Transnet Swazi Rail Link – Section – Gert Sibande District Municipality Socio-economic impact analysis Draft

Glossary

Corridors : A corridor is a linear strip of land or area, connecting large activity nodes, traversing urban or inter-urban areas, surrounding a major transport facility or facilities providing an appropriate regional level of mobility and accessibility to adjacent areas, and containing a high concentration of population and mixed land uses” and “… accommodate major linear transport routes like heavy and light rail and/or freeways, large shopping concentrations etc., social, cultural and sporting facilities as well as a large amount of residential accommodation”. Density : The number of units per unit of land area, e.g. dwelling units/ hectare. There are five measures of density: i. population density: people / hectare. ii. gross dwelling unit density: dwelling units / total land area of a project or suburb including roads, public open space and non-residential land uses. iii. net dwelling unit density: dwelling units / land occupied by residential plots only. iv. building density: area of buildings / hectare. v. settlement density: (dwelling units / total land occupied by settlement) also known as average gross dwelling units density. Densification : Densification is the increased use of space both horizontally and vertically within existing areas/ properties and new developments, accompanied by an increased number of units and/or population threshold. Efficiency : Development that maximises development goals such as sustainability, integration, accessibility, affordability, and quality of living, relative to financial, environmental, and social costs, including on-going and future costs. Infill Development: Development of vacant or under-utilised land within existing settlements in order to optimise the use of infrastructure, increase urban densities and promote integration. Integrated Development Plan: The strategic municipal development plan, reviewed on an annual basis, required by the MSA (Act 32 of 2000) which guides municipal decisions and budgets. Land Use Management: Establishing or implementing any measure to regulate the use or a change in the form or function of land, and includes land development. Land Use Management System : A system used to regulate land use in a municipality, including a town planning or zoning scheme, or policies related to how land is used on a plot by plot basis. Nodes: Nodes are focused areas where a higher intensity of land uses and activities are supported and promoted. Typically any given municipal area would accommodate a hierarchy of nodes that indicates the relative intensity of development anticipated for the various nodes, their varying sizes, and their dominant nature. Spatial planning: planning of the way in which different activities, land uses and buildings are located in relation to each other, in terms of distance between them, proximity to each other and the way in which spatial considerations influence and are influenced by economic, social, political, infrastructural and environmental considerations. Spatial Development Framework: A Spatial development Framework (SDF) is a core component of a Municipality’s economic, sectoral, spatial, social, institutional, environmental vision. In other words it is a tool for moving towards a desired spatial form for the Municipality. Sector Plans: Municipal plans for different functions such as bio-diversity conservation, housing, transport, local economic development and disaster management. They may also be geographically based, for example a sub-region, settlement within a local Municipality or a component of that settlement. Stakeholders : Agencies, organisations, groups or individuals who have a direct or indirect interest in a development intervention or its evaluation.

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Town Planning Scheme or Zoning Scheme: A legal instrument for regulating the use of land in terms of provincial or national legislation, see Land Use Management System. Urban Edge: The urban edge is defined as an indicative boundary within the municipality with the sole purpose of containing physical development and sprawl and re-directing growth towards a more integrated, compact and efficient urban form.

Acronyms

AU Animal unit

CMIP Comprehensive Municipal Infrastructure Plan

CRC Capital Replacement Cost

DEA Department of Environmental Affairs

DFA Development Facilitation Act (Act 67 of 1995)

DoH Department of Health

GSDM Gert Sibande District Municipality

GVA Gross value added

IDP Integrated Development Plan

LED Local Economic Development

LM Local Municipality

LUMS Land Use Management System

MSA Municipal Systems Act (Act 32 of 2000)

MTREF Medium Term Revenue and Expenditure Framework

NEMA National Environmental Management Act (Act 107 of 1998)

NSDP National Spatial Development Perspective

PGDS Provincial Growth and Development Strategy

SANParks South African National Parks

SDF Spatial Development Framework

SEA Strategic Environmental Assessment

SWOT Strengths, Weaknesses, Opportunities, Threats

VIP Ventilated Improved Pit Latrine

WSA Water Services Act (Act 108 of 1997)

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Contents 1 Introduction ...... 6 2 Regional Setting ...... 6 2.1 Location ...... 7 2.2 Administrative Structure ...... 7 3 Socio Economic Profile ...... 8 3.1 Data and data sources ...... 8 3.2 Demographic consideration ...... 8 3.2.1 Size ...... 8 3.2.2 Population age distribution ...... 10 3.2.3 Population growth rate ...... 11 3.2.4 Future expected growth ...... 12 3.2.5 Spatial aspects of the population ...... 13 3.3 Labour and Economic Analysis ...... 15 3.3.1 Economic Overview ...... 15 3.3.2 Description of the labour force ...... 15 3.3.3 Employment productivity ...... 16 3.3.4 Economic structure and performance...... 20 4 Land Use ...... 26 4.1 Gert Sibande District Municpality ...... 26 4.1.1 Settlement Patterns ...... 26 4.1.2 Agriculture ...... 27 4.1.3 Mining ...... 27 4.1.4 Manufacturing ...... 27 4.1.5 Tourism ...... 27 4.1.6 Forestry ...... 28 5 Risks and Benefits ...... 29 6 Works Cited ...... 30

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Index of figures Figure 1: Population Pyramid ...... 10 Figure 2: Annualised Growth rates per LM (%) ...... 12 Figure 3: Projected growth per LM ...... 13 Figure 4: Composition of the labour force ...... 15 Figure 5: Comparative employment situation in 1995 and 2011 (Employment per sector) – Albert Luthuli LM ...... 18 Figure 6: Comparative employment situation in 1995 and 2011 (Employment per sector) – Msukaligwa LM ...... 19 Figure 7: Comparative employment situation in 1995 and 2011 (Employment per sector) – Mkhondo LM ...... 19 Figure 8: GVS per economic sector (R'million) – Albert Luthuli ...... 21 Figure 9: GVS per economic sector (R'million) – Msukaligwa ...... 22 Figure 10: GVS per economic sector (R'million) – Mkhondo LM ...... 22 Figure 11: Comparative GVA ...... 23 Figure 12: Tress index (10 industries) - Mpumalanga Section ...... 23

