Orange IWRMP
Task 10: Demographics & Economic Activity
Study Name:
Orange River Integrated Water Resources Management Plan
Report Title:
Demographic and Economic Activity in the four Orange Basin States
Submitted By: WRP Consulting Engineers, Jeffares and Green, Sechaba Consulting, WCE Pty Ltd,
Water Surveys Botswana (Pty) Ltd
Authors:
D Hall, G Jennings
Date of Issue: August 2007
Distribution:
Botswana: DWA: 2 copies (Katai, Setloboko)
Lesotho: Commissioner of Water: 2 copies (Ramosoeu, Nthathakane)
Namibia: MAWRD: 2 copies (Amakali)
South Africa: DWAF: 2 copies (Pyke, van Niekerk)
GTZ: 2 copies (Vogel, Mpho)
Reports:
Review of Existing Infrastructure in the Orange River Catchment
Review of Surface Hydrology in the Orange River Catchment
Flood Management Evaluation of the Orange River
Review of Groundwater Resources in the Orange River Catchment
Environmental Considerations Pertaining to the Orange River
Summary of Water Requirements from the Orange River
Water Quality in the Orange River
Demographic and Economic Activity in the four Orange Basin States
Current Analytical Methods and Technical Capacity of the four Orange Basin States
Institutional Structures in the four Orange Basin States
Legislation and Legal Issues Surrounding the Orange River Catchment
Summary Report
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TABLE OF CONTENTS
1
INTRODUCTION ..................................................................................................................... 2
2
OVERVIEW OF DEMOGRAPHIC TRENDS IN THE BASIN ................................................. 4
3
BOTSWANA ............................................................................................................................ 6
3.1
Introduction........................................................................................................................ 6
3.2
History of Census Taking in Botswana ............................................................................. 6
3.3
Quality Control Measures.................................................................................................. 9
3.3.1
Census Committees .....................................................................................................9
3.4
Other Relevant Surveys .................................................................................................. 10
3.5
Demographic overview of Botswana .............................................................................. 10
3.6
Demographics of the South East .................................................................................... 11
3.7
Impact of HIV/AIDS ......................................................................................................... 14
3.8
Water Demand ................................................................................................................ 16
3.8.1
Water Demands for the Molopo (Orange) River Basin..............................................21
4
LESOTHO.............................................................................................................................. 24
4.1
Introduction...................................................................................................................... 24
4.2
History of Census Taking in Lesotho .............................................................................. 24
4.3
Quality.............................................................................................................................. 25
4.4
The 2001 Lesotho Demographic Survey ........................................................................ 27
4.5
Broad Demographic Trends ............................................................................................ 28
4.5.1
Internal Migration ........................................................................................................28
4.5.2
External Migration.......................................................................................................29
4.5.3
The Demographic Impacts of HIV/AIDS ....................................................................30
5
NAMIBIA ................................................................................................................................ 34
5.1
Overview.......................................................................................................................... 34
5.2
Introduction to census issues.......................................................................................... 38
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5.3
Background on census issues ........................................................................................ 38
5.4
Census results................................................................................................................. 39
5.5
Impacts of HIV/AIDS ....................................................................................................... 43
5.6
HIV/AIDS Trends ............................................................................................................. 45
5.7
Looking Ahead................................................................................................................. 46
5.8
Key Namibian Literature.................................................................................................. 48
6
SOUTH AFRICA.................................................................................................................... 49
6.1
Introduction...................................................................................................................... 49
6.2
Method............................................................................................................................. 49
6.3
The Orange River Basin & Demographics in South Africa............................................. 49
6.4
Official Demographic Statistics - STATSSA ................................................................... 59
6.5
Census 1996 ................................................................................................................... 61
6.5.1
Census 1996 Background ..........................................................................................61
6.5.2
Census 1996 Problems and Results..........................................................................61
6.6
Census 2001 "How the count was done"..................................................................... 62
6.6.1
Census 2001 Background ..........................................................................................62
6.6.2
Management Structure & Planning ............................................................................62
6.6.3
Pre-enumeration .........................................................................................................64
6.6.4
Enumeration ...............................................................................................................66
6.6.5
Data Processing .........................................................................................................71
6.6.6
Adjustments, Analysis, Results & Dissemination.......................................................72
6.7
Census 2001 Results ...................................................................................................... 77
6.8
STATSSA Post Census Statistics................................................................................... 77
6.9
Demographic Data from Local Government Sources..................................................... 79
6.10 Migrancy .......................................................................................................................... 79
6.11 The Impact of HIV / AIDS on Demographics .................................................................. 82
6.11.1 STATSSA HIV/AIDS Data ..........................................................................................85
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6.11.2 ASSA HIV/AIDS Data .................................................................................................87
6.12 Demographics of the Inter-basin Transfer Areas............................................................ 89
6.12.1 The Tugela-Vaal Transfer Scheme ............................................................................89
6.12.2 The Orange-Fish Transfer Scheme ...........................................................................92
6.12.3 Johannesburg Metropolitan Area ...............................................................................96
6.12.4 The Lesotho Highlands Water Project .......................................................................96
7
REGIONAL AND INTERNATIONAL DATA .......................................................................... 97
7.1
Demographic Health Surveys ......................................................................................... 97
7.2
World Bank Data ............................................................................................................. 99
7.3
Other Useful Data Sets ................................................................................................. 100
7.4
Data on Migration .......................................................................................................... 100
7.5
Data on Gender ............................................................................................................. 104
8
REFERENCES RELATING TO DEMOGRAPHY ............................................................... 107
9
ECONOMIC ACTIVITY ....................................................................................................... 112
9.1
Introduction.................................................................................................................... 112
9.2
Overview of Major Economic Activities and corresponding water use......................... 113
9.3
Transfers ....................................................................................................................... 114
9.3.1
Lesotho Highlands Water Project (LHWP)...............................................................114
9.4
Vaal River Basin ............................................................................................................ 115
9.4.1
Upper Vaal Water Management Area ......................................................................116
9.4.2
Middle Vaal Water Management Area .....................................................................116
9.4.3
Lower Vaal Water Management Area ......................................................................117
9.5
Orange River basin ....................................................................................................... 117
9.5.1
The Upper Orange & Lesotho ..................................................................................118
9.5.2
Lower Orange and the Common Border Area .........................................................118
9.6
Future growth ................................................................................................................ 120
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9.7
Growth prospects by sector .......................................................................................... 121
9.7.1
Irrigated agriculture...................................................................................................122
9.7.2
Urban-industrial sector .............................................................................................127
9.7.3
Mining and mine closures.........................................................................................129
9.7.4
Power generation......................................................................................................131
9.7.5
Water quality and dilution .........................................................................................134
10
REFERENCES ON THE ECONOMIC ACTIVITIES ........................................................... 136
11
THE ECONOMIC VALUE OF WATER ............................................................................... 138
11.1 Introduction.................................................................................................................... 138
11.1.1 Background...............................................................................................................138
11.1.2 Overview of the Paper ..............................................................................................139
11.2 The Concept of Economic Value .................................................................................. 140
11.2.1 Different cost components........................................................................................142
11.2.2 Characteristics and types of water use ....................................................................143
11.3 Economic value of water for individual water users ..................................................... 144
11.3.1 Introduction ...............................................................................................................144
11.3.2 Irrigation Water .........................................................................................................147
11.3.3 Municipal Water ........................................................................................................149
11.3.4 Industrial water demand and value ..........................................................................151
11.4 Economic value and water allocation decisions ........................................................... 154
11.4.1 Opportunity cost and allocative efficiency................................................................155
11.4.2 Water trading and water markets .............................................................................155
11.4.3 Environmental values ...............................................................................................157
11.4.4 Water transfers .........................................................................................................157
11.5 Maximising the system-wide economic benefits of water use...................................... 158
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11.6 Estimated economic value of water for major water users in the Orange-Senqu Basin
160
11.6.1 Municipal water.........................................................................................................162
11.6.2 Irrigation ....................................................................................................................162
11.6.3 Hydro-electric power generation ..............................................................................165
11.6.4 Environmental reserve..............................................................................................166
11.6.5 Impact of water quality on water values ...................................................................166
11.6.6 Water transfers .........................................................................................................167
11.7 Affordability issues and the economic value of water................................................... 168
12
REFERENCES ON THE ECONOMIC VALUE OF WATER............................................... 169
13
APPENDICES...................................................................................................................... 171
13.1 Appendix A South African Migration Statistics for the Orange River Basin by Province
of Previous Residence .................................................................................................. 171
13.2 Appendix B Orange River Basin Population Density Map ........................................ 176
13.3 Appendix C Orange River Basin Population Map ..................................................... 178
LIST OF FIGURES AND TABLES
Table 1: Population of the Orange River Basin 2001 ..................................................................... 4
Table 2: Botswana Population Census Results: 1904 - 1991 ....................................................... 7
Table 3: Botswana 2001 Census Results by Administrative District............................................. 8
Table 4: Summary of Population Statistics - Molopo River Basin Area ....................................... 12
Figure 1: Population Projection for the Molopo River Basin........................................................ 14
Table 5: Rural Village Water Supply Methods ............................................................................. 17
Table 6: Settlement Water Demand Forecasts (Nationwide) ...................................................... 18
Table 7: Percentage Water Demand Usage Nationwide.............................................................. 18
Table 8: Water Demand per demand category............................................................................ 19
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Figure 2: National Water Demand and Usage for 1990 ............................................................... 20
Figure 3: Projected 2000 National Water Demand ....................................................................... 20
Figure 4: Projected National Water Demand & Usage for Year 2020.......................................... 21
Table 9: Water Demand in the Molopo River Basin .................................................................... 22
Table 10: Distribution of Lesotho's Population by Gender and District ........................................ 27
Figure 5: Growth by Settlement Type ........................................................................................... 29
Figure 6: Rainfall distribution of Namibia ...................................................................................... 34
Figure 7: Projections of urbanization rates in Namibia: 1985 to 2025 ......................................... 37
Figure 8: Population distribution map of Namibia ......................................................................... 40
Table 11 Comparative Populations for the Karas, Hardap and Omaheke Regions .................... 41
Figure 9: Distribution Map of HIV Prevalence amongst Pregnant Women in Different Sentinel
Sites, Namibia 2002...................................................................................................... 43
Figure 10: Rates of HIV Infection in Namibia (1998) .................................................................... 44
Table 12 Regional Economic activities ......................................................................................... 47
Table 13: Census 2001 population statistics per DM & LM within the Orange River Basin. ....... 52
Table 14: Comparisons between the 1996 census and the Schlemmer study............................ 59
Figure 11: Hierarchical structure used to define geographical areas in Census 2001. ............... 65
Table 15: Percentage undercount for persons and households per province ............................. 71
Table 16: Census 2001 unadjusted and adjusted population figures per province ..................... 72
Table 17: Adjusted population figures with 95% confidence levels............................................. 74
Table 18: Comparative population data from Census 1996 and Census 2001 ........................... 75
Table 19: STATSSA 2005 population data (compared to other sources).................................... 78
Figure 12: Comparative data on HIV/AIDS prevalence in SA ..................................................... 82
Figure 13: Comparative data on the Percentage of the population with HIV/AIDS .................... 83
Figure 14: Accumulated number of AIDS deaths to mid 2004 .................................................... 83
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Table 20: Selected statistics indicating the impact of HIV/AIDS ................................................. 84
Table 21: STATSSA estimated adult HIV-prevalence rates, 20012005. ................................... 86
Figure 15: Estimated HIV-prevalence rate 2004.......................................................................... 86
Figure 16: Various HIV/AIDS related factors impacting on population levels .............................. 88
Figure 17: Tugela Basin & Tugela-Vaal Transfer Scheme.......................................................... 90
Table 22: Population per LM within the Tugela Basin ................................................................. 91
Figure 18: The Fish & Sundays Basins & Orange-Fish Transfer Scheme.................................. 93
Table 23: Population within the Fish & Sundays River Basins & the Port Elizabeth area. ......... 95
Table 24: Gender Disaggregated Data on Botswana................................................................ 105
Figure 19: Orange River Basin.................................................................................................... 113
Table 25: Water requirements for the year 2000 (million m3/annum)........................................ 114
Table 26: Hydropower Macro plans and Key projects, by SADC country............................... 134
Table 27: Water requirements for the year 2000 (million m3/annum)........................................ 140
Figure 20: Economic Value ......................................................................................................... 141
Figure 21: Properties of water-use.............................................................................................. 143
Figure 22: Water valuation methods ........................................................................................... 147
Table 28: Economic value for water per sector in the Vaal River System ................................. 161
Figure 23: Orange River Basin Population Density Map ........................................................... 177
Figure 24: Orange River Basin Population Map ........................................................................ 179
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LIST OF ABBREVIATIONS
Acronym
Description
AIDS
Acquired Immune Deficiency Syndrome
ART
Anti Retroviral Treatment
ASSA
Actuarial Society of South Africa
BMR
Bureau of Market Research
BNWMP
Botswana National Water Master Plan
BSO
Bureau of Statistics (Lesotho)
CAS
Census Administration System
CBDC
Cross-boundary District Council
CBLC
Cross-boundary Local Council
CSO
Central Statistics Office
DBSA
Development Bank of South Africa
DC
District Council
DHS
Demographic Health Systems
DM
District Municipalities
DMA
District Management Area
DWA
Department of Water Affairs (Botswana)
DWAF
Department of Water Affairs and Forestry (South Africa)
EA
Enumerator Area
EC
Eastern Cape
FS
Free State
GDP
Gross Domestic Product
GIS
Geographic Information Systems
GOB
Government of Botswana
GPS
Global Positioning Systems
HIV
Human immuno-defiency virus
HSRC
Human Sciences Research Council
IT
Information Technology
IWRMP
Integrated Water Resources Management Plan
KZN
KwaZulu-Natal
LDS
Lesotho Demographic Survey
LLWSFS
Lesotho Lowlands Water Supply Scheme Feasibility Study
LM
Local Municipality
Metro
Metropolitan area
MP
Mpumalanga
MRC
Medical Research Council
NC
Northern Cape
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Acronym
Description
NP
Limpopo (former Northern Province)
NW
North West
ORASECOM
Orange / Senqu Commission
PES
Post Enumeration Survey
PMTCT
Prevention of mother-to-child transmissions
RVWSDM
Rural Village Water Supply Design Manual
SAMP
South African Migration Project
SIAPAC
Social Impact and Policy Assessment Corporation
STATSSA
Statistics South Africa
TAMS
TAMS Consulting Engineering
TFR
Total Fertility Rate
TOR
Terms of Reference
UNAIDS
The joint United Nations programme on HIV/AIDS
USAID
United States Aid for International Development
WC
Western Cape
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SECTION ONE
DEMOGRAPHICS
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1
INTRODUCTION
This report is the main output of Task 10 of the ORASECOM study.
The Terms of
Reference (TOR) specify that for this task the consultants should: "Obtain available
information
on
the
population
distribution,
population
growth
and
demographic
movements". Further to this, the TOR required the consultants to: "Provide an overview of
economic activity in the Orange River Basin, and highlight potential economic stimulants
and growth areas (and) include a discussion on the economic value of water for different
uses".
This report presents first the demographic aspects of Task 10, and then looks at the key
economic activities in the Basin followed by a discussion of economic value of water for
different users.
The activities proposed in the TOR to meet the demographic objectives of Task 10
included:
· A review of Census data in all countries;
· A review of post-census reports, particularly those dealing with the impact of
HIV/AIDS;
· The creation of a basic demographic map showing variations in population density
and growth;
· A review of reports dealing with key areas of economic activity, particularly of
water-dependent sectors;
· A review of websites with electronic data and / or reports; and
· The noting of future links from the Information Database to be established as part
of Task 2.
As can be seen from the above list, Task 10 is not intended to generate a definite report
on the demographics of the Basin but rather to review existing data and reports.
In
conducting the review the consultants have been able to comment on issues pertaining to
data quality, accessibility and gaps. This information can be used in the preparation of
comprehensive demographic analysis that will form part of the Integrated Water
Resources Management Plan (IWRMP).
The Consultants have also been able to go
beyond the requirements of the TOR to provide details on the broad demographic trends
of the region.
This report begins with an overview of these trends and then provides
detailed information on Botswana, followed by Lesotho, Namibia and then South Africa.
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The section on South Africa is significantly more detailed, partly due to the volume of
available data, but also due to the largest proportion of the Basin's population being
resident in South Africa. The final sections of the demographic part of the report look
specifically at the crosscutting themes of migration, gender and data sources.
The sections dealing with economic activities and the value of water are presented in a
way that is intended to highlight key issues and stimulate debate regarding the future
management of the Basin's water resources. As will be seen, this is not intended to be a
comprehensive listing of all economic activities, but rather an introduction to major themes.
As one of the ORASECOM Commissioners put: "Please don't throw the Gauteng Yellow
Pages at us."
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2
OVERVIEW OF DEMOGRAPHIC TRENDS IN THE BASIN
The review of reports and data undertaken by the demographic team reveals a number of
trends that will need to be examined in greater detail when the IWRMP is drawn up.
Before looking at these, it is pertinent to note that the Basin as a whole is home to some
15.7 million people, the vast majority of whom (85%) live in South Africa. Botswana and
Namibia have relatively few people living in the Basin (0.3 and 1.1 respectively), while
Lesotho's entire population is in the Basin, making up just over 13% of the total number in
the Basin.
Table 1: Population of the Orange River Basin 2001
% of Number in
Country
Total Population
Number in Basin
% in Basin
Basin
Botswana
1,680,863
47,661
2.8
0.3
Lesotho
2,127,539
2,127,539
100
13.5
Namibia
1,830,330
163,093
8.9
1.3
South Africa
44,819,778
13,357,298
29.8
84.9
Total
50,458,510
15,738,115
100
Source: Country specific reports based on 2001 figures
As will be seen later in this report, although there is considerable variation in the number of
people each country `contributes' to the Basin, they share numerous similarities with
regard to demographic trends. In all cases the following can be observed:
Urbanisation: All Basin states are undergoing rapid urbanisation driven primarily by high
levels of unemployment in rural areas and perceived work opportunities in urban areas.
This results in rural areas experiencing out-migration (population declines), whilst urban
areas especially major conglomerates witness growth rates well in excess of the
national averages.
Migration: Internal migration is closely associated with urbanisation, with all major urban
centres within the Basin states experiencing growth at the expense of their rural
hinterlands. However, the Basin is also a magnet for international migrants and attracts
people from well beyond the national borders of the Basin states. This is particularly the
case for the Gauteng province of South Africa, which is the economic hub of the region.
However, even in sparsely populated parts of Namibia, sudden demographic shifts can
occur in response to new economic opportunities such as the opening of a mine.
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Declining fertility: Fertility rates have been in decline in all Basin states for at least a
decade.
This is attributed partly to increased access to education, contraceptive
prevalence and (in some cases) improved economic status.
However, the HIV/AIDS
pandemic also contributes to this, as women of childbearing age succumb to the disease.
HIV/AIDS:
Without exception, all the Basin states have been severely impacted by
HIV/AIDS, with Botswana and Lesotho being particularly hard hit. While high prevalence
rates will have a long-term demographic impact on national growth rates, they are unlikely
to significantly reduce the growth of key urban areas due to the high demand for jobs and
the availability of a large unemployed (or underemployed) workforce.
These key trends need to be closely monitored as the IWRMP is developed and eventually
implemented. The reason for this is the rapidly changing socio-economic environment that
can have major impacts on demography within a relatively short time.
For example,
should Anti-Retroviral (ARV) medication become available on a wide scale in the next
decade, mortality rates will decline and the current population projections will need to be
adjusted. Equally important, regional migration patterns could be suddenly changed by,
for example, improvements in the political situations of countries such as Zimbabwe which
could result in a decline (or even a reversal) in the number of international migrants
moving to Gauteng. Likewise, the decline of particular industries (such as diamond mines
in the Northern Cape or the garment industry in Lesotho) could have significant
consequences for local population movements. In short, demographic trends are highly
susceptible to forces of change in the broader environment and must be constantly
monitored.
This immediately implies a need for good quality data that is frequently
collected or updated and made easily accessible to policy makers. Determining the status
and availability of such data has been the main undertaking of Task 10 and is the focus of
subsequent sections of this report.
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3
BOTSWANA
3.1
Introduction
The review of existing demographic information showed that, in the case of Botswana,
much of the data and related background information is available electronically through
the well-managed and presented web site of the Central Statistics Office (CSO)
(www.cso.gov.bw). The availability and accessibility of the data will be advantageous in
the preparation of the IWRMP. In this section of the report we briefly describe the history
of census taking in Botswana and present the key findings from the latest census of 2001.
Thereafter we consider some of the control measures that have been put in place to help
ensure the relatively high standard of census data. Mention is also made of other related
studies and the impact of the HIV/AIDS pandemic is discussed. Finally, information is
provided on the relatively small population that live within the part of the country that is
located in the Molopo basin which is a tributary of the Orange River Basin.
3.2
History of Census Taking in Botswana
According to the CSO, census taking in Botswana can be traced back to the beginning of
the twentieth century. The first census was held in 1904 shortly after the Anglo-Boer war.
Despite many difficulties, such as fear and suspicion on the part of the population, poor
communication and the fact that the country was large and sparsely inhabited, the
exercise provided insights that made the later censuses easier.
With no wars or disturbances, the Bechuanaland Protectorate joined the rest of the British
Empire in conducting the 1911 and the 1921 decennial censuses. In view of the worldwide
economic depression of the late twenties and early thirties, there was no census in 1931.
The census was held in 1936 instead. The reliability of the 1936 Census results were
questioned, mainly because the district figures were too different from the 1921 Census
figures. The questionnaire for the 1946 Population Census was much longer and more
complicated than previous ones and this meant that tabulation preparation dragged on
towards the preparatory time for the 1951 census. As a result, the 1951 census was
delayed until 1956. With the urgent need for information on which to base development
plans and accurate population figures for constituency delimitation, the 1964 Population
Census was conducted. This was the first to follow a house-to-house canvassing method.
Following the attainment of independence in 1966 it was found necessary to return to a
year ending with 0 or 1 as recommended by the United Nations, hence the next census
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was re-scheduled for 1971. The 1971 Population Census was the first to be conducted on
a de facto basis with the idea of enumeration areas being applied for the first time in
Botswana's census history. In terms of the quality of data obtained and the wide subject
coverage, it was an important census for future planning. It also provided a basis for
sampling in later surveys.
According to the CSO, the 1981 Census was the first one to include a housing component
in its questionnaire. In view of better cartographic support, effective publicity campaigns
and stronger administrative backing, as well as an improved level of education, the 1981
Census could be considered as having had better coverage than the 1971 Census. The
1991 Population and Housing Census is considered by the CSO to be "another success
story" as it had even more comprehensive coverage of topics and the cartographic work
had greatly improved, resulting in very few incidents of locality omission during
enumeration.
The summary of census results from the 1904 to 1991 is presented in the Table 2 below:
Table 2: Botswana Population Census Results: 1904 - 1991
Year
Population
Growth Rate %
1904
120 776
1911
124 350
0.5
1921
152 983
2.0
1936
265 756
3.8
1946
296 310
1.1
1956
309 175
0.4
1964
549 510
7.5
1971
596 994
1.2
1981
941 027
4.7
1991
1,326,796
3.5
Source: Government of Botswana, CSO.
In 2001 Botswana's census formed part of the SADC 2000 Census Project. This project
has the aim of harmonizing all census-taking activities for better comparability of
demographic characteristics within SADC. Although resources are pooled and common
questions shared, countries are still free to add-in their own specific topics to the regional
minimal set.
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The key findings from the 2001 Census are summarised in the table below. These are
broken down by administrative districts, the key areas of interest to the ORASECOM
project being the South and South East.
Table 3: Botswana 2001 Census Results by Administrative District
Area Sq.
Non-Inst.
Institutional
Total
Code
District
Households
Km
Population
Population
Population
01
Gaborone
169
58,476
181,627
4,380
186,007
02
Francistown
79
23,124
81,003
2,020
83,023
03
Lobatse
42
8,523
28,801
888
29,689
04
Selibe Phikwe
50
15,258
48,825
1,024
49,849
05
Orapa
17
2,578
8,306
845
9,151
06
Jwaneng
100
4,681
14,559
620
15,179
07
Sowa
159
979
2,726
153
2,879
10
Southern
28,470
37,202
170,981
671
171,652
20
South East
1,780
14,780
59,877
746
60,623
30
Kweneng
31,100
52,578
227,986
2,349
230,335
40
Kgatleng
7,960
17,054
73,199
308
73,507
50
Serowe/Palapye
31,381
33,969
151,884
1,151
153,035
51
Central Mahalapye
16,507
23,730
108,324
1,487
109,811
52
Central Bobonong
14,242
15,057
66,602
362
66,964
53
Central Boteti
33,806
10,363
47,738
319
48,057
54
Central Tutume
46,140
27,168
122,696
818
123,514
60
North East
5,120
10,834
49,249
150
49,399
70
Ngamiland
86,400
16,129
72,926
2,144
75,070
71
Okavango
22,730
10,184
49,189
453
49,642
72
Chobe
20,800
4,600
16,547
1,711
18,258
80
Ghanzi
117,910
7,776
32,707
463
33,170
90
Kgalagadi South
32,800
5,679
25,617
321
25,938
91
Kgalagadi North
72,400
3,984
16,067
44
16,111
Total
581,730
404,706
1,657,436
23,427
1,680,863
From the above it is apparent that although Botswana contributes relatively small amounts
of surface water to the Orange River Basin (via the Molopo river catchment), it also has a
relatively small population.
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3.3
Quality Control Measures
As there can be considerable variations in the quality of census exercises it is prudent to
look at what quality control measures were put in place for the most recent census. In the
case of Botswana it is apparent that considerable effort was made to ensure both public
support and quality.
3.3.1
Census Committees
The case of Botswana demonstrates a high level of political will with regard to the national
census. This is very apparent from the measures put in place for the 2001 Census. For
example, in order to ensure that activities were kept on track, the CSO put in place a
Census Technical Advisory Committee (comprising experts from Government Ministries,
Para-statals and Research Institutions) overseen by the Census Central Committee
(membership comprising Permanent Secretaries), which endorsed the topics for inclusion
in the census. Subsequent to that, Cabinet approved the topics in June 2000. Then, in
July 2000, a National Census Communications Committee, with associated lower level
committees in the districts, was formed.
Particular measures taken to ensure quality included the following:
· Launching of census education and publicity campaigns.
· Mapping and listing of all settlements.
· Delineation of Census Districts; Enumeration and Supervision Areas.
· Pre-testing of census questionnaire.
· Identification and contracting of IT Consultants to process census data.
· Development of instruments; and computing software.
· Development of the necessary legislation.
· Conducting of the Pilot Census in the Year 2000.
· Conducting of the main census in 2001 and release of preliminary results.
· Analysis of census data and release of main results.
· Digitisation of census maps.
The experience of Botswana shows that preparatory cartographic work is critical. For the
2001 census this began in October 1999 and involved listing of all dwelling units,
household heads, and the number of persons in the household etc. Through this exercise,
maps for the whole country were compiled and were further divided into enumeration
areas assigned to enumerators.
The enumerators, supervised at district level, wore
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census badges identifying them and were sworn to secrecy to assure the public that the
answers given would be kept in the strictest confidence and only be used for Census
purposes.
A Pilot census of over 7000 households tested both the questionnaire and
administrative logistics. Census data processing was contracted to a private IT Consultant.
All of the above contributed to ensuring quality, indicating that the latest Census results
can be depended upon for the purposes of the ORASECOM project.
3.4
Other Relevant Surveys
While the Census data is most critical for the purposes of determining the overall
population of the country, supplementary household surveys shed light on factors that
influence growth in general, and water demand in particular. Over the last two decades
relevant surveys in the case of Botswana include:
· Household Income and Expenditure Survey -I in 1985/86, 93/94 and 02/03;
· Demographic Survey -I in 1987 and 1998
· Botswana Family Health Survey -II in 1988 and 1996
· Informal Sector Survey in 1999
· Botswana Multiple Indicator Survey (MICS), 2000
· Botswana Aids Impact Survey 2001
3.5
Demographic overview of Botswana
Over the last ten years, the demographics of the country have changed significantly with
increasing numbers concentrated around the urban centres. Botswana's population is
becoming increasingly urbanised. The traditional way of life of people moving between the
village home, the fields, lands and cattle kraals is in decline with more people having
additional town domiciles. Both education and healthcare continue to be priority areas for
Botswana. The Government of Botswana (GOB) continues to improve and expand the
education system, consuming over a fourth of the 2000-2001 allocated expenditure
budget. The health care system has also received substantial inputs resulting in about
85% of the rural population living within 15km of a health facility. Public health expenditure
averaged 5-8% of the national budget between 1980 and 1999.
Between the 1974/75 and 1999/2000 financial years, the Gross Domestic Product (GDP)
of Botswana grew at an average rate of 9.1% increasing from (in Pula) P228 million to P26
billion in 1999/2000. This expansion was fuelled primarily in the structure of the economy
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from agriculture and financial services to mining and public sector. Mining is now the
leading sector in the national economy, constituting more than 50% of Government
revenues and nearly 80% of foreign exchange earnings. Through diversification efforts by
the GOB and shifts in the global economy, the mining sector now represents about one
third of the GDP.
3.6
Demographics of the South East
It can be anticipated that a future ORASECOM water management plan will take particular
interest in the southern parts of Botswana in the Molopo river basin which is a tributary
basin of the Orange River.
Consequently demographic data for the area has been
sourced from the CSO.
Southern Botswana hosts a very low rural population density confined to the sparsely
populated Kgalagadi District and the moderately populated Southern District.
Some 2.8% of Botswana's population lives within the Molopo Basin area. Population
centres
include:
Goodhope,
Gathwane,
Mogojogojowe,
Mmathethe,
Digawana,
Thareseleele, Ramatlabama, Mokatako, Phitshane-Molopo Mmakgori, Tshidilamolomo,
Mabule, Selokolela, Metlobo, Magoriapitse, Sekoma, Khakhea, Makopong, Khisa,
Omaweneno, Maleshe, Tsabong, Werda, Maralaleng, Struizendam, Rappelspan, Khuis,
Bogogobo, Middlepits, Khawa, Gakhibane and Bokspits.
As mentioned above, the Botswana Central Statistic Office (CSO) carries out a national
population census every ten years.
Population statistics derived from the CSO 2001
census and the associated 2010 and 2020 population projections for the Molopo River
Basin are presented in the table below.
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Table 4: Summary of Population Statistics - Molopo River Basin Area
POPULATION
LOCALITY/TOWN
2001
2010
2020
Goodhope
2934
3300
3414
Gathwane
922
927
928
Bogogobo
341
336
335
Sekoma
1033
1151
1188
Magoriapitse
969
1107
1151
Makopong
1501
1577
1600
Khakhea
2035
2096
2114
Omaweneno
1068
1127
1166
Khisa
423
455
464
Khonkhwa
473
490
495
Keng
931
1007
1031
Leporung
582
587
588
Werda,
1961
2108
2153
Tsabong
6591
7546
7848
Maleshe,
389
403
407
Maralaleng
487
567
625
Struizendam
313
318
335
Rappelspan
278
315
327
Khuis
755
791
802
Khawa
517
538
544
Gakhibane
501
515
519
Bokspits
499
528
537
Thareseleele
767
778
781
Selokolela
1188
1296
1329
Metlobo
925
942
947
Mabule
1589
1631
1643
Tshidilamolomo
673
1783
1851
Mokatako
967
978
961
Mmakgori
742
834
863
Ramatlabama,
1174
1179
1180
Phitshane-Molopo
1569
1783
1851
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POPULATION
LOCALITY/TOWN
2001
2010
2020
Mogojogojowe
603
654
670
Kokotsha
1021
1053
1062
Kolonkwaneng
591
594
595
Vaalhoek
346
377
387
Dikhukhung
288
279
276
Bray
899
927
936
Sedibeng
616
701
728
Mmathethe
4415
4809
4930
Middlepits
657
707
722
Maubelo
453
491
516
Digawana
2675
2832
2879
Population in Molopo River Basin area
47661
Total population in Botswana
1680863
Population outside Molopo River Basin
1587038
area
% Population in study area
2.8
Source CSO 2001 Population Census
A map showing the projected population distribution for the year 2020 in settlements within
the study area is shown in the Figure below.
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Figure 1: Population Projection for the Molopo River Basin
3.7
Impact of HIV/AIDS
Of the various supplementary surveys conducted in Botswana in recent times the Aids
Impact Survey of 2001 is probably the most critical. The impact of HIV and AIDS has been
highly significant and has serious consequences for the growth of the population and the
economy, both of which determine water demand. For this reason particular attention has
been paid to reviewing the HIV/AIDS situation in Botswana. Drawing primarily on reports
by the CSO, a summary of the main trends is given below.
The first cases of HIV in Botswana were diagnosed in 1985 and the disease has spread
very rapidly since then, with UNAIDS estimating that by the end of 1999, at least one in
four adults in Botswana was living with HIV.
A variety of programmes have been
implemented to improve the knowledge about how HIV is transmitted as well as strategies
for HIV/AIDS prevention and control. However, despite these efforts, the infection rate has
not declined significantly.
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The HIV / AIDS epidemic continues to deepen in Botswana. The overall, adjusted HIV
prevalence rate for pregnant women aged 15-49 has increased from 33.6% in 2000 to
36.2% in 2001. This increase is reflected across nearly all age groups. The trend of HIV
prevalence from 1993 to 2001 indicates that the prevalence rates for 2001 are double
those for 1993.
In 2001 the CSO conducted an impact survey to obtain more information on topics related
to HIV/AIDS. In particular the survey set out to do the following:
· Assess whether people are changing their sexual behaviour;
· Establish the proportion of people in need of care due to HIV infection;
· Establish the proportion at risk of HIV infection;
· Assess the impact of the pandemic at household level and
· Provide information on issues related to the impact of HIV/AIDS on household
and communities.
The population growth structure continues to be altered as a result of the HIV and the
AIDS epidemic. Mortality across age groups is on the rise in Botswana and life expectancy
has began a steady decline, from a GOB estimated high of about 66.2 years to a projected
low of 47.4 years (1999 & 2000 GOB Human Development Reports). It is estimated that by
the year 2010 life expectancy could reach a staggering low of 29 years. Additionally, if
nothing is done to halt the deepening of the epidemic, 30% of Botswana's adult population
could be lost over the next eight to ten years.
The epidemic has had a significant impact on children. The survey found that 28 percent
of children aged 0-14 are not living with both parents. Children who have lost one or both
parents to death amount to about 13 percent of all children aged 0-14. The mean age at
first marriage or cohabitation is 27 years. The mean duration of current marriage or
cohabitation is 3 years. The survey gives the mean age at first having sex as 24 years,
although other reports indicated a lower and more plausible - age of first sex at 17 to 18
years (Dr D. Cownie, - pers. com).
The structure of the population will shift to increasing numbers of both the very young and
very old. Household income levels are expected to drop at least 8% due to HIV and AIDS,
pushing the number of household below the poverty line up by around 5%. Ever
decreasing household resources may be increasingly channelled to medical and care
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expenses, with less going to education and social amenities. The impact of HIV / AIDS is
keenly felt in the social sector in particular education and health. A high incidence of
morbidity and mortality among teachers reduces the number of classroom hours being
taught. At home, ill health among family reduces the time children spend at school or
attending to schoolwork. Similarly, the nation's health system is stretched to the limit as
the shear magnitude of the epidemic threatens to consume both health resources and
facilities. The massive burden of caring for and treating HIV and AIDS in Botswana will
increasingly limit the health care system to deliver even the most basic care to the rest of
the population.
The epidemic is having a catastrophic impact on the economy with an HIV prevalence of
some 36% among the workforce. The number and quality of people available to work will
decline over the next five to ten years. The loss of skills, institutional memory and
experience will create a vacuum in the labour market. Labour costs will rise along with
recruitment and retraining costs in order to meet the need of business and industry. Added
to that the costs of meeting expected medical and support costs may seriously reduce
corporate earnings, savings and investment levels, depressing the economy. It has been
estimated that the HIV / AIDS epidemic will cause a contraction of GDP by 1.5% over the
next 20-25 years resulting in an economy at least 31% smaller than would otherwise be
projected without the impact of the epidemic. The impact of the epidemic in respect of
water demands has not as yet been quantified by the GOB.
