Transnet - Swazi Rail Link – Msukaligwa Local Municipality - Davel Stock Yard

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. 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.

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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 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 3 Socio Economic Profile ...... 7 3.1 Data and data sources ...... 7 3.2 Demographic consideration ...... 7 3.2.1 Size ...... 8 3.2.2 Population age distribution ...... 8 3.2.3 Population growth rate ...... 9 3.2.4 Future expected growth ...... 10 3.2.5 Spatial aspects of the population ...... 11 3.3 Labour and Economic Analysis ...... 14 3.3.1 Description of the Msukaligwa Local Municipality Labour force ...... 14 3.3.2 Employment productivity ...... 17 3.3.3 Economic structure and performance...... 20 4 Land Use ...... 25 4.1 Msukaligwa Local Municipality ...... 25 4.1.1 Settlement Patterns ...... 25 4.1.2 Agriculture ...... 26 4.1.3 Mining ...... 26 4.1.4 Tourism ...... 26 4.1.5 Forestry ...... 26 5 Risks and Benefits ...... 27 6 Works Cited ...... 28

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Index of figures Figure 1: Population Pyramid ...... 8 Figure 2: Msukaligwa population growth (1996 – 2011) ...... 9 Figure 4: Forecasted population growth ...... 10 Figure 5: Composition of the labour force ...... 14 Figure 6: Change in economic active population (No. of people) ...... 15 Figure 7: Change in formal employment (No. of people) ...... 15 Figure 8: Change of informal employment (No. of People) ...... 16 Figure 9: Change in unemployment (No. of people) ...... 16 Figure 10: Change in unemployment rate (%) ...... 16 Figure 11: Change in labour force participation rate (%) ...... 17 Figure 12: Comparative employment situation in 1995 and 2011 (Employment per sector) ...... 19 Figure 13: GVA output per labour unit 1995 to 2011 (Rand) ...... 19 Figure 14: GVS per economic sector (R'million) ...... 21 Figure 15: Economic sub sectors (R’million) ...... 22 Figure 16: Tress index (10 industries) ...... 22

Index of tables Table 1: Population size ...... 8 Table 2: Households according to population group ...... 8 Table 3: Population Percentage ...... 9 Table 4: Municipal demographic projections ...... 10 Table 5: Population per EA type ...... 11 Table 6: Msukaligwa Local Municipality Labour force ...... 14 Table 7: Employment per sector ...... 17 Table 8: Employment distribution per sector ...... 18 Table 9: GVA output per labour unit (R’million) ...... 20 Table 10: Location coefficient: ...... 23 Table 11: Location coefficient: ...... 23 Table 12: Location coefficient: District ...... 23

Index of maps Map 1: Swazi Rail Link Alignment ...... 6 Map 2: Swazi Rail Link Alignment – Davel Shunting Yard ...... 7 Map 3: Msukaligwa Municipality - Wards ...... 12 Map 4: Population distribution ...... 13 Map 5: Land Cover ...... 25

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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 Davel and surrounding areas 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 • Possible risks and benefits 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, starting at the Davel Shunting Yard in Mpumalanga. This will form the main focus of this study. The areas affected in the neighbouring country of Swaziland will be excluded from this study.

Map 1: Swazi Rail Link Alignment

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2.1 Location The Davel Yard and its associated infrastructure and connections are proposed to be situated within the existing Davel Yard in Mpumalanga (indicated in Map 2), approximately 20km east and approximately 33km west of the Ermelo, Mpumalanga. Due to its strategic location, a shunting yard situated at Davel will form a unique intersection between the existing Coal Line (Webbsrus - Hamelfontein), the Eastern Mainline (Machadodorp - ) as well as the Central Basin (). This shunting yard will facilitate the consolidation of loads into optimised lengths for suitable for the new Swaziland Rail Link, and also possibly the de-consolidation of returning loads in the future.

Map 2: Swazi Rail Link Alignment – Davel Shunting Yard 3 Socio Economic Profile 3.1 Data and data sources 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.