Index of tables Table 1: Population size – Albert Luthuli LM (MP301) ...... 8 Table 2: Population size - Msukaligwa LM (MP302) ...... 8 Table 3: Population size - Mkhondo Local Municipality ...... 9 Table 4: Households per population group - Albert Luthuli LM (MP 301) ...... 9 Table 5: Households per Population Group - Msukaligwa LM (MP302) ...... 9 Table 6: Households per Population Group - Mkhondo LM (MP 303) ...... 10 Table 7: Gender Population Percentages ...... 11 Table 8: Municipal demographic projections ...... 12 Table 9: Population per EA type ...... 14 Table 10: Mpumalanga LM labour force (2011) ...... 16 Table 11: Employment per sector (Mpumalanga LM) ...... 17 Table 12: Employment distribution per sector ...... 18 Table 13: GVA output per labour unit (R’million) ...... 20 Table 14: Location coefficient: ...... 24 Table 15: Location coefficient: Mpumalanga ...... 24 Table 16: Location coefficient: Gert Sibande ...... 25

Index of maps Map 1: Transnet- Swazi Rail Link Alignment...... 6 Map 2: Transnet Swazi Link Alignment - Mpumalanga Section ...... 7 Map 3: Population distribution ...... 14 Map 4: Land Cover ...... 26

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Socio-Economic Impact Assessment

1 Introduction The aim of this document is to provide a socio-demographic profile that captures the relevant characteristics of the areas in Mpumalanga that will be affected by the Transnet-Swazi Rail Link. This baseline socio-economic profile of the study area will be developed through a desktop review of available documentation and will address the following aspects:

• The regional setting of the study area; • A socio-economic profile of the population residing in the study area; • Existing land uses and infrastructure in the vicinity of the project-related infrastructure and along the proposed railway line routes; and • Issues and concerns regarding the proposed undertaking when considering the above mentioned aspects. 2 Regional Setting The study area of this project, as indicated in Map 1, relates to the areas directly affected by the alignment of the Transnet - Swazi Rail link as well as all associated infrastructure. The rail link traverses the provinces of Mpumalanga and KwaZulu Natal, which form the main focus of the study, as well as the neighbouring country of Swaziland, that will be excluded from this study.

Map 1: Transnet- Swazi Rail Link Alignment

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The aim of this document is to contextualise the study by developing a socio-demographic profile that captures the relevant characteristics of the affected areas in Mpumalanga. 2.1 Location Mpumalanga is located in the north eastern part of South Africa, and is bordered by Mozambique and the Kingdom of Swaziland to the south. Mpumalanga also shares common borders with the provinces of Limpopo, Gauteng, Free State and KwaZulu Natal. 2 The province has a land surface area of 76 495km which represents about 6.35% of South Africa’s total land area. 2.2 Administrative Structure Mpumalanga Province consists of three district municipalities and 18 local municipalities.

Three local municipalities in Mpumalanga, all situated in the Gert Sibande District Municipality, will be affected by the proposed line. These municipalities, as indicated in Map 2, are: • Albert Luthuli Local Municipality (MP 301); • Msukaligwa Local Municipality (MP 302); • Mkhondo Local Municpality (MP 303).

Map 2: Transnet Swazi Link Alignment - Mpumalanga Section

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3 Socio Economic Profile 3.1 Data and data sources In order to maintain compatibility and consistency in assessment the Census 2001 and 2011 datasets were used. In terms of the approach to the analysis the data was described at the local municipal level and in the conclusions trends and tendency as extrapolated to draw conclusions on the focus areas of the project. Secondly, local economies and demographic areas are open and it is practically impossible to isolate a local or part of a local economy. The approach therefore was, where appropriate, to do the analysis on a comparative basis by comparing the municipality with the profile of the District Municipality, the economy or demographics of Mpumalanga and also with the broader context of the national economy. 3.2 Demographic consideration The population of a municipal area forms part of the ultimate objective of the development process, as well as being a subject in the process, since the people provide labour and entrepreneurship for production and also consume the output of production. Demography does not form part of economic analysis but needs to be taken into account as the population forms the basis of all economic activity in the area.

3.2.1 Size The size of the population and in particular the number of households are some of the most important determinants of the needs of the inhabitants. These needs are expressed in the demand for infrastructural and social services and the potential on the extent of involvement in economic activities. It also forms the basis from which all other calculations are made. Table 1: Population size – Albert Luthuli LM (MP301) Population Group No. of People % 2%

Black population 181 531 97.59 Black African Coloured population 434 0.23 Coloured Asian population 755 0.41 Indian or Asian White White population 2938 1.58 98% Other Other 353 0.19

Total 186 011 100

Table 2: Population size - Msukaligwa LM (MP302) Population Group No. of People % 10% Black population 131625 88.12 1% 1% Black African Coloured population 892 0.60 Coloured

Asian population 1678 1.12 Indian or Asian 88% White White population 14707 9.85 Other Other 476 0.32

Total 149 378 100

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Table 3: Population size - Mkhondo Local Municipality Population Group No. of People %

Black population 131625 88.12 4% 1% Black African Coloured population 892 0.60 Coloured Asian population 1678 1.12 Indian or Asian 95% White White population 14707 9.85 Other Other 476 0.32

Total 149 378 100

The tables show the population composition of the three local municipalities. The largest population group is Blacks, comprising of over 88% of the municipalities’ total population as estimated in 2011. The second largest group is the white population which accounts for 3% while the coloured population makes up less than 1% of the total population and the Asian and other population 1% of the total population of these municipalities. The following tables describe the number of households per population group in the three local municipalities. The patterns are similar to the way the population is grouped as seen in Table 1, Table 2, and Table 3 above. Table 4: Households per population group - Albert Luthuli LM (MP 301) Population Group No. of Households % 2% 1% Black population 46409 97.28% Black African Coloured population 83 0.17% Coloured Indian or Asian Asian population 230 0.48% 97% White White population 815 1.71% Other