The findings raise cause for concern as 21 percent were unable to mention at least one
way of reducing chances of HIV infection and only 25 percent mentioning both partners
having no other partners as a way of preventing infection.
Only 31 percent of the
population correctly identified three misconceptions about HIV transmission and only 14
percent have been tested for their status. Given these results, there is little to indicate that
the infection rate will be significantly reduced in the coming years.
3.8
Water Demand
The economic success of the Republic of Botswana has translated into improved
infrastructure nationwide and increased household per capita income in rural villages. This
new buying power enables more households to have water connections. These
connections have proved to encourage high water consumption per capita as compared to
public standpipes. As such there is increased demand for a reliable and convenient
potable water supply. Rural village water demand calculations are based on the
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Department of Water Affairs (DWA) (1989) Rural Village Water Supply Design Manual
(RVWSDM). Rural water supply schemes are designed to the following design criteria:
· Public standpipe (30 litres/day/person)
· Yard connection (60 litres/day/person)
· Household connection (150 litres/day/person)
The percentage of the rural population served to these criteria is given in the following
table:
Table 5: Rural Village Water Supply Methods
Connection Type
% Served
House connection
15%
Private connections
Yard connection
20%
Public standpipes
65%
Source DWA 1989 Rural Village Water Supply Demand Manual
The last decade has seen rapid infrastructural developments at rural village level, which
makes many of the RVWSDM water demand calculation formulae obsolete. There has
been a large increase in the number of housing developments at village level, which now
exceeds the 15% stated in the RVWSDM. Waterborne sewage systems are being
emplaced in the larger villages. Primary and Junior Secondary Schools nationwide are
being equipped with waterborne sanitation facilities. The Ministry of Health is establishing
a network of primary hospitals located at sub-district HQ and major villages. This
combined with the many new developments (businesses, shopping malls etc.) in the larger
villages and the resulting urban life style (modern housing) of the population results in a
much higher water demand.
For major villages supplied from well fields separate water demand studies are
implemented (as is the case in the Molopo River Basin). The studies are also based on the
consumption statistics given the RVWSDM and the Botswana National Water Master Plan
(BNWMP) planning documents.
Water demand forecasts contained in the BNWMP (1991) are presented in the table
below. It is understood that the BNWMP is currently under revision.
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Table 6: Settlement Water Demand Forecasts (Nationwide)
Year
1990
2000
2010
2020
Population (103)
Urban Centres
262
437
647
949
Major Villages
294
431
597
816
Rural Villages
289
386
498
629
Minor Settlements
444
515
593
641
Domestic Demands (106m3)
Urban Centres
8.1
17.0
26.6
41.9
Major Villages
3.1
8.8
15.1
24.4
Rural Villages
1.8
3.2
5.1
6.8
Minor Settlements
2.4
2.8
3.3
3.5
Overall Demands (106m3)
Urban Centres
19.6
41.3
68.2
107.2
Major Villages
7.4
17.5
27.9
43.3
Rural Villages
3.6
6.3
9.5
12.6
Minor Settlements
3.3
3.8
4.4
4.7
Source 1991 BNWMP
The 1991 BNWMP identifies five main water demand users: Domestic/settlements, Mines
& Energy, Livestock, Irrigation & Forestry and Wildlife. Percentage usage figures for the
different categories are presented in the Table below as well as the two Figures below.
Table 7: Percentage Water Demand Usage Nationwide
1990
2000
2010
2020
Year Demand Category
%
%
%
%
Settlements
28.6
38
45
51
Mines & energy
19.3
18.6
21.4
17.9
Livestock
30.7
22.7
14.3
14
Irrigation/forestry
16.4
17.4
16.9
15
Wildlife
5
3.3
2.4
2
Source BNWMP, 1991
Water demand projections for the categories are presented in the following table.
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Table 8: Water Demand per demand category
Year Demand Category
1990
2000
2010
2020
Settlements
33.7
68.8
109.9
167.8
Mines & energy
22.9
33.6
52.2
58.7
Livestock
36.5
41
34.8
46.7
Irrigation/forestry
19.5
31.6
41.3
49.8
Wildlife
6
6
6
6
Total
118.6
181
244.2
329
Source BNWMP, 1991. All figures (x 106m3/annum)
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N ational W ater D em and 1990
5%
16%
29%
31%
19%
B NW N P,1991
S ettlem ents
M ines& energy
Livestock
Irrigation/forestry
W ildlife
Figure 2: National Water Demand and Usage for 1990
These water demand figures have been calculated using a range of water consumption
between 15 - 130 litres/day/person.
Figure 3: Projected 2000 National Water Demand
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The domestic water demand for settlements increases with time to a projected total of 51%
of the total national water demand in 2020 as shown in Figure 4. In comparison the
percentage proportion of other users remains static or even declines with time (as in the
case of livestock).
Projected national water demand
180
160
140
120
Settlements
3
Mines& energy
100
m
Livestock
80
106
Irrigation/forestry
60
Wildlife
40
20
0
1990
2000
2010
2020
Figure 4: Projected National Water Demand & Usage for Year 2020
3.8.1
Water Demands for the Molopo (Orange) River Basin
Tsabong, Goodhope, Mmathethe, Digawana and Khakhea are considered major villages
in the Molopo River Basin. These villages have the highest water demand compared to
minor villages. The water demands of these villages are presented in the table below.
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Table 9: Water Demand in the Molopo River Basin
Population
Water Demand (m3/d)
Village
2001
2010
2020
2001
2010
2020
Goodhope
2934
3300
3414
220.05
247.50
256.05
Gathwane
922
927
928
69.15
69.53
69.60
Bogogobo
341
336
335
25.58
25.20
25.13
Sekoma
1033
1151
1188
77.48
86.33
89.10
Magoriapitse
969
1107
1151
72.68
83.03
86.33
Makopong
1501
1577
1600
112.58
118.28
120.00
Khakhea
2035
2096
2114
152.63
157.20
158.55
Omaweneno
1068
1127
1166
80.10
84.53
87.45
Khisa
423
455
464
31.73
34.13
34.80
Khonkhwa
473
490
495
35.48
36.75
37.13
Keng
931
1007
1031
69.83
75.53
77.33
Leporung
582
587
588
43.65
44.03
44.10
Werda,
1961
2108
2153
147.08
158.10
161.48
Tsabong
6591
7546
7848
494.33
565.95
588.60
Maleshe,
389
403
407
29.18
30.23
30.53
Maralaleng
487
567
625
36.53
42.53
46.88
Struizendam
313
318
335
23.48
23.85
25.13
Rappelspan
278
315
327
20.85
23.63
24.53
Khuis
755
791
802
56.63
59.33
60.15
Khawa,
517
538
544
38.78
40.35
40.80
Gakhibane
501
515
519
37.58
38.63
38.93
Bokspits
499
528
537
37.43
39.60
40.28
Thareseleele
767
778
781
57.53
58.35
58.58
Selokolela
1188
1296
1329
89.10
97.20
99.68
Metlobo
925
942
947
69.38
70.65
71.03
Mabule
1589
1631
1643
119.18
122.33
123.23
Tshidilamolomo
673
1783
1851
50.48
133.73
138.83
Mokatako
967
978
961
72.53
73.35
72.08
Mmakgori
742
834
863
55.65
62.55
64.73
Ramatlabama,
1174
1179
1180
88.05
88.43
88.50
PhitshaneMolopo
1569
1783
1851
117.68
133.73
138.83
Mogojogojo
603
654
670
45.23
49.05
50.25
Kokotsha
1021
1053
1062
76.58
78.98
79.65
Kolonkwaneng
591
594
595
44.33
44.55
44.63
Vaalhoek
346
377
387
25.95
28.28
29.03
Dikhukhung
288
279
276
21.60
20.93
20.70
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Population
Water Demand (m3/d)
Village
2001
2010
2020
2001
2010
2020
Bray
899
927
936
67.43
69.53
70.20
Sedibeng
616
701
728
46.20
52.58
54.60
Mmathethe
4415
4809
4930
331.13
360.68
369.75
Middlepits
657
707
722
49.28
53.03
54.15
Maubelo
453
491
516
33.98
36.83
38.70
Digawana
2675
2832
2879
200.63
212.40
215.93
Totals
47661
52417
53678
3574.58
3931.28
4025.85
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4
LESOTHO
4.1
Introduction
Of the four riparian countries that make up ORASECOM, Lesotho is the only one where
the entire population falls within the catchment of the Seqnu-Orange River.
From a
demographic point of view this has a distinct advantage, as national statistics can be
applied for analysis and planning (unlike in the other countries where only a proportion of
the population falls within the basin).
As was the case with Botswana, the initial objective of the demographic work in Lesotho
has been to determine the extent, availability and quality of relevant demographic data. In
reviewing available documents it was found that the recently completed Lesotho Lowlands
Water Supply Scheme Feasibility Study (LLWSFS) provides valuable information on the
country's demographics and water demand. In addition it was found that the Bureau of
Statistics has a range of reports from census and household survey reports, some of
which are available electronically.
It was further found that a number of international
agencies, NGOs and private sector consultants have produced reports which are relevant
to understanding the demographics of the country, particularly with regard to migration and
the impact of HIV/AIDS.
The work carried out so far consists of reviewing the key documents and data sets that are
most readily available. Particular attention has been given to the work done under the
LLWSFS as this is very recent and relevant. The sections that follow build on this growing
body of data that will be substantially augmented in 2006 when the next census is carried
out. The section below begins with a brief overview of Lesotho's census history, followed
by an assessment of the quality of the most recent census, conducted in 1996.
4.2
History of Census Taking in Lesotho
Census taking in Lesotho has a longer history going back to 1875, with subsequent
censuses in 1891, 1904, 1911 and 1921. Since 1936 decennial censuses have been the
norm, with those conducted over the period 1936 to 1986 using schoolteachers as
enumerators. Earlier census reports failed to explain their methodologies, and the
precision of their results cannot be other than spurious. The 1956 census was the first in
which the Census Officer outlined the methodology used in detail. Enumeration was then,
as in all subsequent censuses, by households. In 1965 this amounted to 793,639 people in
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158,569 households, amounting to a household size of almost exactly 5 persons, a figure
which has remained virtually unchanged in subsequent censuses.
Lesotho's demographers are in agreement that, following a series of wars in the 19th
century, the population of Lesotho only began to expand in the early 20th century as a
result of political events across the border that forced many Basotho to move to Lesotho.
This resulted in a high growth rate until the early 1920s. Thereafter, the growth of
economic opportunities in South Africa led not only to a large population of migrant
workers, but also to permanent out-migration, primarily to South African urban and
industrial areas. As a result there was very little growth in the population in the 1930s and
1940s, with it remaining at just over 660,000 for a decade.
After 1948, when the Nationalist Party came to power in South Africa, it became more
difficult for Basotho to settle in South Africa, so the population within Lesotho's boundaries
grew faster than had previously been the case. By Independence in 1966, the de jure
population of Lesotho was almost exactly one million, almost twice that of Botswana.
Although there was initially not much out-migration in the post-Independence period, there
was considerable internal migration that continues to this day. By 1986 the population had
grown to 1,600,000, averaging 2.6 percent per annum over the pervious decade. The
latest census (discussed in more detail below) returned a figure of 1,860,000, however the
Bureau of Statistic concluded that there was a 5 percent under enumeration.
4.3
Quality
One weakness that has been identified in the censuses relates to cartography. To analyse
the growth of particular areas over time it is essential to maintain consistent enumeration
areas that are clearly demarcated on reliable maps. By 1966, a series of 1:50,000 maps
of Lesotho became available for the first time and enumeration areas (EAs) were
delineated on these. However, in the 1976 Census the maps were not always strictly
adhered to, partly because many village names did not appear on the maps. In 1986 new
boundaries were accepted but the EA boundaries were never published, and no backup
reference set was made. As a result, when sheets from the original set were lost they
could not be readily replaced. As a result the 1986 Census, while providing data at
constituency and district levels, does not allow for easy comparisons between census
years at local level. Comparisons of population at local level might still have been possible
from village lists (which have been published or made available electronically for all
censuses since 1956), however, unlike Botswana, Lesotho does not have a standard,
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officially-recognised set of village names. Comparison is therefore very difficult as
Lesotho's villages frequently have more than one commonly used name. Analysts note
that the need for standardised village names is as essential as ever and recommend that
these names be standardised on all maps, and also used for voter registration, censuses
and local government purposes (personal com. Prof. David Ambrose).
The 1996 Census encountered various problems that have been set out in the 1996
Population Census administration report, volume 1 [preface dated July 1999, but published
in 2000]. Suffice it to say that schoolteachers were not used and the 3,077 unemployed
school leavers did not offer the same level of experience or maturity in their approach to
the work. According to the BOS the final total figure for the de jure population of Lesotho
amounted to 1,862,275 compared with a 1986 population of 1,605,177. This implied a
1.5% growth rate between the two censuses, which is believed to be too low, so it is
concluded that coverage errors must have occurred (1996 Population Census Volume IIIA,
p. 4). If the 2.6% growth rate between 1976 and 1986 had continued, the population would
have been 2.1 million, which implies an under-enumeration of some 276,000 people. The
authors of the commentary come to the conclusion that the 1996 census under-
enumeration was about 5%, and provided a method for producing a smoothed age
pyramid (adding 117,702 people aged 0 to 9 years) which results in a total population of
1,960,069, equivalent to a 2.0% inter-census growth rate. The problems with the 1996
census include many hardly credible figures, particularly relating to urban populations. For
example, the Maseru population is given as 137,837, and this corresponds to an inter-
census growth rate of just 3.5%, the lowest growth rate of any urban area except Qacha's
Nek. This is clearly improbable, given the known expansion of Maseru in the period 1986
to 1996, and it seems likely that Maseru was massively under-enumerated, although to
what extent is difficult to assess.
It is not possible to independently assess the quality of the 1996 census data, as the data
are not available to the public.
Given these difficulties it would be wise for the ORASECOM project to treat Lesotho's
1996 census data with a higher degree of caution than that of Botswana. It is hoped that
any uncertainty will be ended in the 2006 census. Early indications are that many of the
problems encountered in 1996 and being addressed in the 2006 census plans. However,
at the time of writing (February 2006) it was not yet possible to assess progress on this.
For this reason, an assessment is made below of the 2001 Lesotho Demographic Survey.
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4.4
The 2001 Lesotho Demographic Survey
Although Lesotho did not conduct a full census in 2001, the BOS conducted a sample
census, or Demographic Survey, which provides useful background detail for population
planning purposes. The key findings from this study include the following1:
· The average household size is 4.9 people.
· Life expectancy, based on an adult HIV/AIDS prevalence rate of 31%, has
declined to 39.1 years for males and 40.8 years for females.
· Females have a greater tendency to move from their places of birth than males,
due to marriage customs and, more recently, work opportunities.
As a result
72.3% of males and 46.0% of females where found to be living in the settlement
where they were born.
The total population, estimated by the Lesotho Demographic Survey (LDS), is shown in
the table below by gender and district:
Table 10: Distribution of Lesotho's Population by Gender and District
District
Male
Female
Total
Butha-Buthe
63,608
63,299
126,907
Leribe
181,627
180,712
362,339
Berea
151,000
149,557
300,557
Maseru
229,573
248,026
447,599
Mafeteng
119,126
119,820
238,946
Mohale's Hoek
101,565
105,277
206,842
Quthing
70,556
70,085
140,641
Qacha's Nek
37,991
42,332
80,323
Mokhotlong
43,707
45,998
89,705
Thaba-Tseka
66,731
66,949
133,680
TOTAL
2,127,539
Source: BOS, Government of Lesotho, 2001
Significantly, the LDS found that the Total Fertility Rate (TFR) had decreased from 4.9 in
1996 to 4.2 in 2001. This is a decrease of 0.7 in 5 years, far higher than would normally
1 The full report of the LDS is available on the internet (www.bos.gov.ls)
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be expected. This rapid drop, which has very significant consequences for population
projections, is considered to be partly due to a higher rate of contraceptive prevalence
than has previously been the case.
The Bureau of Statistics reports a contraceptive
prevalence rate of 41% (high by African standards but similar to rates in South Africa),
which would normally decrease TFR by 0.3 in five years. The fact that the TFR has
dropped by 0.7, well in excess of expected rates, is an indication of the severe impact that
HIV/AIDS has already had on the demographic structure of the population.
The availability of data from the LDS enabled demographers working on an impact study
of HIV/AIDS on the education sector to project the impact of HIV/AIDS on the national
population. This work was reviewed and extensively used by the LLWSSFS team. The
anticipated impact of HIV/AIDS and other major demographic trends is examined in more
detail in the section titled "The Impact of HIV/AIDS" below.
4.5
Broad Demographic Trends
4.5.1
Internal Migration
The first major trend to note is the internal population shifts within the national boundaries
of Lesotho. A water resources study conducted by TAMS in 1996 showed a significant
shift in population was occurring, with many people moving from the Mountains to the
Lowlands of the country.
The study showed that the process of urbanisation and
movement to the Lowlands is one which impacts very strongly on water resource planning
as the shift is from areas of relatively abundant and largely unutilised water resources to
areas of Lesotho where water resources are relatively scarce and under considerable
pressure. The extent of internal migration is such that part of the country particularly the
remote mountains are experiencing a decline in population while the fastest growing
peri-urban areas are growing at well over twice the national average.
This trend was confirmed by the LLWSSFS which used a `block build approach' to project
the expansion of the main settlements (with populations of over 2,500) to the year 2035.
Detailed demographic analysis of these larger settlements revealed that all are likely to
grow faster than the remaining smaller settlements, which are likely to lose population due
to HIV/AIDS and out-migration. However, the larger settlements will themselves grow at
very different speeds.
This will result in fundamental changes to the composition of
Lesotho's settlement pattern in years to come. As can be seen from the figure below, in
1996 the sum total of all the category five small settlements was greater than the capital,
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Maseru, but by the year 2000 Maseru exceeded this sum total and, thereafter, it rapidly
started to exceed the sum total of other category types. The towns with industry also
rapidly started to overtake settlements without any prospects of industrial development.
Figure 5: Growth by Settlement Type
4.5.2
External Migration
Lesotho is entirely surrounded by its much larger neighbour, South Africa. The social and
cultural links between the two countries are very strong, with more Basotho said to be
living on the South African side of the border than within Lesotho itself. The extent and
durability of these links has made it relatively easy for Basotho to move between the two
countries and many have a `foot on each side of the border'. In the last decade important
changes in South Africa have clearly had an influence on external migration. The reasons
for this include the following:
· Many Basotho were able to register as South Africans in the run up to the 1994
elections (when requirements were relatively loose) enabling them to gain access
to resources unavailable in Lesotho, including state pension, social grants,
housing subsidies and low cost education and health services.
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· The announcement by South Africa that long-term Basotho migrants would be
given the opportunity to settle with their families permanently in South Africa
encouraged some to leave. The extent to which this has actually happened is not
easy to quantify, but in 1998 25% of them said it was likely that they would settle
permanently if given the opportunity2.
· An increasing porosity in the Lesotho/South African border, and incentives for
Basotho families to move to South Africa if at least one member could qualify for a
South African pension or other social security benefits.
· An increased opportunity for well qualified Basotho to study and work in South
Africa,
creating
a
brain
drain
of
major
economic
importance,
even
if
demographically relatively slight.
The exact extent of external migration is not known, partly because the last census was
conducted nine years ago, but also because many of those who move retain some contact
with Lesotho so they would also be counted as de jure citizens even if they rarely return
home. The next census should be able to shed more light on external migration patterns
especially if from the de facto point of view.
4.5.3
The Demographic Impacts of HIV/AIDS
Factors driving the epidemic
Earlier it was indicated that HIV/AIDS, rather than contraceptive prevalence, is the key
factor driving down the country's Total Fertility Rate. Lesotho has one of the highest levels
of HIV infection in the world, exceeded only by Swaziland and Botswana. Lesotho's high
prevalence rates are a result of a number of high risk factors that include: long-term male
(and now also female) migration; a high prevalence of multiple partner relationships; low
levels of condom use; inconsistent condom use; and a relatively young age for the onset of
sexual activity.
2 The actual numbers of Basotho migrants to South African mines over the past 12 years has suffered a
fairly steady decline. According to the Central Bank of Lesotho Quarterly Review, which reports the
relevant statistics in each issue, the average number of Basotho mineworkers in South Africa peaked at
an average of 127,386 in 1990, after which it declined steadily to a low in 2001 of 61,412, less than half of
the 1990 figure. There was a slight recovery, possibly due to the improved gold price, to 62,158 in 2002.
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The growth of the epidemic
The first AIDS case was reported in Lesotho in 1986, according to the Ministry of Health
and Social Welfare. Since then, the Ministry has records for a cumulative total of 10,880
AIDS cases reported by the end of 1999. However, most AIDS deaths are not reported as
AIDS deaths, suggesting that the official figure is well below the reality. UNAIDS estimates
that 16,000 people died of AIDS in Lesotho in 1999 alone, rising to 25,000 during the year
2001. The current projections made by the HIV/AIDS Impact Assessment study done by
SIAPAC for the Ministry of Education are similar to that of other agencies, with estimates
of 16,650 people dying of AIDS in 1999, rising to 22,750 during 2001. The estimate of
cumulative deaths by 1999 was 77,540.
Of all AIDS cases, an estimated 12% were under the age of five (World Bank, 2000).
Family Health International estimates that some 20% of all pregnant and lactating mothers
are HIV positive, meaning that each year 2,000 to 3,000 children are born HIV positive
(assuming that one-in-three HIV positive mothers transmit the virus to their children).
A considerable increase in the number of orphans is one of the long-term implications of
the HIV/AIDS epidemic, as parents die before their children can grow up. As of 2001, it
was estimated by the BOS that 73,000 Basotho under the age of fifteen had lost their
mother or father or both parents to AIDS, and were therefore classified as orphans.
HIV Prevalence
Knowing the prevalence rate has, over the years, been hampered by small sample sizes,
insufficient sites, and gaps in years that sero-prevalence data were collected.
However,
by 1999 sufficient data existed for estimates to suggest that adults aged 15-49 had a
prevalence of 23.6%. Then in 2002 a UNDP estimate in 2002 placed the prevalent rate at
31%, a figure confirmed by the LLWSSFS as a high prevalence scenario. Urban and peri-
urban areas were noted to have higher than average prevalence rates.
Modelling done by SIAPAC for the Ministry of Education Study, and confirmed by the
LLWSSFS indicates that, overall, HIV prevalence in Lesotho appears to be close to its
peak.
The report indicated that if the epidemic continued to follow past trends, HIV
prevalence would peak at 33.8% by 2007 based on the high prevalence projection and at
28.63% for the low prevalence projection. It was, however, noted that the estimates were
based on data known to contain a number of gaps that would need to be addressed before
greater certainty could be established.
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Fortunately the 2004 Lesotho Demographic & Health Survey (LDHS) incorporated HIV
testing of adults at the household level and facilitated a better understanding of the
magnitude and patterns of HIV in a wider and more representative sample of the general
population. This showed an adult HIV prevalence rate of 24%, less than had been
anticipated by past modelling exercises. Then, the updated 2005 UNAIDS/WHO
projections, based on the 2005 HIV sentinel survey and calibrated with the 2004 LDHS,
estimated the adult prevalence rate to be approximately 23%. Consistency of the 2005
projections with the 2004 LDHS value validates the official 2005 updated adult HIV
prevalence of 23.2%. Once again urban areas were shown to have a higher rate than
rural ones (28.8% vs 21.8%).
Number of People Infected with HIV
Based on the prevalence projections made above, the LLWSSFS model indicates that
some 384,000 people are presently HIV-positive and that this will rise to 422,000
individuals by 2015 under the high prevalence scenario or 375,000 under the low
prevalence scenario. The number of people infected in the early stages of the epidemic is
increasing rapidly. Gradually, the rate at which people are infected will slow because a
greater proportion of those vulnerable to the disease are already infected with HIV.
Mortality
As the rate of HIV infection declines, the mortality rate will increase as more of those who
are HIV-positive progress to AIDS and die.
The model shows that although HIV
prevalence will stabilise, the number of HIV infected people will continue to increase until
about 2010, despite a constant prevalence rate. At that point the number of infections will
start to stabilise because of the large number of deaths and the consequent decrease in
population size.
While HIV prevalence is close to its peak, the lag between infection and death means that
a full AIDS epidemic is a number of years away. The AIDS death rate is likely to peak only
eight years after HIV has peaked.
Based on the high prevalence scenario, the number of AIDS deaths is projected to
increase to 43,000 AIDS deaths annually, at its peak, by 2010. Under the low prevalence
scenario the number would be about 38,000 annually by the same year. (These figures are
similar to estimations given by UNAIDS).
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By 2010 the cumulative number of AIDS deaths may be as high as 435,000 without
access to anti-retroviral treatment.
By 2015, cumulative AIDS deaths are expected to
exceed 650,000, but it is important to stress that many of these can be avoided.
Following the low HIV prevalence scenario, Lesotho has currently lost 142,000 people; this
will increase to 370,000 by 2010 and 558,000 by 2015.
Impact of mortality on population growth
AIDS-related deaths will decrease the population of the country in two ways. First, the
deaths will directly affect the size of the country's population as people die. Secondly,
HIV/AIDS affects young adults and the death of young adults reduces the number of
children born. Thus the population of the country will, therefore, be smaller than it would
have been without AIDS, as its citizens die and others are never born. The extent to which
this will happen will, however, be largely influenced by the range and effectiveness of
programmes that are introduced and expanded in the next few years.
Of particular
importance are antiretroviral therapies, including the Prevention of Mother to Child
Transmission, which is relatively inexpensive.
With HIV/AIDS, the natural growth rate of Lesotho is declining and is projected to continue
to decline until it reaches a zero growth rate by 2007/2008, and negative rates thereafter.
This will result in a slow population growth rate until 2010 after which a slow reduction in
population size is projected. Without HIV/AIDS the population was projected to increase to
about 3.3 million by 2015 but due to the impact of HIV/AIDS, it will not grow much beyond
2.3 million by the end of the decade, and will thereafter decline if no fundamental changes
are brought about on a national scale.
The socio-economic impact of HIV/AIDS is considerable. This is not only because of the
number of deaths and the resultant reduction in the rate of population growth, but because
those who are dying are in the productive or working age groups. Unlike epidemics of the
past that targeted the weak, the very young and the old, HIV infects the sexually active
population, with infection rates highest in the 25-35 year old age group. The age groups
with the greatest increases in mortality as a result of AIDS are those most responsible for
economic activity and social care. The impact of HIV/AIDS on both the population and the
economy as a whole has important implications for assessing water demand.
It is
important that ORASECOM continue to monitor changes in this critical area for many
years to come.
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5
NAMIBIA
5.1
Overview
The demography of Namibia is strongly influenced by its vast size, its arid climate and its
political history. The country covers an area of 824,116 km2 with a coastline of 1,572
kilometres. It is sub-Saharan Africa's driest country. About 40% of the land surface of
Namibia could be classified as arid, 40% as semi-arid, 5% as sub-humid, and a narrow rim
in the west and southwest that could be better described as hyper-arid or desert (15%).
Over 85% of the country these climatic zones order themselves in savannah landscapes
that are of a varying ecological constitution, but the boundaries between them are neither
static nor abrupt, because drylands are exposed to high inter-annual variability. The main
rainy season occurs from December to March. Rainfall varies from less than 50 mm along
the coast, to 200 mm in the south, 350 mm in the central areas, 450-550 mm in the north
central areas, and 700 mm in the far northeast (see figure below). Rainfall patterns largely
dictated the early patterns of human settlement, with the more arid areas being the least
densely populated still today.
Figure 6: Rainfall distribution of Namibia
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The general settlement patterns in Namibia's past have been altered by its recent complex
political history, which have caused a distinctive skew distribution of population densities
and concentrations of poverty and underdevelopment. The majority of Namibians live in
the north of the country. One explanation for the higher density is the more favourable
natural environment, especially the better availability of surface water and the higher
rainfall. For many years the central and southern parts remained sparsely populated with
only nomads roaming the land as a result white settlers on commercial farms occupied
the "vacant" land. The finding of groundwater was a decisive drive in this regard.
Moreover, the 130 years of colonial rule before independence laid the foundation for the
way in which Namibia's land is currently divided and utilized. Under South African rule, for
example, many indigenous Namibians were forced into reserves, later called "homelands"
and movement between the different parts of Namibia were restricted. In this way the north
of Namibia remained densely populated, compared to the rest of Namibia. Mainly as a
result of colonial rule, less than 10% of the population own all freehold farming areas. This
privately owned land constitutes approximately 44% of the total land area. About 1.5% of
the total land area is comprised of exclusive diamond concession areas and 13.5% has
been proclaimed as conservation areas. An estimated 60% of Namibia's population
practice subsistence agropastoralism on communal land, which constitutes approximately
41% of the total land area.
The post-independent government made considerable efforts to improve living standards
through political and administrative changes. This includes the reorganisation of the
country in to 13 administrative regions. For the purposes of this study three of these
regions the Karas, Hardap and Omaheke Regions, are defined as part of the Orange
River Basin. A fourth one the Khomas Region contains only a tiny part of the Orange
River Basin and is not taken into account for practical reasons. The thirteen regions
provide the spatial framework within which the state functions and within which planning
and development is accommodated. As a result, decentralization became an important
policy and governing approach. Both the 1991 and 2001 national censuses, for example,
made use of the regional divisions in providing demographic data.
Namibia had a particularly large challenge to face not only to shift to a new paradigm of
sustainable development, but also to overcome the legacies of colonialism such as rural
and urban poverty, huge disparities in income distribution, unequal access to land and
natural resources, poor education, health and housing, and many other more subtle
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issues. Greater freedom of movement was allowed and has had demographic
consequences, but recent economic developments such as the establishment of mines
had caused even more sudden changes in population density as people are attracted to
growth nodes in previously uninhabited areas. Nevertheless, the majority of Namibians
remain directly or indirectly dependent on agriculture and this high dependence on primary
production renders the economy vulnerable to climatic and other external forces. The
economy is broadly characterized by low physical investment, low domestic savings and
very high government consumption. While it is government policy to reduce dependence
on the primary sector, the manufacturing base remains small and under-developed.
Increasing economic growth and employment, reducing poverty and improving equity
remain a pivotal part of development objectives. Despite improvements in the education
and health sectors, efforts need to be further intensified at all levels of society in order to
fully redress Namibia's past inequalities and to improve public sector capacity. Namibia
still suffers from comparatively low levels of education and strong social, gender and
regional disparities in educational levels and outputs, low public sector capacity and a high
reliance on foreign technical experts and consultants and a brain drain within the civil
service (NPC, 2001).
The unemployment rate in Namibia is about 31% (NPC, 2003), and poverty and inequity
remained endemic after Independence. Namibia is one of the most unequal societies in
the world, even worse that Brazil and Bangladesh. Income distribution is especially skew -
the richest 10% of society receive 65% of income, leaving only 35% for the remaining
90%. This means that half of Namibia's population survives on approximately 10% of the
average income, while 5% receives incomes that are five times the national average of
about US$2,000 GNP per capita (UNDP, 1998). Steadily growing at the high annual rate of
3%, the Namibian population is young and will sustain high growth rates over the coming
years. The rapid population growth is closely coupled with a fast increase in dependants,
mainly as a result of HIV/AIDS. Furthermore, Namibia is still mainly a rural society and
possible migration to towns and cities tends to put urban settlements under pressure.
Poverty is especially prevalent in the central and southern parts of the country, with more
than 30% of all people living in absolute poverty in 1998 (UNDP, 1998). In summary, the
key socio-economic challenges that threaten sustainable development in Namibia, and
that have stayed top priorities since 1990, are the high dependency on natural resources,
high population growth and skew population distribution patterns, human health and
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HIV/AIDS, poverty and inequality, access to land and natural resources, poor governance,
and knowledge and human capacity.
Much of the post-independent development in Namibia has focused on urban centres. As
a result Namibia is expected to have an increasingly urbanised population in the decades
ahead. This is illustrated in the figure below:
Figure 7: Projections of urbanization rates in Namibia: 1985 to 2025
According to the 2001 Population and Housing Census the unemployment rate of the
Hardap Region is 34%, for the Karas Region 28% and for the Omaheke Region 24%. The
presence of the mining activities at Oranjemund and Rosh Pinah contribute to the lower
unemployment rate in the Karas Region. In contrast the sparsely populated Hardap
Region, which experiences an out-migration, has a higher unemployment rate - most of
the unemployed being pensioners residing within the rural areas. The data on
unemployment should be used with caution as no information was detailed or verified,
either on the number of working hours or on underemployment.
Inadequate sanitation and lack of access to potable water create conducive environments
for the spread of infectious and parasitic diseases and are therefore a pubic health
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concern. The rural water supply schemes introduced since Namibia's independence have
greatly contributed in providing the rural communities with access to safe and drinkable
water supply. Access to safe and potable water is quite high in all three Namibian regions:
95% of the population residing in the Hardap Region, 94% of the Karas Region and 90%
of the Omaheke Regions have access to safe water (NPC, 2003).
5.2
Introduction to census issues
The objective of the activities conducted to date has been to establish the nature and
quality of available demographic data, and identify gaps in data and interpretation. The
task has consisted principally of a review of existing published data, and the interpretation
of available information based on the consultant's knowledge of the project area.
5.3
Background on census issues
During the first population census in 1921 about 229,000 people were reported living
within the country (Mendelsohn et al., 2002). The population has increased more than
eight-fold since then. In 1970 there were an estimated 393,400 males and 400,400
females in the country, the total estimated population being 793,800. The 1981 census
took place at a time when the liberation struggle was especially intense, the results of this
census were not published in the same detail as that of the former censuses. Since no
mid-year estimates or surveys were conducted between the respective dates estimations
had to be made.
Namibia became independent on 21 March 1990, independence preceded by almost
twenty-five years of the liberation struggle. Organisation for the first post-independence
census took place immediately after independence, with the census itself conducted in
August, 1991. Not surprisingly, it suffered from numerous problems associated with lack of
capacity, insufficiently trained personnel, problems with quality control, etc. This was,
however, less of a problem in the central and southern regions when compared to the
populous northern regions. By contrast, the 2001 census was well planned and well
implemented. While there were a number of disputes associated with the census,
especially in urban locations where local authorities felt that undercounts had occurred, the
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most serious disputes were in communities located in regions outside the Orange River
Basin3.
5.4
Census results
The total enumerated population of Namibia on 1 August 2001 was 1,830,330. Of these,
942,572 (51%) were female and 887,721 (49%) were male, giving Namibia a sex ratio of
94.2 males per 100 females. There were a total of 346,455 households.
Rates of population growth increased from about 2% per annum in the first half of the 20th
century to 3% in the last five decades. Projections suggest that the population will rise to
about 2,250,00 in 2010 and 2,600,00 by the year 2020. Even though the total population
will increase by about 800,000 over the next 20 years, the rate of growth is expected to
drop steadily from the current 3% to about 1.5% in 2006. Much of this decline will be due
to the negative impacts of HIV/AIDS while higher levels of education amongst women, and
involvement in the cash economy will lead to lower birth rates and, therefore, lower growth
rates.
Of the total population of 1,830,330 people (2001), 33% were living in gazetted urban
areas and 67% were living in rural areas. A total of 39% of the population were younger
than 15 years of age in 2001. Corresponding figures for the three regions are 41% for
Omaheke, 39% for Hardap and 31% for Karas. The distribution of the population across
Namibia's thirteen regions is indicated in the figure below.
3
As with all censuses, the count is of the number of people who overnight in a particular location on the
night before the census. Urban communities particularly in the west are affected by dramatic population
fluctuations arising from the seasonality of employment in important economic sectors, notably the fishing
and fish processing industries. For this reason, the two communities of Lüderitz and Walvis Bay in the
southern and central coastal areas, respectively, often have populations significantly higher than reflected
in the census. This is also the case for Swakopmund, near Walvis Bay, which has significant short-term
in-migration during weekends and holidays. This may be the root cause of the disagreements by town
officials about population counts.
