POVERTY POCKETS IN GAUTENG: HOW MIGRATION IMPACTS POVERTY. Contents

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3 POVERTY POCKETS IN GAUTENG: HOW MIGRATION IMPACTS POVERTY REPORT TO THE GAUTENG INTERSECTORAL DEVELOPMENT UNIT Contents page 1 INTRODUCTION Poverty pockets: how the study is structured Literature review Gauteng metropolitan areas International migration Internal migration METHODS: DEALING WITH THE DATA Migration data and mapping Poverty data and mapping Combining the poverty and migration data for mapping purposes FINDINGS: MIGRATION AND POVERTY POCKETS Poverty pockets: the spatial distribution of migration and poverty in Gauteng Migration Poverty Combined poverty and migration Potential migration into Gauteng: migrant intentions Global, regional, national and provincial context Migration context Discussion Relating poverty and migration: a rank-ordering approach to identifying poverty pockets A basic poverty and migration profile of Gauteng main places Gauteng s overall trend for poverty and migration Defining poverty targets at sub-place level Identifying poverty pockets Perceptions from the stakeholders: qualitative responses from administrators and communities In-depth interviews Focus group interviews...45 iii

4 page 4 DISCUSSION: INPUTS FROM THE STAKEHOLDER WORKSHOP CONCLUSIONS: EMPTYING POVERTY POCKETS? Indicators Understanding poverty and migration Recommendations: toward a greater understanding of poverty and migration in Gauteng...68 REFERENCES...69 APPENDICES 1 Migration component Poverty component Allocation of main-place migration data to sub places : Stepwise regression for number of in-migrants ( ) Allocation of main-place migration data to sub places : Stepwise regression for number of out-migrants ( ) Combined poverty and migration Migration intentions List of interviewees TABLES In-migration to Gauteng from other provinces ( and ) Out-migration from Gauteng to other provinces ( and ) Net migration into Gauteng from and to other provinces ( and ) Twenty poverty and migration priority areas ( main places ) in Gauteng (2001) Relation of poverty and migration for all 149 main places in Gauteng, High and low migration places in relation to poverty within the priority grouping, Gauteng Gauteng profile for key indicators: all sub-places Categorizing sub-place results for poverty Categorizing sub-place results for migration Making comparisons: population Making comparisons: housing and occupancy Making comparisons: services and poverty...33 iv

5 MAPS page 1 Mean net migration rates for the periods and : Gauteng main places Poverty- values for Gauteng 'sub-places in Superimposed (overlaid) poverty levels and migration rates for Gauteng 'sub-places...14 FIGURES 1 Factor plot of the ten individual poverty indicators...63 v

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7 1 INTRODUCTION Poverty in our cities is probably the key planning question of this millennium, and it is clear that much of the poverty being experienced in Gauteng is driven by migration. The Johannesburg/ Pretoria conurbation probably has the strongest pulling power of any African city, constantly bringing in new streams of the hopeful poor. To deal with in-migration, Gauteng s cities plan on the urban transition, the process by which rural in-migrants coming from the outside become participating citizens of the city. To promote the urban transition, the cities are providing housing, services and education to the arriving poor, intended to establish a platform for rural-born households to become fully integrated, productive urban citizens. There is an implicit anti-poverty model in this planning effort: it is assumed that if new and poor households are able to be provided with housing and services, they will then be able to accumulate an asset base for themselves that will make city life sustainable. It is also assumed that they will be able to provide their children with the education the second generation will need to raise their level of participation in the city economy to a higher level, as the entrant household climbs the urban economic ladder. The cost to Gauteng of providing the initial housing and services benefits is very high, and outcomes are uncertain. Risks are involved. The major metropolitan cities are the national engines of development: delivering these benefits to the arriving poor takes resources away from the cities commerce and industry, which are competing strenuously with other primate cities in the global context and need all the investment capital they can find if South Africa is not to fall behind. Likewise, to work properly, this infrastructural delivery approach to the urban transition requires access to jobs, so that the newly arrived households can accumulate assets and ensure education. In the present state of the South African economy, there is not a guarantee of jobs for the in-migrating rural poor, and therefore the outcome of the national infrastructural delivery project in the cities remains precarious. The country-wide services and housing protests of 2005 underline the tense relation between the cities and the in-migrants around the issues of delivery. As the cities respond, Johannesburg itself is working to promote assimilation and integration of rural in-migrants by providing a package of price discounts on urban infrastructural services, a positive move which will also act to lower barriers to urban migration by the rural poor. Accordingly, a great deal is at stake for Gauteng in the policy issues around poverty. Success means cities of justice, and a beacon for the global South. Failure means cities of poverty, dominated by permanent impoverished shack settlements. In this light, how should we understand migration and urban poverty? 1.1 Poverty pockets: how the study is structured Commissioned by the Gauteng Inter-Sectoral Development Unit, the present study by HSRC looks at the characteristics and the internal and external dynamics of areas in Gauteng province 1

8 which can be designated as poverty pockets that is, localities in which the share of the population in poverty is significantly higher than what is found in surrounding areas. Such poverty pockets are the areas of the province which are the most difficult to assist in terms of poverty reduction, as well as the most difficult for metro management and planning structures to deal with in relation to delivery. In this light, they are of urgent importance to Gauteng s own task of internal governance as well as to the South African national project of providing the poor and destitute with a decent life, in which Gauteng is taking the leading role. Migration has been identified as a possible important factor in relation to the formation and survival of poverty pockets. Concentrations of high in-migration overlap with poverty concentrations, but the two are not identical. The present study addresses the relation of such poverty concentrations to migration and in particular to the arrival of the poor in the Gauteng metro sector. The approach taken by the study rests on first, identifying poverty pockets within Gauteng, using Census data which has been statistically manipulated to bring it down to a level appropriate to communities which are often very small, and displaying the results through the GIS mapping work (see Maps 1, 2 and 3, below). Once identified, poverty pockets are examined in the light of the 12-factor developed by Strategy and Tactics, to the extent that these indicators can be analyzed at the appropriate small-community level. The results in terms of how poverty manifests itself in the areas identified are placed in context through the supporting work on migration intentions on the part of potential migrants in other provinces, and through the qualitative interviews and focus groups conducted with stakeholders at city and province levels, and in the communities on the ground. Research questions include: o What share of migrants becomes economically integrated? o What share of arriving migrants do not have the qualifications to find a foothold? o How are these people distributed in the province? o What are poverty pockets, and how do they form? The ultimate objective of the study is to show how migration trends relate to poverty pockets, and how the province and communities are dealing with these issues. The approach of the research is based mainly on the quantitative desktop analysis, supplemented and filled in to some extent by the qualitative field study. The desktop study is intended to review the available information on migration and poverty in Gauteng, and to consider the literature. It strives to highlight gaps, and to identify key dynamics of the relation between selected indicators and the nature and distribution of the poverty pockets. The qualitative work has been empirically directed, and aimed at obtaining information from practitioners and from people in the communities themselves. The overall approach therefore includes: 2

9 Qualitative: interviews and focus groups, carried out with key informants and in key areas selected for the light they can shed on the issues of migration and poverty; Quantitative: Census 2001 data, backed up by HSRC data and other data sources. Comparisons between key indicators have been made by sorting the data to start to show dynamics. Data has been placed in a spatial framework through GIS mapping, which has pinpointed the physical location of areas shown to be poverty pockets. Time series: This process of research began with a literature review (Jonathan Mafukidze and Marie Wentzel, see 1.2) and the start of the qualitative field study (Marie Wentzel and Khuli Tlabela). These aspects of the research took place at the same time as the quantitative analysis began a first phase of working with the Strategy and Tactics poverty at main place level, using Census data (Pieter Kok, Catherine Cross). When it became clear that Stats SA would not release 2001 data at the necessary level, the first quantitative phase was followed by the construction of the new dataset approximating the sub-place level, and a more detailed quantitative analysis. Gina Weir-Smith from the HSRC s GIS Centre provided the 1996 EA codes covered by the new (2000) local governments and the main places and produced the maps presented here. At this point results were presented to a stakeholders workshop convened by Gauteng Inter-sectoral Development Unit. Very important inputs were received at this workshop (see 4, Discussion: inputs from the stakeholders workshop, below) particularly in connection with the nature and purpose of the poverty. To respond, the HSRC team held intensive internal workshop consultations and carried out factor analysis procedures to explore the issues which had come up. Draft final results as presented here flow from that final phase of re-reflection as well as from analysis of the qualitative and quantitative results. The methods employed in making use of the Census data to consider poverty pockets are described below under 2, Methods. In an attempt to get a spatially more nuanced picture of poverty and migration in Gauteng than was provided in two recent Gauteng studies, by Strategy & Tactics (Jennings, Ntsime & Everatt 2003) and DPRU/SAMP (Oosthuizen, Peberdy et al. 2004; see also 1.2, Literature review), a basic analysis of the situation in the various so-called main places in the province was undertaken on the 2001 census data. Results appear under 3.1, Spatial location. The main results are presented under 3, Findings of the study. The work which followed the identification of poverty pockets made central use of the list of 12 indicators developed by Jennings, Ntsime and Everatt in collaboration with Gauteng Intersectoral Development Unit. These results are reported in 3.3, A ranking analysis of poverty pockets. These findings follow a discussion of the scale and nature of intended migration to Gauteng, in 3.2, Intentions to migrate to Gauteng. Both these sections are placed in perspective through the results of the qualitative study, in 3.4, Qualitative findings from the interviews and focus groups, and in 4, Discussion: inputs from the stakeholder workshop. In the concluding section, 5, two main aspects are considered further: first, in 5.1 Indicators, the characteristics of the Strategy and Tactics list of indicators in revealing the extent and nature of poverty in the areas described as poverty pockets; and second, in 5.2 Migration and poverty, the wider ramifications of the findings in relation to 3

10 poverty pockets in Gauteng. The report concludes in 5.3, with some recommendations for how the present work might be taken forward for best applicability to Gauteng s policy context for urban poverty. 1.2 Literature review Gauteng, the smallest and richest province in South Africa, is the second most populous province in the country after KwaZulu-Natal. As the hub of South Africa s financial and services sectors with its links to the mining industry, the province has a long history of attracting migrants. According to Census 2001 over 40 per cent of the Gauteng population of 8.8 million were born outside the province. Approximately 35 per cent of the population was born in one of the other eight provinces, while 5 per cent are foreign-born (Oosthuizen, Peberdy, et al. 2004). The largest number of South African in-migrants from other provinces to Gauteng between 1996 and 2001 came from Limpopo, followed by KwaZulu-Natal and the North West. Census 2001 shows that the majority of the migrants from outside the South African borders in Gauteng came from the Southern African Development Community (SADC) countries (65%), followed by European migrants (24%) (Oosthuizen, Peberdy et al. 2004). Migration to and within South Africa is a complex phenomenon as it is elsewhere in the world. The causes and impacts remain a subject of intellectual and political debate because it is consequently difficult to come to any definite conclusion (Balan 1981, Peek 1981, Roberts 1981, Skeldon 1990, Massey 1998). The patterns and trends of migration continue to alter as the national, regional and global socio-economic and political terrain change constantly. (For a more detailed discussion of the causes of migration see Appendix 6.) Gauteng metropolitan areas Within South Africa, and for the present study, Gauteng s Johannesburg/ Tshwane conurbation is the key sub-region. Selected key features of the Johannesburg, Tshwane and Ekurhuleni metropolitan municipalities are described below: City of Johannesburg Metropolitan Municipality Currently the City of Johannesburg is the largest city in the country with a population of more than 3,2 million in In-migration is driving population growth. Between 1996 and 2001 the number of people in the typically migrating age bracket (15-34 years) grew by 27,8%, and the number of households with only one member increased to 23,9%. This is keeping unemployment high (37,4%) even while the economy is growing steadily. In 2001 more than one-fifth (22,2%) of households was without formal shelter and almost one-sixth (15,5%) of the households without on-site water (South African Cities Network 2004). City of Tshwane Metropolitan Municipality Tshwane is the country s seat of executive government. Its economy is therefore dominated by the government sector. Economic growth is driven mainly by manufacturing, especially the automotive industry node. Between 1996 and 2001 manufacturing jobs grew by 25%. Fast population growth does, however, bring its own set of challenges. In the period the number of households in informal dwell- 4

11 ings grew by 57,7% and in 2004 almost one-quarter (24,8%) of households did not have adequate accommodation. Census 2001 counted a population of just below two million for Tshwane, and at that time more than one-fifth (20,8%) of households had been without formal shelter and almost one-sixth (15,6%) without on-site water. In 2002 the unemployment rate was 18,9%. Tshwane is the only large city with a boundary overlapping a provincial border, raising complex governance challenges. (South African Cities Network 2004). Ekurhuleni Metropolitan Municipality Ekurhuleni, historically known as the East Rand, has traditionally been South Africa s manufacturing heartland. Industry declined from its peak in the 1970s and between 1980 and 1996 the city lost manufacturing jobs. However, the sector is still strong and showing some signs of rebirth. Manufacturing provides 19,2% of the city s employment and grew by 26% between 1996 and 2001, adding new jobs. This growth attracts large numbers of relatively low-skilled and semi-skilled job seekers. Ekurhuleni currently has the fastest population growth in the country. Its population grew by 4,1% per annum in the period , and it saw a remarkable 39,2% increase in households, most of these moving into two or three-room dwellings. In 2001 the population was almost 2,5 million, with nearly 29,8% of households being without formal shelter and 30,3% of households not having on-site water. In 2002 the unemployment rate was 31,4% (South African Cities Network 2004) International migration International migration to South Africa, and in particular to Gauteng, has a long history. Since earliest times, migrants have been part of the South African industrialisation process. Today international migrants play a significant role in all spheres of the South African economy (Oosthuizen, Peberdy et al. 2004, Adepoju 2003, Neocosmos 1999, Ramphele 1993). The number of migrants coming to South Africa and also to Gauteng, particularly those originating from the African continent, has increased since the early 1990s, and more so after the first democratic elections in The migrants primarily come from South Africa s traditional labour supply areas, which include SADC countries. However, migrants have also come from other African countries, Europe and Asia (Wentzel & Tlabela forthcoming). Traditionally, migration into Gauteng and to the rest of the country was highly institutionalised and regulated. Labour migration largely occurred under the direction of the highly formalised and regulated contract labour system for the South African mines. Throughout the twentieth century at least 40 per cent of the mine workforce was non-south African. This figure peaked in the early 1970s at over 80 per cent and by the late 1990s 60 per cent of the mine workforce was of foreign origin (Crush 2000). Thus, over the years, hundreds of thousands of male migrants from the southern African region have spent most of their working lives in South Africa and in Gauteng in particular. Since the 1980s migration trends to South Africa have changed from the traditionally long established migrant labour system to a more complex and broader flow of diverse people. Skilled professionals (teachers, doctors, lawyers, nurses, engineers 5

12 and university professors) came clandestinely to the country to take up challenging jobs (Adepoju 2003). However, due to the immigration policy of the apartheid state these people were employed in the former homelands (Prah in Adepoju 2003). Since the democratisation of the country in 1994 traders and vendors gradually followed this group (Adepoju 2003). The liberation of South Africa also gave rise to the feminisation of international migration as numerous women took up cross-border trade, investment and employment seeking as sources of livelihood (Crush 2001). Although the majority of these women are engaged in trading and vending some are also skilled professionals. Gauteng is a preferred destination for migrants from many African countries since migration to Gauteng is an established tradition close to two centuries old, people in those countries have well-established social networks, and Gauteng is seen as an African economic powerhouse. Gauteng s flourishing construction and mining industries offer employment to the unskilled and semi-skilled job seekers while industry and the universities attract highly skilled professionals. Furthermore, the large and diverse population offers opportunities for trade and self-employment and for small-scale entrepreneurship (DPRU 2004). Migrants face many difficulties and are a vulnerable component of the population. Cross-border migrants, in particular, also have to deal with xenophobic sentiments among some South Africans. These affect where foreigners live, their access to some services, and the activities in which they participate, thus restricting them from developing their full economic potential. Albeit a small proportion (5%) of the Gauteng population in 2001, immigrants and other cross-border migrants are a potentially important group of workers and entrepreneurs. Yet discrimination in the job market and in the areas where they settle make them vulnerable and may cause them not being able to maximise their contribution to the local economy (Oosthuizen, Peberdy et al. 2004) Internal migration Internal migration in South Africa was characterized by voluntarily and involuntarily processes. Involuntarily migration in South Africa, in particular, was caused by the segregation policies of the apartheid state. This policy led to forced removals of people from certain areas and the establishment of townships such as Soweto in the 1950s and Soshanguve in the 1970s (Harrison n.d., Anderson 1992). Gauteng has a highly mobile population with approximately one-fifth of its residents indicating in 2001 that they had moved during the preceding five years. Of these migrants, just over 1 million (60%) were intra-provincial migrants, having moved within the province (Oosthuizen, Peberdy et al. 2004). The metropolitan municipalities (Johannesburg, Tshwane and Ekurhuleni) account for 84 per cent of migrants in Gauteng, with only Ekurhuleni having a below-average proportion of migrants. Oosthuizen, Peberdy et al. (2004) indicate that there is a clear difference between the metropolitan regions in terms of migration. Johannesburg, the metropolitan municipality with the largest population (37% of the total), receives a relatively large proportion of intra-gauteng migrants (40%). Tshwane, on the other hand, receives a relatively large proportion of non-gauteng migrants (27%) compared to its share of 6

13 the total provincial population (17%). Ekurhuleni accounts for similar proportions of total intra- and total non-gauteng migrants (25%). Oosthuizen, Peberdy et al. (2004) observe as follows regarding the general relationship between migration and poverty in Gauteng in their report on Migration into Gauteng Province: (a) In-migrants (persons born outside Gauteng) tend to respond to a considerable economic incentive to leave their home provinces. The relative abundance of employment opportunities in Gauteng, especially when compared to provinces such as Eastern Cape, Limpopo and KwaZulu-Natal, is therefore an important driving force for continued in-migration into the province. While Gauteng has been successful in attracting many highly educated persons from other provinces, the report shows that in-migrants tend to be employed in less skills-intensive sectors notably women migrants in domestic employment. This observation is confirmed by the generally lower incomes of in-migrants. In that sense in-migration probably contributes to a lowering of income levels in the province. (b) Non-migrants in Gauteng have much higher disability rates than in-migrants (at a ratio of 1,65 to 1). This indicates that in-migrants may actually contribute to lowering the province s dependence on disability grants. (c) Almost a third (31%) of households that have recently migrated to Gauteng reside in informal dwellings, compared to 24 per cent of households that have been living in Gauteng for more than five years. The report also suggests that recent intraprovincial migrants may tend to relocate from informal to new formal dwellings, which helps to explain why a higher proportion of intra-gauteng migrant households reside in formal dwellings than non-migrant households. Details of the reasons and the origins and destinations of intra-provincial migrants are not provided though, making it difficult to understand these processes. (d) Access to household goods such as radios, television and refrigerators, often presumed to be a useful indicator of living standards, tends to differ notably between households having in-migrated recently and more established Gauteng households, with the recent in-migrant households being far worse off. This may be related to a comparative lack of appropriately secured shelter and/or access to electricity in informal housing among recent in-migrant households. (e) Another factor contributing to higher levels of visible poverty in Gauteng is the large number of migrant workers (1,3 million or 46% of the national total), with by far the largest proportion (more than 40%) having moved from (presumed impoverished) rural parts of Limpopo. These migrant workers remitted more than R2,6 billion (mostly in cash) to sending households during the period September 2001 to August The average per migrant worker in Gauteng was more than R1 900 in the same period. While the available data do not allow estimates of the impact of having to send remittances on the amount of money left for spending in Gauteng, it is reasonable to suggest that remittances impact significantly on the worker s ability to save or invest money locally. This may create an important dilemma for the Gauteng Provincial Government in its admirable vision of in the near future having informal housing and single-worker hostels in the province replaced with formal family-residential structures. 7

14 2 METHODS: DEALING WITH THE DATA In the study reported here census data on poverty and migration had to be manipulated, analysed, converted into maps, and used in modelling. In an attempt to get a spatially more nuanced picture of poverty and migration in Gauteng than was provided in two recent Gauteng studies, by Strategy & Tactics (Jennings, Ntsime & Everatt 2003) and DPRU/SAMP (Oosthuizen, Peberdy et al. 2004), a basic analysis of the situation in the various so-called main places and sub-places in the province was undertaken on the 1996 (where appropriate) and 2001 census data. To identify the most important poverty and migration pockets in the province 1 a rough of poverty was at first created and combined with the proportion migrants for each of the 149 main places in Gauteng to obtain a crude measure of poverty and migration priority areas. The shortened or reduced version of the poverty presented in the first interim report was the average of nine (and later ten) of the 12 poverty indicators identified by the Provincial Poverty Alleviation Committee (as reported in Strategy & Tactics 2003: par 38). 2 This section describes how the data were organised to meet the requirements of the study. Attention is given firstly to the migration and poverty data separately and then the process of combining the poverty and migration data is described. 2.1 Migration data and mapping In Appendix 1 a detailed description of the migration analyses is provided at the main-place level, which is the lowest spatial level for which migration data from Census 2001 were made available. Detailed analyses of the (a) out-migration, (b) inmigration and (c) net migration volumes and rates 3 for the various local governments and their constituent main places were undertaken. The 1996 and 2001 censuses offer a unique opportunity to study internal migration in South Africa. There is no longer a problem of the information for some areas not being available, nor are there seriously flawed census questions. The migration studies that can be undertaken on the basis of the 1996 and 2001 census data are numerous and potentially very important. Although a slight redefinition of migration is theoretically needed to analyse the census data correctly, there are opportunities for migration research, including the analysis of migration trends, that has never been possible before. Some examples of the analysis opportunities of the data generated by these two censuses have been provided here. Unfortunately these could only be undertaken 1 Ideally speaking, such pockets should be smaller spatial units than main places, but since the migration data have not been provided at lower levels it was decided to restrict this initial analysis to main places. 2 This adjustment was necessary because appropriate and reliable data for the other three indicators could initially not be found at the level of Census main places. However, it was discovered later that the census data needed for calculating the poverty indicator crowding had in fact also been available and hence the tenth poverty indicator was added subsequently. 3 Migration rates for two periods of approximately five years each, namely 1 January 1992 to 10 October 1996 and 11 October 1996 to 10 October 2001, were calculated in an attempt to obtain an understanding of the demographic impact of migration. 8

15 at a main place level due to the unavailability of spatially more detailed migration data from Census While the level of in-migration is the main cause for concern in government circles in Gauteng and elsewhere, it is important to note that the key issue from a policy and planning perspective should not necessarily be the number of migrants arriving in a particular area but the net gain in respect of in-migrants received and out-migrants having left the area. The net gains and losses (positive and negative net migration numbers) have therefore received particular attention in the analyses reported here. The emphasis in these analyses has furthermore been on the mean rates of migration. The means were calculated for two consecutive periods ( and ) in an attempt to avoid conclusions based on the periodic surges in the levels of either in-migration or out-migration, and rates were used in an attempt to indicate the demographic impact of in-migration and out-migration (on each area s population). The levels of out-migration, probably being of least interest to policy makers and planners in the province, were described first, and this set of analyses was followed by a description of the levels and demographic impact of in-migration and net migration respectively over the two periods studied. Every set of analyses regarding mean outmigration, in-migration and net migration volumes and rates started off with a description of what happened in the 13 local government areas in the province, followed by a description of the mean rates of migration as experienced by the various so-called main places in the province, and lastly we looked briefly at the overall situation in respect of the 14 cross-border main places in the province. Net migration received the most attention in terms of analysis, mapping and interpretation. The last set of analyses reported in Appendix 1 looked at the trends in net migration over the two periods in the main places of five conveniently identified sub-regions of the province and arbitrarily named north, west, central, east and south. Map 1 shows the mean net migration rates for the periods and that were experienced by the main places in Gauteng. It seems that most main places with the highest mean net migration rates (of between 18 and 43 per cent of the 2001 population) are found in a north-south band (of approximately one-third of Gauteng s east-west width) that runs through the centre of the province. 4 However, problems continue to be encountered with erroneous reporting of census data by respondents, which can take place on a considerable scale and which generates compounding distortions when the data is worked with or projected. Poor overall reporting and general confusion and misinterpretation is a constant risk with census data, and has in some cases led to specific areas recording outmigration rates over 100 percent for the five-year intercensal interval. However, rates of erroneous response high enough to cause concern may be due less to the conduct of the census itself, and more to the fears of exclusion or expulsion often felt by respondents who have migrated to any area where resources are contested. This holds particularly for respondents who have moved to the metro cities, often understood to be hostile to outside in-migrants (see for instance Cross 2005 in press). Partly as a result, metro out-migration to other areas seems to be more readily reported than metro in-migration, risking overstatement of out-migration rates on a large scale. In that out-migration rates are key to calculating net migration, the need to project 2001 Census data down to sub-place level results in risk of distortions occurring due to the complexity of statistical manipulation of weak or fuzzy data. 9

16 Map 1 Mean net migration rates for the periods and : Gauteng 'main places 10

17 2.2 Poverty data and mapping The data needed for populating the ten poverty indicators and the overall poverty used in this study were first created at the so-called main place level (which happens to be the level at which the migration data have been made available by Statistics South Africa). The poverty-indicator data were then created at the so-called sub-place level (see Statistics South Africa 2004). Appendix 2 gives details of the poverty analysis at the sub-place level. Ten poverty indicators, identified by the Provincial Poverty Alleviation Committee (see Strategy & Tactics 2003), were used to construct the poverty reported there for Gauteng sub places : (a) dwelling type (proportion of households in dwellings classified informal or traditional); (b) electricity (proportion of households that do not have electricity for lighting purposes); (c) female-headed households (proportion of households headed by women); (d) household income (proportion of households with an annual income of R9 600 or less); (e) illiteracy (proportion of population (15+) who have not completed Std 5/Grade 7); (f) refuse removal (proportion of households whose refuse is not removed by local authority); (g) sanitation (proportion of households that do not have a flush or chemical toilet); (h) unemployment rate (proportion of the economically available population, i.e. all persons aged years, that is unemployed); (i) crowding (proportion of households sharing a room with at least one other household); (j) water (proportion of households that have no tap water inside dwelling or on site). The poverty used here is the average over all 10 these poverty indicators, which were based on Census 2001 data provided by Statistics South Africa. The two poverty indicators identified in Strategy & Tactics (2003: par 38) that were not covered by the reduced poverty are: (a) malnutrition (proportion of children under the age of 5 years visiting provincial or local authority clinics who are severely malnourished), and (b) social security (proportion of population receiving a social security grant). Their exclusion is due to the fact that appropriate and reliable data simply cannot be analysed at the sub-place level. The poverty- values for Gauteng sub-places are shown in Map 2. The subplaces with the highest poverty indices (51% and higher) are found mainly at the peripheries of the three metropolitan areas of the province (Ekurhuleni, Johannesburg and Tshwane). 11

18 Map 2 Poverty- values for Gauteng 'sub-places in

19 2.3 Combining the poverty and migration data for mapping purposes The lowest spatial level at which migration data were made available from Census 2001 is the so-called main place. In the Gauteng poverty and migration study a need existed to disaggregate the main place migration data to the so-called sub places in an attempt to identify and study poverty and migration pockets. The first step was to do a stepwise regression analysis of migration for Gauteng main places based on a number of socio-economic variables that were available at both the main place and the sub place levels. Various alternatives were considered but found not appropriate. The results of the analyses for the best alternatives are shown in Appendix 3 (for in-migration volumes) and in Appendix 4 (for out-migration volumes). From Appendix 3 it is clear that the model covering only the 2001 population ( POP2001 ) explained more than 80 per cent of the variation in the number of inmigrants during the period ( IN96_01 ). Adding the unemployment rate ( UNEMPL ) added a mere 1,3 per cent in predictive power. No other variable met the entry requirements. 5 A similar pattern emerges when one looks at the findings from the regression analysis of the out-migration volumes reported in Appendix 4. Again only population size ( POP2001 ) and unemployment rate ( UNEMPL ) qualified to enter the model. Again population size on its own explained the greatest part of the variation (more than 90%) in out-migration ( OUT96_01 ) and, as should have been expected, even more so than for in-migration. Again the addition of unemployment added only 1,3 per cent to the predictive power of the model. Based on this evidence it was decided to use only population size as the criterion for allocating the number of in-migrants and out-migrants for every main place to its constituent sub places. This was done by multiplying the fraction of the main place population in the particular sub place by the number of in-migrants/outmigrants in the main place. The number of net migrants was calculated for each sub place by subtracting the calculated number of out-migrants from the calculated number of in-migrants. The disadvantage of this approach is that the in-migration and out-migration rates are constant for all sub places within a particular main place. The advantage of this method is that the in-migration and out-migration volumes for all sub places remain consistent with the corresponding migration volumes for their specific main places. These allocations appear in Appendix 5. 5 The other variables considered for inclusion were: (1) persons aged 15+ years with education level of lower than Grade 7 ( EDUCAT ), (2) informal of traditional dwellings ( INFORMAL ), (3) no electricity for lighting ( ELECTR ), (4) no tap water on site ( WATER ), (5) refuse not removed by local authority ( REFUSE ), (6) no flush/chemical toilet ( SANITAT ), (7) annual household income R9 600 or less ( INCOME ), (8) female-headed households ( FEM_HHDS ), and (9) overall poverty ( INDEX ). All these variables were expressed as percentages of the total population (or the total number of households, depending on the context) in the main place concerned. 13

20 Map 3 Superimposed (overlaid) poverty levels and migration rates for Gauteng 'sub-places 14

21 In Map 3 the combined poverty and migration for individual Gauteng subplaces is illustrated. The highest values (which are the product of the two percentages giving the mean net migration rate and the overall poverty level, and can therefore be divided by 100 to obtain values that are easier to interpret) of between 801 (8,01%) and 2822 (28,22%) are found mainly in the northern parts of Tshwane, the eastern parts of Ekurhuleni, and small pockets in the western parts of Lesedi and Johannesburg. 3 FINDINGS: MIGRATION AND POVERTY POCKETS Empirical and analytical results of the study are presented in this section, which covers the spatial distribution of the major concentrations of poverty and migration activity, the scale and character of intended migration now directed toward Gauteng, the use of the combined poverty to identify poverty pockets, and the perceptions and opinions of stakeholders in the administrative departments and in the communities themselves. To guide HSRC s inquiry based on local knowledge, an of poverty has been constructed based on the work of the Gauteng Provincial Poverty Alleviation Committee, reported by Strategy & Tactics, 2003 (see above, 2.3 Methods, for the initial and current lists). As noted, the S&T uses 12 indicators, while HSRC s started with 9 of the 12, and now includes 10. There is no usable data for malnutrition and social grants at the sub-place level. 3.1 Poverty pockets: the spatial distribution of migration and poverty in Gauteng The first question is, where? The GIS maps based on 2001 Census data reflect the overall distribution of migration (Map 1) and of poverty (Map 2), and also show how the combined poverty and migration developed by HSRC from the Strategy & Tactics indicators is distributed spatially for Gauteng (Map 3). The maps for the study make it clear that areas of high poverty and high migration overlap in space, but they do not overlay each other perfectly, so that it can be seen that the two are not identical. What is clear in the distribution is the attraction of in-migration to localities on the borders of existing urban areas that offer income opportunities and a services grid, but which are still relatively thinly settled and not strongly defended by formal urban interests. When in-migration takes place under poverty conditions, areas of this kind will be strategically significant Migration Areas of high migration (Map 1) are both larger and more spatially clustered than areas of high poverty (Map 2). Large areas with relatively heavy rates of net migration are spread around the north of Johannesburg and Diepsloot, in a broad half-circle extending around to the west and north of Tshwane. Major areas of high migration therefore lie in between Johannesburg and Tshwane, in an area of active formal infill as the two metros grow toward each other into a single conurbation. In these municipalities the areas acting as migration destinations are often interspersed with higher-income suburban settlement. To the east, west and south of 15

22 Johannesburg there are only smaller areas of high net migration, though all the east and west semi-rural districts recorded moderately high rates of net inflow. Relatively small patches of high migration activity were found in Emfuleni, Westonaria, Merafong City and Randfontein. There were somewhat larger concentrations southeast of Ekurhuleni in the vicinity of Katlehong and Lenasia, as well as in the Benoni/Daveyton area. For Tshwane/Pretoria, as of 2001 very high in-migration prevailed on all sides except the east, and was particularly extensive in the tracts of land stretching from North West in toward Pretoria via Soshanguve and Ga-Rankuwa. On the east, strong inmigration as reflected in the 2001 Census was limited to relatively small areas around Mamelodi, and further eastward in Nokeng tsa Taemane Poverty Across Gauteng, the current distribution of poverty does appear to show up as pockets, while in-migration can be seen taking place across wider swathes of territory. However, all the undeveloped relatively open areas around both City of Johannesburg and City of Tshwane record fairly high poverty levels, though the population of these more rural areas is often relatively thin except where local concentrations are developing. Identifiable areas of poverty (see Map 1) are found distributed mainly around the outer edges of the Johannesburg conurbation itself, with small concentrations all around the boundaries of Randfontein and Westonaria with Johannesburg City. Significant areas with more than 50 percent of the population rated poor in terms of 2001 Census data are also located to the north in the vicinity of Tweefontein and Lanseria Airport, to the east around Daveyton and Greater Benoni, and on the southern metro margins beyond Vosloorus. While there are also poor areas on Gauteng s southern boundary at the northern edges of Vereeniging and Vanderbijlpark, there are few conspicuously poor settlements located northwards around City of Tshwane itself: Map 1 records only one area of significant size, north of Soshanguve on the border of North West Province. At the same time, with the entire Gauteng hinterland recording from 16 to 50 percent poverty, there are also numbers of small to very small poor settlements around both Johannesburg and Pretoria/ Tshwane that are not visible on the maps Combined poverty and migration The interrelation of poverty and migration can partly be seen on Map 3, showing how the combined identifies areas where high migration activity coincides with high rates of poverty. While not all high-migration areas fall into this category, the map shows that a large share belongs in this grouping. Reasons for this relationship are discussed below, in sections 3.2 and 4. Concentrations of settlement with both migration activity and serious poverty levels are located particularly on the east of Johannesburg and Ekurhuleni, in the Daveyton/ Benoni area. These settlements are close to the N 12 and N 17 highways, which connect eastern Gauteng to reservoirs of poverty around farming districts and former homelands in Mpumalanga and Sekhukhune. To the north, poor areas of high in- 16

23 migration occur particularly around the secondary routes linking the heavily settled eastern districts of North West Province with northern Tshwane through Ga-Rankuwa and Mabopane. Both clusters are close to recognized migration routes, which connect impoverished rural source populations with de facto ports of entry into Gauteng. The major areas of migration north of Johannesburg and south of Tshwane appear to accommodate settlement coming predominantly from or through the urban townships. By comparison with the entry-port settlements on the east and north of Gauteng, those of significant size between Johannesburg and Tshwane often reflected high migration, but did not record comparable high levels of poverty in It is largely but not completely in these entry-port areas that the analysis will focus, since many of the settlements which can be classed as poverty pockets occur as very small areas with a population of less than 500 which can become established on an almost invisible basis. This is particularly true in the inner city of Johannesburg. In addition to the larger and more visible settlements, these very small concentrations of poverty and high migration may also be found in little-noticed localities distributed throughout the Gauteng metro sector. 3.2 Potential migration into Gauteng: migrant intentions Migration results from a complex interplay of demographic, economic and social processes operating at the level of the individual, family, community, local area (municipality or district), province, country, region, continent, and the world at large. It is appropriate, therefore, to look at these processes from a global, regional, national and provincial perspective as a context for the identification of research needs before offering suggestions in this regard Global, regional, national and provincial context Migration should be viewed not only from the perspective of the migrant/non-migrant concerned and the area/country from or to which migration takes place, but also from a larger, global perspective. The global context indicates the past, present and possible future trends in economic conditions and spatial mobility patterns across the world, and provides a useful backdrop against which local migration processes should be evaluated. The regional context is equally important and for the same reasons. For example, something happening in one part of southern Africa has effects, albeit not always predictable, on the rest of the region. However, the key trends in migration to and from Gauteng will be influenced more by national than global factors, suggesting that these need to be given special attention in any provincial analysis of migration. It is commonly accepted that Gauteng is a microcosm of South Africa, and has also been described as South Africa s magnifying glass (Schlemmer 1998). As such, the province largely reflects what is happening in the rest of the country, but being the heart of the South African (and even the southern African) economy, events taking place in Gauteng more often than not will affect what happens elsewhere in the country. 6 The reader may want to also look at the study by Kok and Aliber (2005) that deals extensively with migration from the three most rural or isolated provinces in South Africa (namely Eastern Cape, Northern Cape and Limpopo) to the country s nine major cities, three of which (Ekurhuleni, Johannesburg and Tshwane) are in Gauteng. 17

24 It is useful to express economic, demographic and social processes in terms of indicators with a view to determining where a particular population or area is located within the broader spatial context and time. The appendix provides key indicators of Gauteng s position relative to other spatial entities. In African terms South Africa and Gauteng are well endowed economically. South Africa has by far the largest economy in Africa, and only one other country on the African continent, Egypt, has a larger economy than Gauteng (SakeBeeld, 26 May p. 1). Gauteng, which accommodates three of South Africa s six officially defined metropolitan areas, namely Johannesburg, Tshwane and Ekurhuleni, is the most urbanised (about 96%) of all the South African provinces. Gauteng has by far the largest economy of all South African provinces, with a gross domestic product per region (GDPR) of more than R200 billion (at constant 1995 prices) in the period since 1999, which grew by more than 3 per cent during the period , outperforming the rest of South Africa (excluding Limpopo). Gauteng s official unemployment rate of 27% in 2001 was slightly lower than that of South Africa as a whole, and notably lower than that of Limpopo (36%), KwaZulu- Natal (33%) and the Eastern Cape (31%). Unemployment levels in South Africa and Gauteng increased only marginally between 2001 and From an economic perspective the province is therefore an attractive South African migration destination. Gauteng is also an attractive migration destination for other reasons. The province has by far the greatest concentration of persons aged 20 and older with a Grade 10 or higher educational qualification. More than 92 per cent of Gauteng s population has access to piped water on site, 99 per cent of its residents have access to improved sanitation facilities, and the refuse of 89 per cent of its population is removed by a local government. Almost 89 per cent of its households use electricity for lighting, more than 82 per cent for cooking, and 78 per cent for heating. More than 60% of Gauteng households have a telephone in the dwelling or regular use of a cellular phone. All these compare favourably with the situation in other provinces. The population of Gauteng, which was estimated at 9,4 million in 2003 (almost of which were born in countries abroad), currently grows by approximately 4 per cent per year. A notable proportion of this growth can be ascribed to net inmigration into the province, being the most attractive migration destination in South Africa (see below) Migration context Census analyses Gauteng is clearly the preferred migration destination in South Africa, followed distantly by the Western Cape. Of the 1,1 million persons who migrated from one province to another during the period , more than two-fifths (42%) migrated to Gauteng, as compared to the less than one-sixth (16%) who migrated to the Western Cape (Kok et al. 2003:37 38). During the period these trends continued, with Gauteng attracting 39% of all 1,9 million inter-provincial migrants in South Africa, followed distantly by the Western Cape (15,5%). 18

25 Table gives the levels of in-migration to Gauteng from the other eight South African provinces for two successive five-year periods (1 January 1992 to 10 October 1996 and 11 October 1996 to 10 October 2001). In both periods (each representing a migration interval of approximately five years) a quarter of all in-migrants to Gauteng came from Limpopo, and about one-eighth (13%) from the Eastern Cape and Mpumalanga. The second-largest proportion of inmigrants over the two periods came from North West (21% in and 16% in ), followed closely by KwaZulu-Natal (17% and 19% respectively). TABLE IN-MIGRATION TO GAUTENG FROM OTHER PROVINCES ( AND ) Province of origin Number * ** Proportion of all in-migrants Number Proportion of all in-migrants Western Cape % % Eastern Cape % % Northern Cape % % Free State % % KwaZulu-Natal % % North West % % Mpumalanga % % Limpopo % % Total % % * Source: Kok et al. (2003:37), based on the Migration Community Profile for Census 96, provided by Statistics SA ** Source: South Africa (2004) The out-migration from Gauteng to other provinces over two successive periods ( and ) is given in Table Whereas North West (21%) and Mpumalanga (20%) were the two most popular destinations for migrants from Gauteng during the period , the Western Cape (20%) and North West (18%) were the preferred destinations during the next five-year period ( ). Table gives Gauteng s net migration (in-migration minus out-migration) figures for the periods and Gauteng experienced a net loss of migrants to the Western Cape over both periods, but net gains from all other provinces. The net gain in terms of migration from Limpopo (38% and 33% of the total net migration for the two periods respectively) is particularly noteworthy. 19

26 TABLE OUT-MIGRATION FROM GAUTENG TO OTHER PROVINCES ( AND ) Province of destination * ** Number Proportion of all out-migrants Number Proportion of all out-migrants Western Cape % % Eastern Cape % % Northern Cape % % Free State % % KwaZulu-Natal % % North West % % Mpumalanga % % Limpopo % % Total % % * Source: Kok et al. (2003:37), based on the Migration Community Profile for Census 96, provided by Statistics SA ** Source: South Africa (2004) TABLE NET MIGRATION INTO GAUTENG FROM AND TO OTHER PROVINCES ( AND ) Province of origin and destination * ** Net migration (number) Proportion of total net migration Net migration (number) Proportion of total net migration Western Cape [-]9% [-]7% Eastern Cape % % Northern Cape % % Free State % % KwaZulu-Natal % % North West % % Mpumalanga % % Limpopo % % Total % % * Source: Calculated from Kok et al. (2003:37), based on the Migration Community Profile for Census 96, provided by Statistics SA ** Source: Calculated from South Africa (2004) As indicated earlier, net in-migration is expected to be an important factor in the overall growth of the population of Gauteng. The total net migration figures in the bottom row of Table 3 indicate the extent to which the growth in the population of Gauteng between 1992 and 2001 can be ascribed to migration. In 1996 Gauteng accommodated a total population of , which increased to in 2001, 20

27 giving a difference of to which net migration probably contributed (26,5%). While natural increase still contributes more than 70 per cent of the provincial population growth, migration is expected to continue to play an important role. An analysis of the 10 per cent sample of Census 2001 that was made available by Statistics South Africa shows that migrants who moved to Gauteng from outside the province during the intercensal period (11 October 1996 to 10 October 2001) showed certain characteristics: (a) in the age bracket years (70%) (b) employed (45%) or students (13%), giving a total of 58 per cent, and (c) employed in the sectors manufacturing (11%), trade (15%) or financial, insurance, real estate and business services (17%) or involved in other service activities (10%), giving a total of 53 per cent. As far as in-migrants possible contribution to poverty is concerned, in 2001 only a minority of Gauteng in-migrants: (a) lived in informal or traditional dwellings (31% as compared to the proportion for the entire 2001 Gauteng population of 25%) (b) lived in severely overcrowded circumstances with more than one household sharing a room (21% as compared to the 15% of all Gauteng households) (c) was illiterate, i.e. not having completed Grade 7 (21% as compared to 30%) (d) was unemployed (27% as compared to 26%) (e) lived in households with a total annual income of R9 600 or less (31% as compared to 35%) (f) lived in households with no access to electricity for lighting (25% as compared to 19%) (g) lived in households with no piped water on site (21% as compared to 16%) (h) lived in households with no flush or chemical toilet (22% as compared to 17%) (i) lived in households whose refuse was not removed by the local authority (17% as compared to 14%) (j) lived in households headed by females (37% as compared to 35%). These figures illustrate the point that Gauteng migrants are a relatively advantaged population compared to how they are often imagined. Most were literate and employed, had access to basic services, and lived in relatively adequate housing in married households. In some cases, recent in-migrants seem to have been worse off than the total Gauteng population: this appeared to be true in relation to dwelling type, crowding, unemployment, energy source, water, sanitation, refuse removal, and gender of the household head. In other cases they seem to have been better off, as in relation to literacy and household income. Although it is not entirely clear from the evidence presented here that in-migration into Gauteng did in fact contribute significantly to poverty in the province during the period , most of the evidence presented here seems to indicate such a potentially negative contribution in spite of their relatively high levels of education and other measures of capacity. In other parts of the report this issue is dealt with in more detail. 21

28 Findings by DPRU and SAMP The recent DPRU/SAMP report (Oosthuizen, Peberdy et al. 2004) for the Gauteng Premier s Office appropriately refers to the fact that in-migrants (persons born outside Gauteng) tend to respond to a considerable economic incentive to leave their home provinces. While Gauteng has been successful in attracting many highly educated persons from other provinces, the report shows that in-migrants tend to be employed in less skills-intensive sectors notably women migrants in domestic employment. This observation is confirmed by the generally lower incomes of in-migrants. In that sense in-migration may contribute to a lowering of income levels in the province. In the literature review of Section 1 the DPRU/SAMP report is discussed in some detail. Findings from the HSRC Migration Survey The HSRC Migration Survey covered a national sample of households/respondents. The survey questionnaire covered a wide range of migrationrelated issues, with some emphasis on migration intentions. In this section we restrict ourselves to intentions to migrate permanently over the five years following the survey (i.e. 2001/ /07). The question that was asked in the survey in this regard was: Do you plan to move away from this area 7 to settle permanently in another area in South Africa or in another country in the next 12 months / 5 years? Appendix 6 contains a detailed literature review and an analysis of the factors affecting migration intentions, with the latter referring specifically to the intentions to migrate to Gauteng among persons living in other provinces of South Africa at the time of the survey. What follows here is a profile of those people in the survey who intended to migrate to Gauteng during the period 2001/ /07. More than a third (36%) of all respondents (aged years) living outside Gauteng indicated that they planned to move to Gauteng during this five-year period. Converted into numbers, this proportion translates into 7,2 million potential in-migrants 8 from other provinces during the period 2001/ /07. Of course not all these potential migrants would actually migrate to Gauteng in this period, but such a large number of possible in-migrants is probably quite staggering from a provincial perspective. The migration-intention data from this survey show that adult persons living in South Africa but outside Gauteng who intended to migrate to Gauteng during the five years following the survey (i.e. 2001/ /07) were: (a) significantly 9 younger 10 than (i) the total sample, (ii) those preferring Gauteng as a possible destination but not necessarily planning to move there, and (iii) the Gauteng residents in the sample; 7 This area was defined as this city (if in a city) / this town (if in a town) / the rural part of this district (if in a rural area). 8 Based on the 2001 population outside Gauteng in the age category years. 9 Statistically significant at the 5% level, based on the correct standard errors for such a complex sample. 10 Note: only persons years old were covered in the survey. 22

29 (b) significantly 3 better educated than (i) the total HSRC sample as well as (ii) those preferring Gauteng as a possible destination but not necessarily planning to move there; (c) significantly 11 more likely to have access to a migrant network at the possible destination than (i) the total HSRC sample as well as (ii) those preferring Gauteng as a possible destination but not necessarily planning to move there, (d) significantly better informed about the situation in their possible destination than the total HSRC sample. From a poverty perspective it may be important to note that those respondents in the survey who planned to move to Gauteng during the period 2001/ /07 were not significantly different from the Gauteng residents in the sample. This generalization held true as far as (i) education, (ii) employment rate and (iii) current personal income were concerned. These persons, should they actually migrate to Gauteng as planned, are therefore not likely to have a significant impact on general poverty levels in the province. In fact, being significantly younger adults than the resident population, they may contribute to a lowering of the dependency on social grants. Of course, being predominantly young adults, they are likely to have children and this may increase the burden on some of the education and health-care facilities and services in the province. As mentioned before, in-migration to Gauteng increased from to (from to over five years). At the same time out-migration from Gauteng also increased, but less so (from to ). The effect of the inand out-migration streams was a net increase from to (i.e. a difference of more than 50 per cent). From the survey data presented here, it seems that the in-migration volume may increase even further between 2001/02 and 2006/07. This will inevitably put a strain on infrastructure, housing and service delivery. However, it should be remembered that, of all the provinces in SA, Gauteng is economically best equipped to deal with a large influx of migrants. This will require proactive planning, creative thinking, and a political as well as bureaucratic commitment Discussion The graphical chain modelling presented in Appendix 6 has confirmed beyond doubt that the migration intentions among the South African population are explained significantly by a structural framework that is based on the migration literature. It is a pity that the study could not include an analysis of the extent to and circumstances under which these intentions are converted into actual migration behaviour and what role is played by largely unanticipated constraints and facilitators. The data required for that purpose can only be obtained from a longitudinal study, for which the HSRC study was designed from the outset. Nevertheless, it is clear that migration intentions are determined to a significant extent by goals/values and expectations (as a primary set of predictors), network roles and 11 Statistically significant at the 5% level, based on the correct standard errors for such a complex sample. 23