Other 169 0.35%

Total 47706 100%

Table 5: Households per Population Group - Msukaligwa LM (MP302) Population Group No. of Households % 1% Black population 36473 89.11% 1% 9% Coloured population 189 0.46% Black African Coloured Asian population 381 0.93% Indian or Asian White population 3740 9.14% 89% White Other Other 148 0.36%

Total 40931 100%

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Table 6: Households per Population Group - Mkhondo LM (MP 303)

Population Group No. of Households %

1% 1% 4% Black population 35258 94.19% Black African Coloured population 132 0.35% Coloured Asian population 304 0.81% Indian or Asian 94% White White population 1622 4.33% Other Other 118 0.32%

Total 37434 100%

3.2.2 Population age distribution

A population pyramid graphically Population 2011 displays a population’s age and 80 - 84 gender composition by showing 75 - 79 numbers or proportions of males 70 - 74 and females in each age group; 65 - 69 the pyramid provides a clear 60 - 64 picture of a population’s 55 - 59 characteristics. The sum total of 50 - 54 all the age-gender groups in the 45 - 49 pyramid equals 100 per cent of 40 - 44 the population. 35 - 39 30 - 34 The population pyramid of the 25 - 29 three local municipalities in Figure 20 - 24 1 shows larger numbers in the 15 -19 younger age groups, this indicates 10 - 14 rapid growth. There are a 5 - 9 relatively large number of people 0 - 4 in the economically active age

-10 000 000 -5 000 000 0 5 000 000 10 000 000 group (15-56 years).

Male Female Migrant labour is not a factor in the municipality as there are about Figure 1: Population Pyramid equal amounts of males and females in the municipal area. However, there are anomalies in some cohorts between 20 years and 50 years. There is no apparent reason that explains this.

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Table 7: Gender Population Percentages

Male % Male Female % Female

0 - 4 8,113,048 12.78% 7,981,589 11.65%

5 - 9 7,961,619 12.54% 7,854,662 11.47%

10 - 14 7,430,354 11.71% 7,382,195 10.78%

15 -19 6,528,502 10.29% 6,829,619 9.97%

20 - 24 6,234,379 9.82% 6,760,207 9.87%

25 - 29 5,682,153 8.95% 6,207,818 9.06%

30 - 34 4,686,531 7.38% 5,209,038 7.61%

35 - 39 3,669,745 5.78% 4,098,998 5.99%

40 - 44 3,145,157 4.96% 3,581,366 5.23%

45 - 49 2,643,178 4.16% 2,989,212 4.36%

50 - 54 2,134,808 3.36% 2,473,750 3.61%

55 - 59 1,778,426 2.80% 2,207,737 3.22%

60 - 64 1,307,673 2.06% 1,718,627 2.51%

65 - 69 938,989 1.48% 1,256,361 1.83%

70 - 74 600,400 0.95% 858,323 1.25%

75 - 79 369,638 0.58% 596,526 0.87%

80 - 84 244,773 0.39% 478,442 0.70%

Total 63,469,373 100.00% 68,484,469 100.00%

M/F % 48.10% 51.90%

3.2.3 Population growth rate The population growth rate and future projections are of great importance for planning purposes. A negative or below-average growth rate is indicative of an out-migration of people – normally due to a lack of economic growth and the concomitant loss of job opportunities in the municipal area. The reverse is true for an above-average growth rate. Figure 2 shows the population growth of the three Local Municipality over time. The figure indicates the growth of each population group. Both these groups’ growth rates remain negative. The coloured population has shown a steady decline throughout and in 2010 has indicated a negative figure. The line indicating the total growth rate of the municipality follows almost exactly the same trend as that of the black population group. This is to be expected as this population group comprises about 83% of the total municipal population. The annual growth rate is calculated by looking at the incremental increase/decrease from the 2001 to 2011 census years expressed as a percentage, over the number of observation years in order to get an annual average.

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2.0%

1.5%

1.0%

0.5%

0.0% MP301: Albert Luthuli MP302: Msukaligwa MP303: Mkhondo

-0.5%

Figure 2: Annualised Growth rates per LM (%)

Figure 2 compares the total growth in population of the three Municipalities within the Gert Sibande District Municipality, Mpumalanga. Albert Luthuli shows a slowly declining growth rate which is currently around 1%. This pattern is mostly the same for both the province and the district. The three local municipalities show some very interesting growth patterns. From 2001 to 2008 the municipality’s growth rate has declined rapidly from about 4% to -2%. This decline is a characteristic of modern rural municipalities in South Africa. This can show to large majorities of people migrating to other areas in search of employment opportunities. However, since 2009 the growth rate has improved and in 2011 was calculated at about 1.5%. 3.2.4 Future expected growth Population and household growth is one element that determines the long-term demand for goods and services. Based on historical population figures from 2000 to 2011, a trend analysis was done for the municipality. The trend analysis is based on historical data. This data was used to determine the municipal growth rate and extrapolate the figures to give a projected growth for the municipality up to 2025.

Table 8: Municipal demographic projections

2001 2011 2015 2020 2025 187,864 186,011 185,345 184,516 183,690 Population Albert Luthuli LM 39,652 47,706 51,328 56,246 61,634 (MP 301) Households 4.7 3.9 3.6 3.3 3.0 HH size 124,829 149,378 160,351 175,206 191,439 Population Msukaligwa 29,689 40,931 46,865 55,506 65,740 (MP 302) Households 4.2 3.6 3.4 3.2 2.9 HH size 142,911 171,983 185,062 202,819 222,280 Population Mkhondo 27,888 37,434 42,316 49,322 57,489 (MP 303) Households 5.1 4.6 4.4 4.1 3.9 HH size

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Forecasted Population Growth

250 000

200 000

150 000

100 000

50 000

0 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

MP301: Albert Luthuli MP302: Msukaligwa MP303: Mkhondo

Figure 3: Projected growth per LM

In terms of the projected population growth the following should be noted:

• The growth rate was calculated at 1.5%. • This is similar to other rural municipalities in the country. • The general trend indicates that the municipality’s population will increase at a steady but slow pace. • The priorities, policies and decisions of the local and district municipality can alter the situation. This cannot necessarily be predicted. Household growth was projected in the same way as for population. The following should be noted: • The Household growth rate was calculated to be -0.6% • Household growth shows a steady decline over the next few years Household size is an important indicator for the demand for services since the number of people in a household determines consumption demand for water, electricity, and waste disposal. The estimates shown in the table below was derived from the population and households estimates in the previous sections. Households sizes are average but can be a little below average for a rural environment. This is based on the assumption that there are no structural changes in population. However the higher levels of possible migrant workers can imply that more males are absorbed, which is not necessarily attached to an existing household. This implies more single-person households, which may lower the average household size.