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Figure 8: Population distribution map of Namibia
The following table reflects the population structure and composition residing within the
three regions during the 1991 and 2001 census survey periods.
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Table 11 Comparative Populations for the Karas, Hardap and Omaheke Regions
Population
%
%
Growth
Persons
Region
Females
Males
size
Urban
Rural
rate
per km
Karas:
1991
61,162
27,239
33,923
45
55
2001
69,329
32,346
36,976
54
46
1.3
0.4
Hardap
1991
66,495
32,767
33,728
44
56
2001
68,249
33,665
34,579
46
54
0.3
0.6
Omaheke
1991
52,735
25,423
27,312
16
84
2001
68,039
32,484
35 554
28
72
2.5
0.8
2001 Total
205,617
98,495
107,109
40*
60*
1.37*
0.6*
Source: 2001 Population and Housing Census (* = calculated 2001 average)
Together the three regions falling within the basin comprise 11% of Namibia's total
population residing on 355,478 km² (43%) of Namibia's total area of 824,116 km².
While the Omaheke Region experiences a growth rate of 2.5%, the Karas and Hardap
regions experience a mere 1.3% and 0.3% respectively. This is partially due to out-
migration from the south, arising from the decline in job opportunities and the pull of urban
centres for job seekers. However, it is also due to two other factors: 1) the number of in-
migrant households to the south which are small; and 2) the lower natural population
growth rates among populations in the south compared to other parts of Namibia. The
Karas Region, for example, grew only by 1.3% from 1991 to 2001, and the Hardap Region
by only 0.3% (the lowest in the country), compared to a national rate of 2.6%. This might
also be a result of the undercounts that occurred in the north in 1991, `adjusted' through a
redistribution of population numbers for these regions from 1991 to 2001. Put differently,
the undercount in the north during 1991 meant that the south appeared to have a higher
proportion of the population in 1991. The strong mining sector found within the Karas
region results in a high number of in-migrant households to the region partially balancing
the lower natural population growth rates among populations in the south compared to
those living in the northern parts of Namibia. The low growth rate of the Hardap Region
could be ascribed to limited economic employment opportunities, a high HIV/AIDS rate
and low natural population growth. Consequently, it is expected that the growth rate for the
Hardap region may actually decline in the near future.
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While quite small, the population of the Hardap and Karas Regions would be significantly
lower if it were not for urban settlements, mining activities and irrigation schemes, as the
hyper-arid, arid and semi-arid conditions of the south supports a sparse rural population
distribution. Vast parts of the south, as well as the Omaheke Region, exist of extremely
large but sparsely populated commercial farms.
In the Karas Region 54% of the population lives in urban areas, compared to only 46% in
the Hardap Region and 28% in the Omaheke Region. The low figure of the Omaheke
Region could be explained as a result of the predominant extensive farming areas. The
regions continue to rapidly urbanise, especially in the Karas Region where more and more
of the population is concentrated in the following four settlements: the regional capital of
Keetmanshoop, the mining town of Oranjemund, the port of Lüderitz, and the rapidly-
growing mining town of Rosh Pinah. Of these, three - Keetmanshoop, Oranjemund, and
Rosh Pinah are closely related to the Orange River4.
In all three regions there exists a growing awareness about the economic potential of
tourism. As a result the many emerging tourism enterprises may finally stabilize the
exodus of the population from the rural parts. With the exception of Mariental, where the
Hardap Irrigation Scheme is found, little economic activity other than extensive sheep and
cattle farming and high value tourism in the Naukluft / Sossus Vlei area is experienced in
the Hardap Region. The region is furthermore characterized by a low birth rate (3.7
children per woman), high infant death rate (63 per 1,000 for both sexes) a presumable
high rate of out-migration of farm workers and a high HIV/AIDS rate. Consequently, very
nominal economic development occurs in the region. The economic situation in the
Omaheke Region is very similar to the Hardap Region, but the birth rate is slightly higher
4,9 children per woman and infant death rate slightly lower 55 per 1,000 for both
sexes.
Sex ratios of all three regions reflect the dominance of males related to in-migration from
the north. The Karas Region has 114 males for every 100 females, the highest such ratio
in Namibia. The Omaheke Region has 109 males for every 100 females. In the Hardap
Region, the ratio was 103 males for every 100 females. Many of these migrants will leave
4
Rosh Pinah was not gazetted as an urban area for the 2001 census. It is, nevertheless, perhaps
the fastest growing town in Namibia, due to the recent commissioning of the Skorpion Zinc Mine.
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the south for their permanent northern homes eventually. Pull factors behind the in-
migration relate specifically to mining, irrigation and industrial developments in the two
regions.
5.5
Impacts of HIV/AIDS
Like the rest of sub-Saharan Africa, Namibia is severely affected by HIV/AIDS. The
national infection rate is estimated at 22% for people aged 15-49 (2002 data), with rates
highest in the north and lowest in the south and northwest. Rates of HIV prevalence
amongst pregnant women are indicated in the figure below.
Figure 9: Distribution Map of HIV Prevalence amongst Pregnant Women in Different
Sentinel Sites, Namibia 2002
Infection rates are growing in all locations in Namibia as reflected in the figure below
showing comparative rates of infection in 1998.
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Figure 10: Rates of HIV Infection in Namibia (1998)
Presumably the infection rates in the three regions vary between 10 and 15% in 2005.
Unfortunately, the impacts of HIV/AIDS on the growth rates of the regions are not well
comprehended. Most key informants do not believe that HIV/AIDS will have an important
impact on population growth rates for the regions overall, largely because population
growth is driven principally by economic opportunities in these regions, and not by local
death rates. The loss of working age adults may therefore draw more migrants to the
regions. If, on the other hand, economic opportunities in these regions decline, the
resultant reduction in in-migration may cause the effect of AIDS-related deaths to be more
prominent.
While HIV/AIDS will slow the Namibian population growth rate over time, it is apparent that
the epidemic will not prevent the significant population movements associated with
emerging economic opportunities in the south. This is the key determinant of population
growth in the south, and of particular interest to ORASECOM. If, for example, a mine
opens on the Orange River, and creates 500 jobs (as in the case of the Skorpion Mine), it
will draw about five times more people from the northern parts of Namibia. It is thus clear
that a mine of such magnitude may draw enough people to replace at least half of all
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AIDS-related deaths in the Karas region over the next five years. It also means that if the
mining activities at Oranjemund expand at the same magnitude, in-migration would likely
surpass AIDS-related deaths.
In order to understand the impact that AIDS has on the population as a whole, the
following section explores trends based on modelling similar to that done in Lesotho.
5.6
HIV/AIDS Trends
In 1991, some 14,000 Namibians were HIV positive and had not yet died of AIDS. This has
risen rapidly to 28,200 in 1992; 50,000 in 1993; 77,400 in 1994; and tripled to 240,200 by
2001. Rapid increases will presumably level off by the year 2006 at around 273,300, with
cases averaging near 367,400 from 2016. As noted earlier, it is estimated that the average
HIV positive person will take eight and a half years from infection before AIDS develops.
Therefore, the number of AIDS-related deaths will lag behind HIV-infection by about a ten-
year period. The Spectrum model indicates the exponential growth in cumulative AIDS-
related deaths, rising to over 500,000 total deaths by the year 2020, while the annual
AIDS-related death rate will begin to decrease, based on the current model estimates.
A UNAIDS report from 2000 summarises the demographic changes arising from AIDS as
follows:
"The base of the pyramid is less broad. Many HIV-infected women die or become infertile
long before the end of their reproductive years, which means that fewer babies are being
born; and up to a third of the infants born to HIV-positive mothers will acquire and
succumb to the infection.
But the dramatic change in the population pyramid comes
around 10 or 15 years after the age at which people first become sexually active, when
those infected with HIV early in their sexual lives begin to die off. The populations of
women above their early 20s and men above their early 30s shrink radically. As only
those who have not been infected survive to older ages, the pyramid becomes a chimney."
For the age group 5-18, deaths from AIDS had little impact until the end of the 1990s, with
1995 showing the first year of decline. Most of this decline is due to children never being
born because one or more parent has died of AIDS, and to a lesser extent due to HIV
positive children being born but dying before their fifth birthday.
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5.7
Looking Ahead
Because the population in the study area is relatively small compared to the vast area, the
relative influence of economic developments on the size of the population is considerable.
While most parts of the ORASECOM catchment area receives summer rainfall, the south-
western part is classified as a winter rainfall area. Although experiencing a low annual
rainfall of between 100 mm and 200 mm per annum, the successful production of table
grapes and dates along the northern banks of the Orange (mainly at Aussenkjer) and at
the Naute Dam has demonstrated the possibility to utilize the natural resources, the
sustainability of which need to be monitored.
Tourism, which has developed into Namibia's fastest growing sector after mining and
agriculture, holds potential in further developing the southern and eastern parts of the
ORASECOM catchment area, especially along the Konkiep, Fish River Canyon and the
Karas Mountain area. Recognition must also be given to the fast growing cultural tourism
sector which can capitalize from culture and tradition of the Nama who inhabited Namibia's
south during the 1740's, generally entering the country from South Africa at the
Velloorsdrift area, or the following Orlam Nama (1800's) and Rehoboth Baster (1870)
tribes following the Nama but penetrating even further north.
Studies are currently being conducted to introduce olive and date farming along the
northern banks of the Orange River near Oranjemund while the possibility to introduce
cash crop farming and a horticulture project at Rosh Pinah is also investigated. The town
of Rosh Pinah has probably almost tripled in size because of the development of the
Skorpion Zinc Mine. Alternatively, the reduction in land-based mining activities over the
past ten years near Oranjemund has resulted in a decline in the town's population,
although this appears to have been halted by off-shore developments. A feasibility study
has been conducted for another mine near the Orange River, near Noordoewer but a
number of key informants do not think that the mine will take off.
There is also a feasibility study currently underway investigating the possibility to establish
the Haib copper mine at Noordoewer while an irrigation scheme near Noordoewer is also
investigated. There has also been some discussion of expanding irrigation at Aussenkjer,
but a dispute has arisen with a mining company who has rights over land in the area.
With regard to the Hardap Region, there are not many new economic developments, with
a few exceptions. An irrigation scheme is planned at Brukkaros. The town of Tses, near
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the irrigation site, would probably grow considerably, especially during the construction
phase. The town has a severely restricted water supply and this would temporarily worsen,
until the dam is built and provides water for the town. In addition, the Ministry of Lands and
Resettlement is investigating the possibility to introduce an agricultural scheme at
Voigtsgrund, one of its resettlement farms in the Hardap Region.
In short, economic opportunities have important impacts on how many people will live in
these regions, how many of them would be migrants from outside the region, whether they
will establish themselves for the long-term in the region, and whether they will migrate for
short or long periods of time. The table below illustrates the main economic activities
practiced and development potential of the regions and the economic active urban areas
falling within the Namibian catchment area of the Orange river.
Table 12 Regional Economic activities
Region
Activity / Major Centres
Cattle farming; game (farming, hunting); tourism (cultural, wildlife)
Regional administration and governance; agricultural
Omaheke Region
Gobabis
and transport service centre; tourism; feedlot and
dairy production
Leonardville
Irrigation (cash crops, fodder); tourism
Sheep and cattle farming; tourism (scenery, wildlife)
Tourism; agricultural service centre; irrigation (fodder
Rehoboth
and horticulture)
Hardap Region
Regional administration and governance; regional
Mariental
and agricultural service centre; irrigation scheme
(cash crops, fodder); tourism
Maltahöhe
Tourism; service centre
Mining (diamonds, zinc, lead, copper); sheep and cattle farming; tourism
(scenery, wildlife, cultural); trade (import / export); fishing, irrigation (table
grapes, olives, dates); aquaculture
Regional administration and governance; regional,
Keetmanshoop
agricultural and transport service centre; tourism;
meat processing (sheep, ostrich)
Karas Region
Lüderitz
Service centre; harbour; tourism; aquaculture
Mining; irrigation (dates and olives); eco-tourism;
Oranjemund
aquaculture
Rosh Pinah
Mining; service centre; eco-tourism
Karasburg
Agricultural service centre
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5.8
Key Namibian Literature
Hardap Regional Council. (2001) Regional Development Plan 2001/2002 2005/2006.
Karas Regional Council. (2001) Regional Development Plan 2001/2002 2005/2006.
Mendelsohn JA, Jarvis C, Roberts & T Robertson. (2002) Atlas of Namibia. A portrait of
the land and its people. Cape Town, David Philip Publishers.
Ministry of Environment and Tourism. (2003) Community Tourism Market Research for the
South of Namibia. Windhoek. Government of the Republic of Namibia.
National Planning Commission. (2001) Levels of living survey, 1999 (Main report).
Windhoek, National Planning Commission (Central Statistics Office).
National Planning Commission. (2003) Republic of Namibia 2001 Population and Housing
Census, Hardap Region - Basic Analysis with Highlights. Windhoek. Central
Bureau of Statistics.
National Planning Commission. (2003) Republic of Namibia 2001 Population and Housing
Census, Karas Region Basic Analysis with Highlights. Windhoek. Central
Bureau of Statistics.
National Planning Commission. (2003) Republic of Namibia 2001 Population and Housing
Census, Omaheke Region - Basic Analysis with Highlights. Windhoek. Central
Bureau of Statistics.
Omaheke Regional Council. (2001) Regional Development Plan 2001/2002 2005/2006.
UNDP.
(1998)
Namibia
Human
Development
Report:
Environment
and
Human
Development in Namibia. Windhoek. UNDP.
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6
SOUTH AFRICA
6.1
Introduction
This section of the report seeks to review available information on population distribution,
population growth, demographic movements and the impact of HIV/AIDS on the South
African population living within the Orange River basin. The objective of the review is to
give an indication of the extent, availability, nature and quality of the available
demographic data.
6.2
Method
The task has consisted principally of the collection, review and interpretation of existing
published demographic data.
Particularly data made available by Statistics SA
(STATSSA), the legislated statistical department of the SA government.
The methodology followed in pursuit of the objective includes:
· Sourcing statistical data on population distribution, population growth, migration
and HIV/AIDS from a range of sources including: government departments,
District Municipalities (DM), research institutes, professional bodies and private
companies.
· Establishing what methodologies, approaches and assumptions are being used to
determine population and HIV/AIDS statistics and migration dynamics.
· Reviewing, assessing and comparing data, assumptions and approaches.
6.3
The Orange River Basin & Demographics in South Africa
STATSSA is South Africa's legislated demographer, however there are numerous
institutions generating demographic data for a range of purposes.
This scenario has
produced a lively debate fuelled by a range of interested parties including academics, the
private sector, government (at all levels), NGOs and Community Based Organisations,
politicians and so on.
The interests themselves are equally broad and include those
whose interests lie in methodology and fine tuning models, those, such as the insurance
industry, who need to determine risk profiles, those whose interests lie in the delivery of
services, such as municipalities, and those with political agendas. In general the debate is
healthy.
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With respect to the demographics of specific river basins, South African demographic data
is understandably limited as there is limited demand for data making use of basins as
geographic entities. Nevertheless it is reasonable to assume that one can build a picture
of a particular basin's demographics by making use of geographic entities originally
demarcated for alternative purposes.
What is more problematic in determining basin
demographics is the purpose for collecting such data.
In most cases it is to make
calculations about current and future water requirements and this purpose is complicated
by inter-basin transfers (there are additional complicating factors e.g. groundwater
reserves which obviously do not reflect the same geographic patterns as surface water
basins etc but these are not considered in this study).
The focus of this study is the demographics of the Orange River basin. This basin is by far
the largest in South Africa and covers a considerable extent of the country including most
of the densely populated Gauteng province as well as the sparsely populated areas of the
Northern Cape. In addition to the area of the basin itself there are three significant inter-
basin transfers to be taken cognisance of in determining any relationship between
population and access to water. These are:
· The transfer out of the basin from the Gariep dam to the Eastern Cape via the
Fish River Tunnel,
· The transfer out of the basin by Rand Water to serve the northern parts of the
Johannesburg Metropolitan area (the Witwatersrand which forms the watershed
between the Orange-Vaal basin and the Limpopo basin runs in an east-west
direction through the centre of the Johannesburg Metropolitan area),
· The transfer into the basin from the Tugela River via the Tugela-Vaal pumped
storage scheme.
The Lesotho Highlands Water Project is not considered an inter-basin transfers for
demographic purposes as the transfer is from one area within the basin to another area
within the basin.
Bearing in mind the difficulty of equating exactly the area of the basin with existing
geographic entities where population data is known, the approximate total population of
the Orange Basin in South Africa was some 13,357,298 people, according to STATSSA in
the 2001 Census. This translates in to some 30% of South Africa's population (where the
total population is 44,819,778 according to the 2001 census). A detailed breakdown of the
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Orange River Basin's population per LM is reflected in the Table below. In addition a
population density map is included as an Appendix.
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Table 13: Census 2001 population statistics per DM & LM within the Orange River Basin.
District
Local
Code
Code
Black
Coloured
Asian
White
LM Total
DM Total
Municipality
Municipality
Kgalagadi
CBDC1
Ga-Segonyana
CBLC1
61154
5,385
66
3,786
70,391
176,917
Gamagara
NC01B1
7803
4,070
9
4,298
16,180
Kalahari
NCDMACB1
4672
500
4
1,065
6,241
Moshaweng
NW1a1
83528
523
12
42
84,105
Frances Baard
DC9
Phokwane
CBLC7
47542
6,906
36
6,842
61,326
324,803
Sol Plaatje
NC091
109904
63,860
1,590
26,108
201,462
Dikgatlong
NC092
21659
11,511
54
2,546
35,770
Magareng
NC093
15725
3,695
138
2,174
21,732
Diamondfields
NCDMA09
2274
1,832
407
4,513
West Rand
CBDC8
Merafong City
CBLC8
175770
1,618
279
32,814
210,481
738,369
Mogale City
GT411
219970
2,109
6,365
61,281
289,725
Randfontein
GT412
88680
13,273
221
26,660
128,834
Westonaria
GT414
97464
512
178
11,175
109,329
Ekurhuleni
East
Ekurhuleni Metro
East Rand
1891299
67,210
39,669
482,098
2,480,276
2,480,276
Metropolitan
Rand
Ukhahlamba
DC14
Senqu
EC142
131918
1,542
39
1,643
135,142
203,766
Maletswai
EC143
30756
3,390
30
3,131
37,307
Gariep
EC144
22774
5,878
3
2,650
31,305
Oviston Reserve
ECDMA14
3
9
12
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District
Local
Code
Code
Black
Coloured
Asian
White
LM Total
DM Total
Municipality
Municipality
Xhariep
DC16
Letsemeng
FS161
27893
10,846
36
4,205
42,980
135,237
Kopanong
FS162
40631
9,935
27
5,348
55,941
Mohokare
FS163
32382
1,106
6
2,822
36,316
Motheo
DC17
Naledi
FS171
24790
895
112
1,681
27,478
728,262
Mangaung
FS172
534171
31,953
1,043
78,271
645,438
Mantsopa
FS173
48857
2,483
219
3,787
55,346
Lejweleputswa
DC18
Masilonyana
FS181
59219
862
9
4,319
64,409
657,017
Tokologo
FS182
27319
2,208
6
2,925
32,458
Tswelopele
FS183
50878
720
15
2,107
53,720
Matjhabeng
FS184
355998
9,014
474
42,681
408,167
Nala
FS185
92972
602
18
4,671
98,263
Thabo
DC19
Setsoto
FS191
114922
1,110
636
6,529
123,197
725,942
Mofutsanyane
Dihlabeng
FS192
114671
1,834
175
12,248
128,928
Nketoana
FS193
58211
147
24
3,569
61,951
Maluti a Phofung
FS194
355395
438
439
4,515
360,787
Phumelela
FS195
47436
102
36
3,334
50,908
Golden Gate Park
FSDMA19
162
9
171
Northern Free
DC20
Moqhaka
FS201
144673
4,814
180
18,226
167,893
460,315
State
Ngwathe
FS203
102166
3,638
78
12,930
118,812
Metsimaholo
FS204
93976
617
190
21,188
115,971
Mafube
FS205
53421
312
36
3,870
57,639
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District
Local
Code
Code
Black
Coloured
Asian
White
LM Total
DM Total
Municipality
Municipality
Sedibeng
DC42
Emfuleni
GT421
553297
7,113
5,845
92,163
658,418
794,598
Midvaal
GT422
38163
922
293
25,259
64,637
Lesedi
GT423
57499
607
565
12,872
71,543
Johannesburg
Metro
JHB
Johannesburg
Johannesburg
2369958
206,552
134,046
515,252
3,225,808
3,225,808
Namakwa
DC6
Richtersveld
NC061
1178
7,708
12
1,232
10,130
87,372
Nama Khoi
NC062
1372
39,264
63
4,050
44,749
Hantam
NC065
288
16,417
27
3,087
9,819
Karoo Hoogland
NC066
325
8,321
18
1,849
10,513
Khai-Ma
NC067
1447
8,719
9
1,174
11,349
Namaqualand
NCDMA06
36
593
183
812
Karoo
DC7
Ubuntu
NC071
2850
11,847
18
1,660
16,375
164,614
Umsombomvu
NC072
13883
8,055
18
1,686
23,642
Emthanjeni
NC073
10555
20,650
51
4,294
35,550
Kareeberg
NC074
190
8,271
24
1,001
9,486
Renosterberg
NC075
2491
5,621
6
951
9,069
Thembelihle
NC076
1901
10,118
27
1,937
13,983
Siyathemba
NC077
3013
13,010
15
1,476
17,514
Siyancuma
NC078
9714
22,479
33
3,587
35,813
Bo Karoo
NCDMA07
256
2,360
566
3,182
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District
Local
Code
Code
Black
Coloured
Asian
White
LM Total
DM Total
Municipality
Municipality
Siyanda
DC28
Mier
NC081
87
6,498
3
253
6,841
209,884
Kai Garib
NC082
13207
39,993
24
4,457
57,681
Khara Hais
NC083
14359
48,499
72
10,860
73,790
Kheis
NC084
851
13,739
6
1,434
16,030
Tsantsabane
NC085
14721
12,994
48
3,250
31,013
Kgatelopele
NC086
7313
5,523
72
2,537
15,445
Benede Oranje
NCDMA08
1167
6,304
3
1,610
9,084
Central
DC38
Setla-Kgobi
NW381
103247
793
27
261
104,328
625,569
Tswaing
NW382
106120
1,328
72
6,636
114,156
Mafikeng
NW383
247569
5,484
1,858
4,571
259,482
Ditsobotla
NW384
131693
3,072
410
12,428
147,603
Bophirima
DC39
Kagisano
NW391
93625
1,465
15
1,283
96,388
439,686
Naledi
NW392
43223
7,739
607
6,537
58,106
Mamusa
NW393
44336
1,107
141
2,784
48,368
Greater Taung
NW394
179043
2,085
72
963
182,163
Molopo
NW395
10507
276
3
907
11,693
Lekwa-Teemane
NW396
34909
2,651
81
5,327
42,968
Southern
DC40
Ventersdorp
NW401
36940
1,309
69
4,771
43,089
599,687
Potchefstroom
NW402
90485
8,263
543
29,067
128,358
City Council of
NW403
283944
9,968
1,391
63,900
359,203
Klerksdorp
Maquassi Hills
NW404
60904
1,458
115
6,560
69,037
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District
Local
Code
Code
Black
Coloured
Asian
White
LM Total
DM Total
Municipality
Municipality
Govan Mbeki
DC30
Msukaligwa
MP302
111524
342
811
12,141
124,818
569,176
Lekwa
MP305
89125
1,892
963
1,290
103,270
Dipaleseng
MP306
35122
67
290
3,135
38,614
Highveld East
MP307
185458
2,396
2,036
31,853
221,743
Seme
MP304
74337
422
457
5,515
80,731
TOTAL POPULATION FOR THE ORANGE RIVER BASIN - SOUTH AFRICA
13,368,054
13,357,298
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There are however, as noted above, a range of alternatives to the STATSSA figures which
are regularly used, such as those used by the Demarcation Board, the DBSA, the
Association of SA Actuaries, the CSIR, the Local and District municipalities and others.
Nevertheless, the most accessible data and the data used by the national and provincial
governments in the allocation of resources etc remains the STATSSA data and thus it
remains a benchmark against which other statistics are most often compared. Thus, in
respect of building a data set for the Orange River Basin the point of departure should be
the official STATSSA figures and where detailed planning is required for specific areas or
developmental projects research should be undertaken to address the specific
requirements.
The best practice demographic model currently favoured by statisticians (including
STATSSA) makes use of cohort-component methodology. In general the process followed
in this methodology is as follows:
· A base population where the number of people and their characteristics is well
known is established for a specific year.
· The population is divided into specific age categories (the categories are
determined by the common experiences of people of a similar age).
· Trends in fertility, mortality and migration are analysed and assumptions are
made in respect of these factors for each age category.
· The fertility, mortality and migratory assumptions are then applied to each existing
age categorized cohort (group).
· Each age cohort (group) is then projected through each of the established age
categories and as it moves through each of these categories the age specific
fertility, mortality and migratory assumptions are applied.
Quite simply the model reflects that, as a group of peers moves from one life stage to the
next it experiences different things but that within each life stage most people will have
similar experiences. As a result statisticians are able to calculate the fertility, mortality and
migratory experiences of each group of similar aged people. It follows therefore that as a
group moves from one life stage to another the statistical assumptions relevant to each
new life stage can be applied and hence the group's numbers will rise or fall before the
group moves on to the next life stage where a new set of statistical assumptions will apply.
In this way demographers are able to establish demographic patterns and make
projections in to the future.
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In respect of the objectives of ORASECOM, a recent (released in 2001) study
commissioned by DWAF and led by Professor Lawrence Schlemmer in association with
MarkData and Eric Hall & Associates stands out as particularly relevant in that it
specifically investigates the population of South Africa's urban areas with respect to water
demand.
The Schlemmer study was commissioned in 1995 with the objective of
formulating scenarios of demographic and economic change relevant to the broad
planning and management of water resources, and of providing estimates of water usage
for human consumption in all urban areas (consumption centres) throughout the country in
five-year increments under these scenarios to the year 2025.
The researchers found the most significant challenges in the forecasting exercise were to
correct for probable undercounts and deviations in the census data and to predict the
impact of HIV/AIDS on future growth
The study recognized population growth as the key driver of water use for human
consumption. However it stressed the significant inter-relationship between the economy
and demographics and suggested that a stronger economy would assist in combating
factors associated with the HIV/AIDS epidemic, and that a lower level of AIDS infection
would alleviate constraints on the economy and economic confidence.
The result of the Schlemmer study in respect of the national population revealed there to
be 41,562,000 people in 1995. In addition extrapolations were made in respect of a "Low
scenario" and a "High scenario" population forecast for 2025 with the following results:
· Low forecast: 48 to 50 million
· High forecast: 52 to 54 million
In order to make comparisons between the Schlemmer study and the 1996 Census figure
of 41,945,000 the study's results were extrapolated using a growth rate of 1,9% to a 1996
figure of 42,379,000 (1,8 million above the census) as reflected in the table below.
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Table 14: Comparisons between the 1996 census and the Schlemmer study
Schlemmer etal
1996 CENSUS
Provinces
Rural
Urban
Total
Rural
Urban
Total
Eastern Cape
3,916,000
2,740,000
6,656,000
3,942,000
2,635,000
6,577,000
Free State
843,000
1,982,000
2,825,000
803,000
1,831,000
2,634,000
Gauteng
243,000
7,547,000
7,790,000
217,000
7,823,000
8,040,000
KwaZulu-Natal
4,825,000
3,690,000
8,519,000
4,851,000
3,602,000
8,453,000
Mpumalanga
1,424,000
1,511,000
2,939,000
1,710,000
1,242,000
2,952,000
Northern Cape
203,000
628,000
831,000
264,000
576,000
840,000
Northern Province
3,891,000
1,164,000
5,059,000
4,347,000
590,000
4,937,000
North West
1,980,000
1,527,000
3,507,000
2,183,000
1,172,000
3,355,000
Western Cape
424,000
1,527,000
4,270,000
446,000
3,712,000
4,158,000
South Africa
17,744,000
3,846,000
42,379,000
18,762,000
23,183,000
41,945,000
Note: The rural populations of the Schlemmer study are comparatively lower than the 1996 census
data, because of the inclusion of functionally urban fringe areas in the urban population due to the
growth of informal settlements in many of the areas.
6.4
Official Demographic Statistics - STATSSA
As mentioned above, Statistics SA (STATSSA) is South Africa's official statistics gathering
body. It is a legislated institution falling under the ministry of finance and it is obliged to
collect and disseminate statistics on a wide range of issues including the country's
demographics.
It is governed by the Statistics Council which has a sub-committee
devoted to census issues.
Notwithstanding the fact that STATSSA has recently admitted to errors in certain
calculations and that its figures are at times disputed, its transparency, its openness to
debate, its efforts at making information readily available and its willingness to admit to
errors has won it credibility. As a result it is currently recognised as a competent arm of
government both inside and outside the country and it meets a number of international
statistical reporting standards.
Its importance in collecting data for the development of SA in the post apartheid era is
significant. Its success in carrying out the 1996 census two years after the first democratic
elections (1994) was a major achievement as it was the first census carried out throughout
the entire country since the establishment of homelands which had led to a multiplicity of
statistics gathering institutions.
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With respect to demographics, STATSSA has undertaken two full censuses (1996 and
2001) and has produced mid-year population estimates annually since the advent of
democracy.
In reviewing the extent, availability, nature and quality of the country's official demographic
statistics this study has looked at a range of reports, publications and data sets produced
by STATSSA.
With respect to the availability of data, all STATSSA publications are publicly available and
can be accessed from the STATSSA Library in Pretoria or any of the following libraries:
· National Library of South Africa, Pretoria Division,
· National Library of South Africa, Cape Town Division,
· Natal Society Library, Pietermaritzburg,
· Library of Parliament, Cape Town,
· Bloemfontein Public Library,
· Johannesburg Public Library,
· Eastern Cape Library Services, King William's Town,
· Central Regional Library, Polokwane,
· Central Reference Library, Nelspruit,
· Central Reference Collection, Kimberley and the
· Central Reference Library, Mafikeng.
In addition STATSSA runs an official website (www.statssa.gov.za) which is enabled to
allow one to download most current publications and where this is impractical a number of
digital products can be obtained directly from STATSSA. In the latter respect an easily
available and user friendly set of 12 CDs giving one access to all the information captured
in the 2001 Census, is available for approximately R1000.005. This data set allows one to
access data to sub-place-name level.
5 Following the sale of the Census 1996 data set in a similar format much debate was generated
around the legitimacy of the public having to purchase data which was collected at taxpayer's
expense. As a result and in the light of current access to information policies the data is supplied
free and the costs are associated with producing, package and distributing the data and training
manuals and support in accessing it.
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Information can also be accessed through direct contact with STATSSA6 and their library
service and support for technical enquiries is currently effectively supported.
6.5
Census 1996
The 1996 Census was the first fully comprehensive South African census since 1970 and
the first in a democratic SA.
The extensive use made of the data by a number of
institutions, municipalities and organisations has resulted in this data being the basis for
extrapolations and predictions which continue to be used, notwithstanding the release of
data from the 2001 census. As a result a brief account of it is made in this study.
6.5.1
Census 1996 Background
During Apartheid, census taking was done as separate exercises in the former Transkei,
Bophuthatswana, Venda and Ciskei areas and the detailed life circumstances and
aspirations of the people of these areas was not collected. In contrast Census `96 was a
full census covering the entire country. It was conceptualized and managed in-house as
four separate but sequentially linked processes (phases): pre-enumeration, enumeration,
data processing, and analysis and dissemination. During the pre-enumeration phase the
country was divided into some 86,000 Enumerator Areas (EA) of approximately 150
households each. Thereafter each household was visited and detailed information was
collected about each member from a representative who was either interviewed, or who
filled in the questionnaire themselves, in one of the 11 official languages.
6.5.2
Census 1996 Problems and Results
Generally the 1996 census was a success. However major difficulties were experienced
with the payment of the temporary workers, and the technical and management aspects of
data processing.
The most significant issue with respect to the data from the 1996 Census was that,
STATSSA could not be sure whether 22% of the households visited during the Post
Enumeration Survey (PES) had been visited during the actual count.
The overall result was a population of some 40,583,5573 people.
6 STATSSA Contact details:
User information - Tel: (012) 310 8600, Fax: (012) 310 8500, Email: info@statssa.gov.za
Technical enquiries - Tel: (012) 310 8636, Fax (012) 310 8339, hestonp@statssa.gov.za
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6.6
Census 2001 "How the count was done"7
6.6.1
Census 2001 Background
Democratic SA had its second census in October 2001. STATSSA resolved to carry out a
full census as in 1996. This was done in order to establish how the end of apartheid had
contributed to demographic patterns and to enable comparison between the 1996 and
2001 census data which would be a critical factor in future planning. In order to manage
the 2001 census, a census sub-committee of the Statistics Council was created8 and an
advisory committee comprised of a wide variety of stakeholders and users was set up.
Parliamentary authorization was received and a budget was only approved in late 1999,
resulting in a critically short planning period. International assistance, both financial and
technical, was received from the United States (Census Bureau [USAID]), Sweden (Stats
Sweden), Kenya, Tanzania and the UK.
6.6.2
Management Structure & Planning
Unlike the previous census where tasks were carried out sequentially, Census 2001
followed a project-managed approach and was managed as a series of sub-projects falling
under a central coordinating office.
The following nine operational projects were
established:
· Questionnaire design
· Census mapping (demarcation)
· Geographical information system (GIS) updating and maintenance
· Design and completion of the pilot census
· Enumeration
· Post-enumeration survey (PES) design and execution
· Data processing
· Data tabulation and product planning and implementation.
7 Most of the information presented in the following subsections is taken from various STATSSA
publications. In particular the STATSSA publication "How the count was done" (Ref: 03-02-02).
8 Members of the Statistics Council who were also members of the census sub-committee were Dr
HA Southall (Chair of Council) Prof J Galpin, (Chair of Census sub-committee), Prof RE Dorrington,
Mr LC Fouché, Prof JD May, Mr N Mokhesi and Prof CEW Simkins,
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· Listing (this task was added after the pilot census revealed a large degree of map
illiteracy)
In support of the above operational projects the following seven administrative and support
sub-projects were established:
· Financial management and monitoring
· Information technology
· Human resources
· Provisioning
· Logistics
· Provincial management
· Publicity.
Significant inputs from the private sector were made for the first time. These included:
· The payment of temporary workers,
· The technical and management aspects of data processing,
· Publicity and communications,
· Demarcation in various parts of the country,
· Integrating the various management components,
· The development of a computer-based management system - Census
Administration System (CAS).
In addition to the National and Provincial offices, 95 regional offices were established,
staffed and equipped to carry out the census at local level. The number of regional offices
per province was as follows: Eastern Cape 13, Free State 6, Gauteng 16, KwaZulu-Natal
16, Limpopo 6, Mpumalanga 7, Northern Cape 11, North West 7, and Western Cape 13.
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6.6.3
Pre-enumeration
Demarcation and map production
With the aid of a Geographic Information System (GIS) the country was demarcated in to
some 81,000 Enumerator Areas (EA)9.
For the first time, on-screen demarcation was
possible using the GIS and use was also made of Global Positioning Systems (GPS) to
undertake fieldwork in areas of rapid development such as informal settlements.
A geographical hierarchy was established where a number of EAs constituted a sub-place
and a number of sub-places constituted a main place and a number of main places
constituted an LM or a DMA, and so on, aggregated up to LM, DM, provincial and national
level (see figure below). This hierarchy was fundamental in demarcating EAs and later in
disseminating census information. In addition to defining the EA boundary, attribute data
such as the place name, the type of settlement, and in the case of institutions, the type
and name of institution was captured in order to classify the EAs into four primary
categories and seven sub-categories according to land use. The four primary categories
are as follows:
· Urban Formal Area,
· Urban Informal Area,
· Rural Commercial Farms,
· Tribal Traditional areas.
Within these four primary categories EAs were sub-typed as being:
· Residential,
· Farm,
· Small Holding,
· Recreation/Park/State Land,
· Institution,
· Hostel
· Vacant.