30 information, 12 the spatial context and selectivity factors. Some of the detailed conclusions flowing from the analysis are listed below. 1) It has been shown in Appendix 6 that people intend to migrate (a) when their expectations for the current area become lower than those in respect of an alternative place of residence (b) which are often influenced by the information received about the alternative place of abode from relatives and friends living there (c) if they have reason to believe that these networks at the possible destination will provide assistance and support during and after the move (d) when they become sufficiently dissatisfied with their lives in the current area of residence. Although most people do not necessarily prefer to move to Gauteng, they frequently end up here because of the factors described above. High poverty levels in the local government area where people reside are an inhibiting factor in the decision to move away permanently, indicating that a significant proportion of people in very poor areas may be trapped there. At the same time, it appears that people with a higher score on the scale for risk-taking ability are more likely to plan a migratory move than their more risk-averse counterparts. In addition, younger, unmarried adults (especially in the age category years as we saw earlier) will be more inclined to migrate than their older, married counterparts. Likewise, persons who have migrated before are more likely to consider migrating again. Other factors associated with an intention to migrate are: a higher educational attainment, being a black African person, and not currently living in Gauteng. 2) It can also be concluded that people s expectations for an alternative place of residence, compared to their expectations for the current area are higher, on the one hand, if: (a) if they are part of a larger household (b) they are still relatively young (c) have a high efficacy level (d) have access to more information about the possible place of residence, (e) have a higher occupational status. On the other hand, people tend to have higher expectations for their current place of residence if: (a) they have a lower ability to take risks (b) they are satisfied with their lives at present (c) have a higher personal income (d) live in an urban area (e) are females (f) live in poorer areas (g) prefer Gauteng as a possible destination. 12 The role of family influences could, unfortunately, not be empirically confirmed here, but based on other studies (e.g. De Jong 2000) the model should hold for this subset of factors as well. 24

31 3) Preference for Gauteng as a possible destination is associated with (a) a higher occupational status (b) not being employed (c) having a higher educational attainment (d) being a black African person (e) possibly not currently living in Gauteng. It therefore appears that individuals who are likely to want to migrate to Gauteng may well be relatively young and relatively self-confident and assertive, and at the same time relatively well-informed; they are also likely to pursue a comparatively highstatus occupation. These characteristics apply to migrants generally, but are likely to be stronger in people who want to move to Gauteng. By contrast, those who may well prefer not to migrate at all are often urban women with comparatively satisfactory incomes who are living in poorer areas, are of a cautious disposition, and would prefer to be in Gauteng if in fact they do move. Lastly, the prospective migrants who would specifically choose Gauteng as a destination can be described in aggregate as unemployed but well qualified for the job market, well educated, black African, and not at the moment living in Gauteng. 3.3 Relating poverty and migration: a rank-ordering approach to identifying poverty pockets This section addresses identifying poverty pockets, and looks first at how migration and poverty are related in Gauteng as a whole, considering first main place level (3.3.1 below), and then using a constructed dataset to explore relations at sub-place level (3.3.2) A basic poverty and migration profile of Gauteng main places To explore the relation between poverty and migration in the province 13 a rough of poverty was created and combined with the proportion of migrants recorded in each of the 149 main places in Gauteng. This exercise identifies priority areas for migration and poverty, and lays the groundwork for the subsequent analysis of poverty pockets carried out at sub-place level. The shortened or reduced version of the poverty given in this section is the average of nine of the 12 poverty indicators identified by the Provincial Poverty Alleviation Committee (as reported in Strategy & Tactics 2003; for precise definitions of these indicators, see Appendix 2). This adjustment has been necessary because appropriate and reliable data for the other three indicators could not be found at the level of Census main places. The nine poverty indicators which were used at this stage of the analysis included: o Dwelling type o Electricity access o Female-headed households o Household income 13 Ideally speaking, such pockets should be smaller spatial units than main places, but since the migration data have not been provided at lower levels it was decided to restrict this initial analysis to main places. 25

32 o Illiteracy o Refuse removal o Sanitation o Unemployment rate o Water access The three indicators which could not be included in our shortened poverty due to lack of usable data include crowding, malnutrition, and access to social grants, the last of which is described by Strategy and Tactics (2003) as social security. Later in the analysis, data was secured which enabled crowding to be added to the list of indicators used in 3.3.3, below. There is still no government data available in relation to malnutrition or for access to social grants, either for main places or for sub-places. Table lists the 20 most problematic main places in order of importance in terms of high levels of poverty in 2001 and in-migration during the period The entire priority list for all 149 main places is provided in Appendix 1. TABLE TWENTY POVERTY AND MIGRATION PRIORITY AREAS ( MAIN PLACES ) IN GAUTENG (2001) Order Main Place Poverty % Migranttion Poverty & Migra- Index 1 Kekana Gardens (Nokeng tsa Taemane) 67% 99% 83% 2 Temba (City of Tshwane) 72% 94% 83% 3 Nooitgedacht (City of Johannesburg) 68% 94% 81% 4 Pipeline (City of Johannesburg) 70% 60% 65% 5 Tshepisong (City of Johannesburg) 36% 93% 64% 6 Lindelani Village (Ekurhuleni Metro) 60% 67% 63% 7 Vlakfontein (City of Johannesburg) 67% 52% 59% 8 Ekurhuleni Metro (Rural) 55% 53% 54% 9 Slovoville (City of Johannesburg) 23% 84% 53% 10 Bapsfontein (Ekurhuleni Metro) 73% 32% 53% 11 Ekandustria (Kungwini) 24% 80% 52% 12 Nellmapius (City of Tshwane) 25% 79% 52% 13 Zenzele (Randfontein) 76% 27% 51% 14 Cerutiville (Ekurhuleni Metro) 77% 23% 50% 15 Orient Hills (Mogale City) 75% 23% 49% 16 Wheeler's Farm (City of Johannesburg) 71% 26% 48% 17 Sweetwaters (City of Johannesburg) 81% 15% 48% 18 Olievenhoutbos (City of Tshwane) 48% 46% 47% 19 City of Tshwane (Rural) 47% 47% 47% 20 Ebony Park (City of Johannesburg) 17% 75% 46% Eight of these 20 priority areas (main places) are located in the City of Johannesburg, four are in the City of Tshwane, four in Ekurhuleni, and one each in Kungwini, Mogale City, Nokeng tsa Taemane and Randfontein. Some of these places have only small populations, with Ekandustria (population of only 20 in 2001) being the smallest. The next smallest priority area, with a population of 357 is Pipeline, followed by Bapsfontein (935) and Nooitgedacht (951). The remaining 16 priority places have populations in excess of 1 000, with City of Tshwane Metro (Rural) the largest (with a 26

33 population of ), followed by Tshepisong (population: ) and Nellmapius (23973) Gauteng s overall trend for poverty and migration Although the priority areas listed in Table 1 show clearly that very high levels of migration or mobility by no means always go along with very high levels of poverty; however, across all the 149 main places in Gauteng, the trend is strong and linear poverty is linked to migration. Table summarizes the trend of the place-level data on poverty and migration given in Appendix 1. It shows the overall relation between Census-designated main places which record poverty levels higher than 35 percent arbitrarily taken as a significant level of poverty and places which show migration levels of more than 20 percent, which can be taken as reflecting significant migration over the period It can be seen that the relation of poverty to migration is both very strong and also nearly linear when considered for all of Gauteng: that is, in Gauteng province higher migration levels are closely linked on average to higher levels of poverty, and on average the poorest areas tend to be those with higher levels of migration. TABLE RELATION OF POVERTY AND MIGRATION FOR ALL 149 MAIN PLACES IN GAUTENG, 2001 Poverty & Migration Ranking % Main places recording more than 35 % poor in 2001 % Main places recording more than 20 % migration in ( Priority areas ) It follows that the approach taken by the GIDU as the sponsors of the present study in commissioning research work to investigate the relation of poverty and migration in poverty pockets at a micro-local level would appear to be on the right track and well justified. Likewise, it appears to be the case that the selection of the top-ranking 20 areas on a combined poverty and migration ranking would be an appropriate approach, and a sound method of identifying localities on which to concentrate. However, it is also clear that this priority grouping is not internally consistent (Table 3.3.3). 27

34 TABLE HIGH AND LOW MIGRATION PLACES IN RELATION TO POVERTY WITHIN THE PRIORITY GROUPING, GAUTENG 2001 High poverty % Moderate poverty % Low poverty 0-35 % High migration % Moderate migration % Low migration 0-20 % Table illustrates the internal divergence of the priority category of main places. Poverty is high to relatively moderate for three quarters of the places in this priority category, and migration is high or moderate for all but one. However, there are five main places in this priority category where migration is high but poverty is relatively low, under 35 percent of the population according to the 2001 Census. These observations underline the importance of unpacking, so far as possible using currently available data, more precisely what the profiles of these areas look like in terms of the factors which affect both poverty and migration. By doing this it will be possible to assess the internal and external dynamics which create and maintain poverty pockets, and to start to understand the relation of poverty and migration in enough depth so that the policy and delivery processes can begin to deal effectively with these concentrations of greatest need Defining poverty targets at sub-place level The second part of the study has attempted to drill down closer to the local level. Gauteng includes 2022 named sub-places the smallest spatial unit for which data can be developed. Poverty pockets are often very small areas, and identifying them means adjusting the focus of the inquiry down to local level. Reaching sub-place level has required manipulating the Census data as it was released to develop a constructed dataset by projecting existing main-place data down to assign values within sub-place boundaries. Errors can be involved in the difficult and unsatisfactory mechanics of this process, and findings here in relation to provincial and local migration rates are therefore provisional and under revision. All sub-places have been ranked on four key indicators which address poverty and migration: 1. The composite ten-part poverty 2. Poverty-level household income (total income <R 800/ month) 3. Rate of in-migration over 5-year Census period 4. Net rate of migration (inflow outflow) Using the same rank-ordering approach employed above, the top 24 areas for each indicator have been selected to profile the specific characteristics for areas of high migration and areas of high poverty. 28

35 Table shows average values for the whole of Gauteng in respect of the most important indicators for this part of the study. It can be seen that on average, subplaces in this province have a population of about 4000, and that a relatively small share of the population is still living in shacks at 17 percent. Accordingly, the share of the population which is still without hard services water, electricity and sanitation appears to vary between 15 and 19 percent, depending on the area. The share of population in absolute poverty is represented by the 29 percent of households that record incomes of less than R 800 per month, a fairly high level for a province where the economy is strong and the formal unemployment rate is relatively low at 18 percent. Illiteracy, defined as education levels below Grade 7, is also at 29 percent, a serious problem in terms of access to a job market moving further toward knowledge work. TABLE GAUTENG PROFILE FOR KEY INDICATORS: ALL SUB-PLACES Poverty indicator Average for Gauteng sub-places Population Informal housing 17 % Crowding (+1 household in room) 3 % Women-headed households 32 % Household income < R 800/m 29 % Illiteracy/ education < Grade 7 29 % Strict unemployment rate 18 % In-migration rate 23 % Net migration rate 5 % Services (water, electricity, sanitation) % lacking % Composite poverty 19 % Source: Census 2001; migration data projected down to sub-place level The overall in-migration rate estimated from Census data is 23 percent, and the net migration rate at sub-place level is projected at 5 percent. Women-headed households represent a high share of all households at 32 percent, but relatively few families altogether are living with more than one household to a room: extreme crowding of this kind is a phenomenon of inner city living for the most part, and for the whole province it is characteristic of a small number of sub-places only. However, in these areas residential crowding can spike to high peaks, giving an average crowding value of 3 percent for Gauteng as a whole. Comparable figures for poverty at sub-place level are given in Table below. 29

36 TABLE CATEGORIZING SUB-PLACE RESULTS FOR POVERTY Poverty indicator Household income < R 800 Illiteracy % Unemployment rate Composite poverty Gauteng average High poverty group Low income group High inmigration group High net migration group Source: Census 2001; migration data projected down to sub-place level It can be seen that the groupings established by ranking the S&T composite poverty alongside sub-places with most households in absolute poverty reflects significant variation within the province. At 72 per cent, the high poverty- grouping has well over double the share of households in absolute poverty as the population at large. However, at the same time it is important to note that the grouping of sub-places ranked for absolute poverty itself is in fact the one which reflects the highest percentage of extreme income poverty at 84 percent. This is well above the income poverty level for the poverty grouping itself. In income terms, this second grouping represents the poorest of the poor, a point to which the analysis will return later. For migration, the sub-places with highest migration rates and the sub-places with the highest levels of in-migration over out-migration are significantly poorer than average. However, they are much less poor than either the grouping selected by ranking the poverty or the one selected for very low incomes alone. It is a truism of migration studies that the very poorest households are not usually able to migrate over significant distances, since they cannot accumulate the resources needed to cover the costs of a major move. The grouping of sub-places which shows the highest migration involvement also scores far below the poverty-defined groupings though still higher than average for illiteracy, unemployment, and the poverty itself. Results here begin to suggest that the poor are more mobile than the general Gauteng population, but it is not the very poorest areas which are most involved in migration activity. Instead, the relation between poverty and migration is one where the middle poor grouping is the most mobile, with the poorest more likely to remain trapped where they find themselves, and less able to use migration to improve their life chances. Further light is shed on this key relationship by Table 3.3.6, which shows how on average the sub-places with the most active migration at the time of the Census relate to the sub-places with the most indications of poverty. 30

37 TABLE CATEGORIZING SUB-PLACE RESULTS FOR MIGRATION Poverty indicator In-migration rate Net migration rate Gauteng average % High poverty group Low income group High inmigration group High net migration group Source: Census 2001; migration data projected down to sub-place level Migration rates for the severest poverty sub-places are perhaps as low as a third of the rates for the high-migration areas, and seem to be about average for Gauteng as a whole. In contrast, for the areas of high migration, in-migration is estimated at more than two and a half times as high as it is for the poverty-related groupings, and net migration is seven or eight times as high. Although as shown in above there is a general linear relation between poverty and migration at main place level, it appears to be doubtful that the very poorest areas as reflected on the poverty and the absolute poverty measures are in the grouping of poverty pockets that are most unstable in relation to population movement. The high migration areas appear to be very much more active, though it is interesting that the areas selected for lowest incomes appear to have developed a negative migration rate over the five-year period between the Censuses. Turning to the characteristics of the poorest sub-places in relation to those with highest migration rates, Table shows significant variation in sub-place area population. TABLE MAKING COMPARISONS: POPULATION Poverty indicator Sub-place population, 2001 (rounded) Gauteng average High poverty group Low income group High inmigration group High net migration group Women-headed households % Source: Census 2001; migration data projected down to sub-place level For the groupings selected on poverty measures, the projected population of subplaces is less than half the Gauteng average, underlining the small size of poverty pockets in general. There are also significant differences between the grouping of places chosen on the combined poverty, and the grouping selected on absolute poverty: the income poverty sub-places average only 1300 people, with the poverty sub-places appearing almost half again as large on average at 1900 inhabitants. These results 31

38 again suggest that the poverty grouping of poverty pockets and the absolute poverty grouping are not drawn from exactly the same population. The same is probably true of the high-migration sub-places, which appear to be larger than the Gauteng average. The high in-migration grouping is slightly larger than the mean provincial size at people, but the sub-places chosen for retaining migration seem to average as large as in population. If it can be confirmed, this pattern would seem to suggest that the middle poor in settlements of substantial size are the poverty category most involved in migration activity. This result is not surprising, in that it is the larger settlements which are more widely known, and which can attract migrants from a larger catchment area than the smaller and newer settlements. However, if smaller poverty pockets are actually more demographically stable than larger ones, it is a result worth noting. In this light it is perhaps disappointing that the important women-headed household indicator tells little in respect of poverty and migration as it is currently defined. Households run by women are generally known to be poorer and more subject to economic barriers than those based on marriage relations: however, the share of women s households in the different poverty and migration categories does not vary significantly in Table It is possible to speculate that women-headed households as a category no longer contains a homogeneous grouping of poor women with limited access to wage income, but now includes both educated and employed women households heads and unemployed single mothers whose situation is very precarious, as well as both relatively well-off widows and poor grandmothers supporting large families on pension income. Further work with the Census data is needed to show whether adding an age parameter to female-headed status would help to centre the category on younger single mothers with marginal incomes. TABLE MAKING COMPARISONS: HOUSING AND OCCUPANCY Poverty indicator Gauteng average High poverty group Low income group High inmigration group High net migration group Informal housing % Crowding: % families share one room Source: Census 2001; migration data projected down to sub-place level Rates of informal shack housing and occupancy rates are shown in Table It is clear immediately that the share of each category living in shacks differs drastically between the different poverty and migration groupings chosen on the indicators. Nearly all the sub-places selected by the poverty as it has been measured here are located in shack settlements, at 98 percent. It appears that the poverty, at the moment, only selects shack areas, and does not take in inner city rental housing or run-down areas in older townships. It seems to be significant that the sub-places 32

39 identified for absolute poverty are not nearly so tightly associated with shack housing. About two thirds of this very poorest category is located in shack areas, leaving 32 percent in other types of settlement. At the moment, it appears that a large share of Gauteng s poverty pockets may not be identified by the composite poverty as measured here. The high-migration categories were much less likely to be located in shack settlements. Only one third of the high in-migration sub-places were shack areas, as against 42 percent of the high net migration grouping. It may be likely a priori that rental housing of different kinds is subject to more rapid turnover than many of the more stable shack areas: if so, it is a point worth noting, as poor shack populations are often described as highly mobile and unstable for planning purposes. The crowding indicator provides much less information. Though it may be important that the shack areas representing the high poverty grouping reflect almost ten percent multiple family occupation of single rooms, there is generally little variation among the poverty and migration categories. Crowding as measured here runs perhaps one or two percentage points above the Gauteng average of 3 percent, as might be expected in poor and demographically unstable populations, but the variation is not large or obviously systematic, and crowding does not appear to be an effective indicator for either migration of poverty as it stands at the moment. The last major factor involved with identifying poverty pockets for Gauteng province relates to delivery of services, shown in Table below. TABLE MAKING COMPARISONS: SERVICES AND POVERTY INDEX Poverty indicator Water % Electricity % Sanitation % Refuse % Composite poverty Gauteng average High poverty group Low income group High inmigration group High net migration group Source: Census 2001; migration data projected down to sub-place level It is immediately clear that the pool of shack areas selected by the composite poverty almost uniformly lacks service delivery. Either 98 or 99 percent of this grouping has no services in the categories of water delivery, electricity, sanitation, or refuse removal. The average score on the composite poverty itself is 70, which can probably be taken as predicting shack housing and lack of hard services in the areas selected. The absolute poverty grouping is much less consistent in this respect. For the poorest areas in income terms, from 70 to 77 percent had no hard services, leaving about a 33

40 quarter which were recorded as having some kind of service delivery at the same time as high levels of poverty. The composite poverty for this absolute poverty group averaged only 56, suggesting that absolute poverty in income terms and the kind of poverty manifestations identified by the composite in its 10-item form are fairly far apart in relation to real communities. The sub-places in the high migration categories were significantly more likely to have services. The high in-migration grouping was lacking most services in from 34 to 38 percent of the sub-places identified, and the high net migration grouping in 41 to 45 percent of areas. The exception here was electricity, which was not supplied in 48 percent of the high in-migration areas and 59 percent of the high net-migration subplaces. It is evident that having access to services does not prevent migration, and the areas that were retaining high migration inflows were somewhat worse off for service delivery than those selected for high in-migration. The picture here is consistent with busy areas of moderate poverty and high residential turnover Identifying poverty pockets Using the composite poverty as it stands for this exercise, restricted to ten items measurable at a projected sub-place level to identify poor sub-places draws attention to the role of services and housing in living standards. At the same time, it also pushes the definition of poverty pockets very strongly toward the remaining informal shack areas which have almost no delivery. The sub-places selected as scoring highest on the poverty had 98 percent informal housing, and 98 percent did not have delivery of hard services, including water, electricity and sanitation. That is, the areas selected by the poverty show a relatively uniform profile as poor shack settlements. However, these areas were not as poor in terms of income as the sub-places chosen because they record the highest share of residents with incomes under R 800 per month. Nearly a third of the income poverty sub-places were not in the shacks and upwards of a third did have some delivery of services. It appears that there is an unknown share of poverty pockets that are not located in shack areas, and are probably in the inner cities, in rental housing, in backyards, or elsewhere in the older townships. These other poverty pockets are not yet being captured by the combined poverty. To define poverty pockets in a way that is generally acceptable to decision makers, it will be necessary to look more closely at the question of income poverty as against housing and service delivery as a standard measure: these points are taken up in sections 4 and 5 below. Further GIS analysis to geolocate all the poverty pockets identified and categorize them according to housing type would be desirable at this point. Beyond the specific question of the composite poverty, some wider observations about the relation between poverty and migration can be made here. First, the relationship of poverty to migration is not linear once we start to disaggregate levels and types of poverty. Further, the following apply here: o Very poor sub-places seem to have about average migration, or mobility o Middle poor areas have fairly high to very high rates of migration 34

41 o There seems to be high outflow from many very small and poor sub-places o Larger settlements seem to retain more migrants As is widely the case in migration studies, it looks as if sub-places of high poverty are likely to have low rates of migration, due to the relatively high costs and high risk involved in migration for the very poor. As incomes rise toward middle poverty, migration potential increases rapidly, and then may tail off as areas with adequate incomes come to provide a decent lifestyle. 3.4 Perceptions from the stakeholders: qualitative approaches to obtaining information from administrators and communities A qualitative research component was included in the project to help fill important gaps in the available existing information and to pave the way for possible empirical research in a later phase. However, given limitations on time and available resources, the scope and coverage of the qualitative research component were necessarily limited. The qualitative research component consisted of nine in-depth interviews with national, provincial and local government officials and key service providers in the province. Three focus group interviews, among members of the public in selected areas within the three metropolitan areas of Gauteng, were also conducted In-depth interviews It is not only jobs and wages that attract poor people to the cities: there are also other factors, such as availability of housing and social services that play an important role as attractors. At the same time, deterrents such as denied access, high costs and low availability of housing, services and other needs will hold back migration. Some of these factors which depend on government delivery and which have been identified through previous research include housing, employment, education, health care and hard services (including electricity and water). The interviews Interviews were conducted with officials in the three metropolitan areas, Tshwane, Johannesburg and Ekurhuleni. It was envisaged that officials in the planning (for services and housing) and health departments (for the provision of primary health care) would be interviewed. (See Appendix 7 for a list of the interviewees.) In some instances the researchers were referred to officials who were not directly involved in the planning or provision of services. This could have an impact on the information obtained during the interviews. It was also planned to interview officials from the Gauteng Provincial Departments of Health and Education regarding their perceptions on health and education related issues. Despite numerous attempts no interview could be secured with any official from the Gauteng Provincial Department of Education. This is perceived as a serious shortcoming/limitation in the research since no perceptions on education are included in the study. To obtain a national perspective on population and development and on the provision of welfare services to migrants and the poor, officials from the national Department of 35

42 Social Development were interviewed. In addition a spokesperson of the Salvation Army was interviewed on the perceptions on poverty related issues by an NGO operating in Gauteng. Stakeholder perceptions Issues raised during the discussions with the interviewees included their perceptions on migration and poverty and how these factors relate to the provision of urban services and infrastructure. Matters specifically dealt with include the extent to which migration and poverty are perceived as problems in the urban context, the nature and extent of these problems, perceptions of best practices in addressing the problems of urban migration and poverty, how easily available affordable housing and job opportunities are to the in-migrating poor and the settled population, how far service departments are able to provide for the needs of the arriving poor and through what mechanisms, and how effectively the needs of the poor have been dealt with. An information sheet explaining the aim and objectives of the study, the in-depth interview schedule and the consent form that had to be signed by participants (see Appendix) were presented to the Ethics Committee of the HSRC. The interviews were recorded on tape, transcribed and archived upon completion of the project. Perceptions of the relationship between migration and poverty in Gauteng It is clear from the series of interviews conducted in Gauteng that both migration and poverty are perceived as major challenges in the urban context. Both Johannesburg and Tshwane commissioned research in this regard (see Peberby, Crush & Msibi 2004; Umhlaba Development Services 2004; Office of the City Manager 2004 and Erasmus & Weir-Smith 2003). The importance of migration was highlighted by the Executive Mayor of the City of Johannesburg in his 2004 State of the City address: Johannesburg has become a magnet for people from other provinces, the African continent, and indeed, the four corners of the world. He also referred to the challenges posed by migration: While migrancy contributes to the rich tapestry of the cosmopolitan city, it also places a severe strain on employment levels, housing and public services (as quoted in Gotz and Landau 2004:13, 14). Some of the interviewees were of the opinion that the high rate of in-migration caused enormous problems for planning since service providers are not able to provide in the needs of all the newcomers to the cities. Poverty is seen as multi-faceted and as long as people do not have access to basic needs they are perceived as poor. In many instances the service providers did not budget for additional people and are thus not in a position to cater for the in-migrants needs in that specific year. This seems to have a snowball effect and the service providers are yearly faced by the same problem. As one interviewee explained, one cannot plan for migration; it is forever a problem and it is forever a factor that contributes to poverty. In some areas there is simply not enough land for new housing developments to cater for the cities inhabitants. An interviewee commented in this regard: The figures in terms of the number of people that are either homeless, looking for houses or on the 36

43 waiting list for houses is phenomenal. We don t know if we will ever catch up. Not with the land that we ve got available. Furthermore, in some areas planners planned to relocate people closer to job opportunities but instead those peripheral areas just kept on growing with no available land for the relocation of the people. Orange Farm, in the City of Johannesburg Metropolitan area, is a good example of the latter. The rapid urbanization also caused huge densities in some areas, especially with backyard shacks being erected, e.g. in Alexandra. However, housing and available land are an enormous problem for attempts to de-densify some of those areas. Diepsloot was initially developed to act as a transition area for people being relocated from Alexandra due to the very high densities in the area and the danger of living on the Jukskei River Bank, until other housing could be provided. However, in the past few years Diepsloot has established itself as a permanent residential area with a huge influx of people. According to some interviewees the metropolitan areas in Gauteng did not always have the necessary funding available to provide basic services, e.g. electricity and water, to the poor areas in Gauteng, especially due to the rapid increase in the number of poor areas and the high population growth of the poor areas themselves. Since no distinction can be made between poor internal migrants and poor city dwellers and the basic needs of people are the same, these have to be addressed in the same manner. Migration is also seen as an important factor to stimulate poverty and add to the already high unemployment rate of Gauteng. An interviewee commented that the high unemployment rate has a huge impact on the high crime levels, especially in Johannesburg. According to the interviewees the majority of the in-migrants to Gauteng were internal migrants from other parts of South Africa looking for a better future. However, there are also many migrants from the SADC countries and elsewhere in Africa living in Gauteng. One of the most contentious issues in the migration debate in South Africa is the number of foreign nationals living in the country and especially in Gauteng. The census cannot assure accurate statistics for people who, as a general rule, do not want their presence to be officially registered. Although some estimates have been made over the last decade, they tended to be more speculation than fact (South African Cities Network 2004:42). Some of the interviewees were of the opinion that migration is a global phenomenon and thus inevitable, especially in large cities, e.g. in Johannesburg, and that the planners have to learn to respond in the best possible way to the constant substantial population growth. Since Johannesburg is perceived as a wealthy city and a city of great opportunities people have since the earliest times migrated to the city. Both highly skilled and less skilled people have migrated to the city. Some of the inmigrants have therefore added to the growth of the city s poor population, and in addition, the demand for services. 37

44 The discovery of minerals on the Witwatersrand led to the establishment of a migrant labour system with workers coming from South and southern Africa to Gauteng. This created perceptions that many job opportunities were available in the area and according to some of the interviewees such perceptions still exist today. It was also mentioned that there has been a very unsophisticated understanding of the dynamics of migration which makes it even more difficult to plan appropriately. Although research has been done on areas with high population growth, the impact of HIV/AIDS on the population structure of Gauteng are also to be taken into account. For example, the household sizes of the City of Johannesburg have shrunk while the number of households has grown substantially. An interviewee commented in this regard: So there are those kind of dynamics which say something, but we don t at this stage have a sufficiently good understanding of what they are saying. And so, those are some of the migration dynamics that we haven t as yet, I think, come to terms with in the best possible way. An interviewee was of the opinion that some people might migrate to Gauteng because they have the perception that services are better in Gauteng than, for example, in the Mpumalanga and North West Provinces. Furthermore, it seems that people are of the opinion that there is a relationship between distance from the provincial capital and the quality of services that are provided. An interviewee from the Tshwane Metropolitan Council indicated that Tshwane is closer to the north than the other metropolitan areas in Gauteng. Consequently, Tshwane is the first stop for migrants from poorer provinces, e.g. Limpopo. That is also true for cross-border migrants from Zimbabwe and Mozambique. Gotz and Landau (2004:18) expressed the opinion that metropolitan municipalities should give attention to the presence of migrants in their areas and starting to formulate appropriate responses to migrants within social and economic development strategies, because there are good reasons, however, to expect that migration of all kinds will continue. Gauteng and in particular Johannesburg will continue to be a primary destination and host to both internal migrants and migrants from outside South Afica s borders. The 1996 census showed that 4,8% of the Gauteng population was not born in South Africa. In 2001 this had grown to 5,4%. Johannesburg alone saw an increase of 57% of residents with non-south African citizenship between the two censuses. According to Census 2001 Gauteng cities have seen a substantial rise in their population due to in-migration from other provinces between 1996 and More than one-ninth (11,3%) of Johannesburg s total population had migrated to the city in the last five years (Gotz & Landau 2004:18, 19). According to Gotz and Landau (2004:19) there is evidence that temporary labour migration patterns, established through the apartheid schemes of Bantustans and pass laws, continue to be an important feature of South African society. It was expected that circular migration would rapidly dissipate after the end of apartheid as people could permanently migrate to urban areas but this has not happened. 38

45 Circular migration poses a particular challenge for urban municipalities, as migrants are still committed to live in rural areas and thus use temporary bases in the cities to accumulate resources for re-investments in housing and economic activities back home. Cities with large numbers of circular migrants struggle with the effects of the tendency amongst migrants to try to minimize the cost of urban life, which often contributes to non-payment for services, and to sustained leakages from the urban economy. Simultaneously, the cities have to contend with under-investment in urban assets and infrastructures that add value to the tax-base (Gotz & Landau, 2004:19). Impact of migration and poverty on service delivery The rapid increase in the population of the metropolitan areas in Gauteng quite often resulted in inadequate resources for the provision of services to the population, including housing, educational needs, health and infrastructural development such as water and sanitation. All these services are linked to and have an impact on one another. For example, the lack of proper sanitation services could result in sewerage flows into some of the rivers which could in turn infect people using the water. The lack of clean water would thus lead to a greater demand for health services. Some interviewees observed that high levels of in-migration, causing rapid urbanization, lead to enormous shortages of both land and housing. In some areas there is simply not enough land for new housing developments to cater for the cities settled inhabitants. Furthermore (as mentioned before), in some areas planners wanted to relocate people in outlying settlements closer to job opportunities but instead those peripheral areas just kept on growing with no land available for the relocation of the people. Service providers indicated that the planning of infrastructure and services is based on the national census figures with provisions for an increase, but normally the facilities are quite soon inadequate for the number of people demanding services. This is mainly true for facilities and services in and around informal settlements and in areas where transport is easily available, such as inner city areas. One interviewee noted that the number of women migrants in the City of Johannesburg has grown substantially. Service providers, such as those rendering health services, thus have to cater for the health requirements of women migrants that are different to the needs of men, for instance for family planning services. Peberdy, Crush and Msibi (2004:14) confirm that the Census 2001 data suggest that, following global trends, women are increasingly becoming migrants in their own right, as well as being partners of migrants as was the traditional perception. For example, in the population of the City of Johannesburg, the proportion of male and female internal migrants born in Gauteng showed a near 50:50 balance. Women migrants from the Eastern Cape, Free State, Northern Cape and Western Cape exceed the number of male migrants in Johannesburg, but more men than women migrated from the other provinces to the city. An interviewee indicated that decisions by the Constitutional Court, such as the Court case regarding squatters on farm land in Ekurhuleni, meant that the government has no option but to provide for the arriving poor. According to the interviewee, this is a major financial challenge. 39

46 International migrants, especially those with permanent resident status, could have an impact on social grants because it was decided by the Constitutional Court in 2003 that they are eligible to receive social grants. Previously this category of people had not been included in the government s budgetary estimates. The Court ordered that social grants should be made available to citizens as well as permanent residents. Permanent residents are thus now also eligible for old-age grants, and they can apply for child-support grants and care-dependency grants (Bhamjee & Klaaren, 2003:57). Some of the interviewees expressed concern that health care services have to be provided to migrants from outside South Africa s borders although no provision was made for them in annual budgets. In reality migrants from outside South Africa have different rights of access to health services, but anyone who is in a life-threatening situation cannot be refused health care. Citizens, permanent residents, asylum seekers, refugees and other immigrants have the right to access government health services as well as private health services. They cannot be turned away from state services because of their inability to pay, since all categories of people should be treated in the same way. Undocumented migrants, however, have no right to access to the South African health care system, but they probably access health care services in life threatening situations or through private doctors who are not concerned with the legal status of their patients (Peberdy, Crush & Msibi 2004:42). Clinics in Tshwane, amongst others, experienced an increase in the demand for their chronic and curative services, which are services used mainly by adults. Although no research has yet been done in this regard they are of the opinion that this is due to an influx of people. It was also mentioned during some of the interviews that patients from certain outlying areas travel to nearby clinics in more central districts which they perceive as having better clinics than in their own areas, as when people from KwaMhlanga visit the Tshwane clinics in Eersterust or East Lynne. This practice also affects the number of patients demanding services at specific clinics, but it cannot be ascribed to migration. The negative impact of HIV/AIDS on existing poor communities was stressed during some of the interviews. Apart from households being affected by HIV/AIDS, the disease also impacts negatively on the provision of health services. The rising number of HIV/AIDS patients is causing an increased demand for these services. The Health Department of the City of Johannesburg has identified four groups to be targeted in HIV/AIDS prevention programmes: the youth, women, informal settlements and hostel dwellers. Hostels provide accommodation to migrants from other areas in South Africa as well as from the neighbouring countries. In many instances migrant workers do not have their families with them and thus get involved with local sex workers. Quite often sex workers are from poor communities and thus resort to commercial sex to secure an income. Some of the interviewees also stressed the difficulties in trying to improve the lives of people in poor communities. People in poor communities were said not to have the necessary skills to uplift themselves. It was also very difficult for service providers to conduct skills development programmes for such people because many of them had never attended school. Such a process of developing poor people can only be done over an extended period of time. 40

47 Provision for the service needs of in-migrants Since there is no real distinction between poor in-migrants and poor city dwellers, the basic needs of the people are the same and have to be addressed equally and at the same time. An interviewee involved in the physical development planning of a city indicated that in terms of the capital investment frame of the city, their model is designed in such a way that poverty is one of the major criteria and gets one of the highest ratings. Poor areas will thus be rated much higher for areas where services, e.g. waste management, electricity supply, roads or water should be provided. Transportation cost is one of the highest costs for the poor in Gauteng. The international standard for transportation cost is not more than 10 to 15 % of a person s monthly income. However, in urban areas in Gauteng it could be as high as 50% of a person s monthly income. For the City of Johannesburg s planners the provision of a public transportation system in the poor areas of the city is very important. Furthermore, an important aim is the provision of job opportunities closer to the people. They thus try to address some of the issues around poverty, as for instance by promoting ways in which the poor can increase their monthly disposable income, by reducing their transportation costs. When, for example, new housing developments are being planned, the availability of job opportunities within a reasonable distance of such a new development is being taken into account. If that is not viable the planners would investigate the possibility to encourage economic development with a view to creating jobs in that particular area. Interviewees from the planning departments stressed the fact that they are only involved in the planning of services but that utilities and agencies are responsible for implementation. An interviewee commented in this regard: Their (the agencies ) strategy around how they deal with those poor areas and what we would like to see are two totally different things, and we re not in charge of that, we re not the decision makers. We send it through the committee cycle but we don t always have the kind of an impact that we d like to have. According to an interviewee from the Salvation Army many migrants from the rest of the country travel to Gauteng looking for work opportunities. They usually require a place to stay while looking for employment. The Salvation Army provides accommodation in shelters (for a maximum of three months) to homeless people including migrants looking for work. They also provide them with access to services, for instance health care services, by giving them information leaflets. After the initial three months the Salvation Army assists them to rent a flat. Although the Salvation Army is not an employment agency, employers would regularly contact them requesting all kinds of labourers. According to the interviewee many in-migrants are very keen to find employment because they have responsibilities towards their families back home. Rural in-migrants often came to Gauteng without proper identification documents and that poses a real problem when looking for jobs. The Salvation Army s shelter in Pretoria offers a skills development programme where welding and furniture making are taught. The Krugersdorp shelter offers a painting programme whereby jobless people are employed to paint houses. 41

48 Apart from shelters, the Salvation Army also runs a few old age and children s homes with the aid of the Department of Social Development. The children are admitted to the children s homes through the child welfare system and are often orphans or children whose parents are not in a position to care for them. The Salvation Army also operates a feeding scheme on a daily basis in Johannesburg. At the time of the interview the scheme was limited to women and children only. Previously everybody was accommodated in the scheme, but due to certain problems men have now been excluded. Best practices in addressing migration and poverty in Gauteng s metropolitan areas The need for up-to-date data to understand the extent of the problem of poverty and migration was stressed by some of the interviewees. Planning relies heavily on the census data, but in many instances it is outdated and not accurate. An interviewee commented in this regard: When it comes to housing delivery and other issues where you try to address the issue of urbanization and the influx of the poor in the area, you don t have substantial data or correct data to actually understand the nature and extent of the problem. Some of the interviewees were of the opinion that there has to be co-ordination between all tiers of government in the provision of services. Currently, for example, all tiers of government are working in the same community, but there is no co-ordination between the various programmes. This causes problems and confusion within the communities. Another problem is that some people see this lack of co-ordination in programmes as an opportunity to apply for funding for their projects at local, provincial and also national level. An interviewee from the City of Johannesburg indicated that the City of Johannesburg is still fragmented in terms of the world of primary health care. However, Pursell (2004) observes that this is true for the entire country, since South Africa s health system is three tiered and each tier provides different levels of care. The Johannesburg Metropolitan Council runs some of the clinics in the city, while others are run by the province. All health services are thus not provided at all clinics, and this affects the poor in terms of having to travel to different clinics. Service delivery would thus be much more efficient if it was co-ordinated by a single institution. Since migration as a phenomenon and the specific needs of migrants are extremely complex, the need for research regarding these issues was stressed. An interviewee commented in this regard: Do we particularly know that migrants have a problem with service delivery whereas people who have lived in the city for five to ten years don t? I would like to see research that says that migrants to the city, settling in any place in South Africa, settle in these particular types of areas and they have these particular types of problems. I would imagine that it s not solely the domain of migrants. It was felt that the flow of internal migration to Gauteng could be downsized by the economic development of sending areas (and thus job provision), and by the provision of adequate social services and infrastructure (schools, hospitals. clinics, roads, etc.). People would thus in many instances not be compelled to migrate to Gauteng or to 42

49 any other wealthy province. Development thus has to benefit people in the sending areas. Health sector service providers are aware of poor patients doing clinic hopping, by visiting more than one clinic for the same health problem and then presumably selling the additional medication or, in the case of formula milk given to babies, using it in their households. This practice has serious implications for the health services since fewer poor people can now be treated with the remaining available resources. An interviewee indicated that this problem can only be combated by a computer system that links all the health centres. That will enable personnel to immediately determine whether a patient had already been treated for a specific problem. At this stage many clinics are computerized and maintain databases, but these are not linked. An interviewee suggested that, since migration cannot be curbed, there has to be an integrated approach by government to provide services in areas where the migrants settle. In the past the government introduced pilot programmes that involved micro scale job creation programmes. However, it only succeeded as long as the government was funding it and came to a standstill as soon as the government funding stopped. Job creation at that level is thus not a solution. The provision of social welfare services is not the final solution to achieve the alleviation of poverty because the numbers of the poor in South Africa are increasing at a very high rate. This is especially due to the continuous widening of the gap between rich and poor. South Africa would thus not be able to continue to provide social services to a high percentage of its population in future. Projects aiming at the sustainable development of people will be a more viable solution. The importance of community development programmes and plans aimed at uniting the community to assist one another in the fight against poverty was also stressed by an interviewee in this regard. Sometimes communities are sceptical towards the motives of local governments in attempts to improve living conditions and thus do not want to participate in programs. This became real obstacles to development and the provision of services such as housing. For projects to succeed, ways thus have to be found to ensure the trust and co-operation of communities. When discussing best practices in addressing the problems of urban migration and poverty an interviewee suggested that the overall solution would be an urban renewal project that takes the social, human and economic factors into account. The interviewee from the Salvation Army is of the opinion that the Johannesburg Metropolitan Council does not have a coordinating approach dealing with homeless people. Social Services from the Johannesburg Metropolitan Council, e.g., approached the Salvation Army to provide food and shelter to homeless people in Region 9. The Salvation Army does not have any accommodation for homeless people available in Region 9, but nevertheless offered to provide food to the homeless. However, the law enforcement agencies of the Council did not allow them to distribute the food to the homeless and chased them away. In another area in the City of Johannesburg the Salvation Army provided blankets to the homeless people, but 43

50 this initiative was also stopped by law enforcement agencies. The Johannesburg Metropolitan Council thus needs to have a coordinated approach in dealing with poverty. According to the interviewee of the Salvation Army newcomers to the city have to be accommodated in shelters as soon as they arrive in the city, because once they have established social networks it became quite comfortable for them to stay in the uncomfortable situation on the streets. He commented in this regard: Because they become part of a clique they are introduced to all kinds of other means to mask the effects of discomfort, e.g by drinking alcohol or sniffing glue. After they have been around for two weeks and they have learned how things run it is already too late. The interviewee from the Salvation Army proposed the introduction of overnight shelters where migrants can overnight while paying for the service at a low fee. He also suggested that capacity building, including programmes aimed at developing the skills of the poor, should be a priority for NGOs working with the poor. Finally, when asking about best practices in addressing migration and poverty, an interviewee commented as follows: There is no best way. I ll tell you why. The needs of people are forever changing. It s a question of understanding a situation in a particular environment at a specific time, and assisting people at that time. The survival skills of people are different; the understanding of their poverty is also different. Location of recent in-migrants The interviewees indicated that internal migrants quite often live in areas where they have family members or other established social networks. They normally settle in areas according to their means. Some of the poor migrants are to be found on farms, informal settlements, townships and in rural areas. In some instances they are also among the homeless in the cities. The poor, including the in-migrants, are also found in slum areas in the inner cities that are normally manufacturing, labour intensive and, low skill development areas, e.g. the Yeoville, Bertrams, Troyville and Brixton parts of Johannesburg. The City of Johannesburg has done research on areas where poverty was growing and found much higher densities within the peripheral areas of the city. They are of the opinion that migrants will often settle in those peripheral areas because land is cheaper there and because it is easier to access housing in those parts of the city. An interviewee from Johannesburg commented in this regard: You see the informal settlements mushrooming and even growing. We ve got Soweto that is growing, but it s not really growing at that fast a rate any more as some of the areas to the north, and I think that is purely because of more proximate job opportunities. The interviewee ascribed the phenomenal growth of Diepsloot to the fact that it is centrally located with easy access to areas where job opportunities are available. Destinations with possible job openings include areas such as Wynberg, Midrand (although firms in that area are not really labour intensive) and Modderfontein. Peberdy, Crush and Msibi (2004:31) found in their study on migrants in the City of Johannesburg that internal migrants could be found living across the city s suburbs, townships and informal settlements. Migrants, particularly from the rest of Africa rather than from the SADC countries, are more likely to live in the inner city and suburbs than in township areas. Research suggests that xenophobia plays a part in these 44

51 decisions. Migrants from outside Southern Africa are less likely to have existing social networks than those from the other SADC countries. They are also more visible and less likely to speak South African languages. The inner city may thus provide a safer place to live than townships. SADC migrants often stay in townships and informal settlements where they may have established social networks prior to moving. In the City of Johannesburg migrants from West and East Africa chose to live in the inner city while migrants from Somalia, India and Pakistan often live in Fordsburg or Mayfair, because the local Muslim community provided support and rents housing to non-nationals sharing the same faith. Job opportunities and making a living Many poor residents of the cities have excellent survival skills and somehow make a living with virtually no economic resources. In some informal areas there is still a communal kind of approach whereby the community assists the needy. However, it was indicated that the situation is somehow different for newcomers since they are unknown in the community and do not necessarily have the required social networks. In many instances it is difficult for in-migrants to find jobs because they do not have proper social networks. Furthermore, securing employment is already problematic for the regular inhabitants of the cities. Urban poor families, including in-migrants, make a living in various ways: It ranges from informal trading on the streets, by running spaza shops, shebeens and crèches, by having phones on the streets and letting people make phone calls, by working as domestic workers and living on child support grants, old age pension and disability grants. Some of the poor also cultivate vegetables and maize in their backyards for own consumption, but often also to sell to the community. However, it was indicated that in view of the limited market/resources it is difficult for newcomers in the city to embark on and continue with many of the above-mentioned activities since those who have been there the longest try to protect their turf until such time that you have merged well with them in the survival struggles, in the environment and in the communities. According to Peberdy, Crush and Msibi (2004:25) internal migrants in Johannesburg are most likely to be found working in the service, financial, community, and manufacturing sectors as well as private households. Migrants born outside South Africa are predominantly employed in the construction, service, and financial sectors and show relatively high rates of employment in private households. The income levels in sectors where both categories of migrants are working are usually low, the employment insecure and working conditions often poor Focus group interviews This section contains the responses of focus group interviews conducted to augment the quantitative research data and to elicit more contextual information on the perceptions, experiences, nuances and underlying factors on why people move to Gauteng as well as why those already inside the province move from place to place. 45

52 Three focus group interviews were conducted among members of the public in each of the three metropolitan areas of Gauteng: Tshwane, Johannesburg and Ekurhuleni. Having seen on the basis of the proportion of migrants and number of individuals with no income, focus groups were conducted with members of Nellmapius in Tshwane, Slovoville in Johannesburg and Lindelani Village in Ekurhuleni. Fieldworkers familiar with these areas helped to organise and recruit participants who would be able to generate free-flowing discussions about migration and poverty. As the study was not aimed at statistical representivity, a purposive sample of participants willing to share ideas about migration and poverty formed the focus of this part of the qualitative study. These focus groups were held between 22 and 26 February All focus groups comprised eight members between the ages of 20 and 50 years. There were five males and three female participants in Lindelani Village, four males and four female participants in Nellmapius and five females and three male participants in Slovoville. The researchers moderated all focus group interviews using a pre-designed focus group schedule. Open-ended type of questions were asked to find contextual information on the perceptions, experiences and underlying factors why people move to Gauteng as well as why those already inside the province move from place to place. Furthermore, the questions tried to establish the interrelationship between poverty and migration. A tape recorder was used to capture all the discussions. At the beginning of each focus group, the researcher outlined the purpose of the study and provided participants with an information sheet, describing the purpose of the study. Participants were requested to sign a consent form to show that they understood the purpose of the study and that they give consent to have the discussion recorded on tape. They were also requested not to disclose their migrant or legal status in the country. However, besides providing information on the study, the participants wanted to know how the research would benefit them. There seemed to be a general expectation that since poverty related issues would be discussed, there might be direct benefits flowing from the research, for instance in employment, housing, or related areas. These issues were thus clarified. It then became clear how important it is to avoid raising any expectations or implicitly making any promises that might be difficult to keep. This introductory conversation helped to establish rapport and facilitate discussion throughout the focus group interviews. At the end of the focus group discussion, the participants were provided with refreshments. Their transport costs were also covered. All the procedures that were to be followed in conducting the focus groups were presented to the Ethics Committee of the HSRC, including the focus group schedule, indepth interview schedule and consent forms that had to be signed by participants. The results of the focus groups were translated and transcribed for use as source material of the qualitative report. Analyses of the transcriptions were undertaken by means of developing codes and themes emanating from the findings of all focus group interviews. 46