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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 Population Group No. of people % 1% 3% Black African 8106 95.57% Black African Coloured 67 0.79% Coloured Indian or Asian Indian or Asian 11 0.13% 96% White White 287 3.38% Other 11 0.13% Other Total 8482 100% Table 1 shows the population composition of Davel, situated in the Msukaligwa local municipality. Black African is the majority population group, comprising over 88% of the municipality’s 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 the municipality. The household representation of the population is indicated in Table 2, and shows a similar distribution as the overall population profile.

Table 2: Households according to population group Population Group No. of Households % 4% 1% Black African 2056 94.75% Black African Coloured 11 0.51% Coloured Indian or Asian 7 0.32% Indian or Asian 95% White 89 4.10% White Other 7 0.32% Other

Total 2170 100%

3.2.2 Population age distribution Population 2011 A population pyramid graphically 80 - 84 displays a population’s age and gender composition by showing 70 - 74 numbers or proportions of males and 60 - 64 females in each age group; the 50 - 54 pyramid provides a clear picture of a population’s characteristics. The 40 - 44 sum total of all the age-gender 30 - 34 groups in the pyramid equals 100 per cent of the population. 20 - 24 The population pyramid of Davel 10 - 14 (Figure 1) shows larger numbers in 0 - 4 the younger age groups, this -600 -400 -200 0 200 400 600 indicates rapid growth. Male Female Figure 1: Population Pyramid

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Migrant labour is not a factor in the municipality as there are about equal amounts of males and females in the municipal area. However, there are anomalies in some cohorts between 30 years and 34 years. There is no apparent reason that explains this.

Table 3: Population Percentage Age Groups Male % Male Female % Female 0 - 4 518 12.33% 434 10.20% 5 - 9 508 12.10% 450 10.58% 10 - 14 450 10.71% 494 11.61% 15 -19 447 10.64% 470 11.05% 20 - 24 452 10.76% 399 9.38% 25 - 29 372 8.86% 356 8.37% 30 - 34 246 5.86% 290 6.82% 35 - 39 255 6.07% 261 6.14% 40 - 44 223 5.31% 208 4.89% 45 - 49 167 3.98% 166 3.90% 50 - 54 158 3.76% 174 4.09% 55 - 59 136 3.24% 170 4.00% 60 - 64 108 2.57% 136 3.20% 65 - 69 62 1.48% 73 1.72% 70 - 74 53 1.26% 80 1.88% 75 - 79 30 0.71% 56 1.32% 80 - 84 15 0.36% 37 0.87%

Total 4,200 100.00% 4,254 100.00% M/F % 49.68% 50.32%

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 indicates the growth in population for the Msukaligwa LM between 1996 and 2011. From this figure it can be seen that the municipality is experiencing a positive growth.

Population growth - Msukaligwa LM 160000 140000 120000 100000 80000 60000 40000 20000 0 1996 2001 2011

Figure 2: Msukaligwa population growth (1996 – 2011)

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Based on the population size over the past years, the population growth rate of the Msukaligwa LM was calculated to be 1.8% per annum. This is slightly lower than the Mkhondo LM but higher than the Albert Luthuli LM has seen a decline in population size. This pattern revealed is mostly the same for both the province and the district. It should be noted that the growth in the five year period between 1996 and 2001 was the almost the same as for the period between 2001 and 2011, indicating a slightly slower growth over the past few years. 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 1.8%.

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 4: Municipal demographic projections

2001 2011 2015 2020 2025

Population 124,829 149,378 160,351 175,206 191,439 Households 29,689 40,931 46,865 55,506 65,740 HH size 4.2 3.6 3.4 3.2 2.9 Average Pop Growth Rate (last 12 years) 1.8% Average HH Growth Rate (last 12 years) -0.09%

Figure 3 indicates the forecasted population growth graphically, compared to the expected growth of two neighbouring LMs.

Forecasted Population Growth 250 000

200 000

150 000

100 000

50 000

0 201120122013201420152016201720182019202020212022202320242025

MP301: Albert Luthuli MP302: Msukaligwa MP303: Mkhondo

Figure 3: Forecasted population growth

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

• The growth rate was calculated at 1.8%. • This is similar to other rural municipalities in the country.