9 Eastern Cape - 18370, Free State 5183, Gauteng 13367, KwaZulu-Natal 12752, Limpopo
1661, Mpumalanga 5813, Northern Cape 10325, North West 6215, Western Cape 7101.
Total 80787.
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Each EA received a unique code number, which was also associated with a bar code and
linked to its geographical entities. All subsequent census processes used this number as
an identifier and maps were produced for every EA (Approximately 81,000 A3 maps).
The diagram below10 explains the hierarchical structure used for geographical areas in
Census 2001.
Figure 11: Hierarchical structure used to define geographical areas in Census 2001.
10 STATSSA publication "How the count was done" (Ref: 03-02-02).
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Listing
Listing refers to a list of visiting points within an EA and it has been a common feature of
South African census taking and ensures that all households within an EA are visited. The
process is relatively difficult in tribal areas and informal areas where formal street
addresses do not exist.
In previous censuses listing was undertaken as part of the demarcation and mapping
process and included a written description of the EA. In the 2001 census the improvement
in mapping technology was presumed originally to be such as to enable the doing away
with listing and written description of the EA boundary.
After the pilot census, it was
discovered that most of those employed were relatively map illiterate and listing was
reintroduced as a separate process that was to be undertaken between demarcation and
enumeration. Thus a process of employing and training specialized listers to list and write
a boundary description for each EA before the census was embarked upon. Ultimately,
not all areas in the country were completely listed by the time enumeration began.
Questionnaire
Using the `96 census as a point of departure and after a long process of engaging with
stakeholders, three lengthy questionnaires were developed one for households, one for
individuals in institutions and one for the institutions themselves.
The length of the
questionnaire was related to the shortage of available data at local level and the desperate
need for information on which to plan for the development of people at grassroots level.
Publicity
Publicity was undertaken to raise awareness and assist in soliciting support from the public
to participate in the census. It was one of the areas where the private sector assisted and
was approached in a number of ways through various media campaigns and the Census
at Schools campaign.
6.6.4
Enumeration
Staff Recruitment
The recruitment, training, and payment for carrying out of enumeration of an enumerator
for every EA and a supervisor for every five or so enumerators (approximately 100,000
temporary employees) was an immense task which was doubly difficult considering the
short period of time in which it had to be achieved. It is thus not surprising that staff
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recruitment, training and payment was a weakness of the census and contributed to a fair
share of frustrations.
The staff recruitment function was decentralized to regional offices. Enumerators were
expected to have matric, literacy in english, map-reading ability, and knowledge of the
language of the area. In addition it was desirable that enumerators come from the area in
which they would enumerate in order to facilitate communication and co-operation. Every
applicant wrote a standard test and the score was used as a guide to employment.
Regrettably the huge applicant list and the time it took to assess tests and appoint staff
resulted in the census starting late in some areas.
One of the shortcomings was the
insufficient number of enumerators recruited from certain communities particularly areas
previously designated white. The result was that enumerators designated to these areas
knew very little about their areas, had to travel great distances to begin enumeration and
were not able to enumerate in the evenings.
Staff Training
Initial training plans relied on cascade training whereby people trained in the processes
and methodology, in turn trained further groups of people, and so on down the line. The
pilot census revealed an over emphasis on training methods and not enough emphasis on
census methodology and a severe lack of map reading skills. Training was subsequently
amended to include better training manuals and centralized training. This involved making
use of trainers with knowledge of the census process being video-taped and these tapes
being used as training materials with head office staff being available for online questions.
All enumerators had manuals with instructions for each question.
Staff Payment
Following administrative problems in the 1996 census it was resolved to outsource the
payment of staff and the Post Office was initially appointed to undertake the task.
However, following payment problems in the payment of those who listed and in the
payment of advances to enumerators and supervisors to cover costs of food and travel
during enumeration, regional managers were required to become paymasters and later a
commercial bank was commissioned to undertake the task.
In some areas payment
problems delayed the start of the census. On account of the delay in starting and the
extension to the counting period, temporary staff in some areas were unsure of whether or
not they would be paid for the additional period worked which complicated human
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resource problems.
Finally it was resolved that once counting was completed and
enumerators returned their questionnaires etc they were paid by cheques, which were
redeemable from one of the commercial banks.
Pilot
A pilot census was conducted in February and March 2001 which highlighted:
· Limitations to the task-based project management approach which hindered both
communication and integration between processes, resulting in task duplication
and overlapping, gaps in planning, and in some cases, related tasks being tackled
in non-compatible ways.
· A deficiency in lister and enumerator map literacy.
· Differing interpretations of the questionnaires.
· An inadequacy in the administration system, which was unable to capture all the
necessary administrative information and was not used correctly.
After the pilot census, revisions to both processes and management methods, including
the following, were introduced:
· A revised management process which focused on daily 'Nerve Centre' meetings
attended by project and sub-project management to encourage greater integration
across sub-projects in order to identify problems and take immediate action. In
addition, regular video-conferencing sessions ensured that provincial offices were
kept abreast of issues, and a private-sector consultancy was commissioned to
assist in management.
· Establishing listing as a process separate from enumeration.
· A review of training methodology which shifted the focus from cascading training
downwards to `narrowcasting' which emphasised direct training through video
linkages etc.
· The development of a census information and management system, (Census
administration system [CAS]) in collaboration with a private sector company.
· The use of a barcode based management system to manage the flow of
questionnaires, questionnaire boxes, data etc throughout all processes of the
census.
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In addition to the pilot census a census test was undertaken in August 2001 to further test
logistics and the PES questionnaire and also to verify whether the use of barcode stickers
on all visiting points assisted in the matching process, which it did.
Census Enumeration
Enumeration through personal interview or self-completion of the questionnaire took place
throughout South Africa. The experience differed from one area to another and between
one EA and the next depending on particular circumstances.
In some areas language became an issue as people refused to engage with enumerators
who did not speak their language or to fill in questionnaires not in their language.
Questionnaires were printed in English, Afrikaans and Zulu but the logistics of accessing
those in Afrikaans or Zulu from the field was difficult.
Some people politicized the process and refused to participate, others were reluctant to
divulge information seeing it as a security risk and others were not available or were left
out by enumerators for a range of reasons.
The management hierarchy during enumeration relied on Regional Office Managers
overseeing Fieldwork Co-ordinators who managed the feedback of data from Supervisors
in the field who in turn managed groups of Enumerators.
The key role was that of the Supervisors who co-ordinated and managed Enumerators by
reporting on progress and ensuring that they completed their work and submitted accurate
completed questionnaires and summary books and in addition they were responsible for
enumeration quality. However, control processes required by both the enumeration sub-
project and the data-processing sub-project were not adequately integrated and had not
been tested in the field and thus daily progress reporting was complicated and
burdensome.
Quality control during enumeration by supervisors was planned to include assessments of
the first batch of questionnaires completed by each enumerator, including visits to
randomly-chosen enumerated households in order to correct any errors and avoid
repeating these. However the revisiting of enumerated households as a check was not
included in training and nor had it been tested and very few supervisors were able to do
the necessary verification
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Fieldwork Co-ordinators experienced similar checking and progress reporting difficulties to
Supervisors and on account of payment problems they ended up focusing on staffing,
money and transport problems, at the expense of quality and coverage.
As the period for enumeration neared its end, it became clear that an extension was
essential in most areas to increase coverage. The extension was referred to as the 'mop-
up' operation and the rationale for it included:
· Abnormally cold and wet weather conditions prior to and in the early stages of
enumeration;
· Delayed recruitment and appointment of temporary staff;
· Initial delays in enumerator payment, which delayed the start of counting;
· Difficulties in finding people at home during the day, and insufficient arrangements
for enumerating after hours;
· Difficulties in obtaining access to high-walled properties, areas with heavy
security, and commercial farms; and
· Problems with the CAS
The extension certainly assisted in increasing coverage but was not without its own
problems. In particular there was confusion surrounding the extension, which resulted in
management and human resource problems as people expected more money to cover the
extended period or were not prepared to continue where they had been paid and were
thus no longer employed.
In addition to the extension STATSSA instituted a call centre, which allowed the public to
report that they had not been enumerated and resulted in a follow up visit to the caller.
Provision was also made for people who chose to complete their questionnaires
themselves to deposit their questionnaires at post offices.
Post Enumeration Survey (PES)
Immediately following the census a Post Enumeration Survey (PES) of 600 EAs, making
up a representative sample of the country, was undertaken along similar lines to the
census, in November 2001. The aim of the PES was to determine the degree of difference
between the census and the survey in order to make adjustments for errors of coverage
and errors of content so as to achieve more reliable data.
Most of the enumerators used in the PES were selected from the pool of annual October
Household Survey fieldworkers and were considered to be highly competent.
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The critical factor in such a survey is to match the questionnaire completed during the
census with that completed during the PES. This issue was addressed through the use of
barcode stickers from the census questionnaire being left at the address and then
recorded later at the time of the PES.
This innovation was particularly successful in
achieving the necessary matching which was undertaken manually.
After adjustments the PES revealed the following percentage undercount per province.
Table 15: Percentage undercount for persons and households per province
Percentage undercount for persons and households per province
Province
Persons %
Households %
Eastern Cape
14,74
15,55
Free State
17,63
20,60
Gauteng
18,74
23,02
KwaZulu-Natal
22,51
26,21
Limpopo
14,36
17,04
Mpumalanga
16,08
17,24
Northern Cape
14,07
17,81
North West
16,02
20,29
Western Cape
16,27
16,93
South Africa.
17,64
20,52
6.6.5
Data Processing
Once all the questionnaires were returned to a central processing centre in Pretoria, data-
capturing was largely a digital exercise founded on scanning each questionnaire into a
database as an image and interpreting the image with appropriate optical character
recognition technology. The result, after a year and a half (February 2003) of capturing
cleaning and editing was a digital database (with an accuracy level of some 98.9%) which
was much easier to manage, and allowed for effective utilization of the data.
"Imputation was used to allocate values for unavailable, unknown, incorrect or inconsistent
responses. The editing system uses a combination of both 'logical' imputation techniques
and 'hot decks' (dynamic imputation). 'Undetermined' values were used for only a few
variables in a few cases (such as industry and occupation). Logical imputations, in which a
consistent value is calculated or deduced from other information in the household, are
usually preferred over hot deck imputations. Generally, the editing system attempts to
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resolve inconsistencies first by looking at other characteristics of the household (for
example, a married person with an invalid response for sex would be assigned the
opposite sex to their spouse). If this is unsuccessful, then a consistent value is imputed
from a hot deck, which bases the imputation on nearby persons or households that share
similar characteristics". 11
The complete set of editing specifications for Census 2001 is
available upon request from Statistics South Africa (Ref: 03-02-43).
6.6.6
Adjustments, Analysis, Results & Dissemination
Once data processing was complete the census results were assessed and compared with
the PES results and various statistical processes were applied. The unadjusted (persons
actually counted) and adjusted results (results after the PES and other statistical
adjustments were made) were as follows:
Table 16: Census 2001 unadjusted and adjusted population figures per province
Persons
Households
Unadjusted
Adjusted
Unadjusted
Province
Adjusted count
count
count
count
Eastern Cape
5 537 841
6 436 763
1 288 456
1 512 664
Free State
2 255 442
2 706 775
587 518
733 302
KwaZulu-Natal
7 392 274
9 426 017
1 572 591
2 086 250
Gauteng
7 270 597
8 837 178
2 079 100
2 651 244
Limpopo
4 543 051
5 273 642
1 000 619
1 179 965
Mpumalanga
2 641 152
3 122 990
617 505
733 131
Northern Cape
714 708
822 727
172 870
206 842
North West
3 108 050
3 669 349
753 410
929 004
Western Cape
3 839 068
4 524 335
985 411
1 173 304
South Africa
37 302 183
44 819 778
9 057 480
11 205 705
The census sub-committee reported that the 2001 census probably resulted in:
· An underestimate of the number of children below age five (this is a common
feature of censuses, particularly in developing countries)
· An over-estimate of the number of teenagers aged between 10 and 20
11 STATSSA, Census in Brief 2001 Report No 03-02--03 (2001)
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· An underestimate of the number of men relative to the number of women (This is
a common feature of censuses, particularly in developing countries)
· An underestimate of the white population's numbers
· Higher than expected numbers aged 80 and older, in the African population
· An underestimate of the number of foreign-born, since some identified themselves
incorrectly as having been born in South African
· Age misstatement in the range 60-74
· An overestimate of the extent of unemployment
· An underestimate of those who were employed for only a few hours per week
· An underestimate of household income
· An overestimate in the number of paternal orphans and the number of fathers
missing from the household.
In addition the census sub-committee noted that:
· "Scanning problems caused some births to be recorded in the wrong province.
The number of cases is relatively small and should not lead to too much distortion
for most purposes for which these data are used; however, it does produce
obviously erroneous results when one tries to estimate the extent of inter-
provincial migration of those born since the previous census."
· The fertility data (numbers of children ever born, children surviving) is
problematic.
· Adjusted population figures by province, population group, sex and age, with 95%
confidence limits are as indicated in the following table.
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Table 17: Adjusted population figures with 95% confidence levels
95% Confidence Interval Limits
Category
Estimate
Lower
Upper
Province
Eastern Cape
6,436,763
6,286,402
6,587,125
Free State
2,706,775
2,665,303
2,748,247
Gauteng
8,837,178
8,520,018
9,154,338
KwaZulu-Natal
9,426,017
9,030,906
9,821,128
Limpopo
5,273,642
5,244,376
5,302,907
Mpumalanga
3,122,990
3,081,917
3,164,064
Northern Cape
822,727
812,071
833,384
North West
3,669,349
3,608,191
3,730,507
Western Cape
4,524,335
4,439,010
4,609,661
Total
44,819,776
43,688,194
45,951,361
Population group
Estimate
Lower
Upper
Black African
35,416,166
34,923,119
35,909,213
Coloured
3,994,505
3,917,140
4,071,871
Indian or Asian
1,115,467
1,084,589
1,146,345
White
4,293,640
4,205,194
4,382,086
Sex
Estimate
Lower
Upper
Male
21,434,040
21,182,666
21,685,415
Female
23,385,737
23,108,636
23,662,839
Age group
Estimate
Lower
Upper
0-4
4,449,816
4,390,734
4,508,897
05-Jan
9,915,472
9,768,819
10,062,125
15-19
4,981,721
4,920,430
5,043,011
20-29
8,229,462
8,133,430
8,325,494
30-44
9,032,136
8,925,942
9,138,330
45-64
5,995,960
5,930,696
6,061,224
65+
2,215,211
2,191,652
2,238,771
Thus the population of South Africa according to STATSSA as a result of the 2001 Census
is 44,819,778 people as compared with 40,583,573 people found in the 1996 Census, a
summary per province is given in the table below.
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Table 18: Comparative population data from Census 1996 and Census 2001
October 2001
October 1996
Province
No.
%
No.
%
Eastern Cape
6 436 763
14,4
6 302 525
15,5
Free State
2 706 775
6,0
2 633 50
6,5
Gauteng
8 837 178
19,7
7 348 423
18,1
KwaZulu-Natal
9 426 017
21,0
8 417 021
20,7
Limpopo
5 273 642
11,8
4 929 368
12,1
Mpumalanga
3 122 990
7,0
2 800 711
6,9
Northern Cape
22 727
1,8
840 321
2,1
North West
3 669 349
8,2
3 354 825
8,3
Western Cape
4 524 335
10,1
3 956 875
9,7
South Africa
44 819 778
100,0
40 583 573
100,0
Dissemination
STATSSA has effectively utilized a range of dissemination vehicles in both print and
electronic format to disseminate their findings. In general their data is readily and easily
available and requests to the organization are ordinarily well received and effectively
addressed. Libraries, government offices and educational institutions have access to their
data and in most cases make it available to the public.
Essentially the data itself is
available free of charge though fees are incurred for the services offered in printing,
downloading, disseminating etc.
The STATSSA internet site is relatively user friendly and provides access to information of
a generalized nature going down to ward level. For more detailed data which allows for
focused searches down to sub-place name level a series of 12 CDs can be purchased
(R1,000.00) and utilized in most GIS packages.
A variety of other census products are available from STATSSA including the following:
· Printed Reports (also available in PDF format on the internet):
· Key census results, a pamphlet aimed at the general public, which outlines briefly
how the count was done and contains a few highlights of the results (Ref: 03-02-
01).
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· Census in brief, an A6 booklet consisting of over 80 tables and graphs at national
and provincial level, for an extensive range of individual and household variables
(Ref: 03-02-03).
· How the count was done (Ref: 03-02-02).
· Thematic and other posters.
· Primary tables, giving more detailed information on the results in tabular form, for
the country as a whole (Ref: 03-02-04) and for each province (Ref: 03-02-05 to
03-02-13).
· Post-enumeration methodology (Ref: 03-02-17)
· Census review (Ref: 08-02-18)
· Key municipal data, which contains breakdowns at municipal level for a range of
individual and household variables (Ref: 30-02-21).
Electronic products available on the internet or on request:
· Interactive Internet products: a series of interactive products, for users to compile
tables according to their own specifications.
· Community profiles: for users who wish to arrange and combine information into
their own unique tables, at different levels of geography (Ref: 03-02-22).
· Age tables by single-year breakdowns for the country as a whole (Ref: 03-02-30)
and for each province (03-02-31 to 03-02-39).
· Census concepts and definitions: an alphabetical listing of concepts and
definitions used during the census, with some methodological notes (Ref: 03-02-
26).
· Other general and geographical metadata files. The general metadata files
include, among other things, the exact wording of each question, the guidelines
that were given to the enumerators on how to interpret the replies, and the final
code lists for all census data (Ref: 03-02-24). The geographical metadata file
explains the geography of the census and the coding of all the geographic areas
in the country (Ref: 03-02-25).
· Demographic atlas. This product will display the demographic characteristics of
various towns, cities and municipalities (Ref: 03-02-28).
· In addition, the following electronic products are available on request:
· CD Rom disks containing the community profiles described above (Ref: 03-02-
22).
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· My constituency: a CD with census findings for all electoral wards, designed to
give parliamentarians more information about the wards they represent (Ref: 03-
02-29).
· Special requests. A set of tables can be produced by Stats SA, either at head
office or in each province, providing specific information, at any level of
geography: EA, sub place, main place, municipal, magisterial district or provincial
level.
· Sample database in SuperCross and ASCII. This sample of census records is
designed for researchers wishing to do their own analyses. (Ref: 03-02-23).
· CD with spatial (GIS) data. This product is designed for users with their own GIS
software.
These digitised enumeration areas and boundaries can serve as a
backdrop for any GIS system.
The CD contains information about all
geographical areas in the country, from provincial to the smallest area (Ref: 03-
02-27).
Publications can be ordered from: Printing and Distribution, Statistics South Africa, Tel:
(012) 310 8251, Fax: (012) 322 3374, E-mail: distribution@statssa.gov.za
6.7
Census 2001 Results
The Census 2001 population statistics per District and Local municipalities which fall within
the Orange River basin are highlighted in Table 11 of section 6.3 above.
6.8
STATSSA Post Census Statistics
Annually STATSSA publishes its "Mid-year population estimates" which is an updated
overview of the population based on statistical growth rates. These estimates are limited
in that they specifically apply to national and provincial level but not to municipal or local
level. The mid-year estimates ordinarily give indications of assumptions and methods and
are relatively comprehensive and informative.
The latest release is given in the table
below and it is notable that STATSSA has given some indications of alternative data
sources in their release.
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Table 19: STATSSA 2005 population data (compared to other sources)
Model
2000
2005
Estimated total population in millions
ASSA 2002*
44,0
46,0
ASSA 2002**
44,0
46,2
BMR 2004
-
47.0
HSRC
43,1
45,1
Stats SA
44,5
46,9
Life expectancy at birth
ASSA 2002*
55
46
ASSA 2002**
56
49
BMR 2004
-
46
HSRC
50
45
Stats SA
53
47
Infant mortality rate
ASSA 2002*
65,6
68,0
ASSA 2002**
63,5
52,3
BMR 2004
-
72,1
HSRC
65,5
56,2
Stats SA
54,3
53,6
Total annual number of deaths in millions in the year starting 1 July
ASSA 2002*
0,6
0,8
ASSA 2002**
0,5
0,8
BMR 2004
-
0,9
HSRC
0,6
0,8
Stats SA
0,5
0,7
HIV-prevalence rate for adults aged 1549 years
ASSA 2002*
15,4
20,3
ASSA 2002**
14,7
18,8
HSRC
17,0
16,3
Stats SA
14,2
16,7
Total fertility rate
ASSA 2002*
2,7
2,5
ASSA 2002**
2,7
2,5
Stats SA
2,9
2,8
Birth rate
ASSA 2002*
24,8
22,4
ASSA 2002**
24,8
22,3
HSRC
25,9
23,5
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Model
2000
2005
Stats SA
24,6
23,8
Annual number of births in millions in the year starting 1 July
ASSA 2002*
1,09
1,03
ASSA 2002**
1,09
1,03
BMR 2004
-
1,18
HSRC
1,12
1,06
Stats SA
1,09
1,09
Notes:
ASSA 2002. Results from running ASSA2002 with "no" to interventions (see
http//www.assa.org.za)
** ASSA 2002. Results from running ASSA2002 with "yes" to all interventions (see
http//www.assa.org.za)
BMR: Bureau of Market Research, 2004
HSRC: Rehle & Shisana, 2003
6.9
Demographic Data from Local Government Sources
On account of the necessity to deliver services to people within their jurisdictions
municipalities are one of the most significant users and generators of demographic data.
In this respect it is pertinent to note the statistics municipalities are using in their official
planning.
It is also notable that in speaking to municipalities, which make use of
STATSSA, figures, the claim is often made that the official figures reflect smaller
populations than the real situation. This is often contentious in respect of bread and butter
issues such as the extent to which there is a backlog of services, housing etc as official
STATSSA figures may present a lesser problem than that faced by officials at municipal
level (the coalface). It is also worth noting that in the annual division of revenue process
municipalities with greater needs are able to present stronger cases for funding than their
better off neighbours and thus municipalities have an interest in presenting an image of
need.
6.10 Migrancy
It is very difficult to establish the complex nature of migration, especially with respect to a
geographic area as large and as varied as the Orange River Basin. As a result there is a
tendency either to establish broad migratory patterns on a macro scale or to focus on the
micro scale and investigate migratory dynamics in particular communities.
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Focusing on South African as a whole, migratory dynamics present a picture of significant
out-migration by the white community (some 325,000 people between 1996 and 2001) and
significant in-migration by people from SADC countries and to a lesser extent from other
African Countries. Between the 1996 and 2001 census there was a recorded increase of:
· 158,000 people from SADC countries (excl SA)
· 21,800 people from other African countries (non-SADC), and
· 12,300 people from Asian countries.
Notwithstanding the above figures, it is possible that the number of in-migrants from
African countries is higher than that officially reflected as many immigrants move to South
Africa illegally in order to work and would deliberately avoid being included in any official
statistics for fear of deportation. In addition, the in-migration from SADC and other African
countries is expected to increase. With respect to the out-migration of the white population
it is expected that this trend has peaked and will decline substantially over time.
With respect to the Orange River Basin, migratory dynamics differ from one area to
another. In general the following trends are observed from a reading of the Census 2001
migratory statistics (see Appendix 1):
· The already sparsely populated areas of the lower Orange River Basin
experience significant out-migration, particularly of those with skills in the
economically active age groups.
· The rural areas in general experience significant out-migration.
· The larger and more economically robust urban areas experience the most
significant in-migration.
· Within the basin there is a tendency to migrate to those urban nodes closest to
one's original base rather than to those further away.
· Within the basin, particularly in the Northern Cape and North West, there is a
tendency to migrate from poorer municipalities to those with urban nodes based
on mines.
For example 46% of those who migrated in to the Dikgatong LM
(Francis Baard DM north-west of Kimberley) are from the North West Province.
· There are significant migratory trends between the rural areas such as the
Eastern Cape, KwaZulu-Natal, and Western Cape to mining centres such as
Kimberley (10% and 6% and 11% respectively of the total number of people who
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migrated in to the Sol Plaatjie / Kimberley municipality), which echoes apartheid
migratory trends.
· The establishing of provincial capitals in places such as Kimberley has boosted
the economies of these centres and has led to significant in-migration.
· In the sparsely populated Northern Cape and North West there is a tendency to
migrate to or from mining centres (depending on the economic viability of the new
or old mines), which underlies the significance of the mining industry in these
provinces. Though the numbers may be small, the percentage increase made by
new in-migrants has a significant impact on the total population. For example
some 51% (1,139 people) of the in-migrants to the Nama Khoi LM (Namakwa
DM) came from the Western Cape. This represents an increase to the existing
population in the area of some 2.68%.
· The area within the basin which reflects the largest number of people who moved
from their original province to another province within the basin are those from
North West (164,639 people).
· The in-migration from provinces outside of the basin or substantially outside of the
basin (Eastern Cape & Mpumalanga) is as follows: 109,389 from Eastern Cape,
134,840 from KwaZulu-Natal, 120,794 from Limpopo, 60,268 from Mpumalanga.
In all cases some 50% of those people migrating in to the basin went to Gauteng.
It is also significant to note that a trend highlighted by Schlemmer12 may well be more
broadly relevant that is that "in many areas (Northern Province, Central Mpumalanga, and
the North West) employment prospects have ceased to be a factor in residential migration
because of the exceedingly high levels of unemployment. Investments in housing, the
presence of social services and important social networks are now the major factors that
influence families' movement or their disinclination to move, despite very low household
incomes. These trends have important implications for service provision and affordability
of service charges".
12 The Distribution of South Africa's Population, Economy and Water Usage into the Long Term
Future. Schlemmer L, MarkData (PTY) Ltd and Eric Hall & Associates 2001.
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6.11 The Impact of HIV / AIDS on Demographics
Generally it is agreed that the impact of HIV/AIDS on South Africa is likely to be
considerable. Demographically the impact will include:
· An increased general mortality rate,
· An increased infant mortality rate,
· A decrease in life expectancy,
· A decrease in the fertility rate,
· A decrease in the population growth rate,
· An increase in deaths amongst the economically active age groups.
Clearly these issues will result in a range of negative social and economic consequences
for the country and thus it is imperative that interventions be made to reduce the impact of
the disease.
Regrettably, both quantifying the extent of the problem (a demographic
issue) and planning and executing the interventions required to address the problem have
become politicised with a number of different agendas being pursued.
This document
seeks to highlight some of the demographic issues.
Depending on factors such as data sources, assumptions made, the models used in
analysis and so on, a range of statistics exist relating to the impact of HIV/AIDS. This is
illustrated by the following graph from a report presented by the Actuarial Society of South
Africa (ASSA) and the Medical Research Council (MRC), which compares data from,
STATSSA, the ASSA, UNAIDS, the Human Sciences Research Council (HSRC) and the
Department of Health.
Figure 12: Comparative data on HIV/AIDS prevalence in SA
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Similarly the following graph compares the percentage of the population with AIDS from
different sources.
Figure 13: Comparative data on the Percentage of the population with HIV/AIDS
In respect of the impact of HIV/AIDS on the population of South Africa the following graph
indicates the total deaths to date from a number of sources.
Figure 14: Accumulated number of AIDS deaths to mid 2004
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Similarly the following table 13 from the STATSSA publication "Mid-year population
estimates, South Africa 2005"14 highlights estimates from various sources for the years
2000 and 2005. It is worth noting the differences between the two years for factors such
as Life expectancy at birth, Total Fertility Rate and Birth Rate as these are significant
indicators of the impact of HIV/AIDS.
Table 20: Selected statistics indicating the impact of HIV/AIDS
Model
2000
2005
Life expectancy at birth
ASSA 2002*
55
46
ASSA 2002**
56
49
BMR 2004
-
46
HSRC
50
45
Stats SA
53
47
Infant mortality rate
ASSA 2002*
65,6
68,0
ASSA 2002**
63,5
52,3
BMR 2004
-
72,1
HSRC
65,5
56,2
Stats SA
54,3
53,6
HIV-prevalence rate for adults aged 1549 years
ASSA 2002*
15,4
20,3
ASSA 2002**
14,7
18,8
HSRC
17,0
16,3
Stats SA
14,2
16,7
Total fertility rate
ASSA 2002*
2,7
2,5
ASSA 2002**
2,7
2,5
Stats SA
2,9
2,8
Birth rate
ASSA 2002*
24,8
22,4
ASSA 2002**
24,8
22,3
HSRC
25,9
23,5
Stats SA
24,6
23,8
13 Some of the data in this table was included in an earlier table and is repeated here for the sake of
convenience and further explanation.
14 STATSSA - Mid-year population estimates, South Africa 2005, Statistical Release PO302, 2005
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Model
2000
2005
Annual number of births in millions in the year starting 1 July
ASSA 2002*
1,09
1,03
ASSA 2002**
1,09
1,03
BMR 2004
-
1,18
HSRC
1,12
1,06
Stats SA
1,09
1,09
Notes: ASSA 2002. Results from ASSA2002 with "no" to interventions (see: www.assa.org.za)
** ASSA 2002. Results from ASSA2002 with "yes" to all interventions (see www.assa.org.za)
BMR: Bureau of Market Research, 2004
HSRC: Rehle & Shisana, 2003
With respect to the impact of HIV/AIDS on the demographics of the Orange River basin
there are different impacts to HIV/AIDS in different areas depending on a range of related
factors.
For example, a high HIV/AIDS death rate could lead to a reduction in the
economically active population in an area and an increase in opportunities for those living
outside the area who will be drawn to the jobs of those who are ill or who have died. Thus,
whilst there will be an impact from HIV/AIDS on the sparsely populated areas of the
Northern Cape, the overall population change in the area is more likely to be as a result of
economic opportunities or the lack of economic opportunities, which will give rise to
migratory shifts. In the case of a lack of economic opportunities and the out-migration of
the economically active, the HIV/AIDS rate is likely to increase the rate of depopulation in
the area.
6.11.1
STATSSA HIV/AIDS Data
As noted in the STATSSA publication, "Mid-year population estimates, South Africa 2005"
the 2005 midyear annualised growth rates are calculated using the cohort-component
method by "applying the Spectrum Policy Modelling System. The integration is based on
DemProj, which supports many of the calculations in the other components FamPlan,
Benefit-Cost, AIM [version 4] and RAPID.
DemProj is used to make the demographic
projection, while the AIDS Impact Model (AIM) is used to incorporate the impact of HIV on
fertility and mortality"15.
15 STATSSA - Mid-year population estimates, South Africa 2005, Statistical Release PO302, 2005.
pg 3.
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In determining the annualised growth rate STATSSA recognises the impact of HIV/AIDS
thereon and makes use of the AIDS Impact Model to determine the estimated adult HIV
prevalence rate, which it then uses in its various growth rate models. Thus the prevalence
rates used by STATSSA are indicated in the following Table.
Table 21: STATSSA estimated adult HIV-prevalence rates, 20012005.
2001
2002
2003
2004
2005
Women 1549 years
15,8
16,3
16,7
17,4
18,1
Women 2064 years
14,4
14,8
15,1
15,6
16,1
Men 2064 years
14,4
14,8
15,1
15,7
16,3
Adults 2064 years
14,4
14,8
15,1
15,6
16,2
Adults 1549 years
14,7
15,1
15,4
16,1
16,7
Total population
8,4
8,7
9,0
9,4
9,8
The following graph, produced by the ASSA16 indicates a similar pattern to the above data.
Figure 15: Estimated HIV-prevalence rate 2004
16 "The Demographic Impact of HIV/AIDS in South Africa. National Indicators for 2004". Dorrington
etal 2004.
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6.11.2
ASSA HIV/AIDS Data17
The ASSA has developed an updated version of their ASSA2002 demographic model
(ASSA2002), which integrates a range of epidemiological and demographic data including
antenatal surveys.
The updated model (ASSA2002) reflects two scenarios; the first
considers the demographic situation where no interventions are made to counter the
effects of HIV/AIDS, whilst the second considers the demographic situation where all
possible interventions are made to counter the effects of HIV/AIDS. The presumption is
that reality lies somewhere between these positions.
According to the ASSA model for the year 2004, there are some 5 million South Africans
out of a total population of some 46 million18 who are HIV positive. This translates in to a
prevalence rate of 11%. The revisions in the model result in a population projection that is
higher than previous estimates. This suggests that Anti Retroviral Treatment (ART) and
the prevention of mother-to-child transmissions (PMTCT) have led to a reduction in the
number of AIDS deaths per year.
The model predicts that without ART and other
interventions the expected deaths from HIV/AIDS in 2010 will be some 495,000, with ART
and other interventions this will be reduced to approximately 380,000. It should be noted
however that the model suggests that by mid-2004 approximately 500,000 people were in
need of treatment yet by October 2004, only 19,500 people were receiving ART in the
public sector. In addition the default scenario with respect to interventions assumes that
approximately 50% of those who need ART will be able to access it. Nevertheless, life
expectancy is projected to fall to just under 50 years by 2010 compared with the previous
estimate of 43 years and the number of deaths in 2010 could be anything between
290,000 and 450,000.
Based
on
the
default
scenario
the
ASSA2002
model
estimates
the
following
approximations for 2004:
17
Most of the information in this section is taken from the following paper entitled "The
Demographic Impact of HIV/AIDS in South Africa. National Indicators for 2004" by Dorrington RE,
Bradshaw D, Johnson L, Budlender D. Cape Town: Centre for Actuarial Research, South African
Medical Research Council and Actuarial Society of South Africa. 2004.
18 The size of SA's population itself is a debatable issue. This in turn impacts on the calculation of
the prevalence rate.
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· AIDS deaths 311,000 (44% of total deaths total deaths 701,000)
· Accumulated AIDS deaths mid-year 1,212,000
· Life expectancy of 48.5 years for males and 52.7 years for females or 51.0 years
generally (it is presumed that it would be 63.9 years without HIV/AIDS).
· Infant mortality rate of 56 per 1000 live births.
· Over 1.2 million people have already died as a result of AIDS.
· Just over 5 million are infected with HIV.
· Just over 500 000 are AIDS sick.
The ASSA2002 model's default scenario reveals projected population, number of HIV+,
AIDS sick and cumulative AIDS deaths for 1990-2015, in the following graph.
Figure 16: Various HIV/AIDS related factors impacting on population levels
This positive trend is believed to be a result of a combination of factors including the
impact of ART, the PMTCT programme, behavioural changes (increased use of condoms)
etc. Hence the rate at which these programmes are implemented (or not implemented) is
integral to the demographic future of the country.
Like the downward revisions of the ASSA model, estimates by UNAIDS have also been
revised downward (5.6m by mid 2004) and thus by the start of 2004, the UNAIDS model
predicted 5.6 million infected people in South Africa.
Some projections made by the ASSA model between 1990 and 2015 include the following:
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· The total population is expected to increase at a decreasing rate over the period.
· From 2011, the expected annual rate of increase is 0.4%.
· The number of people infected with HIV is expected to peak in 2013, at just over
5.4 million, after which it starts to decrease slowly.
· The number of people sick with AIDS in the middle of each year is expected to
rise over the period, reaching nearly 743,000 in 2015.
· Accumulated AIDS deaths are expected to be close to 5.4 million by 2015.
6.12 Demographics of the Inter-basin Transfer Areas
6.12.1
The Tugela-Vaal Transfer Scheme
The Tugela-Vaal transfer scheme (see Figure 17 below) was completed in 1974 and
transfers water from the Tugela Basin over the Drakensberg escarpment in to the Vaal
Basin. The scheme allows for the storage of water from the Tugela in the Sterkfontein
dam above the escarpment and when necessary water is released from Sterkfontein in to
the Nuwejaarspruit River, which flows into the Wilge River and then into the Vaal Dam for
use in the Witwatersrand area. As mentioned on the Rand Water website, "During the
drought of 1995, when the level of the Vaal Dam was below 15%, the transfer of water
from the Tugela River to the Sterkfontein Dam and releases from this dam to the Vaal
Dam was the life blood of the Gauteng area. Without this supply, homes and industries
would have run dry".