53 Findings from the focus groups The results of the study show that urban poor families are often found in spatially identifiable areas. They are found in peripheral location and in the inner city. There are also migrants from other countries outside South Africa who stay in the informal settlements or rent cheap accommodation in the inner city. Peripheral area locations The majority of participants mentioned that urban poor families are found in selfconstructed shelters in informal settlements, squatter camps, shacks, townships, RDP houses and hostels. These areas are characterised by poverty, unemployment and a lack of services such as electricity, running water or proper sanitation. The houses in these areas are small and built from diverse materials, such as plastic, wood, bricks or corrugated iron. Some participants also referred to poor families that lodge in the backyards of other people in the townships. The backyards rooms could also be built from wood, bricks or corrugated iron. To emphasise the lack of services in informal settlement, some participants mentioned the following: Poor people are those who stay in the informal settlement, they do not have water, there are no toilets and they do have not enough space in the shack, they have small children and dirty water is found at the doorstep and there is no decent life. Most poor people stay in small houses in squatter camps, RDP houses, hostels or townships. Most houses are built from wood, other are built of plastic, corrugated iron. Only a few are built of bricks. Most of them do not have electricity or water. In areas where there is water and electricity such as in the townships and RDP houses, affordability of these services presents many challenges. One participant indicated the following: We cannot even pay rent, services are high and we owe large amounts of money in the range of R The inspectors raid us and we are arrested and forced to pay the money that we do not have. That is the kind of live we live. Inner city accommodation For other participants, poor people are also found in inner city streets and areas such as parks, train stations and under bridges. These participants specifically referred to homeless people without any form of shelter: Usually they are found roaming the streets in towns, looking for job opportunities and they end up here. Most of them are found in the city centre as well as in the city parks looking for work and if they cannot find work, they eventually decide to stay in town, on the streets or parks. Cross-border migrants in informal settlements and the inner city While some participants focused on urban poor families in the country, others referred to migrants from other countries outside South Africa as being among the urban poor populations. 47

54 Many of these poor families comprise foreigners in the squatter camps. There are many foreigners in Hillbrow who are also very poor. They rent old and dilapidated flats. Survival strategies of the urban poor When asked how urban poor families make a living, the majority of participants mentioned that most urban poor people are unemployed and have little or no capital to make a living. This sometimes leads to many criminal activities taking place in the urban areas. Others commit various crimes like breaking into other peoples properties, and stealing their assets and belongings. Those who live in deep poverty, who have nothing and that is why they go out to steal cars and commit housebreaking. Despite increasing unemployment, a number of other livelihood strategies other than crime often take place. Some households have access to some form of cash economy through informal cash earning: They sell a lot of things in the streets like bags, curtains, and cellphones you will also see them even at night, doing house- to- house selling. Others do washing and ironing for other people to buy food and e others sell vegetables and cold d rinks so they can survive. Some are lucky because they have backyard hair salons while others repair shoes because of the experience they gained when working for other people. Other households access the cash economy through social grants. However, there are various problems surrounding the use of social grants and some participants feel that they do not necessarily assist the intended beneficiaries. Other people rely on social grants that they get for their children. But, the problem is that some young mothers abuse this money as they use it to buy cellphones and not food for their children. There are those families that totally depend on their grandparents pension money. A family of 10 people can depend on the granny s R780. Neighbours, family and friends also play an important role in helping some households to cope with their situation. I sometimes visit my friends and neighbours and eat at their places and that is how I make a living. What happens is that an in-migrant would stay with someone until they are able to support themselves. 48

55 Participants also highlighted the survival strategies of the urban poor in the inner city. While some roam around the city collecting and selling recyclables as well as looking after parked vehicles and washing cars, others resort to begging and prostitution. Some serve as parking attendants at shopping malls so that car owners can give them some money they can use to buy food. Others turn to prostitution to be sex workers so that they can live. They also ask money from other people in the streets or they collect bottles for recycling. Children of urban poor families When asked whether children of urban poor families are able to find work, participants mentioned that many of these children drop out of school at an early age and thus unable to find work. As a result many end up committing crime, doing drugs, running away from home becoming pregnant during their teens and others end up as prostitutes, homeless or street children. Children of urban poor families are unable to find work because they drop out of school very early. As such they find it difficult to find work. They start committing crime as a way of living. Other children of poor families run away from home because of poverty. They stay in the streets of the city, sniffing glue, committing crime and girls end up in prostitution. However, only a few children are able to find employment, mostly doing domestic work. The problem is that they can t afford formal housing, so they also end up staying in the informal settlements. Most children from poor families are unable to find work and formal housing because they drop out of school at an early age. Some end up in crime while others end up doing odd jobs that do not pay them enough to afford formal housing. So they also usually end up in the informal settlements. Sometimes children from poor families end up working for other people like washing cars and becoming domestic workers. Moving from an informal to a formal location Participants described the circumstances that could make urban poor families to move to formal populations as better employment opportunities, education and marriage. Most poor families move from being in the informal settlement when they get formal employment, but this happens especially when their children get employment and take their parents with them to stay in a formal settlement. If you marry a rich man, then you can move to a formal settlement. 49

56 Migration to Gauteng In response to perceptions of where people who enter Gauteng come from, the participants from the three focus groups differentiated among in-migrants from any of South Africa s eight provinces and cross-border migrants. The participants also distinguished between in-migrants who are in Gauteng on a temporary basis such as students, and those working and taking up permanent residency in Gauteng. The participants mentioned that the majority of Gauteng in-migrants come from any of the other eight provinces while other people come from other parts of Gauteng. There are also cross border migrants from other countries such as Zimbabwe, Mozambique, Pakistan, Nigeria and Swaziland. Similar responses to those relating to where people who enter Gauteng come from were provided for those entering the metropolitan areas of Johannesburg, Tshwane and Ekurhuleni. Across the three metropolitan areas, the majority of people are seen to come from other provinces, other parts of Gauteng as well as other countries outside South Africa. With regard to where people who enter specific cities come from, the participants stated that the majority of people who enter these cities come from elsewhere in that same city. In Slovoville, an RDP housing settlement in the Johannesburg metropolitan area, the majority of people came from other informal settlements areas and backyard rooms in Johannesburg, particularly from Soweto. People in Slovoville are people who were tenants they were lodging in the backyards of other people in Soweto locations such as Zola, Diepkloof and Meadowlands and some come from other informal settlements. That is why we are here now as the community of Slovoville. In Nellmapius, an RDP housing settlement in Tshwane Metropolitan City, the majority of people came from other informal settlements and backyard rooms elsewhere in the city, particularly from Mamelodi. Most people in Nellmapius come from Mandela Village, Stanza Bopape and Lusaka. Others come from Mamelodi West. These people were tenants at the backyards of other people. In Lindelani, an informal settlement in the Ekurhuleni metropolitan city, the majority of people come from other informal settlements and backyard rooms, particularly from Daveyton and Springs. Some people left their shacks to come a stay here. Others were tenants in Daveyton and Springs. Others were crowded in four-roomed houses and wanted to have their own places. Preference to move to Gauteng province instead of anywhere else Participants cited a number of reasons why people prefer to move to Gauteng instead of anywhere else. Similar reasons were also cited for preferring specific metropolitan cities. Job opportunities, access to resources as well as gaining security were cited as 50

57 main reasons why people prefer to move to Gauteng and also the metropolitan cities of Johannesburg, Tshwane and Ekurhuleni. Job opportunities By far the main reason in-migrants prefer to move to Gauteng as well as the metropolitan cities of Johannesburg, Tshwane and Ekurhuleni is in response to perceived job opportunities. Participants mentioned that Gauteng is seen as a province where people come to look for employment: Gauteng is the only place where there are more job opportunities than in other provinces. There are no industries in rural areas. So people know that industries are in Gauteng and hope that they might get employment in those industries. Participants also referred to other cross-border migrants who create job opportunities to survive in Gauteng. Some of these migrants even employ South Africans. these people are different from those who come from Pakistan or Nigeria, because they are here as entrepreneurs. They sell curtains, wall pictures, cell phones and they also own hair salons and curio shops because they do not have the necessary documents to work in South Africa. Some South Africans work n these shops. Access to resources For some participants people prefer to move to Gauteng in order to access resources that are unavailable in their places of origin, but are also seen to be better in this province. There is a lack of facilities such as recreational and health facilities in other provinces as compared to Gauteng where there are services that benefit many people. There are schools, clinics and recreational facilities for young people and people can use their talents to the fullest. Such talents are not utilised in Limpopo province. Most people who enter Gauteng come from those provinces that have fewer or no resources and services and they come here because they know that they would be able to access better schools and hospital services. Most informal settlements also have water and electricity. Gaining security Political instability in other countries was seen as a factor that can cause people to move in search of safety in South Africa, particularly in Gauteng. Foreigners from other African countries come here because they know that South Africa has no violence tendencies and some are war refugees Crime is low as compared to their countries Life in Gauteng is better. Settlement-specific preferences The responses from the focus group discussions show that preferences to settle in specific parts of the city are motivated by a number of reasons. By far the main reasons 51

58 cited in all the focus groups as to why people prefer to settle in specific areas is to be nearer to place of work, be able to have a house and to have access to free or affordable basic services. Other reasons cited include protection against xenophobia as well as being able to exercise independence from parents. Proximity to place of work The majority of participants mentioned that the main reason why people prefer to move to Slovoville, Nellmapius and Lindelani is to be able to be nearer a place of work so that they could save on transport costs. People prefer to stay next to their workplace so that they do not spend much on their transport. Affordable housing Some participants mentioned that the people prefer to move to Slovoville, Nellmapius and Lindelani in order to have a house of their own. They stated that people prefer to move to settlements where they know that they would be able to afford a house. People will choose settlements that they know will not require money from them because most people in the informal settlement are not working. Those who are working earn very little. The best settlement opportunities for poor people are in the informal settlements. As long as there is a piece of land, people can erect a structure they can afford, be the form of bricks, plastic, wood. They settle in these areas because they can t afford to pay for water and electricity in formal settlements. Gaining independence from landlords or parents For some participants, people prefer to settle in Slovoville, Nellmapius and Lindelani because they want to gain some form of independence from landlords or parents. They indicated that tenants renting shacks in the backyards of landlords prefer to move to RDP houses or informal settlements where they know that they would have a house of their own and be able to exercise some independence. Furthermore, young people were also seen to move to these areas in order to assume a sense of independence from parental control. They leave to have their own houses, do their own things and not live in somebody s backyard. If you want to leave your parent s house and settle in an informal area, you can easily get a cheap stand and then erect your own shack. Accessing free/affordable basic services Another reason cited for people preferring to move to specific settlements relates to free or affordable services that are offered at those settlements. The participants stated that the amount of money paid for services in an informal settlement is less than that paid in formal settlements. However, others were attracted to these settlements by the free services being provided. 52

59 Some people stay in the hostels because they know that it is cheap to stay there. People stay free in some hostels. They know that they do not have to pay anything here. Although they know that there are no basic facilities like toilets and water here, they are happy to stay in these areas because they do not have to pay for accommodation. Clustering for fear of xenophobia Fear of xenophobia was also identified as a reason why some people prefer specific settlements. The participants stated that cross-border migrants prefer to stay together to protect themselves against South Africans who are xenophobic. Those from other countries stay in a group. You will find that more than 10 people occupy a shack or flat. This helps them to share rental payment. The other thing is their safety because when they are in a group they can protect themselves from people who want to attack them, as some South Africans do no like foreigners. What people expect and obtain When asked what the people moving to the Gauteng province expect and obtain, all participants in the various groups mentioned that in-migrants expect to find jobs in Gauteng. Similar to reasons cited why people prefer to move to Gauteng instead of anywhere else, job opportunities, access to resources and educational opportunities were cited as the main expectations to find in Gauteng and specifically also in the metropolitan cities of Johannesburg, Tshwane and Ekurhuleni. However, the majority of in-migrants do not always secure the job they expected to find in Gauteng. In-migrants mainly expect to find job opportunities in Gauteng. Most migrants come to Gauteng mainly for job opportunities. Some do find work, but the majority does not. People expect jobs in Gauteng. Unfortunately, most do not get them. Some participants ascribed the difficulty in finding jobs to the increased number of job seekers, particularly the growing number of cross-border migrants who are willing to accept low paying jobs. Yes, they get what they expect. It is just that jobs are now scarce because the population of job seekers has increased. But there is a lot of economic activity here in Gauteng. If only foreigners could go back to their homes, many opportunities would open up. It is very easy for foreigners to get a job. They are not choosy; they accept any type of job. That is something that South Africans would not do. However, while participants felt that it is easy for cross-border migrants to find work because they tend to accept any type of work, they did not see the same happening to in-migrants from other provinces. They said in-migrants from other provinces find it difficult to find work in Gauteng. 53

60 Migrants from other province struggle to find jobs. These days, it is very difficult to get a job, whether you have qualifications or not. I have a teacher s diploma but I am not employed. I keep myself busy by volunteering to do home-based care with a hospice. I would not say that it is easy for settled populations to find jobs better than migrants because these days, most people are unemployed, and they struggle to find work. There is a lot of nepotism and other things that make it difficult for some people to get work. Other participants were of the opinion that, similar to cross-border migrants, some inmigrants from rural areas are able to accept any kind of work being offered to them. In-migrants from rural areas find it easy to get employment at municipal offices because there are various types of work that do not necessarily require training or qualification. For example, digging graves, work roads, gardening, rubbish removal, etc. Settled populations would not accept all these types of work. They see these types of work as lowering their dignity. Truly, people from rural areas grab any work opportunity. They are not choosy. You might have a degree, but because you have been looking for work for some time and could not find it, you will have to take any work that comes along. Access to basic municipal infrastructure and services such as water, sanitation, houses and health services were seen as what people expect to find in Gauteng. However, these services are expected only once they have found jobs. People who are moving to Gauteng expect to get jobs. That is the first thing for most people. Once they are here, they expect to get a house and have access to services like water and electricity and for their children to be able to go to school. For others, basic services like health services are major attractors. Hospitals and specialist doctors are mostly found in Gauteng. They expect to get work and then other services. When asked how easily are social and infrastructural services available to in-migrants as compared to the settled populations, participants said it is not always easy for inmigrants from other countries to access government services, as they do not possess the required documentation. As one participant indicated it is only in those situations where they do not have legal documents that they would not be able to have services. However, there are cross border migrants who bribe government officials to get fraudulent documents that allow them to access government services. Migrants with money are able to access services. They can bribe officials to have houses. Similar to cross-border migrants who cannot access government services because of lack of proper documentation, in-migrants from other provinces without necessary documents cannot access government services either. 54

61 Those from the rural areas have access to services in the same way as the settled population do, as long as they have relevant documents like IDs or clinic cards. Services are not easily available to migrants who do not have the necessary documents. But it is the same as with settled populations - if they do not have relevant documents then they would not be able to access services. However, children are not required to have any relevant documentation to access government services. As one participant stated: All children, no matter where they come from have access to educational and health services. They are allowed in state schools, clinics and hospitals. Other expectations mentioned relate to access to educational opportunities. Not only do people expect job opportunities; some are here to seek educational opportunities. In Gauteng there are many better schools that offer different courses, universities, technikons, teacher and nursing colleges, IT colleges. Educational opportunities also cause migration. Integration of in-migrants with the settled population When asked how the in-migrants integrate with settled populations, participants mentioned that the relationships that in-migrating poor already have or establish with settled populations helps them to integrate with settled populations. While some inmigrants come to Gauteng already knowing someone, others establish some form of relationship with settled populations when they arrive in the province. Knowing someone in Gauteng was seen as being important to helping in-migrants to socialise and integrate with settled populations. Those migrants from other provinces integrate easily because they come here knowing somebody, friends or relatives. For in-migrants who do not know anyone in Gauteng, establishing new friendships often helps them to integrate with the settled populations. For example, a man from outside Gauteng who wants to socialize with people will talk to me and I will tell my friends about him and he will make friends with them and he will go to fetch his brothers and friends because he is now used to the social life in Gauteng. Intimate relationships established with the settled population were seen as facilitating socialisation and integration of in-migrants. However most of the relationships are not truly intimate, they only help the in-migrants to obtain a South African citizenship. Some have intimate relationships and are even married to our brothers and sisters. However many of these are marriages of convenience, because they want to have a South African citizenship. 55

62 Skills of in-migrants Some participants were of the opinion that the technical skills that in-migrants have often help them to easily socialise and integrate with settled populations. These oftensought and scarce skills or abilities are also used to survive in Gauteng. Manual labour and informal trading were seen as important to help in-migrants to survive. However, differences were noted between migrants from other provinces and those from across the country s borders. Cross-border migrants were seen to be entrepreneurs and to use their skills to access income-generating activities while in-migrants from other provinces would merely like to have a job in order to survive. Most foreigners have skills that some people here do not have. They are builders, shoe-repairers and entrepreneurs because they sell many things, towels, cell phones, curtains, etc... Others own saloons or clothing shops. Educational qualifications seem to be important for in-migrants from other provinces to survive in Gauteng. However, those without formal qualifications tended to engage in the informal sector. I think that it is important for migrants who are from other provinces, to have qualifications to be able to support themselves. You must have been trained and have a certificate for that training. You must also be able to communicate in English, so that when you get employment, you are able to communicate with other employees. For some participants, many in-migrants with no qualification often resort to any type of work as long at it can bring them some money. A person who grew up in Gauteng will never accept to work as a rubbish bin collector. A person from a rural area would accept this job as long as he knows that he would be paid. Network connections As seen earlier the relationships that e in-migrants already have or establish with settled populations help them socialise and integrate with the settled population. The same relationships are also used to find a foothold and survive in Gauteng. These network connections are used to identify specific areas in which to settle and to identify certain opportunities such as jobs and basic services. Identification of places to settle Relationships with family and friends were seen to help in-migrants to find a place to stay because when they arrive in Gauteng they would need accommodation. It is because you meet in a taxi and talk to one another and a migrant will ask you where you stay or sometimes you meet in town or at the workplace and the relationship grows. Sometimes the migrant will lodge at my sister s place and naturally there will be a friendship that develops. Identification of job opportunities For some participants in-migrants have connections with family and friends that help them to find work in Gauteng. These participants emphasised the role that social network play in popularising perceptions of Gauteng as a province with many job 56

63 opportunities. Some of these networks emanate from those who currently work and those who have worked in Gauteng. In-migrants have many connections. When they arrive in Gauteng, they already know someone, either a relative or a friend, who is going to help them find a job. People from countries like Zimbabwe, Mozambique are mostly invited by their friends and relatives who might have been in Gauteng for some time to come and work on the farms and in other areas where cheap labour is required. We also know that even in the past, our parents came to Gauteng to work, so we all think that this is where one can get a job. Identification of basic services Friends also help in-migrants to access government services: They have connections in the Department of Home Affairs and use those connections to acquire ID documents that they use to access services such as houses and health services. Intention to return to place of birth The participants in all groups mentioned that many people move to Gauteng with a clear intention of eventually returning to their place of origin. These participants stated that people moving to Gauteng still maintain ties with family and friends in the place of origin. They invest and improve the living conditions in their place of origin. Furthermore, although they spend their whole lives in Gauteng, they would want to go back to their places of origin when they retire or die. The main reason to want to go back to their places of origin is that most African people see place of origin as home while Gauteng is a place of work and not really home. They said when you ask most black people where home is they would point their place of birth. I would say most of them move to Gauteng with the intention of returning where they came from because even if they are here, they still go home over Easter and Christmas holidays. Furthermore, whenever they buy furniture, they always take it home because they have proper houses at home. So, although they stay here, they regard their place of birth as home and I think that is where most would want to return to when they are old or when they die so that they can be buried there. I think it is the culture of most black people to see their place of origin as their home. Gauteng is only a place of work. I am sure that they would want to go back home. Perceived threats to the settled population All participants in all three groups perceived people moving to Gauteng as a threat to settled populations. While in-migrants in general were seen to be benefiting from social and infrastructural opportunities planned for Gauteng residents, those from other countries were seen to be taking jobs that actually belong to South Africans. Furthermore cross-border migrants were seen to be committing various types of crimes such as selling drugs, hijacking cars, faking documents and recruiting young girls to prostitution. The majority of migrants seen to be committing these crimes 57

64 were perceived to come from countries like Nigeria, Zimbabwe and Mozambique. However, Pakistanis, Chinese and Indians in particular, were seen to be taking over businesses and selling fake goods and clothing. Threats posed by in-migrants from other provinces In-migrants from other provinces were seen to enjoy the benefits that were supposed to be enjoyed by people who originally come from Gauteng. They were seen to be taking jobs and houses that settled populations in Gauteng should benefit from The settled populations always see in-migrants as a threat. In-migrants are always seen to be taking the jobs that settled populations should be doing. Furthermore, housing is a problem. There are more people from rural areas in the informal settlements than people from Gauteng. Threats posed by cross-border migrants Cross-border migrants were seen to be posing a major threat to settled populations. They were seen to be taking jobs, committing crime, selling drugs, transmitting diseases like HIV/AIDS, bribing officials to access services, involving girls in prostitution and also being involved in child-trafficking. In certain instances they were seen to be taking low-paid jobs, thereby lowering wages paid generally by employers. They are a problem because they grab any job opportunity even if the wage is very low. Migrants are a threat to us, they sell drugs they hijack cars, they forge documents so that they can have a South African citizenship, they involve young girls in prostitution, they sell fake clothing, etc. There are so many bad things that they do. Migrants are always called with different names. They are mostly not welcomed in Gauteng because they are seen to be taking jobs, bringing diseases like HIV/AIDS, bringing drugs and committing crimes like hijacking. Other cross-border migrants were seen to be running the majority of businesses in certain areas. In-migrants from other countries own businesses You will find that all the hair salons in this area belong to them. Some cross-border migrants were superstitiously even seen to be capable of bewitching settled populations: They are a threat because they can let you buy from them on credit and if you fail to pay, they can bewitch you because they are capable of performing magic. You might find yourself following him to his country because of his magic and this is a threat. Another threat presented by cross-border migrants was the relationships they establish with South African women. Many are seen to marry South Africans merely to get South African citizenship. They are a problem to us as South Africans, sometimes when our sisters go to the department of Home Affairs they find that they were married. Cross-border migrants also commit Internet banking fraud and sell drugs to little children. 58

65 They are seen as bad people who are only interested in South African women only to have a get a citizenship. Threats posed by the in-migrating poor The in-migrating poor were seen to be posing different kinds of problems, as they are seen to be committing robbery, burglary or hijacking in formal settlements, particularly those near the informal settlements. From my experience, I think that people who are in the informal settlement are seen as a threat to settled populations. As long as you are from a rural area and staying in an informal settlement or hostel you are a threat to those staying in formal settlements. For the in-migrating poor, they are seen as the ones committing crimes. They are the first suspects for burglaries, robbery and hijacking Summary The research shows that while the majority of people entering Gauteng come from any of the other eight provinces, other areas within Gauteng or other countries outside South Africa, those who enter specific settlements often come from elsewhere in that specific city. Perceived job opportunities, access to affordable and better services, educational opportunities as well as gaining security as a result of political instability were cited as main reasons why people prefer to move to Gauteng as compared to anywhere else. These reasons, together with gaining independence from landlords or parents and clustering for fear of xenophobia, were cited as main reasons why people prefer to move to specific cities. However, despite these some pull factors, people often move to Gauteng with a clear intention of eventually returning to their place of origin. Culture seems to play a role in facilitating this intention to return, which is seen to be more prominent among black Africans. The relationship that in-migrants already have or establish with settled populations in Gauteng helps them to socialise and integrate with the settled population. These network connections are also used to find a foothold in specific places to stay; areas with opportunities such as jobs, housing and government services. While people moving to Gauteng have relationships with settled inhabitants, they were perceived to pose a threat to inhabitants of Gauteng. Migrants in general were seen to be benefiting from social and infrastructural opportunities planned for Gauteng residents, while those from other countries were also seen to be taking jobs and houses that South Africans should have benefited from, and to be committing various types of crimes and merely becoming involved in intimate relationships to obtain a South African citizenship. Urban poor families, including those from outside South Africa, were seen to be in spatially identifiable areas in the peripheral area locations and in the inner city. These areas are characterised by poverty, unemployment and a lack of services such as electricity, running water or proper sanitation. In areas where there is water and electricity such as in the townships and RDP housing developments, affordability of these services presents many challenges. Despite increasing poverty and unemployment, poor people often engage in informal cash earning livelihood strategies such as street trad- 59

66 ing, domestic work and access to social grants. In addition, employment, education and marriage could make urban poor families move from informal to formal populations. However, children of urban poor families are unable to find work as many drop out of school at an early age. While a few are able to find employment, they fail to afford formal housing, so they also end up staying in the informal settlements. The rapid increase in the population of the metropolitan areas in Gauteng impacted negatively on planning and quite often resulted in inadequate resources for the provision of services to the population, including housing, educational needs, health and infrastructural development such as water and sanitation. Furthermore, high levels of in-migration lead to enormous shortages of both land and housing. Recent decisions by the Constitutional Court meant that the government now has to provide for the arriving poor. This has major financial implications. Migrants from outside the South African borders have different rights of access to health services. However, anyone who is in a life-threatening situation cannot be refused health care. Some of the interviewees were concerned that health care services have to be provided to migrants from outside South Africa s borders although no provision were made for them in annual budgets. The negative impact of HIV/AIDS on existing poor communities was highlighted. Apart from households being affected by HIV/AIDS, the disease also impacted negatively on the provision of health services. When looking at best practices to address migration and poverty the need for up-todate, correct data to understand the nature and the extent of the problem of poverty and migration, was stressed by some of the interviewees. Planning relies heavily on the census data, but in many instances the data are outdated and not accurate. Furthermore, migration as a phenomenon and the specific needs of migrants are extremely complex. There is thus a need for research on these topics. The provision of social welfare services is not the final solution to the alleviation of poverty because the numbers of the poor in South Africa are increasing at a high rate. Projects aimed at the sustainable development of people will be a more viable solution. Furthermore, there has to be co-ordination between all tiers of government in the provision of services to both the in-migrating poor and the poor settled residents of the cities. The Salvation Army indicated that newcomers to the city have to be accommodated in shelters as soon as they arrive in the city, because once they have established social networks it becomes quite acceptable for them to stay on the streets. The organization also suggested that capacity building, including programmes aimed at developing the skills of the poor, should be a priority for NGOs working with the poor. 4 DISCUSSION: INPUTS FROM THE STAKEHOLDER WORKSHOP The stakeholder workshop convened by Gauteng Intersectoral Development Unit to consider the early results of the study played an important role in how the initial quantitative and qualitative findings were taken forward to this point. The workshop considered how the indicator list was developed, and how the indicators could best be 60

67 used. It focussed particularly on the expectations of the Gauteng Province Poverty Alleviation Committee in looking to create a broad-based able to identify different forms of poverty, in a way that would relate to government anti-poverty spending objectives. This interchange gave the research team valuable insight into how the stakeholders in government saw the issue of poverty in a policy context. The workshop discussion was closely focussed, and critical to confirming the objectives the research should address. The priority highlighted by participants targeted the need for statistics and measures to resolve problems of resource management and allocation. Policy-makers attending the workshop noted that the S&T indicators list was selected carefully in order to be broad-based and comprehensive, with more effective decisions concerning provincial and local resource allocation as its goal. Objectives the S&T indicators tried to address included helping to convince stakeholders about what poverty means and where it is located, in order to persuade all tiers of government to allocate funds to poverty as against competing needs, and to justify allocating funds to specific areas. Workshop participants noted that at present, government resources don t necessarily coincide with poverty pockets, and asked four key questions: o How can poverty be identified in differing areas? o Where do we locate anti-poverty resources? o Will the indicators we can develop match up with the way people see it on the ground? o In any decision to allocate money to a particular area, can we show that the standards in use are informed by objective indicators and are actually equal and just? The questions raised by the inputs of the stakeholders related closely to the findings of the qualitative aspect of the research, which reflected shortage of resources for addressing the urgent needs created by in-migration of the poor, with administrators struggling to to obtain anti-poverty funds and allocate them to where they could best have impact and create multiplier effects. Likewise, both the workshop and the qualitative research inputs emphasized incomprehension in government concerning how poverty works, and the lack of data on the subject to inform officials. The critical role of Johannesburg/Tshwane as South Africa s primate city, in creating economic growth and maintaining international competitiveness, provided a constant background to the discussions. With regard to migration, workshop participants also pointed out that surviving in Gauteng needs education and skills which are supplied at a cost, so that it is important to persuade people who have received expensive skills and qualifications through government investment not to move away from the province. To support this goal, there was concern that new in-migrants should be fully integrated into the city in all aspects of urban life. But there was also concern for how rising levels of migration could be controlled, influenced or managed within the provisions of the Constitution, in such a way that Gauteng should not receive more in-migrants than could be provided with a decent life within a sustainable budget and time frame. 61

68 Given this background of urgent choices in policy and resource allocation, the researchers noted in the workshop discussions that the present version of the poverty is actually narrow rather than broad-based in how it operates: that is, because there are a large number of indicators on which the poor must qualify, the in practice selects certain kinds and levels of poverty which are tied to lack of formal housing and services. In that these housing-related indicators dominate the poverty, and because of the difficulty of getting the data needed to include the malnutrition and social grants indicators, there is a risk of identifying poverty very narrowly, in pre-determined categories only. That is, results available at the time of the workshop suggested that if the poverty were used as it is currently structured only shack settlements would be likely to qualify as poverty pockets. This outcome could occur because the ten-item version of the is more heavily weighted toward lack of housing and services rather than toward income poverty. As a result, income poverty in the older townships would not be prioritized, and such areas might not be identified as poverty pockets even if they were very poor in terms of income and unemployment, and even if housing was decaying and service delivery was very ineffective. Much the same would apply to very low-cost rental accommodation. The relation to migration would also be identified fairly narrowly, in that the present ten-item prioritizes the middle poor in shack areas, and in these areas migration is high. In the poorest areas, which may sometimes be missed by the, migration is usually moderate unless the settlement is still in the process of becoming established. In order to address the issues raised at the stakeholder workshop, the HSRC research team subsequently held in-house discussions on the ideal composition of the and how possible modifications might affect outcomes (results and recommendations are presented in 5.1, under Conclusions, below). To move the discussions forward, a principal-components factor analysis statistics procedure was also carried out in order to show more clearly how the ten items of the actually relate to each other. The results of the factor analysis which shows how closely associated the different indicators are when treated as statistical variables are presented here (Figure 1). The factor analysis procedure largely confirms our thinking concerning how the separate indicators fit together to make up the, and shows why the current version of the relies most heavily on the cluster of indicators linked to informal housing. Factor analysis is based on the statistical correlations among all the variables entered, and shows how they fit together based on how closely inter-correlated they are as they work to explain the variation in the data. 62

69 FIGURE 1 FACTOR PLOT OF THE TEN INDIVIDUAL POVERTY INDICA- TORS GAUTENG POVERTY INDICATORS Rotation Method: Promax (power = 3) Plot of Reference Structure for Factor1 and Factor2 Reference Axis Correlation = Angle = Factor1 1 G.9 I.A D.7 C H.6.5 B.3.4 F.2 J F a c 1.0t o r Informal=A Fem_hhds=B Water=C Electr=D Sanitat=D Refuse=F Income=G Educat=H Unempl=I Crowded=J 63

70 Figure 1 shows the final rotation for Factor 1, the main explanatory cluster of indicators, cantered on informal housing. This Factor 1 cluster is located at the top end of the vertical axis of the factor plot, and shows informal housing very closely associated with absence of electricity, sanitation, and water delivery. These last three indicators are so closely linked that electricity overlies sanitation on the plot. This tight combination is not surprising, since as of 2001 these services were routinely delivered as part of a package with formal housing, and it could be statistically predicted that shack areas would lack such services although, in the intervening years, the major metro cities have become efficient in providing temporary services to new shack areas. This tight cluster of four indicators dominates the statistical results for the as a whole. These housing and services indicators are also linked closely to illiteracy, while poverty-level incomes and unemployment are also part of this cluster, but a little further away, and on the opposite side of the axis. Altogether, Factor 1 explains most of the statistical variation accounted for by the as a whole. In total, it comprises the seven indicators informal housing, electricity, sanitation, water, illiteracy, poverty incomes and unemployment, but it is dominated by the first five, which stick closely together on the right of the upper axis. By comparison, Factor 2 is weak and scattered, and explains much less of the statistical variation in the data. In this second cluster, female head is very loosely associated with crowding and refuse removal, but the interrelations of these indicators are not at all close. This result is to be expected in that, as indicated in 3.2 above, all three of these indicators predicted weakly in relation to both poverty and migration, showing little variation between areas with high poverty and areas with high migration. While it is normal in factor analysis procedures for the first cluster of variables Factor 1 to absorb most of the variation in the data and leave much less for the second independent cluster to account for, in this case Factor 2 is particularly weak and ineffectual. The results here highlight the combined predictive power of the housing and services cluster of indicators in Factor 1, with poverty incomes and unemployment as outlying members of this tight grouping. In terms of what kinds of poverty the in its current form will select, the factor analysis procedure shows the centrality of informal housing, and helps to confirm that shack settlements which are characterized by informal housing and lack of formal services will tend to be selected as poor in preference to poor areas with other kinds of housing. This is likely to occur because the indicators linked to absolute poverty unemployment and poverty-level income will be outweighed by the combined force of the other five strong indicators associated with informal housing. That is, as the data reviewed in 3.3 suggest, likely poverty pockets in settlements such as some of the older townships or inner-city rentals, which may be very poor but which are recorded on the Census as housed and serviced, may be missed out by the poverty in its current form. 64

71 5 CONCLUSIONS: EMPTYING POVERTY POCKETS? The results of this study are still at an early stage of inquiry, and require further exploration in order to confirm and understand the trends identified. Further work should be done with the indicator list using Census data as indicated below (Section 5.3, Recommendations). More work is also required in order to better differentiate migration and poverty constituencies in relation to the kinds of areas they select, as shown in the qualitative interviews and focus groups, and in relation to the important finding about whether and how migration rises as government delivers successfully and poverty abates. From the current study, main findings address the indicators in relation to poverty and migration on the one hand, and the wider policy implications on the other. 5.1 Indicators As reflected in the stakeholder workshop and in the study brief to HSRC, the clear intent behind the poverty has been to obtain a broad-based set of indicators which could identify different forms of poverty across the province, so that official spending could be efficiently and justly prioritized. A range of twelve indicators was identified by the Strategy & Tactics consultancy in consultation with the Gauteng Provincial Poverty Alleviation Committee, and were included in the poverty list for this purpose. o An explicit concern has been that all the indicators chosen would be both measurable and subject to government delivery, so that government could effectively address their effects o The implicit assumption appears to have been that all these indicators would have approximately equal weight in selecting areas which could be designated as poverty pockets. To date, and partly through unforeseen circumstances, the trial use of the poverty to identify poverty pockets at sub-place level has not entirely met these priorities. As of the time of the stakeholder workshop, Census data needed for the indicators was not available at a level that would identify poverty pockets, which often contain a population of less than 300 people. Although HSRC was able to use statistical manipulations to create a dataset based on the Census for most of the indicators at sub-place level, this improvised solution did not resolve all the problems created by the unexpected non-release of Census data at enumerator-area level. The need to project data down to sub-place level risks picking up and multiplying fuzziness or distortions created by reporting errors. Using available official datasets, data cannot be produced at sub-place level for the indicators concerning malnutrition and social grants. In the light of the factor analysis and the other findings to date, it appears that the absence of these indicators leaves the poverty seriously unbalanced. 65

72 The factor analysis confirms very tight clustering of the four indicators linked to informal housing and to lack of hard services in such areas. Together, these closely linked indicators may be able to outweigh the others in the poverty list, and the data given in 3.3 above suggests that they act to tilt the toward selecting shack areas, ahead of other types of area which may be extremely poor in absolute terms but have at some point had some housing and services delivery. The factor analysis process suggests that adding 2-3 additional indicators would help significantly to improve results. Speculating ahead of any empirical test, it looks fairly likely that having data on the other Strategy & Tactics indicators social grants and child malnutrition would yield two factor clusters of more equal weight, so that the might become more genuinely broad-based in line with the original intent. Both social grants and child malnutrition are often associated with the households of poor women, and might allow a stronger second factor to form around the femaleheads indicator, especially if a weighting for age allowed for greater concentration on younger women who are often more likely to be poor than older widows. Such a new cluster might pull the income and unemployment indicators to a more central position, where in principle they would be expected to locate. Conversely, since the electricity, water and sanitation indicators are at present so closely correlated that any one of the three will almost perfectly predict the other two, it may be useful to consider stripping out one or two of these three. Any area which receives electricity will almost certainly have the other two, and this kind of redundancy implies there is limited need to assign three slots in the poverty. Including all three indicators in effect ensures that the hard-services issue is counted three times whenever the is applied, risking unintended bias. The effects of adding in crime, as proposed by the research team at the stakeholder workshop, are less easy to predict. However, crime is a major factor in relation to poverty, and also relates closely to migration (see for instance Kok et al 2003). The more serious crimes are fairly well measured in police statistics and may be accessible at sub-place level, while anti-crime measures through the police are part of service delivery, and can be acted on directly by government. Crime rates are unlikely to be tightly clustered with either informal housing or poor women s households. Assuming availability of the data, a measurement for local crime rates could further broaden the base of the poverty by dispersing the present unintended concentration of the weight of the indicators. 5.2 Understanding poverty and migration Results of the quantitative work confirm the complexities shown by the interviewing and focus groups. Although initial results at main-place level suggested that the relation between poverty and migration in Gauteng might be a simple linear one, this has not proved to be the case. Instead, closer to ground level, the amounts of migration activity associated with poverty pockets that accommodate severe income poverty are average for the province as a whole: at the same time, poverty areas associated with middle poverty show up as extremely turbulent demographically, and often seem to have very high migration levels. It appears that high migration is most strongly linked to middle poverty. As in 66

73 other countries, it may take place with rising frequency as poor but determined households accumulate the resources they need to change their life chances by moving to new localities. The choice of such destination areas is not the same for all sub-groups, as shown both in the qualitative work and in the literature. Young mobile individuals may often choose the inner city, and youth looking for independence may break with parental households and put up shacks. Poor people moving from within the city may choose the expanding shack areas in the rapidly-developing settlements between Johannesburg and Tshwane, while rural-born migrants are rapidly moving into and through the entry-port areas with access to the farms and homelands, and many international migrants choose rental accommodation in particular localities where their networks are localized. The larger areas of high migration appear on the GIS maps as concentrated in the entry-port areas funnelling in rural-to-urban migration, but not enough is currently known about movement to and through the inner cities. Further mapping work and field study is needed here. The relation of poverty to migration as perceived in policy circles will therefore depend on how the poverty pockets are identified. If pockets are defined in terms of income poverty only, then migration activity associated with poverty will probably appear as strong but less significant. If pockets are identified using a spread of indicators, then migration is likely to appear as an extremely important factor. The cumulative effect of this population movement on delivery and anti-poverty options for the province and for the cities needs to be unpacked in greater detail, with inquiries anchored at the micro-local level. The reported subjective effect is that both established residents, city officials and other migrants all see Gauteng s pervasive and turbulent migration processes as a threat to their interests. Perhaps one of the most significant findings implied in the apparent distribution of migration activity across the poverty pockets identified in the study is the likely prospect that successful government delivery to the very poor will cause a rise in migration activity. This so because it is the middle poor much more than the very poor who migrate. To the extent that government efforts to reach the poor justly and effectively such as the work being done with the poverty meet their goals and channel official spending to where it is most needed, one of the immediate rewards to Gauteng will not be greater demographic stability, but increased population movement as the escape from poverty gathers speed. This finding underlines the need for an understanding in depth of migration dynamics and determinants. Whether or not it appears as a risk to provincial goals, migration is a highly diffuse phenomenon protected explicitly by human rights conventions, and therefore cannot be ordered or controlled. For Gauteng to find some level of predictability in its unstable migration context, the critical requirement will be to grasp the underlying inducements and incentives affecting different groupings involved in migration. There is probably no other way 67

74 for the province and the cities to become able to exert influence over this apparently chaotic process than through the levers of the policy process itself. 5.3 Recommendations: toward a greater understanding of poverty and migration in Gauteng Flowing from the research so far, it is possible to suggest at least two concrete steps that may be considered: It is recommended that: The poverty be adjusted to include a total of ten indicators, namely informal housing, electricity, illiteracy, income, unemployment, younger female head, crowding, social grants, child malnutrition, and crime rates. At present, sanitation and water delivery appear to be redundant and a cause of over-concentration, and refuse collection is a very weak indicator with little prediction potential. It should be noted that it will not be possible to add social grants and child malnutrition to the indicator list without the use of local survey work to obtain data directly. It is further recommended that: Local surveys of poverty pockets be carried out regularly on a rotating basis, allowing (1) monitoring of the development of poverty pockets and of change over time (2) monitoring the relative success of Gauteng anti-poverty efforts (3) collecting data at local level to measure those poverty indicators that are not now available from Census data (4) use of appropriate imputation techniques to create and update city-wide maps of poverty and migration, based on data from a limited number of local surveys to be conducted yearly. 68

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81 APPENDIX 1 MIGRATION COMPONENT An analysis of the and migration patterns to, from and within Gauteng was undertaken on the basis of Migration Community Profile data that were made available by Statistics South Africa from the 1996 and 2001 censuses. 14 Separate data sets were provided for each of the main political or administrative spatial units in the country: for Census 1996 the migration data were provided at an enumerator area (EA) level, and for the 2001 census in respect of province, metropolitan/district municipality, metropolitan/local municipality, magisterial district and main place (the latter being an easily recognisable, small-area geographical entity at local level, and corresponds, for example, to the name of the city, town, tribal area or administrative area (Statistics South Africa 2004:7). 15 The lowest spatial level for which both origin and destination (migration) data were made available is therefore main place. In-migration data are, however, available as far down in the geographical hierarchy as the ward level, but without migration-origin data being available at the same (i.e. ward) level it is not possible to calculate net migration rates at this level of spatial detail. The net migration analyses to be presented in this report are therefore reported at the main place or higher geographical level. Migration rates The most frequently used migration indicator is the so-called migration rate, which refers to the level of in-migration, out-migration or net migration (being in-migration minus outmigration) compared to the size of the population concerned. The generic formula for the calculation of a migration rate is as follows: where: m ij = M P m ij = rate of migration from i to j during a specified time interval; M ij = number of migrants moving from i to j during the given time interval; P = the population concerned (i.e. either in i or j) at a particular point in the given time interval, and k = a constant (usually 100, so as to express the quotient as a percentage). There is an interesting theoretical debate around the issue of what P should be. The general agreement seems to be, however, that P should be the population at the risk of migration (which could be interpreted as either the population at risk of migrating or the population at risk of receiving migrants ). This issue does not create problems in the case of out-migration rates, but arises when in-migration rates or net migration rates must be calculated. ij k 14 The Migration Community Profile data from Census 1996 and Census 2001 were made available by Mr Piet Alberts and Ms Lana Evtimova (respectively) of Stats SA. Their kind assistance in this regard is greatly appreciated and acknowledged with gratitude. 15 A total of unique main places were recorded in the country for the purposes of Census 2001, but because of the coding structure and because some main place names cross the boundaries of adjacent municipalities, a total of main place names were coded (Statistics South Africa 2004:7). The five-digit code for each main place was constructed as follows: The first digit denotes the province, the second and third digits indicate the municipality, and the last two digits identify a unique main place in that municipality. 75

82 Another topic of some debate, also relating to the population at the risk of migration (P), is what the most appropriate point in the given time interval should be. The consensus seems to be that it should, for various reasons, preferably be the mid-point of the time interval. If, however, the population at the mid-point cannot be directly obtained or reliably estimated, the population at either end point of the interval can be used, provided that a clear motivation for selecting that particular end point is provided. Three sets of migration rates are provided in this report, namely for in-migration, outmigration and net migration. The 1996 and 2001 South African census data will be used here to populate the rates. For the calculation of the migration rates only the endpoint (1996) population can be used as P, because the 1991 census excluded the populations of the former homeland areas of Gauteng. Although it would be possible to use an estimated mid-point population for the period , it is suggested that the end-point (2001) population is used for P here as well so as to ensure the necessary comparability between the periods and Furthermore, since the full 2001 migration data have so far only been released at the provincial level, only inter-provincial migration rates can be calculated. To populate the migration indicators from the 1996 census data, the period 1 January 1992 to 10 October 1996 will be used. Similarly, the period 11 October 1996 and 10 October 2001 will be used for the period. These roughly represent five-year intervals, and the time that elapsed between the beginning and end of the interval is short enough to warrant the use of the end-point population instead of the (ideal) mid-point population sizes. Out-migration rates The formula for calculating the out-migration rate can be given as follows: m i = M P i i k where: m i = rate of migration from i to all other destinations during a particular time interval; M i = number of migrants moving from i to all other destinations during the given time period; P i = the population concerned (in i) at the end of the given time period (10 October 1996/2001), and k = constant (100). The out-migration rates for the periods and for the various local governments are given in Table 1 and in respect of their constituent main places in Table 2. In Table 3 the in-migration details are provided for main places found in more than one local government. The numbers and rates provided in Tables 1 3 were calculated from Migration Community Profile data provided by Statistics South Africa (Census 1996/2001). 76

83 Table 1 Out-migration from Gauteng local governments during the periods and Local government Mean Out-migrants Rate* Out-migrants Rate Out-migrants Rate* Mogale City % % % West Rand * % * Randfontein % % % Westonaria % % % Emfuleni % % % Midvaal % % % Lesedi % % % Nokeng tsa Taemane % % % Ekurhuleni % % % City of Johannesburg % % % City of Tshwane % % % Kungwini % % % * Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved. Table 1 shows that West Rand s non-urban Gauteng component clearly had the greatest mean out-migration rate (in excess of 100%), but this can be ascribed to an unrealistically high outmigration rate for Other local governments in the province with total mean outmigration rates in excess of one-fifth of the mean population were Merafong City (35%), Midvaal (32%), City of Johannesburg (23%), Kungwini (21%), and City of Tshwane (21%). Nokeng tsa Taemane and Randfontein (both 13%) had the lowest mean out-migration rates. From Table 2 it can be concluded that that 14 main places had mean out-migration rates in excess of 100 per cent over the two periods. These are: (1) the City of Johannesburg part of Randfontein, (2) Springs (Lesedi part), (3) Vaal Marina (Midvaal), (4) Vergenoeg (Nokeng tsa Taemane), (5) Ekandustria (Kungwini), (6) Blybank (Merafong City), (7) Edenvale (City of Johannesburg part), (8) Pretoria (Kungwini part), (9) Kempton Park (City of Johannesburg part), (10) Western Deep Levels Mine (Merafong City part), (11) Bultfontein (City of Johannesburg), (12) West Rand (Gauteng non-urban part), (13) Evaton (Midvaal part), and (14) Modderfontein (in Westonaria). The main places with these excessive mean outmigration rates are (in general) small areas or components of main places with mean population sizes ranging from 27 (Ekandustria, a predominantly industrial part of Kungwini) to 8192 (a small part of Randfontein situated in the City of Johannesburg), with a mean population size of only Ignoring rates in excess of 100 per cent, the following main places had mean out-migration rates much higher than the average for all Gauteng main places (of 21 per cent) in order of magnitude: Brandvlei (Randfontein):...78% Bapsfontein (Ekurhuleni):...77% Suikerbosrand Nature Reserve (Midvaal):...67% Devon (Lesedi):...57% Muldersdrift (Mogale City):...46% Waterpan (Westonaria):...40% 77

84 Table 2 Out-migration from Gauteng local governments and main places during the periods and Local government Mogale City Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Kagiso % % % Krugersdorp % % % Magaliesburg % % % Mogale City Non-urban % % % Muldersdrift % ** % Munsieville % % % Orient Hills % % Rietvallei % % % Mogale City ** % % Total % % % West Rand West Rand Non-urban % ** Randfontein Bhongweni % % Brandvlei ** % Mohlakeng % % % Panvlak Gold Mine % % % Randfontein % % % Toekomsrus % % % Zenzele % % Randfontein Non-urban % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

85 Table 2 (continued) Local government Westonaria Emfuleni Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Bekkersdal % % % Cooke Mine % % % Elsburg Gold Mine % % % Etlebeni % % Glen Harvie % % % Hills Haven % % % Johannesburg % % Kloof Gold Mine % % % Leeudoorn Mine % % % Libanon Gold Mine % % % Modderfontein ** ** Panvlak Gold Mine % % % Randfontein Mine % % % Venterspost % % % Waterpan % ** % Westonaria % % % Westonaria Non-urban % % % Total % % % Boipatong % % % Bophelong % % % Emfuleni Non-urban % % % Evaton % % % Orange Farm % % % Sebokeng % % % Sharpeville % % % Tshepiso % % % Vanderbijlpark % % % Vereeniging % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