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• 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 -0.09% • 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 in shown in Table 4 were derived from the population and households estimates in the previous sections. Household sizes are averaged but is regarded as slightly 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 on ward level. The various wards locality are indicated by the map below. The data shows that the municipality’s population is largely formal in nature with little informal and no traditional residential population. Davel is situated in Ward 10, and consist primarily of formal residential buildings and farms.

Table 5: Population per EA type Formal residential Informal residential Traditional residential Farms Parksand recreation living Collective quarters Industrial Small holdings Vacant Commercial

Ward 1 6100 0 0 0 0 0 0 0 0 0 Ward 2 7906 986 0 0 0 0 0 0 0 0 Ward 3 7034 1313 0 0 0 887 85 0 0 30 Ward 4 4297 0 0 0 0 739 0 0 4 0 Ward 5 2807 623 0 0 0 0 0 0 0 0 Ward 6 3142 2162 0 0 0 0 0 0 0 0 Ward 7 5275 0 0 0 0 32 125 0 0 180 Ward 8 9175 1460 0 2710 0 155 6 0 92 0 Ward 9 6386 881 0 2442 0 0 81 0 0 0 Ward 10 4974 0 0 3508 0 0 0 0 0 0 Ward 11 3397 0 0 3886 0 0 0 0 5 0 Ward 12 302 1305 0 1408 0 0 260 0 0 0 Ward 13 8295 299 0 359 0 0 0 0 0 0 Ward 14 5578 174 0 619 0 0 0 0 0 0 Ward 15 2965 1267 0 5870 0 0 0 0 0 0 Ward 16 10299 4351 0 1013 0 0 25 0 32 0 Ward 17 7154 0 0 0 0 0 0 0 0 0 Ward 18 197 0 0 6052 207 0 0 0 0 0

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Ward 19 4284 2564 0 1602 0 0 0 0 11 0 Total 99567 17385 0 29469 207 1813 582 0 144 210

Map 3 below illustrates the various locations of the different wards with in the Msukaligwa Municipality.

Map 3: Msukaligwa Municipality - Wards

Map 4 below presents the population data spatially. One can immediately see specific population concentrations. Ermelo shows the densest populated area in the municipality. The rest of the population is spread throughout the municipal area show some densification close to natural resources. It is interesting to note that the densification area is close to main access routes, emphasising the importance of accessibility in development. 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.

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Map 4: Population distribution

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3.3 Labour and Economic Analysis In the analysis of the labour and employment situation in a municipal area, 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.

3.3.1 Description of the Msukaligwa Local Municipality Labour force Table 6: Msukaligwa Local Municipality Labour force Description Number Population 126 687 Economically active 64 560 Formal and informal (Total) 36 971 Formal 26 104 Formal - Highly skilled 4 402 Formal - Skilled 10 739 Formal - Semi- and unskilled 10 963 Informal 10 867 Unemployed 3 033 Unemployment rate (Percentage) 20.80 % Labour force participation rate (Percentage) 57.27 %

Table 6 describes the labour force of the Msukaligwa LM. According to the 2011 data acquired from the Quantec database, Msukaligwa Local Municipality has 64,560 economically active persons. 26,104 people in the municipality are employed in the formal sector while 10,867 people are active in the informal sector of the economy and 3 033 are shown to be unemployed. The municipality has an unemployment rate of 20.80% and a labour force participation rate of 57.27%.

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The size of the informal sector includes subsistence agriculture (not necessarily applicable in the municipal area), 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. 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. The following figures describe each of the most important elements over time. This is done to establish patterns in the Labour force and to assess any changes that might have taken place.