Although this drought occurred before the Lesotho Highlands
scheme became operative it is clear that the Witwatersrand may well rely on water from
the Tugela basin in times of need. Hence the population of the Tugela Basin could be
affected by the extraction of water from the Tugela to the Vaal Basin and thus should be
factored into deliberations on water demand in the Orange River Basin.
As an aside the Tugela-Vaal transfer scheme includes the Drakensberg Pumped Storage
hydroelectric power station, which uses water pumped and stored above the escarpment
in low electricity demand periods to drive turbines to produce electricity in high demand
periods.
The Table 22 below, sourced from Census 2001 statistics reflects the approximate
population in the municipalities falling within the Tugela basin.
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Figure 17: Tugela Basin & Tugela-Vaal Transfer Scheme
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Table 22: Population per LM within the Tugela Basin
District
Code
Local Municipality
Code
Black
Coloured
Asian
White
Total
Municipality
Umgungundlovu
DC22
Mooi Mpofana
KZ223
33,151
195
809
2,663
36,818
Highmoor/Kamberg Park
KZDMA22
6
0
0
3
9
Uthukela
DC23
Emnambithi/Ladysmith
KZ232
202,756
2,217
11,531
8,960
225,464
Indaka
KZ233
113,581
33
3
27
113,644
Okhahlamba
KZ235
134,344
152
261
2,770
137,527
Imbabazane
KZ236
119,684
90
27
129
119,930
Umtshezi
KZ234
50,154
1,514
5,390
2,867
59,925
Giants Castle Reserve
KZDMA23
322
0
15
173
510
Umzinyathi
DC24
Nqutu
KZ242
144,939
42
9
45
145,035
Msinga
KZ244
167,659
114
69
186
168,028
Umvoti
KZ245
86,751
543
2,431
2,559
92,284
Endumeni
KZ241
40,576
1,964
4,024
4,532
51,096
Amajuba
DC25
Newcastle
KZ252
302,579
2,207
10,110
18,087
332,983
Utrecht
KZ253
29,273
622
81
2,303
32,279
Dannhauser
KZ254
99,751
204
1,696
1,125
102,776
Uthungulu
DC28
Nkandla
KZ286
133,479
48
15
63
133,605
Illembe
DC29
eNdondakusuka
KZ291
122,605
635
3,096
2,333
128,669
Maphumulo
KZ294
120,564
54
18
6
120,642
TOTAL POPULATION - TUGELA CATCHMENT
2,001,224
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6.12.2
The Orange-Fish Transfer Scheme
The Orange-Fish River Transfer Scheme transfers water from the Gariep Dam on the
Orange River to the Fish River Basin in the Eastern Cape. Thereafter some of this water
is transferred from the Fish River Basin to the Sundays River Basin and since 1992, water
from this basin has been transferred to Port Elizabeth.
The infrastructure of the scheme (see Figure 18 below) begins with a tunnel transferring
water from the Gariep dam to the headwaters of the Great Fish River where it flows down
stream to the Elandsdrift Weir.
From the weir water is channelled via a 65 km long
aqueduct (which includes a 13km tunnel) into the Little Fish River near Somerset East
(completed in 1978).
At the De Mist Weir (completed in 1987) 40km downstream the
water enters the Skoenmakers Canal, which leads to the Darlington Dam on the Sundays
River. With the shortage of water in Port Elizabeth the scheme was extended to allow the
drawing of water from the Sundays (some 60km downstream of Darlington Dam) and
transfer it to the city. It is estimated that 200 million m³ of Orange River water could be
transferred to Port Elizabeth annually.
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Figure 18: The Fish & Sundays Basins & Orange-Fish Transfer Scheme
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In addition to the transfer from the Fish to the Sundays the lower Fish River Scheme was
initiated in 1985 and completed in 1992. This scheme's primary objective is to provide
water for irrigation purposes along the river, but it also makes provision for Grahamstown's
increasing water demands.
Table 23 sourced from Census 2001 statistics reflects the approximate population in the
municipalities falling within the Sundays and Fish River Basins and the Nelson Mandela
Metropolitan area (Port Elizabeth).
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Table 23: Population within the Fish & Sundays River Basins & the Port Elizabeth area.
District / Metropolitan
Code
Local Municipality
Code
Black
Coloured
Asian
White
Total
Municipality
Cacudu
DC10
Camdeboo
EC101
9,697
29,827
60
4,784
44,368
Blue Crane Route
EC102
20,860
11,468
33
2,648
35,009
Ikwezi
EC103
3,790
5,618
3
956
10,367
Makana
EC104
57,604
9,203
432
7,302
74,541
Ndlambe
EC105
43,733
3,939
42
7,772
55,486
Sunday's River Valley
EC106
31,804
7,501
9
2,268
41,582
Aberdeen Plain
ECDMA10
778
4,942
9
808
6,537
Mountain Zebra National
ECDMA13
75
3
-
9
87
Park
Amatole
DC12
Nxuba
EC128
18,670
4,531
36
1,587
24,824
Nkonkobe
EC127
122,572
4,813
63
1,207
128,655
Chris Hani
DC13
Inxuba Yethemba
EC131
32,576
21,113
45
6,558
60,292
Tsolwana
EC132
29,765
1,829
3
922
32,519
Nelson Mandela
Nelson Mandela
PE
592,412
236,094
11,218
166,056
1,005,780
TOTAL POPULATION - FISH & SUNDAYS CATCHMENTS
1,520,047
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6.12.3
Johannesburg Metropolitan Area
The Witwatersrand, as the name implies, is the watershed between the Vaal and Limpopo
basins and the "rand" (range of hills) runs in an east-west orientation through metropolitan
Johannesburg.
The result is a metropolis that straddles the two basins and a water
service provider, Johannesburg Water, that supplies water to the entire metropolis.
Furthermore the water is sourced from Rand Water, which sources its water from the Vaal
(Orange) basin. In this respect there is essentially a "transfer" of water from one basin to
another. As a result, in determining the population within the Orange River Basin, the
entire population of the Johannesburg Metropolitan area, some 3,225,81219 people has
been used.
6.12.4
The Lesotho Highlands Water Project
The Lesotho-Highlands Water Project (the first phase of which was completed in 1998) is
located in the mountains of Lesotho and one of its principal aims is to store and transfer
water from the Orange-Senqu River Basin within Lesotho to the Witwatersrand in South
Africa. It does so by transferring water via the Ash River, near Clarens, which then flows
in to the Saulspoort Dam from where it flows in to the Liebenbergsvlei River, which in turn
flows in to the Wilge River and then into the Vaal Dam.
With respect to the impact of this scheme on the population of the Orange River Basin the
transfer essentially is from one secondary basin within the Primary Orange Basin to
another secondary basin the Vaal Basin within the Primary basin.
As a result, the
population dynamics are not specifically considered in this study. Though it is recognized
that the transfer of this water does have socio economic spin-offs on the populations within
each of the secondary basins.
19 STATSSA as per Census 2001 data.
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7
REGIONAL AND INTERNATIONAL DATA
7.1
Demographic Health Surveys
There are a number of regional and international data sets that provide valuable
information on demographic trends for the four Basin countries.
One of these, the
Demographic Health Surveys (DHS), funded by USAID, has already been referred to on a
number of different occasions above. Here, it is pertinent to summarise what the DHS is,
and how the data can be obtained.
Demographic and Health Surveys are nationally-representative household surveys with
large sample sizes (usually between 5,000 and 30,000 households). DHS surveys provide
data for a wide range of indicators in the areas of population, health, and nutrition.
Typically, DHS surveys are conducted every 5 years, to allow comparisons over time, and
mix basic indicators with flexibility. The use of a standardised core questionnaire allows for
comparisons in indicators across different countries while special modules can also be
added to the questionnaires to meet country-specific needs. The standard DHS survey
consists of a household questionnaire and a women's questionnaire. A nationally
representative sample of women age 1549 is interviewed.
The DHS website indicated that the household questionnaire contains information on the
following topics:
· Household listing: For every usual member of the household and visitor,
information is collected about age, sex, relationship to the head of the household,
education, parental survivorship and residence.
· Household characteristics: Questions seek to establish factors including the
source of drinking water, toilet facilities, cooking fuel, and assets of the
household. I n areas with a high prevalence of malaria, questions about the use of
bed nets in the household are added.
· Nutritional status and anaemia: The height and weight of women age 1549 and
young children are measured to assess nutritional status. For the same
individuals, the level of haemoglobin in their blood is measured to assess the level
of anaemia.
The women's questionnaire contains information on the following topics:
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· Background characteristics: Questions on age, marital status, education,
employment, and place of residence provide information on characteristics likely
to influence demographic and health behaviour.
· Reproductive behaviour and intentions: Questions cover dates and survival status
of all births, pregnancies that did not end in a live birth, current pregnancy status,
fertility preferences, and future childbearing intentions of each woman.
· Contraception: Questions cover knowledge and use of specific contraceptive
methods, source of contraceptive methods, exposure to family planning
messages, informed choice, and unmet needs for family planning. For women not
using contraception, questions are included on knowledge of a source of
contraception and intentions about future use.
· Antenatal, delivery, and postpartum care: The questionnaire collects information
on antenatal and postpartum care, place of delivery, who attended the delivery,
birth weight, and the nature of complications during pregnancy for recent births.
· Breastfeeding and nutrition: Questions cover feeding practices, the length of
breastfeeding, and children's consumption of liquids and solid food.
· Children's health: Questions examine immunization coverage, vitamin A
supplementation, recent occurrences of diarrhea, fever, and cough for young
children and treatment of childhood diseases.
· Status of women: The questionnaire asks about various aspects of women's
empowerment, including decision making and autonomy, and attitudes about
domestic violence.
· AIDS and other sexually transmitted infections: Questions assess women's
knowledge of AIDS and other sexually transmitted infections, the sources of their
knowledge about AIDS, knowledge about ways to avoid getting AIDS, and high-
risk sexual behavior.
· Husband's background: Currently married women are asked about the age,
education, and occupation of their husbands.
· Other topics: Questions examine behavior related to environmental health and the
use of tobacco.
Interim Surveys focus on the collection of information on key performance monitoring
indicators but may not include data for all impact evaluation measures (such as mortality
rates). These surveys are conducted between rounds of DHS surveys and have shorter
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questionnaires than DHS surveys. Although nationally representative, these surveys have
smaller samples than DHS surveys (2,0003,000 households).
DHS survey results can be obtained at www.measuredhs.com. DHS data distribution is
managed by a company called ORC Macro which is authorized to distribute, at no cost,
unrestricted survey data files for legitimate academic research, with the condition that they
are provided with an abstract or a detailed description of any project that will be using the
data. Once received, the datasets must not be passed on to other researchers without the
written consent of DHS. Copies of all reports and publications based on the requested
data must be sent to the DHS Data Archive in sufficient number for DHS to forward copies
to the countries whose data have been used.
In the cases of Botswana, Namibia and South Africa, it may be necessary to abstract from
the raw data sets information on the specific areas of interest that fall into the Orange
River Basin catchment.
Lesotho is fortunate in this sense, as all national data are
pertinent as the entire country is within the Basin.
It is worth noting that DHS assists institutions in developing countries in collecting and
analysing data needed to plan, monitor, and evaluate population, health, and nutrition
programs. By building data collection systems in developing countries, DHS works to
increase local capacity in research design and implementation, sampling, data processing,
analysis, and dissemination.
Equally important, it is pertinent that DHS has a section that specialises in gender. This
section produces specialised reports either on specific countries or topics. For example,
recent publications cover topics such as "Women's Status and Empowerment", "Domestic
Violence" and "Female Genital Cutting".
7.2
World Bank Data
The World Bank website is a rich source of demographic and socio-economic data. The
data are most useful when used to compare the status of any given country with others in
the region (note the use of gender data earlier in the report). The World Bank derives its
data either directly or indirectly, from official statistical systems organized and financed by
national governments. The World Bank, in collaboration with many other agencies is
actively involved in improving both the coverage and effectiveness of these systems. The
World Bank offers multiple databases online, some free of charge and some on an annual
subscription basis.
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From a demographic and socio-economic point of view, HNPStats - the World Bank's
Health, Nutrition and Population data platform, is the most relevant.
HNPStats is
described as "a one-stop data source for health, nutrition and population indicators from
various national and international data sources". It provides direct access to more than 100
indicators, with time series for countries and country groups from 1960 to the most recent
year, where data are available. In this single data platform, HNPStats compiles key health,
nutrition and population indicators for data access, comparison and analysis. Its dynamic
data query system is designed in a user-friendly format and creates ready-for-use reports.
A section of HNPStats contains an atlas database where users can access and download
maps, although these do not show regional variations within countries. HNPStats has links
to many other websites of international agencies and country statistical offices. The
website is continuously updated as new information becomes available.
7.3
Other Useful Data Sets
While it was beyond the scope of this study to list all international data sets it is worth
highlight the usefulness and relevance of data available from two other agencies: UNAIDS,
UNHABITAT and UNDP.
In all cases these include critical demographic and socio-
economic issues, although from different angles.
7.4
Data on Migration
The Terms of Reference for Task 10 make mention of the need to document "demographic
movements" which essentially entails studying the migration patterns of the region. All the
censuses discussed earlier were found to contain information on both internal and external
migration. The most critical `gap' identified in this regard relates to the census information
from Lesotho. As noted earlier, because the last census was in 1996, the data on
migration is largely out of date, especially as there have been important changes in
Lesotho's socio-economic development (notably the growth of the textile factory and
decline in migrant labour) between 1996 and 2005.
In addition to the information contained in the census reports there is now a very
significant body of data emerging from the Southern African Migration Project (SAMP).
SAMP was formed in recognition of the fact that "migration is one of the major
development and management challenges confronting the SADC region in the 21st
century". The Project was established in 1996 to encourage and support new regional
approaches and policies on migration.
SAMP believes that to have any chance of
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success, national and regional immigration policy must be based on the best possible
information and analysis.
SAMP is an international partnership network linking organizations in Canada and six
Southern African states committed to collaborative research, training, public education and
policy development on migration issues, funded by the Canadian International
Development Agency. It has four main components: applied migration research, policy
advice and monitoring, migration training and public education. During the period 1996-
99, the primary focus was on cross-border migration to South Africa, from 2000 the
program gained more of a regional character. According to the SAMP website, the key
objectives are:
· To generate sound and reliable information on migration dynamics, trends and
impacts and to disseminate such information to decision-makers; and
· To promote awareness of the role and contribution of migrants, immigrants and
refugees to host societies.
The first objective above is of particular interest to ORASECOM as it provides a source of
information on migration with details and discussions of issues at a level not generally
available from census data. This information has been generated through a multi-year
programme of cooperative research on key policy-related dimensions of contemporary
migration in the Southern African region involving all the key partners.
A series of
workshops, conferences and seminars have been convened to discuss the findings and to
develop new policy initiatives around the issue of intra-regional migration.
To achieve its objectives SAMP has a major ongoing programme of applied research
focusing on priority themes that include:
· Establishment of electronic regional migration data base
· National household poverty and migration surveys
· Citizen attitudes to immigrants, refugees and migration policy
· Border management and regional cooperation
· Women's and children migration
· Regional labour market and economic impacts of migration
· Globalisation and transnational migration
· Immigration and refugee law
· Migration and HIV/AIDS
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All of these themes should be of interest to ORASECOM as it moves towards developing
an IWRMP.
SAMP makes information available on such topics in a variety of ways. Firstly, it manages
a bibliography containing a large number of reports dealing specifically with migration.
Many of these deal with migration into the Basin, in particular to Gauteng. A link to the
SAMP bibliography will be created in the ORASECOM database, so there is no need to list
all the publications in this report.
However, a few examples of studies dealing with
migration into South Africa (mostly to Guateng) are listed below:
Chimere-Dan, O. (1996). Migrants From Other African Countries in South Africa. SA
Labour Bulletin, 20, 45-47.
Chirwa, W.C. (1995). Malawian Migrant Labour and the Politics of HIV/AIDS, 1985 to
1993. In J. Crush & W. James. (eds.) Crossing Boundaries: Mine Migrancy in a
Democratic South Africa. Cape Town/Ottawa: Institute for Democracy in South
Africa/International Development Research Centre.
Crush, J. (1991). The Chains of Migrancy and the Southern African Labour Commission.
In C. Dixon & M.J. Heffernan. (eds.) Colonialism and Development in the
Contemporary World. London/New York: Mansell Publishing.
Crush, J. (1992). Inflexible Migrancy: New Forms of Migrant Labour on the South African
Gold Mines. Labour, Capital and Society, 25, 46-71.
Crush, J. (2000). Migrations Past: An Historical Overview of Cross-Border Movement in
Southern Africa. In D. McDonald. (ed.) On Borders: Perspectives on Cross-Border
Migration in Southern Africa. New York: St Martin's Press
de Vletter, F. (2000). Labour Migration to South Africa: The Lifeblood for Southern
Mozambique. In D. McDonald. (ed.) On Borders: Perspectives on International
Migration in Southern Africa. New York: St Martin's Press.
Fraser, G. (1993). An Economic Analysis of Factors Influencing Rural-Urban Migration to
Southern Africa. Development Southern Africa, 10, 437-442.
Hough, M. (1995). Illegal Aliens in South Africa: Causes and Facilitating Factors. Strategic
Review for Southern Africa, 17, 1-23.
Human Sciences Research Council. (1995). A Research Review of the Policies
Surrounding the Issue of the Free Movement of People Across International
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Borders with Specific Reference to Southern Africa and the Particular Effect
Thereof on South Africa. Pretoria: Human Sciences Research Council.
International Labour Organisation, & SAMAT. (1998). Labour Migration to South Africa in
the
1990s.
Harare:
International
Labour
Organisation/Southern
Africa
Multidisciplinary Advisory Team.
Isserow, M., Morrison, L., Belvedere, F., & Selabe, B. (1998). 'Voting With Their Feet': A
Study of Cross-Border Migration into South Africa. Braamfontein: Community
Agency for Social Enquiry (CASE).
The SAMP bibliography contains many other reports dealing with migration within the
Basin, for example:
Cobbe, J. (1997). Labour Migration from Lesotho: Implications for South African Labour
Market Policies. In H. Bass, R. Kappel, F. Messner, M Wauschkuhn, K.
Wohlmuth. (eds.) Regional Perspectives on Labour and Employment: African
Development Perspectives Yearbook. Münster: LIT Verlag.
Coplan, D. (2001). A River Runs Through It: The Meaning of the Lesotho-Free State
Border. African Affairs, 100, 81-116
Gay, J. (2000). Migration Attitudes of Skilled Professionals in Lesotho. Africa Insight, 30,
65-74.
Matlosa, K. (1992). The Future of International Labour Migration in Southern Africa: Focus
on Lesotho. International Affairs Bulletin, 16, 32-51.
Ulicki, T., & Crush, J. (2000). Gender, Farmwork, and Women's Migration from Lesotho to
the New South Africa. Canadian Journal of African Studies, 34, 64-79.
What is striking about the bibliography is that there is no information on migration from
Botswana and Namibia to South Africa, but a great deal on Lesotho.
The second important resource created by the SAMP project is its own reports.
By
September 2005 SAMP had published 39 reports, many dealing with topics that are not
addressed in Census reports, such as "HIV/AIDS and Children's Migration in Southern
Africa", or "Migration, Sexuality, and the Spread of HIV/AIDS in Rural South Africa".
Others address specific migration issues within Basin countries, such as: "Mobile Namibia:
Migration Trends and Attitudes"; "Riding the Tiger: Lesotho Miners and Attitudes Towards
Permanent Residence in South Africa" and "Botswana: Migration Perspectives and
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Prospects". All of these can be downloaded from the SAMP website either in a summary
format or in full. Again, a link will be created between the ORASECOM database and
SAMP to facilitate access to these reports.
Before moving to the next section, it is pertinent to note that SAMP takes a special interest
in gender. The SAMP website, for example, points out that:
"Research and policy debates on cross-border migration in Southern Africa have tended to
focus on labour migrants, and hence on men. Women have traditionally been looked at as
those 'left behind': as de facto heads of household and bearers of additional burdens of
domestic and agricultural work. Over the past decade, although cross-border migration
has remained male-dominated, more and more women have been crossing borders
between Southern African countries. New social, spatial and temporal patterns of female
migration are evolving, with various forms of cross-border mobility driven by a variety of
social and economic motives. In direct and tangible ways, women are the agents by which
goods and capital circulate in the region. They are thus potentially powerful agents of
development. Yet most migration policy and law hinders rather than facilitates their
mobility, discriminating against women and perpetuating the male bias in migration flows".
To address this SAMP "continues to prioritise gender concerns in its inputs into the policy-
making process, and plans to conduct further gender-based research to inform ongoing
policy debates in the region" (www.queensu.ca/samp)
7.5
Data on Gender
The review of available data indicates that SAMP is not the only source of information
dealing with gender. Indeed, in each of the Basin countries the official census data can be
disaggregated and analysed from a gender perspective, although in certain cases there
are constraints to manipulating the data as they are not easily available electronically.
With respect to national overviews presented from a gender perspective data can be
obtained from various international agencies. Earlier it was noted that DHS has a specific
section dealing with Gender. Another example of this is the World Bank, which makes
gender-specific data available for all countries, including the ORASECOM states. A useful
feature of these data is that they enable comparison with other sub-Saharan countries. As
can be seen from the sample below, Botswana is far better off socio-economically than the
average Sub-Saharan country. Although life expectancy is lower (due to HIV/AIDS) there
are no differences between males and females (both 39 years in 2000). Females are only
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slightly less literate than males in Botswana, but are far more literate than their
counterparts in other Sub-Saharan countries. The situation of young women, aged 15-24,
is undermined by the HIV/AIDS pandemic with prevalence rates being twice as high as
their male counterparts and over three times as high as the Sub-Saharan rate for women
the same age.
Table 24: Gender Disaggregated Data on Botswana
SUB-SAHARAN
INDICATORS
BOTSWANA
AFRICA
1980
1990
1995
2000
1980
2000
GNP per capita (US$)
1,110
2,750
3,190
3,040
660
480
POPULATION
Total (millions)
0.9
1.3
1.5
1.7
383.2
658.3
Female (% of total)
52.2
51.7
51.4
50.3
50.2
50.5
Life expectancy at birth (years)
Male
56
55
49
39
46
46
Female
60
59
51
39
49
47
Adult illiteracy rate (% of people aged 15+)
Male
44.5
34.3
30.0
25.5
50.8
30.3
Female
40.9
29.7
25.0
20.2
72.4
46.8
LABOR FORCE PARTICIPATION
Total labor force (millions)
0
1
1
1
173
290
Labor force, female (% of total labor force)
50
47
46
45
42
42
Unemployment
Total (% of total labor force)
21.5
15.8
Female (% of female labor force)
23.9
17.2
EDUCATION ACCESS AND ATTAINMENT
Net primary school enrollment rate
Male
69
90
79
78
Female
82
97
83
81
Progression to grade 5 (% of cohort)
Male
80
94
87
87
Female
84
98
93
92
Primary completion rates (% of relevant age
group)
Male
102
96
88
58
Female
126
107
96
49
Youth illiteracy Rate (% of people aged 15-
24)
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SUB-SAHARAN
INDICATORS
BOTSWANA
AFRICA
Male
32.1
20.7
17.9
15.5
34.4
17.9
Female
24.9
12.8
10.0
7.9
56.2
27.5
HEALTH
Total fertility rate (births per woman)
6.1
5.1
4.6
4.0
6.6
5.3
Contraceptive prevalence (% of women
aged 15-49)
Births attended by health staff
78
87
99
Maternal mortality ratio (per 100,000 live
100
917
births)
Child malnutrition prevalence, weight for
17
13
age (% of children under 5)
HIV prevalence rate (% of people aged 15-
24)
Male
15.8
4.4
Female
34.3
9
Source: The World Bank
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8
REFERENCES RELATING TO DEMOGRAPHY
Bourne D. (2000). Demographic implications for development in South Africa as a result
of the AIDS epidemic a graphical review.
South African Medical Research
Council.
Bureau of Statistics (1986). Population Census Analytical Report Vol.IV. 1986. Bureau of
Statistics Lesotho.
Bureau of Statistics (1996).
Population Census Analytical Report Vol. IIIB: Socio-
Economic Characteristics and Population Projections 1996. Bureau of Statistics
Lesotho.
Bureau of Statistics (1996). Population Census Statistical Tables Vol.II 1996. Bureau of
Statistics Lesotho.
Bureau of Statistics (1996). Statistical Report No.15: 1996. Bureau of Statistics Lesotho.
Bureau of Statistics / UNFPA (1996). Population Census Projections 1996. Bureau of
Statistics / United Nations Population Fund, Lesotho.
Bureau of Statistics (1999). Labour Force Survey 1999. Employment Policy Formulation
and Labour Market Analysis (LES/004/94). Ministry of Labour and Employment /
Bureau of Statistics Lesotho.
Bureau of Statistics (2001). 2001 Lesotho Demographic Survey, Analytical Report, Vol. I,
prepared by the UNFPA Associate Consultant to the Bureau of Statistics Lesotho.
Bureau of Statistics (2003).
Kingdom of Lesotho 2000 End Decade Multiple Indicator
Cluster Survey (EMICS), Bureau of Statistics Lesotho.
Corcora, B. (1995). Report of HIV/ TB Sentinel Surveillance, mimeo, Maseru.
Dorrington RE, Bradshaw D, Johnson L, Budlender D. (2004) The Demographic Impact of
HIV/AIDS in South Africa. National Indicators for 2004. Cape Town: Centre for
Actuarial Research, South African Medical Research Council and Actuarial
Society of South Africa.
Family Health International (2002). HIV/AIDS Behavioural Surveillance Survey. Lesotho
2002, executed by Family Health International in collaboration with the Lesotho
Ministry of Health, Lesotho AIDS Programme Co-ordinating Authority and
Sechaba Consultants, Maseru.
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Family Health International / IMPACT (2001).
Lesotho & Swaziland: HIV/AIDS Risk
Assessments at Cross Border and Migrant Sites in South Africa. Family Health
International - IMPACT Project.
Government of Lesotho (2000).
National AIDS Strategic Plan 2000/2001-2003/4, A
three year rolling plan for the national response to the HIV/AIDS Epidemic in
Lesotho 2000, Government of Lesotho.
Government of Lesotho (Sept. 2000). Policy Framework on HIV/AIDS Prevention, Control
and Management 2000. Government of Lesotho.
Hardap Regional Council. (2001). Regional Development Plan 2001/2002 2005/2006.
IUCN Water Demand Management Country Study Namibia (1999).
Integrated Development Plans from DMs and selected LMs within the Orange River Basin.
Karas Regional Council ((2001). Regional Development Plan 2001/2002 2005/2006.
Khomas Regional Council. (2001). Regional Development Plan 2001/2002 2005/2006.
Mendelsohn J. (2003). Atlas of Namibia
Ministry of Health and Social Welfare (1996).
AIDS Epidemiology in Lesotho 1996
STD/HIV/AIDS Prevention and Programme. Annual Report, prepared by DR M.A.
Maw for the Disease Control and Environmental Health Division, Ministry of
Health & Social Welfare Lesotho.
Ministry of Health and Social Welfare (1996). Update on AIDS in Lesotho, June 1996,
STD/AIDS Unit Disease Control and Environmental. Health Division Ministry of
Health & Social Welfare Lesotho.
Ministry of Health and Social Welfare (1997).
Update on AIDS in Lesotho 1997,
STD/HIV/AIDS Prevention and Control Programme, Disease Control and
Environmental Health Division, Ministry of Health & Social Welfare Lesotho.
Ministry of Health and Social Welfare (1998). AIDS Epidemiology in Lesotho,
STD/HIV/AIDS Prevention and Programme, prepared by DR M.A. Maw for the
Disease Control and Environmental Health Division, Ministry of Health & Social
Welfare Lesotho.
Ministry of Health and Social Welfare (1999).
AIDS Epidemiology in Lesotho 1999
STD/HIV/AIDS Prevention and Programme, prepared by DR M.A. Maw for the
Disease Control and Environmental Health Division, Ministry of Health & Social
Welfare Lesotho.
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Ministry of Health and Social Welfare (2000).
AIDS Epidemiology in Lesotho 2000,
STD/HIV/AIDS Prevention and Programme, prepared by Dr MOE AUNG MAW for
the Disease Control and Environmental Health Division, Ministry of Health &
Social Welfare Lesotho.
Ministry of Health and Social Welfare (2000). HIV Sentinel Surveillance Report 2000,
STD/HIV/AIDS Prevention and Programme, prepared by Dr M. A. Maw et al for
the Ministry of Health & Social Welfare Lesotho.
Ministry of Health and Social Welfare (2000). HIV/AIDS and STD Situation in Lesotho.
2000, STD/HIV/AIDS Prevention and Control Programme, Disease Control and
Environmental Health Division, Ministry of Health and Social Welfare, Maseru.
Namibian Ministry of Environment and Tourism. (2003). Community Tourism Market
Research for the South of Namibia.
Namibian Central Bureau of Statistics. (2005). Republic of Namibia 2001 Population and
Housing Census; Khomas Region Basic Analysis with Highlights.
Namibian Central Bureau of Statistics. (2005). Republic of Namibia 2001 Population and
Housing Census; Omaheke Region Basic Analysis with Highlights.
Namibian Central Bureau of Statistics. (2005). Republic of Namibia 2001 Population and
Housing Census; Hardap Region Basic Analysis with Highlights.
Namibian Central Bureau of Statistics. (2005). Republic of Namibia 2001 Population and
Housing Census; Karas Region Basic Analysis with Highlights.
Omaheke Regional Council. (2001). Regional Development Plan 2001/2002 2005/2006.
SA Institute of Race Relations. (2003). Report on HIV/AIDS. SAPA 28-08-2003.
Sechaba Consultants (2000). Condoms in Lesotho. Young People's Perceptions of the
Lovers Plus Campaign and the Barriers to Consistent Condom Use, Sechaba
Consultants, Maseru.
Schlemmer L, MarkData, Eric Hall & Associates. (2001) The Distribution of South Africa's
Population, Economy and Water Usage into the Long Term Future.
STATSSA. (2003) Mid-year 2003. Statistical Release PO302.
STATSSA. (2003) Census 2001 Census in Brief. Report No 03-02-03 (2001).
STATSSA (2003) How the count was done. Report No. 03-02-02 (2001).
STATSSA. (2004) Stats in Brief, 2004 Ten years of democratic governance.
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STATSSA. (2005) Mid-year population estimates, South Africa 2005. Statistical Release
PO302.
TAMS et al, 1996: Water Resource Management: Policy and Strategies, final report,
prepared for the Government of the Kingdom of Lesotho, Ministry of Natural
Resources, Department of Water Affairs, Maseru.
UNICEF/UNAIDS (2002). Orphans in Lesotho: Urgent Action for Children on the Brink
2002. UNICEF/UNAIDS.
World Bank (2000). Lesotho. The Development Impact of HIV/AIDS. Selected Issues
and Options, Macroeconomic Technical Group, Africa Region, World Bank,
Washington.
World Bank (2000). The Developmental Impacts of HIV/AIDS Selected Issues & Opinions
and Strategies October 18, 2000. World Bank Lesotho.
Ziken-Sechaba Joint Venture
(2001).
Lesotho Health Sector Reform Baseline
Assessment, prepared by J. Bloem, D. Gill, D. Hall, H. Lucas, R. Schumacher and
G. Singleton, Ziken-Sechaba Joint Venture Consultants, Maseru.
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SECTION TWO
ECONOMIC ACTIVITY
AND THE VALUE OF WATER
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9
ECONOMIC ACTIVITY
9.1
Introduction
The aim of this section of the Task 10 report is to provide a brief overview of the key water
dependent economic activities of the Orange-Senqu River Basin, highlighting potential
economic stimulants and growth areas.
Due to the time limitations in compiling this report, only information readily available on the
internet or through primary contacts in the South African Department of Water Affairs or
other consulting companies could be used in this report. It should be noted that information
of this nature is not easily available over the internet, and the next phase of this project will
have to allocate time to locating existing reports, which may not be readily available to the
public.
Growth projections for any industry depend on a wide range of variables and assumptions,
and are notoriously difficult to make with any degree of accuracy. This report has therefore
focussed on those sectors and activities which are receiving priority attention and support
from national governments or private investors.
The first half of section of the report provides an overview of the key economic activities in
the different areas of the Orange River Basin, together with an indication of their relative
share of water consumption.
The final portion introduces the strategic growth prospects of certain key water dependent
sectors, namely irrigated agriculture, the urban-industrial sector, mining, and hydro-electric
power generation.
It has become apparent that economic growth in South Africa has been more rapid than
was anticipated. The South African government had outlined a set of interventions to boost
the sum of all products and services in the country to 6% by 2010. This has important
implications for water requirement projections, particularly with regards to power
generation. In October 2004, Cabinet approved the implementation of the Vaal River
Eastern Subsystem Augmentation Project (VRESAP) which is a 124 km pipeline to deliver
water from the Vaal Dam into the Vaal River Eastern Sub-System. VRESAP is to be
implemented
to
meet
the
growing
water
demands
of
Eskom,
Sasol
and
Emalahleni/Middelburg local municipalities in the Mpumalanga Highveld region.
More
work is required nationally to revise growth estimates for all water intensive industries, and
the implications for water demand throughout the country.
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9.2
Overview of Major Economic Activities and corresponding water use
Economic activity in the Orange-Vaal river system is dominated by the urban-industrial
centre in Gauteng, the economic hub of Southern Africa, which accounts for 38% of South
Africa's GDP, and nearly 9 million people, or 20% of South Africa's population.
Other major water-dependent economic activities in the basin include mining, the energy
sector, and irrigated agriculture.
The major water users in the Orange-Senqu and Vaal basins change over the length of the
river, with urban-industrial uses predominating in the Upper Vaal area, while irrigation is
the dominant water use in all of the other areas with the exception of the Upper Vaal
urban-industrial centre, which includes much of Gauteng Province. Irrigation is the most
important water consumer overall, accounting for over 60% of the local water requirements
in the entire area in 2000.
Figure 19: Orange River Basin
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The major demand centres of water demand supplied from the Orange River system
include:
· The Vaal River System, which in turn can be broken up into Upper, Middle and
Lower Water Management Areas;
· The Upper Orange River, including Lesotho, which extends as far as the
Vanderkloof Dam upstream of the Orange/Vaal confluence. This area is also the
source of transfers to both Gauteng and the Eastern Cape; and
· The Lower Orange River, which extends from downstream of the Orange/Vaal
confluence to the river mouth, and includes the Common Border Area between
Namibia and South Africa.
The table below shows the distribution of local water use between different sectors overall
and in each of the areas.
Table 25: Water requirements for the year 2000 (million m3/annum)
Water
Mining & Bulk
Management
Irrigation
Urban
Rural
Other
Total
Industry
Area (WMA)
Upper -Vaal
11%
61%
4%
17%
8%
26%
Middle Vaal
43%
25%
9%
23%
9%
Lower Vaal
82%
11%
7%
1%
16%
Upper Orange
81%
13%
6%
0%
24%
Lower Orange
95%
2%
2%
1%
25%
Total
63%
23%
5%
7%
2%
100%
Notes: Urban includes basic needs reserve of 25l/c/d and Mining & industrial that are not part of urban
systems.
Source: DWAF 2004
9.3
Transfers
9.3.1
Lesotho Highlands Water Project (LHWP)
The LHWP was initiated in the 1970's as a dual-purpose project. The water from the
LHWP feeds the industrial and domestic needs of six provinces of South Africa under
normal circumstances. During severe droughts, water can also be channeled via the
Caledon River to provide the needs of the central Free State, as well as the Eastern Cape
provinces. The largest user of the Lesotho water is Gauteng Province, the industrial
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heartland of South Africa, which relies mainly on the Vaal River System. The LHWP adds
water to this system, as the residents and activities require more water than the Vaal River
system can provide. The second purpose is to generate hydropower to meet the needs of
Lesotho.
In addition to being the centre of financial and manufacturing activity in the country, the
bulk of South Africa's energy is generated by water-cooled, coal-fired power stations
situated in the central plateau, known as the Highveld. This region also relies on the
LHWP - augmented Vaal River system for its water supply. The industrial development of
Gauteng would simple not be sustainable without water supplied through the LHWP.