86 Table 2 (continued) Local government Midvaal Lesedi Nokeng tsa Taemane Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Alberton % % % Evaton ** ** Meyerton % % % Midvaal Non-urban % % % Randvaal % % % Suikerbosrand Nature Reserve % ** % Vaal Marina % ** ** Vereeniging % % % Walkerville % % % Total % % % Devon % ** % Heidelberg % % % Impumelelo % % % Lesedi Local Municipality % % % Nigel % % % Ratanda % % % Springs % ** ** Total % % % Baviaanspoort % % Cullinan ** % % Kekana Gardens % % Nokeng tsa Taemane Non-urban % % % Onverwacht % % Rayton % % % Refilwe % % % Roodeplaat Dam Nature Reserve % ** % Vergenoeg ** ** Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

87 Table 2 (continued) Local government Ekurhuleni Metro Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Alberton % % % Bapsfontein ** % Bedfordview % % % Benoni % % % Boksburg % % % Brakpan % % % Cerutiville % % Chief Albert Lithuli Park % % Daveyton % % % Duduza % % % Dukathole % % % Edenvale % % % Ekurhuleni Metro Non-urban % % % Etwatwa % % % Germiston % % % Katlehong % % % Kempton Park % % % KwaThema % % % Lindelani Village % % Midrand % % % Nigel % % % Reiger Park % % % Springs % % % Tembisa % % % Thokoza % % % Tsakane % % % Vosloorus % % % Wattville % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

88 Table 2 (continued) Local government City of Johannesburg Metro Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Alexandra % % % Bultfontein ** ** City of Johannesburg Non-urban % % % Diepkloof % % % Diepsloot % % % Ebony Park % % % Edenvale ** ** ** Ivory Park % % % Johannesburg % % % Kagiso % ** % Kempton Park % ** ** Klipfontein View % % Mayabuye % % Meadowlands % % % Midrand % % % Nooitgedacht % % Orange Farm % % % Pipeline % % Poortjie % % % Rabie Ridge % % % Randburg % % % Randfontein % ** ** Roodepoort % % % Sandton % % % Slovoville % % Soweto % % % Sweetwaters % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

89 Table 2 (continued) Local government City of Johannesburg Metro (continued) City of Tshwane Metro Kungwini Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Tshepisong % % % Vlakfontein % % % Wheeler's Farm % % % Zandspruit % % % Zevenfontein % % % Total % % % Akasia % % % Atteridgeville % % % Centurion % % % City of Tshwane Non-urban % % % Ga-Rankuwa % % Hammanskraal % % % Knopjeslaagte % % Mabopane % % % Mamelodi % % % Nellmapius % % % Olievenhoutbos % % Pretoria % % % Saulsville % % % Soshanguve % % % Temba ** % % Total % % % Bronkhorstspruit % % % Ekandustria ** ** Kungwini Non-urban % % % Pretoria % ** ** Rethabiseng % % % Sehlakwana % % Zithobeni % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

90 Table 2 (continued) Local government Merafong City Out-migration over the two periods Main place * Means: & Outmigrants (1996) migrants (2001) migrants population Population Out- Population Out- Mean Rate** Rate** Rate** Blybank ** % ** Blyvooruitzicht % % % Carletonville % % % Deelkraal % % % Doornfontein % % East Driefontein Mine ** % % Elands Ridge % % % Khutsong % % % Letsatsing % % % Merafong City Non-urban % % % Oberholzer % % % Phomolong % % Welverdiend % % % West Driefontein % % % Western Deep Levels Mine ** % ** Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

91 Table 3 Main places found in more than one local government: Out-migration volumes and rates for two periods ( and ) and the population sizes on which the migration rates are based Out-migration for two periods ( and ) and population sizes on which the migration rates are based Main place Alberton Edenvale Evaton Johannesburg Kagiso Kempton Park Midrand Nigel Orange Farm Local government Outmigrants Means: & Outmigration 2001 population migration migration Out- Out Outmigrantmigrants population Out- Mean population rate* rate* rate* Ekurhuleni % % % Midvaal % % % Total % % % City of Johannesburg * * * Ekurhuleni % % % Total % % % Midvaal % % % Emfuleni * * Total % % % City of Johannesburg % % % Westonaria * * Total % % % City of Johannesburg % % % Mogale City % % Total % % % City of Johannesburg % * % Ekurhuleni % % % Total % % % City of Johannesburg % * * Ekurhuleni % % % Total % % % Ekurhuleni % % % Lesedi % % % Total % % % City of Johannesburg % % % Emfuleni % % % Total % % % * Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

92 Table 3 (continued) Main place Panvlak Gold Mine Pretoria Randfontein Springs Vereeniging Local government Out-migration for two periods ( and ) and population sizes on which the migration rates are based Outmigrants Means: & Outmigration 2001 population migration migration Out- Out Outmigrantmigrants population Out- Mean population rate rate rate Westonaria % % % Randfontein % % % Total % % % Kungwini % % % City of Tshwane % % % Total % % % City of Johannesburg % % % Randfontein % % * Total % % % Ekurhuleni % * * Lesedi % % % Total % % % Midvaal % % % Emfuleni % % % Total % % % * Where the calculated out-migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

93 Please note, however, that migration and population data were used for each portion of every cross-boundary main place, and some small portions of main places might have had unrealistic migration volumes and rates. For that reason Table 3 is also provided, and it largely confirms this conclusion. The mean out-migration rates for the totals for the abovementioned main places with out-migration rates in excess of 100% that are spread out over two local governments, namely Randfontein (26%), Springs (29%), Edenvale (26%), Pretoria (31%), Kempton Park (23%) and Evaton (28%) are far more realistic. The seven main places with the lowest mean out-migration rates (of one per cent or less) were: (1) Mayabuye (City of Johannesburg), (2) Olievenhoutbos (City of Tshwane), (3) Chief Albert Lithuli Park (Ekurhuleni), (4) Tshepisong (City of Johannesburg), (5) Kekana Gardens (Nokeng tsa Taemane), (6) Panvlak Gold Mine (Randfontein part), and (7) Nellmapius (City of Tshwane). The mean population sizes of these areas range from 1276 (Panvlak) to (Tshepisong). Their mean population size is Looking only at the mean out-migration numbers over the periods and , it is clear that the volume of out-migration over the two periods combined were greatest for Johannesburg ( ), and Soweto ( ), Pretoria ( ), Katlehong (48 270), Roodepoort (47 625), Mamelodi (42 660), and Tembisa (41 424). The mean out-migration rates for these areas, Johannesburg (15%), and Soweto (10%), Pretoria (15%), Katlehong (8%), Roodepoort (15%), Mamelodi (10%), and Tembisa (7%), indicate that the impact of the out-migration might not have been as great. In-migration rates For the in-migration rate the following formula applies: where: m j = M P j j m j = rate of migration from all other origins to the destination j during a particular time interval; M j = number of migrants moving from all other origins to the destination j during the given time period; P j = the population concerned (in j) at the end-point of the given time period (in this case, 10 October 1996/2001), and k = constant (100). The in-migration rates for the periods and for the various local governments are given in Table 4 and in respect of their constituent main places in Table 5. In Table 6 the in-migration details are provided for main places found in more than one local government. The numbers and rates provided in Tables 4 6 were again calculated from Migration Community Profile data provided by Statistics South Africa (Census 1996/2001). k 87

94 Table 4 In-migration into Gauteng local governments during the periods and Local government * Mean No. Rate No. Rate No. Rate Mogale City % % % West Rand 143 9% % 389 5% Randfontein % % % Westonaria % % % Emfuleni % % % Midvaal % % % Lesedi % % % Nokeng tsa Taemane % % % Ekurhuleni % % % City of Johannesburg % % % City of Tshwane % % % Kungwini % % % Merafong City % % % Table 4 shows that the following local governments experienced mean in-migration rates in excess of 20% for the periods and : Nokeng tsa Taemane (31%), Midvaal (23%), Kungwini (23%), City of Tshwane (22%), and Westonaria (20%). Only one local government experienced a mean in-migration rate of less than 10 per cent, namely the Gauteng part of West Rand Non-urban (5%). From Table 5 can be concluded that 10 main places in Gauteng experienced mean inmigration rates in excess of 30 per cent. These are: (1) Kekana Gardens (42%), (2) Nooitgedacht (42%), (3) Temba (41%), (4) Tshepisong (40%), (5) Slovoville (36%), (6) Cullinan (36%), (7) Nellmapius (34%), (8) Baviaanspoort (32%), (9) Ebony Park (32%), and (10) the City of Johannesburg part of Kagiso (31%) 16. The lowest mean in-migration rates (of two per cent) were experienced by four main places, namely (1) Impumelelo (Lesedi), (2) West Driefontein (Merafong City), (3) Modderfontein (Westonaria), and (4) Atteridgeville (City of Tshwane). The main places with mean in-migration rates much higher than the average for all Gauteng main places (of 18%) were (in order of magnitude): Kekana Gardens (Nokeng tsa Taemane):...42% Nooitgedacht (City of Johannesburg):...42% Temba (City of Tshwane):...41% Tshepisong (City of Johannesburg):...40% Slovoville (City of Johannesburg):...36% Cullinan (Nokeng tsa Taemane):...36% Nellmapius (City of Tshwane):...34% Baviaanspoort (Nokeng tsa Taemane):...32% Ebony Park (City of Johannesburg):...32% Kagiso (City of Johannesburg part):...31% 16 Table 6 shows, however, that Kagiso as a whole (i.e. covering both the parts in Mogale City and the City of Johannesburg) experienced an in-migration rate of only 13%. 88

95 Table 5 In-migration into Gauteng local governments and main places during the periods and Local government Mogale City In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Kagiso % % % Krugersdorp % % % Magaliesburg % % % Mogale City Non-urban % % % Muldersdrift % % % Munsieville % % % Orient Hills % % Rietvallei % % % Mogale City % % % Total % % % West Rand West Rand Non-urban % % % Randfontein Bhongweni % % Brandvlei % % Mohlakeng % % % Panvlak Gold Mine % % % Randfontein % % % Toekomsrus % % % Zenzele % % Randfontein Non-urban % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

96 Table 5 (continued) Local government Westonaria Emfuleni In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Bekkersdal % % % Cooke Mine % % % Elsburg Gold Mine % % % Etlebeni % % Glen Harvie % % % Hills Haven % % % Johannesburg % % Kloof Gold Mine % % % Leeudoorn Mine % % % Libanon Gold Mine % % % Modderfontein % % Panvlak Gold Mine % % % Randfontein Mine % % % Venterspost % % % Waterpan % % % Westonaria % % % Westonaria Non-urban % % % Total % % % Boipatong % % % Bophelong % % % Emfuleni Non-urban % % % Evaton % % % Orange Farm % % % Sebokeng % % % Sharpeville % % % Tshepiso % % % Vanderbijlpark % % % Vereeniging % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

97 Table 5 (continued) Local government Midvaal Lesedi Nokeng tsa Taemane In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Alberton % % % Evaton % % Meyerton % % % Midvaal Non-urban % % % Randvaal % % % Suikerbosrand Nature Reserve % % Vaal Marina % % % Vereeniging % % % Walkerville % % % Total % % % Devon % % % Heidelberg % % % Impumelelo % % % Lesedi Local Municipality % % % Nigel % % % Ratanda % % % Springs % % Total % % % Baviaanspoort % % Cullinan % % % Kekana Gardens % % Nokeng tsa Taemane Non-urban % % % Onverwacht % % Rayton % % % Refilwe % % % Roodeplaat Dam Nature Reserve % % % Vergenoeg % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

98 Table 5 (continued) Local government Ekurhuleni Metro In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Alberton % % % Bapsfontein % % Bedfordview % % % Benoni % % % Boksburg % % % Brakpan % % % Cerutiville % % Chief Albert Lithuli Park % % Daveyton % % % Duduza % % % Dukathole % % % Edenvale % % % Ekurhuleni Metro Non-urban % % % Etwatwa % % % Germiston % % % Katlehong % % % Kempton Park % % % KwaThema % % % Lindelani Village % % Midrand % % % Nigel % % % Reiger Park % % % Springs % % % Tembisa % % % Thokoza % % % Tsakane % % % Vosloorus % % % Wattville % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

99 Table 5 (continued) Local government City of Johannesburg Metro In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Alexandra % % % Bultfontein % % City of Johannesburg Non-urban % % % Diepkloof % % % Diepsloot % % % Ebony Park % % % Edenvale % % % Ivory Park % % % Johannesburg % % % Kagiso % % % Kempton Park % % % Klipfontein View % % Mayabuye % % Meadowlands % % % Midrand % % % Nooitgedacht % % Orange Farm % % % Pipeline % % Poortjie % % % Rabie Ridge % % % Randburg % % % Randfontein % % % Roodepoort % % % Sandton % % % Slovoville % % Soweto % % % Sweetwaters % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

100 Table 5 (continued) Local government City of Johannesburg Metro (continued) City of Tshwane Metro Kungwini In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Tshepisong % % % Vlakfontein % % % Wheeler's Farm % % % Zandspruit % % % Zevenfontein % % % Total % % % Akasia % % % Atteridgeville % % % Centurion % % % City of Tshwane Non-urban % % % Ga-Rankuwa % % Hammanskraal % % % Knopjeslaagte % % Mabopane % % % Mamelodi % % % Nellmapius % % % Olievenhoutbos % % Pretoria % % % Saulsville % % % Soshanguve % % % Temba % % % Total % % % Bronkhorstspruit % % % Ekandustria % % Kungwini Non-urban % % % Pretoria % % % Rethabiseng % % % Sehlakwana % % Zithobeni % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

101 Table 5 (continued) Local government Merafong City In-migration over the two periods Main place * Means: & Inmigrants (1996) migrants (2001) migrants population Population In- Population In- Mean Rate Rate Rate Blybank % % % Blyvooruitzicht % % % Carletonville % % % Deelkraal % % % Doornfontein % % East Driefontein Mine % % % Elands Ridge % % % Khutsong % % % Letsatsing % % % Merafong City Non-urban % % % Oberholzer % % % Phomolong % % Welverdiend % % % West Driefontein % % % Western Deep Levels Mine % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.)

102 Table 6 Main places found in more than one local government: In-migration volumes and rates for two periods ( and ) and the population sizes on which the migration rates are based In-migration for two periods ( and ) and population sizes on which the migration rates are based Main place Alberton Edenvale Evaton Johannesburg Kagiso Kempton Park Midrand Nigel Orange Farm Local government Inmigrants Means: & Inmigration 2001 population migration migration In- In Inmigrantmigrants population In- Mean population rate rate rate Ekurhuleni % % % Midvaal % % % Total % % % City of Johannesburg % % % Ekurhuleni % % % Total % % % Midvaal % % % Emfuleni % % Total % % % City of Johannesburg % % % Westonaria % % Total % % % City of Johannesburg % % % Mogale City % % % Total % % % City of Johannesburg % % % Ekurhuleni % % % Total % % % City of Johannesburg % % % Ekurhuleni % % % Total % % % Ekurhuleni % % % Lesedi % % % Total % % % City of Johannesburg % % % Emfuleni % % % Total % % %

103 Table 6 (continued) Main place Panvlak Gold Mine Pretoria Randfontein Springs Vereeniging Local government Out-migration for two periods ( and ) and population sizes on which the migration rates are based Outmigrants Means: & Outmigration 2001 population migration migration Out- Out Outmigrantmigrants population Out- Mean population rate rate rate Westonaria % % % Randfontein % % % Total % % % Kungwini % % % City of Tshwane % % % Total % % % City of Johannesburg % % % Randfontein % % % Total % % % Ekurhuleni % % % Lesedi % % Total % % % Midvaal % % % Emfuleni % % % Total % % %

104 The highest mean in-migration volumes were found in Johannesburg (City of Johannesburg part): ; Pretoria (City of Tshwane part): ; Soweto (City of Johannesburg): ; Soshanguve (City of Tshwane): ; Katlehong (Ekurhuleni Metro): ; Tembisa (Ekurhuleni Metro): , and Roodepoort (City of Johannesburg): The mean in-migration rates for these areas were as follows: Johannesburg (10%), Pretoria (12%), Soweto (4%), Soshanguve (12%), Katlehong (7%), Tembisa (7%), and Roodepoort (13%), being far below the highest mean rate of 42% (as experienced by Kekana Gardens and Nooitgedacht). Table 6 shows the mean in-migration rates for the cross-border main places (that are found in two local governments). Midrand (38%), Edenvale (24%), Pretoria (24%), Orange Farm (24%), Kempton Park (23%), Randfontein (21%), Springs (21%), Johannesburg (20%) and Vereeniging (20%) all had mean in-migration rates of 20 per cent or higher. Cross-border main places with mean in-migration rates lower than 20 per cent were Kagiso (13%), Panvlak Gold Mine (15%), Evaton (17%), Nigel (18%) and Alberton (18%). Net migration rates The formula for the net migration rate is as follows: nm i M = where: nm i = net migration rate in respect of area i during a particular time interval; M i = number of migrants moving from all origins to i during the given time period; M i = number of migrants moving from i to all destinations during the given time period; P i i P i M = the population concerned (in i) at the end-point of the given time period (in this case, 10 October 1996/2001), and k = constant (100). Arguably the most significant of the migration rates, the net migration rates for the different areas illustrate the differential net impacts on their populations. In Table 7 the net migration data are provided for the local governments and Table 8 for their constituent main places. In Table 9 the net migration details are provided for main places found in more than one local government. Table 7 Net migration in Gauteng local governments during the periods and Local government Mean No. Rate No. Rate No. Rate Mogale City % % % West Rand % % Randfontein % % % Westonaria % % % Emfuleni % % % Midvaal % % % Lesedi % % % Nokeng tsa Taemane % % % Ekurhuleni % % % City of Johannesburg % % % City of Tshwane % % % Kungwini % % 81 1% Merafong City % % % i k 98

105 Table 8 Net migration in Gauteng local governments and main places during the periods and Local government Mogale City Net migration over the two periods Main place * Means: & Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Kagiso % % % Krugersdorp % % % Magaliesburg % % % Mogale City Non-urban % % % Muldersdrift % % Munsieville % % % Orient Hills % % Rietvallei % % % Mogale City % % Total % % % West Rand West Rand Non-urban % % Randfontein Bhongweni % % Brandvlei Mohlakeng % % % Panvlak Gold Mine % % % Randfontein % % % Toekomsrus % % % Zenzele % % Randfontein Non-urban % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

106 Table 8 (continued) Local government Westonaria Emfuleni Net migration over the two periods Main place * * * Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Bekkersdal % % % Cooke Mine % % % Elsburg Gold Mine % % % Etlebeni % % Glen Harvie % % % Hills Haven % % % Johannesburg % % Kloof Gold Mine % % % Leeudoorn Mine % % % Libanon Gold Mine % % % Modderfontein Panvlak Gold Mine % % % Randfontein Mine % % % Venterspost % % % Waterpan % % Westonaria % % % Westonaria Non-urban % % % Total % % % Boipatong % % % Bophelong % % % Emfuleni Non-urban % % % Evaton % % % Orange Farm % % % Sebokeng % % % Sharpeville % % % Tshepiso % % % Vanderbijlpark % % % Vereeniging % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

107 Table 8 (continued) Local government Midvaal Lesedi Nokeng tsa Taemane Net migration over the two periods Main place * * * Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Alberton % % % Evaton Meyerton % % % Midvaal Non-urban % % % Randvaal % % % Suikerbosrand Nature Reserve % % Vaal Marina % % Vereeniging % % % Walkerville % % % Total % % % Devon % % % Heidelberg % % % Impumelelo % % % Lesedi Local Municipality % % % Nigel % % % Ratanda % % % Springs % % Total % % % Baviaanspoort % % Cullinan % % % Kekana Gardens % % Nokeng tsa Taemane Non-urban % % % Onverwacht % % Rayton % % % Refilwe % % % Roodeplaat Dam Nature Reserve % % % Vergenoeg Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

108 Table 8 (continued) Local government Ekurhuleni Metro Net migration over the two periods Main place * * * Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Alberton % % % Bapsfontein Bedfordview % % % Benoni % % % Boksburg % % % Brakpan % % % Cerutiville % % Chief Albert Lithuli Park % % Daveyton % % % Duduza % % % Dukathole % % % Edenvale % % % Ekurhuleni Metro Non-urban % % % Etwatwa % % % Germiston % % % Katlehong % % % Kempton Park % % % KwaThema % % % Lindelani Village % % Midrand % % % Nigel % % % Reiger Park % % % Springs % % % Tembisa % % % Thokoza % % % Tsakane % % % Vosloorus % % % Wattville % % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

109 Table 8 (continued) Local government City of Johannesburg Metro Net migration over the two periods Main place * * * Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Alexandra % % % Bultfontein City of Johannesburg Non-urban % % % Diepkloof % % % Diepsloot % % % Ebony Park % % % Edenvale % % Ivory Park % % % Johannesburg % % % Kagiso % % % Kempton Park % % Klipfontein View % % Mayabuye % % Meadowlands % % % Midrand % % % Nooitgedacht % % Orange Farm % % % Pipeline % % Poortjie % % % Rabie Ridge % % % Randburg % % % Randfontein % % Roodepoort % % % Sandton % % % Slovoville % % Soweto % % % Sweetwaters % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

110 Table 8 (continued) Local government City of Johannesburg Metro (continued) City of Tshwane Metro Net migration over the two periods Main place * * * Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Tshepisong % % % Vlakfontein % % % Wheeler's Farm % % % Zandspruit % % % Zevenfontein % % % Total % % % Akasia % % % Atteridgeville % % % Centurion % % % City of Tshwane Non-urban % % % Ga-Rankuwa % % Hammanskraal % % % Knopjeslaagte % % Mabopane % % % Mamelodi % % % Nellmapius % % % Olievenhoutbos % % Pretoria % % % Saulsville % % % Soshanguve % % % Temba % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

111 Table 8 (continued) Local government Kungwini Merafong City Net migration over the two periods Main place * * * Net Population Net Population Net Mean Rate** Rate** volume (1996) volume (2001) volume population Rate** Bronkhorstspruit % % % Ekandustria Kungwini Non-urban % % % Pretoria % % Rethabiseng % % % Sehlakwana % % Zithobeni % % % Total % % % Blybank % % Blyvooruitzicht % % % Carletonville % % % Deelkraal % % % Doornfontein % % East Driefontein Mine % % Elands Ridge % % % Khutsong % % % Letsatsing % % % Merafong City Non-urban % % % Oberholzer % % % Phomolong % % Welverdiend % % % West Driefontein % % % Western Deep Levels Mine % % Total % % % * Since the 1996 census-based migration origins were given at magisterial district level only, probability-based conclusions were made as to which main place in the district of origin the migratory move had been made from. (The figures may therefore not be correct in all cases.) ** The net migration rates are based on the population sizes at the end of the migration interval concerned. Where the net migration rates exceeded 100% (in absolute terms) they were not included, because these may indicate possible data problems. (There is no guarantee that data errors do not occur in other cases either.)

112 Table 7 shows that five of the 13 Gauteng local governments experienced positive mean net migration rates during the periods and : Nokeng tsa Taemane (18%), Westonaria (4%), Mogale City (3%), City of Tshwane (2%), and Kungwini (1%). Over the same periods six local governments experienced negative net migration rates: West Rand (-25%), Merafong City (-22%), Midvaal (-9%), City of Johannesburg (-5%), Lesedi (-4%), and Emfuleni (-4%). Zero mean net migration rates were observed in respect of two local governments (Ekurhuleni and Randfontein). The following main places had mean net migration rates much higher than the average for Gauteng main places (-3%): Kekana Gardens (Nokeng tsa Taemane):... 83% Temba (City of Tshwane):... 70% Nellmapius (City of Tshwane):... 59% Slovoville (City of Johannesburg):... 56% Lindelani Village (Ekurhuleni):... 53% Baviaanspoort (Nokeng tsa Taemane):... 47% Nooitgedacht (City of Johannesburg):... 47% Mayabuye (City of Johannesburg):... 44% Diepsloot (City of Johannesburg):... 42% Cullinan (Nokeng tsa Taemane):... 41% From Table 9, which shows the net migration rates for cross-border main places found in more than one local government, it can be concluded that Orange Farm (12%), Midrand (10%) and Panvlak Gold Mine (10%) had positive mean net migration rates. Negative mean net migration rates were experienced by the following cross-border main places: Evaton (-11%), Johannesburg (-10%), Vereeniging (-9%), Springs (-9%), Pretoria (-7%), Randfontein (-5%), Edenvale (-2%), Alberton (-2%). Kagiso, Nigel and Kempton Park had mean net migration rates of zero per cent. Graphs 1 12 and Maps 1 12 illustrate the mean net migration rates for the Gauteng local governments and the main places. 17 The net migration numbers and rates provided in Tables 7 9, Graphs 1 12 and Maps 1 12 were calculated from Migration Community Profile data provided by Statistics South Africa (Census 1996/2001). Graph 1 Mean net migration rates for the periods and : Mogale City and its 'main places' as well as West Rand (Non-urban) 40% 30% 20% Net migration rate* 10% 0% -10% -20% -30% Kagiso Krugersdorp Magaliesburg Mogale City Non-urban Muldersdrift Munsieville Orient Hills Rietvallei Mogale City Total West Rand Non-urban -40% -50% -60% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. 17 The GIS Centre of the HSRC provided the mean net migration maps for the local governments and their main places. The assistance of Ms Gina Weir-Smith in providing these maps is gratefully acknowledged. 106

113 Table 9 Main places found in more than one local government: Net migration volumes and rates for two periods ( and ) and the population sizes on which the migration rates are based Net migration for two periods ( and ) and population sizes on which the migration rates are based Main place Alberton Edenvale Evaton Johannesburg Kagiso Kempton Park Midrand Nigel Orange Farm Local government Means: & Net volume 1996 population Net rate* Net volume 2001 population Net rate* Net volume Mean population Net rate* Ekurhuleni % % % Midvaal % % % Total % % % City of Johannesburg % * % Ekurhuleni % % % Total % % % Emfuleni % % % Midvaal * * Total % % % City of Johannesburg % % % Westonaria % % Total % % % City of Johannesburg % % % Mogale City % % % Total % % % City of Johannesburg % % Ekurhuleni % % % Total % % % City of Johannesburg % % % Ekurhuleni % % % Total % % % Ekurhuleni % % % Lesedi % % % Total % % % City of Johannesburg % % % Emfuleni % % % Total % % % * Where the calculated net migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

114 Table 9 (continued) Main place Panvlak Gold Mine Pretoria Randfontein Springs Vereeniging Local government Net migration for two periods ( and ) and population on which rates are based Means: & Net volume 1996 population Net rate* Net volume 2001 population Net rate* Net volume Mean population Net rate* Westonaria % % % Randfontein % % % Total % % % Kungwini % * % City of Tshwane % % % Total % % % City of Johannesburg % * % Randfontein % % % Total % % % Ekurhuleni % % % Lesedi % * % Total % % % Midvaal % % % Emfuleni % % % Total % % % * Where the calculated net migration rate exceeds 100% it is not shown, because it indicates a serious data problem that could not be solved.

115 Map 4 Mean net migration rates for the periods and : Mogale City and its 'main places', as well as West Rand (Non-urban) 109

116 Map 5 Mean net migration rates for the periods and : Randfontein and its 'main places' 110

117 From Table 8, Graph 1 and Map 1 it is clear that Rietvallei (29%) and Mogale City (26%) experienced the highest positive net migration rates. Magaliesburg (-50%), Muldersdrift (-25%) and West Rand Non-urban (-25%) experienced negative mean net migration rates during the periods and Table 8 shows that by far the highest positive mean net migration volumes were experienced by Rietvallei (6 487) and Mogale City Non-urban (3 548), and their mean net migration rates were 29 and 9 per cent respectively. Negative mean net migration volumes were found in Krugersdorp (-1 222), Magaliesburg (-951), Kagiso (-774), Muldersdrift (-539), and Munsieville (-428), and the corresponding rates were -1% (Krugersdorp), -50% (Magaliesburg), -1% (Kagiso), -25% (Muldersdrift), and -3% (Munsieville). In Graph 2 and Map 2 the mean net migration rates for Randfontein main places are shown. Randfontein Non-urban (12%) and Panvlak Gold Mine (9%) experienced the highest positive mean net migration rates in Randfontein. Graph 2 and Map 2 also show that Bhongweni (-21%) and Zenzele (-6%) had the highest negative mean net migration rates. Graph 2 Mean net migration rates for the periods and : Randfontein and its 'main places' 15% 10% 5% Net migration rate* 0% -5% -10% -15% Bhongweni Brandvlei Mohlakeng Panvlak Gold Mine Randfontein Toekomsrus Zenzele Randfontein Non-urban Total -20% -25% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. The mean net migration position in Westonaria s main places is shown in Graph3 and Map 3. Randfontein Mine (29%), Hills Haven (21%), Glen Harvie (15%), Leeudoorn Mine (15%), Westonaria Non-urban (14%), Panvlak Gold Mine (13%), Kloof Gold Mine (11%) and Elsburg Gold Mine (10%) had the highest positive net migration rates (of 10 per cent or greater). Westonaria (-30%), the Westonaria part of Johannesburg (-23%) and Etlebeni (-10%) had negative mean net migration rates of 10 per cent or more. 111

118 Map 6 Mean net migration rates for the periods and : Westonaria and its 'main places' 112

119 Graph 3 Mean net migration rates for the periods and : Westonaria and its 'main places' 40% 30% 20% Net migration rate* 10% 0% -10% -20% Bekkersdal Cooke Mine Elsburg Gold Mine Etlebeni Glen Harvie Hills Haven Johannesburg Kloof Gold Mine Leeudoorn Mine Libanon Gold Mine Modderfontein Panvlak Gold Mine Randfontein Mine Venterspost Waterpan Westonaria Westonaria Non-urban Total -30% -40% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. Graph 4 and Map 4 illustrate the situation in Emfuleni s main places regarding mean net migration over the two periods and While Tshepiso (28%) and Bophelong (13%) experienced the highest positive net migration rates, Boipatong (-11%) and Sharpeville (-17%) had the highest negative net migration rates. Graph 4 Mean net migration rates for the periods and : Emfuleni and its 'main places' 35% 30% 25% Net migration rate* 20% 15% 10% 5% 0% -5% -10% -15% Boipatong Bophelong Emfuleni Non-urban Evaton Orange Farm Sebokeng Sharpeville Tshepiso Vanderbijlpark Vereeniging Total -20% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. As is clear from Graph 5 and Map 5, which denote the situation in Midvaal Local Municipality, only Vaal Marina (26%) and Walkerville (2%) had positive net migration rates. Vereeniging (-32%) and Alberton (-11%), Suikerbosrand Nature Reserve (-8%), Midvaal Non-urban (-5%) and Randvaal (-4%) experienced negative mean net migration rates over the same two periods. 113

120 Graph 5 Mean net migration rates for the periods and : Midvaal and its 'main places' 30% 20% Net migration rate* 10% 0% -10% -20% -30% Alberton Evaton Meyerton Midvaal Non-urban Randvaal Suikerbosrand Natuurresevaat Vaal Marina Vereeniging Walkerville Total -40% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. Graph 6 and Map 6 show that only one Lesedi main place, Lesedi Local Municipality, experienced a positive mean net migration rate over the periods and The Lesedi part of Springs and Devon had comparatively very high negative mean net migration rates for these two periods (both -38%). Graph 6 Mean net migration rates for the periods and : Lesedi and its 'main places' 10% 5% Net migration rate* 0% -5% -10% -15% -20% -25% -30% -35% -40% Devon Heidelberg Impumelelo Lesedi Local Municipality Nigel Ratanda Springs Total -45% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. In Graph 7 and Map 7 the mean net migration rates experienced by main places in Nokeng tsa Taemane are illustrated. Kekana Gardens (83%), Baviaanspoort (47%), Cullinan (41%) and Refilwe (17%) experienced the highest positive mean net migration rates, while only Roodeplaat Dam Nature Reserve (-17%) experienced negative net migration. 114

121 Graph 7 Mean net migration rates for the periods and : Nokeng tsa Taemane and its 'main places' 100% 80% Net migration rate* 60% 40% 20% 0% -20% -40% Baviaanspoort Cullinan Kekana Gardens Nokeng tsa Taemane Non-urban Onverwacht Rayton Refilwe Roodeplaat Dam Nature Reserve Vergenoeg Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. Total Graph 8 Mean net migration rates for the periods and : Ekurhuleni Metro and its 'main places' 60% 50% Net migration rate* 40% 30% 20% 10% 0% -10% -20% Alberton Bapsfontein Bedfordview Benoni Boksburg Brakpan Cerutiville Chief Albert Lithuli Park Daveyton Duduza Dukathole Edenvale Ekurhuleni Metro Non-urban Etwatwa Germiston Katlehong Kempton Park KwaThema Lindelani Village Midrand Nigel Reiger Park Springs Tembisa Thokoza Tsakane Vosloorus Wattville Total Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. In Graph 8 and Map 8 the mean net migration rates for Ekurhuleni s main places are illustrated. The highest positive mean net migration rates were recorded in Lindelani Village (53%), Ekurhuleni Metro Non-urban (21%) and Cerutiville (15%). KwaThema (-12%) was the only main place in Ekurhuleni with a negative mean net migration rate in excess of (minus) 10 per cent. 115

122 Graph 9 Mean net migration rates for the periods and : City of Johannesburg Metro and its 'main places' 80% 60% 40% Net migration rate* 20% 0% -20% Alexandra -40% -60% City of Johannesburg Non-urban -80% Diepsloot Edenvale Johannesburg Kempton Park Mayabuye Midrand Orange Farm Poortjie Randburg Roodepoort Slovoville Sweetwaters Vlakfontein Zandspruit Total -100% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. Graph 9 and Map 9 show that 14 main places in the City of Johannesburg experienced comparatively high positive mean net migration (of 10 per cent or more). These are: Slovoville (56%), Nooitgedacht (47%), Mayabuye (44%), Diepsloot (42%), Ebony Park (36%), Klipfontein View (33%), Poortjie (32%), Pipeline (28%), Wheeler's Farm (21%), Zandspruit (18%), Randfontein (14%), Orange Farm (14%), Midrand (14%) and Zevenfontein (14%). Main places that experienced negative net migration rates of (minus) 10 per cent or higher were: Edenvale (-88%), Alexandra (-10%), Johannesburg (-10%), Soweto (-11%) and Diepkloof (-11%). In Graph 10 and Map 10 the City of Tshwane main places situation regarding net migration during the periods and is shown. On the one hand Temba (70%), Nellmapius (59%), Olievenhoutbos (38%), City of Tshwane Non-urban (24%), Knopjeslaagte (14%), Soshanguve (13%) and Akasia (13%) all experienced positive mean net migration rates in excess of 10 per cent. Ga-Rankuwa (-33%), Hammanskraal (-21%) and Atteridgeville (-19%), on the other hand, experienced negative mean net migration rates in excess of (minus) 10 per cent. In Kungwini Local Municipality (see Graph 11 and Map 11) three main places experienced positive mean net migration rates of 10 per cent or above: Rethabiseng (16%), the Kungwini part of Pretoria (13%) and Zithobeni (10%). Only Sehlakwana (-15%) had a negative mean net migration rate in excess of 10 per cent. From Graph 12 and Map 12 it should be clear that the following main places in Merafong City experienced mean net migration rates of at least +10 per cent: Blybank (28%), Elands Ridge (22%), Merafong City Non-urban (12%), Deelkraal (11%), Western Deep Levels Mine (10%) and East Driefontein Mine (10%). Over the same periods negative net migration rates of (minus) 10 per cent or higher were experienced by Phomolong (-47%) and Carletonville (-23%). 116

123 Graph 10 Mean net migration rates for the periods and : City of Tshwane Metro and its 'main places' 80% 60% Net migration rate* 40% 20% 0% -20% -40% Akasia Atteridgeville Centurion City of Tshwane Non-urban Ga-Rankuwa Hammanskraal Knopjeslaagte Mabopane Mamelodi Nellmapius Olievenhoutbos Pretoria Saulsville Soshanguve Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. Temba Total Graph 11 Mean net migration rates for the periods and : Kungwini and its 'main places' 20% 15% 10% Net migration rate* 5% 0% -5% -10% Bronkhorstspruit Ekandustria Kungwini Nonurban Pretoria Rethabiseng Sehlakwana Zithobeni Total -15% -20% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. 117

124 Map 7 Mean net migration rates for the periods and : Emfuleni and its 'main places' 118

125 Map 8 Mean net migration rates for the periods and : Midvaal and its 'main places' 119

126 Map 9 Mean net migration rates for the periods and : Lesedi and its 'main places' 120

127 Map 10 Mean net migration rates for the periods and : Nokeng tsa Taemane and its 'main places' 121

128 Map 11 Mean net migration rates for the periods and : Ekurhuleni Metro and its 'main places' 122

129 Map 12 Mean net migration rates for the periods and : City of Johannesburg Metro and its 'main places' 123

130 Map 13 Mean net migration rates for the periods and : City of Tshwane Metro and its 'main places' 124

131 Map 14 Mean net migration rates for the periods and : Kungwini and its 'main places' 125

132 Map 15 Mean net migration rates for the periods and : Merafong City and its 'main places' 126

133 Graph 12 Mean net migration rates for the periods and : Merafong City and its 'main places' 40% 30% 20% Net migration rate* 10% 0% -10% -20% -30% -40% Blybank Blyvooruitzicht Carletonville Deelkraal Doornfontein East Driefontein Mine Elandsridge Khutsong Letsatsing Merafong City Non-urban Oberholzer Phomolong Welverdiend Westdriefontein Western Deep Levels Mine Total -50% -60% Main place * The mean net migration rates are based on the mean 1996 and 2001 population size. Where migration rates exceeded 100% (in absolute terms) they are not shown, because they may indicate notable data problems. Migration trends over the periods and So far we have looked mainly and in some detail at the mean migration levels and rates for the periods and But what are the trends? Which Gauteng main places were experiencing an increase and for which do we observe a decrease in these levels and rates? The main places in their totality (i.e. not divided into the parts in different local governments) in the northern, western, central, eastern and southern sub-regions of Gauteng are used in the trend analyses reported here. For the purposes of this unofficial regional division of the province, northern Gauteng is seen to be comprised of the main places in the City of Tshwane and Nokeng tsa Taemane. Western Gauteng is regarded as consisting of main places in Merafong City, Mogale City, Randfontein, Westonaria and the Gauteng part of West Rand. The main places of the City of Johannesburg are viewed as forming central Gauteng. Eastern Gauteng is regarded as comprising the main places forming part of Ekurhuleni Metro, Kungwini and Lesedi. Southern Gauteng is seen to be made up of the main places in Emfuleni and Midvaal. The net migration changes from to are given as the differences between the two periods, with the figures being subtracted from the figures. In Tables the details of the changes are given and these net differences are illustrated in Graphs The net migration rates in these tables and graphs are based on the 1996 population sizes. (Rates in excess of 100 per cent are again not shown.) 127

134 Table 10 Trends in net migration over the periods and : Northern Gauteng main places Main place Net migration difference ( minus volume) 1996 Population Difference Net migration Net migration Volume Rate Akasia % Atteridgeville % Baviaanspoort Centurion % City of Tshwane Non-urban % Cullinan % Ga-Rankuwa Hammanskraal * Kekana Gardens Knopjeslaagte Mabopane * Mamelodi % Nellmapius * Nokeng tsa Taemane Non-urban % Olievenhoutbos Onverwacht Pretoria % Rayton % Refilwe % Roodeplaat Dam Nature Reserve % Saulsville % Soshanguve % Temba * Vergenoeg Total % Graph 13 Trends in net migration rates over the periods and : 'Northern Gauteng' main places 80% 60% Net migration rate for difference 40% 20% 0% -20% -40% -60% Akasia Atteridgeville Baviaanspoort Centurion City of Tshwane Non-urban Cullinan Ga-Rankuwa Hammanskraal Kekana Gardens Knopjeslaagte Mabopane Mamelodi Nellmapius Nokeng tsa Taemane Non-urban Olievenhoutbos Onverwacht Pretoria Rayton Refilwe Roodeplaat Dam Nature Reserve Saulsville Soshanguve Temba Vergenoeg -80% Main place From Table 10 and Graph 13 it can be concluded that the two rural parts of the northern Gauteng local governments, City of Tshwane Non-urban (67%) and Nokeng tsa Taemane Non-urban (26%), followed by Saulsville (16%) experienced the highest increases in net migration over the two 128

135 periods. The greatest decreases in net migration were experienced by Rayton (-64%), Roodeplaat Dam Nature Reserve (-39%), Soshanguve (-26%), Atteridgeville (-22%), Refilwe (-21%), Cullinan (-19%), Mamelodi (-17%), Pretoria (-14%) and Akasia (-12%). As a whole, main places in northern Gauteng experienced a decrease in net migration of (minus) 11 per cent. Table 11 Trends in net migration over the periods and : Western Gauteng main places Main place Net migration difference ( minus volume) 1996 Population Difference Net migration Net migration Volume Rate Bekkersdal % Bhongweni Blybank Blyvooruitzicht % Brandvlei Carletonville % Cooke Mine % Deelkraal % Doornfontein East Driefontein Mine Elands Ridge % Elsburg Gold Mine % Etlebeni Glen Harvie % Hills Haven % Kagiso % Khutsong % Kloof Gold Mine % Krugersdorp % Leeudoorn Mine % Letsatsing % Libanon Gold Mine % Magaliesburg Merafong City Non-urban % Modderfontein Mogale City Mogale City Non-urban % Mohlakeng % Muldersdrift Munsieville % Oberholzer % Orient Hills Panvlak Gold Mine % Panvlak Gold Mine % Phomolong Randfontein % Randfontein Mine % Randfontein Non-urban % Rietvallei % Toekomsrus % Venterspost % Waterpan Welverdiend % West Rand Non-urban West Driefontein % Western Deep Levels Mine

136 Main place Net migration difference ( minus volume) 1996 Population Difference Net migration Net migration Volume Rate Westonaria % Westonaria Non-urban % Zenzele Total % Table 11 and Graph 14 shows that Blyvooruitzicht (46%), Mogale City Non-urban (40%), Randfontein Non-urban (21%), Elands Ridge (19%) Letsatsing (13%), Randfontein Mine (11%) and Westonaria Non-urban (10%) experienced the highest net migration rate increases (all in excess of 10 per cent). Carletonville (-81%), Cooke Mine (-57%), Rietvallei (-31%), Elsburg Gold Mine (-31%), Randfontein (-30%), Hills Haven (-29%), Westonaria (-20%), Krugersdorp (-19%), Libanon Gold Mine (-17%), Venterspost (-17%), Deelkraal (-15%), Leeudoorn Mine (-13%), Glen Harvie (-13%), Toekomsrus (-11%) and Khutsong (-11%) experienced the greatest decreases in net migration rates (all in excess of -10 per cent). Main places in western Gauteng as a whole experienced a slight increase in net migration of one per cent. Graph 14 Trends in net migration rates over the periods and : 'Western Gauteng' main places 60% 40% Net migration rate difference 20% 0% -20% Bekkersdal -40% Blybank Brandvlei Cooke Mine Doornfontein Elandsridge Etlebeni Hills Haven Khutsong Krugersdorp Letsatsing Magaliesburg Modderfontein Mogale City Non-urban Muldersdrift Oberholzer Panvlak Gold Mine Phomolong Randfontein Mine Rietvallei Venterspost Welverdiend Westdriefontein Westonaria Zenzele -60% Main place It can be concluded from Table 12 and Graph 15 that only Rabie Ridge (49%), Wheeler's Farm (16%) and Zevenfontein (12%) experienced increases in net migration rates over the two periods. Over the same two periods Poortjie (-68%), Sweetwaters (-48%), Roodepoort (-32%), Zandspruit (-24%), Alexandra (-17%), Sandton (-16%), Soweto (-14%), Randburg (-14%), City of Johannesburg Non-urban (-13%), Ivory Park (-12%) and Meadowlands (-12%) experienced decreases in net migration rates in excess of -10 per cent though. Main places in the sub-region central Gauteng in total experienced a decrease in net migration of (minus) 12 per cent. 130

137 Table 12 Trends in net migration over the periods and : Central Gauteng main places Main place Net migration difference ( minus volume) 1996 Population Difference Net migration Net migration Volume Rate Alexandra % Bultfontein City of Johannesburg Non-urban % Diepkloof % Diepsloot % Ebony Park Edenvale Ivory Park % Johannesburg % Klipfontein View Mayabuye Meadowlands % Midrand % Nooitgedacht Orange Farm % Pipeline Poortjie % Rabie Ridge % Randburg % Roodepoort % Sandton % Slovoville Soweto % Sweetwaters % Tshepisong Vlakfontein Wheeler's Farm % Zandspruit % Zevenfontein % Total % Graph 15 Trends in net migration rates over the periods and : 'Central Gauteng' main places Net migration rate difference 60% 40% 20% 0% -20% -40% Alexandra Bultfontein City of Johannesburg Non-urban -60% -80% Diepkloof Diepsloot Ebony Park Edenvale Ivory Park Johannesburg Klipfontein View Mayabuye Meadowlands Midrand Nooitgedacht Orange Farm Pipeline Poortjie Rabie Ridge Main place Randburg Roodepoort Sandton Slovoville Soweto Sweetwaters Tshepisong Vlakfontein Wheeler's Farm Zandspruit Zevenfontein 131

138 Table 13 Trends in net migration over the periods and : Eastern Gauteng main places Main place Net migration difference ( minus volume) 1996 Population Difference Net migration Net migration Volume Rate Alberton % Bapsfontein Bedfordview % Benoni % Boksburg % Brakpan % Bronkhorstspruit % Cerutiville Chief Albert Lithuli Park Daveyton % Devon Duduza % Dukathole % Edenvale % Ekandustria Ekurhuleni Metro Non-urban % Etwatwa % Germiston % Heidelberg % Impumelelo % Katlehong % Kempton Park % Kungwini Non-urban % KwaThema % Lesedi Local Municipality % Lindelani Village Midrand % Nigel % Nigel % Ratanda % Reiger Park % Rethabiseng % Sehlakwana Springs % Tembisa % Thokoza % Tsakane % Vosloorus % Wattville % Zithobeni % Total % 132

139 Graph 16 Trends in net migration rates over the periods and : 'Eastern Gauteng' main places 100% 80% Net migration rate difference 60% 40% 20% 0% -20% Alberton -40% -60% Bedfordview Boksburg Bronkhorstspruit Chief Albert Lithuli Park Devon Dukathole Ekandustria Etwatwa Heidelberg Katlehong Kungwini Non-urban Lesedi Local Municipality Midrand Nigel Reiger Park Sehlakwana Tembisa Tsakane Wattville -80% Main place From Table 13 and Graph 16 it can be concluded that Ekurhuleni Metro Non-urban (87%), Dukathole (45%), Kungwini Non-urban (35%), Rethabiseng (34%) and Zithobeni (15%) experienced the greatest increases in net migration rates (all in excess of 10 per cent). On the other hand a large number of main places in eastern Gauteng, namely Heidelberg (-54%), Midrand (-53%), Benoni (-32%), Bronkhorstspruit (-32%), Brakpan (-23%), Nigel (-23%), Alberton (-22%), Nigel (-21%), Thokoza (-20%), Kempton Park (-19%), Germiston (-17%), Boksburg (-17%), Tsakane (-16%), Daveyton (-15%), Edenvale (-14%), Tembisa (-14%), Springs (-13%), Vosloorus (-13%), Wattville (-12%) and Duduza (-10%), experienced decreases in net migration rates of -10 per cent or more. Eastern Gauteng main places in total experienced a decrease in net migration of (minus) 11 per cent. Table 14 Trends in net migration over the periods and : Southern Gauteng main places Main place Net migration difference ( minus volume) 1996 Population Difference Net migration Net migration Volume Rate Boipatong % Bophelong % Emfuleni Non-urban % Evaton % Meyerton % Midvaal Non-urban % Randvaal % Sebokeng % Sharpeville % Suikerbosrand Nature Reserve % Tshepiso % Vaal Marina Vanderbijlpark % Vereeniging % Walkerville % Total % 133

140 Graph 17 Trends in net migration rates over the periods and : 'Southern Gauteng' main places 80% 60% Net migration rate difference 40% 20% 0% -20% -40% -60% Boipatong Bophelong Emfuleni Non-urban Evaton Meyerton Midvaal Non-urban Randvaal Sebokeng Sharpeville Suikerbosrand Natuurresevaat Tshepiso Vaal Marina Vanderbijlpark Vereeniging Walkerville -80% -100% Main place Table 14 and Graph 17 show that Bophelong (61%) and Meyerton (11%) experienced increases in net migration rates of 10 per cent or more over the periods and Suikerbosrand Nature Reserve (-91%), Tshepiso (-39%), Evaton (-31%), Boipatong (-26%), Vereeniging (-22%), Sebokeng (-20%), Vanderbijlpark (-17%), Sharpeville (-15%), Randvaal (-13%), on the other hand, all experienced decreases in net migration rates in excess of -10 per cent. In total, main places in southern Gauteng experienced a decrease in net migration of (minus) 18 per cent. 134