3.3.1.1 Economic active population Includes the formally employed, the 39000 unemployed, and those persons active in 38000 the informal/ unregistered sector. The terms 37000 ‘supply of labour’ and the ‘labour force’ are 36000 used as synonyms for the economically 35000 active population. 34000 The number of people in the economically 33000 active population has shown an overall 32000 decline over the study period as indicated in 31000 Figure 5. There has been quite a drastic decrease in this figure between 2000 and 2003, and again between 2007 and 2010. This however correlates to the economic Figure 5: Change in economic active population (No. of down turn experienced in 2008. people)

3.3.1.2 Formal employment

35000 The formal employment sector follows a similar trend to that of the economically 30000 active population graph above, although the 25000 differences were less extreme. Since 2000 20000 there has been a decline in the number of people that are formally employed in the 15000 municipality. The graph does show that this 10000 figure has become more stable since 2009. 5000

0

Figure 6: Change in formal employment (No. of people)

3.3.1.3 Informal employment

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12000 The informal employment graph, depicted in Figure 7, shows an increase in people 10000 employed informally from 2001 to 2011. 8000 6000 4000 2000 0

Figure 7: Change of informal employment (No. of People)

3.3.1.4 Unemployment

10000 The change in the unemployed section of the labour force shows a steep fall from 2000 to 8000 2003/2004 and then an increase from that period to 2008, followed by a decrease with the 6000 onset of the recession. The decline in 4000 employment corresponds with the increase in employment in both the formal and informal 2000 sectors but the impact of the economic decline is 0 also evident in the trend towards the end of the period. It can be expected that it will start to

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 increase as the current economic conditions prevail. Figure 8: Change in unemployment (No. of people)

3.3.1.5 Unemployment Rate

25 The rate of changes in unemployment indicates the number of people unemployed as a percentage of the total economically active 20 population (labour force). 15 The unemployment rate follows much the same pattern as the change in the unemployed. The 2009 figure would suggest a rise in the 10 unemployment rate. This can again be explained by the economic recession. 5

0

Figure 9: Change in unemployment rate (%)

3.3.1.6 Labour force participation rate

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Labour force participation rate indicates the 60 labour force (economically active population) as a 50 percentage of the population in the age group 15– 64 years 40 An increase in the participation rate can be the 30 result of more women entering the labour market 20 or the outflow of the potential economically active people from a municipal area due to harsh 10 economic conditions, which would ‘artificially’ increase the participation rate. A low participation 0 rate in a municipal area can be ascribed to the large number of male migrant workers moving out of the municipal area or the proliferation of Figure 10: Change in labour force participation rate (%) peripheral activities in the municipal area. The latter does not seem to be the case in the Msukaligwa Municipality. 3.3.2 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.

3.3.2.1 Employment per sector The table below shows the employment per sector. The structure of employment and the extent of the link between employment and the level of economic activity are important.

Table 7: Employment per sector Agriculture, forestry and fishing and Mining quarrying Manufacturing gas and Electricity, water Construction retail and Wholesale trade, catering and accommodation storage Transport, and communication Finance, insurance, and real estate business services Community, social and personal services General government Total

1995 14225 2929 2669 244 2478 6074 1543 2131 3504 3318 39114 1996 14155 2833 2850 224 2301 6183 1558 2213 3483 3425 39225 1997 14048 2687 2872 207 2149 6300 1516 2290 3633 3512 39215 1998 13816 2463 2832 205 1890 6571 1396 2316 3810 3535 38833 1999 13286 2249 2790 200 1563 6999 1316 2301 4008 3508 38221 2000 12543 2076 2741 188 1397 7143 1234 2309 4215 3461 37306 2001 10568 1970 2685 199 1349 7355 1224 2397 4134 3468 35350 2002 9491 1753 2689 185 1367 7894 1225 2512 4237 3542 34895 2003 8651 1614 2612 189 1225 7851 1355 2582 4319 3694 34092 2004 8107 1547 2665 211 1316 8351 1491 2641 4466 3858 34654 2005 7909 1511 2681 262 1572 9255 1667 2553 4651 4073 36134 2006 7711 1380 2621 320 1842 10025 1761 2700 4823 4289 37471 2007 6887 1434 2492 429 1906 10887 1863 2810 5141 4517 38365 2008 5482 1490 2237 543 1823 11537 2051 2858 5433 4794 38247 2009 4338 1524 1897 641 1731 11900 2033 2665 5602 5051 37384 2010 4162 1654 1722 644 1644 11707 2058 2536 5414 5203 36743 2011 3950 1689 1610 654 1701 11931 2133 2518 5293 5494 36971 Avg. -7.5% -3.7% -3.0% 7.2% -2.9% 4.4% 2.2% 1.1% 2.7% 3.2% 3.8% Change