Vaal River Eastern Subsystem Augmentation Project (VRESAP)
On 6 October 2004, the South African cabinet approved the implementation of the
VRESAP which is a 124 km pipeline to deliver water from the Vaal Dam into the Vaal River
Eastern Sub-System (VRESS).
VRESAP is to be implemented to meet the growing water demands of Eskom, Sasol and
Emalahleni/Middelburg local municipalities in the Mpumalanga Highveld region. The
scheme transfers water via a 124 km long pipeline from the Vaal Dam near Vaal Marina to
the Knoppiesfontein Diversion Structure which discharges into either the Trichardtsfontein
or Bosjesspruit Dams near Secunda. The total anticipated capital cost is R2.4 billion. Each
main user will be required to redeem the debt based on the cost allocation via a water use
tariff based on actual use out of the VRESS and not just what is required from the
VRESAP. Under this arrangement Eskom pays for 61% of the project, but is allocated 50
% of the capacity, likewise Emalahleni/Middelburg only pays for 15% of the project, but are
entitled to 24% of its capacity.
9.4
Vaal River Basin
Domestic, mining and industrial water requirements predominate in the Vaal River
catchment, and these activities account for most of the urban-industrial activity in the
Orange System. Hydroelectric power generation, irrigation and mining are the major water
users in the rest of the basin.
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9.4.1
Upper Vaal Water Management Area
There are currently major water transfers both into and out of the Upper Vaal area
including a major transfer from Lesotho in the Upper Orange. With the completion of the
Mohale Dam in Lesotho, this amount will be increasing from 491 to 835 million m3/annum.
More growth in water demand is expected in the area as a result of ongoing economic
growth and continued urbanisation. With only marginal potential for further resource
development, water allocation decisions are paramount for this area.
The Vaal River supplies water to the industrial hub of Southern Africa, which includes the
Greater Pretoria and Johannesburg area, where mining, urban and industrial concerns
predominate. This area produces around 50% of South Africa's GDP as well as more than
80% of the country's electricity requirements - more than 50% of all the electricity
generated in Africa. (DWAF, 2005)
The Upper Vaal area also includes much of the gold and coal mining areas in Gauteng
and the eastern North-West province. Economic activity in the remainder of the area
consists mainly of livestock farming and rain-fed cultivation. The area stretches from
Potchefstroom in the west, down to Qwa-Qwa in the south, while the northern part extends
to the south of Pretoria and the Crocodile-Marico WMA.
The industrial hub of South Africa straddles the watershed between four international
shared river systems, namely the Limpopo, Incomati, Maputo and Orange Rivers.
A
number of water transfer schemes have been developed to meet the water requirements
of the area.
An important future development may concern the valuation of the economic impact of
poor water quality on downsteam water users.
9.4.2
Middle Vaal Water Management Area
The Middle Vaal area extends from Klerksdorp in the North West Province to the Bloemhof
dam to the west, and includes much of the north-western Free-State, extending almost to
the border of Lesotho.
While there is some irrigated agriculture in the area, economic activity is dominated by
gold mining, which accounts for 45% of the area's gross geographic product. No significant
economic growth is expected in future, in fact a slight decline in economic activity with the
decline in gold mining is expected.
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There is no more development potential for surface water, but this is not anticipated to be
an issue due to the limited future growth prospects.
Water quality issues are most important, and are likely to increase in importance with poor
quality, saline water. Water quality is carefully managed currently.
9.4.3
Lower Vaal Water Management Area
This area includes the Molopo River basin in the Kalahari, shared between South Africa
and Botswana, down to Douglas and the confluence of the Orange and Vaal rivers. The
largest urban centre in the area is Kimberley, with a population of approximately 200,000
people. It is situated on the southern edge of the area, and no significant growth in
economic activity is expected.
Major economic activities currently include iron ore, diamond and manganese mining.
Irrigation accounts for 80% of the total water use, which is concentrated in the Vaalharts
irrigation scheme.
Water quality is again a major issue, with the high salinity of leach water from the
Vaalharts irrigation scheme. Water transfers currently take place from the Orange River to
the Douglas weir for waste dilution. The value of water quality management, such as
through waste dilution, is likely to become increasingly important.
9.5
Orange River basin
Most water used in the Orange River basin is for irrigation with a relatively small portion
being used for urban and industrial purposes. River losses through evaporation also
represent a large portion of the total requirement
Although irrigation is by far the largest user of Orange River water, some of the largest
mines in the country are located in the Orange River basin. It is estimated that mining
accounts for more than 50% of the Gross Domestic Product (GDP) in the Northern Cape
Province compared to less than 15% generated from agriculture.
The mines use relatively small amounts of water, however, this water must be supplied at
a very high level of assurance due to the large investment involved in any major mining
concern. The mines in the Orange River basin are famous for diamonds, copper,
manganese and iron.
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9.5.1
The Upper Orange & Lesotho
The Orange River starts as the Senqu in Lesotho, where water demand stems primarily
from domestic and stock-watering needs. Some potential for further development of water
resources still exists in order to meet increasing transfer demands, particularly from the
Gauteng region. The region covers much of the southern Free State up to Douglas in the
Northern Cape and the confluence of the Vaal and Orange Rivers.
Transfers from Gariep Dam through the Orange/Fish tunnel are mainly used to support
irrigation developments in the Eastern Cape. A transfer agreement of a maximum 643
million m3 (capacity of current transfer system) is currently in place to meet the needs of
users in the Fish-Sundays system, and future urban growth in the Nelson Mandela
Metropolitan area. (Muir et al, 2003)
Maseru and Bloemfontein are the major urban centres in the area. There are no strong
economic stimulants for economic growth, but urbanisation is expected to continue within
the Free State, with continuing migration to Bloemfontein from within the Free State in
particular.
There is potential for the development of approximately 4,000ha of new irrigated land in
the Upper Orange, and an additional 4,000ha in the Fish-Sundays area. These areas have
been earmarked for irrigation development for developing farmers.
The key water resource issue is the allocation of the available surplus, and realisation of
development potential. Future growth demands are anticipated to come from the Nelson
Mandela Municipality, Mangaung Municipality, Gauteng, and also Namibia.
9.5.2
Lower Orange and the Common Border Area
Current activities
The Common Border Area of the Lower Orange River falls in the Northern Cape Region in
South Africa and in the Karas Region in Namibia. The economy of both regions is based
on mining and agriculture, which is estimated to contribute between 80 and 90% of the
region's economic activity. The local economy is generally seen as underdeveloped and
unsophisticated. (Muir et al, 2004)
Both South African interests and Namibian interests are broadly the same with respect to
water requirements for socio-economic development, domestic use, stock farming, mining,
irrigation and tourism.
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The economy of the Northern Cape is heavily dependent on the primary sector of the
economy. The largest sector is mining, which has declined in contribution to the Provincial
GDP from 25,8% in 1996 to 23,7% in 2002. Agriculture, however, increased its
contribution from 6,2% to 7,3%. There is only a limited amount of processing of the
primary commodity output in mining and agriculture, and manufacturing contributes only
4,2% towards the Provincial GDP.
There appears to be a trend in the Northern Cape for people from the more rural areas to
migrate into the larger towns where access to opportunities and services are significantly
better. This is reflected in the increase in the proportion of people living in urban areas
from 75,2% in 1996 to 82,7% in 2001. (Northern Cape provincial Government, 2005)
The South African water demand is mostly from irrigation, but water is also transported to
towns such as Pofadder, Aggeneis and Springbok. There is also a major rural water
supply project in the Kalahari to the north of the river, as well as various mining
developments.
The major urban consumers on the Namibian side are the towns of Oranjemund, Rosh
Pinah (including the Skorpion Mine) and Noordoewer.
The major towns on the Fish River include Keetmanshoop and Mariental. There are
currently 2 490 ha under irrigation at the two main irrigation projects in the Fish River
Basin.
Water usage
The Lower Orange area is completely dependent on upstream flows. Over 90% of the
water used in the Lower Orange is currently used for irrigation. Most of the high value
irrigation takes place along the lower Orange River downstream of Prieska. Most of the
area's wealth west of Douglas comes from either agriculture or mining, both of which
depend to some degree on Orange River water.
The Namibian government has estimated long-term water requirements in the order of
200Mm3/annum. The current allocation to Namibia is 50 Mm3/annum, with approximately
35 Mm3/annum being used for irrigation.
An additional 60 Mm3/annum has been
allocated to Namibia until the end of 2007, when South Africa is estimated to need this
allocation.
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The South African water focus is on improving efficiency of current water use, in particular
in irrigation, and the provision of water for socio-economic, rural community development.
9.6
Future growth
SA and Namibia have identified considerable development opportunities along the lower
Orange River. In Namibia, such developments include:
· The Skorpion lead and zinc mine;
· The proposed Kudu gas-fired power station at Oranjemund, and Haib copper
mine; and
· Irrigation projects for commercial and communal farmers.
Similar potential also exists in South Africa, with a particular need to develop irrigation
opportunities for resource-poor farmers and to support poverty alleviation. The Northern
Cape Government has also identified the possibility of growth in the agro and mineral
processing sectors, along with manufacturing.
There are currently 4,115 ha under irrigation along the common border on the South
African side of the Orange River. This is expected to increase for two main reasons; the
South African Government has allocated 4,000 ha of irrigable land for the establishment of
small farmers from previously disadvantaged groups, and there is likely to be further
demand from commercial farmers for irrigation of high value crops in the area. (Muir et al,
2004) However no additional new water quotas are anticipated to be allocated in South
Africa beyond that allocated for developing farmers. This policy may be revised if
additional water can be made available.
A slight decline is population is expected on the Northern Cape side, and future economic
activities will include a modest contribution from eco-tourism. Future growth opportunities
are expected to consist mainly of the production of high value crops along the common
border area. Approximately 60,000 ha has been identified on both sides of the border that
is suitable for the development of high value crops, such as table grapes and dates which
are discussed below. (Muir et al, 2004)
Most of the future irrigation potential lies on the Namibian side of the river, where there are
government-backed plans to expand the land under irrigation, and to increase production
of high-value crops such as export table grapes and dates. The Namibian National
Development Plan (Phase II) and Vision 2030 aim to make Namibia food self sufficient,
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with a key focus on development along the major rivers, including the Lower Orange. The
Namibian Government is therefore seeking as much water as possible from the river.
Namibia is heavily dependent on the availability of water along the lower Orange River.
The possibilities for socio-economic development in this area are limited to extensive small
stock farming, mining, irrigation and tourism. The water requirements for domestic use, a
number of diamond mines, 2 zinc mines and the future development of the proposed Kudu
gasfield power station at Oranjemund will require an assured source of water. (Heyns,
2004)
The availability of water for large scale development may be a constraint to develop the full
potential in the Lower Orange River. The following measures may affect this availability:
· Savings through water demand management initiatives, which are being
investigated as part of the Lower Orange River Management Study (LORMS).
· Reduction of losses due to peak hydro power releases that do not coincide with
peak irrigation demands.
· Reduction of operational losses in the river.
· The environmental requirements of the river.
However water savings through improved water use efficiency and Water Demand
Management (WDM) initiatives upstream of the Common Border will not necessarily be
available for use in the Common Border Area, and may possibly be used to support further
development in the Upper Orange River Region.
9.7
Growth prospects by sector
This section focuses more broadly on the future growth prospects of key water dependent
sectors in the Orange-River Basin, taking a sectoral view. The most important water using
sectors from an economic point of view are:
· Irrigated agriculture;
· The Urban-industrial sector;
· Mining and mine-closures; and
· Power generation
The last section briefly addresses the issue of water dilution. Although not a sector itself,
the quality of water has significant cost implication for all sectors.
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9.7.1
Irrigated agriculture
The primary information sources for information on potential in the irrigated agriculture
section were studies on the Lower Orange River. It was not possible in the time available
to obtain detailed information on the potential for irrigation in the rest of the Orange basin,
and more work will have to be done to identify other irrigation opportunities. However in
terms of potential economic impact, the development of further land for irrigation in the
area would provide employment and stimulate regional infrastructure development in an
impoverished area with little other potential for development.
Current role of agriculture in ORASECOM countries
Agricultural production in Lesotho has declined substantially over the past decade. The
sector's contribution to GDP has fallen from around 24% in 1990 to 15.4 % in 2001. The
decline is particularly worrying given that about 75% of the country's population depends
on agriculture for their livelihood. Apart from erratic weather conditions, several factors
have constrained the development of agriculture in Lesotho. The quantity of arable land
has declined from around 13 % of total land in the 1960s to around 9% today, putting
pressure on the fertility of a limited land base. Approximately 40 percent of the population
is landless.
The Agricultural sector has also depended heavily on external assistance. With the
general decline of donor support in the country, the sector became the hardest hit. The
average agriculture share of total development assistance declined significantly. The
sector is however expected to show some recovery with implementation of the Agricultural
Sector Investment Programmes. The challenge is for Lesotho to increase agricultural
productivity to sustain food security in the country (Vision 2020)
The agricultural challenge in the Northern Cape in South Africa has been identified as
growing the agricultural sector and increasing its contribution to provincial GDP,
employment and income while at the same time increasing access to agricultural
resources by the previously excluded sections of society.
The Provincial government
seeks to promote:
· New investments in primary agricultural production,
· The more efficient use of irrigation water, by promoting crop diversification and
the reduction of levels of risk in agriculture,
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· The development of agro-processing and the addition of value to primary
agricultural output and by stimulating increased export of high value agricultural
produce.
Government is also committed to promoting transformation in agriculture through land
reform, the allocation of water rights, transfer of skills and knowledge and agricultural
credit to emerging black farmers.
Agriculture is Namibia's most important economic sector. Although it provides only around
6% of GDP, it employs 37 % of the Namibian work force. With its low and highly-variable
rainfall pattern, Namibia is the driest country in Sub-Saharan Africa. The most important
challenge facing the country's agricultural sector is dealing with the low rainfall and
exploiting the existing water resources sustainably.
Although irrigation potential is limited
in Namibia, there is room for expansion beyond present levels. As part of its policy of crop
diversification, government is encouraging cultivation of high-value crops. Recent studies
have shown that non-traditional crops such as table grapes, dates, cotton, tobacco,
lucerne and devil's claw have potential for expansion. Fruit growing for export also has
good potential as Namibia can harvest earlier in the season than South Africa, providing
substantial price advantages in the European Union. Namibia's table grape production is
expanding rapidly, mainly to meet demand in the European Union. The Namibian
Government has completed a series of feasibility studies for the expansion of table grape
production along the Orange river. (DTI, USAID-Namibia)
Preconditions for successful irrigation projects
The economic success of irrigation projects is difficult to predict due to its complexity. Very
few irrigation projects in the world would have been viable without some or other form of
subsidy. Even the currently lucrative table grape farming practices along the Lower
Orange would be unlikely to be able to carry the present day costs of the dams and the
other water supply infrastructure on which they are dependent. Add the market complexity
of currency fluctuations, technology development and changes in world markets together
with the influence of climatic conditions then it is clear that the irrigation potential is volatile.
The provision of large dams to regulate stream-flow, along with other infrastructure such
as roads and airfreight facilities provided by the state creates an enabling environment for
the implementation of irrigation projects.
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The viability of irrigation schemes in the Orange River Basin can be enhanced through
encouraging the cultivation of high value crops through marketing support incentives to the
farmers. The use of more efficient irrigation systems will also allow a farmer with the same
volume of water allocation to cultivate a larger area. (Muir et al, 2003)
The projections of future water demand in the Lower Orange depend largely on the
development of further irrigation projects.
In considering the potential of these future
developments, the following factors need to be considered:
· The available irrigable land;
· The financial viability of irrigation;
· The availability of markets and the long-term prospects of the crops grown;
· Effective marketing of produce is essential, particularly as high value produce
grown in the area will have to compete with other global players competing for the
same markets; and
· The economic and socio-economic benefits to the region and to the country.
Large profits and valuable foreign exchange can be earned from well-managed
irrigation projects that grow high value crops. (Muir, 2003)
Potential Significant Irrigation Opportunities
Irrigation farming, particularly for high value crops, is labour intensive and the expansion of
irrigation will provide many employment opportunities.
The development of further irrigation projects is not constrained by the amount of irrigable
land, but rather by the cost of making the land suitable, transporting water, and of course,
the availability of water.
In a recent study of the competitive potential of agriculture throughout Africa, the FAO
identified several potentially competitive country-specific crops which are relevant to the
Orange River Basin. The recommended crops included:
· sunflowers in Botswana,
· potatoes and wheat in Lesotho,
· and millet in Namibia. (Minoiu, 2003)
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Both South Africa and Namibia have identified considerable development opportunities
along the Lower Orange River.
The bulk of the opportunities have been identified in
Namibia, including irrigation projects for both commercial and communal farmers.
Similar potential also exists in South Africa, which has identified a particular need to
develop irrigation opportunities for resource-poor farmers and to support poverty
alleviation. It appears that the only irrigation developments for which new water rights will
be released in South Africa in the Orange River system will be for small-scale farming.
Three allocations have been made, including 4 000 hectares each for the Lower Orange
WMA, the Upper Orange WMA and for the Fish to Tsitsikama WMA.
Irrigation potential also exists in the Eastern Cape for new initiatives such as sugar beet
and chicory production.
The growth of crops produced according to organic principles may create further major
opportunities in the international market if it is marketed correctly.
It is doubtful if the growing of low value crops will be viable at all, except for small scale
production for own consumption due to the great distances to markets, high pumping
costs, low value of products and other negative factors.
The presumed development of high value crops is related to high pumping cost, market
advantages for certain crops, water quality, soil conditions and climatic factors. Although
the growing of high value crops can result in substantially greater profits, this is also
coupled to far greater capital investments, higher management inputs and higher risks.
There are a number of other crops that can be considered as alternatives, but none of
these have proved to be as profitable as table grapes. In the long-term the role that table
grapes are playing now can be taken over by dates, which are already being produced
successfully along the Lower Orange River. Both dates & grapes have been identified as
important growth sectors for the US market. (USAID) In future other crops may emerge
that will equal or exceed the potential of dates and grapes.
Table grapes
An important growth industry in the Lower Orange River is the export table grape industry
for the European and American market. Table grapes have been tried and tested in this
region and are a viable crop for the immediate future. However the success of the table-
grape growing industry depends on the early harvesting of crops due to a climatic
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advantage, and getting goods to market before competitors, which results in very
favourable prices.
Unfortunately the weakening of the dollar in recent years has reduced the economic
viability of this industry relative to earlier expectations. (pers comm, P Pike, DWAF, 2005)
which in most cases assumed an exchange rate of between R8 and R9 to the dollar (Muir,
2003). Production costs in the Orange River (due to its distant location) are relatively
higher compared to other production regions. If table grape prices come under pressure,
the profitability and return on capital investment will decline substantially.
The Lower Orange has traditionally produced a relatively small volume of table grapes
early in the season. Production of table grapes early in the season in the Lower Orange
soared in the late 1990's and early 2000's. However there have been concerns that the
comparative advantage with regard to a relatively "small" volume early in the season will
gradually be eroded.
The period of time during which the grapes fetch phenomenally high prices is so brief that
it is virtually impossible to determine the extent of the demand during this period. It occurs
during the first week or two that grapes become available from the Lower Orange. Within
one week, the price drops to 50% of its highest level.
This dependence on a very narrow market window makes the table grape industry in the
Lower Orange very vulnerable. The profitability of the grapes that are marketed at the right
time slot is such that competition is likely to develop. There is even a risk that the increase
in production along the Orange River can saturate the market for early-season, higher-
priced grapes.
New markets are however being investigated and developed, and the
world markets currently being supplied by table grape growers in the region have the
capacity overall to absorb substantial expansion.
Dates
While table-grapes appear to have the greatest potential in the short to medium term, they
might be overtaken by dates as the table-grape industry becomes saturated. The market
for dates is unlimited at this point in time, and dates appear to be more stable than table
grapes in the long-term.
The fruit can be stored for longer periods of time and marketing is not subject to a window
period that is determined by global climatic conditions. In terms of profitability dates can
compete with grapes marketed in the peak period (Muir, 2003).
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However the switch over to the production of dates will be a slow process. A major
drawback to the production of dates is the time that it takes to come into production.
Dates start to produce after five years and only come into full production after ten years.
Land is unlikely to be switched over within a short period and the input costs, capitalised
interest, and operating costs will have to be carried for long periods of time.
Vegetables
Most vegetables are highly profitable due to shortages caused by erratic weather
conditions.
Due to the considerable fluctuations in the market for vegetables, it is not
possible to make medium or long-term predictions. However, vegetables have an inherent
strong demand in the local market as part of a staple diet.
Because of the difficulty in market projections, as well as the relatively high management
requirement, vegetables are considered to be high-risk crops. The three main vegetables
grown in South Africa in terms of value of production are potatoes, tomatoes and onions.
The per capita consumption of potatoes in South Africa is very low compared to other
countries. This creates an enormous potential for market development, which Lesotho in
particular is well placed to take advantage of, due to their comparative advantage in potato
production.
Lucerne
As a major livestock-producing country, there is strong demand for lucerne in Namibia,
particularly in years of low rainfall. At present most of the lucerne is imported from South
Africa. Local production is increasing and expansion of lucerne production could
significantly reduce the country's stockfeed imports. (DTI)
9.7.2
Urban-industrial sector
The growth of the urban-industrial sector will be determined largely by demographic trends
which are dealt with under a separate report.
In general, the vast majority of future urban-industrial growth in the Orange Basin will take
place in the Upper Vaal area. According to the State of the Cities Report (2004), the urban
portion of Gauteng - comprised primarily of the cities of Johannesburg, Ekurhuleni and
Tshwane will be a polycentric urban region with a projected population of some 14.6
million people by 2015, making it one of the largest cities in the world. This growth will be a
significant source of growing water demand in the future.
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With the possible exception of Bloemfontein which is expected to continue to grow, the
urban-industrial sector accounts for a minor share of water consumption in the rest of the
Orange River Basin, or 36% of water consumption overall.
Although the irrigation water demand along the Orange River far outweighs the urban,
mining and industrial water demand, it is important to study these demands to identify
mismanagement of a valuable resource. In general, effective water management is not
practised along the Orange River for urban water consumption. Along the Lower Orange,
this is mainly because domestic water is supplied without cost in mining towns, where the
water account is paid by the mine and not by the consumer.
Certain towns situated in the Orange River Basin currently obtain water from their own
sources, mainly boreholes. As these towns grow, it is possible that the local groundwater
sources will not be able to sustain the growth of the towns and it is assumed that these
towns will eventually obtain water from the Orange River. (Muir, 2004)
Both in South Africa and Namibia, manufacturing activities are normally concentrated in
larger cities and towns. Although the contribution of the manufacturing sector to the
Northern Cape provincial economy is extremely low, the Provincial Government is
promoting economic diversification and the processing of primary mineral and agricultural
products.
Lesotho's manufacturing sector grew rapidly in 2001, aided by the depreciation of the loti,
which boosted demand for manufactured exports. In addition, investor sentiment has
remained strong since the country's designation by the US government as one of the least
developed countries to benefit from the Africa Growth and Opportunity Act (AGOA). The
manufacturing sector is highly concentrated, with a few firms in the textile industry
accounting for the bulk of total production in manufacturing as well as total manufactured
exports. The lack of diversification exposes the sector to a high degree of risk. The
manufacturing sector needs to diversify to reduce the reliance on textiles and clothing as
the primary source of export earnings. The World Bank has identified three potentially
profitable areas of investment: the bottling of mineral water, sandstone cutting, and the
promotion of the tourism industry (DTI, 2005)
For Namibia, increased manufacturing, in addition to increased local value adding in the
resource sectors of the economy such as mining and agriculture is a priority. Namibia's
manufacturing sector is relatively small as compared to the other sectors of the economy
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and it contributes little to the GDP. However the Namibian Government is currently
focusing on programmes to achieve greater diversification in the manufacturing sector.
(DTI, 2005)
9.7.3
Mining and mine closures
Current activities
Although irrigation is by far the largest user of Orange River water, some of the largest
mines in the country are located in the Orange River basin. It is estimated that mining
accounts for more than 50% of the GDP in the Northern Cape compared to less than 15%
generated from agriculture.
The mines use relatively small amounts of water, however, this water must be supplied at
a very high level of assurance due to the large investment involved in any major mining
concern. The mines in the Orange River basin are famous for diamonds, copper,
manganese and iron. Most of the country's gold mines are located in the Vaal River Basin.
(DWAF, ORP website) The Northern Cape Manganese Deposits to the north and west of
Kuruman are the largest manganese deposits in the world. It is estimated that more than
80% of the world's known manganese reserves are situated in the Northern
Cape. (Minerals Bureau, 1992)
Mining activities normally have a limited life span and cannot be regarded as a permanent
production source. They are therefore generally seen as a short to medium term demand.
The success of mining activities is dependent on commodity prices and related global
markets, as opposed to the availability of water.
The economic development of the Northern Cape in South Africa was based on a rich
base of mineral resources, particularly the diamond, copper, manganese and iron ore
deposits. Mining in the Northern Cape accounted for 23,7% of Provincial GDP in 2002.
The mining sector is the largest contributor to Namibian GDP after government services
and is a major contributor to exports, accounting for 40 % of foreign exchange earnings;
however, the sector's share of GDP declined considerably over the 1990s, falling from 20
% of GDP in 1990 to 12.5 % of GDP in 1999.
Growth opportunities
SA & Namibia have identified considerable development opportunities along the Lower
Orange River. In Namibia, possible developments include:
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· The Skorpion lead and zinc mine, which is earmarked to be the world's lowest
cost zinc producer;
· The proposed Kudu gas-fired power station at Oranjemund, where seawater will
most probably be used for the cooling operations of the power station. The only
fresh water required from the Orange River will be the demineralised water used
in the boilers;
· Haib copper mine. The water demand of Haib Mine would overshadow any other
mining enterprises along the Orange River on the Namibian side. Estimates of
the water which will be needed annually by the mine range from the initial figure of
60 million m3 to more recent estimates of approximately 20 million m3/. This high
water consumption rate is as a result of the water intensive processes that the
mine intends to use. There is a 25 year life span for the mine. The viability of the
Haib Mine depends to a great extent on the world copper price which in turn
depends upon demand and supply.
These proposed developments will lead to a substantial increase in water demand.
Lesotho is believed to have significant mineral deposits, but attempts at exploitation have
been limited due to a lack of investment. Known deposits include diamonds, uranium, base
metals, high quality stone and clay. Recent policy initiatives are aimed at encouraging
greater private sector participation in the mining industry. To date interest has mainly
focused on diamonds, although output is modest at around 1,000 carats a year. There are
plans to revive the diamond industry by reopening the Letseng-la-Terae mine, owned by
the Letseng Diamond Company, a company in which the government has a 24 percent
shareholding. The mine was operated by De Beers between 1976 and 1992, but has been
closed for more than a decade. The viability of mining Lesotho's reserves of uranium,
base metals and clays is being evaluated. Reserves of coal and bituminous shale have
also been identified in several areas of the country. (DTI)
The Botswanan government is currently focusing on diversifying away from the mining
sector. The production of diamonds has reached a plateau, following the completion of the
Orapa mine expansion in 2000, and no significant growth impetus is expected to originate
from this sector in the near future. An aeromagnetic survey has identified three deep
sedimentary basins in western Kalahari, and initial prospects for locating either oil or gas
were considered good. (DTI)
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Mine closures and acid water
In general, mining in the Orange River System is in its mature phase, and is expected to
decline in the foreseeable future. There are concerns that current and impending gold-
mine closures may result in devastating consequences for the environment unless
managed properly.
Together, the South African Department of Water Affairs (DWAF) and Minerals and
Energy (DME) intend to ensure that mining companies implement remedial measures
towards final closure that will not only facilitate environmentally and socially sustainable
future land use, but also secure a water use that will support such land use. It has been
suggested that mines investigate establishing a water utility to process and sell the water,
turning a potential environmental hazard into a job-creating asset.
DWAF has highlighted gold-mines in the Randfontein area where the western basin is
decanting towards the Tweelopies spruit, and mines in the Boksburg and Benoni area
where dewatering of the eastern basin is affecting the Vaal river system. There are
concerns that unregulated dewatering will affect the sustained use of the Vaal as well as
the agricultural industry. When gold-mining ceases, water levels in the basins will rise and
eventually decant at the lowest geographical points in the Vaal river system, threatening
the quality of the water resources.
DWAF, DME and the mining sector will have to cooperate to ensure that the Vaal river is
protected from uncontrolled and illegal discharges of highly-polluted underground water
from mines. The scale and allocation of the costs required to do so are at this stage both
undecided, and contentious.
9.7.4
Power generation
South Africa's higher than anticipated economic growth has resulting in the country
running out of generating capacity sooner than anticipated. In order to keep pace with
higher than expected sales growth, Eskom has begun accelerating its return to service
project, which includes demothballing 3 water-cooled coal-power stations. This has
significant implications for water demand in the Vaal River System.
Demothballing the three power stations in Mpumalanga is expected to add an additional 3
500 MW of electricity to the South African power pool, the first coming on stream this year,
the last by 2011. The Camden power station, near Ermelo in Mpumalanga will comprise
the first phase of the programme. Two generating units with combined capacity of 380MW
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will be brought on stream at Camden every year from 2005 to 2008. Grootvlei which is
situated near the town of Balfour, will have one unit recommissioned in 2007, two units in
2008 and 2009, and one in 2010. The Komati plant between Middelberg and Bethal will
recommission two units a year from 2010 to 2013 and one in 2013. At the end of the
programme, Eskom will have additional generation capacity of 3612 MW. The three
stations will each have an operating life of 15 to 20 years.
It was estimated that South Africa would need almost 65 000 MW of electricity capacity by
2024 to meet growing demand. However, the demand-growth projections going forward to
2025 were based on gross domestic-product growth of 4%. These figures need to be
adjusted based on government's planned growth of 6%.
Eskom is the main beneficiary of the water to be transferred in the VRESAP scheme,
discussed elsewhere in this report.
The most significant future hydro-electric power generation opportunities in the Orange-
Senqu Basin are in Lesotho. Lesotho used to rely on South Africa for 98% of its power
requirements until the implementation of the Lesotho Highlands Water Project (LHWP),
which has changed Lesotho from an importer to an exporter of electricity. The royalties
generated from these sales to South Africa will be an important source of income for
Lesotho in coming years, in addition to the payment of water royalties for water transferred
to the Gauteng region.
The Project (LHWP) is already generating enough hydroelectricity to meet almost all of the
country's needs and provide a profitable source of export earnings. The project combines
water storage and electricity generation, and involves the construction of an extensive
system of pipelines and tunnels to deliver water to Gauteng province in South Africa.
There is a possibility of a Phase 2 of the project, which has the potential to generate up to
2,000 MW of electricity. However, it has yet to be decided just what phase 2 will involve.
Uncertainty stems from the fact that expectations of population growth in Gauteng
province are lower than when the first projections were drawn up in 1980.
Uncertainty also stems from the focus in the African energy sector, which is generally on
regional cooperation and on resources of sub-regional significance. This may lead to other
schemes being implemented prior to or instead of Phase II of the LHWP.
Of Africa's technically feasible hydropower potential of 1 750 000 GWh/year, only 3% has
been exploited. However, the location of hydro resources and the demand for power are
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poorly matched. Few households have access to electricity, consumption is low, and
industrial and commercial developments are practically non-existent. The result is a lack of
demand for electricity to economically justify rapid exploitation of the vast hydro potential
that exists. The role of regional hydropower is viewed as providing a sustainable, low-cost
source of electricity. One of the most high-profile ways of attaining this is through the
proposed Inga scheme in the Congo basin. It is hoped that a supply of cheap and plentiful
electricity will help to stimulate industrial developments and help reduce poverty.
SADC is promoting regional electricity cooperation and power pooling through the
extension of grid interconnections to cover all Member States and the transformation of the
Southern African Power Pool (SAPP) from a co-operative to a competitive pool and create
a regional electricity market. The SAPP is a core component in NEPAD's regional energy
plans. It was created in 1995, to link SADC member states into a single electricity grid.
Botswana, Lesotho, Namibia and South Africa all belong to the SAPP, which saw the
bringing together of two main networks in the region, namely:
· A southern network (consisting of mainly thermal generation), with transmission
links interconnecting South Africa, Namibia, Mozambique, Swaziland and
Lesotho; and
· A northern network (mainly hydropower), with transmission links interconnecting
Democratic Republic of the Congo, Zambia, Mozambique and Zimbabwe.
The proposed $1-billion Inga hydropower project in the Congo Basin involves an initial
transmission line which will run from the power station in the Democratic Republic of
Congo (DRC) through to South Africa via Angola, Namibia and Botswana. This is to be
implemented by the Western Corridor Steering Committee (Westcor). Westcor was set up
by SAPP to investigate the feasibility of this corridor and is a joint-venture between the
power utilities of Namibia, Botswana, South Africa, Angola and the DRC. The goal is to
complete the Westcor project by 2015.
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Table 26: Hydropower Macro plans and Key projects, by SADC country
Country
Major national plans and initiatives
Botswana
Internal electrical power is generated entirely from thermal sources at two major coal-
fired plants
Will benefit from regional hydropower initiatives such as Inga 3 and the Westcor
transmission project. Could import from Angola
Lesotho
Sale of hydropower to SA is a significant revenue generator. The inauguration of the
Muela hydroelectric power station now means that Lesotho no longer needs to import
electricity from South Africa, but can instead export power to South Africa.
There is considerable hydroelectric potential in Lesotho. Estimates have equaled 1260
GWh/year. Internal demand is low, with only 2% of the population currently electrified
Namibia
Current supply not adequate for industrial demand, imports most of its power. Will
benefit from the Western Corridor Project (Westcor), part of the Inga3 project.
Namibia and Angola are considering the development of a hydroelectric facility on the
Cunene River that would provide electricity to both countries. Two possible sites for the
dam are being considered, Baynes and Epupa Falls. The proposed facility would have
a generating capacity of about 360 megawatts (MW) and provide power to the
Angolan, Namibian and South African grids.
South Africa
Insignificant internal potential. Main international opportunities include the Westcor
project to utilise DRC potential, and the recently completed Maguga Dam in Swaziland
Source: Reports compiled for SADC 2004, World Bank 2004.
9.7.5
Water quality and dilution
Poor water quality increases the cost of water purification and decreases the productivity
of irrigated agriculture. Water dilution is therefore practiced in an attempt to improve water
quality. Upstream polluters are therefore passing on the costs of poor water management
practices to downstream users both through lower quality water, and the use of water for
dilution which could be better used elsewhere in the system.
Water dilution is a sub-optimal use of scarce water resources, and upstream users should
be encouraged to improve their water-use practices to reduce the need to reserve water
for this purpose.
The quality of the water in the Orange River system has systematically been degrading.
Reasons for this include the increasing agricultural and industrial activities which are
upstream from Upington, as well as the lessening of the inflow of high quality water from
Lesotho.
The quality of the water varies with the seasons, as well as depending on which river feeds
the main inflow. If it is the Orange River, the turbidity, sand and salt content is usually high.
If the inflow comes mainly from the Vaal River one finds a light nutrient content which
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leads to algae growth. The removal of large concentrations of both silt/sand and algae is
problematic at times.
Salinity in the Orange River has increased due to the transfer of high quality water out of
the Orange River (in Lesotho and the Upper Orange WMA) and as a result of high salinity
irrigation return flows along the Orange River. Poor quality water from the Vaal River,
which contains a high proportion of irrigation return flows as well as treated urban effluent,
also enters the Orange. Salinity is at present still moderate along the main stem of the
Orange River. Deterioration can be expected with increased upstream irrigation and the
situation must be closely monitored.
Water quality of the surface water in the Upper Orange is generally good except for the
high sediment load in the Caledon and the salinity problems in the Lower Riet. The water
quality in the Lower Orange has, however, been severely impacted upon by extensive
upstream developments. It is possible that the water quality problems in the Orange is
coming from the Vaal as water quality in the Vaal becomes worse as one proceed along
the Vaal. Under normal operating conditions very little water from the Vaal reach the
Orange River and it is mainly under flood conditions that large volumes will enter the
Orange.