141 APPENDIX 2 POVERTY COMPONENT Table 15 gives the poverty indicators and the overall poverty for each of the (populated) Gauteng sub places, by municipality and main place. An analysis of the data showed that Randfontein (Randfontein Part 1, Randfontein), Bophelong (Emfuleni), Sweetwaters (City of Johannesburg), Mohlakeng (Randfontein), Witpoort Estates (Brakpan, Ekurhuleni Metro), Mawag (Cerutiville, Ekurhuleni Metro), Somalia Park (Boksburg, Ekurhuleni Metro), Tswaing (Soshanguve Part 1, City of Tshwane), and Union Ext (Alberton, Ekurhuleni Metro) had very high poverty levels in 2001 (all nine with poverty indices in excess of 70 per cent). 18 In Maps the overall poverty indicators for sub-places in the various Gauteng municipalities are shown. 18 Tembisa Station (Tembisa, Ekurhuleni Metro), Roshasia (Vereeniging, Emfuleni), and Villa Liza Camp (Boksburg, Ekurhuleni Metro) actually had the highest poverty indices, but these three sub-places had populations of fewer than ten persons in

142 Map 16 Overall poverty levels in sub-places : City of Johannesburg Metro 136

143 Map 17 Overall poverty levels in sub-places : City of Tshwane Metro 137

144 Map 18 Overall poverty levels in sub-places : Ekurhuleni Metro 138

145 Map 19 Overall poverty levels in sub-places : Emfuleni 139

146 Map 20 Overall poverty levels in sub-places : Kungwini 140

147 Map 21 Overall poverty levels in sub-places : Lesedi 141

148 Map 22 Overall poverty levels in sub-places : Merafong City 142

149 Map 23 Overall poverty levels in sub-places : Midvaal 143

150 Map 24 Overall poverty levels in sub-places : Mogale City and West Rand (Non-urban) 144

151 Map 25 Overall poverty levels in sub-places : Nokeng tsa Taemane 145

152 Map 26 Overall poverty levels in sub-places : Randfontein 146

153 Map 27 Overall poverty levels in sub-places : Westonaria 147

154 TABLE 15 POVERTY INDICATORS AT SUB-PLACE LEVEL, BY MAIN PLACE AND METROPOLITAN/LOCAL MUNICIPALITY Municipality Main-place name Sub-place name Mogale City Kagiso Krugersdorp 148 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Kagiso SP 17% 33% 44% 45% 8% 10% 55% 41% 39% 8% 30% Chamdor 4% 4% 0% 13% 0% 15% 11% 30% 8% 8% 9% Dr Masibilanga 14% 33% 2% 3% 1% 0% 19% 23% 25% 0% 12% Father Gerald 1% 43% 0% 0% 1% 3% 18% 20% 19% 0% 10% Hillsview 7% 30% 2% 2% 3% 0% 18% 28% 26% 1% 12% Hospital View 13% 26% 4% 13% 12% 15% 20% 26% 23% 0% 15% Kagiso 99% 27% 98% 99% 95% 58% 72% 46% 58% 8% 66% Kagiso 2 19% 43% 1% 1% 1% 0% 25% 24% 37% 1% 15% Kagiso Central 32% 44% 1% 7% 1% 1% 48% 32% 46% 6% 22% Kagiso Ext 0% 100% 0% 0% 0% 0% 100% 40% 33% 0% 27% Kagiso Ext 12 79% 44% 3% 10% 1% 2% 45% 35% 34% 4% 26% Kagiso Ext 14 51% 33% 1% 10% 2% 0% 40% 33% 43% 0% 21% Kagiso Ext 6 3% 27% 3% 0% 1% 1% 19% 27% 31% 1% 11% Kagiso Ext 8 10% 33% 2% 3% 1% 2% 25% 33% 27% 1% 14% Kagiso Ext 9 1% 26% 1% 1% 1% 14% 10% 26% 29% 0% 11% Reservoir Ridge 8% 47% 0% 0% 0% 0% 9% 24% 26% 0% 11% Riverside 4% 40% 1% 1% 1% 1% 23% 25% 31% 2% 13% Tsakane 25% 30% 1% 6% 3% 0% 36% 29% 35% 5% 17% Krugersdorp SP 74% 49% 42% 94% 45% 96% 64% 53% 20% 1% 54% African Fauna Bird Park 0% 30% 41% 59% 3% 64% 21% 26% 8% 0% 25% Agavia 0% 23% 0% 0% 0% 3% 13% 18% 9% 3% 7% Apple Park 6% 38% 15% 4% 2% 0% 18% 30% 14% 0% 13% Azaadville 4% 33% 3% 2% 2% 1% 27% 24% 4% 2% 10% Beckedan AH 5% 21% 4% 47% 52% 94% 37% 42% 12% 1% 31% Boltonia 0% 25% 0% 8% 1% 2% 18% 26% 16% 0% 9% Breaunanda 2% 24% 4% 1% 0% 1% 7% 16% 4% 0% 6% Burgershoop 1% 30% 2% 1% 1% 1% 23% 24% 8% 0% 9% Chancliff AH 5% 22% 11% 4% 6% 5% 26% 19% 4% 3% 11% Coronation Park 0% 29% 0% 0% 0% 0% 14% 26% 0% 0% 7% Dan Pienaarville 3% 27% 4% 2% 1% 0% 10% 16% 7% 0% 7% Delporton 59% 18% 0% 6% 6% 39% 67% 52% 4% 13% 26% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

155 Municipality Main-place name Sub-place name Mogale City (continued) 149 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Eljeesee AH 11% 28% 24% 16% 22% 100% 24% 45% 5% 1% 28% Factoria 9% 9% 0% 0% 0% 20% 30% 0% 0% 7% Helderblom AH 2% 18% 8% 18% 16% 6% 30% 36% 4% 14% 15% Kenmare 2% 26% 3% 0% 2% 1% 13% 17% 5% 1% 7% Krugersdorp Central 3% 36% 3% 5% 2% 0% 12% 19% 8% 1% 9% Krugersdorp Game Reserve 0% 17% 0% 17% 0% 100% 33% 38% 13% 0% 22% Krugersdorp North 1% 42% 2% 1% 2% 0% 12% 16% 4% 1% 8% Krugersdorp Prison 22% 39% 43% 4% 0% 4% 39% 22% 5% 4% 18% Krugersdorp West 3% 23% 3% 3% 2% 0% 15% 20% 8% 1% 8% Krugersdorp West Mine 0% 0% 0% 0% 0% 100% 0% 75% 33% 0% 21% Lewisham 1% 23% 2% 4% 2% 0% 13% 16% 11% 1% 7% Luipaardsvlei Estate 31% 15% 7% 13% 11% 11% 38% 31% 29% 8% 19% Marabeth AH 11% 13% 5% 53% 26% 97% 30% 41% 7% 1% 29% Mindalore 1% 25% 9% 1% 1% 1% 9% 16% 7% 1% 7% Monument 4% 35% 7% 1% 1% 1% 14% 15% 4% 1% 8% Noordheuwel 2% 27% 4% 2% 1% 1% 13% 16% 3% 0% 7% Krugersdorp (continued) Oaktree AH 6% 20% 16% 33% 33% 65% 27% 41% 5% 0% 25% Oatlands AH 5% 12% 17% 2% 2% 0% 17% 20% 8% 3% 9% Olivanna 2% 47% 3% 3% 1% 1% 29% 22% 23% 1% 13% Paardekraal 5% 26% 5% 0% 0% 0% 10% 13% 3% 0% 6% Paardekraal Hospital 0% 38% 13% 0% 0% 0% 0% 14% 6% 0% 7% Pienaarville 2% 30% 1% 0% 1% 1% 9% 16% 9% 1% 7% Pretorius Park 2% 46% 17% 2% 4% 0% 7% 19% 8% 2% 11% Protea Rige AH 5% 20% 7% 9% 0% 11% 22% 25% 6% 7% 11% Quellerie Park 2% 24% 8% 1% 4% 1% 18% 21% 9% 0% 9% Randfontein Estate Gold Mine Rangeview 3% 28% 3% 2% 1% 1% 8% 12% 6% 0% 6% Rant-en-Dal 1% 30% 3% 1% 1% 0% 13% 18% 2% 0% 7% Silverfields 8% 28% 2% 1% 1% 1% 15% 14% 4% 0% 7% Sterkfontein 13% 30% 14% 30% 25% 57% 22% 34% 6% 2% 23% Vlakplaas AH 14% 37% 34% 58% 83% 98% 49% 57% 17% 3% 45% Waterval AH 5% 14% 0% 23% 9% 0% 14% 26% 10% 0% 10% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

156 Municipality Main-place name Sub-place name Mogale City (continued) 150 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Wentworth Park 3% 24% 3% 1% 1% 1% 11% 22% 7% 1% 7% Wes-Rand Consolidated Centre 32% 10% 0% 0% 1% 56% 20% 34% 22% 0% 18% West Village 8% 26% 6% 6% 6% 6% 22% 21% 24% 4% 13% Krugersdorp (continued) Westrand Consolidated Gold Mine 0% 18% 40% 42% 23% 32% 32% 30% 19% 4% 24% Witpoortjie 0% 0% 0% 0% 0% 100% 0% 0% 0% 11% Wolfelea AH 59% 28% 64% 77% 69% 98% 45% 53% 14% 6% 51% Blaawbank 20% 16% 30% 28% 28% 19% 23% 38% 14% 2% 22% Magaliesburg Boystown 0% 25% 11% 7% 7% 47% 11% 30% 7% 4% 15% Magaliesburg SH 17% 29% 51% 27% 22% 42% 25% 41% 10% 12% 28% Vaalbank 10% 26% 36% 18% 33% 61% 24% 40% 10% 16% 27% Diswilmar AH 3% 25% 10% 20% 28% 50% 20% 32% 11% 3% 20% Driefontein SH 2% 23% 43% 46% 54% 87% 34% 44% 17% 2% 35% Elandsdrif Estates 10% 27% 18% 25% 59% 95% 59% 42% 10% 23% 37% Heuningklip AH 3% 26% 48% 16% 26% 95% 58% 42% 15% 1% 33% Krugersdorp NU 19% 28% 36% 46% 61% 90% 49% 53% 10% 6% 40% Lammermoor 2% 18% 6% 21% 32% 88% 35% 39% 12% 7% 26% Lindley SH 20% 53% 84% 33% 52% 99% 72% 34% 38% 38% 52% Mogale City Part 1 Nooitgedacht SH 30% 27% 41% 40% 41% 86% 39% 38% 24% 5% 37% Northvale AH 1% 31% 12% 37% 13% 100% 32% 32% 13% 0% 27% Rietfontein AH 10% 23% 4% 32% 34% 92% 28% 31% 15% 3% 27% Rietvlei AH 18% 12% 9% 42% 58% 96% 31% 31% 26% 37% 36% Seekoeihoek AH 17% 20% 37% 9% 16% 82% 22% 42% 8% 1% 25% Steynsvlei AH 29% 35% 16% 39% 35% 96% 20% 23% 11% 0% 30% Van Wyksrestant AH 32% 15% 26% 52% 53% 94% 22% 39% 22% 1% 36% Zwartkop SH 20% 36% 19% 25% 45% 84% 46% 38% 10% 13% 34% Muldersdrift Honingklip 0% 11% 5% 20% 59% 93% 21% 45% 16% 2% 27% Oak Tree AH 0% 0% 0% 0% 0% 0% 0% 22% 22% 0% 4% Munsieville Munsieville SP 36% 42% 2% 19% 14% 2% 40% 34% 34% 9% 23% Munsieville 99% 23% 94% 99% 99% 99% 57% 57% 37% 4% 67% Orient Hills Orient Hills 93% 31% 99% 98% 99% 100% 65% 70% 17% 2% 67% Rietvallei Rietvallei SP 81% 31% 1% 4% 2% 0% 60% 47% 46% 0% 27% Rietvallei Ext 2 88% 34% 1% 6% 64% 1% 59% 51% 32% 2% 34% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

157 Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- Municipality Main-place name Sub-place name type: Informal household sal income ment source Rietvallei Ext 3 92% 36% 4% 5% 97% 1% 50% 38% 32% 0% 35% Mogale City (continued) Randfontein Rietvallei (continued) Zwaneville 85% 35% 1% 20% 1% 12% 59% 49% 40% 2% 30% Mogale City Part 2 Mogale City Part2 SP 2% 40% 0% 3% 2% 0% 44% 41% 18% 1% 15% Mogale City 100% 37% 100% 100% 100% 100% 57% 59% 35% 0% 69% Bhongweni Bhongweni SP 1% 14% 2% 0% 2% 0% 13% 31% 16% 2% 8% Brandvlei Brandvlei 42% 60% 72% 46% 49% 49% 20% 39% 16% 11% 40% Mohlakeng SP 29% 40% 2% 24% 2% 3% 43% 31% 39% 5% 22% Mohlakeng 92% 50% 98% 98% 98% 97% 89% 85% 17% 0% 72% Mohlakeng Mohlakeng Ext 1 3% 30% 1% 2% 0% 2% 33% 28% 33% 0% 13% Mohlakeng Ext 3 8% 32% 3% 5% 2% 0% 21% 30% 27% 0% 13% Mohlakeng Ext 4 2% 66% 0% 25% 1% 0% 55% 40% 38% 1% 23% Panvlak Gold Mine Randfontein Part 1 Mohlakeng Ext 7 7% 52% 1% 63% 1% 0% 62% 44% 41% 0% 27% Panvlak Gold Mine SP 0% 0% 0% 6% 0% 11% 17% 5% 0% 4% Randfontein Harmony Gold Mine 0% 0% 1% 0% 1% 0% 6% 48% 2% 0% 6% Randfontein Part1 SP 62% 26% 79% 82% 89% 97% 54% 52% 32% 5% 58% Aureus 75% 0% 0% 0% 0% 0% 20% 0% 0% 11% Botha AH 27% 32% 40% 47% 35% 93% 41% 42% 13% 2% 37% Culemborg Park 2% 26% 4% 1% 2% 0% 13% 16% 4% 2% 7% Delmas 0% 0% 0% 0% 0% 0% 100% 0% 25% 14% Dwarskloof AH 15% 35% 13% 50% 54% 99% 46% 46% 8% 3% 37% Eikepark 3% 16% 5% 5% 3% 4% 13% 18% 5% 1% 7% Eland SH 49% 28% 40% 65% 65% 100% 42% 46% 16% 2% 45% Finsbury 4% 20% 3% 5% 3% 3% 19% 23% 20% 5% 10% Green Hills 2% 35% 5% 1% 2% 0% 16% 19% 5% 1% 9% Groot-Elandsvlei AH 18% 54% 31% 49% 61% 73% 33% 56% 28% 12% 42% Hectorton 0% 44% 4% 2% 2% 0% 20% 20% 11% 0% 10% Helikon Park 4% 26% 8% 3% 3% 1% 11% 18% 5% 0% 8% Hillside AH 7% 26% 13% 40% 49% 99% 45% 48% 16% 7% 35% Home Lake 4% 35% 9% 2% 1% 1% 22% 18% 11% 2% 11% Kocksoord 5% 17% 4% 6% 4% 2% 16% 31% 17% 0% 10% Loumarina AH 18% 31% 14% 36% 27% 93% 43% 40% 8% 0% 31% Middelvlei AH 9% 21% 16% 38% 44% 95% 44% 41% 12% 3% 32% 151

158 Municipality Main-place name Sub-place name Randfontein (continued) Westonaria Randfontein Part 1 (continued) 152 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Millside 0% 27% 0% 0% 9% 0% 45% 98% 0% 33% 21% Pelzvale AH 4% 16% 16% 23% 20% 94% 26% 28% 18% 19% 26% Randfontein 100% 18% 97% 100% 100% 100% 97% 68% 78% 8% 77% Randfontein Estate Gold Mine 0% 17% 3% 0% 7% 75% 30% 34% 6% 7% 18% Randfontein South AH 15% 14% 2% 26% 46% 98% 38% 40% 40% 0% 32% Randgate 3% 35% 3% 3% 1% 1% 17% 21% 9% 2% 9% Randpoort 4% 47% 1% 1% 1% 1% 20% 18% 4% 1% 10% Rikasrus AH 6% 24% 32% 59% 66% 100% 60% 49% 12% 0% 41% Robin Park 5% 21% 4% 0% 4% 49% 34% 30% 14% 4% 16% Tenacre AH 29% 34% 17% 43% 51% 100% 40% 50% 11% 5% 38% West Porges 1% 45% 1% 3% 1% 2% 36% 23% 23% 23% 16% Westergloor 4% 30% 4% 3% 3% 0% 17% 25% 22% 0% 11% Wheatlands AH 33% 18% 13% 46% 49% 99% 41% 46% 13% 1% 36% Wilbotsdal AH 0% 40% 13% 26% 18% 51% 31% 57% 6% 9% 25% Toekomsrus Toekomsrus SP 20% 39% 3% 9% 2% 0% 28% 30% 28% 0% 16% Zenzele Zenzele 98% 33% 99% 99% 98% 99% 61% 57% 40% 0% 68% Randfontein Part 2 Randfontein NU 14% 39% 31% 44% 64% 86% 47% 57% 16% 6% 41% Bekkersdal SP 1% 27% 0% 11% 3% 42% 75% 50% 61% 0% 27% Ghana Section 69% 49% 6% 30% 20% 6% 52% 39% 32% 1% 30% Holomisa Section 96% 44% 83% 99% 98% 9% 63% 57% 36% 8% 59% Mandela Section 96% 54% 52% 97% 99% 8% 66% 47% 41% 7% 57% Bekkersdal Simunye 1% 39% 1% 53% 1% 1% 40% 47% 24% 0% 21% Skierlik Section 51% 44% 4% 27% 20% 12% 50% 41% 36% 2% 29% Spoke Town 98% 53% 52% 100% 99% 8% 62% 52% 39% 3% 57% Tambo Section 91% 52% 74% 99% 99% 9% 69% 53% 37% 9% 59% Uptown Section 39% 53% 3% 15% 8% 4% 48% 36% 32% 4% 24% X-Section 99% 43% 88% 85% 95% 23% 62% 46% 35% 5% 58% Cooke Mine Cooke Mine SP 0% 0% 2% 0% 2% 76% 8% 59% 0% 0% 15% Elsburg Gold Mine Elsburg Gold Mine SP 78% 0% 1% 1% 1% 68% 4% 58% 1% 0% 21% Etlebeni Etlebeni 0% 3% 0% 0% 0% 0% 3% 41% 12% 0% 6% Glen Harvie Glen Harvie Ext 1 12% 27% 1% 2% 3% 1% 20% 26% 7% 8% 11% Glen Harvie Ext 2 4% 25% 3% 0% 4% 0% 17% 25% 8% 6% 9% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

159 Municipality Main-place name Sub-place name Westonaria (continued) Emfuleni 153 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Glen Harvie (continued) Glen Harvie Ext 3 3% 27% 2% 0% 2% 0% 13% 29% 9% 0% 9% Hills Haven Hills Haven SP 1% 23% 3% 0% 1% 1% 20% 25% 12% 5% 9% Johannesburg Lenz 0% 34% 37% 37% 40% 43% 28% 33% 17% 0% 27% Kloof Gold Mine Kloof Gold Mine SP 4% 1% 39% 0% 1% 33% 7% 51% 3% 0% 14% Leeudoorn Mine Leeudoorn Mine SP 2% 1% 7% 0% 2% 1% 7% 50% 5% 0% 8% Libanon Gold Mine Libanon Gold Mine SP 6% 5% 1% 0% 5% 21% 14% 51% 9% 2% 11% Modderfontein Seberuberung 89% 37% 93% 89% 99% 100% 54% 53% 37% 4% 66% Panvlak Gold Mine Panvlak Gold Mine SP 20% 3% 55% 0% 2% 56% 16% 61% 4% 0% 22% Randfontein Mine Randfontein Mine SP 0% 0% 1% 0% 2% 2% 55% 42% 4% 0% 11% Thabony 0% 9% 0% 0% 0% 2% 13% 45% 20% 0% 9% Venterspost Venterspost SP 4% 26% 4% 12% 2% 1% 36% 28% 14% 3% 13% Venterspost Gold Mine 0% 0% 0% 44% 33% 44% 56% 48% 23% 13% 26% Waterpan Waterpan SP 2% 22% 0% 0% 2% 4% 26% 29% 8% 6% 10% Dennydale SH 15% 29% 54% 55% 27% 95% 50% 46% 13% 5% 39% Westonaria Part 1 Vanderbijlpark NU 15% 16% 55% 53% 74% 100% 55% 62% 5% 2% 44% West Rand Garden AH 14% 30% 12% 38% 46% 92% 51% 40% 25% 2% 35% Westonaria NU 50% 27% 47% 59% 58% 84% 50% 47% 23% 2% 45% Westonaria Part 2 Westonaria Part 2 SP 4% 30% 7% 2% 1% 0% 16% 24% 13% 5% 10% Boipatong Boipatong SP 15% 38% 2% 11% 2% 2% 43% 35% 32% 2% 18% Boipatong 69% 29% 10% 100% 11% 99% 70% 48% 39% 5% 48% Bophelong Bophelong SP 5% 45% 2% 5% 2% 77% 65% 40% 42% 1% 28% Bophelong 98% 21% 97% 100% 100% 100% 93% 58% 76% 18% 76% Emfuleni Vanderbijlpark NU 11% 17% 26% 52% 66% 93% 58% 58% 18% 2% 40% Beverly Hills 30% 38% 28% 31% 31% 94% 52% 40% 35% 0% 38% Debonair Park 31% 19% 10% 43% 37% 20% 28% 39% 24% 1% 25% Evaton Central 55% 36% 28% 18% 59% 89% 62% 45% 34% 2% 43% Evaton Evaton North 49% 39% 23% 3% 4% 89% 48% 41% 31% 0% 33% Evaton Small Farms 51% 38% 31% 13% 62% 98% 62% 43% 35% 1% 43% Evaton West 3% 44% 2% 10% 1% 99% 64% 38% 41% 0% 30% Ironsyde 60% 22% 4% 67% 62% 43% 46% 41% 30% 0% 38% Lakeside 30% 37% 1% 4% 1% 99% 40% 43% 28% 1% 28% Orange Farm Stretford 1% 33% 4% 0% 1% 99% 35% 29% 31% 0% 23% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

160 Municipality Main-place name Sub-place name Emfuleni Sebokeng Sharpeville Tshepiso Vanderbijlpark 154 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Sebokeng SP 14% 32% 4% 7% 9% 92% 62% 43% 44% 1% 31% Boitumelo 8% 42% 1% 5% 5% 94% 69% 46% 41% 20% 33% Johandeo 69% 36% 8% 3% 1% 99% 63% 44% 42% 3% 37% Polokong Sebokeng 98% 41% 14% 20% 96% 99% 73% 50% 50% 5% 55% Sebokeng Hospital 80% 0% 0% 0% 0% 40% 48% 5% 0% 19% Sebokeng Unit 10 13% 26% 10% 12% 12% 13% 25% 30% 22% 0% 16% Sebokeng Unit 11 9% 40% 6% 4% 1% 0% 49% 38% 36% 1% 18% Sebokeng Unit 12 13% 41% 9% 11% 9% 11% 52% 42% 36% 1% 22% Sebokeng Unit 13 6% 43% 3% 2% 1% 1% 56% 38% 41% 1% 19% Sebokeng Unit 14 3% 35% 3% 2% 1% 0% 39% 30% 31% 1% 15% Sebokeng Unit 16 2% 20% 0% 0% 1% 0% 39% 29% 30% 0% 12% Sebokeng Unit 17 6% 32% 5% 4% 4% 34% 49% 37% 34% 5% 21% Sebokeng Unit 19 81% 38% 79% 81% 80% 81% 63% 49% 32% 4% 59% Sebokeng Unit 3 4% 35% 2% 3% 2% 87% 43% 33% 36% 1% 25% Sebokeng Unit 6 57% 37% 5% 8% 5% 95% 55% 41% 32% 2% 34% Sebokeng Unit 7 9% 44% 7% 4% 5% 82% 54% 39% 36% 0% 28% Sebokeng Unit 8 3% 30% 4% 2% 1% 11% 43% 40% 32% 0% 17% Westside Park 2% 29% 6% 12% 5% 53% 41% 34% 28% 0% 21% Sharpeville SP 12% 41% 3% 9% 2% 5% 51% 31% 36% 3% 19% Sharpeville 99% 27% 96% 99% 97% 99% 77% 47% 43% 4% 69% Sharpville Ext 17 13% 47% 4% 14% 0% 0% 51% 29% 40% 3% 20% Tshepiso SP 18% 42% 3% 10% 4% 53% 59% 41% 36% 2% 27% Tshepiso 88% 25% 76% 93% 75% 98% 70% 48% 42% 9% 62% Vanderbijlpark SP Ardenwold AH 4% 16% 44% 12% 39% 80% 31% 26% 12% 0% 26% Bloempark AH 5% 13% 0% 26% 37% 100% 44% 35% 9% 0% 27% Bonanne 3% 33% 1% 7% 1% 86% 55% 35% 35% 1% 26% Cyferpan 36% 18% 7% 29% 39% 86% 46% 33% 6% 7% 31% Flora Gardens 1% 21% 2% 2% 1% 0% 4% 15% 4% 0% 5% ISCOR 70% 15% 1% 64% 0% 16% 37% 39% 27% 2% 27% Kaalplaats East 0% 26% 23% 23% 23% 100% 40% 37% 4% 0% 27% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

161 Municipality Main-place name Sub-place name Emfuleni (continued) Vanderbijlpark (continued) 155 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Kaalplaats West 2% 18% 7% 5% 12% 84% 23% 59% 11% 0% 22% Lasiandra AH 9% 26% 9% 13% 17% 74% 26% 36% 15% 9% 23% Linkholm AH 25% 13% 63% 50% 50% 100% 50% 57% 26% 0% 43% Loch Vaal 15% 10% 15% 17% 29% 100% 27% 54% 7% 2% 28% Louisrus 7% 12% 21% 27% 54% 97% 42% 37% 12% 0% 31% Louisrus SH 26% 11% 23% 30% 34% 100% 30% 32% 13% 0% 30% Marlbank AH 7% 17% 7% 7% 42% 100% 33% 61% 10% 4% 29% Mooiwater AH 6% 15% 14% 35% 39% 100% 45% 32% 12% 0% 30% Mullerstuine AH 14% 20% 13% 31% 39% 93% 48% 39% 19% 5% 32% Nanescol AH 15% 18% 16% 20% 22% 96% 39% 34% 15% 2% 28% Noordloch AH 5% 26% 11% 18% 11% 95% 18% 34% 8% 0% 23% Northdene AH 2% 25% 7% 27% 32% 91% 41% 41% 8% 2% 28% Riverside AH 12% 31% 0% 29% 40% 100% 54% 55% 22% 0% 34% Roodia AH 0% 17% 0% 22% 39% 100% 39% 48% 13% 0% 28% Rosashof AH 17% 16% 26% 35% 38% 97% 50% 43% 26% 6% 35% Rusticana AH 7% 19% 26% 33% 37% 96% 37% 26% 11% 0% 29% Staalrus AH 2% 25% 5% 17% 11% 55% 40% 32% 13% 0% 20% Steelvalley AH 18% 0% 83% 58% 50% 100% 42% 56% 33% 0% 44% Stefanopark AH 5% 10% 19% 24% 19% 95% 43% 22% 13% 0% 25% Sylviavale AH 7% 24% 0% 13% 4% 63% 39% 38% 11% 7% 21% Theoville AH 10% 24% 13% 36% 42% 100% 44% 37% 16% 3% 32% University & College 0% 0% 0% 0% 0% 50% 50% 25% 50% 0% 18% Vaalview AH 9% 18% 14% 27% 29% 98% 41% 36% 12% 6% 29% Vanderbijlpark CE 1 1% 27% 3% 0% 3% 0% 9% 14% 8% 1% 7% Vanderbijlpark CE 2 1% 34% 11% 0% 0% 0% 18% 20% 12% 1% 10% Vanderbijlpark CE 3 1% 25% 2% 1% 2% 1% 13% 18% 8% 1% 7% Vanderbijlpark CE 4 1% 17% 5% 1% 2% 1% 14% 21% 13% 5% 8% Vanderbijlpark CE 5 2% 37% 3% 0% 0% 0% 30% 18% 4% 3% 10% Vanderbijlpark CE 6 0% 50% 0% 0% 0% 50% 100% 63% 0% 0% 26% Vanderbijlpark Central 0% 34% 14% 0% 5% 0% 11% 13% 13% 0% 9% Vanderbijlpark CW 1 1% 32% 3% 0% 1% 0% 12% 14% 8% 1% 7% Vanderbijlpark CW 2 2% 28% 2% 1% 2% 0% 18% 17% 10% 3% 8% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

162 Municipality Main-place name Sub-place name Emfuleni (continued) Vanderbijlpark (continued) Vereeniging 156 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Vanderbijlpark CW 3 2% 25% 4% 0% 1% 0% 13% 13% 8% 1% 7% Vanderbijlpark CW 4 1% 25% 3% 0% 0% 12% 36% 20% 16% 2% 12% Vanderbijlpark CW 5 2% 29% 2% 1% 1% 1% 19% 20% 12% 0% 9% Vanderbijlpark CW 6 1% 21% 1% 2% 1% 1% 17% 20% 11% 2% 8% Vanderbijlpark SE 1 3% 29% 3% 0% 2% 0% 11% 21% 6% 1% 8% Vanderbijlpark SE 2 0% 15% 1% 0% 1% 1% 10% 16% 3% 1% 5% Vanderbijlpark SE 3 2% 17% 1% 1% 0% 1% 12% 18% 2% 0% 5% Vanderbijlpark SE 4 3% 16% 2% 1% 1% 1% 7% 16% 1% 1% 5% Vanderbijlpark SE 5 0% 25% 0% 2% 3% 0% 8% 15% 4% 0% 6% Vanderbijlpark SE 6 4% 14% 4% 1% 2% 0% 8% 18% 4% 1% 6% Vanderbijlpark SE 7 1% 33% 2% 0% 1% 0% 18% 15% 6% 1% 8% Vanderbijlpark SW 1 1% 24% 1% 1% 1% 0% 8% 17% 5% 0% 6% Vanderbijlpark SW 2 2% 15% 1% 1% 1% 0% 8% 15% 4% 1% 5% Vanderbijlpark SW 5 1% 24% 4% 0% 1% 0% 12% 14% 3% 1% 6% Vereeniging SP 13% 11% 70% 68% 22% 90% 53% 40% 29% 0% 40% Aerovaal 3% 37% 2% 5% 6% 0% 28% 24% 4% 0% 11% Arcon Park 0% 28% 5% 0% 1% 4% 11% 17% 5% 0% 7% Bedworth Park 1% 30% 4% 2% 2% 2% 39% 10% 8% 4% 10% Dadaville 1% 34% 2% 5% 2% 1% 28% 26% 4% 1% 10% Dickinsonville 20% 0% 0% 0% 0% 100% 40% 36% 22% 0% 22% Dreamland AH 18% 13% 42% 45% 44% 93% 49% 40% 16% 4% 36% Drie Riviere 2% 31% 9% 1% 2% 3% 17% 15% 5% 1% 9% Duncanville 3% 27% 3% 2% 3% 3% 16% 30% 7% 2% 9% Falcon Ridge 4% 12% 3% 2% 2% 3% 7% 19% 5% 1% 6% Harmoniesrus AH 4% 11% 28% 24% 26% 93% 28% 33% 8% 0% 25% Homer 0% 19% 0% 2% 0% 0% 9% 21% 14% 2% 7% Homer AH 16% 16% 8% 13% 11% 53% 34% 26% 20% 8% 20% Houtkop AH 23% 18% 60% 29% 35% 85% 32% 32% 6% 6% 33% Klipplaatdrift 3% 18% 3% 2% 0% 0% 13% 30% 4% 4% 8% Kwaggafontein 97% 26% 97% 97% 97% 97% 81% 38% 35% 0% 66% Leeuhof 1% 34% 1% 4% 2% 2% 27% 22% 21% 0% 11% Leeukuil 6% 44% 0% 0% 12% 0% 0% 26% 7% 0% 9% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

163 Municipality Main-place name Sub-place name Emfuleni (continued) Midvaal 157 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Lenteland AH 11% 24% 7% 49% 27% 91% 51% 48% 10% 5% 32% Peacehaven 2% 24% 17% 2% 1% 0% 11% 19% 10% 4% 9% Peacehaven Industrial 19% 14% 0% 11% 7% 14% 14% 17% 17% 3% 12% Powerville 3% 44% 17% 88% 23% 43% 52% 31% 26% 5% 33% Redan 0% 35% 11% 4% 4% 19% 33% 16% 7% 2% 13% Roshasia 67% 100% 83% Roshnee 2% 34% 2% 3% 2% 0% 32% 23% 5% 1% 10% Rust-ter-Vaal 20% 38% 2% 14% 2% 0% 39% 31% 22% 1% 17% Sonlandpark 2% 19% 10% 2% 2% 3% 9% 18% 6% 0% 7% Springcol 0% 36% 0% 10% 0% 3% 18% 31% 17% 0% 11% Springfield Collieries 55% 31% 77% 82% 87% 90% 67% 58% 18% 13% 58% Steelpark 2% 19% 1% 0% 1% 0% 9% 21% 14% 1% 7% Three Rivers 0% 51% 0% 0% 0% 0% 10% 14% 1% 5% 8% Vereeniging (continued) Three Rivers 8% 35% 1% 0% 1% 0% 7% 13% 4% 0% 7% Three Rivers 0% 48% 0% 1% 1% 0% 10% 10% 0% 0% 7% Three Rivers East 1% 31% 1% 2% 2% 1% 14% 19% 3% 0% 8% Three Rivers North 2% 32% 9% 2% 2% 2% 19% 12% 6% 0% 9% Unitas Park 0% 48% 1% 3% 1% 0% 14% 38% 22% 0% 13% Unitas Park AH 3% 22% 12% 9% 4% 5% 32% 26% 11% 2% 13% Unitas Park Ext 0% 0% 50% 63% 75% 100% 63% 68% 43% 0% 46% Unitas Park Ext 1 3% 22% 1% 2% 0% 1% 10% 21% 9% 0% 7% Vereeniging Aerodrome 14% 19% 14% 65% 50% 92% 50% 44% 13% 0% 36% Vereeniging Central 3% 35% 2% 3% 2% 2% 23% 17% 15% 4% 10% Vereeniging Ext 1 0% 14% 4% 2% 4% 4% 11% 20% 6% 0% 6% Vereeniging Industrial 5% 7% 1% 2% 3% 2% 6% 27% 9% 0% 6% Waldrift 1% 25% 3% 1% 3% 2% 14% 20% 6% 1% 8% Waterdal AH 42% 22% 36% 79% 78% 99% 64% 44% 42% 1% 51% Alberton Zwartkopjes 4% 10% 13% 2% 2% 2% 8% 22% 7% 2% 7% Evaton Lakeside 1% 37% 2% 98% 2% 1% 70% 45% 48% 5% 31% The Evaton Estate Meyerton Boltonwold SH 32% 22% 28% 51% 48% 93% 54% 55% 20% 1% 41% Buyscelia AH 12% 18% 15% 35% 35% 96% 38% 40% 15% 2% 31% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

164 Municipality Main-place name Sub-place name Midvaal (continued) Meyerton (continued) Midvaal Randvaal 158 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Golf Park 6% 14% 14% 1% 2% 1% 4% 16% 3% 0% 6% Helderstrome AH 22% 16% 42% 52% 59% 96% 44% 54% 14% 0% 40% Homelands AH 22% 16% 9% 45% 21% 91% 45% 40% 13% 2% 30% Kookrus 0% 31% 5% 3% 10% 8% 21% 29% 13% 0% 12% Meydustria 0% 18% 44% 81% 0% 81% 10% 47% 0% 0% 28% Meyerton Central 3% 31% 4% 1% 1% 2% 18% 19% 6% 1% 8% Meyerton Ext 6 2% 18% 0% 0% 0% 0% 14% 19% 3% 0% 6% Meyerton Exts 1/3 0% 0% 0% 0% 0% 0% 14% 40% 0% 0% 5% Meyerton Park 71% 21% 69% 74% 71% 14% 56% 44% 33% 15% 47% Meyerton South 1% 16% 7% 8% 5% 3% 13% 18% 4% 0% 8% Mooilande AH 12% 20% 7% 36% 37% 93% 42% 45% 15% 0% 31% Nelsonia AH 10% 23% 25% 35% 36% 91% 39% 31% 17% 1% 31% Noldick 0% 25% 0% 13% 38% 75% 63% 36% 0% 0% 25% Randvaal 21% 18% 14% 51% 59% 86% 46% 43% 10% 8% 36% Riversdale 5% 21% 4% 7% 8% 7% 22% 25% 12% 1% 11% Rothdene 2% 30% 31% 2% 2% 0% 20% 18% 7% 2% 12% Sybrand van Niekerk Park Van Der Westhuizen AH 12% 8% 20% 32% 48% 100% 56% 50% 14% 0% 34% Heidelberg (Gauteng) NU 15% 14% 28% 62% 93% 83% 60% 81% 29% 4% 47% Heidelberg NU 12% 49% 51% 28% 78% 88% 44% 62% 11% 0% 42% Klipview AH 65% 16% 60% 72% 80% 87% 52% 48% 26% 6% 51% Koolfontein AH 17% 13% 9% 53% 61% 100% 59% 46% 14% 5% 38% Schoongezicht AH 18% 20% 16% 41% 67% 72% 45% 43% 8% 2% 33% Tedderfield AH 5% 16% 6% 35% 37% 98% 47% 41% 8% 14% 31% Vereeniging NU 19% 27% 33% 58% 60% 91% 54% 57% 15% 10% 42% Daleside 5% 23% 2% 15% 14% 0% 30% 30% 25% 17% 16% Gardenvale AH 2% 23% 8% 28% 42% 74% 36% 38% 15% 0% 27% Glen Douglas 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Henley-on-Klip 5% 22% 22% 6% 5% 1% 12% 21% 7% 1% 10% Highbury 2% 18% 0% 7% 2% 2% 18% 18% 10% 0% 8% Kliprivier 19% 43% 17% 31% 46% 74% 29% 59% 5% 3% 33% Klipwater 10% 48% 8% 4% 2% 0% 22% 23% 13% 0% 13% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

165 Municipality Main-place name Sub-place name Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- type: Informal household sal income ment source Randvaal (continued) Valley Settlement 15% 17% 11% 29% 36% 37% 38% 37% 16% 5% 24% Waterval 52% 43% 37% 42% 40% 57% 40% 50% 13% 2% 38% Suikerbosrand Nature Reserve Suikerbosrand Nature Reserve SP Vaal Marina Vaal Marina SP 0% 0% 0% 0% 0% 50% 0% 50% 0% 0% Balmoral Estates 12% 12% 8% 25% 25% 33% 49% 24% 19% 8% 21% De Deur Estates 21% 19% 13% 32% 41% 44% 41% 42% 11% 6% 27% Vereeniging Duncanville 27% 14% 0% 0% 0% 0% 0% 25% 0% 0% 7% Glen Donald AH 5% 19% 17% 18% 18% 85% 30% 34% 6% 1% 23% Midvaal (continued) Lesedi Mckay Estates 6% 22% 25% 13% 29% 90% 50% 38% 3% 4% 28% Risiville 4% 23% 2% 5% 1% 10% 13% 19% 5% 0% 8% Blignautsrus AH 1% 36% 20% 26% 27% 93% 35% 34% 8% 0% 28% Drumblade AH 9% 18% 13% 17% 20% 53% 34% 37% 7% 5% 21% Elandsfontein AH 18% 29% 21% 39% 49% 97% 47% 43% 9% 9% 36% Golf View SH 0% 28% 4% 7% 7% 79% 44% 30% 11% 3% 21% Walkerville Hartzenbergfontein AH 4% 16% 2% 20% 47% 78% 49% 38% 5% 2% 26% Homestead Apple Orchards AH 5% 23% 3% 23% 28% 98% 34% 30% 17% 2% 26% Ironside AH 12% 14% 3% 24% 10% 84% 45% 33% 18% 4% 25% Ohenimuri 0% 51% 19% 8% 3% 11% 19% 36% 19% 0% 16% Walker's Fruit Farms AH 10% 21% 11% 32% 30% 88% 46% 37% 13% 11% 30% Walkerville AH 1% 25% 6% 11% 22% 78% 38% 28% 8% 0% 22% Devon Devon SP 0% 16% 7% 0% 2% 2% 23% 21% 10% 0% 8% Bergsig 8% 11% 12% 9% 8% 9% 15% 20% 9% 0% 10% Heidelberg Central 3% 26% 3% 2% 2% 0% 12% 18% 6% 0% 7% Heidelberg Ext 2 0% 33% 0% 0% 0% 100% 0% 29% 6% 0% 17% Heidelberg Ext 5 1% 20% 1% 3% 0% 1% 10% 14% 4% 1% 6% Heidelberg Jordaan Park 1% 21% 5% 0% 1% 0% 9% 19% 1% 0% 6% Military Camp 0% 0% 0% 0% 0% 0% 0% 3% 1% 0% 0% Overkruin 1% 32% 0% 1% 0% 0% 18% 16% 4% 0% 7% Rensburgdorp 3% 23% 3% 1% 1% 1% 6% 20% 5% 1% 6% Shalimar Ridge 3% 11% 4% 1% 0% 0% 11% 23% 12% 0% 7% Springfield 72% 0% 36% 159

166 Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- Municipality Main-place name Sub-place name type: Informal household sal income ment source Impumelelo SP 8% 34% 6% 8% 5% 5% 64% 50% 44% 1% 23% Lesedi (continued) Nokeng tsa Taemane Impumelelo Lesedi Local Municipality Nigel Ratanda Springs Baviaanspoort Impumelelo 80% 37% 100% 97% 97% 92% 69% 67% 33% 1% 67% Blue Valley AH 0% 20% 80% 100% 100% 100% 40% 67% 40% 0% 55% Boschfontein AH 23% 20% 39% 46% 43% 89% 37% 40% 4% 7% 35% Bothasgeluk AH 0% 0% 14% 48% 38% 100% 48% 53% 0% 0% 30% Die Tuine AH 0% 27% 56% 72% 85% 92% 56% 64% 16% 0% 47% Endicott AH 18% 20% 12% 38% 48% 22% 38% 59% 26% 4% 28% Hallgate AH 23% 15% 22% 34% 34% 96% 44% 42% 15% 7% 33% Heidelberg AH 0% 20% 13% 7% 3% 97% 32% 38% 12% 10% 23% Heidelberg NU 16% 23% 38% 45% 62% 90% 50% 53% 11% 5% 39% Kaydale AH 15% 21% 15% 43% 43% 72% 36% 30% 15% 6% 30% Nigel NU 45% 20% 59% 61% 76% 93% 54% 65% 19% 3% 49% Rusoord 11% 21% 21% 46% 46% 89% 21% 32% 14% 0% 30% Spaarwater AH 0% 20% 25% 44% 43% 100% 32% 41% 15% 1% 32% Springs NU 4% 22% 0% 2% 100% 6% 61% 67% 20% 2% 28% Viskuil AH 8% 14% 11% 32% 31% 8% 33% 37% 10% 6% 19% Jameson Park 16% 23% 17% 42% 11% 12% 29% 45% 32% 1% 23% Kaydale AH 0% 0% 80% 100% 80% 100% 40% 55% 10% 0% 46% Sonstraal AH 0% 64% 73% 9% 73% 82% 27% 44% 27% 0% 40% Ratanda SP 5% 40% 14% 4% 2% 0% 35% 35% 27% 1% 16% Ratanda Ext 1 51% 37% 43% 50% 44% 18% 57% 52% 38% 1% 39% Ratanda Ext 2 98% 28% 63% 97% 90% 64% 77% 62% 22% 1% 60% Ratanda Ext 3 48% 42% 45% 48% 44% 7% 59% 49% 30% 2% 38% Ratanda Ext 4 24% 36% 0% 7% 3% 8% 40% 37% 13% 1% 17% Ratanda Ext 5 41% 43% 30% 42% 31% 4% 62% 48% 41% 0% 34% Ratanda Ext 6 42% 42% 92% 41% 34% 23% 56% 55% 37% 0% 42% Ratanda Ext 7 7% 40% 0% 8% 1% 0% 52% 51% 27% 0% 19% Aston Lake East Daggafontein Gold Mine Baviaanspoort SP 2% 14% 2% 0% 0% 0% 6% 22% 6% 0% 5% Baviaanspoort Prison 29% 0% 15% Cullinan Cullinan SP 1% 27% 1% 1% 1% 1% 11% 28% 7% 1% 8% 160

167 Municipality Main-place name Sub-place name Nokeng tsa Taemane Ekurhuleni Metro 161 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Elandia 0% 0% 0% 0% 0% 0% 0% 3% 12% 0% 1% Erica 0% 50% 0% 0% 0% 0% 0% 7% 0% 0% 6% Cullinan (continued) Premier Diamond Mine 3% 5% 26% 1% 2% 2% 8% 45% 23% 0% 11% Protem 8% 33% 0% 0% 7% 7% 7% 23% 2% 0% 8% Zonderwater 0% 21% 1% 0% 1% 2% 7% 30% 24% 0% 9% Kekana Gardens Kekana Gardens SP 94% 34% 100% 99% 100% 100% 56% 46% 46% 0% 68% Kekana Gardens 96% 28% 99% 42% 99% 98% 52% 49% 25% 1% 59% Buffelsdrift AH 7% 20% 18% 10% 16% 98% 10% 24% 4% 2% 21% Cullinan NU 16% 19% 29% 40% 50% 96% 47% 38% 8% 3% 34% Derdepoort AH 4% 19% 12% 7% 10% 79% 16% 23% 3% 1% 17% Doornkraal 8% 12% 3% 9% 32% 90% 40% 27% 11% 0% 23% Nokeng tsa Taemane Downbern AH 14% 28% 5% 32% 43% 94% 30% 35% 10% 0% 29% Kameeldrift SH 10% 32% 6% 28% 36% 94% 34% 34% 11% 3% 29% Kameelfontein SH 8% 29% 6% 18% 19% 87% 27% 31% 2% 2% 23% Leeuwfontein SH 11% 20% 18% 10% 18% 97% 13% 24% 6% 0% 22% Lewzene Estate 6% 23% 13% 21% 32% 63% 38% 33% 10% 1% 24% Wonderboom NU 12% 25% 5% 27% 28% 80% 35% 32% 3% 2% 25% Onverwacht Onverwacht SP 12% 41% 46% 43% 97% 10% 66% 53% 27% 0% 40% Rayton Rayton SP 1% 21% 4% 0% 1% 1% 13% 17% 6% 0% 6% Refilwe Refilwe SP 48% 37% 3% 53% 14% 12% 47% 42% 34% 2% 29% Refilwe 98% 38% 52% 98% 87% 77% 73% 64% 45% 4% 64% Roodeplaat Dam Nature Roodeplaat Dam Nature Reserve SP Reserve 0% 11% 33% 0% 33% 67% 11% 12% 0% 0% 17% Vergenoeg Vergenoeg SP 17% 27% 9% 6% 12% 58% 27% 30% 2% 0% 19% Alberante 1% 37% 5% 2% 1% 0% 17% 18% 5% 2% 9% Alberante Ext 1 0% 19% 3% 1% 0% 0% 4% 19% 1% 3% 5% Alberton Central 3% 31% 5% 1% 1% 0% 11% 15% 7% 2% 8% Alberton Alberton South 2% 29% 8% 0% 2% 1% 10% 18% 6% 2% 8% Albertsdal 6% 24% 4% 2% 2% 0% 11% 23% 7% 0% 8% Alrode 0% 20% 40% 0% 0% 20% 0% 60% 0% 0% 14% Alrode South 29% 23% 77% 83% 77% 0% 65% 50% 3% 0% 41% Brackendowns 2% 25% 6% 1% 1% 1% 9% 18% 5% 1% 7% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