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The most noticeable is the differential growth rates in employment creation between the sectors. Agriculture has shown an average decrease of about 7.5% of the jobs between 2011 and 1995. The implication is important since these workers are jobless and have to leave farms. They usually end up in informal settlements on the urban fringes. This amounts to about 7886 people being affected or it implies a need for about 2 500 new households to be provided with services. The primary and secondary sectors are both shedding jobs while the tertiary sector is growing strongly and emphasising the role of the Msukaligwa Municipality.

3.3.2.2 Changes in employment Table 8 below presents the employment distribution per sector over the past 17 years. These figures are expressed in terms of the distribution of employment across the sectors. One noticeable aspect is that the largest economic sectors are not necessarily the biggest contributors to employment creation.

Table 8: Employment distribution per sector

Agriculture, forestry and and Agriculture, forestry fishing quarrying and Mining Manufacturing water gas and Electricity, Construction retail trade, and Wholesale cateringand accommodation and storage TG: Transport, 7] [SIC: communication real insurance, Finance, TH: business services and estate [SIC: 8] and Community, TI:social 92, [SIC: services personal 95-6, 99, 0] government TJ: General [SIC:94] 91, Total

1995 36% 7% 7% 1% 6% 16% 4% 5% 9% 8% 100% 1996 36% 7% 7% 1% 6% 16% 4% 6% 9% 9% 100% 1997 36% 7% 7% 1% 5% 16% 4% 6% 9% 9% 100% 1998 36% 6% 7% 1% 5% 17% 4% 6% 10% 9% 100% 1999 35% 6% 7% 1% 4% 18% 3% 6% 10% 9% 100% 2000 34% 6% 7% 1% 4% 19% 3% 6% 11% 9% 100% 2001 30% 6% 8% 1% 4% 21% 3% 7% 12% 10% 100% 2002 27% 5% 8% 1% 4% 23% 4% 7% 12% 10% 100% 2003 25% 5% 8% 1% 4% 23% 4% 8% 13% 11% 100% 2004 23% 4% 8% 1% 4% 24% 4% 8% 13% 11% 100% 2005 22% 4% 7% 1% 4% 26% 5% 7% 13% 11% 100% 2006 21% 4% 7% 1% 5% 27% 5% 7% 13% 11% 100% 2007 18% 4% 6% 1% 5% 28% 5% 7% 13% 12% 100% 2008 14% 4% 6% 1% 5% 30% 5% 7% 14% 13% 100% 2009 12% 4% 5% 2% 5% 32% 5% 7% 15% 14% 100% 2010 11% 5% 5% 2% 4% 32% 6% 7% 15% 14% 100% 2011 11% 5% 4% 2% 5% 32% 6% 7% 14% 15% 100%

From Table 8 it is confirmed that employment is not a static issue and changes in employment are very important. Figure 11 below gives a comparison between the employment situation in 1995 and in 2011.

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Changes in employment sector between 1995 and 2011 40% 35% 30% 25% 20% 15% 10% 1995 5% 0% 2011

Figure 11: Comparative employment situation in 1995 and 2011 (Employment per sector)

From Figure 11 an overall decline in primary and secondary employment is evident, with the only exception of a slight increase in the utilities industry. Also, despite the large decrease in agricultural employment, the agriculture sector remains one of the larger employers in the LM. The tertiary sector has however shown an overall increase, especially in the wholesale industry.

3.3.2.3 Labour output Table 9 and Figure 12 below shows the Gross Value Added (GVA) output per labour unit between 1995 and 2011.