The water quality issues in the catchment relate to the management of the water quality
passed down between WMAs and can therefore not be solved on a WMA basis alone. An
integrated water quality management tool is required to allow for the rational assessment
of the factors that impact on water quality in the Orange River.
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10
REFERENCES ON THE ECONOMIC ACTIVITIES
Gauteng Provincial Government, 2005, Gauteng Provincial Growth Strategy, April 2005,
www.gpg.gpv.za
C Muir / F du Plessis / F Oosthuizen / P de Wet / T Hart, 2004, Vioolsdrift and Noordoewer
Joint Irrigation Scheme
(JIA) : Assessment of Viability with Particular
Reference to JIA Request for Further Investment. Draft final report
Minoiu D, 2003, Products with competitive potential in African agriculture, Report produced
by the FAO in support to the agricultural sector component of the Nepad
secretariat.
Heyns P, 2004, Achievements of the Orange-Senqu River Commission in Integrated
Transboundary Water Resource Management, Paper presented at the INBO
World Assembly, January 2004, The Martinique, French Antilles.
European
Commission,
2004,
Development
Country
Profiles,
http://europa.eu.int/comm/development/index_en.htm
Northern Cape, Northern Cape Provincial Growth and Development Strategy Abridged
Discussion Document
van der Linden E, 2000, Namibia's position in the Southern African economy
"Opportunities and challenges for the future, Paper commissioned by the Royal
Netherlands Embassy, Windhoek, November 2000.
DTI, Economic Overview of Lesotho, Botswana and Namibia www.dti.gov.za
Chris Muir; Manie Maré; Francois du Plessis; Ben van der Merwe; Charles Crosby, 2005,
Water Requirements, Report for Prefeasibility Study Into Measures To Improve
The Management Of The Lower Orange River And To Provide For Future
Developments Along The Border Between Namibia And South Africa, July 2004.
USAID/Namibia, Country Strategic Plan, FY 2004 2010
Northern Cape Provincial Growth Strategy, 2004-2014
Namakwa District Municipality. 2006, Draft IDP
2020 national vision for Lesotho: Empowerment for sustainable prosperity,
Mariaan Olivier, 2006, 65 000 MW needed by 2024, says Eskom, Engineering Weekly, 27
March 2006
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Shona Kohler, 2004, Big changes in store for SA's energy sector, Electra Mining Online
Supplement, Nasrec, Johannesburg, South Africa, 6-10 September 2004.
Nicola Mawson, 2005, Legacy of drowning, Mining Weekly, 3 June 2005.
Northern Cape Provincial Growth Strategy, 2004-2014
Department of Water Affairs and Forestry, South Africa. 2004. Internal Strategic
Perspective: Lower Orange Water Management Area. Prepared by PDNA, WRP
Consulting Engineers (Pty) Ltd, WMB and Kwezi-V3 on behalf of the Directorate:
National Water Resource Planning. DWAF Report No P WMA 14/000/00/0304
Department of Water Affairs and Forestry, South Africa. 2004. Internal Strategic
Perspective: Orange River System Overarching. Prepared by PDNA, WRP
Consulting Engineers (Pty) Ltd, WMB and Kwezi-V3 on behalf of the Directorate:
National Water Resource Planning. DWAF Report No P RSA D000/00/0104
Northern Cape Province, 2005, Northern Cape Mineral Sector Strategy, www.northern-
cape.gov.za
Government websites:
www.siyanda-dm.co.za
www.namakwa-dm.gov.za
www.northern-cape.gov.za
www.dti.gov.za
www.dwaf.gov.za/orange/default.htm
www.dme.gov.za
www.grnnet.gov.na
www.eskom.co.za
www.gpg.gov.za
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11
THE ECONOMIC VALUE OF WATER
11.1 Introduction
11.1.1
Background
The Orange River Basin extends into four countries; The Kingdom of Lesotho, Republic of
Botswana, Republic of South Africa and Republic of Namibia. It includes the total land
area of Lesotho, most of the central part of South Africa and reaches to the southern part
of Botswana as well as draining most of the southern half of Namibia. The Orange-Senqu
River Commission (ORASECOM) came into existence on 3rd November 2000 by
agreement among the four basin member states in terms of the SADC Protocol on Shared
Watercourse Systems, with one of the primary aims being the integrated development and
management of the water resources of the Orange River to the mutual and equitable
benefit of all parties.
At the stage that ORASECOM was founded, extensive developments had already taken
place with respect to water resource infrastructure and utilisation of the resource. Amongst
others, large inter-basin transfer schemes have been developed which transfer water from
several other basins into the Orange River Basin as well as from the Orange River Basin
to other adjoining river basins. Plans have also been developed by some of the co-basin
countries with respect to possible further developments and aspects pertaining to the
future management and utilisation of the resources of the Orange River Basin. To facilitate
the integrated development and management of the resources of the Orange River jointly
by the four basin member countries, it is essential that common ground exist among the
basin countries with respect to the principles and objectives salient to the joint
management and that appropriate strategies and plans be developed to achieve this. A
key component and common reference base being the development of an Integrated
Water Resources Management Plan (IWRMP) for the Orange River Basin. This paper
deals with the issue of the economic value of water in the Orange River Basin and is an
output of Task 10.
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11.1.2
Overview of the Paper
Water has an economic value in all its competing uses and should be recognised as
an economic good.
Within this principle, it is vital to recognise first the basic right of all human beings to have
access to clean water and sanitation at an affordable price. Past failure to recognize the
economic value of water has led to wasteful and environmentally damaging uses of the
resource. Managing water as an economic good is an important way of achieving efficient
and equitable use, and of encouraging conservation and protection of water resources.
Principle no. 4, International Conference on Water and the Environment, Dublin, January
1992
There is an extensive body of literature available on the economic value of water, and the
various techniques for evaluating the economic value of water. However this report serves
only as a summary introduction to some of the main concepts and techniques. Where
possible this information has been tailored with regard to the major categories of water use
in the Orange-Senqu Basin. References are supplied for readers who would like to
investigate these methods in more detail.
In determining the optimal allocation of water resources, a system-wide perspective has to
be taken. In the Orange-River Basin, extensive transfers out of the basin to the Gauteng
and Fish-Sundays River System must be taken into account. For this reason, the water
uses and values considered in this report will extend beyond the strict boundaries of the
river basin itself.
It explores the generic conditions driving the demand for water in each of the major water-
using sectors in the Orange-Senqu, and introduces some of the methods which are used
to determine the economic value of water in each of these sectors.
Irrigation and urban-industrial users account for the bulk of consumption in the Orange
basin, or 86% overall. The table below illustrates the major water users by sub-basin, and
their relative share of total water consumption. The prospects for future growth and
development are dealt with in other reports.
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Table 27: Water requirements for the year 2000 (million m3/annum)
Mining &
Water Management Area
Irrigation
Urban
Rural
Bulk
Other
Total
(WMA)
Industry
Upper -Vaal
11%
61%
4%
17%
8%
26%
Middle Vaal
43%
25%
9%
23%
9%
Lower Vaal
82%
11%
7%
1%
16%
Upper Orange
81%
13%
6%
0%
24%
Lower Orange
95%
2%
2%
1%
25%
Total
63%
23%
5%
7%
2%
100%
Notes: Urban includes basic needs reserve of 25l/c/d
Mining & industrial that are not part of urban systems
Source: DWAF 2004
The types of values allocated by the different users to water is explored, along with the
role that water markets might play in improving the efficiency of water use in the basin.
The report concludes with a brief discussion of the issue of affordability and access to
water for the poor.
11.2 The Concept of Economic Value
Total economic value is loosely defined as the maximum amount a user would be willing to
pay for the use of a resource, and derives from the specific use to which this resource will
be put. (Gibbons, 1986) Economic value is the economic benefit derived from using a
given quantity of water.
In a world of perfect competition for water, with perfect information, functioning markets, no
externalities, and no distribution concerns, the public and private value of water are equal.
Under these conditions, the economic value would be equal to the market price.
However, in the absence of these conditions, there is most likely a mismatch between
price and value, and price is no longer necessarily representative of economic value.
Public and private values differ because an individual's use of water at one time and place
may impose unintended consequences, or externalities, on others.
To satisfy their highest priority needs, users are typically willing to pay a premium for the
first units of water. In most cases the total value of water to a user will increase as the
quantity used increases, but at a decreasing rate. This suggests that the marginal value of
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each additional unit of water decreases as use increases because additional units are put
to less valuable uses.
This assumption of decreasing marginal returns causes the familiar downward slope of the
demand curve. This relationship between the quantity of water used and the marginal
value of water holds for groups as well as for individuals. It is the marginal value of water
(the value to the user of the last unit purchased or used) that will determine the user's
economic value of it.
Figure 20: Economic Value
Source: Agudelo, 2001
Scarce water resources require that decisions be made as to how to allocate these
resources. Demand curves can be derived which show the marginal benefit obtained from
consumption and the willingness to pay. Supply curves can be deduced showing the
marginal cost of supply and the willingness to supply at given prices. The point where the
demand and supply curves intersect defines the theoretical price at which economic
efficiency and welfare is maximised.
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However, applying market principles to water management is complicated in practice,
because water does not easily fit the economist's model of a perfect market. Often water
providers have a monopoly, and there may be values attached to water (as for instance
non-use values) which are difficult to quantify in monetary terms.
Both average and marginal values are used for estimating water values although marginal
values are the relevant measure for assessing the efficiency with which water is allocated
among alternative uses.
11.2.1
Different cost components
The economic cost of water does not only include the supply cost, which comprises
operation, maintenance and capital costs. In addition to the direct financial costs, it also
includes the external costs, and the opportunity cost for water.
There are three components to estimating the economic costs of water.
· Use values, also referred to as financial costs or full supply costs, are the costs
associated with physically supplying water to a consumer. These are the
traditional capital and operating costs, the financial expenses required to access
the resource. This reflects the value of water to the user;
· Economic costs, or full-use values consist of the financial use costs, in addition to
any externalities associated with a particular pattern of use. The standard
economic approach is to define the system in such a way as to internalise the
externalities. (Rogers, 1996) Externalities occur when the actions of one water
user affect the interests or well-being of another. Externalities can be separated
into economic and environmental externalities, and can be either positive or
negative. Economic costs can be defined as either the maximum amount a user
will pay for the use of a unit of water, or the cost of the least expensive alternative,
or opportunity cost (see below);
· Full values consist of the full economic costs plus the non-use values attached to
water. These relate to the benefits and costs derived from current use, both
directly and indirectly. Information on these impacts is seldom available.
(Gibbons, 1986; Grey, 2003: 20; Agudelo, 2001)
Direct uses include uses such as water as a factor of production in industry and
agriculture, water for drinking, and waste disposal.
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Non-use, indirect uses are those where water is not directly used to produce a commodity,
for example supporting recreation and tourism, or water required to sustain a healthy
environment. These are values placed on the mere existence of a resource and its
physical, biological or cultural characteristics, even though the individual may not ever
directly experience it. These values thus are not associated with any specific use, and the
measurement of non-use values is more controversial than that of use values.
11.2.2
Characteristics and types of water use
As water use has a number of dimensions, such as quantity, quality, timing and location,
many issues arise as soon as water uses are further specified. It is important to be aware
of these other dimensions when comparing marginal values between sectors to assess the
economic efficiency of allocations among them.
Quantity is the dimension considered most often in value estimates. Due to the law of
diminishing marginal utility, the larger the quantity used, the lower the marginal value.
Water uses can be further grouped under several categories, namely subtractability,
location and economic role.
Figure 21: Properties of water-use
Source: Agudelo, 2001
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Consumptive use is not only defined by a reduction in water quantity, but can also result
from a loss of water due to a change in water quality. It is therefore not only a reduction in
water quantity that defines a consumptive use.
Another way of classifying water use is by location those uses occurring in a river and
dependent on its flow characteristics are called instream uses, such as hydroelectric
power generation, recreation and, importantly for the Vaal River, waste dilution. Off-stream
uses include municipal, agricultural and industrial water demand.
The expense of transporting water means that it is important to be aware of location in
describing use and allocating value. For example, for water values of an instream use to
be comparable to those of an offstream use, adjustments have to be made to reflect the
site-specific nature of any offstream water value.
Water can also be defined by its economic role as an intermediate producer or final
consumer good. Water used in the production of another good, such as water used in
irrigation or industrial processes or to generate electricity, is an intermediate good.
Household demand and waste dilution are the only water uses where water is consumed
directly, while navigation is a non-consumptive final use of water. The concept of economic
value also differs slightly according to its economic role water used by a consumer
provides direct utility or improved well-being, while the value that a producer places on the
use of water depends on the final value of the resulting goods or services. (Gibbons, 1986)
Timing can also have an important influence on a water value. Irrigation water is more
valuable when applied during periods of critical plant growth and during times of drought,
when crops are water stressed. This may in turn have an impact on related, usually
complementary uses such as hydro-electric power generation. (Gibbons, 1986; Agudelo,
2001; Louw, 2003)
11.3 Economic value of water for individual water users
11.3.1
Introduction
The following section investigates the different methods of calculating the value of water to
different water users in the Orange-Senqu Basin.
There are two main approaches to valuing all natural resources, including water. The first
asks individuals directly what they would be willing to pay for a given amount of water,
using methods such as contingent valuation surveys. The second, used where water is
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being used as a factor of production such as by agriculture or industry, calculates the
value of water as a residual after deducting the costs of all other inputs. The value of water
can be calculated by taking the unit cost, less the unitised value of all inputs.
These methods and others will be discussed in reference to the major different types of
water users in the Orange River.
The various different methods of calculating water value result in values which are not
necessarily directly comparable. The estimates may have fundamental, definitional
differences. For example, some values are specific to a certain time-frame, and short and
long-run value can differ significantly. Average and marginal values are based on very
different concepts of value that cannot be equated in most instances. Care must therefore
be taken to be clear how values have been defined, and for what purpose.
(Gibbons
1986)
The different valuation methods usually estimate on-site water values, which includes the
costs of transporting water. For these values to be comparable to instream water values,
and to water values in other on-site, offstream sectors that have been calculated at the
source, the costs of transporting water to the site, on-site irrigation or pumping water from
an aquifer would have to be subtracted. The economic value of water at the source of
supply is generally less than the on-site estimated values.
There are several methods which are used to calculate the economic value of water.
There are essentially 3 types of valuation methods, namely:
1) Methods that infer values from information regarding markets of water or water-related
benefits.
2) Methods that infer values from the derived demand for water, where water is taken as
an intermediate (production) good, i.e. as an input to the production of other goods or
services; water is an intermediate good for instance in the cases of irrigation of crops,
cooling, processing or manufacturing operations, or driving of turbines to make electricity;
These include:
· Residual approaches, where financial budget information on a single productive
process can be used to calculate the share of the total product value that can be
attributed to water. If all factors of production are paid at their marginal
productivities, the remainder, after subtracting all other inputs, is assumed to be
the maximum economic return to the water input.
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· The concept of alternate cost can also be used to value water. The cost of the
least expensive alternative to water is used as a proxy for the maximum amount
the user might be willing to pay for water
· Estimating production functions and simulating the loss of output which would
result from the use of one unit less water.
3) Methods that infer values from a direct consumer demand, in cases where water is
considered a final (consumption) good, used directly by the final consumer. (Agudelo,
2001)
· If there are any market-type transactions, payments of this kind for water indicate
that the user is willing to pay at least a certain amount, which can be viewed as a
lower limit on value for that sector. (Gibbons, 1986)
· If enough price and quantity data is available, a water demand curve can be
estimated, from which estimates can be made of marginal values of the water use
at different quantities. For example, Greengrowth Strategies (2003) derived
demand functions of water for the Vaal River system areas for the different water
user categories, and calculated the price elasticity of demand for water, in order to
determine how responsive water demand is to changes in tariff. (These values will
be presented in Section 4 of this report.)
· Using structured "contingent valuation" questions to determine what value user's
place on a given resource.
· Public goods such as the valuation of water for recreational or environmental
purposes are estimated using techniques such as revealed preference (e.g. travel
cost method, or hedonic pricing model) or stated preference approaches (also
known as contingent valuation used for valuation of in-stream flows and water
quality benefits. (Gibbons, 1986; Agudelo, 2001)
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Figure 22: Water valuation methods
Source: Agudelo, 2001
11.3.2
Irrigation Water
Demand characteristics
Most irrigation in developing countries is used in the production of food grains, a high
volume, low value use of water. It is also used for growing high value irrigation of fruits,
vegetables and flowers. The supply cost of irrigation water is usually low, but when there is
competition with either urban uses or high-value irrigation, the opportunity cost is high.
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The challenge in treating water as an economic good, is to ensure that farmers consider
these opportunity costs, and that there are institutional arrangements to facilitate
movement of water to higher-valued uses.
Demand for irrigation water is a derived input demand, as irrigation is a factor of
production. Demand for water for crop irrigation is influenced by season, location and
quality requirements and effects, as the quality of irrigation water can affect crop yields.
The value of water will thus depend on the relative scarcity of water, for example in times
of droughts, over the short-term farmers will be prepared to pay substantially higher
amounts for additional water in order to save the current crop. In the short-run, with the
growing season underway, irrigation water has a very inelastic price elasticity, and
demand is very unresponsive to price changes. However over the longer run, farmers are
able to increase irrigation efficiency, switch to a higher value or more water efficient crop,
or reduce the land under irrigation, so demand is much more responsive to increased
costs over the long run.
Valuation methods
Irrigation water value estimates are heavily dependent on crop prices. Each physical or
financial method of determining values takes crop price or revenue as the basis for the
value of water in crop production. All estimates depend on assumptions about the
efficiency of the irrigation system.
The basic methods for estimating irrigation water values are crop-water production
function analyses and farm crop budget analyses that use linear programming. In spite of
the differences in methodologies used, the primary factors underlying the wide variations
in the estimated irrigation water values are the crop grown, the location , the time-frame
(long-run or short-run) and the year of the estimate rather than the methodology employed
(Gibbons, 1986; Young, 1996; Louw, 2003).
The relationship between inputs and outputs of crop production can be expressed
mathematically as the crop production function. If all other inputs are held constant, the
marginal productivity of water for each unit of water used on the crop can be calculated.
The marginal value of each cubic meter is the marginal physical product times the crop
price. These values are therefore related only to the crop selling price and the physical
productivity of the water unit. However in most places and for most crops the actual
physical productivity of water is not known.
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In crop-budgeting or residual analyses, water values are also dependent on non-water
input costs. As the prices of other inputs go up, the estimated value of water declines.
Representative farm crop budgets can be used to estimate the maximum revenue share of
the water input. The total crop revenue less the cost of all non-water inputs results in a
residual amount, the maximum amount the farmer could pay for water and still cover costs
of production. This represents the on-site value of water. If supply costs are subtracted,
the net value for irrigation is then comparable to in-stream water values. This amount,
divided by the total quantity of water used on the crop, determines a maximum average
value, or willingness to pay for water for that crop. Depending on whether or not fixed
costs are included, such values can be short-run or long-run average values. (Gibbons,
1986) This method is most suitable when water is a significant factor of production in terms
of the value of the output, as for irrigation. (Agudelo, 2001)
A variation on the theme of crop budgeting can be used where dry-land and irrigated
production of a crop occur within a homogenous farming area. When all other factors such
as soil type and climate are similar, the difference in net returns can be attributed to the
irrigation water. According to Gibbons (1986) this method of calculating values is seldom
used, but it is an interesting method in that it allows the separation of normal profits from
the value of the water.
The negative indirect results of irrigation include water quality externalities. Not
incorporating these negative effects into irrigation water values will result in an
overestimation of the true value of irrigation water. (Gibbons, 1986)
11.3.3
Municipal Water
Demand characteristics
Municipal water demand consists of residential, commercial and public uses. It is
influenced by several factors, such as climate, population density, income level and water
price.
The marginal value of water in municipal uses depends on how much of it is available.
Very little and the value will be high, an abundance and the value will be close to zero. In
these cases the cost of water would be based completely on the actual physical, financial
costs of transporting and providing water.
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Residential demand consists of indoor uses, such as drinking, cooking, washing, and
outdoor uses such as garden watering. These different uses tend to have different
reactions to changes in price, as a different value is placed on the different uses.
Households place a higher value on the first units, and demand is inelastic for lower
amounts, as water is essential for life. Residential water is not price responsive in the short
run, confirming its status as an essential good. However, water demand is more price
elastic in areas where outdoor water use is a larger part of consumption. Outdoor demand
tends to vary between seasons, particularly where summers are hot and dry.
Household demand is also more responsive to price over the longer term, as there is
frequently still plenty of opportunity for reducing consumption without hardship.
Conradie found (2002) that poor and wealthy households have similar price elasticity of
demand for water. However the reasons for this are assumed to be different. Low income
household demand for water is already fairly constrained to using water for basic needs,
which is generally accepted as being essential, and therefore fairly inelastic. The
inelasticity of demand for higher income households on the other hand is assumed to be
due to the fact that the price of water is a smaller part of household income. However, the
price elasticity of higher-income households for increases in water tariffs which double or
more has not yet been established.
Valuation methods
The principle way to measure the marginal value of water to an individual is through the
use of water demand functions. A consumer's willingness to pay for an increment of
supply is the corresponding area under the demand curve, although the amount the
consumer actually pays for the increment is the water price times the quantity.
For the value calculated for municipal water be comparable to the value of water in in-
stream uses, the costs of bringing water to the urban user must be subtracted from the
overall willingness to pay. The value of municipal water at its source is net of the water
utility or supply costs and is represented by the consumer surplus.
The contingent valuation method is also frequently used for estimating domestic
demand. This approach makes use of user surveys and questionnaires to determine the
value that user's place on water. However the limitation of this approach is that outcomes
are based on expectations, rather than being observed.
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11.3.4
Industrial water demand and value
Demand characteristics
The elasticity of demand for water is expected to be low in sectors where the cost of water
is a relatively small share of the value of the final product and where water cannot be
replaced by other factors of production. A low price elasticity of demand implies that a high
premium is placed on sufficient water and a high level of assurance.
Water costs are typically a fraction of total costs for industrial processes. As a result,
decisions on water use are usually secondary to a firm's initial profit-maximising decisions
on process technology, inputs, output mix and scale of operations. These primary
decisions on technology and outputs usually determine the amount of water required per
unit of output or time involved in the production process.
Since water for industrial and power purposes is required through-out the year, including
the dry season, the provision of water for both of these uses results in high opportunity
costs and high supply costs, due to higher infrastructure and storage costs required to
provide this level of assurance. These costs should be taken into account while evaluating
the benefits and costs of industrial water supplies. If it results in a smaller area under
irrigation during the dry season, this has to be factored in when calculating the opportunity
costs of water in the industrial and urban sectors.
Valuation methods
The most popular method for valuing industrial water is to use the opportunity or
alternative cost of reusing water i.e. the costs of effluent water treatment as the economic
value of water.
There is very little evidence of industrial water demand functions, due to the individual
nature of each production process, and the small cost contribution of water to overall
production costs. Due to methodological difficulties, value has sometimes been equated
with the internal cost of water recirculation. (Gibbons, 1986)
There are also residual approaches, where financial budget information on a single
productive process can be used to calculate the share of the total product value that can
be attributed to water. If all factors of production are paid at their marginal productivities,
the residual, after subtracting all other inputs, is assumed to be the maximum economic
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return to the water input. However, this approach can be problematic due to the small cost
contribution of water to the overall production process.
Waste assimilation and water quality
Different water uses require different intake water quality, and also result in different
degrees of water quality. The capacity of a water body to assimilate or dilute wastes
represents a real economic value when the costs of water quality effects are considered.
Water managers rely on dilution flows in maintaining water quality standards in rivers. The
release of water from storage for low-flow augmentation is a recognised use of multiple-
purpose reservoirs. The value of water in this use is related to the variation in natural
streamflows.
The value of water for waste dilution is usually calculated as either the waste-treatment
costs foregone or downstream damages avoided. But damages are hard to estimate
reliably. Although less direct than damage estimation, the economic value can also be
calculated by using an alternate cost framework in which the value of dilution water is
assumed to be no greater than the cost of providing the same water quality through pre-
treatment of the effluent. Water quality can be maintained through the treatment of and
reduction of wastes entering the river, which is usually less expensive as it doesn't require
the construction of expensive storage. This is applicable to point-source pollution only.
Non-use values
Non-use, indirect uses are those where water is not directly used to produce a commodity,
for example supporting recreation and tourism, or the water required to sustain a healthy
environment. These are values placed on the mere existence of a resource and its
physical, biological or cultural characteristics, even though the individual may not ever
directly experience it. These values thus are not associated with any specific use, and the
measurement of non-use values is by far more controversial than that of use values.
A variety of approaches have been developed, based on user surveys of actual or
hypothetical behaviour: these include observed indirect (also called revealed preference)
methods and the hypothetical questioning (or contingent valuation) method.
Related-market approaches are based on the links between environmental assets and
markets for related private goods and services. If the use of water-based recreational
services influences the demand for any marketed commodity, observations on purchasing
behaviour related to that commodity can be analysed to derive information on the
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preferences and WTP for the environmental amenity. Two of these methods are the travel
cost approach and the hedonic price approach.
The travel cost method is the most widely used example of the observed indirect
methods. The technique assumes that visitors to a particular site incur economic costs, in
the form of outlays on time and travel, to visit the site. These economic expenditures
reflect the `price' of the goods and services provided by the site, and are an indirectly
observable indication of the minimum amount a visitor is willing to pay to use the site (with
all its associated attributes).
By observing the characteristics of individuals visiting the site, economists are able to
estimate the derived demand for the site. That is, for any given or implicit price, the derived
demand relationship will determine the number of visits consumers will `purchase' at that
site. (Agudelo, 2001)
The hedonic pricing method is applicable when data can be inferred from markets, which
can then be used to measure willingness to pay for water supply or environmental quality
differences. In their earliest applications these techniques were meant to capture the
variations in property values, resulting from the presence or absence of specific
environmental attributes recognised by purchasers, such as water views, noise, or air
pollution. These attributes cannot be separated when purchasing the property. By
comparing the market value of two properties which differ only in respect of these
environmental features, economists may assess the implicit price of that feature as shown
by the behaviour of buyers and sellers.
The hedonic technique is as yet relatively rarely applied to measuring values of water or
water quality. To estimate economic values of environmental resources through hedonic
methods is quite difficult in practice, and the technique is subject to serious limitations.
There are cases in which it is not possible to derive value measures from observing
individual choices through a market. The methods developed to measure environmental
values in such cases are referred to as `hypothetical methods'. The most common form of
questioning on hypothetical futures is called the contingent valuation method, the only
method available for measuring `existence values', the value that individuals place on
simply knowing the natural resource exists in an improved state. It involves directly asking
individuals what they would be willing to pay for particular goods or services contingent on
some hypothetical change in the future state of the world. A limitation on ascertaining the
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marginal value of water may occur, because the questions asked do not relate to
incremental changes in water supply or quality, but to the value of the site or policy itself.
However, the method has been able to provide useful estimates of the marginal value of
streamflow for uses such as water for fishing, boating or streamside recreation. (Agudelo,
2001; Young, 1996)
11.4 Economic value and water allocation decisions
The previous sections discussed the various methods for establishing the value of water to
different types of users, or economic sectors as the case may be.
However, when any resource becomes scarce, methods have to be found of allocating this
scarce resource between different water users, in a way that is most economically
beneficial. If water markets functioned perfectly, all users would be paying the true value,
but this doesn't work in reality. The concept of opportunity cost is used to allocate
resources under conditions of scarcity.
"When water is locked into uses that are no longer high-valued, inefficiency abounds.
When the distribution of resource use cannot adapt to changing economic conditions,
conflict escalates." (Gibbons, 1986)
To treat water as an economic good means that water should be produced and consumed
in an efficient way. Efficiency means that a scarce good is allocated in such a way that it
cannot be redistributed without someone loosing from this change (Hansson, 2004).
Efficient consumption of water means that the value of water for the person consuming it
must exceed or at least be equal to the cost to produce that good. If the value is less than
the cost, it means that it is possible to use the water used to produce that good, in an
alternative and more productive way.
The conventional strategy to cope with an increasing water demand has been to augment
the supply, which is obviously an unsustainable strategy. Inefficient water use leads to an
over-use of water, and over-investment in water supply facilities relative to investment in
other methods of providing or conserving water and relative to expenditure on other goods
and services. (Louw, 2003)
To decide which sectors should be given preference in water allocation during scarcity,
information is needed on the value of water in these sectors. The economic valuation of
water provides a basis for sharing the benefits of the river water between all potential
users, thereby aiming to improve the net economic benefits and the efficiency of water
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use. An examination of marginal benefits in competing uses can help to identify large
disparities. Economic efficiency and fiscal responsibility are promoted where the marginal
benefits of water use are equal to the marginal costs. (Gibbons, 1986)
11.4.1
Opportunity cost and allocative efficiency
Opportunity costs are the benefits that could have been generated had water been put to
its next-best use. They address the fact that by consuming water, the user is depriving
another user of the water, including non-consumptive uses. If that user had a higher value
for the water, then there are opportunity costs to society for the misallocation of resources.
Opportunity costs (or foregone opportunities) will outweigh the use value generated by
water when it is not put to its highest value use. The opportunity cost of water is zero only
when there is a surplus of water.
Ignoring opportunity costs leads to undervaluing water, failures to invest, and misallocation
of resources between users. (Louw, 2003)
The opportunity cost of water cannot be captured properly if sectors are modelled
separately. The opportunity cost is critical in modelling the value of water and the impact of
water markets. Water markets act as a buffer when water becomes scarcer. If water can
be sold or leased to anyone for any purpose, this provides an incentive to the owners of
the water right to conserve water and sell the surplus to those willing to pay a higher price
than the value that the present owner attaches to the right, thereby allowing water to be
reallocated to higher valued uses.
11.4.2
Water trading and water markets
Water trading appears to be a more effective way of improving the efficiency of water use
throughout a river system. In theory, a person having a low-value use could sell it to
another person willing to pay more. The seller would not do so without getting paid more
than the value they place on the good, while the purchaser would not do so if the price
paid were not below the their maximum willingness to pay.
The existence of a market presents water users with the real opportunity cost of their water
use decisions, and forces them to take this opportunity cost into account. If a water market
is based on the opportunity costs, it creates a built-in incentive to conserve water and put it
to the most productive use. (Hansson, 2004; Louw, 2003)
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Water markets can provide a more flexible mechanism for reallocating water use among
water users, and between riparian countries, within an agreed compensation structure.
Water markets would allow users to buy and sell fixed-term water use rights that would not
affect accepted water treaty rights. The price and quantity of water use rights could be
decided by market forces or negotiated as a means of benefit sharing.
Tradable water rights can help to shift water to higher-value use in a way that is cheaper
and fairer than building new infrastructure, confiscating water from farmers, or substantially
raising water charges to force farmers to conserve water and to free-up water for higher-
value uses. In addition, water rights may also serve as an asset that can be used as
collateral for lower-interest loans. (Louw, 2003)
The efficient construction of any water market requires the existence of 3 conditions for
trading to occur:
· Well-defined water rights;
· Public information on the supply of and demand for water, and
· The physical and legal possibility for trading to take place. (Louw, 2003)
While the National Water Act of 1998 provides the framework for water markets in South
Africa, enabling CMAs to design water allocation strategies for each of the major
catchments, preference is still given to administrative price setting for water resources.
There is also uncertainty about the provision for legal transfer of water use licenses.
Procedures are required for formalising water licenses and resolving disputes.
In light of the importance of high assurance for some users, the current licensing system
may have to be amended. The current system provides for a license for a maximum of 40
years, renewable every 5 years with no guarantee that the user will receive the same
volume of water. This insecurity may lead to a lower valuation of water.
In a water market allocation, potential buyers will bid for water in-stream, thereby removing
their differential supply costs from the equation. However, the regulators of a water market
will have to be careful to incorporate the external costs imposed on the resource, to ensure
that the true economic costs of water are being reflected, and that private and public water
values are equivalent. For example, the use of water by two farmers results in increased
salinity downstream, which imposes costs on downstream users.
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11.4.3
Environmental values
In South Africa, a certain volume of water is needed to ensure a water resource, such as
an estuary, of a certain quality. Water sufficient for this purpose is therefore allocated from
the available stream-flow, for purposes such as the in-stream flow requirement, or even
water for dilution purposes where salinity has become a problem.
This therefore imposes costs on the system by removing water from the system that might
have been used by productive uses. The amount that these users would have been willing
to pay for the water has now been foregone. This method can be used to determine the
opportunity cost of preserving a desired environmental quality.
Under conditions of scarcity, where water allocation decisions have to be made, there is
therefore a direct trade-off between the quality of the environment preserved, and the
economic value foregone.
11.4.4
Water transfers
Water transfers imply a water surplus in one system, and therefore a zero opportunity cost
to the system providing the water. However there is a time dimension to this arrangement,
as demand in both basins may change over time. To achieve an economically efficient
allocation of water rights, if the opportunity cost is higher than the value to be derived from
the destination, then water shouldn't be transferred. In other words, water should only be
transferred if users in the destination basin are willing to pay more (at the source or in-
stream value) than other users in the value, i.e. excluding the supply costs.
For example, when comparing the value that farmers in the Fish-Sundays, compared to
farmers in the lower Orange, place on any given unit of water, it will have to be calculated
at the source, less the supply costs of the Fish-Sundays transfer scheme. After subtracting
supply costs, it is highly unlikely that, given the current market conditions for table grapes
and dates, farmers in the Fish Sundays system will value water more highly than farmers
in the Lower Orange. Under current market conditions, economic efficiency would seem to
indicate that the quota to the Fish-Sunday system should be reduced, and allocated
instead to farmers in the Lower Orange.
However, if changing demand patterns over time are taken into account, in particular the
anticipated future urban-industrial demand from Nelson-Mandela Metro and Buffalo City,
the current allocation may be economically efficient.
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11.5 Maximising the system-wide economic benefits of water use
There are downstream externalities which result from any water use, whether through
affecting water quality, or reducing the quantity of water available in the system. The
economic cost of these effects should be deducted from the economic value of any given
use, when calculating the combined economic gains of different water uses to the entire
river system.
Sadoff and Grey (2003) have proposed that a system-wide approach be used to maximise
the benefits from and "beyond the river", with particular reference to internationally shared
river-courses. This requires discerning between the value of one water use within a river
system (the user value) and the aggregate value of a pattern of multiple uses within the
river basin (the system value). The system value is the aggregate value that a unit of water
can generate as it moves through the river system before it is consumed or lost. Or to put
it another way, it is the sum of benefits and costs to all the users under a specific
configuration of uses or development path. By aggregating the value of water in all of its
uses within the river basin, this approach effectively forces an integrated systems
management perspective by internalizing the externalities (and opportunity costs) of a
given development path or configuration of water uses in a basin.
This approach looks at the total economic value generated by a cubic meter of water in a
particular water management strategy for all users in the river system, rather than the
economic values of allocating a cubic meter of water to one particular user. The first level
of economic benefits from cooperation is achieved with a shift from maximizing user
values to maximizing system values.
Analysis of user values and system values can, however, identify potential benefits and
clarify the benefit distribution associated with different management scenarios. When
these are made explicit, the equity of various scenarios can be assessed and
compensation mechanisms considered. While questions of equity are beyond the scope of
user values and system values, these calculations can prove useful for quantifying the
payoffs of alternative outcomes, thus providing the basis of comparison and information on
which judgments on fairness can be made. (Sadoff, et al, 2003)
Direct payments might be made for water itself or for the benefits to be shared or forgone
in the context of a cooperative scheme. The Orange basin already provides a good
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example of this with the Lesotho Highlands Water Project agreement, where South Africa
agreed to pay Lesotho for water delivered.
Where system values exceed user values, there is strong incentive for cooperative
management. The economic benefits of systemwide cooperative management may not,
however, be equitably distributed among riparian nations, and the optimal development
path from a systems perspective may not be the best option for any single riparian. In the
context of international rivers such as the Orange-Senqu, it is difficult to find interventions
that result in improved efficiency, because someone almost always loses in large-scale
investment projects. Given the transaction costs and political overtones of international
shared waters negotiations, it is unlikely that a plan representing a potential efficiency
improvement benefiting one country disproportionately would be accepted by all, much
less preferred.