168 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) 162 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Brackenhurst 2% 26% 4% 1% 2% 1% 8% 16% 3% 0% 6% Eden Park 14% 32% 3% 2% 1% 0% 29% 31% 28% 2% 14% Florentia 4% 31% 5% 1% 1% 1% 12% 18% 7% 3% 8% Genl Alberts Park 1% 23% 2% 2% 2% 0% 3% 15% 7% 0% 5% Mayberry Park 3% 30% 5% 1% 1% 0% 8% 21% 8% 1% 8% Alberton (continued) Meyersdal 3% 32% 6% 4% 3% 3% 9% 18% 4% 0% 8% New Redruth 3% 37% 3% 1% 1% 0% 9% 13% 5% 1% 7% Newmarket 1% 39% 2% 35% 1% 0% 24% 28% 22% 6% 16% Randhart 4% 29% 3% 1% 2% 1% 11% 17% 4% 2% 7% South Crest 2% 31% 11% 1% 1% 0% 14% 13% 6% 1% 8% Union Ext 98% 36% 93% 98% 93% 98% 84% 28% 76% 0% 71% Verwoerdpark 2% 29% 3% 1% 1% 1% 12% 15% 4% 1% 7% Bapsfontein Bapsfontein 88% 36% 98% 96% 98% 100% 61% 57% 30% 12% 68% Bedfordview SP 2% 40% 4% 1% 1% 1% 13% 18% 3% 2% 8% Bedford Gardens 2% 42% 5% 2% 3% 3% 10% 13% 6% 0% 9% Bedford Park 1% 50% 1% 1% 0% 0% 11% 15% 4% 2% 8% Bedfordview Essexwold 0% 28% 6% 0% 0% 0% 12% 16% 3% 0% 6% Morninghill 3% 40% 5% 2% 1% 1% 9% 14% 2% 3% 8% Oriel 1% 38% 5% 3% 5% 0% 16% 19% 3% 3% 9% Senderwood 5% 42% 6% 0% 3% 0% 9% 28% 3% 0% 10% St Andrews 1% 38% 3% 0% 2% 0% 12% 16% 3% 2% 8% Alphen Park 0% 17% 6% 0% 2% 2% 4% 14% 2% 0% 5% Apex 0% 50% 0% 23% 16% 23% 20% 24% 36% 0% 19% Atlasville 4% 19% 4% 11% 4% 4% 4% 13% 5% 0% 7% Benoni AH 3% 22% 7% 22% 17% 19% 33% 26% 11% 2% 16% Benoni Central 5% 36% 5% 5% 3% 1% 27% 18% 19% 1% 12% Benoni Benoni East AH 0% 25% 13% 13% 0% 0% 25% 40% 4% 0% 12% Benoni North AH 12% 39% 25% 15% 4% 34% 30% 33% 4% 3% 20% Benoni Orchards AH 2% 39% 0% 12% 12% 31% 44% 27% 5% 0% 17% Benoni Small Farms 2% 38% 7% 2% 7% 7% 18% 19% 8% 0% 11% Benoni South 0% 0% 0% 0% 0% 0% 10% 11% 0% 2% Brakpan Mines 8% 16% 0% 0% 5% 0% 16% 22% 25% 0% 9% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

169 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Benoni (continued) 163 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Brentwood 0% 38% 1% 1% 1% 0% 18% 15% 2% 1% 8% Brentwood Ext 1 0% 62% 0% 0% 0% 0% 7% 21% 9% 0% 10% Brentwood Ext 3 0% 32% 1% 0% 0% 1% 6% 15% 2% 0% 6% Brentwood Park AH 27% 27% 15% 27% 16% 20% 41% 30% 7% 3% 21% Breswol AH 42% 17% 25% 8% 8% 100% 8% 50% 24% 7% 29% Crystal Park 4% 24% 3% 1% 1% 0% 8% 24% 12% 1% 8% Dewald Hattingh Park 3% 34% 6% 2% 2% 1% 20% 20% 8% 2% 10% Fairlead AH 4% 41% 2% 8% 5% 18% 27% 23% 5% 2% 13% Farrarmere 3% 31% 4% 1% 1% 1% 12% 17% 3% 1% 7% Goedeburg Ext 3 4% 47% 3% 3% 0% 0% 10% 16% 2% 1% 9% Hillcrest AH 26% 10% 54% 73% 82% 96% 65% 40% 21% 0% 47% Inglethorpe AH 10% 23% 5% 15% 7% 73% 25% 31% 9% 0% 20% Jatniel 0% 38% 1% 0% 0% 4% 40% 6% 2% 0% 9% Kingsway 2% 45% 3% 80% 1% 0% 58% 51% 44% 9% 29% Lakefield 2% 35% 2% 2% 1% 2% 9% 16% 2% 0% 7% Lilyvale AH 3% 18% 8% 17% 20% 34% 34% 26% 15% 1% 18% Mackenzie Park 5% 22% 5% 3% 2% 0% 19% 25% 6% 2% 9% Marister AH 10% 17% 11% 17% 25% 30% 33% 28% 12% 0% 18% Modder B 0% 13% 0% 0% 0% 6% 0% 24% 22% 0% 6% Modderfontein Deep Levels Morehill 2% 31% 11% 2% 2% 0% 14% 17% 5% 0% 8% Morehill Ext 1 1% 22% 5% 2% 1% 0% 13% 19% 2% 0% 7% Morehill Ext 8 Myneskool 0% 0% 0% 0% 0% 0% 27% 6% 0% 4% Nestadt 0% 0% 53% 0% 53% 68% 43% 0% 0% 24% New Modder 6% 52% 0% 15% 3% 0% 20% 26% 14% 8% 14% North Villa 2% 29% 6% 2% 1% 0% 10% 23% 3% 0% 8% Northmead 2% 29% 3% 2% 1% 0% 11% 17% 4% 1% 7% Norton Park 0% 58% 4% 0% 0% 0% 16% 1% 0% 0% 8% Norton's Home Estate 13% 29% 17% 19% 22% 34% 42% 28% 7% 8% 22% Putfontein AH 5% 22% 4% 11% 28% 57% 33% 32% 17% 2% 21% Rynfield 3% 45% 17% 0% 1% 2% 24% 20% 3% 1% 12% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

170 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Benoni (continued) Boksburg 164 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Rynfield AH 4% 28% 7% 7% 2% 15% 21% 32% 5% 8% 13% Rynfield Ext 0% 53% 0% 0% 0% 3% 5% 22% 0% 0% 8% Rynfield Ext % 68% 10% 3% 2% 2% 3% 25% 3% 0% 11% Rynfield Ext % 57% 0% 0% 0% 0% 11% 46% 0% 0% 11% Rynsoord 2% 38% 1% 2% 2% 0% 32% 21% 7% 5% 11% Sentrarand Sesfontein AH 5% 39% 2% 29% 23% 55% 38% 57% 10% 2% 26% Slaterville AH 6% 50% 28% 33% 33% 11% 33% 26% 3% 0% 22% The Stewards 3% 40% 0% 3% 0% 0% 9% 16% 8% 0% 8% Van Ryn AH 6% 14% 5% 15% 4% 9% 18% 23% 13% 6% 11% Van Ryn Gold Mine 0% 21% 21% 21% 0% 54% 25% 46% 15% 10% 21% Vista University Westdene 1% 30% 9% 1% 1% 1% 17% 23% 3% 1% 9% Boksburg SP 82% 27% 91% 91% 91% 100% 82% 60% 22% 8% 65% Anderbolt 11% 18% 12% 24% 18% 12% 18% 50% 19% 7% 19% Atlasville 2% 26% 3% 2% 2% 0% 7% 16% 4% 1% 6% Bardene Ext 1% 21% 5% 1% 4% 1% 5% 20% 2% 1% 6% Bartlett 5% 29% 3% 3% 3% 0% 15% 22% 4% 0% 8% Bartlett AH 4% 28% 7% 6% 7% 6% 16% 13% 8% 10% 10% Berton Park 2% 21% 4% 1% 1% 5% 9% 16% 5% 1% 7% Beyers Park 1% 27% 5% 2% 2% 1% 10% 14% 4% 0% 7% Boksburg 91% 24% 86% 91% 86% 86% 47% 34% 38% 10% 59% Boksburg Central 5% 37% 8% 8% 7% 3% 23% 21% 20% 4% 14% Boksburg Ext 1 0% 34% 3% 1% 0% 1% 18% 21% 3% 1% 8% Boksburg North 6% 27% 5% 4% 2% 1% 18% 21% 13% 5% 10% Boksburg Oos 20% 11% 21% 26% 16% 42% 32% 30% 20% 20% 24% Boksburg South 3% 39% 16% 5% 5% 4% 22% 17% 10% 4% 12% Cambrian 0% 41% 25% 80% 89% 89% 64% 56% 5% 4% 45% Caro Nome AH 0% 14% 14% 0% 0% 0% 0% 44% 0% 0% 7% Cason 1% 54% 2% 1% 0% 0% 41% 20% 10% 3% 13% Chris Hani 98% 23% 99% 99% 96% 100% 54% 33% 38% 4% 64% Cinderella 5% 26% 5% 2% 1% 1% 12% 18% 4% 1% 7% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

171 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Boksburg (continued) 165 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Comet 2% 17% 4% 0% 1% 1% 21% 31% 12% 0% 9% Dawn Park 2% 25% 1% 5% 4% 5% 13% 28% 19% 2% 10% Dawn Park Ext 7% 22% 3% 7% 2% 1% 15% 30% 9% 8% 10% Dayanglen 2% 37% 2% 1% 1% 0% 7% 14% 4% 0% 7% Delmore 96% 20% 81% 81% 81% 82% 40% 34% 36% 3% 55% Delmore Park 2% 23% 4% 4% 4% 1% 9% 19% 22% 1% 9% Delmore Park Ext 1% 18% 1% 5% 1% 0% 15% 26% 26% 3% 9% East Rand Proprietary Mines 20% 20% 40% 37% 37% 54% 50% 28% 37% 7% 33% Everleigh 4% 14% 3% 10% 5% 7% 3% 22% 4% 0% 7% Farrar Park 0% 23% 12% 4% 2% 0% 16% 20% 5% 2% 8% Freeway Park 3% 28% 5% 3% 1% 0% 8% 16% 4% 0% 7% Groeneweide 6% 22% 3% 4% 2% 1% 11% 22% 8% 0% 8% Hughes Ext 10% 26% 15% 21% 18% 21% 26% 23% 15% 7% 18% Impala Park 4% 22% 2% 2% 2% 0% 10% 18% 5% 1% 7% Jansen Park 3% 17% 6% 4% 7% 1% 8% 30% 5% 8% 9% Jerusalem 98% 25% 99% 99% 98% 99% 50% 31% 44% 4% 65% Jetpark 22% 0% 0% 0% 0% 22% 0% 39% 0% 14% 10% Kanana Village 99% 19% 97% 100% 96% 99% 74% 36% 31% 10% 66% Klippoortje 1% 29% 20% 19% 19% 18% 25% 28% 13% 2% 17% Libradene 4% 25% 2% 3% 1% 0% 13% 18% 3% 4% 7% Lilianton 9% 20% 6% 5% 2% 8% 13% 21% 9% 1% 9% Lindelani Ext 8 32% 44% 3% 32% 1% 0% 57% 46% 34% 3% 25% Mapleton AH 13% 28% 29% 41% 48% 65% 52% 40% 22% 3% 34% Morganridge 6% 20% 4% 2% 0% 0% 12% 12% 2% 8% 7% Parkdene 1% 12% 3% 1% 1% 0% 46% 27% 4% 1% 10% Parkhaven 0% 32% 0% 0% 0% 0% 0% 17% 0% 0% 5% Parkrand 2% 25% 3% 1% 2% 1% 8% 17% 3% 1% 6% Plantation 6% 44% 13% 1% 0% 5% 9% 21% 12% 8% 12% Ravensklip 2% 36% 5% 1% 2% 0% 8% 16% 5% 0% 7% Ravenswood 4% 32% 7% 5% 3% 4% 12% 18% 6% 1% 9% Reigerpark 85% 22% 99% 100% 99% 63% 56% 39% 48% 17% 63% Salfin 3% 11% 0% 1% 2% 27% 3% 18% 9% 1% 7% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

172 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Boksburg (continued) Brakpan 166 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Somalia Park 98% 27% 99% 99% 99% 100% 77% 49% 45% 17% 71% Sunward Park 2% 23% 3% 1% 1% 1% 9% 18% 3% 1% 6% Talbot Park 0% 13% 0% 0% 2% 0% 6% 18% 5% 7% 5% Van Dyk Park 1% 24% 6% 2% 2% 1% 14% 19% 5% 1% 7% Villa Liza 98% 33% 83% 99% 82% 96% 64% 47% 34% 3% 64% Villa Liza Camp 67% 100% 83% Villa Liza Ext 1 98% 30% 52% 99% 98% 74% 71% 53% 35% 3% 61% Villa Liza Ext 8 2% 42% 1% 1% 1% 1% 28% 30% 30% 0% 14% Villa Liza Ext A 14% 32% 6% 0% 1% 0% 41% 31% 29% 0% 15% Vredebos 1% 32% 20% 70% 43% 87% 51% 34% 47% 1% 39% Westwood AH 1% 37% 10% 0% 1% 1% 10% 12% 3% 1% 8% Windmill Park 7% 45% 79% 86% 86% 93% 55% 39% 15% 27% 53% Windmill Park Ext 0% 17% 0% 5% 3% 0% 15% 28% 18% 0% 9% Windmill Park Ext 1 5% 28% 2% 0% 0% 0% 29% 34% 37% 0% 13% Windmill Park Ext 2 0% 31% 0% 0% 0% 2% 35% 42% 16% 0% 13% Windmill Park Ext 8 98% 29% 43% 100% 43% 55% 54% 46% 37% 6% 51% Wit Deep Gold Mine Witfield 2% 26% 13% 2% 1% 1% 10% 15% 6% 2% 8% Brakpan SP 5% 29% 38% 42% 42% 79% 38% 42% 19% 4% 34% Brakpan Central 2% 34% 6% 4% 1% 1% 19% 19% 9% 2% 10% Brakpan Mines 0% 8% 0% 0% 0% 15% 18% 54% 0% 0% 9% Brakpan North 3% 22% 3% 2% 2% 1% 12% 28% 11% 2% 9% Brenthurst 3% 28% 5% 3% 3% 1% 13% 19% 7% 3% 8% Dalpark 1% 18% 5% 1% 0% 0% 6% 18% 4% 0% 5% Dalpark Ext 1 0% 24% 2% 1% 0% 0% 7% 17% 5% 0% 6% Dalpark Ext 11 6% 28% 3% 2% 0% 0% 12% 23% 10% 1% 8% Dalview 2% 29% 3% 4% 2% 0% 14% 20% 8% 2% 8% Denneoord SH 11% 17% 15% 8% 13% 4% 17% 27% 8% 0% 12% Geluksdal 2% 42% 0% 3% 0% 4% 30% 40% 32% 0% 15% Geluksdal Ext 1 1% 25% 5% 2% 2% 0% 15% 24% 21% 0% 10% Geluksdal Ext 2 3% 42% 1% 25% 0% 0% 60% 31% 41% 0% 20% Goverment Gold Mine 66% 26% 66% 71% 61% 85% 71% 54% 37% 0% 54% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

173 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) 167 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Huntingdon 0% 35% 3% 0% 3% 0% 15% 16% 3% 0% 7% Kenleaf Ext 9 2% 20% 4% 0% 2% 0% 4% 13% 4% 0% 5% Labore 14% 0% 0% 0% 86% 14% 21% 14% 0% 17% Langaville Ext 6 98% 22% 98% 98% 98% 97% 78% 41% 51% 8% 69% Larrendale 22% 27% 2% 16% 2% 22% 21% 23% 4% 0% 14% Leachville 4% 25% 2% 10% 6% 5% 17% 24% 11% 3% 11% Leachville Ext 3 3% 20% 1% 3% 1% 0% 15% 31% 26% 0% 10% Minnebronne 3% 25% 0% 1% 2% 2% 7% 18% 7% 0% 7% Rand Colleries SH 4% 11% 11% 11% 0% 11% 0% 22% 5% 4% 8% S.A. Land Exploration Gold Mine 0% 0% 0% 0% 0% 0% 33% 17% 0% 6% Sallies Village 0% 19% 3% 3% 0% 0% 22% 26% 22% 5% 10% Brakpan (continued) Sonneveld 2% 27% 12% 0% 0% 0% 5% 19% 2% 0% 7% Sunair Park 0% 15% 0% 2% 0% 0% 11% 16% 4% 0% 5% Sunair Park Ext 3 5% 47% 0% 2% 0% 0% 60% 43% 0% 0% 16% Van Dyk Park 3% 34% 0% 5% 0% 2% 25% 27% 15% 35% 15% Vlakfontein 6% 30% 36% 35% 4% 2% 53% 31% 38% 2% 24% Vulcania 0% 13% 3% 10% 7% 20% 27% 21% 2% 0% 10% Vulcania South 0% 38% 0% 15% 38% 31% 38% 30% 22% 7% 22% Withok AH 3% 18% 30% 58% 36% 37% 49% 33% 13% 11% 29% Withok Estates 0% 13% 0% 0% 0% 100% 0% 8% 8% 0% 13% Witpoort Estate AH 0% 9% 0% 95% 5% 67% 59% 51% 0% 65% 35% Witpoort Estates 99% 33% 100% 99% 98% 99% 69% 50% 58% 13% 72% Witpoort Estates AH 16% 20% 0% 83% 49% 80% 49% 23% 35% 25% 38% Cerutiville Mawag 99% 34% 99% 100% 100% 82% 84% 69% 34% 14% 71% Chief Albert Lithuli Park Daveyton Chief Albert Lithuli Park 0% 41% 0% 4% 0% 0% 42% 23% 13% 0% 12% Chief Albert Lithuli Park Block A 1% 35% 3% 4% 1% 0% 37% 44% 33% 0% 16% Chief Albert Lithuli Park Block B 1% 25% 2% 5% 1% 0% 32% 33% 38% 0% 14% Basotho Section 34% 31% 1% 7% 2% 0% 44% 35% 41% 4% 20% Boyas View 4% 33% 3% 3% 0% 0% 16% 24% 19% 0% 10% Chris Hani 70% 31% 61% 78% 68% 38% 65% 44% 43% 6% 50% Daveyton Central 19% 43% 1% 5% 2% 0% 38% 49% 33% 0% 19% Daveyton Ext 3 3% 33% 6% 1% 2% 0% 16% 25% 13% 1% 10% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

174 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Daveyton (continued) Duduza Dukathole Edenvale 168 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Garden Village 0% 32% 2% 2% 1% 0% 13% 27% 19% 0% 10% Pedi Section 41% 35% 14% 4% 1% 0% 47% 34% 43% 1% 22% Sgodi Phola 26% 40% 2% 2% 0% 0% 44% 36% 37% 0% 19% Swazi Section 32% 40% 5% 10% 2% 0% 45% 34% 40% 3% 21% Tsonga Section 45% 39% 1% 6% 1% 0% 49% 41% 37% 4% 22% Vergenoeg 13% 44% 1% 7% 2% 1% 42% 34% 40% 1% 18% Xhosa Section 43% 43% 5% 16% 1% 0% 49% 31% 45% 2% 23% Zenzele Village 93% 28% 62% 99% 96% 9% 56% 49% 38% 4% 53% Duduza SP 2% 43% 2% 3% 1% 0% 39% 34% 31% 1% 15% Blue Gum View 51% 35% 0% 5% 1% 17% 55% 45% 25% 3% 24% Blue Gum View Ext 1 1% 49% 0% 3% 0% 3% 62% 33% 32% 11% 19% Blue Gum View Ext 2 84% 35% 6% 4% 0% 0% 55% 58% 30% 0% 27% Blue Gum View Ext 3 90% 38% 50% 47% 46% 40% 67% 52% 37% 2% 47% Blue Gum View Ext 4 86% 36% 55% 6% 5% 1% 50% 42% 27% 1% 31% Blue Gum View Ext 5 72% 30% 41% 43% 40% 38% 65% 56% 41% 2% 43% Blue Gum View Ext 6 84% 32% 44% 42% 40% 25% 62% 54% 38% 1% 42% Duduza Central 3% 46% 1% 2% 1% 1% 52% 34% 34% 1% 18% Duduza Ext 2 5% 55% 4% 2% 1% 2% 61% 30% 46% 9% 21% Duduza Ext 3 1% 40% 3% 1% 1% 21% 51% 37% 33% 0% 19% Masechaba Ext 1 87% 37% 12% 17% 46% 3% 59% 53% 34% 2% 35% Masechaba Ext 2 2% 51% 0% 2% 1% 0% 64% 37% 43% 2% 20% Masechaba Ext 3 2% 41% 13% 4% 1% 2% 68% 41% 46% 0% 22% Masechaba View 50% 35% 2% 12% 3% 1% 67% 52% 41% 2% 27% Phumla Mqashi 2% 41% 1% 3% 0% 0% 47% 39% 26% 0% 16% Rose View 13% 33% 19% 9% 0% 0% 42% 24% 11% 0% 15% Dikathole Ext 5 98% 25% 99% 100% 97% 17% 52% 33% 42% 17% 58% Dikathole Ext 9 93% 28% 66% 88% 53% 73% 53% 32% 44% 9% 54% Dukathole Ext 9 1% 41% 1% 100% 1% 1% 43% 37% 45% 0% 27% De Klerkshof 2% 26% 7% 0% 2% 0% 11% 21% 4% 2% 7% Dowerglen 2% 44% 2% 0% 1% 0% 11% 18% 2% 0% 8% Dowerglen Ext 1 1% 36% 3% 0% 1% 1% 10% 18% 2% 1% 8% Dowerglen Ext 3 0% 45% 1% 0% 2% 0% 11% 16% 1% 2% 8% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

175 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Edenvale (continued) Ekurhuleni Metro Etwatwa 169 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Dowerglen Ext 4 3% 30% 7% 1% 1% 1% 10% 17% 3% 2% 7% Dowerglen Ext 5 4% 33% 6% 0% 0% 1% 19% 16% 1% 1% 8% Dunvegan 1% 30% 2% 0% 1% 1% 21% 20% 3% 1% 8% Eastleigh 2% 37% 5% 2% 2% 0% 12% 17% 4% 4% 8% Eden Glen 1% 37% 3% 1% 1% 1% 9% 13% 3% 1% 7% Eden Glen Ext 38 2% 41% 5% 3% 2% 1% 9% 13% 5% 2% 8% Edendale 2% 34% 6% 1% 1% 0% 14% 17% 6% 2% 8% Edenvale Ext 1% 45% 6% 0% 3% 1% 6% 10% 4% 0% 8% Elmapark 2% 26% 3% 1% 2% 0% 13% 16% 4% 1% 7% Hurlyvale 2% 31% 4% 3% 2% 0% 7% 16% 5% 2% 7% Illiondale 1% 28% 9% 1% 2% 0% 13% 14% 6% 1% 8% Isandovale 5% 18% 9% 3% 0% 0% 10% 17% 5% 2% 7% Marais Steyn Park 1% 33% 6% 1% 1% 1% 8% 14% 2% 1% 7% Sebenza 0% 0% 0% 0% 0% 0% 37% 86% 5% 11% 14% Benoni NU 97% 25% 96% 97% 89% 99% 68% 41% 46% 1% 66% Bronkhorstspruit NU 90% 24% 92% 87% 94% 16% 66% 45% 41% 3% 56% Elandsfontein AH 9% 30% 55% 46% 68% 79% 53% 46% 15% 3% 41% Geesteveld AH 0% 53% 0% 97% 100% 100% 50% 43% 0% 3% 45% Heidelberg NU 61% 23% 73% 97% 95% 100% 52% 51% 31% 5% 59% Kempton Park NU 14% 29% 37% 33% 35% 77% 50% 42% 14% 6% 34% Springs NU Tweefontein AH 3% 18% 25% 30% 69% 63% 33% 46% 1% 0% 29% Vereeniging NU 24% 45% 33% 55% 20% 24% 62% 48% 21% 16% 35% West Park AH 14% 16% 15% 29% 21% 91% 21% 34% 10% 4% 25% Etwatwa SP 82% 27% 33% 24% 58% 20% 70% 40% 40% 5% 40% Albertina 88% 40% 33% 29% 57% 28% 58% 50% 35% 3% 42% Besta Ext 2 28% 25% 2% 3% 33% 2% 50% 38% 38% 0% 22% Besta Ext 3 18% 22% 4% 4% 1% 0% 18% 25% 27% 4% 12% Emaphopheni 79% 44% 5% 1% 72% 0% 54% 53% 45% 3% 36% Emaphopheni Ext 36 96% 39% 59% 47% 87% 5% 58% 54% 34% 2% 48% Emaphopheni Section 11 71% 70% 15% 2% 85% 0% 55% 41% 2% 2% 34% Emaphopheni Section 12 85% 35% 21% 2% 3% 1% 52% 47% 23% 0% 27% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

176 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Etwatwa(continued) Germiston 170 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Emaphopheni Section 14 87% 32% 10% 2% 11% 1% 50% 48% 31% 5% 28% Emaphopheni Section 15 14% 27% 2% 3% 1% 0% 43% 32% 37% 2% 16% Emaphopheni Section 18 79% 50% 0% 1% 2% 1% 63% 46% 39% 0% 28% Emaphopheni Section 21 78% 35% 14% 1% 47% 0% 56% 44% 30% 1% 31% Emaphopheni Section 30 49% 40% 16% 3% 2% 1% 54% 45% 39% 2% 25% Emaphopheni Section 7 62% 41% 12% 2% 50% 0% 63% 50% 32% 0% 31% Emaphopheni Section 8 22% 37% 1% 1% 15% 0% 62% 44% 41% 2% 23% Etwatwa East 66% 38% 2% 2% 1% 0% 46% 42% 38% 1% 24% Etwatwa Ext 1 9% 33% 4% 4% 5% 1% 33% 36% 29% 2% 16% Etwatwa Ext 23 87% 36% 43% 16% 69% 6% 55% 44% 37% 3% 39% Etwatwa Ext 25 97% 32% 79% 100% 98% 3% 59% 45% 31% 2% 55% Etwatwa Ext 26 98% 36% 65% 86% 86% 13% 49% 42% 40% 2% 52% Etwatwa Ext 27 97% 35% 89% 99% 100% 26% 61% 51% 42% 2% 60% Etwatwa Ext 3 3% 17% 2% 4% 0% 0% 21% 30% 26% 0% 10% Etwatwa Ext 7 10% 28% 2% 2% 3% 0% 32% 30% 30% 0% 14% Etwatwa Square 90% 36% 6% 8% 6% 0% 59% 55% 43% 1% 30% Etwatwa West 12% 43% 15% 2% 1% 1% 46% 41% 31% 2% 19% Knoppiesfontein 45% 22% 41% 87% 86% 89% 62% 50% 47% 1% 53% Thulani Section 2% 25% 1% 10% 0% 1% 28% 34% 22% 1% 12% Xtombiza 95% 32% 98% 100% 100% 11% 79% 72% 48% 2% 64% Germiston SP Activia Park 19% 19% 0% 19% 0% 0% 38% 44% 0% 17% 15% Albemarle 3% 26% 7% 2% 3% 0% 8% 19% 5% 2% 7% Albemarle Ext 2 3% 20% 4% 0% 0% 0% 6% 16% 3% 0% 5% Buhle Park 2% 20% 4% 27% 2% 11% 28% 31% 28% 1% 15% Buurendal 9% 25% 11% 5% 5% 5% 9% 10% 5% 6% 9% Castleview 4% 37% 2% 0% 0% 0% 20% 21% 4% 4% 9% Cruywagen Park 8% 38% 34% 8% 3% 2% 16% 29% 9% 3% 15% Dawnview 1% 36% 2% 3% 1% 0% 21% 19% 5% 4% 9% Delmore Hospital 63% 47% 0% 47% 39% 100% 47% 28% 6% 0% 38% Delport 99% 18% 99% 100% 97% 94% 56% 29% 44% 1% 64% Delville 4% 32% 10% 3% 1% 0% 16% 19% 10% 6% 10% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

177 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Germiston (continued) 171 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Denlee 2% 38% 2% 1% 3% 1% 11% 16% 3% 0% 8% Dinwiddie 2% 26% 4% 1% 1% 0% 16% 19% 8% 1% 8% Driehoek 90% 24% 86% 85% 85% 26% 46% 30% 40% 6% 52% East Rand Proprietary Mines 0% 0% 25% 0% 0% 25% 25% 29% 14% 0% 12% Elandsfontein 33% 30% 5% 23% 5% 2% 35% 21% 26% 0% 18% Elandsfontein Rail 11% 38% 2% 12% 4% 1% 21% 20% 30% 1% 14% Elandshaven 3% 10% 3% 0% 0% 0% 7% 12% 8% 0% 4% Elsburg 3% 25% 6% 1% 2% 0% 16% 17% 12% 2% 8% Elspark 2% 22% 4% 0% 1% 0% 14% 17% 6% 3% 7% Elspark Ext 4 3% 20% 9% 2% 1% 2% 12% 25% 29% 0% 10% Estera 5% 28% 9% 3% 2% 0% 16% 25% 9% 3% 10% Fishers Hill 2% 36% 4% 3% 0% 1% 22% 25% 8% 0% 10% Freeway Park 2% 37% 3% 4% 0% 1% 7% 21% 5% 1% 8% Georgetown 5% 31% 11% 2% 3% 3% 19% 20% 15% 9% 12% Gerdview 2% 29% 5% 1% 1% 1% 13% 20% 7% 1% 8% Germiston Central 3% 34% 43% 9% 0% 8% 29% 19% 22% 5% 17% Germiston Ext.3 0% 0% 0% 23% 0% 0% 0% 17% 0% 0% 4% Germiston South 2% 34% 3% 3% 2% 1% 21% 20% 13% 5% 10% Germiston Station 45% 23% 54% 94% 60% 83% 23% 42% 31% 11% 46% Germiston West 2% 31% 2% 1% 3% 1% 8% 14% 12% 0% 7% Glen Deep 4% 13% 0% 4% 0% 4% 35% 22% 14% 0% 10% Gosforth Park 3% 24% 0% 0% 0% 0% 2% 22% 8% 0% 6% Harmelia 7% 21% 4% 1% 0% 1% 9% 15% 3% 2% 6% Hazeldene 2% 27% 7% 0% 0% 2% 9% 21% 7% 0% 8% Hazelpark 1% 26% 3% 1% 1% 2% 15% 19% 6% 1% 7% Highway Gardens 2% 26% 6% 1% 2% 0% 11% 15% 3% 1% 7% Homestead 2% 38% 3% 2% 1% 0% 19% 18% 6% 1% 9% Industries East 0% 1% 4% 1% 1% 0% 47% 46% 37% 1% 14% Junction Hill 0% 0% 0% 33% 0% 0% 0% 25% 17% 0% 8% Klippoortje 3% 38% 59% 3% 0% 3% 6% 16% 2% 0% 13% Klippoortje North 2% 34% 5% 3% 2% 1% 17% 20% 3% 0% 9% Klippoortjie AL 11% 20% 29% 30% 39% 33% 21% 29% 11% 2% 23% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

178 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Germiston (continued) 172 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Klopperpark 7% 30% 7% 2% 1% 1% 12% 19% 11% 2% 9% Knights 13% 27% 0% 18% 0% 27% 45% 47% 24% 0% 20% Lambton 3% 35% 3% 3% 1% 0% 17% 19% 5% 1% 9% Lambton Gardens 2% 19% 5% 2% 2% 2% 11% 19% 7% 1% 7% Leondale 1% 27% 8% 1% 1% 0% 12% 22% 16% 2% 9% Marathon 49% 36% 49% 86% 48% 38% 65% 35% 49% 9% 46% Marlands 2% 28% 3% 1% 1% 1% 14% 16% 7% 0% 7% Meadowbrook 2% 46% 6% 2% 0% 1% 18% 19% 6% 0% 10% Meadowdale 33% 0% 0% 0% 10% 20% 0% 50% 0% 0% 11% Parkhill Gardens 1% 31% 5% 1% 0% 1% 16% 18% 6% 1% 8% Pirowville 28% 24% 1% 6% 1% 1% 35% 23% 13% 28% 16% Primrose 3% 36% 4% 3% 2% 1% 20% 21% 8% 1% 10% Primrose Hill 1% 38% 2% 2% 1% 1% 13% 18% 5% 1% 8% Rand Airport 0% 50% 0% 0% 0% 0% 13% 33% 9% 20% 12% Rietfontein 3% 27% 0% 2% 2% 2% 12% 18% 8% 5% 8% Rondebult 5% 26% 11% 13% 15% 17% 23% 26% 14% 1% 15% Rondebult Bird Sanctuary Rondebult Ext 2 0% 39% 2% 50% 1% 0% 58% 41% 52% 1% 24% Roodebult 1% 24% 3% 0% 1% 0% 9% 23% 17% 1% 8% Roodekop 53% 0% 0% 18% 18% 65% 18% 50% 0% 0% 22% Rose Deep 96% 27% 99% 100% 99% 98% 53% 34% 35% 1% 64% Simmer and Jack Gold Mine 45% 33% 44% 54% 44% 54% 25% 31% 26% 24% 38% Simmerfield 2% 27% 0% 2% 2% 0% 17% 17% 10% 1% 8% Solheim 2% 55% 3% 1% 2% 0% 9% 20% 7% 2% 10% South Germiston Ext 7 0% 40% 0% 0% 0% 20% 40% 0% 0% 25% 13% South Germiston Exts 8% 4% 0% 0% 4% 0% 4% 51% 19% 0% 9% Summerfield 7% 28% 3% 6% 1% 0% 17% 17% 12% 1% 9% Sunnyridge 2% 35% 5% 1% 1% 0% 13% 19% 6% 0% 8% Sunnyrock 3% 31% 3% 4% 3% 3% 10% 10% 3% 1% 7% Symhurst 0% 39% 1% 1% 0% 0% 10% 19% 8% 1% 8% Tedstoneville 1% 26% 3% 1% 2% 1% 10% 22% 10% 2% 8% Ulana 98% 38% 99% 100% 50% 50% 45% 30% 51% 20% 58% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

179 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Germiston (continued) Katlehong 173 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Union 88% 21% 83% 88% 89% 4% 52% 38% 40% 2% 51% Union Ext 2% 30% 18% 9% 1% 9% 15% 22% 8% 1% 11% Wadeville 14% 38% 6% 23% 0% 6% 38% 39% 13% 6% 18% Wannenburghoogte 3% 36% 9% 1% 1% 0% 7% 24% 8% 1% 9% Webber 0% 37% 12% 0% 2% 0% 10% 18% 2% 2% 8% Wilbart 0% 0% 0% 0% 0% 0% 25% 50% 17% 0% 9% Witfield 4% 29% 6% 6% 4% 2% 21% 22% 4% 2% 10% Witwatersrand Gold Mining 0% 9% 0% 2% 0% 0% 9% 41% 10% 0% 7% Woodmere 4% 28% 7% 0% 0% 1% 10% 18% 5% 3% 8% Wychwood 3% 41% 5% 4% 4% 3% 23% 18% 12% 8% 12% Wychwood Ext. 100% 0% 63% 63% 63% 100% 19% 45% 20% 0% 47% Katlehong SP 4% 27% 3% 12% 1% 5% 62% 44% 41% 9% 21% Admin Triangle 0% 43% 7% 0% 0% 0% 40% 30% 22% 0% 14% Everest 43% 35% 1% 6% 1% 0% 52% 33% 52% 0% 22% Goba 19% 38% 1% 12% 1% 0% 47% 34% 44% 0% 20% Greenfield 23% 35% 15% 87% 18% 11% 70% 44% 50% 3% 36% Hlahatsi 15% 36% 6% 18% 1% 0% 40% 34% 25% 6% 18% Hlongwane 14% 46% 1% 16% 1% 0% 53% 36% 44% 0% 21% Kathlehong 99% 33% 100% 96% 98% 100% 59% 50% 36% 13% 68% Katlehong South 51% 35% 10% 6% 2% 0% 65% 41% 48% 2% 26% Khumalo Section 4% 32% 1% 3% 1% 0% 40% 30% 35% 2% 15% Kwenele South 2% 43% 2% 16% 2% 1% 49% 33% 36% 0% 18% Likole 8% 35% 2% 5% 1% 0% 24% 31% 18% 2% 12% Likole Ext 1 5% 30% 3% 2% 0% 0% 35% 29% 33% 2% 14% Likole Ext 2 2% 30% 13% 2% 1% 0% 66% 44% 40% 3% 20% Lindela Section 23% 38% 2% 15% 1% 1% 43% 29% 36% 4% 19% Magagula Heights 61% 34% 0% 38% 0% 9% 56% 43% 43% 0% 28% Mandela Park 94% 36% 50% 98% 49% 44% 75% 39% 58% 4% 55% Maphanga 32% 40% 1% 24% 2% 0% 43% 34% 33% 8% 22% Mavimbela Section 21% 38% 1% 21% 1% 0% 49% 38% 46% 0% 21% Mnisi 6% 40% 1% 3% 1% 0% 40% 37% 32% 2% 16% Mokwena 12% 39% 2% 12% 1% 0% 51% 40% 34% 1% 19% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

180 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Katlehong (continued) 174 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Moleleki 77% 32% 77% 80% 23% 77% 65% 46% 40% 2% 52% Moleleki Ext 2 49% 37% 1% 4% 1% 0% 55% 38% 42% 1% 23% Moleleki Ext 3 77% 31% 79% 75% 75% 32% 63% 49% 39% 3% 52% Moleleki Ext 4 91% 18% 9% 91% 55% 91% 45% 29% 50% 0% 48% Mopedi Section 28% 33% 1% 20% 2% 1% 45% 31% 37% 1% 20% Moseleke 10% 39% 3% 9% 1% 0% 57% 35% 44% 2% 20% Moseleke East 4% 38% 1% 4% 1% 0% 40% 29% 39% 2% 16% Natalspruit 40% 30% 1% 100% 1% 0% 71% 43% 43% 6% 33% Ncala 23% 37% 17% 12% 1% 0% 50% 37% 39% 1% 22% Ndhlazi 17% 44% 1% 19% 1% 0% 47% 44% 38% 5% 22% Ngema 22% 34% 1% 10% 3% 1% 45% 35% 37% 0% 19% Nhlapo 38% 39% 5% 22% 2% 0% 57% 38% 40% 5% 25% Palime 13% 37% 1% 24% 2% 0% 47% 37% 32% 5% 20% Palm Ridge 35% 32% 35% 37% 35% 34% 36% 31% 25% 1% 30% Palm Ridge Ext 2 98% 32% 96% 99% 97% 86% 63% 44% 45% 9% 67% Phake Section 22% 41% 1% 31% 1% 0% 47% 33% 50% 0% 23% Phooko 20% 40% 1% 24% 1% 0% 49% 37% 42% 3% 22% Phumula 2% 26% 2% 0% 0% 0% 18% 28% 30% 0% 11% Radebe 10% 44% 1% 17% 1% 1% 50% 37% 46% 6% 21% Ramakonopi East 3% 35% 1% 3% 0% 0% 42% 26% 29% 2% 14% Ramakonopi West 3% 37% 1% 3% 0% 0% 40% 29% 30% 1% 14% Sali 7% 40% 8% 2% 0% 0% 51% 38% 49% 0% 19% Seloma A 0% 29% 0% 1% 0% 0% 40% 41% 36% 0% 15% Seloma B 6% 24% 34% 2% 0% 0% 34% 32% 25% 8% 16% Shongweni Ext 9 30% 35% 1% 14% 2% 0% 48% 33% 42% 5% 21% Siluma Shacks 4% 30% 0% 8% 4% 4% 23% 35% 35% 11% 15% Siluma View 20% 22% 2% 5% 2% 0% 29% 29% 32% 3% 14% Skosana 22% 36% 1% 12% 1% 0% 51% 37% 42% 4% 21% Spruitview 14% 30% 13% 13% 13% 3% 20% 28% 18% 2% 15% Tsietsi Phase 1 98% 31% 99% 100% 100% 79% 65% 43% 49% 3% 67% Tsietsi Phase 2 98% 31% 98% 100% 100% 80% 60% 49% 36% 11% 66% Tsietsi Phase 3 96% 27% 99% 99% 98% 85% 62% 45% 41% 5% 66% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

181 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Katlehong (continued) Kempton Park 175 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Tsietsi Phase 5 99% 27% 99% 100% 94% 95% 69% 49% 44% 5% 68% Tsolo 18% 34% 0% 20% 0% 0% 44% 29% 45% 9% 20% Tsongweni 41% 40% 1% 24% 1% 1% 50% 38% 41% 2% 24% Twala 30% 36% 0% 15% 2% 1% 46% 31% 36% 2% 20% Umgadi Section 3% 47% 1% 2% 1% 0% 37% 32% 48% 2% 17% Zonkizizwe 79% 32% 10% 17% 7% 1% 65% 52% 38% 2% 30% Zonkizizwe Ext 80% 30% 17% 20% 16% 0% 62% 48% 37% 2% 31% Zonkizizwe Ext 2 67% 41% 2% 4% 1% 0% 62% 44% 41% 2% 27% Zuma Section 26% 36% 0% 15% 1% 1% 44% 38% 37% 9% 21% Allen Grove 3% 48% 2% 0% 0% 0% 21% 13% 2% 1% 9% Aston Manor 1% 30% 4% 1% 2% 0% 14% 17% 3% 0% 7% Atlas 75% 25% 0% 0% 0% 100% 25% 7% 1% 0% 23% Birch Acres 4% 25% 3% 1% 1% 0% 12% 19% 7% 2% 7% Birch Acres Ext 2 5% 21% 5% 3% 2% 1% 14% 23% 8% 2% 8% Birch Acres Ext 6 2% 27% 2% 2% 3% 0% 9% 20% 11% 1% 8% Birchleigh 2% 29% 5% 1% 1% 1% 9% 16% 5% 1% 7% Birchleigh North 3% 23% 3% 2% 1% 1% 9% 17% 8% 2% 7% Bonaero Park 1% 26% 3% 1% 1% 3% 11% 18% 4% 1% 7% Bredell AH 12% 21% 16% 14% 10% 4% 25% 22% 7% 5% 14% Brentwood Park AH 4% 38% 13% 5% 1% 1% 21% 14% 4% 1% 10% Chloorkop 20% 33% 13% 8% 15% 20% 18% 27% 27% 6% 19% Chloorkop Ext 3% 24% 2% 0% 0% 3% 16% 60% 14% 2% 12% Cresslawn 5% 25% 3% 2% 3% 0% 9% 16% 5% 1% 7% Croydon 4% 32% 6% 1% 1% 1% 8% 15% 6% 2% 8% Edleen 2% 34% 3% 1% 1% 1% 11% 16% 5% 2% 8% Edleen Ext 2 2% 34% 4% 1% 3% 0% 8% 13% 5% 0% 7% Esselen Park Ext 1 20% 28% 8% 8% 5% 29% 28% 15% 19% 2% 16% Esther Park 5% 21% 1% 0% 3% 1% 10% 18% 7% 3% 7% Esther Park Ext 4% 26% 19% 2% 1% 0% 14% 20% 7% 0% 9% Esther Park Ext 9 Glen Marais 2% 33% 6% 1% 2% 1% 10% 16% 3% 1% 8% Glen Marais Ext 1% 34% 3% 1% 1% 0% 10% 15% 2% 3% 7% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

182 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Kempton Park (continued) KwaThema 176 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Isando 0% 9% 5% 0% 5% 0% 9% 8% 4% 0% 4% Johannesburg International Airport 0% 0% 0% 0% 0% 0% 0% 11% 0% 0% 1% Kempton Park AH 16% 26% 10% 14% 4% 6% 25% 25% 8% 12% 15% Kempton Park Central 3% 37% 4% 2% 1% 2% 9% 15% 10% 2% 8% Kempton Park West 4% 24% 1% 1% 1% 0% 14% 19% 16% 2% 8% Nimrodpark 2% 46% 6% 0% 4% 1% 16% 18% 4% 1% 10% Norkem Park 3% 30% 6% 2% 1% 0% 9% 17% 6% 1% 8% Pomona 14% 18% 6% 2% 6% 2% 16% 23% 3% 4% 10% Pomona AH 10% 20% 19% 25% 15% 10% 27% 26% 11% 14% 18% Pomona Ext 3 6% 52% 4% 5% 2% 0% 2% 21% 4% 0% 10% Restonvale AH 4% 54% 5% 4% 0% 3% 12% 14% 6% 3% 11% Rhodesfield 2% 34% 12% 2% 2% 1% 16% 19% 9% 2% 10% Spartan 0% 43% 0% 0% 0% 0% 0% 22% 11% 0% 8% Spartan Ext 1 0% 4% 0% 3% 3% 75% 3% 39% 9% 1% 14% Terenure 1% 18% 2% 0% 2% 1% 7% 17% 5% 0% 5% Terenure AH 3% 32% 36% 5% 5% 7% 7% 21% 3% 5% 12% Terenure Ext 2% 46% 1% 1% 1% 1% 13% 14% 4% 1% 8% Van Riebeeck Park 4% 36% 2% 1% 1% 1% 14% 17% 4% 1% 8% Witfontein 0% 21% 100% 100% 100% 100% 59% 50% 23% 0% 55% KwaThema SP 0% 24% 17% 73% 4% 1% 91% 91% 81% 3% 38% Deeplevels 30% 41% 31% 36% 1% 3% 50% 37% 38% 4% 27% Gugulethu 2% 24% 2% 1% 1% 1% 22% 22% 20% 0% 9% Highlands 2% 50% 2% 2% 2% 1% 55% 34% 41% 5% 19% KwaThema 86% 25% 86% 88% 89% 84% 68% 51% 42% 0% 62% KwaThema Central 0% 63% 3% 1% 0% 0% 46% 32% 47% 0% 19% KwaThema Ext 2% 31% 1% 11% 1% 0% 36% 31% 25% 1% 14% KwaThema Phase 1 3% 20% 2% 3% 1% 0% 25% 27% 23% 0% 10% KwaThema Phase 2 39% 34% 5% 47% 4% 0% 46% 38% 33% 0% 25% KwaThema Phase 3 94% 27% 44% 90% 26% 36% 62% 42% 49% 5% 47% Masimini 4% 44% 0% 2% 1% 0% 46% 38% 37% 3% 18% Mthembu Village 2% 44% 1% 1% 1% 0% 41% 27% 29% 0% 15% New Payneville 1% 52% 0% 0% 4% 0% 43% 29% 46% 0% 18% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

183 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) KwaThema (continued) Lindelani Village Midrand Nigel 177 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Overline 3% 46% 1% 0% 1% 1% 64% 45% 54% 4% 22% Phelendaba 4% 37% 1% 0% 0% 0% 52% 44% 42% 0% 18% Phumulo 1% 47% 0% 3% 2% 0% 51% 30% 45% 1% 18% Rest In Peace 3% 43% 1% 4% 1% 0% 43% 31% 32% 2% 16% Riverside 12% 36% 0% 4% 0% 0% 42% 40% 40% 10% 18% Thembilisha 1% 30% 2% 0% 1% 0% 29% 28% 25% 2% 12% Tornado 3% 39% 0% 3% 0% 0% 40% 35% 42% 2% 17% Vergenoeg 3% 47% 0% 16% 1% 0% 46% 36% 40% 1% 19% White City 4% 44% 1% 16% 1% 0% 50% 33% 45% 1% 20% Lindelani Village 97% 19% 97% 99% 97% 2% 51% 38% 41% 2% 54% SANTA 0% 38% 0% 100% 0% 0% 75% 45% 13% 0% 27% Clayville 3% 25% 2% 3% 1% 3% 10% 19% 13% 2% 8% Clayville East 3% 20% 5% 2% 1% 0% 13% 20% 11% 3% 8% Olifantsfontein 8% 18% 12% 0% 0% 33% 0% 15% 3% 7% 10% Sun Lawns AH 15% 73% 5% 31% 58% 100% 68% 42% 2% 17% 41% Nigel SP 11% 18% 18% 51% 44% 51% 44% 52% 8% 11% 31% Alra Park 2% 35% 2% 5% 1% 0% 24% 33% 19% 1% 12% Alra Park Ext 4% 72% 2% 4% 0% 0% 41% 33% 10% 2% 17% Alra Park Ext 1 5% 36% 2% 12% 3% 2% 21% 23% 19% 0% 12% Alra Park Ext 2 20% 29% 6% 15% 25% 3% 60% 59% 45% 0% 26% Cerutiville 68% 35% 2% 32% 26% 2% 49% 35% 44% 10% 30% Cerutiville Ext 1 30% 49% 0% 7% 19% 0% 57% 49% 31% 0% 24% Dunnottar 1% 23% 3% 2% 2% 1% 13% 19% 8% 1% 7% Ferryvale 0% 22% 4% 2% 0% 0% 12% 13% 4% 0% 6% Glen Varloch 0% 24% 0% 0% 0% 0% 10% 9% 5% 0% 5% Laversburg 3% 46% 3% 0% 3% 0% 10% 22% 6% 0% 9% Mackenzieville 0% 38% 4% 5% 0% 2% 23% 45% 10% 1% 13% Marievale 0% 8% 48% 27% 22% 27% 32% 38% 13% 9% 22% Nigel Central 4% 31% 14% 2% 1% 0% 9% 16% 4% 2% 8% Nigel Gold Mine 7% 13% 34% 91% 32% 84% 72% 50% 47% 0% 43% Noycedale 1% 21% 8% 1% 2% 0% 15% 15% 8% 0% 7% Pretoriusstad 0% 16% 45% 43% 5% 12% 21% 26% 8% 5% 18% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