3.2.5 Spatial aspects of the population This section investigates how the population of the municipality is distributed and in what type of areas people lives. This helps to visualise the data presented previously in this section. The table below shows the population per Enumerator Area type in the municipality. The data shows that the municipality’s population is largely formal in nature with little informal and traditional population.

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Table 9: Population per EA type

Albert Luthuli LM Msukaligwa LM Mkhondo LM Totals

Formal residential 58845 99565 67425 225835

Informal residential 12983 17386 4345 34714

Traditional residential 97439 - 36822 134261

Farms 12062 29470 60882 102414

Parks and recreation 573 207 11 791

Collective living quarters 110 1813 1689 3612

Industrial 110 582 - 692

Small holdings 422 - 561 983

Vacant 3226 144 247 3617

Commercial 241 210 - 451

Map 3: Population distribution below presents the spatial distribution of the population for the areas affected by the alignment. The most significant observation is the density of the population at Ermelo. The rest of the population is spread throughout the municipal area. Due to the disparity of the population it can be assumed that the alignment of the Mpumalanga section of the rail link will not have a direct effect on the location of the population.

Map 3: Population distribution

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3.3 Labour and Economic Analysis In the analysis of the labour and employment situation in municipal areas, it is necessary to focus attention on the size and spatial distribution of the labour force. Secondly, the characteristics of the labour market should be analysed. To this end, it is necessary to examine the supply of labour, which is derived from figures on the economically active population in a municipal area. The demand for labour, on the other hand, is an indication of employment opportunities, which are determined by the economic structure of an area along with the level and growth in economic activities. Unemployment, and in a sense transfrontier commuting, provides an indication of the difference between supply and demand and implies that equilibrium in the labour market necessitates both expansion of economic activity and the curtailment of population growth.

Figure 4: Composition of the labour force

A third issue that should be addressed is involvement in the peripheral sector, as not all potential workers are active in the labour market. Finally, the quality of the labour force needs to be analysed as it provides information on the employability of the workers. The term labour force refers to those people who are available for employment in a certain area. Figure 4 illustrates the different components of the labour force and the relationship between them. Formally employed refers to people who are selling their labour or who are self-employed in the formal sector of the economy, for pay or profit. Informally employed includes all people who are active, for pay or profit, in the informal or unregistered sector of the economy. Unemployed are persons actively looking for a job, but who are not in any type of paid employment. 3.3.1 Economic Overview The three main urban centres of the municipality, i.e. Carolina, Piet Retief, and Ermelo, plays an important role on district level with regard to economy, providing the most prominent opportunities for economic diversification and value-adding activities.

3.3.2 Description of the labour force Table 10 describes the labour force of the three LMs that will be directly affected by the proposed rail line. According to the 2011 data acquired from the Quantec database the three municipalities have 135 895, 91 854, and 79 314 economically active persons in the Albert Luthuli, Msukaligwa, and Mkhondo Local Municipalities respectively. From the data is can be seen that the majority of the population in the first two municipalities are employed, with the majority of the employment in the formal sector. In the Mkhondo local municipality the unemployment rate is higher at nearly 50%. In all three of the municipalities the formal employment rate is higher than the informal employment rate. The unemployment rate of the three municipalities varies between 20% and 48% and the labour force participation rate between 45% and 57%.

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Table 10: Mpumalanga LM labour force (2011) Albert Luthuli Msukaligwa Mkhondo Local Local Local Municipality Municipality Municipality

Description Number Number Number 196,412 126,687 110,575 Population 67,419 64,560 32,246 Economically active 30,650 36,971 17,857 Formal and informal (Total) 22,642 26,104 13,385 Formal

4,423 4,402 1,556 Formal - Highly skilled 9,841 10,739 4,509 Formal - Skilled 8,378 10,963 7,320 Formal - Semi- and unskilled 8,008 10,867 4,471 Informal 19,797 9,707 16,202 Unemployed 39.24 20.80 47.57 Unemployment rate (%) 45.46 57.27 52.14 Labour force participation rate (%)

The three LMs clearly face a huge challenge with regard to unemployment. This can be attributed to the large influx of unskilled and semi-skilled labour that comes for seasonal work, but then remain in the area. The current demand in the economic industry is mostly for skilled labour, and the main economic activities in the LMs can therefore not absorb most of the semi and unskilled labour force, leading to higher unemployment levels (Gert Sibande District Municipality, 2008). It should however be noted that the values can be slightly skew since the size of the informal sector, which includes subsistence agriculture that is highly applicable in the concerned municipal areas, is difficult to establish with a reasonable degree of accuracy and can easily be under-estimated. One reason for this is that people involved in informal activity often classify themselves as unemployed. Obtaining the participation rates, involves calculating the labour force or the economically active population relative to the potential labour force, (i.e. the population in the age group 15 to 64 years). These rates reflect the percentages of the said population that are actually economically active.

3.3.3 Employment productivity Employment is always a priority for the Council and it is obvious that development and growth strategies will have to support job creation.

3.3.3.1 Employment per sector The table below shows the employment/workforce per sector for each of the affected local municipalities over a 16 year interval, as well as the average per sector. The structure of employment and the extent of the link between employment and the level of economic activity are important.

From Table 11 it can be seen that the overall employment figures for the three municipalities remained relatively constant over the past 16 years. There are however a few aspects that need to be discussed.