8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 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 TI: Community, social and personal services General government Figure 12: GVA output per labour unit 1995 to 2011 (Rand)

It is not possible to draw any specific conclusion regarding labour productivity. The interplay between labour and capital is not assessed. All labour units, with the exception of the mining industry showed an increase in GVA output since 1995. The tertiary sector shows a significant contributing of 60%. 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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Table 9: GVA output per labour unit (R’million)

retail trade,

Primary sector Primary and fishing forestry Agriculture, quarrying and Mining sector Secondary Manufacturing water gas and Electricity, Construction sector Tertiary and Wholesale accommodation catering and and storage Transport, communication real estate insurance, Finance, business services and and social Community, TI: services personal government General Total 1995 969 157 812 373 237 97 40 1,531 319 345 388 166 314 2,873 1996 982 211 771 390 252 100 38 1,601 325 363 420 169 324 2,972 1997 975 195 780 408 272 97 39 1,614 323 379 423 168 322 2,998 1998 946 203 743 388 268 81 38 1,626 321 391 416 177 321 2,960 1999 969 222 748 370 265 69 36 1,670 341 403 419 183 324 3,009 2000 952 226 726 382 285 71 26 1,675 359 415 381 190 329 3,009 2001 885 192 693 395 299 66 30 1,708 371 426 387 195 329 2,988 2002 861 230 630 447 340 75 32 1,743 387 451 374 201 330 3,051 2003 831 223 609 448 323 91 35 1,835 396 501 374 213 351 3,114 2004 788 223 564 496 342 116 38 1,944 434 536 395 219 359 3,227 2005 716 208 508 552 363 145 45 2,063 478 577 409 226 372 3,331 2006 647 186 461 614 379 184 51 2,222 533 622 439 242 387 3,484 2007 617 186 431 685 401 223 61 2,355 582 675 439 256 402 3,658 2008 607 215 392 735 415 250 70 2,465 614 731 441 266 413 3,807 2009 563 212 351 784 424 282 78 2,483 640 740 419 259 425 3,830 2010 574 213 361 787 445 265 77 2,499 643 749 417 257 433 3,860 2011 560 212 348 802 467 258 77 2,583 669 785 419 263 446 3,945

Average 791 207 584 533 340 145 48 1,977 455 535 409 215 364 3,301 Average % contribution 24% 6% 18% 16% 10% 4% 1% 60% 14% 16% 12% 7% 11% 100% % growth pa -2.48% 2.05% -3.36% 6.78% 5.73% 9.85% 5.56% 4.04% 6.46% 7.50% 0.47% 3.46% 2.48% 2.19%

3.3.2.4 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 Msukaligwa local municipality since the male to female ratio is almost a 50/50 split. 3.3.3 Economic structure and performance

3.3.3.1 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 Gross Value Added (GVA). The analysis will focus on the GVA produced by the primary, secondary and tertiary economic sectors over time; the GVA produced by

5 July 2013 Page 20 each economic sector in the municipality and compare the GVA of the Municipality to the Gert Sibande DM, the Mpumalanga province and the country as a whole. 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.

Sectoral GVA contributions - Msakaligwa LM 3 000

2 500

2 000

1 500

1 000

500

GVA ContributionGVA (R'million) 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 13: GVS per economic sector (R'million)

Figure 13 indicates the largest and strongest growing sector in Msukaligwa’s economy is 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. Going further and analysing the sub sectors in the economy (see Figure 14), it is clear that Msukaligwa is experiencing strong growth in all industries with the exception of the mining industry. It also becomes apparent why Msukaligwa’s tertiary economic sector is so strong. The economy is driven by manufacturing activities with the finance, insurance, real estate and business services showing strong growth. General government is also a sector that is improving and contributing to the economy more at a steady increasing pace.

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GVA output per industry - Msakaligwa LM 900 800 700 600 500 400 300 200 100 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity, gas and water Construction Wholesale and retail trade, catering and accommodation Transport, storage and communication Finance, insurance, real estate and business services TI: Community, social and personal services General government

Figure 14: Economic sub sectors (R’million)

3.3.3.2 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 - Msukaligwa LM 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 Msukaligwa Local Municipality

Figure 15: Tress index (10 industries)

The comparative tress index displayed in Figure 15 shows that Msukaligwa LMs economy is diversifying. Not only is the municipality’s economy less vulnerable/diversified than it was in 1995, but it is also showing higher diversification than the Mpumalanga province and South Africa.