184 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Nigel (continued) Reiger Park Springs 178 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Sharon Park 1% 24% 1% 3% 0% 0% 8% 20% 9% 1% 7% Sharondale 3% 27% 0% 0% 0% 0% 20% 22% 4% 0% 8% Sub-Nigel Gold Mine 0% 8% 8% 0% 0% 0% 38% 27% 19% 0% 10% Visagie Park 1% 26% 3% 0% 2% 0% 10% 16% 6% 0% 6% Vorsterkroon 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Reiger Park SP 0% 100% 100% 0% 0% 0% 100% 0% 0% 0% 30% Ramaphosa Ext 1 97% 30% 41% 85% 38% 41% 52% 41% 39% 5% 47% Ramaphosa Ext 2 99% 23% 99% 100% 99% 100% 45% 33% 29% 10% 64% Reigerpark 9% 38% 2% 4% 1% 0% 26% 27% 25% 3% 14% Barkerton Ext 3 4% 19% 15% 7% 1% 1% 12% 20% 10% 0% 9% Barkerton Ext 4 4% 33% 3% 4% 1% 1% 18% 23% 7% 0% 9% Casseldale 4% 34% 13% 1% 2% 1% 18% 22% 5% 0% 10% Daggafontein 59% 16% 46% 52% 46% 3% 50% 40% 23% 5% 34% Dal Fouche 2% 16% 3% 1% 1% 3% 9% 16% 8% 0% 6% Dersley 3% 21% 3% 3% 1% 1% 12% 19% 7% 1% 7% East Geduld 0% 15% 2% 0% 0% 0% 10% 24% 8% 0% 6% Eastvale 3% 19% 2% 4% 1% 0% 9% 22% 7% 1% 7% Edelweiss 1% 22% 3% 2% 2% 0% 9% 16% 5% 0% 6% Everest 97% 26% 98% 99% 98% 100% 59% 45% 41% 6% 67% Fulcrum 0% 0% 0% 0% 0% 0% 100% 100% 0% 0% 20% Fulcrum Ext 2 1% 52% 0% 47% 1% 1% 58% 28% 39% 2% 23% Geduld 5% 29% 8% 2% 1% 0% 22% 20% 15% 4% 11% Geduld Ext 1 2% 24% 3% 0% 1% 0% 14% 17% 11% 3% 8% Geduld Proprietary Mines 0% 0% 0% 0% Golden Springs 0% 20% 0% 0% 0% 20% 20% 17% 0% 0% 8% Grootvaly AH 23% 48% 18% 51% 26% 29% 60% 33% 28% 12% 33% Grootvlei Proprietary Mines 0% 6% 2% 1% 1% 1% 53% 30% 14% 1% 11% Gugulethu 99% 22% 99% 100% 99% 99% 65% 46% 44% 12% 68% Krugersrus 37% 18% 33% 33% 31% 34% 47% 21% 32% 1% 29% Large Colliery 0% 27% 95% 95% 95% 100% 80% 89% 11% 0% 59% Lodeyko 31% 45% 23% 21% 20% 29% 58% 16% 17% 4% 26% New Era Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

185 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Springs (continued) Tembisa 179 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation New State Area 2% 22% 0% 2% 2% 0% 12% 19% 5% 0% 6% Nuffield Oranjehof 5% 16% 7% 24% 2% 5% 33% 30% 31% 6% 16% Paul Krugersoord 3% 50% 5% 0% 1% 1% 44% 27% 14% 0% 14% Payneville 0% 18% 4% 4% 3% 4% 28% 25% 32% 1% 12% Petersfield 1% 24% 5% 1% 0% 3% 16% 19% 4% 2% 8% Pollak Park 4% 18% 5% 5% 1% 3% 10% 19% 11% 6% 8% Presidentsdam 0% 6% 16% 0% 0% 0% 0% 20% 1% 0% 4% Rowhill 0% 15% 3% 2% 2% 0% 10% 18% 4% 2% 5% Selcourt 3% 25% 4% 1% 2% 1% 10% 17% 4% 2% 7% Selection Park 2% 31% 5% 3% 2% 1% 16% 20% 7% 1% 9% Slovo Park 14% 38% 2% 86% 2% 0% 65% 53% 40% 2% 30% Springs Airfield 0% 0% 100% 0% 0% 0% 0% 0% 0% 11% Springs Central 2% 32% 2% 3% 1% 0% 24% 16% 16% 8% 10% Strubenvale 1% 27% 5% 2% 2% 0% 14% 23% 6% 1% 8% Struisbult 0% 26% 10% 3% 1% 0% 14% 15% 6% 1% 8% Welgedacht 3% 13% 5% 14% 3% 1% 16% 25% 18% 1% 10% Welgedacht SH 13% 17% 14% 41% 32% 37% 23% 34% 13% 1% 23% Wright Park 5% 36% 4% 2% 2% 0% 13% 22% 17% 1% 10% Duduza 98% 26% 96% 99% 96% 77% 56% 34% 46% 7% 64% Ecaleni 7% 42% 7% 13% 6% 7% 49% 40% 30% 24% 22% Ehlanzeni 44% 32% 27% 35% 30% 19% 49% 38% 48% 4% 33% Elindinga 0% 15% 0% 0% 0% 1% 24% 29% 35% 0% 10% Emangweni 2% 39% 2% 6% 2% 0% 47% 33% 45% 5% 18% Emfihlweni 2% 37% 2% 8% 1% 1% 36% 38% 29% 9% 16% Emkatini 2% 53% 12% 1% 0% 0% 20% 22% 21% 14% 14% Emoyeni 2% 33% 1% 4% 0% 0% 31% 33% 23% 1% 13% Emthafeni 2% 34% 1% 1% 1% 0% 35% 32% 31% 9% 14% Endayini 4% 36% 1% 7% 3% 2% 34% 31% 33% 1% 15% Endulweni 5% 41% 14% 11% 3% 3% 40% 34% 41% 9% 20% Entshonalanga 6% 44% 1% 5% 1% 1% 35% 29% 39% 0% 16% Esangweni 2% 47% 2% 4% 0% 0% 40% 33% 23% 0% 15% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

186 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Tembisa (continued) 180 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Esiziba 15% 34% 0% 28% 0% 0% 43% 42% 34% 1% 20% Hospital View 4% 31% 14% 3% 3% 1% 16% 23% 25% 4% 12% Ibaxa 3% 33% 1% 1% 3% 1% 33% 32% 41% 1% 15% Ibazelo 2% 59% 0% 0% 0% 0% 15% 26% 28% 0% 13% Igqagqa 53% 42% 52% 73% 52% 36% 53% 40% 47% 3% 45% Ililiba 0% 24% 1% 0% 2% 0% 16% 31% 31% 1% 11% Inxiweni 11% 35% 11% 26% 6% 0% 46% 41% 35% 8% 22% Isekelo 98% 30% 97% 99% 96% 28% 63% 41% 45% 6% 60% Isiphetweni 5% 25% 3% 5% 3% 11% 22% 35% 32% 0% 14% Isithame 5% 34% 3% 18% 2% 1% 33% 33% 36% 15% 18% Jiyana 6% 31% 0% 4% 1% 0% 46% 31% 32% 2% 15% Khatamping 15% 33% 0% 5% 0% 0% 38% 29% 45% 16% 18% Kopanong 3% 46% 27% 11% 1% 1% 31% 33% 39% 21% 21% Leboeng 2% 44% 5% 0% 0% 0% 26% 31% 30% 12% 15% Lekaneng 4% 31% 1% 8% 1% 0% 31% 28% 26% 7% 14% Lifateng 3% 35% 10% 7% 1% 1% 28% 31% 38% 1% 15% Makulong 1% 34% 1% 5% 11% 1% 29% 31% 35% 24% 17% Maokeng 6% 44% 3% 1% 0% 0% 21% 34% 37% 12% 16% Mashimong 1% 38% 2% 6% 1% 0% 29% 32% 28% 18% 16% Meriting 36% 27% 37% 41% 37% 37% 51% 33% 42% 20% 36% Moedi 2% 46% 0% 5% 0% 0% 35% 28% 39% 0% 16% Moteong 3% 40% 12% 5% 1% 0% 37% 27% 35% 13% 17% Motsu 2% 38% 1% 1% 0% 0% 23% 25% 35% 9% 13% Mpho 3% 23% 18% 8% 3% 1% 30% 29% 33% 9% 16% Mqantsa 2% 43% 6% 9% 1% 1% 40% 31% 40% 11% 18% Phomolong 32% 37% 9% 13% 6% 5% 41% 32% 37% 7% 22% Sedibeng 3% 39% 1% 9% 2% 1% 40% 37% 33% 10% 17% Seotloana 9% 53% 1% 5% 3% 1% 34% 43% 31% 15% 20% Sethokga 1% 2% 3% 1% 3% 1% 71% 21% 63% 1% 17% Teanong 0% 35% 3% 0% 0% 0% 31% 37% 40% 6% 15% Tembisa Ext 11 30% 24% 29% 33% 15% 13% 44% 33% 25% 1% 25% Tembisa Ext 5 2% 35% 3% 0% 1% 0% 33% 27% 33% 7% 14% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

187 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) Tembisa (continued) Thokoza 181 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Tembisa Ext 7 38% 38% 1% 26% 4% 0% 46% 31% 38% 8% 23% Tembisa Station 100% 100% Temong 9% 43% 17% 7% 1% 1% 33% 27% 33% 6% 17% Thiteng 1% 54% 0% 3% 1% 0% 42% 24% 50% 1% 18% Tlamatlama 5% 44% 1% 2% 2% 0% 30% 31% 36% 16% 17% Tsepo 3% 39% 1% 5% 1% 0% 30% 27% 43% 21% 17% Umfuyaneni 5% 36% 1% 14% 1% 0% 41% 29% 44% 9% 18% Umnonjaneni 2% 46% 24% 5% 0% 0% 34% 27% 37% 3% 18% Umthambeka 14% 36% 7% 15% 7% 0% 48% 39% 45% 4% 21% Vusimusi 59% 33% 57% 67% 57% 62% 42% 35% 37% 9% 46% Welomlambo 2% 32% 2% 6% 1% 0% 40% 34% 27% 2% 15% Winnie Mandela 91% 26% 32% 99% 28% 12% 50% 36% 40% 9% 42% Winnie Mandela Ext 10 97% 22% 11% 99% 11% 7% 46% 35% 39% 5% 37% Winnie Mandela Ext 12 98% 20% 12% 100% 12% 13% 52% 33% 48% 4% 39% Winnie Mandela Ext 4 95% 27% 29% 99% 27% 10% 47% 39% 38% 6% 42% Winnie Mandela Ext 5 96% 24% 40% 100% 37% 10% 44% 36% 40% 6% 43% Winnie Mandela Ext 6 96% 21% 14% 100% 11% 8% 51% 39% 53% 3% 40% Winnie Mandela Ext 7 95% 31% 31% 100% 31% 21% 58% 41% 47% 3% 46% Winnie Mandela Park 6% 25% 1% 5% 1% 0% 36% 39% 33% 6% 15% Xubene 3% 30% 1% 11% 1% 0% 28% 36% 41% 13% 16% Basothome 46% 34% 1% 15% 1% 0% 51% 36% 47% 5% 24% Everest 27% 35% 3% 9% 2% 1% 45% 35% 36% 0% 19% Khumula Baji Section 38% 36% 0% 11% 0% 0% 38% 29% 32% 5% 19% Mpilisweni 61% 45% 64% 73% 39% 43% 66% 47% 45% 4% 49% Phenduka 34% 42% 1% 6% 1% 3% 48% 35% 39% 0% 21% Phomolamqashi 43% 38% 9% 11% 1% 1% 51% 43% 38% 1% 24% Polar Park Ext 1 55% 33% 39% 78% 38% 13% 61% 41% 48% 1% 41% Thokoza Ext 1 47% 35% 0% 26% 1% 0% 46% 41% 40% 3% 24% Thokoza Ext 2 16% 32% 10% 23% 1% 0% 40% 32% 38% 2% 19% Thokoza Ext 5 21% 33% 2% 37% 2% 0% 48% 34% 34% 4% 21% Thokoza Ext 6 36% 35% 20% 58% 23% 18% 52% 41% 49% 12% 34% Thokoza Gardens 6% 24% 1% 1% 3% 0% 22% 26% 30% 0% 12% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

188 Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- Municipality Main-place name Sub-place name type: Informal household sal income ment source Thokoza Hostels 57% 24% 32% 96% 27% 25% 83% 52% 45% 1% 44% Ekurhuleni Metro (continued) Thokoza (continued) Tsakane Vosloorus Thokoza Unit F 16% 36% 5% 19% 3% 2% 36% 34% 36% 2% 19% Langaville 59% 33% 14% 62% 15% 3% 65% 40% 44% 2% 34% Langaville Ext 17 96% 35% 70% 72% 69% 58% 72% 49% 39% 2% 56% Langaville Ext 2 74% 35% 0% 50% 1% 0% 65% 40% 42% 2% 31% Langaville Ext 3 61% 33% 2% 55% 3% 8% 68% 53% 44% 9% 33% Langaville Ext 4 89% 25% 2% 56% 2% 0% 61% 45% 37% 2% 32% Langaville Ext 5 82% 29% 35% 70% 26% 6% 70% 50% 46% 5% 42% Langaville Ext 7 37% 45% 5% 36% 5% 1% 55% 32% 38% 1% 25% Tsakane Ext 1 8% 35% 2% 3% 3% 0% 37% 33% 26% 2% 15% Tsakane Ext 11 18% 42% 4% 7% 2% 1% 47% 33% 35% 3% 19% Tsakane Ext 12 6% 31% 2% 3% 2% 1% 53% 42% 44% 1% 19% Tsakane Ext 13 67% 34% 0% 5% 1% 0% 63% 48% 38% 3% 26% Tsakane Ext 15 12% 45% 1% 10% 1% 0% 61% 34% 40% 3% 21% Tsakane Ext 5 2% 24% 4% 7% 1% 0% 33% 29% 27% 1% 13% Tsakane Ext 8 44% 32% 5% 5% 2% 2% 48% 40% 34% 0% 21% Tsakane Ext 9 18% 39% 15% 27% 5% 3% 67% 37% 42% 1% 25% Tsakane Proper 15% 46% 5% 5% 1% 1% 54% 38% 33% 2% 20% Eastfield 3% 28% 12% 3% 1% 0% 15% 27% 23% 2% 11% Eastfield Ext 10 1% 30% 2% 1% 2% 0% 12% 23% 14% 0% 9% Eastfield Ext 16 4% 34% 1% 1% 2% 1% 17% 28% 19% 0% 11% Eastfield Ext 23 0% 25% 0% 2% 0% 2% 12% 24% 32% 0% 10% Eastfield Ext 24 2% 33% 0% 0% 0% 2% 6% 30% 26% 0% 10% Herwin 0% 0% 0% 0% 0% 75% 0% 17% 40% 0% 13% Mabuya Park 0% 31% 0% 0% 3% 0% 23% 36% 32% 1% 13% Marimba Gardens 2% 32% 1% 0% 0% 0% 23% 34% 27% 1% 12% Marimba Gardens Ext 9 2% 27% 3% 0% 2% 0% 22% 26% 28% 1% 11% Mfundo Park Ext 30 17% 29% 2% 1% 1% 0% 27% 27% 30% 0% 13% Nguni Section 4% 34% 2% 1% 2% 2% 47% 31% 38% 4% 16% Nguni Section Ext 8 1% 39% 1% 1% 0% 0% 24% 25% 30% 1% 12% Nguni Section Ext 9 2% 33% 0% 0% 0% 0% 19% 25% 16% 3% 10% Sotho Section 23% 0% 5% 23% 19% 100% 52% 47% 11% 0% 28% 182

189 Municipality Main-place name Sub-place name Ekurhuleni Metro (continued) City of Johannesburg Metro Vosloorus (continued) Wattville Alexandra 183 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Sotho Section Ext 1 3% 36% 12% 15% 12% 6% 50% 35% 36% 4% 21% Vosloorus 81% 36% 97% 100% 100% 97% 76% 41% 19% 8% 66% Vosloorus Ext 40% 44% 3% 6% 3% 1% 62% 39% 41% 0% 24% Vosloorus Ext 12 20% 32% 12% 14% 13% 10% 35% 27% 28% 2% 19% Vosloorus Ext 14 1% 32% 1% 1% 1% 0% 38% 34% 31% 1% 14% Vosloorus Ext 2 2% 29% 2% 1% 1% 0% 19% 27% 30% 1% 11% Vosloorus Ext 25 2% 36% 2% 4% 1% 0% 33% 28% 28% 2% 14% Vosloorus Ext 7 1% 34% 1% 2% 1% 1% 32% 31% 29% 2% 14% Vosloorus South 32% 37% 28% 34% 26% 10% 60% 42% 40% 4% 31% Actonville 2% 28% 2% 5% 2% 0% 22% 23% 19% 1% 11% Actonville Ext 4 1% 30% 3% 1% 1% 0% 24% 20% 21% 1% 10% Emolotheni 1% 36% 96% 100% 100% 4% 46% 42% 41% 1% 47% Harry Gwala 86% 26% 87% 98% 83% 34% 57% 39% 48% 2% 56% Lakeview 96% 25% 65% 69% 68% 37% 46% 38% 43% 16% 50% Rangeview Camp 97% 36% 98% 99% 84% 0% 57% 46% 43% 11% 57% Tamboville 55% 39% 21% 24% 20% 3% 53% 38% 44% 7% 30% Tamboville Ext 1 83% 33% 1% 22% 5% 0% 56% 42% 40% 0% 28% Wattville 97% 22% 0% 17% 10% 0% 47% 49% 44% 17% 30% Wattville Ext 2 11% 31% 28% 0% 28% 0% 48% 41% 26% 4% 22% Wattville Ext 3 14% 39% 10% 13% 11% 4% 31% 28% 37% 4% 19% Wattville Proper 9% 38% 5% 7% 1% 0% 35% 33% 40% 10% 18% Watville Ext 1 7% 38% 1% 15% 1% 0% 34% 34% 35% 1% 17% Alexandra SP 31% 34% 22% 24% 6% 1% 42% 32% 36% 11% 24% Alexandra 71% 22% 66% 58% 58% 6% 43% 33% 37% 23% 42% East Bank 2% 36% 1% 1% 1% 1% 16% 27% 20% 8% 11% Tsutsumani 2% 43% 5% 17% 2% 1% 24% 34% 22% 13% 16% Bultfontein Bultfontein 75% 4% 0% 98% 96% 0% 2% 41% 3% 0% 32% Althea AH 0% 9% 44% 34% 25% 91% 16% 47% 18% 9% 29% City of Johannesburg Metro Johannesburg NU 19% 37% 27% 34% 38% 57% 33% 37% 17% 2% 30% Randburg NU 32% 25% 54% 55% 57% 74% 35% 45% 25% 4% 41% Stesa AH Vereeniging NU 46% 26% 62% 60% 57% 96% 57% 49% 20% 2% 47% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

190 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) City of Johannesburg Metro (continued) 184 Dwelling type: Informal Femaleheaded Water source household Electricity Sanitation Refuse disposal Household income Westonaria NU 59% 28% 79% 77% 74% 96% 67% 51% 22% 3% 56% Diepkloof SP 1% 11% 30% 100% 53% 59% 76% 35% 38% 3% 41% Diepkloof Ext 1% 37% 1% 1% 1% 0% 10% 18% 17% 1% 9% Diepkloof Zone 1 22% 48% 2% 2% 0% 0% 37% 27% 34% 5% 18% Diepkloof Diepkloof Zone 2 33% 39% 2% 2% 2% 0% 40% 38% 36% 1% 19% Diepkloof Zone 3 55% 41% 50% 50% 20% 13% 47% 34% 42% 1% 35% Diepkloof Zone 4 19% 47% 3% 3% 1% 1% 33% 30% 38% 4% 18% Diepkloof Zone 5 35% 45% 3% 6% 1% 0% 42% 37% 33% 5% 21% Diepkloof Zone 6 33% 45% 1% 7% 2% 3% 36% 34% 36% 8% 20% Diepsloot Diepsloot SP 72% 33% 50% 56% 46% 14% 52% 31% 41% 12% 41% Diepsloot 95% 25% 83% 85% 81% 21% 57% 35% 45% 3% 53% Ebony Park Ebony Park SP 3% 36% 2% 2% 1% 1% 37% 31% 35% 7% 16% Edenvale Hospital 0% 78% 1% 0% 2% 2% 43% 17% 8% 0% 15% Edenvale Ivory Park Johannesburg Sebenza Sizwe Tropical Disease Hospital 0% 0% 0% 0% 0% 100% 100% 0% 0% 0% 20% South African Institute For Medical Research 0% 50% 3% 1% 0% 0% 3% 35% 8% 2% 10% Ivory Park SP 65% 33% 24% 24% 21% 12% 52% 40% 40% 8% 32% Ivory Park 70% 24% 46% 55% 52% 37% 50% 42% 35% 14% 43% Johannesburg SP 4% 18% 12% 14% 12% 28% 32% 32% 27% 0% 18% Abmarie SH 14% 20% 13% 31% 43% 91% 36% 29% 20% 2% 30% Aeroton 50% 0% 0% 0% 0% 50% 100% 0% 25% Alan Manor 0% 37% 3% 0% 0% 0% 9% 17% 4% 2% 7% Alberts Farm 4% 49% 14% 4% 0% 4% 0% 4% 0% 0% 8% Albertskroon 2% 52% 7% 18% 5% 0% 35% 20% 7% 1% 15% Albertsville 1% 46% 4% 0% 2% 23% 14% 19% 7% 0% 12% Amalgam 50% 0% 0% 50% 50% 0% 0% 23% 20% 0% 19% Anchorville 91% 25% 88% 90% 86% 90% 52% 40% 38% 5% 61% Armadale 12% 35% 6% 48% 6% 54% 23% 29% 33% 31% 28% Ashanti 98% 37% 93% 99% 89% 2% 77% 36% 63% 18% 61% Auckland Park 0% 54% 4% 1% 1% 0% 38% 12% 4% 2% 12% Bassonia 2% 40% 2% 1% 2% 2% 16% 19% 3% 4% 9% Illiteracy Unemployment Crowding Poverty

191 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 185 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Bassonia Rock 0% 64% 4% 0% 3% 0% 5% 9% 3% 0% 9% Bellavista Estates 2% 53% 5% 1% 0% 0% 27% 25% 25% 1% 14% Bellevue 0% 40% 2% 1% 2% 0% 22% 19% 26% 6% 12% Bellevue East 3% 35% 6% 1% 2% 0% 25% 17% 30% 4% 12% Benrose 58% 12% 23% 21% 8% 1% 51% 41% 54% 8% 28% Berario 3% 37% 4% 1% 2% 1% 6% 16% 3% 0% 7% Berea 2% 35% 2% 1% 1% 1% 28% 17% 30% 4% 12% Betrams 5% 35% 5% 7% 4% 0% 37% 35% 37% 2% 17% Bezuidenhout Valley 5% 34% 6% 3% 1% 1% 24% 22% 16% 3% 11% Birdhaven 1% 42% 2% 1% 2% 0% 9% 17% 3% 0% 8% Blackheath 3% 40% 3% 1% 1% 2% 14% 18% 4% 1% 9% Booysens 3% 25% 30% 33% 1% 0% 26% 27% 17% 16% 18% Booysens Reserve 0% 15% 15% 8% 0% 0% 60% 33% 32% 26% 19% Bosmont 2% 36% 12% 2% 0% 0% 19% 21% 10% 2% 11% Braamfontein 3% 40% 6% 5% 6% 15% 37% 11% 20% 6% 15% Braampark 0% 37% 0% 0% 0% 0% 0% 13% 46% 4% 10% Bramley 2% 44% 4% 1% 2% 0% 39% 18% 4% 1% 11% Bramley Gardens 3% 33% 64% 3% 3% 1% 13% 20% 10% 19% 17% Bramley View 3% 22% 6% 0% 0% 0% 11% 24% 18% 0% 8% Brixton 6% 31% 9% 1% 1% 7% 25% 21% 7% 13% 12% Bruma 3% 46% 7% 3% 1% 2% 16% 16% 3% 1% 10% Casey Park 0% 43% 4% 1% 2% 0% 10% 19% 7% 0% 9% Cheltondale 3% 48% 0% 1% 3% 3% 27% 20% 8% 0% 11% Chrisville 0% 37% 1% 0% 1% 1% 14% 18% 5% 8% 9% City & Suburban 28% 36% 30% 27% 22% 17% 50% 35% 28% 3% 27% City Deep 4% 0% 0% 0% 1% 0% 42% 34% 39% 1% 12% City Deep Gold Mine Claremont 35% 37% 36% 37% 3% 1% 38% 28% 34% 6% 26% Cleveland 17% 50% 13% 8% 4% 1% 49% 29% 46% 1% 22% Comptonville 6% 21% 2% 0% 4% 2% 10% 26% 15% 2% 9% Corlett Gardens 1% 44% 5% 1% 1% 1% 15% 16% 8% 1% 9% Coronationville 3% 45% 15% 6% 4% 0% 26% 22% 25% 3% 15% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

192 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 186 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Craighall 1% 37% 5% 1% 3% 1% 6% 19% 2% 3% 8% Craighall Park 4% 46% 7% 2% 3% 1% 9% 19% 3% 2% 10% Croesus 0% 0% 0% 0% 0% 0% 12% 50% 0% 23% 8% Crosby 2% 29% 5% 4% 2% 1% 22% 22% 12% 5% 10% Crown 0% 17% 9% 21% 0% 41% 21% 26% 5% 0% 14% Crown Ext. 50% 0% 0% 17% Crown Gardens 0% 58% 1% 1% 0% 0% 27% 19% 12% 3% 12% Crown Mines 3% 17% 0% 0% 0% 0% 12% 29% 13% 12% 9% Crystal Gardens 3% 27% 6% 1% 3% 0% 55% 17% 23% 1% 14% Cyrildene 1% 41% 9% 1% 2% 0% 22% 16% 6% 3% 10% Delta Park 0% 27% 0% 0% 0% 0% 0% 13% 0% 0% 4% Denver 23% 17% 16% 17% 17% 1% 66% 30% 56% 14% 26% Denver Ext 7 40% 20% 20% 0% 0% 0% 40% 50% 11% 0% 18% Devland 2% 49% 2% 0% 2% 1% 48% 34% 35% 12% 18% Dewetshof 6% 48% 3% 1% 2% 2% 24% 17% 7% 1% 11% Doornfontein 20% 41% 1% 14% 0% 11% 84% 18% 13% 1% 20% Droste Park 63% 3% 40% 6% 6% 7% 69% 32% 44% 1% 27% Dunkeld 2% 39% 2% 0% 2% 0% 6% 22% 3% 0% 8% Dunkeld West 2% 38% 3% 2% 3% 0% 9% 17% 1% 4% 8% Dunkeld West Ext 3% 50% 1% 1% 1% 1% 14% 14% 4% 2% 9% East Town 0% 32% 2% 0% 2% 0% 12% 14% 8% 0% 7% Eikenhof 86% 26% 86% 95% 95% 20% 55% 53% 44% 9% 57% Elandspark 5% 28% 4% 3% 1% 0% 13% 20% 9% 1% 8% Eldorado Estate 2% 30% 2% 0% 2% 0% 12% 27% 13% 0% 9% Eldorado Park 6% 47% 5% 2% 2% 1% 32% 28% 29% 1% 15% Eldorado Park Ext 8 19% 39% 1% 8% 3% 1% 58% 30% 44% 1% 21% Electron 100% 20% 0% 0% 0% 0% 80% 23% 7% 0% 23% Elton Hill 7% 48% 2% 0% 2% 0% 18% 16% 2% 3% 10% Emmarentia 3% 39% 3% 1% 1% 1% 13% 21% 3% 2% 9% Ennerdale Ext 1 2% 28% 2% 1% 2% 3% 17% 23% 23% 0% 10% Ennerdale Ext 10 2% 23% 3% 3% 2% 3% 17% 22% 25% 1% 10% Ennerdale Ext 2 2% 26% 0% 1% 1% 1% 19% 24% 25% 0% 10% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

193 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 187 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Ennerdale Ext 3 3% 44% 3% 0% 2% 1% 11% 20% 23% 2% 11% Ennerdale Ext 5 3% 36% 5% 2% 4% 1% 17% 25% 26% 0% 12% Ennerdale Ext 6 4% 41% 1% 3% 5% 6% 35% 34% 36% 0% 17% Ennerdale Ext 8 3% 29% 1% 1% 2% 2% 30% 31% 35% 1% 14% Ennerdale Ext 9 4% 29% 0% 0% 2% 1% 15% 24% 23% 0% 10% Ennerdale South 88% 39% 89% 37% 77% 40% 55% 44% 36% 1% 51% Evans Park 0% 46% 0% 0% 0% 0% 26% 18% 3% 9% 10% Fairland 1% 42% 6% 1% 1% 1% 12% 17% 3% 2% 9% Fairland Ext 3 0% 45% 1% 3% 3% 1% 8% 13% 7% 2% 8% Fairmount 1% 43% 2% 0% 1% 0% 11% 22% 7% 0% 9% Fairvale 0% 42% 8% 1% 3% 2% 15% 15% 4% 3% 9% Fairway 2% 62% 16% 1% 1% 0% 20% 15% 5% 2% 13% Fairwood 2% 27% 2% 0% 0% 0% 2% 21% 7% 5% 7% Fellside 0% 48% 7% 2% 0% 0% 15% 14% 7% 6% 10% Ferreirasdorp 21% 23% 26% 21% 7% 12% 35% 39% 23% 28% 23% Finetown 86% 40% 89% 27% 56% 55% 57% 47% 38% 6% 50% Forbesdale 1% 56% 1% 1% 5% 0% 16% 16% 7% 1% 10% Fordsburg 10% 39% 3% 9% 3% 1% 25% 24% 19% 7% 14% Forest Hill 3% 36% 3% 2% 1% 0% 18% 20% 14% 1% 10% Forest Town 1% 41% 5% 1% 2% 1% 8% 24% 4% 1% 9% Franklin Roosevelt Park 1% 42% 4% 5% 4% 2% 18% 17% 3% 1% 10% Gillview 1% 53% 2% 1% 3% 2% 28% 18% 3% 0% 11% Glenanda 3% 31% 3% 0% 1% 0% 17% 19% 3% 1% 8% Glenesk 3% 53% 1% 7% 2% 0% 41% 30% 24% 1% 16% Glenhazel 9% 48% 4% 1% 1% 1% 15% 22% 3% 2% 11% Glenkay 0% 51% 0% 2% 0% 0% 7% 12% 0% 0% 7% Glensan 3% 54% 4% 2% 4% 1% 14% 13% 4% 5% 10% Glenvista 1% 34% 5% 0% 1% 0% 15% 20% 2% 1% 8% Grasmere 89% 47% 99% 99% 73% 31% 61% 46% 44% 0% 59% Greenside 1% 41% 5% 2% 3% 1% 12% 19% 3% 1% 9% Greenside East 1% 40% 4% 0% 1% 0% 9% 20% 1% 0% 8% Gresswold 1% 45% 4% 1% 1% 1% 18% 13% 3% 3% 9% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

194 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 188 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Greymont 4% 36% 13% 2% 3% 1% 13% 14% 10% 1% 10% Haddon 1% 38% 1% 2% 1% 0% 15% 16% 9% 0% 8% Hawkins Estate 2% 37% 3% 2% 5% 0% 16% 17% 4% 2% 9% Heriotdale 40% 20% 0% 33% 0% 3% 66% 32% 52% 3% 25% Highlands North 3% 46% 3% 1% 2% 1% 15% 19% 6% 3% 10% Hillbrow 2% 36% 3% 1% 2% 2% 27% 15% 28% 5% 12% Hiltonia 19% 26% 67% 85% 89% 100% 56% 46% 34% 0% 52% Homestead Park 5% 37% 1% 1% 0% 0% 29% 25% 5% 4% 11% Hopefield 89% 30% 96% 98% 21% 94% 87% 56% 54% 5% 63% Hospital Hill 58% 33% 98% 99% 99% 82% 65% 52% 40% 0% 63% Houghton Estate 2% 45% 4% 2% 2% 1% 15% 19% 7% 4% 10% Hurst Hill 1% 31% 5% 2% 2% 1% 20% 19% 17% 7% 11% Illovo 3% 51% 21% 0% 1% 1% 28% 17% 4% 13% 14% Industria 0% 3% 1% 1% 0% 0% 72% 28% 71% 1% 18% Industria West 0% 0% 0% 0% 0% 0% 0% 33% 29% 0% 6% Jan Hofmeyer 5% 71% 2% 10% 5% 0% 31% 28% 33% 12% 20% Jeppestown 11% 38% 14% 10% 7% 1% 33% 27% 32% 5% 18% Johannesburg CBD 2% 35% 8% 3% 2% 1% 21% 16% 33% 6% 13% Johannesburg Zoo 0% 0% 0% 0% 0% 0% 0% 43% 0% 0% 4% Joubert Park 2% 34% 9% 2% 3% 1% 28% 17% 30% 7% 13% Judiths Paarl 11% 24% 22% 8% 2% 1% 24% 24% 22% 5% 14% Kapok Informal 94% 35% 98% 100% 90% 62% 68% 47% 54% 5% 65% Kenilworth 2% 34% 4% 2% 1% 0% 21% 25% 18% 1% 11% Kensington 2% 38% 4% 1% 1% 0% 18% 18% 6% 2% 9% Kensington South 2% 35% 1% 3% 1% 0% 14% 19% 5% 3% 8% Kent View 1% 50% 3% 3% 0% 4% 11% 8% 5% 4% 9% Kew 6% 43% 3% 5% 2% 2% 17% 19% 12% 4% 11% Kew Informal 99% 13% 98% 99% 22% 0% 55% 29% 54% 72% 54% Kibler Park 1% 24% 15% 2% 2% 2% 15% 15% 7% 3% 9% Killarney 6% 54% 8% 5% 5% 1% 24% 17% 8% 1% 13% Klipriviersberg 0% 21% 11% 11% 7% 11% 14% 9% 9% 0% 9% Klipriviersberg Estate 1% 38% 2% 0% 1% 0% 44% 20% 23% 2% 13% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

195 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 189 Dwelling type: Informal Femaleheaded Water source household Electricity Sanitation Refuse disposal Household income Klipriviersberg Nature Reserve Klipriviersoog Estate 14% 46% 31% 31% 7% 31% 62% 29% 12% 0% 26% Klipspruit West 3% 47% 3% 3% 1% 1% 23% 26% 29% 1% 14% La Rochelle 10% 41% 16% 3% 2% 1% 23% 22% 20% 9% 15% Langlaagte North 7% 32% 3% 2% 0% 0% 20% 17% 11% 2% 9% Lawley Estate 94% 35% 97% 96% 94% 98% 65% 41% 45% 6% 67% Lawley Ext 1 3% 36% 2% 1% 2% 1% 22% 29% 29% 0% 12% Lenasia 0% 66% 2% 3% 0% 2% 45% 32% 0% 5% 16% Lenasia Ext 1 2% 48% 1% 6% 1% 0% 33% 25% 7% 1% 12% Lenasia Ext 10 2% 34% 2% 3% 1% 2% 22% 22% 18% 2% 11% Lenasia Ext 11 11% 41% 10% 9% 8% 6% 30% 24% 14% 3% 16% Lenasia Ext 11&13 0% 33% 67% 83% 67% 83% 33% 45% 12% 0% 42% Lenasia Ext 13 4% 45% 3% 4% 2% 2% 29% 28% 13% 1% 13% Lenasia Ext 2 4% 41% 2% 4% 2% 1% 27% 26% 13% 1% 12% Lenasia Ext 3 1% 23% 2% 1% 2% 1% 20% 19% 2% 0% 7% Lenasia Ext 5 2% 42% 2% 3% 2% 1% 23% 22% 5% 1% 10% Lenasia Ext 6 9% 39% 3% 8% 1% 2% 31% 24% 12% 2% 13% Lenasia Ext 7 3% 53% 0% 1% 1% 1% 21% 20% 4% 3% 11% Lenasia Ext 8 2% 39% 1% 2% 0% 0% 25% 19% 5% 1% 9% Lenasia Ext 9 4% 34% 1% 3% 1% 1% 27% 20% 9% 1% 10% Lenasia South 3% 22% 5% 2% 3% 1% 15% 19% 8% 0% 8% Lenasia South Ext 1 2% 31% 4% 2% 1% 2% 13% 18% 10% 0% 8% Lenasia South Ext 2 4% 22% 12% 3% 0% 3% 10% 23% 10% 0% 9% Lenasia South Ext 4 10% 37% 11% 10% 10% 2% 25% 29% 23% 0% 16% Lenasia South Ext 7 2% 29% 2% 2% 2% 1% 16% 28% 29% 0% 11% Lindbergh Park 4% 60% 9% 4% 2% 0% 29% 25% 25% 25% 18% Linden 3% 39% 2% 1% 1% 0% 13% 19% 4% 3% 8% Linksfield 4% 37% 0% 0% 0% 0% 17% 20% 1% 1% 8% Linksfield North 0% 10% 3% 0% 3% 3% 13% 32% 11% 0% 7% Linksfield Ridge 2% 53% 4% 1% 2% 0% 13% 20% 7% 1% 10% Linmeyer 3% 44% 3% 0% 2% 0% 21% 18% 5% 0% 10% Lombardy East 2% 45% 4% 7% 2% 2% 20% 23% 14% 4% 12% Illiteracy Unemployment Crowding Poverty

196 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 190 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Lombardy West 7% 24% 9% 5% 6% 3% 7% 25% 3% 5% 9% Longdale 0% 0% 0% 100% 0% 100% 100% 100% 0% 0% 40% Lyndhurst 4% 41% 3% 2% 1% 1% 14% 15% 7% 2% 9% Lyndhurst Estates 2% 38% 5% 3% 2% 3% 8% 8% 13% 0% 8% Malvern 3% 40% 10% 3% 3% 0% 29% 21% 26% 5% 14% Marshalltown 3% 25% 4% 2% 3% 1% 41% 18% 31% 8% 14% Martindale 5% 30% 0% 0% 3% 2% 11% 18% 5% 0% 7% Maryvale 0% 55% 0% 0% 0% 0% 22% 19% 7% 3% 11% Mayfair 5% 35% 6% 4% 3% 2% 21% 22% 9% 8% 12% Mayfair West 2% 33% 2% 2% 1% 0% 22% 22% 8% 3% 9% Mayfield Park 8% 26% 7% 3% 2% 1% 13% 21% 8% 1% 9% Melrose 1% 50% 7% 3% 2% 1% 14% 18% 3% 2% 10% Melrose Estate 2% 33% 7% 3% 5% 0% 8% 20% 2% 3% 8% Melrose North 0% 41% 2% 3% 0% 1% 8% 17% 2% 3% 8% Melville 2% 48% 4% 2% 2% 1% 18% 14% 2% 1% 9% Melville Koppies 1% 42% 4% 1% 0% 0% 16% 10% 2% 3% 8% Meredale 1% 30% 3% 1% 1% 2% 12% 18% 7% 1% 8% Mid Ennerdale 5% 33% 2% 9% 11% 2% 23% 27% 25% 3% 14% Moffatt View 1% 43% 3% 1% 1% 0% 41% 17% 22% 1% 13% Mondeor 1% 38% 1% 1% 1% 1% 14% 20% 4% 1% 8% Montclare 7% 38% 4% 5% 3% 1% 16% 22% 9% 7% 11% Montgomery Park 3% 35% 5% 0% 2% 0% 12% 14% 4% 1% 8% Montroux 0% 40% 2% 0% 0% 0% 17% 14% 5% 2% 8% Mountain View 3% 34% 6% 1% 1% 0% 11% 20% 6% 1% 8% Mulbarton 2% 30% 6% 1% 2% 1% 15% 20% 4% 2% 8% Nancefield 72% 48% 81% 80% 25% 71% 57% 40% 37% 14% 53% Nancefield Industrial 20% 40% 30% 20% 0% 70% 30% 38% 25% 30% 30% Naturena 4% 34% 9% 2% 3% 0% 8% 22% 10% 1% 9% New Doornfontein 2% 21% 7% 1% 0% 0% 34% 27% 36% 25% 15% Newclare 2% 51% 3% 2% 2% 0% 38% 26% 33% 3% 16% Newlands 4% 38% 8% 7% 3% 1% 24% 24% 16% 9% 13% Newtown 5% 15% 21% 9% 8% 3% 27% 51% 12% 4% 15% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

197 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 191 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Noordgesig 16% 50% 3% 1% 1% 0% 33% 32% 29% 1% 17% Northcliff 3% 36% 5% 1% 2% 1% 12% 19% 3% 3% 8% Norwood 2% 51% 4% 2% 2% 1% 14% 16% 5% 0% 10% Oakdene 2% 39% 3% 0% 2% 0% 15% 20% 3% 1% 9% Oaklands 3% 43% 8% 1% 3% 1% 14% 25% 3% 3% 10% Observatory 8% 43% 3% 1% 1% 1% 16% 21% 6% 2% 10% Ophirton 6% 23% 0% 8% 3% 0% 40% 25% 35% 0% 14% Orange Grove 3% 44% 4% 1% 3% 0% 18% 16% 9% 3% 10% Orchards 2% 52% 27% 1% 0% 1% 13% 19% 4% 1% 12% Ormonde 1% 38% 3% 0% 1% 0% 8% 17% 10% 3% 8% Paarlshoop 2% 25% 3% 1% 5% 7% 18% 26% 17% 11% 11% Park Central 45% 14% 7% 39% 25% 25% 59% 30% 46% 25% 31% Parkhurst 3% 47% 4% 1% 2% 1% 11% 13% 3% 2% 9% Parktown 1% 58% 4% 1% 1% 1% 36% 18% 6% 2% 13% Parktown North 2% 45% 4% 2% 2% 0% 9% 16% 2% 2% 8% Parkview 1% 44% 3% 1% 0% 0% 10% 21% 3% 1% 8% Parkwood 3% 47% 3% 0% 1% 1% 12% 22% 3% 2% 9% Percelia Estate 4% 31% 0% 2% 2% 0% 0% 30% 0% 0% 7% Prison 2% 24% 2% 0% 2% 2% 12% 21% 1% 0% 7% Prolecon 0% 0% 0% 0% Queenshaven 6% 44% 2% 6% 1% 1% 43% 18% 11% 4% 14% Raedene 5% 36% 1% 5% 1% 0% 17% 25% 10% 3% 10% Rand Afrikaans University 0% 69% 2% 0% 2% 0% 3% 24% 8% 0% 11% Raumarais Park 2% 37% 3% 2% 3% 5% 11% 29% 5% 2% 10% Regents Park 6% 36% 25% 4% 1% 0% 20% 21% 12% 11% 14% Rembrandt Park 1% 41% 4% 0% 1% 2% 9% 16% 4% 3% 8% Rembrandt Ridge 1% 37% 6% 4% 0% 4% 9% 16% 6% 4% 9% Reuven 0% 70% 9% 3% 0% 0% 79% 7% 12% 1% 18% Rewlatch 1% 19% 1% 0% 0% 1% 6% 16% 6% 0% 5% Richmond 2% 34% 6% 0% 0% 2% 14% 13% 12% 0% 8% Ridgeway 2% 47% 1% 0% 1% 6% 8% 15% 6% 0% 9% Ridgeway A 0% 34% 2% 1% 0% 3% 12% 17% 8% 1% 8% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

198 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 192 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Ridgeway B 1% 33% 5% 0% 1% 0% 11% 14% 1% 1% 7% Risana 3% 21% 3% 0% 0% 0% 10% 19% 6% 0% 6% Risidale 1% 32% 5% 1% 0% 0% 10% 15% 3% 1% 7% Rispark AH 12% 20% 31% 14% 13% 21% 38% 36% 12% 1% 20% Riverlea 38% 36% 34% 33% 4% 19% 29% 29% 30% 3% 26% Riverlea Ext 2 1% 41% 1% 1% 3% 2% 19% 17% 11% 0% 9% Riviera 1% 44% 2% 2% 2% 0% 12% 17% 1% 0% 8% Robertsham 2% 35% 2% 2% 1% 1% 21% 21% 6% 5% 10% Roseacre 1% 36% 6% 2% 1% 0% 23% 19% 9% 5% 10% Rosebank 1% 55% 6% 2% 1% 1% 17% 9% 7% 1% 10% Rosettenville 2% 38% 5% 2% 2% 1% 19% 21% 15% 2% 11% Rosherville 7% 14% 4% 2% 3% 3% 7% 21% 5% 0% 7% Rossmore 0% 50% 75% 0% 0% 0% 0% 0% 0% 25% 15% Rouxville 2% 37% 1% 0% 1% 1% 9% 15% 4% 8% 8% Sandringham 1% 47% 2% 1% 2% 0% 13% 19% 4% 1% 9% Savoy Estate 4% 46% 7% 1% 1% 1% 17% 25% 6% 1% 11% Saxonworld 6% 48% 5% 5% 2% 1% 20% 26% 2% 1% 11% Selby 5% 2% 2% 7% 6% 3% 47% 29% 46% 3% 15% Selby Ext 0% 0% 25% 0% 0% 25% 25% 100% 17% 0% 19% Sophiatown 4% 34% 4% 1% 2% 1% 18% 23% 9% 3% 10% South Hills 2% 37% 3% 2% 2% 1% 22% 21% 13% 4% 11% South View 11% 33% 0% 15% 6% 3% 13% 20% 9% 6% 12% Southdale 0% 43% 2% 0% 0% 1% 13% 16% 10% 4% 9% Southfork 6% 10% 8% 4% 4% 33% 42% 26% 13% 15% 16% Southgate 0% 0% 0% 0% 0% 0% 100% 14% Stafford 0% 0% 0% 0% 16% 0% 8% 21% 39% 0% 8% Steeledale 50% 25% 50% 42% Suideroord 5% 41% 6% 0% 2% 1% 15% 19% 5% 1% 9% Sunningdale Rigde 1% 47% 2% 0% 1% 1% 15% 22% 5% 1% 9% Sydenham 1% 52% 2% 2% 3% 1% 20% 22% 5% 1% 11% Talboton 5% 45% 0% 0% 0% 0% 22% 22% 6% 3% 10% The Gardens 5% 42% 4% 2% 1% 2% 15% 15% 2% 8% 10% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

199 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 193 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation The Hill 2% 40% 3% 0% 1% 0% 18% 17% 6% 2% 9% Thembalihle 92% 41% 13% 100% 99% 22% 60% 46% 38% 9% 52% Towerby 14% 27% 16% 9% 1% 0% 14% 22% 12% 0% 12% Townsview 3% 42% 0% 1% 3% 0% 17% 20% 10% 1% 10% Troyeville 4% 34% 3% 5% 1% 0% 31% 19% 36% 4% 14% Tulisa Park 0% 25% 7% 2% 1% 1% 14% 22% 6% 2% 8% Turf Club 3% 42% 0% 2% 0% 0% 13% 14% 19% 23% 12% Turffontein 4% 30% 2% 3% 1% 3% 24% 28% 24% 4% 12% Unaville AH 53% 49% 56% 73% 41% 84% 49% 42% 26% 0% 47% Valeriedene 3% 30% 1% 3% 0% 4% 7% 20% 2% 0% 7% Victoria 23% 59% 2% 0% 0% 0% 8% 16% 2% 0% 11% Victory Park 2% 39% 6% 1% 1% 1% 10% 16% 4% 3% 8% Village Main 50% 0% 33% 17% 17% 0% 0% 43% 43% 17% 22% Village Main Reef Gold Mine 50% 7% 34% 34% 34% 66% 34% 24% 12% 0% 29% Vrededorp 4% 34% 25% 8% 2% 1% 34% 22% 21% 13% 16% Vredepark 0% 52% 2% 0% 2% 0% 36% 27% 14% 0% 13% Wanderers Club 0% 18% 18% 0% 0% 47% 18% 38% 0% 0% 14% Waterval Estate 8% 53% 4% 3% 1% 3% 19% 14% 6% 4% 12% Waverley 3% 41% 3% 2% 2% 1% 20% 20% 3% 1% 10% Wemmerpan West Gate 3% 0% 1% West Park 0% 11% 2% 0% 0% 9% 2% 32% 11% 0% 6% West Turffontein 3% 45% 5% 6% 1% 1% 21% 21% 21% 2% 13% Westbury 2% 55% 11% 0% 2% 1% 33% 27% 33% 1% 16% Westcliff 4% 27% 19% 3% 4% 0% 6% 20% 2% 0% 8% Westdene 4% 45% 4% 2% 2% 0% 20% 14% 5% 3% 10% Whitney Gardens 5% 31% 4% 2% 2% 0% 4% 26% 3% 7% 9% Willowdene 4% 30% 3% 0% 2% 0% 7% 21% 14% 0% 8% Winchester Hills 0% 41% 1% 0% 1% 1% 6% 10% 3% 0% 7% Winchester Hills Ext 1 5% 30% 5% 0% 1% 1% 16% 17% 4% 1% 8% Winchester Hills Ext 3 0% 35% 2% 0% 1% 0% 11% 17% 4% 0% 7% Winnie Camp 86% 43% 95% 97% 64% 69% 59% 40% 46% 13% 61% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