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Table 11: Employment per sector (Mpumalanga LM)

services -6, 99, 0] -6,0] 99,

Agriculture 1] [SIC: Mining 2] [SIC: Manufacturing 3] [SIC: Utilities 4] [SIC: Construction 5] [SIC: Trade 6] [SIC: Transport 7] [SIC: Business services 8] [SIC: Community 92, [SIC:95 General government 91, [SIC:94] Total

1995 11,466 2,108 1,604 96 1,548 3,435 739 795 2,473 3,988 28,251

2001 8,949 752 1,836 130 1,436 4,655 588 1,226 3,034 4,210 26,815

2005 6,033 748 1,992 115 1,433 6,431 752 1,964 3,461 4,884 27,813

2011 2,720 1,398 1,379 141 1,222 9,403 884 3,063 3,884 6,554 30,650 Albert Luthuli LM LM Albert Luthuli Avg. Change -4.5% -2.0% -0.8% 2.8% -1.2% 10.2% 1.2% 16.8% 3.4% 3.8% 0.5%

1995 14,225 2,929 2,669 244 2,478 6,074 1,543 2,131 3,504 3,318 39,114

2001 10,568 1,970 2,685 199 1,349 7,355 1,224 2,397 4,134 3,468 35,350

2005 7,909 1,511 2,681 262 1,572 9,255 1,667 2,553 4,651 4,073 36,134

2011 3,950 1,689 1,610 654 1,701 11,931 2,133 2,518 5,293 5,494 36,971 Msukaligwa LM Msukaligwa Avg. Change -4.2% -2.5% -2.3% 9.9% -1.8% 5.7% 2.2% 1.1% 3.0% 3.9% -0.3%

1995 16,992 706 4,395 75 1,350 3,876 645 743 1,897 1,448 32,128

2001 12,378 677 3,878 70 1,042 5,421 500 1,086 2,744 1,859 29,655

2005 9,225 1,267 2,058 50 1,039 4,922 715 1,298 2,234 1,581 24,389

2011 3,958 3,454 375 60 911 4,068 970 1,282 1,588 1,190 17,857 Mkhondo LM LM Mkhondo

Avg. Change -4.5% 22.9% -5.4% -1.2% -1.9% 0.3% 3.0% 4.3% -1.0% -1.0% -2.6%

The most noticeable is the differential growth rates in employment creation between the sectors. In all three of the municipalities there has been a decrease in employment in the Agriculture sector. The implication is important since these workers are jobless and have to leave farms. They usually end up in informal settlements on the urban periphery.

In the Albert Luthuli LM there is has been a significant increase in the secondary and tertiary employment sectors, especially in trade in business services. The high increase in these sectors possibly contributed to the overall growth in employment for this municipality.

The Msukaligwa LM has seen a large decrease in overall primary sector employment. All of the other employment sectors have however indicated an overall growth and it is assumed that most of the people who lost their jobs in the primary sector got employed in the secondary and tertiary sector, which showed strong increases, with the exception of the construction industry. There is however a slight decrease in overall employment.

The Mkhondo LM shows a high increase in employment in the mining and quarrying sector. This can be attributed to the vast amount of mineral deposits in the area. There has however been a decrease in the overall employment of the LM.

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Overall the primary and secondary sectors are both shedding jobs while the tertiary sector is growing strongly.

Table 12 shows the employment distribution per sector. These figures are expressed in terms of the distribution of employment across the sectors. The moat noticeable aspect is that the largest economic sectors are not necessarily the biggest contributors to employment creation.

Table 12: Employment distribution per sector

-6, 99, 0] -6,0] 99,

tilities tilities Agriculture 1] [SIC: Mining 2] [SIC: Manufacturing 3] [SIC: U 4] [SIC: Construction 5] [SIC: Trade 6] [SIC: Transport 7] [SIC: Business services 8] [SIC: services Community 92, [SIC:95 General government 91, [SIC:94] Total Albert Luthuli LM 8.9% 4.6% 4.5% 0.5% 4.0% 30.7% 2.9% 10.0% 12.7% 21.4% 100.0%

Msukaligwa LM 10.7% 4.6% 4.4% 1.8% 4.6% 32.3% 5.8% 6.8% 14.3% 14.9% 100.0%

Mkhondo LM 22.2% 19.3% 2.1% 0.3% 5.1% 22.8% 5.4% 7.2% 8.9% 6.7% 100.0%

3.3.3.2 Changes in employment Employment is not a static issue and changes in employment are very important, and can shed light on the development of the municipalities over the past few years. The tables below give a comparison between the employment situation in 1995 and in 2011 for each of the local municipalities under consideration.

Albert Luthuli

45% 40% 35% 30% 25% 20% 15% 1995 10% 5% 2011 0%

Figure 5: Comparative employment situation in 1995 and 2011 (Employment per sector) – Albert Luthuli LM

Figure 5 shows an overall decrease in primary sector employment, with the most significant decrease in the agricultural sector. There is however a clear increase in secondary and tertiary sector employment.

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Msukaligwa

Changes in Employment - Msukaligwa LM 40% 35% 30% 25% 20% 15% 10% 1995 5% 0% 2011

Figure 6: Comparative employment situation in 1995 and 2011 (Employment per sector) – Msukaligwa LM

Figure 6 clearly indicates a decline in primary and secondary employment, but also indicates a significant increase in employment in the tertiary sector.

Mkhondo

Changes in Employment - Mkhondo LM 60% 50% 40% 30% 20% 1995 10% 2011 0%

Figure 7: Comparative employment situation in 1995 and 2011 (Employment per sector) – Mkhondo LM

Figure 7 shows a high decrease in agriculture, which has also been identified in the other two local municipalities in the study area. In contrast to the other two municipalities, there is a high increase in mining employment. Similar to the other two municipalities, the increase in the secondary and tertiary employment sectors is noted. It is not possible to draw any specific conclusion regarding labour productivity. The interplay between labour and capital is not assessed. The Albert Luthuli and Msukaligwa LMs indicate a decrease in GVA output for the primary sector, but an increase for the secondary and tertiary sectors. This confirms the trends that were identified in the comparative employment figures. The Mkhondo LM in contrast indicated a growth in GVA output for the primary sector, and a decrease in the secondary sector. If one assumes that these labour units show significant opportunities for substituting labour with capital, then one might conclude that there was an overall increase in labour productivity over the assessment period.