Yet cooperative action on international rivers can enable riparian nations to move closer to
realizing the greatest potential system values of the river. Under such circumstances,
compensation, the redistribution of benefits, or both will need to be explored to reach
agreements among riparian countries. (Grey & Sadoff, 2003)
While it can't address equity issues, economic analyses can delineate efficient
distributions of water and alternative distributions of the benefits derived from its use. Such
information can serve as a basis for comparison for those who must make equity
judgments. It can provide criteria for comparison among alternative investment and
management strategies. (Grey & Sadoff, 2003)
Hoekstra (2001) has also proposed a method for finding the value of water throughout its
movement along the hydrological cycle throughout a river system, known as the `value-
flow concept'. This approach involves the consideration of the values of water in all its
potential uses within a region. The value-flow concept is a conceptual tool for dealing with
the fact that water uses and services within a river basin are not only or even largely
competitive, but rather a combination of competitive and complementary. The value-flow
concept allows the recognition of all those uses and services (and their interdependencies)
and the 'routing' of their values throughout the whole basin over a given interval of time.
This helps in making the correct comparisons that lead to efficient allocation of water
among all water-using sectors within the basin.
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A change in water quality resulting from one use can quite seriously affect a later,
subsequent use. For example, a quantity of water may be diverted from a river for
irrigation, after which it is be assumed to be lost as a water resource. Alternatively, the
water may be left to flow in the river, to be used first for hydropower, then for industrial and
finally for recreational uses downstream. In such a case, the power, industrial and
recreational demands are complementary, but jointly they are in competition with the use
of water for agriculture. In this situation, when demands are complementary instead of
competitive, the marginal values for each use by the members of the complementary
group have to be added up to determine a joint marginal value, for comparison with the
marginal value of competitive demands. Therefore water should be divided among the
agricultural use and the allied hydropower, industrial and recreational uses in such a way
that the marginal value of water for irrigation equals the sum of the marginal values of the
power, industrial and recreational uses. (Hoekstra, 2001)
11.6 Estimated economic value of water for major water users in the Orange-Senqu
Basin
Very little work has been found in the compilation of this report that investigates the
economic value of water in the Orange-Senqu River Basin itself. The WRC commissioned
several research reports aimed at determining the value of water in different sectors of the
country and different parts of the country. Water values were found to differ significantly
between sectors, between geographic areas and within geographic areas. (Nieuwoudt,
2004)
The use of different valuation techniques means that it is very difficult to compare water
values between different regions and uses. Values within the same sector or broad user
types differ greatly, for instance according to whether they are marginal or average values,
whether a short- term, or long-term view is taken (which affects the treatment of capital
costs), the time of year, region, reliability of supply, access to water-saving techniques,
type of crop or product, crop prices at the time of study, and so on. Care has to be taken
therefore when using these values outside of the sector or location where they were
derived. As Winpenny cautions, (1996) `exaggerated claims for water valuation should be
avoided, and excessive precision in such estimates is a cause for suspicion' (Agudelo,
2001).
However a US review of 500 water values from 41 different studies found that the
methodology used was not as important as the crop grown, location, and year. Industrial
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processing and domestic uses are generally the highest-value uses based on both
average and median figures. On the other hand recreation use, fish and wildlife habitat
and irrigation, which together accounts for nearly 80 percent of all the estimates, have the
highest individual estimated values. Water values also tend to be higher in the drier, water
scarce areas of the country (Louw, 2003).
Bearing in mind these caveats, the following values have been calculated for the Vaal
River System.
Table 28: Economic value for water per sector in the Vaal River System
% Share of
Share of
User
Value/m3
Economic
water use*
value of water
Municipal use
82.4%
39%
High income household - indoor
R6.94
High income household - outdoor
R7.91
Low income household - indoor
R4.81
Low income household - outdoor
R3.88
Light industry
R9.86
37.6%
Parks
R7.87
Irrigation use
R0.07
1.2%
39%
Electricity use (cooling water)
R6.44
12.8%
4%
Heavy industry use
R3.68
3.6%
13%
*based on total water requirements in the 3 Vaal WMAs in 2001. Rural consumption accounts for the
missing 6%
Sources: Greengrowth Strategies, 2003; DWAF 2004
Agriculture is an inefficient user of water the table above shows that while it used 39% of
the water in the Vaal River system, it accounts for only 1.2% of the total economic value of
water in the Basin.
A study by Conningarth (2001, quoted in Nieuwoudt, 2004) found that agriculture supports
the lowest GDP per million m3 while it creates the fewest jobs per million m3. It found that
1m3 of water adds R1.5 in agriculture, R157.4 in industry, R39.5 in mining and R44.4 in
eco-tourism.
Despite the current high values placed on water by non-agricultural users, they place a
high value on water assurance, but little value on more than it already currently uses. For
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this reason, Nieuwoudt et al (2004) speculate that although water is expected to transfer
out of agriculture in the long-run, in the short-run agriculture may be its best use.
11.6.1
Municipal water
Conradie (2002) demonstrated that the price elasticity of municipal demand is low and that
marginal water values are noticeably higher than irrigation values. He estimated demand
functions for household, commercial and industrial consumption in the Nelson Mandela
municipality. Urban users were found to attach a high value to assurance and a low value
to additional water, with a price elasticity of -0.47.
Urban water use accounts for 39% of the total water requirements, which amounts to 82%
of the total economic value of water use in the Vaal River system. Urban water supply is a
low volume, high-value use. Supply costs are high, while opportunity costs are quite low.
Therefore the priority issue for the economic management of urban supplies is the supply
cost.
As expected, the value of high quality water for basic human needs is much higher than
the value for discretionary demand, such as garden watering. Similar to the demand for
industrial water, a key element of value is the assurance of supply. (Louw, 2003).
11.6.2
Irrigation
Commercial irrigation
Irrigated agriculture accounts for a large proportion of water use. The value of water for
many low-value crops (such as food grains and fodder) is universally very low. Where
reliable supplies are used on high-value crops, the value of water can be high, sometimes
of an order of magnitude similar to the value of water in municipal and industrial end uses.
How much a farmer is willing to pay for irrigation water depends on factors such as the
crop being cultivated, the amount of rainfall, the prices of agricultural products, and the
prices of other inputs such as fertilizer and labour, but it is typically between $0.01-$0.25
per cubic meter internationally. The user value for large-scale irrigation of cereal crops
such as wheat is at the low end of this range while the user value for the irrigation of high-
value fruits and vegetables is occasionally at the high end of this range but depends to a
great extent on market conditions and the transportation costs of delivering the produce to
market (Grey & Sadoff, 2002).
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In public irrigation systems in developing countries where the quality of irrigation supply is
poor, food-grains are the major crop produced, and the value of water is typically only
about US$0.005 per m3 (Louw, 2003).
It is estimated that 30% of the value of South Africa's agriculture is produced under
irrigation. It also consumes 54% of the total water consumed in the country (Nieuwoudt,
2004). While no estimates could be found for the Orange-Senqu Basin, in the Vaal River
System, irrigated agriculture accounts for 39% of the water used, while only generating
1.2% of the estimated economic value of water in the system.
In irrigated agriculture a high assurance of supply is needed where the capital value
invested in orchards and vineyards is high and crops are of a long-term nature. Therefore
table grape farmers along the Orange do not rent but purchase water rights, because the
investment in table grapes is high and more assurance is required. Renting of water
becomes more feasible where annual crops are grown.
To overcome the lack of assurance in water rights, South African farmers have in the past
sought water rights for water surplus or additional to their normal needs, to cater for
drought years in the Lower Orange River where capital investment is high (under riparian
principles the share of all irrigators is reduced by the same fraction when river flows
decrease during dry periods). They may not be able to do this in future if non-use rights
(sleepers) are lost. Another practise is to include a low-income crop such as lucerne, from
which water can be diverted in drought conditions at relatively low cost. However dams
have resulted in fairly stable flow over recent years, and this practise is not used much
currently.
Water rights between Kakamas and Keimoes were sold for between R8,000 and
R10,000/ha, or an average of R0.60/m3 in 2003, while water rights in the Sundays River
trade for about R2,000/ha orR0.22/m3. The market price of water in the Sundays River is
therefore about a third compared to the Orange, and water would move from the
Fish/Sundays to the lower Orange if transfers were permitted (Nieuwoudt, 2004).
Moller (2003) found that the selling prices of water are responsive to economic conditions
such as the price of the product. Buyers of water rights were table grape farmers and had
a higher return per unit of water.
He concludes that "the water-market in the Lower
Orange promotes the efficiency of water use" (Nieuwoudt, 2004).
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Although agriculture creates few jobs per unit of water compared to other sectors, it
generates more jobs per value of output than in other sectors (Conningarth Consultants,
2001) For instance production of R1 million in agriculture creates 24 jobs, mining creates
10.9 jobs, and manufacturing creates 9 jobs per R1 million. Agriculture also generates
more jobs per R1million investment than the other sectors. This has more relevance to
irrigation agriculture, which is not constrained by suitable land, but rather water availability
and investment in irrigation could create more jobs in the fruit and vegetable enterprises.
However agriculture creates the least employment per unit of water, as shown in the
previous section. (Nieuwoudt, 2004)
Conradie (2002) analysed the economic efficiency of water allocation on the Fish-Sundays
scheme, using the residual approach for valuing water in commercial irrigation. He found
that the current allocation of water is not efficient, since it is possible to reallocate water
from farms which do not need it at the margin, to municipalities who are willing to pay
R0.256/m3 for additional water. It is possible to transfer 77 million m3/ year, or 13% of the
water resource, away from irrigation at a zero opportunity cost or without direct loss to
commercial irrigation, while 60% of the current allocation to irrigation can be bid away at a
price of R0.0352 /m3. Up to this point all the water released will come from the Fish River,
but at this level, the first water from the Sundays River becomes available.
Focusing only on significant differences between marginal water values, Conradie found
that irrigators and stock farms consistently record low water values, while farm businesses
value the marginal unit of water at between R150/ha and R200/ha. Dairy farms place a
value about 3 times larger again, around R500/ha. He concludes that citrus producers as a
group are able to bid water away from fodder crop producers in the Fish River region, and
water is therefore expected to migrate from the Fish to the Sunday's area. Reallocating
water between agricultural users could increase the current value of water to the sector by
as much as 43% in the Upper Fish, and 100% in the Sunday's area.
While 77million m3 can be transferred out of the Fish River at a zero opportunity cost,
there is currently a capacity limit of 18 million m3/year to the Sundays River, while they
could currently absorb a maximum of 40 million m3 based on their valuation of water. After
this point, some Sundays' Farms would attach a zero marginal value to additional water,
so that the region as a whole would change from being a buyer to a potential seller of
water. (Conradie, 2002)
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Smallholder irrigation
Using a residual approach to calculate the value of water for smallholder irrigation in the
Lower Fish River, Conradie (2002) found that the value of water for smallholders is
negative, which means that in the face of water scarcity it is not economically efficient to
allocate water to smallholders. The total value of water in the Fish-Sundays scheme is
reduced by just over R500 000/year if water is allocated for 644ha of smallholder irrigation.
Conradie refutes the claim that small-scale traditional farmers are more efficient resource
users than their commercial counterparts. He therefore finds that claims of fairness and
equity considerations do have to enter into the water allocation debate, for small-scale
traditional irrigation farming to be economically viable. Ignoring issues of equity with
regards to water allocation will undermine existing water rights, and negatively effect the
functioning of a water market.
He proposes that while economic theory offers no suggestions for awarding rights, once
rights are defined, rights holders should not be locked into present use patterns. Instead,
an institutional framework that allows the exchange of water rights through water trading
will make society better off. Safeguards can be built into the framework to restrict the
exchange of water rights where necessary.
11.6.3
Hydro-electric power generation
The value of water for hydropower is quite low, often the same as for irrigated agriculture.
Long-run values are even lower. Whether hydropower is an economic proposition depends
on the particular conditions of the economy, of the power sector and the water sector.
Where water is abundant and there are few competing uses, hydropower is likely to be
economically viable; where water is scarce and competition is therefore high, the case for
hydropower is less clear-cut. It is sometimes argued that hydropower is a non-
consumptive use. However, there is debate about this, as the modification of flow regimes
and the timing of water to downstream users, imposes costs on downstream users. The
key issue is therefore not if it is consumptive or not, but the value of the costs imposed on
downstream users. (Louw, 2003)
In the Orange-Senqu basin, most of the consumptive requirements occur during the
summer months from October through until March. This is to be expected due to the large
influence of irrigation on the total requirement. It does, however, present problems with
regards to hydro-power generation at the Gariep and Vanderkloof dams. ESKOM currently
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use the two hydro-electric power stations for reducing peak system demand, and would
therefore ideally like to generate most of the power during the cold winter months when
demands for electricity are at their highest. However when water is scarce, power can only
be generated in accordance with downstream requirements. Additional power can only be
generated when the main storage reservoirs are at or near full supply level. This ensures
that no water is wasted and that any excess water can be used productively to generate
power. (DWAF, 2005)
11.6.4
Environmental reserve
Typical values for environmental purposes, such as maintenance of wetlands and river
flows varies widely, but typically falls between agriculture and municipal values. Hosking
(2002) estimated the value of freshwater inflows in the Keurboom Estuary using the
Contingent Valuation Method, by asking how much respondents were willing to pay to
prevent the loss of environmental services provided by the estuary due to reduced
freshwater
inflows.
The
total
recreational
value
of
water
was
estimated
at
R0.046/m3/annum.
11.6.5
Impact of water quality on water values
Water of differing qualities has different values associated with it, no matter what sector:
domestic, industrial or irrigation.
Poor water quality imposes economic costs on irrigated agriculture through reduced yields
on certain crops, and the loss or withdrawal of more profitable crops. Even where quality is
affected on a seasonal basis, this contributes to both private and external costs. Private
costs involve the need for artificial drainage, and the application of additional water to
leach salts while external costs are imposed on downstream users in the form of more
saline water.
Water quality is a major concern for users in the Lower and Middle Vaal. The Fish River in
the Eastern Cape is also frequently flushed as the return flow is not suitable for irrigation.
Recent work by Viljoen and Armour (2002) provided an indication of the value of the
dilution effect of Orange River water. Their results clearly indicate that irrigation waters of
different qualities are different commodities for which different rates should be charged,
and that the cost of poor quality, saline irrigation water can be calculated.
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They found that small and resource poor farmers will be most affected by poor quality,
saline irrigation water, and are forced out of production by poor quality water while larger
farms are not as dramatically affected by the same water quality. One of the reasons for
this is the smaller crop choice of smaller farmers due to management, labour and
mechanisation constraints, and their generally poorer access to resources. At the worst-
case scenario in terms of salinity conditions, farmers with below 60 ha water rights and
who do not grow cotton, are forced out of production.
11.6.6
Water transfers
The economic value of water transfers in the Orange-Senqu Basin is most vividly
illustrated by the sale of water by Lesotho to South Africa through the Lesotho Highlands
Water Project.
The development of water resources in the Upper Orange-Senqu Basin has had a
profound impact on Lesotho's economy. Hydropower sales and water transfer royalties are
the main permanent benefits. The Government of Lesotho currently gets fixed and variable
royalties from the transfer of water to South Africa. On average the royalties come to
M15million/month. (www.lhwp.org.ls)
The Upper Orange WMA has also delivered between 65 and 95% of the water used in the
Fish-Sundays scheme over the past 6 years. (Nieuwoudt, 2004)
Where significant differences in the value of water exist between areas in a river system, it
should be possible to achieve economic gains by providing for trading in water licenses.
Conradie (2002) found that for marginal water values for the Fish-Sundays System, citrus
producers would be able to bid water away from fodder producers, while water will migrate
from the Fish to the Sundays, due to the higher water rental and purchase prices. It is
estimated that 77 million m3 or 13% of the resource can be redistributed away from
irrigation at zero opportunity cost, while two-thirds of the current allocation can be bid away
at a price of R0.035/m3. He concluded that the Fish-Sundays may be a possible source of
cheap water for the Orange-Senqu, through the reduction of transfers.
The opportunity cost of diverting water from existing uses is the cost of expanding storage
in the Orange River system. Limited additional capacity of 315 million m3/year can be
created in the Orange Basin as an average cost of R0.05/m3 according to Basson (1999).
Only after a second stage of development on the Orange River at an average cost of
R1.27/m3, might the Fish-Sundays be a possible source of further cheap water, where if
13/11/2007
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Task 10: Demographics & Economic Activity
efficient reallocation takes place, the average value of water increases from 0.046 to
R0.082/m3/year.
11.7 Affordability issues and the economic value of water
Water is not only an economic good, but also a social good. It is therefore necessary to
find a compromise between the goals of giving incentives for saving water to promote
efficient water use, and equity concerns with ensuring that the poor have access to water.
The drive to maximise the overall economic value from water use must be moderated by
recognition of the possible distributional effects of allocation decisions. Allocating the
existing water supply to those with the highest willingness to pay, or highest value water
use might prove regressive in its distributional impacts. For example, where wealthy
farmers with more capital-intensive production capabilities can generate higher returns
than poorer farmers, the allocation of water resources to their highest value uses will
compound income disparities. An economically more efficient, and more complex, solution
would be to allocate water resources to those who generate the greatest value for the
economy, while charging those users an economic price for the water. This revenue could
then be used to fund poverty interventions (Agudelo, 2001)
`Getting prices right' is seen as a reasonable way to allocate water efficiently, but how to
accomplish it remains a debatable issue, since water pricing mechanisms are sensitive to
the physical, social, institutional and political setting in each region. If water is viewed
simply as a commodity, it would be reasonable to expect that it should be priced to cover
at least supply costs, and priced so that low-value uses are discouraged and supplies are
available for the higher-value users who are able and willing to pay for it. However, a strict
application of these principles needs to be handled with some caution, to ensure that the
poorest people in the community are not disadvantaged.
The development of a pricing policy to manage water demand requires a methodology for
estimating the value of water, in order to determine in a reasonable and equitable fashion
what prices are to be charged. In addition, it is necessary to ensure that the impacts of the
pricing policy on all the affected stakeholders are understood and considered. While the
concept of realistic pricing holds the promise of a better allocation of water, it has to be
introduced in a manner that will not penalise communities whose opportunities were
already limited.
13/11/2007
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12
REFERENCES ON THE ECONOMIC VALUE OF WATER
Agudelo JI & Hoekstra AY, 2001, Valuing Water For Agriculture: Application To The
Zambezi Basin Countries, globalization and water resources management: the
changing value of water, August 6-8 AWRA/IWLRI-University of Dundee
international specialty conference 2001
Agudelo JI, 2001, The Economic Valuation Of Water: Principles And August 2001, Value
Of Water Research Report Series No. 5, IHE Delft
Conradie B, 2002, The Value of Water in the Fish-Sundays Scheme of the Eastern Cape,
WRC Report No 987/1/02
DWAF, 2004, National Water Resource Strategy, First Edition September 2004.
Gibbons DC, 1986, The Economic value of water
Greengrowth Strategies, 2003, The Value of Water as an Economic Resource in the Vaal
River Catchment, WRC Report No.990/1/03
Hansson L, 2004, Water as an Economic and Social Good: some socio-economic
principles for Indian water management, International Institute for industrial
environmental economics, Lund University, Sweden.
Hoekstra AY, Savenije HHG & Chapagain SK, 2001, An integrated approach towards
assessing the value of water: a case study on the Zambezi basin, Published in
Integrated Assessment 2: 199-208, 2001, Delft, the Netherlands
Louw DB, 2002, The Development of a Methodology to Determine the True Value of Water
and the Impact of a Potential Water Market on the Efficient Utilisation of Water in
the Berg River Basin, WRC Report No. 943/1/02.
Macgregor J, Masirembu S, Williams R & Munikasu C, 2000, Estimating the Economic
Value of Water in Namibia, 1st WARFSA/Waternet Symposium: Sustainable Use
of Water Resources; Maputo; 1-2 November 2000
Niewoudt WL, Backeberg GR & Du Plessis HM, 2004, The value of water in the South
African Economy: some implications, Agrekon, Vol 43, No 2, June 2004
Renzetti
S & Dupont DP,
, The Value of Water in Manufacturing, CSERGE Working
Paper ECM 03-03
Sadoff CW, Whittington D & Grey D, 2003, Africa's International Rivers: An Economic
Perspective, Directions in Development, World Bank, Washington DC.
13/11/2007
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169
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Task 10: Demographics & Economic Activity
Viljoen MF; Armour RJ, 2002, The Economic Impact of Changing Water Quality on
Irrigated Agriculture in the Lower Vaal and Riet Rivers, Water Research
Commission Report No: 947/1/02
Young RA, 1996, Measuring Economic Benefits for Water Investments and Policies, World
Bank Technical Paper No 338, World Bank, Washington DC.
Young RA, 2005, Determining the Economic Value of Water: Concepts and Methods.
13/11/2007
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13
APPENDICES
13.1 Appendix A South African Migration Statistics for the Orange River Basin by
Province of Previous Residence
13/11/2007
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op
e
ts
on
N
Cape
FS
GT
m
Cape
Cap
an
e
KZ
a
NW
e
d
E
m
m
e
m
tal
m
pe
Limpo
fro
N
st
m
W
origin
Migran
cipality
pality
m
ration
at
fro
fro
m
m
ine
fro
d
Cap
fro
m
Ca
fro
Cap
m
of
n
fro
St
fro
We
fro
ine
population
e
fro
ople
h
ter
ve
Migration
mig
m
tMuni
ople
Munici
ople
ople
tern
re
Zulu-Na
ople
ople
IN
in
IN
ople
pe
F
Gauteng
Limpopo
pe
ople
Nort
ople
pe
se
Easter
pe
pe
ople
Mpumalanga
of
pe
of
pe
Kwa
Mpumalanag
Northern
Wes
Unde
of
of
pe
%
pe
of
pe
of
Inclusi
Total
Distric
Local
of
a/c
of
%
%
%
%
undeter
of
of
%
of
%
increa
%
%
%
Total
%
Ukha-
Senqu
131,977
541
17
1,234
39
159
5
110
3
88
3
109
3
178
6
499
16
247
8
135,142
3,165
2
hlamba
Maletswai
35,906
487
35
345
25
133
9
51
4
30
2
33
2
79
6
223
16
21
1
37,308
1,402
4
Gariep
30,195
208
19
303
27
66
6
33
3
63
6
113
10
38
3
212
19
69
6
31,300
1,105
4
Oviston
Nature
15
0
15
0
0
Reserve
Xhariep
Letsemeng
129
7
41,199
211
12
33
2
77
4
47
3
890
50
93
5
168
9
129
7
42,976
1,777
4
Kopanong
393 22
54,176
385
22
136
8
127
7
85
5
242
14
103
6
135
8
161
9
55,943
1,767
3
Mohokare
1,038 53
34,374
253
13
76
4
70
4
30
2
195
10
138
7
106
5
38
2
36,318
1,944
5
Motheo
Naledi
130 19
26,802
153
23
30
4
60
9
35
5
39
6
78
12
39
6
114
17
27,480
678
2
Mangaung
5,253 20 619,576
5,044
19
1,976
8
1,033
4
782
3
3,444
13
2,968
11
2,122
8
3,245
13
645,443
25,867
4
Mantsopa
133 10
53,981
412
30
148
11
70
5
84
6
57
4
84
6
228
17
148
11
55,345
1,364
2
Lejwele-
Masilonyana
311 21
62,935
295
20
130
9
82
6
112
8
90
6
121
8
154
10
173
12
64,403
1,468
2
putswa
Tokologo
64
4
30,811
160
10
46
3
21
1
36
2
602
37
374
23
30
2
307
19
32,451
1,640
5
Tswelopele
108
8
52,431
299
23
50
4
15
1
189
15
67
5
406
32
55
4
89
7
53,709
1,278
2
Matjhabeng
5,739 34 391,298
3,509
21
1,471
9
669
4
923
5
468
3
1,693
10
685
4
1,714
10
408,169
16,871
4
Nala
126
5
95,904
275
12
79
3
39
2
830
35
46
2
780
33
63
3
115
5
98,257
2,353
2
Thabo
Mofutsan-
Setsoto
327
9 119,551
1,035
28
297
8
172
5
131
4
660
18
176
5
189
5
653
18
123,191
3,640
3
yane
Dihlabeng
380 11 125,477
1,086
31
440
13
240
7
224
6
106
3
204
6
195
6
577
17
128,929
3,452
3
Nketoana
61
7
61,080
373
43
141
16
46
5
45
5
9
1
54
6
25
3
123
14
61,957
877
1
Maluti a
547
9 354,594
1,759
28
2,190
35
264
4
292
5
70
1
318
5
134
2
622
10
360,790
6,196
2
Phofung
Phumelela
58
4
49,600
383
29
334
26
76
6
223
17
24
2
57
4
52
4
94
7
50,901
1,301
3
Golden Gate
6 100
169
175
6
3
Highlands
13/11/2007
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Task 10: Demographics & Economic Activity
op
e
ts
on
N
Cape
FS
GT
m
Cape
Cap
an
e
KZ
a
NW
e
d
E
m
m
e
m
tal
m
pe
Limpo
fro
N
st
m
W
origin
Migran
cipality
pality
m
ration
at
fro
fro
m
m
ine
fro
d
Cap
fro
m
Ca
fro
Cap
m
of
n
fro
St
fro
We
fro
ine
population
e
fro
ople
h
ter
ve
Migration
mig
m
tMuni
ople
Munici
ople
ople
tern
re
Zulu-Na
ople
ople
IN
in
IN
ople
pe
F
Gauteng
Limpopo
pe
ople
Nort
ople
pe
se
Easter
pe
pe
ople
Mpumalanga
of
pe
of
pe
Kwa
Mpumalanag
Northern
Wes
Unde
of
of
pe
%
pe
of
pe
of
Inclusi
Total
Distric
Local
of
a/c
of
%
%
%
%
undeter
of
of
%
of
%
increa
%
%
%
Total
%
National Park
Northern
Moqhaka
611 11 162,372
1,604
29
408
7
214
4
255
5
306
6
1,395
25
356
6
371
7
167,892
5,520
3
Free State
Ngwathe
174
5 115,104
1,880
51
197
5
163
4
269
7
81
2
634
17
148
4
151
4
118,801
3,697
3
Metsimaholo
1,013 11 106,434
5,294
55
595
6
588
6
748
8
170
2
538
6
251
3
350
4
115,981
9,547
8
Mafube
247 10
55,263
745
31
154
7
211
9
391
17
55
2
107
5
85
4
373
16
57,631
2,368
4
Sedibeng
Emfuleni
4,442 14
11,781
38
627,683
2,873
9
2,962
10
2,195
7
380
1
2,541
8
707
2
2,858
9
658,422
30,739
5
Midvaal
594 12
1,653
33
59,589
734
15
485
10
506
10
75
1
412
8
228
5
361
7
64,637
5,048
8
Lesedi
262
6
745
18
67,497
590
15
369
9
1,279
32
64
2
274
7
160
4
305
8
71,545
4,048
6
Ekurhuleni Ekurhuleni
28,000 15
13,510
7 2,294,241
40,347
22 47,419
25
27,143
15
2,565
1
8,550
5
6,717
4
11,777
6 2,480,269
186,028
8
Metro
Metro
Joburg
Johannesburg
32,856 14
15,539
7 2,987,252
63,715
27 52,750
22
15,997
7
3,603
2
26,052
11
13,144
6
14,910
6 3,225,818
238,566
7
Metro
West Rand Merafong City
4,226 30
2,260
16
135,432
1,698
12
778
6
617
4
149
1
3,401
24
331
2
461
3
149,353
13,921
9
Mogale City
2,434 11
1,566
7
268,310
2,930
14
3,673
17
1,368
6
411
2
7,058
33
784
4
1,191
6
289,725
21,415
7
Randfontein
908 10
929
11
120,109
627
7
1,120
13
546
6
197
2
3,539
40
273
3
601
7
128,849
8,740
7
Westonaria
4,424 39
1,303
12
98,044
1,714
15
850
8
726
6
110
1
1,521
13
311
3
330
3
109,333
11,289
10
Govan
Msukaligwa
355 10
228
6
904
25
1,084
30
262
7 121,215
81
2
189
5
163
5
332
9
124,813
3,598
3
Mbeki
Lekwa
405
9
938
20
1,419
31
1,085
24
264
6
98,679
70
2
165
4
105
2
135
3
103,265
4,586
4
Dipaleseng
73
4
319
17
912
49
203
11
120
6
36,750
33
2
69
4
42
2
94
5
38,615
1,865
5
Govan Mbeki
2,524 19
1,872
14
3,241
24
2,231
17
1,302
10 208,445
247
2
670
5
476
4
741
6
221,749
13,304
6
Municipality
Seme
109
4
202
8
764
31
1,086
44
90
4
78,269
30
1
60
2
36
1
87
4
80,733
2,464
3
Ga-
Kgalagadi
36
2
106
5
135
6
51
2
98
4
87
4
12,150
1,697
73
80
3
30
1
14,470
2,320
16
Segonyana
13/11/2007
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173
Orange IWRMP
Task 10: Demographics & Economic Activity
op
e
ts
on
N
Cape
FS
GT
m
Cape
Cap
an
e
KZ
a
NW
e
d
E
m
m
e
m
tal
m
pe
Limpo
fro
N
st
m
W
origin
Migran
cipality
pality
m
ration
at
fro
fro
m
m
ine
fro
d
Cap
fro
m
Ca
fro
Cap
m
of
n
fro
St
fro
We
fro
ine
population
e
fro
ople
h
ter
ve
Migration
mig
m
tMuni
ople
Munici
ople
ople
tern
re
Zulu-Na
ople
ople
IN
in
IN
ople
pe
F
Gauteng
Limpopo
pe
ople
Nort
ople
pe
se
Easter
pe
pe
ople
Mpumalanga
of
pe
of
pe
Kwa
Mpumalanag
Northern
Wes
Unde
of
of
pe
%
pe
of
pe
of
Inclusi
Total
Distric
Local
of
a/c
of
%
%
%
%
undeter
of
of
%
of
%
increa
%
%
%
Total
%
Gamagara
45
2
143
6
332
14
94
4
93
4
95
4
13,781
1,427
60
131
5
30
1
16,171
2,390
15
Kalahari
63
6
61
6
120
12
24
2
57
6
39
4
5,234
597
60
27
3
12
1
6,234
1,000
16
Moshaweng
63
6
122
12
131
13
56
6
84
9
55
6
425
43
83,119
31
3
18
2
84,104
985
1
Frances
Phokwane
109
4
411
14
267
9
68
2
51
2
88
3
36,752
1,799
60
140
5
79
3
39,764
3,012
8
Baard
Sol Plaatje
1,085 10
2,682
24
1,965
17
628
6
343
3
281
2
190,101
2,344
21
1,298
11
738
6
201,465
11,364
6
Dikgatlong
126
7
207
12
301
17
66
4
54
3
57
3
33,975
819
46
90
5
64
4
35,759
1,784
5
Magareng
30
4
117
15
190
24
39
5
18
2
15
2
20,936
312
39
63
8
15
2
21,735
799
4
Diamondfields
70 22
51
16
30
10
9
3
6
2
9
3
4,200
86
27
33
10
21
7
4,515
315
7
Namakwa
Richtersveld
162 23
27
4
87
12
36
5
12
2
9
1
9,421
39
6
322
46
12
2
10,127
706
7
Nama Khoi
256 11
129
6
235
11
115
5
79
4
69
3
42,517
125
6
1,139
51
83
4
44,747
2,230
5
Kamiesberg
9
2
12
2
57
10
12
2
6
1
3
1
10,157
21
4
447
75
30
5
10,754
597
6
Hantam
45
4
15
1
67
5
24
2
21
2
24
2
18,579
24
2
789
64
225
18
19,813
1,234
6
Karoo
40
7
9
2
27
5
6
1
6
1
3
1
9,954
9
2
439
78
21
4
10,514
560
5
Hoogland
KhGi-Ma
63
5
64
5
112
8
21
2
21
2
36
3
9,991
795
59
223
17
12
1
11,338
1,347
12
Namaqualand
0
0
6
15
00
3
8
771
0
27
69
3
8
810
39
5
Karoo
Ubuntu
123 15
69
9
66
8
60
8
3
0
18
2
15,579
9
1
427
53
24
3
16,378
799
5
Umsombomvu
480 36
166
13
198
15
48
4
27
2
48
4
22,320
21
2
263
20
76
6
23,647
1,327
6
Emthanjeni
363 25
177
12
221
15
73
5
24
2
30
2
34,070
88
6
452
31
51
3
35,549
1,479
4
Kareeberg
33
5
33
5
88
13
18
3
15
2
12
2
8,805
39
6
324
47
123
18
9,490
685
7
Renosterberg
36
8
122
28
91
21
27
6
6
1
9
2
8,633
24
6
90
21
30
7
9,068
435
5
Thembelihle
113 13
198
23
165
19
27
3
33
4
75
9
13,105
75
9
155
18
39
4
13,985
880
6
Siyathemba
30
6
64
13
110
22
33
7
18
4
18
4
17,014
51
10
143
29
27
5
17,508
494
3
Siyancuma
100
6
366
22
260
15
51
3
24
1
63
4
34,130
502
30
254
15
67
4
35,817
1,687
5
Bo Karoo
12
6
52
24
27
13
9
4
12
6
33
15
2,963
27
13
36
17
6
3
3,177
214
7
Siyanda
Mier
18
5
58
17
33
10
9
3
32
10
18
5
6,515
15
5
54
16
95
29
6,847
332
5
Kai Garib
97
2
142
3
351
7
57
1
55
1
66
1
52,747
3,482
70
645
13
45
1
57,687
4,940
9
Khara Hais
293
9
418
13
593
18
193
6
90
3
147
4
70,492
444
13
881
27
236
7
73,787
3,295
4
13/11/2007
Final
174
Orange IWRMP
Task 10: Demographics & Economic Activity
op
e
ts
on
N
Cape
FS
GT
m
Cape
Cap
an
e
KZ
a
NW
e
d
E
m
m
e
m
tal
m
pe
Limpo
fro
N
st
m
W
origin
Migran
cipality
pality
m
ration
at
fro
fro
m
m
ine
fro
d
Cap
fro
m
Ca
fro
Cap
m
of
n
fro
St
fro
We
fro
ine
population
e
fro
ople
h
ter
ve
Migration
mig
m
tMuni
ople
Munici
ople
ople
tern
re
Zulu-Na
ople
ople
IN
in
IN
ople
pe
F
Gauteng
Limpopo
pe
ople
Nort
ople
pe
se
Easter
pe
pe
ople
Mpumalanga
of
pe
of
pe
Kwa
Mpumalanag
Northern
Wes
Unde
of
of
pe
%
pe
of
pe
of
Inclusi
Total
Distric
Local
of
a/c
of
%
%
%
%
undeter
of
of
%
of
%
increa
%
%
%
Total
%
Kheis
27
5
77
14
193
35
39
7
12
2
42
8
15,480
49
9
103
19
3
1
16,025
545
3
Tsantsabane
99
5
192
10
270
14
39
2
80
4
43
2
29,016
814
41
341
17
112
6
31,006
1,990
6
Kgatelopele
150 13
177
15
154
13
39
3
60
5
39
3
14,264
449
38
88
7
27
2
15,447
1,183
8
Benede
30
5
58
10
67
12
15
3
33
6
24
4
8,512
220
38
127
22
6
1
9,092
580
6
Oranje
Central
Setla-Kgobi
33
6
60
10
319
54
12
2
39
7
33
6
50
9 103,740
21
4
21
4
104,328
588
1
Tswaing
184
9
205
10
783
38
129
6
229
11
145
7
134
7 112,103
103
5
141
7
114,156
2,053
2
Mafikeng
460
8
611
11
2,362
42
533
9
473
8
261
5
490
9 253,790
210
4
284
5
259,474
5,684
2
Ditsobotla
249
9
391
14
1,193
43
128
5
208
7
169
6
210
8 144,803
116
4
134
5
147,601
2,798
2
Bophirima
Kagisano
100 11
55
6
127
14
62
7
70
8
64
7
280
32
95,495
45
5
82
9
96,380
885
1
Naledi
102
6
184
10
408
23
109