3.3.3.3 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

5 July 2013 Page 22 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 10: Location coefficient: South Africa

vices vices Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity,water gas and Construction Wholesale and retail cateringtrade, and accommodation Transport, storage and communication insurance,realFinance, and business estate ser and Community, social personal services governmentGeneral Msukaligwa Local 2.19 1.50 0.69 3.19 0.57 1.22 1.95 0.45 1.09 0.74 Municipality

When compared at a national level Utility services shows the best performance, followed by transport, agriculture, and mining. It is interesting to note the strong mining position the LM still has on national level despite the decrease in mining production in the LM over the past years. Table 11: Location coefficient: Mpumalanga

vices vices Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity,water gas and Construction Wholesale and retail cateringtrade, and accommodation Transport, storage and communication insurance,realFinance, and business estate ser and Community, social personal services governmentGeneral Msukaligwa Local 1.57 0.48 0.57 1.44 0.80 1.50 2.06 0.80 1.16 1.09 Municipality

In the provincial context transport services and agriculture are highlighted as the most important economic activities, followed by trade and utilities. This shows the importance of the municipality in the province and the role it plays as a service centre starts to emerge. Table 12: Location coefficient: District

orestry and orestry

Agriculture, f fishing Mining and quarrying Manufacturing Electricity, gas and water Construction Wholesale retail and trade, catering and accommodation Transport, and storage communication insurance,Finance, real estate and business services Community, and social personal services government General Msukaligwa Local 1.05 0.43 0.53 1.45 0.85 1.09 2.46 0.99 1.79 1.59 Municipality

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When economic sectors are analysed in terms of how well it functions at district level, some important aspects emerges. In Msukaligwa the tertiary services are the strongest sector and this emphasises the role the municipality plays as an important service centre for the people in the municipality and those surrounding Msukaligwa.

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

Map 5: Land Cover 4.1 Msukaligwa Local Municipality 4.1.1 Settlement Patterns The Local Municipality has a large agricultural sector with strong service centre Ermelo. 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 varying in size and function throughout the area. Informal settlements are also found scattered in this local municipality.

The Mpumalanga Land Use Management Plan (Gert Sibande 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

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Settlement Type Town/City Development Directive

• ; 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.

It is important to note that the major urban centre within Msukaligwa LM, is Ermelo and is in close proximity to the new rail line in question.

4.1.2 Agriculture The Gert Sibande area 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 has rich coal and 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 and Tutuka, both situated in the 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). Despite the strong mining presence that remains in the Msukaligwa LM it is noted that the mining activities have declined drastically since 1995.

4.1.4 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 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).

4.1.5 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 Gert Sibande, namely the Piet Retief mill.

There exists tension between agriculture and forestry activities over the use of land (CSIR, 2007).

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5 Identified Risks and Benefits Due to its strategic location, the shunting yard will be situated at Davel which forms a unique intersection between the existing Coal Line (Webbsrus - Hamelfontein), the Eastern Mainline (Machadodorp - Breyten) as well as the Central Basin (Trichardt). This shunting yard will facilitate the consolidation of loads into optimised lengths for suitable for the new Swaziland Rail Link, and also possibly the de-consolidation of returning loads in the future. 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. Taken into account the various land uses that may be affected by the Transnet - Swazi Rail link, the risk on a sector does exist but the possibility of it having a great impact on the Msukaligwa Municipality as an entire body is very slim. Risks may be that of the current unemployment percentage within the LM, and how the Transnet - Swazi Rail link may bring relief to this high percentage. The new line will generate more trips on the lines and result in change in local movement patterns, movement being 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). Other prominent modes were private cars, buses, and minibus-taxis. Trains contributed only 0.1% of all working trips originating in the province (Department of Transport, 2008).

The Msukaligwa Local Municipality will benefit from the Transnet - Swazi rail link greatly. The risks and benefits weight the reward of the Transnet - Swazi Rail link will be superior.

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6 Works Cited

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.

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

Mpumalanga Department of Agriculture and Land Administration. (2005). Integrated Resource Information Report.

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