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3.3.3.3 Migrant labour To calculate both the size of the migrant labour force and the spatial distribution of their areas of origin, male absenteeism ratios are utilised. In the process, it is firstly assumed that only males in the 15 to 64 year age group will migrate, meaning work on a contract basis in another area and return home at least once a year on average. This assumption is patently invalid as substantial numbers of females also migrate, but it is the only plausible way of establishing a minimum level of male migrant workers. Indications are that migrant labour does not play a role in the studied LMs. The Male to Female ratio is almost a 50/50 split.

3.3.4 Economic structure and performance

3.3.4.1 Labour output The table below shows the Gross Value Added (GVA) output per labour unit. GVA is an economic measure of the value of goods and services that are produced in an area.

Table 13: GVA output per labour unit (R’million)

Albert Luthuli Local Msukaligwa Local Municipality Mkhondo Local Municipality Municipality

Primary Primary Sector Secondary Sector Tertiary Sector Primary Sector Secondary Sector Tertiary Sector Primary Sector Secondary Sector Tertiary Sector 1995 719 203 1,001 969 373 1,531 372 506 726

1996 682 223 1,049 982 390 1,601 451 534 784

1997 615 250 1,065 975 408 1,614 442 574 813

1998 519 256 1,088 946 388 1,626 441 559 837

1999 498 262 1,129 969 370 1,670 496 536 881

2000 460 279 1,159 952 382 1,675 540 540 898

2001 395 286 1,192 885 395 1,708 493 523 918

2002 390 334 1,241 861 447 1,743 536 531 916

2003 399 327 1,325 831 448 1,835 605 441 917

2004 404 348 1,411 788 496 1,944 676 397 913

2005 415 369 1,522 716 552 2,063 759 359 909

2006 373 394 1,647 647 614 2,222 771 317 913

2007 367 420 1,784 617 685 2,355 857 282 930

2008 377 444 1,888 607 735 2,465 984 242 918

2009 359 462 1,958 563 784 2,483 1,023 195 887

2010 395 509 1,994 574 787 2,499 1,058 180 880

2011 386 516 2,077 560 802 2,583 1,097 176 890

-2.72% 9.04% 6.32% -2.48% 6.78% 4.04% 11.48% -3.84% 1.33%

% Growth % Per annum

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The table is structured according to primary, secondary, and tertiary sector that is grouped as follows:

• Primary Sector - Agriculture, forestry and fishing; - Mining and quarrying.

• Secondary Sector - Manufacturing; - Electricity, gas, and water; - Construction. • Tertiary Sector - Wholesale and retail trade, catering and accommodation; - Transport, storage and communication; - Finance, insurance, real estate and business services; - Community, social and personal services; - General government.

3.3.4.2 Gross value added Economic performance of a municipal area’s economic system in terms of, factors such as production activity can be measured by the GVA. The analysis will focus on the GVA produced by the primary, secondary and tertiary economic sectors over time. The primary sector of the economy involves changing natural resources into primary products. Most products from this sector are considered raw materials for other industries. Major businesses in this sector normally include agriculture, agribusiness, fishing, forestry and all mining and quarrying industries. The secondary sector generally takes the output of the primary sector and manufactures finished goods or where they are suitable for use by other businesses, for export, or sale to domestic consumers. This sector is often divided into light industry and heavy industry. The sector is made up of manufacturing, electricity, gas and water, and construction. The tertiary or services sector consists of the "soft" parts of the economy, i.e. activities where people offer their knowledge and time to improve productivity, performance, potential, and sustainability. The basic characteristic of this sector is the production of services instead of end products. Businesses in this sector include wholesale and retail trade, catering and accommodation, transport, storage, communication, finance, insurance, real estate, business services, community, social and personal services, and general government.

Albert Luthuli

2 500 Sectoral GVA contribution - Albert Luthuli LM

2 000

1 500

1 000

500

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Primary sector Secondary sector Tertiary sector

Figure 8: GVS per economic sector (R'million) – Albert Luthuli

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Msukaligwa

Sectoral GVA contribution - Msukaligwa LM 3 000 2 500 2 000 1 500 1 000 500 0 19951996199719981999200020012002200320042005200620072008200920102011

Primary sector Secondary sector Tertiary sector

Figure 9: GVS per economic sector (R'million) – Msukaligwa

Mkhondo

Sectoral GVA contribution - Albert Luthuli LM 1 200

1 000

800

600

400

200

0 19951996199719981999200020012002200320042005200620072008200920102011

Primary sector Secondary sector Tertiary sector

Figure 10: GVS per economic sector (R'million) – Mkhondo LM

Figure 8, Figure 9, and Figure 10 indicates the largest and strongest overall growing sector in the economy for the three LMs are the tertiary sector. This means its economy is dominated by the service sector. The secondary sector has shown some increase in recent years. The primary sector is getting smaller and does not contribute a lot to the economy in terms of GVA. The Mkhondo LM is however an exception, indicating a strong primary sector growth and a decrease in the secondary sector. Despite the differences in growth for the employment sectors for the three LMs, Figure 11 indicates an overall growth in total GVA for each of the LMs.

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Municipal GVA contributions 4 500 4 000 3 500 3 000 2 500 2 000 1 500 1 000 500

Total GVA GVA Total(R'millions) 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Albert Luthuli Local Municipality Total Msukaligwa Local Municipality Total Mkhondo Local Municipality Total

Figure 11: Comparative GVA

3.3.4.3 Diversification and concentration in the economy The level of diversification or concentration of a municipal area’s economy is measured by a tress index. A tress index of zero represents a totally diversified economy. On the other hand, the higher the index (closer to 100), the more concentrated or vulnerable the municipal area’s economy to exogenous variables, such as adverse climatic conditions, commodity price fluctuations, etc.

Tress Index 60

50

40

30

20

10

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

South Africa Mpumalanga Albert Luthuli Local Municipality Msukaligwa Local Municipality Mkhondo Local Municipality

Figure 12: Tress index (10 industries) - Mpumalanga Section

The comparative tress index displayed in Figure 12 shows that the overall economy of the studied LMs is diversifying. Msukaligwa LM is the most diversified of the three LMs, and has higher diversification that Mpumalanga and South Africa. The diversity of the municipality also increased

5 July 2013 Page 23 from 1995 to 2011. The Albert Luthuli LM shows an increase in diversification from 1995 and 2011, although there is a lot of variability in the trend. The diversification is lower than the total Mpumalanga’s diversification, but still higher than South Africa’s diversification. Mkhondo however showed a decrease in diversification. The level of diversification is also lower than the national and provincial level.

3.3.4.4 Location coefficient Basic/Non-Basic ratios are calculated in order to determine the drivers of an economy. The ratio is expressed as the employment in a sector in the local economy divided by the total employment in the local economy. This is in turn divided by the same ratio for the district, provincial or national economy. A ratio greater than one, implies that there is relatively more employment in this sector than in the corresponding economy it is compared to. It therefore generates more than what can locally be consumed and the sector is thus a net exporting sector. This implies that it generates income for the local economy. The opposite is then true for ratios smaller than one. Table 14: Location coefficient: South Africa

Agriculture, forestry and fishing and forestry Agriculture, quarrying and Mining Manufacturing water and Electricity, gas Construction trade, retail Wholesale and accommodation and catering and Transport, storage communication estate real insurance, Finance, services business and and social Community, services personal government General Albert Luthuli Local Municipality 2.14 1.31 0.81 0.86 0.51 1.34 0.71 0.65 1.16 1.40

Msukaligwa Local Municipality 2.19 1.50 0.69 3.19 0.57 1.22 1.95 0.45 1.09 0.74

Mkhondo Local Municipality 5.98 6.14 0.28 0.59 0.64 0.80 1.13 0.44 0.59 0.29

When compared at a national level all three LMs show good performance for Agriculture and Mining. Additionally Albert Luthuli shows good performance for trade, community and governmental services, Msukaligwa shows good performance for in utilities, trade, transport and community services, and the Mkhondo LM shows good performance for transport. At this level agriculture is the municipalities’ strongest performing sector as well as mining for Mkhondo LM and electricity for Msukaligwa. Table 15: Location coefficient: Mpumalanga

Agriculture, forestry and and forestry Agriculture, fishing quarrying and Mining Manufacturing water and Electricity, gas Construction trade, retail Wholesale and catering and accommodation and Transport, storage communication real insurance, Finance, business estate and services and social Community, services personal government General Albert Luthuli Local Municipality 1.54 0.42 0.67 0.39 0.71 1.64 0.75 1.17 1.23 2.04

Msukaligwa Local Municipality 1.57 0.48 0.57 1.44 0.80 1.50 2.06 0.80 1.16 1.09

Mkhondo Local Municipality 4.29 1.95 0.23 0.27 0.90 0.98 1.19 0.79 0.63 0.42

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In the provincial context Agriculture, construction and the government sector are highlighted as the most important economic activities. This shows the importance of the municipality in the province and the role it plays as a service centre starts to emerge. Table 16: Location coefficient: Gert Sibande

and and

ersonal services ersonal services Agriculture, forestry Agriculture, fishing quarrying and Mining Manufacturing water gas and Electricity, Construction trade, retail Wholesale and and catering accommodation and storage Transport, communication real insurance, Finance, and business estate services and social Community, p government General Albert Luthuli Local Municipality 1.02 0.38 0.62 0.39 0.76 1.19 0.89 1.44 1.90 2.99

Msukaligwa Local Municipality 1.05 0.43 0.53 1.45 0.85 1.09 2.46 0.99 1.79 1.59

Mkhondo Local Municipality 2.86 1.78 0.21 0.27 0.95 0.71 1.43 0.98 0.97 0.62

When economic sectors are analysed in terms of how well it functions at district level, some important aspects emerges. In the district, the municipalities’ agricultural contribution is no longer seen as an important economic sector. This indicates that other local municipalities in the district contribute much more to this sector than municipal. Wholesale, Finance, and general government are the strongest sectors and this emphasises the role the municipality plays as an important service centre for the people in the municipality and those surrounding municipal areas.

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4 Land Use This section will provide a general discussion of settlement patterns and major land uses in the Gert Sibande District Municipality.

The main urban centres that are included in the study area are Carolina (Albert Luthuli LM), Ermelo (Msukaligwa LM) and Piet Retief (Mkhondo LM). Outside of the main urban centres the district is predominantly rural in nature, comprising extensive farming, forestry, nature reserves and mining areas. The land cover of the study area is indicated in Map 4: Land Cover below.

Map 4: Land Cover

Based on a high level assessment the following observations can be made: • The Mpumalanga section of the rail line will primarily traverse across cultivated land, forest, woodlands, and plantations, as well as some water bodies and very few mining areas; Some detailed aspects of the land cover of the affected areas will be discussed in the following sections. 4.1 Gert Sibande District Municpality 4.1.1 Settlement Patterns The Gert Sibande district is the home of major industrial complexes in the province, such as the petro- chemical industries. This district also has a large agricultural sector with strong service centres like , Ermelo, and Piet Retief. The settlement pattern in this area has developed in orientation to the resource base and economic potential of the area. The agricultural sector, petrochemical industries and mining activities in the area have led to the distribution of service centres

5 July 2013 Page 26 varying in size and function throughout the area. Informal settlements are also found scattered in this district municipality (CSIR, 2007).

The Mpumalanga Land Use Management Plan (Sisonke Development Planners, 2005est.) identified the following important settlements in the Gert Sibande district:

Settlement Type Town/City Development Directive

Major Urban Centre • Bethal; Aim of centres is to retain the current • Carolina; engineering, social, economic and • ; institutional infrastructure and to • Ermelo; strengthen and diversify the economy • Evander; in order to achieve growth, prosperity • Piet Retief; and sustainability. The following • Secunda; industrial clustering opportunities • Standerton; should be explored: agriculture, • . chemical, forestry, and mining.

Rural settlement hubs A total of 39 rural settlement hubs Hubs should fulfill a rural support were identified. function. Each hub must accommodate the primary range of social and economic services. The hubs must provide accessibility to the hinterland, not only in the form of roads but also public transport facilities.

4.1.2 Agriculture The Gert Sibande DM has a strong agricultural sector that produces maize, sunflower, grain, sorghum, beef, dairy, wool, sheep and wheat. Other types of crops produced in the area incorporate potatoes, oil, seeds, maize and soybeans. The area between Carolina, Bethal and Ermelo is one of the largest wool-producing areas in the country. The Standerton area is known for its large dairy industry and maize agriculture. The area of Ogies shows high soil potential for irrigation farming. The district has an estimate of 1,750 commercial farmers, 2,300 emerging, and 5,300 subsistence farmers (MDALA, 2007b).

4.1.3 Mining The Gert Sibande area is well endowed with coal and other mineral deposits and has some of the largest coal mines in South Africa. The major areas for coal in Gert Sibande District are concentrated around Bethal, Secunda, Standerton and Carolina. Linked with these coal mines are some of the world’s largest coal fired power stations, such as Majuba, , Camden and Tutuka in Gert Sibande District. The area is however confronted by the tension between the agricultural activities in the sense that valuable agricultural land is being sterilised by mining activities (CSIR, 2007).

4.1.4 Manufacturing The Gert Sibande area has a strong manufacturing component, which is concentrated in the western part of the district, specifically the Secunda area (CSIR, 2007). 4.1.5 Tourism There is potential to expand small-scale eco-tourism activities in the vicinity of , because of the unique grassland habitats and the bird life associated with the grassland and wetland

5 July 2013 Page 27 areas. The establishment of stop-over facilities linked to the development of the bird watch tourism cluster adds value to the tourism potential of the Gert Sibande District (CSIR, 2007).

4.1.6 Forestry The forestry activities in the Gert Sibande area relate to pine, eucalyptus and wattle plantations, which are concentrated in the eastern parts of the region stretching from Carolina, Lothair, and Amsterdam down to Piet Retief in the south. Mondi has a manufacturing facility in the Gert Sibande District Municipality, namely the Piet Retief mill. Total plantation area in Mpumalanga is about 525 000 ha, which contributes about 39.4% of the total plantation area in the country. Forestry mainly takes place in the eastern parts of Gert Sibande District Municipality (e.g. Carolina, Lothair and Amsterdam down to Piet Retief) and in Ehlanzeni District Municipality (e.g. Nelspruit, Pilgrim’s Rest, Sabie and Graskop). There also exists tension between the agriculture and forestry activities over the use of land in the District Municipality (CSIR, 2007).

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5 Risks and Benefits Mkhondo Municipality's primary economic activities, cantered in the town of Piet Retief, are timber, coal, food and wool production. The Albert Luthuli Local Municipality’s primary economy relies on timber and coal production as well as tourism. Various conservation projects, historic buildings, cultural diversity, and small enterprises such as weaving and painting entrepreneurships are aimed at the tourist market. Although the new rail line link will not directly influence the Mkhondo and Albert Luthuli Local Municipalities, the impact will filter through and have a secondary socio economic impact, but to a very small scale. The Msukaligwa Local Municipality will be most directly influenced by the new rail link. The location of the rail line can be noted as a transportation spine, crossing the centre section of the Msukaligwa Local Municipality. The spine does not directly affect any known environmental concerns. The town which will mostly be affected by the new rail link within Msukaligwa will be that of . A new shunting yard will be constructed within Davel. The impact will be fairly small and will most probably create a few permanent jobs within the town of Davel, as well as temporary job opportunities. Taken into account the various land uses that may be affected by the new rail link, the risk on a sector does exist but the possibility of it having a great impact on the Msukaligwa LM as an entire body is very slim. Factors of concern may be that of the current unemployment percentage within the LM, and how the new rail link may bring relief to this high percentage. Sectors of note in the Msukaligwa Municipality which have shown growth over the past 16 years are the tertiary sector and the secondary sector, with a decline of the primary sector. The greatest growth of the sectors is the tertiary sector and therefore the leading focus must be on sustaining this sector. Gert Sibande District Municipality faces the challenge of a fragmented development pattern which is the result of past planning and the uneven distribution of mineral resources. The seven local municipalities also face the challenge of achieving an integrated development plan in a district of this size and complexity. The provision of adequate housing, clinics, schools and government services is hindered by the spatial nature of the area, low payment rates for services, the small tax base and little economic activity. Community involvement and key stakeholder participation remain a challenge as well. Furthermore, people residing in rural areas do not own the land on which they live, which means they do not qualify to receive housing subsidies, which come with proper services (Anonumous). Risks may be that of the current unemployment percentage within the LM, and how the new rail link may bring relief to this high unemployment rate. The new line will generate more trips on the existing lines and may result in change of local movement patterns, such as the movement of pedestrians crossing the line. This is a risk that must be taken into consideration as the most frequent used transport mode for both work trips and education trips originating in Mpumalanga Province is walking (31.5% and 80.3% of work and education trips respectively) (Department of Transport, 2008). Taking all of this into consideration in can be concluded that the benefits for the new rail link within the Gert Sibande District Municipality are much bigger than the risks, and the anticipated socio-economic impact will result in a positive outcome.

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6 Works Cited Anonumous. (n.d.). The South African LED Network . Retrieved July 04, 2013, from http://led.co.za/municipality/gert-sibande-district-municipality

Department of Transport. (2008). National Transport Master Plan 2050 .

Gert Sibande District Municipality. (2008). Gert Sibande District Municipality IDP.

Gert Sibande District Municipality. (2009). Gert Sibande District Municipality SDF.

CSIR, 2007. Integrated Spatial Framework Update, 2005, Part I & II (First Edition). Mpumalanga Provincial Department, Office of the Premier. Nelspruit, South Africa.

Sisonke Development Planners, 2005est. Mpumalanga Land Use Management Plan. Mpumalanga Provincial Government, Department of Agriculture and Land Administration. Nelspruit, South Africa.

MDLALA( Mpumalanga Provincial Government, Department of Agriculture and Land Administration), 2007b. 2007/08 – 2009/10 Strategic Plan. Nelspruit, South Africa.

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