200 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Johannesburg (continued) 194 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Winston Ridge 3% 40% 9% 0% 2% 3% 16% 20% 5% 2% 10% Wits University 0% 44% 3% 1% 2% 2% 73% 1% 1% 0% 13% Wolhuter 48% 36% 16% 12% 4% 26% 60% 24% 53% 5% 28% Wyman Park 27% 20% 64% 24% 22% 28% 42% 18% 17% 54% 32% Yeoville 2% 38% 8% 3% 2% 0% 28% 21% 29% 4% 14% Zakariya Park 3% 24% 1% 2% 2% 0% 15% 23% 12% 1% 8% Kagiso Leratong 96% 33% 99% 99% 84% 56% 58% 48% 42% 11% 63% Antwerp 2% 39% 0% 2% 7% 0% 8% 19% 4% 5% 9% Commercia Ext 9 63% 36% 52% 57% 55% 2% 27% 29% 31% 2% 35% Kempton Park Lakeside 0% 31% 8% 0% 0% 0% 11% 33% 2% 0% 8% Modderfontein 13% 19% 23% 0% 3% 14% 11% 29% 7% 17% 14% Modderfontein Conservation Area 100% 33% 33% 33% 0% 33% 33% 50% 25% 0% 34% Klipfontein View Chloorkop Ext 2% 37% 1% 50% 1% 0% 30% 37% 34% 1% 19% Mayabuye Commercia Ext 34 46% 41% 1% 12% 1% 1% 47% 38% 47% 23% 26% Meadowlands Zone 1 10% 39% 37% 2% 2% 1% 50% 32% 41% 5% 22% Meadowlands Zone 10 25% 42% 7% 4% 3% 2% 44% 31% 37% 6% 20% Meadowlands Zone 2 11% 48% 9% 5% 3% 1% 38% 29% 38% 4% 19% Meadowlands Zone 3 9% 51% 1% 2% 0% 2% 38% 30% 35% 3% 17% Meadowlands Midrand Meadowlands Zone 4 13% 45% 11% 2% 2% 1% 49% 44% 45% 5% 22% Meadowlands Zone 5 16% 46% 1% 1% 0% 0% 47% 29% 40% 8% 19% Meadowlands Zone 6 21% 41% 1% 2% 1% 2% 41% 33% 42% 6% 19% Meadowlands Zone 7 22% 45% 2% 1% 2% 6% 47% 28% 50% 6% 21% Meadowlands Zone 8 17% 41% 1% 1% 1% 0% 39% 29% 48% 4% 18% Meadowlands Zone 9 6% 44% 5% 5% 2% 1% 45% 29% 41% 3% 18% Midrand SP 5% 42% 10% 13% 4% 3% 58% 35% 33% 6% 21% Austin View 4% 26% 5% 6% 2% 0% 29% 23% 8% 1% 10% Barbeque Downs 5% 21% 3% 15% 8% 9% 14% 13% 5% 1% 10% Blue Hills AH 6% 30% 11% 5% 6% 5% 20% 23% 7% 6% 12% Bridle Park AH 10% 18% 37% 6% 6% 41% 15% 25% 4% 21% 18% Carlswald AH 17% 29% 0% 3% 0% 1% 11% 25% 5% 2% 9% Country View 2% 28% 3% 0% 2% 0% 13% 20% 7% 2% 8% Crowthorne AH 5% 28% 2% 8% 1% 0% 25% 25% 10% 6% 11% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

201 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Midrand (continued) 195 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Diepsloot AH 2% 47% 20% 24% 23% 86% 34% 30% 14% 13% 29% Erand 1% 34% 2% 2% 2% 0% 10% 14% 4% 0% 7% Erand AH 6% 36% 10% 5% 5% 1% 17% 19% 7% 3% 11% Glen Austin AH 12% 22% 6% 5% 3% 1% 23% 19% 5% 10% 11% Glen Fernes 5% 24% 0% 17% 8% 35% 34% 30% 13% 2% 17% Glenferness AH 0% 27% 5% 3% 3% 0% 21% 27% 0% 0% 9% Halfway Gardens 1% 46% 3% 0% 1% 3% 8% 13% 2% 0% 8% Halfway House 17% 38% 38% 13% 13% 13% 38% 50% 60% 0% 28% Kyalami 5% 37% 1% 3% 2% 2% 8% 26% 3% 2% 9% Kyalami Estates 3% 41% 3% 1% 2% 1% 12% 22% 1% 1% 9% Noordwyk 1% 33% 2% 2% 1% 0% 6% 19% 6% 0% 7% President Park AH 3% 26% 77% 11% 11% 15% 25% 50% 5% 5% 23% Randjesfontein AH 2% 21% 4% 1% 3% 0% 12% 38% 2% 8% 9% Vorna Valley 2% 35% 3% 3% 3% 4% 13% 16% 3% 1% 8% Waterfall 26% 41% 80% 21% 0% 94% 37% 43% 3% 10% 35% Witpoort 3% 26% 6% 2% 3% 1% 16% 24% 2% 5% 9% Nooitgedacht Nooitgedacht 92% 41% 94% 94% 94% 98% 37% 27% 35% 1% 61% Orange Farm SP 44% 51% 20% 4% 85% 0% 61% 41% 33% 2% 34% Drieziek 51% 40% 2% 6% 94% 1% 56% 39% 39% 1% 33% Drieziek Ext 1 24% 48% 29% 20% 96% 3% 54% 37% 35% 3% 35% Drieziek Ext 2 42% 37% 39% 44% 43% 2% 60% 31% 41% 0% 34% Drieziek Ext 4 65% 51% 37% 17% 93% 1% 60% 43% 39% 4% 41% Lakeside 6% 54% 6% 11% 3% 4% 63% 37% 40% 2% 23% Orange Farm Orange Farm Ext 1 15% 48% 6% 5% 29% 11% 57% 45% 41% 0% 26% Orange Farm Ext 3 23% 45% 28% 9% 34% 3% 60% 42% 33% 1% 28% Orange Farm Ext 4 58% 42% 21% 8% 94% 6% 58% 39% 47% 2% 38% Orange Farm Ext 6 66% 55% 17% 5% 50% 0% 61% 44% 32% 1% 33% Orange Farm Ext 7 31% 46% 19% 9% 88% 0% 57% 40% 36% 2% 33% Orange Farm Proper 37% 47% 31% 22% 55% 5% 59% 41% 40% 2% 34% Stretford 55% 50% 17% 4% 42% 1% 62% 42% 40% 2% 32% Stretford Ext 2 46% 48% 24% 4% 79% 2% 55% 39% 25% 4% 33% Stretford Ext 3 8% 33% 39% 14% 96% 6% 57% 39% 34% 0% 33% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

202 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Orange Farm (continued) Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- type: Informal household sal income ment source Stretford Ext 4 57% 47% 17% 6% 96% 0% 57% 43% 30% 3% 36% Stretford Ext 6 64% 50% 25% 6% 86% 0% 60% 41% 42% 4% 38% Stretford Ext 8 47% 56% 25% 7% 72% 2% 61% 43% 34% 3% 35% Pipeline Pipeline 96% 33% 100% 100% 98% 100% 52% 24% 4% 79% 69% Poortjie Poortjie SP 91% 47% 12% 14% 17% 10% 65% 52% 32% 7% 35% Rabie Ridge Randburg Rabie Ridge 44% 36% 37% 32% 30% 3% 37% 35% 34% 7% 30% Rabie Ridge Ext 5 67% 43% 6% 10% 7% 1% 40% 31% 45% 1% 25% Beverley Gardens 3% 18% 2% 0% 0% 0% 13% 43% 2% 0% 8% Blairgowrie 3% 38% 3% 2% 1% 0% 12% 17% 5% 2% 8% Bloubosrand 1% 35% 6% 2% 3% 1% 8% 18% 12% 1% 9% Bordeaux 3% 43% 2% 1% 1% 0% 7% 16% 4% 2% 8% Boskruin 1% 35% 5% 1% 2% 0% 10% 19% 1% 4% 8% Boskruin Ext 2% 38% 4% 2% 2% 1% 9% 16% 2% 1% 8% Broadacres AH 2% 39% 35% 2% 0% 25% 4% 16% 2% 0% 12% Bromhof Ext 2% 39% 9% 1% 2% 4% 6% 13% 4% 1% 8% Bryanston Ext 3 2% 40% 4% 2% 4% 1% 11% 16% 4% 2% 9% Bryanston Ext 5 0% 31% 5% 1% 1% 1% 6% 24% 2% 2% 7% Bush Hill Estate 0% 19% 3% 2% 0% 3% 8% 9% 2% 0% 5% Cedar Lakes 0% 21% 5% 0% 0% 0% 0% 23% 4% 5% 6% Chartwell AH 6% 32% 7% 1% 2% 73% 26% 21% 4% 3% 18% Craigavon AH 4% 16% 6% 4% 6% 9% 7% 18% 4% 4% 8% Cresta 4% 54% 1% 3% 3% 0% 14% 15% 5% 0% 10% Dainfern 2% 29% 5% 4% 1% 0% 5% 23% 1% 0% 7% Dainfern Ridge 1% 41% 4% 1% 1% 0% 4% 25% 1% 2% 8% Darrenwood 0% 50% 3% 1% 2% 2% 12% 13% 4% 4% 9% Farmall AH 5% 23% 13% 15% 11% 93% 18% 27% 4% 9% 22% Ferndale 2% 41% 4% 1% 2% 1% 11% 17% 6% 2% 9% Ferndale Ext 3 33% 10% 10% 5% 5% 0% 10% 22% 0% 0% 9% Fontainebleau 3% 35% 12% 3% 2% 1% 14% 19% 7% 5% 10% Fontainebleau Ext 1 2% 30% 2% 4% 2% 1% 4% 15% 9% 1% 7% Fourways Gardens 1% 35% 3% 3% 2% 1% 13% 22% 2% 1% 8% Golden Harvest 3% 39% 0% 1% 0% 0% 4% 12% 1% 0% 6% 196

203 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Randburg (continued) 197 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Hunters Hill 4% 39% 7% 13% 7% 2% 3% 12% 17% 3% 11% Inadan AH 22% 23% 54% 29% 13% 69% 28% 26% 24% 10% 30% Jacanlee 4% 37% 21% 0% 1% 1% 11% 16% 2% 1% 9% Johannesburg North 2% 48% 4% 2% 2% 0% 11% 18% 3% 4% 9% Jukskei Park 2% 44% 4% 2% 1% 3% 12% 17% 3% 1% 9% Kaya Sand 97% 22% 72% 97% 97% 99% 71% 30% 42% 36% 66% Kelland 0% 49% 7% 0% 3% 1% 14% 12% 4% 2% 9% Kensington B 4% 44% 5% 1% 2% 0% 10% 17% 6% 4% 9% Kya Sand 20% 40% 20% 20% 20% 20% 0% 33% 17% 0% 19% Linden Ext 4% 44% 6% 6% 2% 0% 17% 16% 3% 3% 10% Malanshof 1% 36% 2% 1% 2% 2% 9% 15% 3% 1% 7% Maroeladal 3% 22% 5% 15% 8% 10% 18% 18% 5% 0% 10% Maroeladal Ext 4 8% 23% 8% 0% 0% 0% 0% 5% 7% 0% 5% Moret 3% 43% 2% 1% 0% 1% 23% 23% 8% 8% 11% Noordhang 3% 37% 3% 3% 2% 7% 6% 11% 5% 0% 8% North Riding 6% 33% 3% 4% 2% 4% 10% 16% 3% 7% 9% North Riding AH 4% 26% 8% 9% 6% 25% 13% 20% 5% 2% 12% Northgate Ext 4 0% 39% 3% 2% 2% 3% 5% 12% 3% 0% 7% Northworld 2% 32% 3% 1% 1% 1% 8% 16% 3% 1% 7% Northworld Ext 2% 48% 17% 2% 0% 5% 10% 6% 3% 0% 9% Olivedale 4% 23% 6% 2% 3% 4% 9% 22% 3% 3% 8% President Ridge 2% 35% 6% 1% 1% 0% 19% 16% 5% 7% 9% Randburg Waterfront 1% 45% 0% 0% 0% 0% 4% 9% 2% 0% 6% Randpark 1% 34% 4% 0% 1% 0% 13% 18% 4% 1% 8% Randparkrif 3% 33% 2% 1% 1% 1% 9% 20% 2% 3% 7% Randparkrif Ext 2% 31% 3% 2% 1% 1% 7% 17% 3% 2% 7% Robin Acers 9% 48% 0% 0% 0% 0% 10% 18% 4% 0% 9% Robin Hills 6% 31% 3% 2% 0% 0% 12% 22% 2% 2% 8% Robindale 2% 45% 5% 1% 1% 1% 10% 17% 2% 1% 9% Ruiterhof 0% 34% 4% 3% 0% 4% 19% 21% 1% 0% 9% Sharonlea 1% 36% 5% 1% 1% 0% 7% 18% 3% 2% 8% Sonnedal 44% 27% 24% 41% 56% 97% 25% 27% 17% 6% 36% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

204 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Randburg (continued) 198 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Sonnedal AH 8% 42% 8% 29% 12% 77% 21% 29% 18% 0% 24% Sonneglans Ext 1% 48% 8% 2% 1% 1% 8% 23% 3% 0% 10% Strydom Park 0% 0% 0% 0% 0% 0% 0% 75% 0% 8% Sundowner 2% 32% 3% 3% 2% 1% 7% 17% 3% 2% 7% Sundowner Ext 2% 52% 5% 3% 1% 1% 3% 10% 3% 2% 8% Vandia Grove 0% 36% 5% 0% 0% 0% 5% 20% 1% 0% 7% Windsor East 1% 41% 3% 1% 2% 0% 9% 14% 10% 1% 8% Windsor Glen 1% 30% 2% 2% 1% 1% 13% 19% 3% 2% 7% Windsor West 2% 47% 3% 2% 2% 1% 15% 12% 8% 1% 9% Witkoppen 1% 41% 5% 2% 1% 0% 9% 19% 2% 1% 8% Zandspruit 2% 31% 19% 41% 23% 57% 29% 36% 20% 4% 26% Randfontein Randfontein Estate Gold Mine 100% 0% 50% 100% 0% 100% 50% 33% 33% 50% 0% Aanwins AH 6% 50% 23% 25% 32% 21% 10% 20% 9% 9% 21% Allen's Nek 3% 35% 7% 4% 4% 1% 10% 18% 3% 3% 9% Alsef AH 0% 30% 16% 16% 30% 27% 32% 35% 0% 0% 19% Amorosa AH 3% 44% 1% 19% 21% 21% 21% 33% 11% 26% 20% Bergbron 1% 36% 5% 0% 1% 1% 6% 11% 2% 1% 7% Bergbron Ext 1 5% 29% 8% 5% 0% 1% 16% 14% 1% 4% 8% Carenvale 0% 42% 9% 2% 0% 0% 19% 16% 8% 13% 11% Consolidated Main Reef Gold Mine 75% 17% 17% 17% 11% 23% 28% 29% 20% 0% 24% Constantia Kloof 4% 36% 6% 3% 3% 1% 13% 18% 3% 4% 9% Roodepoort Constantia Park 0% 25% 22% 0% 0% 0% 16% 22% 3% 3% 9% Creswell Park 2% 27% 0% 0% 1% 0% 21% 84% 15% 0% 15% Davidsonville 5% 36% 27% 6% 3% 4% 26% 29% 27% 3% 16% Delarey 2% 37% 18% 1% 4% 0% 16% 21% 12% 3% 11% Discovery 3% 36% 4% 1% 2% 1% 15% 16% 5% 3% 9% Doornkop AH 0% 38% 77% 69% 62% 100% 38% 34% 44% 0% 46% Durban Roodepoort Deep 56% 30% 51% 80% 33% 29% 59% 34% 51% 10% 43% Fleurhof 1% 23% 3% 1% 4% 0% 10% 15% 11% 3% 7% Floracliffe 2% 28% 15% 0% 0% 0% 18% 19% 5% 5% 9% Florida 2% 39% 3% 1% 1% 1% 14% 17% 9% 2% 9% Florida Glen 1% 27% 1% 1% 0% 0% 13% 16% 3% 8% 7% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

205 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Roodepoort (continued) 199 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Florida Hills 1% 33% 5% 0% 2% 1% 11% 16% 4% 4% 8% Florida Lake 5% 30% 7% 1% 0% 1% 10% 22% 5% 7% 9% Florida Lakes 0% 40% 4% 0% 0% 0% 2% 13% 7% 0% 7% Florida North 1% 31% 2% 1% 1% 0% 10% 15% 4% 2% 7% Florida Park 1% 39% 4% 1% 2% 0% 13% 14% 4% 1% 8% Georginia 4% 37% 7% 0% 1% 1% 16% 19% 10% 3% 10% Groblerpark 78% 21% 76% 83% 82% 41% 51% 37% 31% 8% 51% Groblerpark Ext 26% 31% 30% 27% 2% 1% 19% 22% 19% 1% 18% Hamberg 3% 40% 7% 3% 1% 0% 9% 19% 6% 0% 9% Harveston AH 27% 15% 26% 62% 36% 25% 17% 39% 10% 31% 29% Haylon Hills AH 9% 38% 25% 29% 21% 33% 46% 26% 29% 0% 26% Helderkruin 7% 30% 9% 1% 1% 1% 12% 17% 3% 0% 8% Hillfox 2% 24% 2% 0% 2% 12% 17% 19% 6% 1% 9% Honey Dew 0% 33% 0% 8% 0% 0% 33% 26% 0% 0% 10% Honey Hill 0% 45% 6% 1% 0% 0% 16% 20% 2% 0% 9% Honeydew 2% 38% 6% 4% 2% 0% 9% 11% 6% 0% 8% Horison 3% 35% 4% 2% 2% 0% 16% 18% 5% 2% 9% Horison Park 7% 31% 3% 3% 1% 0% 11% 14% 3% 0% 7% Horison View 3% 40% 11% 2% 2% 0% 5% 15% 5% 1% 9% Industria North 11% 31% 0% 0% 0% 5% 5% 69% 18% 10% 15% Kimbult AH 13% 21% 38% 50% 46% 56% 33% 42% 21% 4% 32% Kloofendal 3% 33% 1% 2% 1% 1% 13% 19% 1% 2% 8% Lindhaven 3% 20% 8% 2% 2% 0% 8% 21% 8% 0% 7% Little Falls 2% 29% 8% 2% 3% 1% 8% 16% 3% 1% 7% Manufacta 2% 18% 4% 2% 0% 0% 13% 24% 15% 8% 9% Maraisburg 3% 37% 3% 3% 2% 1% 16% 21% 15% 4% 11% Matholesville 96% 38% 33% 86% 53% 5% 55% 39% 46% 2% 45% Northcliff Ext 2% 35% 5% 3% 3% 1% 14% 14% 5% 2% 8% Ondekkerspark 2% 32% 10% 1% 2% 0% 11% 15% 4% 0% 8% Panorama 4% 38% 2% 1% 2% 1% 2% 10% 5% 1% 7% Poortview AH 0% 18% 6% 0% 2% 4% 8% 21% 3% 0% 6% Princess 95% 32% 98% 99% 31% 2% 62% 36% 44% 9% 51% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

206 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Roodepoort (continued) Sandton Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Princess AH 42% 23% 29% 36% 41% 34% 29% 30% 18% 1% 28% Quellerina 2% 34% 3% 0% 2% 0% 15% 18% 3% 3% 8% Radiokop 1% 41% 2% 2% 0% 1% 7% 13% 2% 0% 7% Rand Leases Gold Mine 17% 12% 23% 17% 14% 18% 24% 32% 32% 4% 19% Reefhaven 4% 29% 4% 4% 0% 0% 23% 22% 9% 4% 10% Rietfontein AH 0% 24% 56% 24% 8% 76% 48% 23% 8% 0% 27% Robertville 5% 11% 0% 0% 5% 0% 0% 30% 0% 33% 9% Roodekrans 1% 32% 6% 1% 1% 1% 12% 19% 2% 3% 8% Roodepoort Central 5% 34% 8% 5% 2% 0% 21% 22% 17% 8% 12% Roodepoort North 3% 36% 4% 2% 1% 1% 18% 17% 13% 3% 10% Roodepoort West 0% 29% 20% 1% 1% 0% 12% 21% 7% 0% 9% Ruimsig 92% 0% 84% 0% 100% 100% 41% 96% 0% 9% 52% Ruimsig AH 68% 30% 3% 26% 9% 69% 34% 22% 30% 0% 29% Selwyn 2% 34% 2% 0% 0% 0% 18% 20% 5% 0% 8% South Roodepoort Main Reef Areas Gold Mine 4% 16% 4% 35% 33% 31% 31% 60% 14% 12% 24% Stormill 27% 20% 10% 3% 0% 87% 37% 18% 22% 0% 22% Strubensvallei 4% 35% 5% 1% 2% 1% 9% 19% 3% 2% 8% Tres Jolie AH 3% 25% 10% 12% 4% 4% 31% 28% 9% 4% 13% Uitsig 3% 24% 9% 0% 0% 0% 12% 9% 2% 0% 6% Weltevreden Park 2% 36% 5% 2% 2% 1% 9% 16% 4% 1% 8% White Ridge 1% 38% 3% 0% 3% 0% 20% 23% 8% 2% 10% Wilfordon 6% 18% 2% 4% 2% 2% 40% 31% 16% 4% 12% Wilgeheuwel 4% 29% 16% 7% 0% 2% 0% 17% 4% 0% 8% Wilgeheuwel Ext 3 8% 22% 4% 0% 2% 0% 8% 16% 4% 0% 6% Wilropark 2% 35% 9% 2% 2% 1% 17% 16% 4% 2% 9% Witpoortjie 4% 27% 14% 2% 2% 2% 13% 19% 7% 2% 9% Airdlin 0% 36% 2% 15% 2% 0% 29% 33% 7% 0% 12% Atholl 11% 38% 5% 2% 3% 1% 5% 21% 3% 3% 9% Atholl Ext 12 13% 52% 6% 8% 21% 8% 16% 14% 13% 15% 17% Atholl Gardens 1% 40% 4% 2% 2% 3% 9% 12% 1% 1% 8% Athollhurst 6% 56% 0% 0% 2% 6% 26% 35% 0% 4% 14% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty 200

207 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Sandton (continued) 201 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Benmore Gardens 0% 51% 18% 0% 0% 0% 13% 19% 4% 0% 10% Beverley 3% 23% 3% 3% 4% 16% 21% 12% 2% 1% 9% Beverley Estates 4% 36% 2% 1% 0% 1% 6% 24% 9% 0% 8% Bramley North 0% 37% 4% 1% 1% 0% 17% 18% 4% 1% 8% Bramley Park 1% 50% 1% 0% 1% 0% 28% 15% 7% 2% 10% Bryanston 3% 43% 3% 2% 1% 1% 11% 20% 2% 2% 9% Bryanston East 2% 59% 2% 0% 1% 2% 10% 6% 3% 0% 9% Bryanston West 4% 54% 4% 0% 0% 4% 14% 18% 3% 4% 10% Buccleuch 1% 35% 2% 1% 2% 4% 8% 16% 5% 2% 8% Commercia 33% 33% 0% 67% 67% 67% 33% 75% 0% 0% 38% Country Life Park 2% 40% 4% 4% 1% 0% 4% 13% 4% 2% 7% Cowdray Park Ext 1 0% 26% 4% 0% 0% 0% 13% 22% 6% 2% 7% Dennehof 0% 0% 0% 0% 0% 50% 0% 33% 0% 0% 8% Douglasdale Ext 2% 38% 6% 2% 2% 1% 8% 16% 2% 2% 8% Duxberry 3% 32% 4% 1% 4% 0% 8% 19% 1% 3% 7% Edenburg 4% 42% 5% 3% 2% 0% 10% 14% 3% 1% 9% Epsom Downs 0% 41% 5% 0% 4% 0% 11% 14% 6% 1% 8% Field and Study Centre 91% 0% 45% Fourways 4% 39% 6% 3% 2% 0% 10% 18% 3% 2% 9% Frankenwald Gallo Manor 2% 42% 4% 1% 1% 1% 14% 19% 3% 4% 9% Glenadrienne East 0% 41% 3% 1% 1% 0% 9% 12% 6% 0% 7% Glenadrienne West 0% 57% 6% 0% 5% 1% 5% 25% 2% 3% 10% Hurlingham 24% 47% 1% 1% 1% 2% 15% 20% 3% 4% 12% Hurlingham Manor 1% 38% 1% 2% 3% 1% 7% 21% 2% 2% 8% Hurlpark 0% 50% 7% 2% 2% 2% 7% 26% 0% 7% 10% Hyde Park 5% 43% 3% 1% 3% 0% 16% 22% 3% 3% 10% Illovo 0% 43% 3% 0% 1% 1% 8% 20% 2% 2% 8% Inanda 3% 42% 3% 0% 0% 0% 5% 15% 3% 0% 7% Johannesburg Country Club 0% 75% 0% 0% 0% 0% 0% 20% 0% 0% 10% Kelvin 1% 32% 1% 2% 1% 1% 16% 17% 7% 3% 8% Kramerville 0% 37% 0% 0% 0% 0% 14% 14% 2% 0% 7% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

208 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Sandton (continued) Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Leeuwkop 1% 19% 1% 1% 3% 1% 6% 17% 4% 8% 6% Lonehill 1% 35% 4% 1% 2% 1% 7% 13% 2% 2% 7% Magaliessig 3% 47% 2% 1% 3% 1% 6% 12% 3% 0% 8% Marlboro 45% 23% 2% 43% 2% 42% 35% 43% 37% 30% 30% Marlboro Ext 1 0% 100% 0% 0% 0% 0% 100% 0% 0% 22% Marlboro Gardens 4% 31% 3% 3% 2% 2% 20% 23% 11% 3% 10% Marlboro South 54% 24% 23% 13% 10% 3% 27% 21% 26% 1% 20% Mill Hill Ext 2 4% 41% 5% 1% 2% 2% 9% 12% 5% 0% 8% Modderfontein AH 7% 26% 10% 3% 6% 4% 21% 21% 11% 15% 12% Morningside 2% 43% 4% 2% 1% 2% 9% 14% 3% 1% 8% Morningside Ext 40 2% 32% 3% 1% 3% 0% 4% 18% 0% 0% 7% Morningside Manor 2% 37% 3% 0% 0% 0% 10% 17% 2% 3% 7% Norscot 2% 43% 4% 1% 2% 4% 10% 15% 2% 2% 9% Parkmore 2% 48% 5% 2% 1% 0% 11% 18% 2% 3% 9% Paulshof 1% 42% 2% 1% 1% 3% 6% 14% 2% 1% 7% Paulshof Ext 13% 47% 5% 9% 2% 10% 13% 18% 8% 0% 12% Petervale 1% 44% 3% 3% 2% 1% 12% 20% 3% 0% 9% Riepen Park River Club 2% 47% 6% 1% 2% 1% 7% 15% 1% 1% 8% Rivonia 2% 45% 1% 2% 0% 1% 13% 14% 3% 3% 8% Sandhurst 1% 40% 3% 0% 2% 2% 27% 18% 2% 2% 10% Sandhurst Ext 4 1% 37% 5% 2% 2% 1% 11% 20% 1% 1% 8% Sandown 5% 46% 4% 3% 4% 5% 12% 13% 4% 1% 10% Sandown Ext 2% 38% 9% 4% 4% 2% 10% 13% 2% 0% 9% Solridge 0% 25% 0% 0% 0% 0% 5% 26% 0% 0% 6% St Sithians 1% 31% 3% 0% 4% 24% 27% 21% 5% 4% 12% Strathavon 0% 47% 1% 1% 3% 0% 15% 12% 4% 0% 8% Sunninghill 2% 34% 3% 2% 2% 2% 6% 15% 3% 2% 7% Sunset Acres 1% 50% 4% 0% 0% 3% 7% 15% 3% 1% 8% The Woodlands 0% 70% 5% 0% 0% 0% 25% 30% 8% 0% 14% University or the Witwatersrand Research Site and Sports Ground 38% 38% 50% 38% 50% 0% 0% 28% 16% 0% 26% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty 202

209 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Sandton (continued) 203 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Wendywood 2% 42% 4% 1% 2% 1% 12% 20% 2% 2% 9% Wierda Valley 3% 40% 0% 3% 1% 0% 20% 22% 0% 8% 10% Willowild 1% 56% 2% 0% 4% 1% 10% 19% 2% 1% 10% Woodmead 3% 36% 4% 3% 2% 3% 10% 16% 3% 2% 8% Woodmead East 0% 43% 0% 0% 1% 0% 7% 23% 3% 1% 8% Woodmead Value Mart 0% 52% 1% 1% 0% 3% 13% 17% 2% 4% 9% Wynberg 65% 21% 58% 60% 55% 0% 47% 27% 42% 1% 38% Slovoville Slovoville SP 1% 42% 4% 12% 2% 9% 58% 35% 43% 7% 21% Bambayi 90% 34% 91% 93% 98% 73% 73% 49% 45% 1% 65% Chiawelo 26% 33% 11% 10% 3% 7% 36% 31% 34% 9% 20% Chiawelo Ext 3 15% 33% 1% 1% 1% 10% 30% 25% 31% 5% 15% Devland 98% 30% 96% 99% 86% 72% 55% 41% 38% 2% 62% Dlamini 46% 42% 44% 47% 25% 13% 50% 37% 38% 5% 35% Dobsonville 21% 39% 3% 2% 1% 1% 33% 29% 34% 3% 16% Dobsonville Gardens 1% 25% 2% 1% 1% 0% 16% 31% 25% 0% 10% Doornkop 1% 26% 1% 1% 1% 0% 19% 30% 31% 0% 11% Dube 3% 37% 7% 10% 1% 1% 48% 25% 34% 3% 17% Emdeni 11% 47% 5% 2% 2% 1% 41% 30% 41% 4% 18% Freedom Park 100% 26% 93% 100% 97% 93% 54% 41% 48% 2% 65% Soweto Goldev 98% 26% 89% 100% 99% 69% 50% 40% 36% 5% 61% Jabavu 12% 51% 3% 5% 2% 1% 47% 31% 37% 2% 19% Jabulani 8% 39% 6% 3% 1% 3% 49% 32% 39% 3% 18% Klipriviersoog 37% 7% 0% 49% 0% 96% 7% 42% 27% 7% 27% Klipspruit 28% 44% 7% 12% 7% 2% 43% 30% 39% 3% 22% Klipspruit Ext 17% 29% 11% 10% 7% 3% 60% 35% 52% 2% 23% Kliptown 73% 40% 81% 89% 64% 25% 63% 38% 39% 1% 51% Mapetla 6% 36% 5% 9% 1% 0% 50% 27% 42% 3% 18% Mmesi Park 1% 30% 3% 0% 2% 0% 10% 19% 21% 0% 9% Mofolo Central 15% 48% 15% 10% 9% 8% 39% 26% 38% 2% 21% Mofolo North 15% 41% 2% 4% 1% 1% 43% 31% 39% 2% 18% Mofolo South 7% 49% 0% 3% 2% 1% 46% 36% 39% 2% 18% Molapo 6% 47% 2% 2% 1% 0% 41% 27% 43% 9% 18% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

210 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) Soweto (continued) 204 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Moletsane 5% 45% 3% 4% 2% 0% 39% 28% 33% 1% 16% Moroka 4% 47% 1% 1% 1% 1% 36% 26% 36% 2% 16% Motsoaledi 94% 35% 97% 97% 46% 0% 64% 39% 51% 8% 53% Naledi Naledi Ext 1 6% 46% 2% 2% 1% 1% 40% 31% 39% 3% 17% Naledi Ext 2 4% 45% 4% 1% 1% 0% 31% 23% 32% 1% 14% Orlando East 56% 39% 6% 16% 3% 3% 43% 30% 38% 11% 24% Orlando West 20% 48% 6% 4% 2% 1% 42% 26% 36% 7% 19% Phiri 13% 48% 3% 5% 1% 0% 50% 33% 48% 2% 20% Pimville 11% 25% 94% 94% 94% 94% 58% 40% 43% 5% 56% Pimville Zone 1 16% 49% 1% 3% 1% 0% 38% 31% 43% 14% 20% Pimville Zone 2 19% 46% 1% 1% 1% 0% 39% 26% 35% 1% 17% Pimville Zone 3 14% 56% 0% 2% 2% 0% 36% 28% 39% 10% 19% Pimville Zone 4 21% 37% 2% 1% 1% 0% 37% 27% 46% 7% 18% Pimville Zone 5 3% 39% 2% 1% 2% 0% 27% 20% 30% 2% 13% Pimville Zone 6 13% 41% 1% 1% 2% 0% 40% 24% 37% 1% 16% Pimville Zone 7 6% 41% 1% 0% 2% 0% 17% 25% 23% 1% 12% Power Park 0% 44% 2% 0% 0% 2% 33% 22% 28% 18% 15% Protea Gardens 2% 23% 1% 0% 2% 0% 7% 24% 25% 0% 8% Protea Glen 1% 32% 3% 0% 1% 1% 15% 29% 23% 0% 11% Protea Glen Ext 2% 25% 1% 0% 1% 0% 13% 25% 27% 0% 9% Protea North 1% 37% 1% 1% 1% 0% 13% 22% 20% 0% 10% Protea South 76% 32% 42% 94% 64% 10% 61% 51% 46% 6% 48% Senaone 8% 43% 3% 2% 2% 1% 37% 32% 39% 7% 17% Thulani 55% 42% 19% 15% 49% 6% 54% 36% 39% 4% 32% Tladi 15% 42% 13% 10% 2% 0% 34% 27% 43% 3% 19% Zola 11% 46% 6% 2% 2% 3% 44% 33% 41% 4% 19% Zondi 11% 42% 8% 2% 1% 0% 48% 36% 40% 10% 20% Sweetwaters Sweetwaters 97% 46% 99% 98% 98% 99% 77% 63% 53% 0% 73% Tshepisong Tshepisong SP 17% 41% 18% 40% 5% 2% 60% 39% 45% 1% 27% Tshepisong 97% 22% 98% 99% 34% 10% 59% 37% 49% 4% 51% Vlakfontein Vlakfontein 91% 52% 63% 100% 92% 45% 66% 49% 44% 6% 61% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

211 Municipality Main-place name Sub-place name City of Johannesburg Metro (continued) City of Tshwane Metro Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- type: Informal household sal income ment source Wheeler's Farm SP 91% 36% 99% 99% 51% 82% 77% 58% 44% 5% 64% Wheeler's Farm Wheeler's Farm 85% 41% 99% 99% 71% 81% 69% 60% 38% 0% 64% Zandspruit Zandspruit 94% 31% 67% 98% 52% 29% 46% 42% 33% 9% 50% Zevenfontein Pipeline 95% 33% 98% 98% 98% 9% 43% 40% 31% 15% 56% Amandasig 2% 37% 5% 1% 3% 3% 20% 18% 4% 2% 9% Chantelle 5% 23% 3% 4% 2% 3% 8% 23% 7% 0% 8% Clarina 4% 41% 12% 1% 4% 1% 11% 21% 10% 1% 11% Eldorette 0% 39% 0% 6% 0% 0% 11% 20% 10% 0% 9% Hartebeesthoek 3% 32% 1% 21% 11% 57% 32% 30% 8% 0% 20% Heatherdale AH 7% 43% 3% 18% 17% 14% 39% 32% 9% 8% 19% Hesteapark 1% 25% 1% 1% 0% 1% 6% 19% 5% 1% 6% Akasia Karenpark 2% 36% 3% 2% 2% 2% 11% 22% 4% 0% 8% Klerksoord 11% 32% 18% 48% 54% 89% 38% 33% 22% 3% 35% Ninapark 2% 35% 4% 2% 1% 1% 13% 18% 3% 2% 8% Onderstepoort Nature Reserve 0% 13% 2% 15% 13% 39% 13% 38% 17% 0% 15% Rosslyn 52% 17% 27% 30% 27% 63% 23% 43% 25% 29% 34% The Orchards 3% 30% 3% 2% 2% 0% 13% 22% 8% 1% 8% Theresa Park 3% 31% 4% 3% 1% 2% 10% 18% 5% 1% 8% Winternest AH 21% 32% 22% 15% 6% 35% 30% 23% 8% 9% 20% Wonderboom AH 36% 12% 24% 89% 85% 100% 83% 54% 20% 13% 52% Atteridgeville Atteridgeville SP 7% 45% 4% 1% 1% 1% 26% 27% 29% 5% 15% Bronberrick 1% 31% 6% 0% 2% 1% 11% 20% 4% 1% 8% Centurion 60% 26% 60% 62% 58% 63% 56% 33% 29% 10% 46% Centurion Central 20% 9% 15% Christoburg 0% 25% 25% 50% 75% 0% 25% 67% 0% 33% 30% Clubview 5% 42% 2% 2% 2% 2% 12% 18% 3% 3% 9% Centurion Clubview Ext 2 5% 25% 6% 0% 0% 0% 5% 15% 3% 0% 6% Cornwall Hill 0% 33% 0% 1% 1% 2% 15% 29% 11% 2% 9% Die Hoewes Ext 0% 44% 4% 2% 2% 2% 7% 9% 3% 0% 7% Die Hoewes Ext 52 0% 31% 2% 2% 2% 0% 2% 9% 1% 0% 5% Die Hoewes Ext 100 0% 33% 5% 5% 5% 0% 5% 10% 4% 2% 7% Die Hoewes Ext 102 0% 37% 2% 0% 1% 0% 7% 9% 3% 1% 6% 205

212 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Centurion (continued) 206 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Die Hoewes Ext 119 0% 35% 8% 5% 2% 0% 5% 7% 1% 0% 6% Die Hoewes Ext 14 0% 21% 0% 0% 0% 0% 8% 18% 3% 2% 5% Die Hoewes Ext 16 4% 49% 1% 0% 0% 0% 0% 15% 2% 0% 7% Die Hoewes Ext 19 0% 100% 0% 0% 0% 0% 0% 0% 0% 11% Die Hoewes Ext 25 1% 45% 3% 0% 1% 1% 5% 6% 2% 0% 6% Die Hoewes Ext 31 2% 80% 9% 0% 1% 0% 52% 0% 22% 0% 17% Die Hoewes Ext 32 1% 38% 0% 0% 4% 0% 6% 13% 4% 0% 7% Die Hoewes Ext 51 8% 0% 4% Die Hoewes Ext 54 3% 5% 4% Die Hoewes Ext 6 8% 34% 8% 4% 8% 0% 13% 15% 16% 4% 11% Die Hoewes Ext 95 1% 34% 6% 1% 0% 0% 14% 14% 3% 0% 7% Doringkloof 3% 24% 2% 1% 1% 0% 6% 17% 3% 1% 6% Eldoraigne 2% 29% 2% 1% 1% 0% 8% 18% 2% 1% 7% Erasmia 3% 28% 5% 2% 3% 0% 17% 24% 4% 1% 9% Gerardsville AH 11% 26% 22% 27% 38% 93% 25% 33% 13% 1% 29% Hennops Park Industrial Ext 50% 10% 90% 30% 0% 20% 30% 16% 0% 44% 29% Hennopspark 1% 32% 3% 0% 1% 0% 15% 17% 2% 0% 7% Hennopspark Ext 5&9 1% 39% 2% 1% 2% 1% 10% 17% 4% 2% 8% Heuweloord 6% 28% 3% 5% 2% 1% 13% 25% 8% 0% 9% Highveld Exts 1% 27% 2% 2% 1% 1% 4% 15% 2% 0% 6% Irene 3% 35% 12% 0% 1% 2% 23% 27% 2% 2% 11% Irene Research Institute 5% 5% 0% 0% 0% 0% 10% 21% 12% 4% 6% Kloofsig 3% 39% 2% 1% 3% 0% 13% 16% 2% 0% 8% Kosmosdal 8% 8% 33% 8% 8% 17% 0% 16% 5% 0% 10% Laudium 27% 36% 25% 27% 12% 2% 32% 24% 14% 4% 20% Louwlardia 48% 37% 53% 43% 60% 67% 23% 32% 19% 15% 40% Lyttelton 0% 35% 28% 0% 0% 0% 21% 19% 8% 11% 12% Lyttelton AH 3% 42% 10% 4% 0% 2% 8% 13% 5% 0% 9% Lyttleton Manor 3% 33% 3% 2% 2% 1% 10% 18% 3% 1% 8% Mnandi AH 5% 27% 14% 7% 6% 82% 17% 23% 6% 1% 19% Monavoni AH 9% 37% 22% 19% 11% 19% 33% 28% 9% 3% 19% Monrick 7% 49% 18% 14% 25% 72% 28% 27% 0% 27% 27% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

213 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Centurion (continued) City of Tshwane Metro Part Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Olievenhoutsbos 1% 55% 0% 0% 3% 0% 48% 47% 36% 0% 19% Pierre van Ryneveld 3% 25% 3% 2% 1% 1% 8% 18% 2% 1% 6% Raslouw 1% 35% 5% 1% 1% 0% 11% 14% 3% 0% 7% Raslouw AH 10% 35% 6% 12% 8% 5% 31% 27% 4% 10% 15% Rooihuiskraal 7% 28% 5% 1% 1% 1% 14% 22% 3% 2% 8% Rooihuiskraal Ext 48% 0% 0% 49% 5% 71% 11% 36% 12% 28% 26% Rooihuiskraal North 3% 30% 5% 3% 2% 1% 8% 12% 2% 1% 7% Snake Valley 0% 67% 11% 11% 0% 0% 11% 0% 15% 0% 12% Sunderland Ridge 100% 0% 100% 0% 0% 0% 33% Tamara Park 1% 37% 4% 0% 5% 1% 8% 14% 2% 3% 7% The Reeds 6% 26% 5% 2% 3% 3% 11% 22% 5% 1% 8% The Reeds Ext 10 6% 20% 4% 0% 0% 0% 11% 28% 3% 5% 8% Timsrand AH 27% 25% 27% 40% 42% 88% 38% 37% 25% 6% 35% Villa Rosa 1% 33% 0% 3% 0% 0% 13% 11% 3% 0% 6% Waterkloof AFB 0% 36% 8% 0% 2% 0% 11% 14% 3% 0% 7% Wierda Park 3% 26% 3% 1% 1% 1% 9% 20% 3% 2% 7% Zwartkop Ext % 25% 5% 1% 2% 1% 7% 18% 3% 0% 6% Zwartkop Ext 7 1% 34% 3% 0% 0% 0% 5% 11% 3% 0% 6% Zwartkop Ext % 49% 5% 3% 2% 0% 8% 7% 1% 0% 8% Bon Accord AH 5% 25% 15% 23% 26% 74% 41% 31% 13% 0% 25% Bultfontein AH 9% 20% 41% 36% 42% 98% 44% 35% 10% 2% 34% Gerardsville AH 5% 32% 10% 21% 30% 66% 38% 35% 1% 0% 24% Grootvlei AH 2% 25% 3% 37% 42% 99% 52% 36% 14% 2% 31% Laezonia AH 17% 27% 7% 21% 10% 81% 47% 39% 9% 6% 26% Onderstepoort SH 28% 35% 7% 72% 49% 100% 45% 38% 15% 3% 39% Pretoria NU 47% 32% 44% 51% 53% 64% 50% 36% 23% 7% 41% Wonderboom NU 55% 31% 50% 58% 61% 85% 45% 41% 22% 2% 45% Ga-Rankuwa Part 1 Ga-Rankuwa Zone 2 4% 57% 9% 1% 1% 24% 80% 18% 8% 3% 21% Hammanskraal 79% 36% 1% 1% 99% 0% 54% 62% 19% 1% 35% Hammanskraal Part 1 Mandela Village 56% 36% 1% 2% 90% 3% 45% 48% 22% 0% 30% Rens Town 1% 24% 1% 1% 2% 4% 11% 25% 9% 0% 8% Knopjeslaagte Knopjeslaagte 97% 23% 98% 96% 19% 21% 55% 42% 19% 4% 47% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

214 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) 208 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Itsoseng 95% 32% 60% 56% 56% 46% 54% 42% 41% 1% 48% Soshanguve South 99% 39% 98% 54% 97% 1% 53% 40% 49% 0% 53% Mabopane Part 1 Soshanguve TT 93% 32% 23% 22% 22% 0% 48% 42% 39% 3% 32% Winterveldt Area 97% 24% 98% 100% 100% 100% 47% 52% 34% 2% 65% Winterveldt Ward 2 97% 27% 97% 99% 96% 99% 46% 63% 31% 3% 66% Mamelodi SP Lusaka 97% 25% 97% 99% 98% 99% 45% 33% 39% 4% 64% Mahube Valley 60% 31% 47% 60% 51% 49% 40% 36% 30% 2% 41% Mamelodi East 12% 38% 4% 3% 1% 2% 27% 29% 27% 5% 15% Mamelodi Mamelodi Sun Valley 1% 18% 9% 0% 3% 4% 6% 22% 17% 1% 8% Mamelodi West 24% 31% 12% 33% 4% 9% 44% 29% 35% 7% 23% Mandela Village 77% 37% 52% 53% 53% 26% 43% 40% 35% 3% 42% Moretele View 3% 18% 0% 0% 0% 0% 7% 28% 13% 1% 7% Stanza Bopape 86% 38% 40% 46% 45% 26% 45% 36% 33% 4% 40% Nellmapius SP 3% 23% 2% 3% 3% 1% 15% 28% 19% 1% 10% Nellmapius Nellmapius Ext 3 7% 52% 0% 3% 1% 0% 42% 29% 24% 3% 16% Nellmapius Ext 4 6% 45% 1% 96% 1% 0% 50% 38% 35% 25% 30% Olievenhoutbos Olievenhoutbos 66% 32% 71% 74% 61% 12% 41% 41% 37% 8% 44% Pretoria SP 27% 11% 62% 53% 43% 69% 43% 30% 5% 11% 36% Alphen Park 0% 45% 3% 0% 3% 0% 14% 12% 0% 0% 8% Andeon AH 22% 32% 14% 35% 26% 46% 23% 23% 7% 10% 24% Annlin 0% 36% 5% 0% 1% 1% 16% 14% 3% 4% 8% Annlin Ext 37 3% 26% 1% 3% 5% 2% 5% 17% 6% 0% 7% Annlin West Ext 3 0% 45% 0% 2% 0% 4% 15% 20% 2% 2% 9% Pretoria Arcadia 2% 47% 7% 2% 2% 1% 24% 11% 6% 2% 10% Ashlea Gardens 0% 35% 5% 4% 1% 2% 5% 14% 1% 0% 7% Asiatic Bazaar 93% 43% 94% 95% 91% 61% 72% 37% 53% 25% 66% Bailey's Muckleneuk 9% 29% 22% 0% 0% 0% 8% 13% 1% 0% 8% Bellevue 4% 48% 5% 0% 2% 0% 14% 16% 2% 4% 9% Bon Accord 18% 12% 0% 41% 12% 65% 18% 51% 11% 13% 24% Booysens 1% 25% 2% 0% 1% 0% 11% 20% 8% 2% 7% Brooklyn 1% 40% 4% 1% 3% 0% 14% 16% 3% 1% 8% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

215 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Pretoria (continued) 209 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Brummeria 3% 30% 3% 0% 0% 0% 16% 17% 0% 0% 7% Brummeria Exts 2% 58% 4% 3% 0% 3% 6% 5% 1% 3% 9% Bryntirion 8% 44% 19% 0% 3% 0% 24% 12% 6% 3% 12% Capital Park 1% 34% 2% 1% 0% 1% 13% 15% 5% 2% 7% Claremont 5% 29% 9% 1% 2% 0% 13% 21% 10% 2% 9% Colbyn 1% 31% 3% 1% 0% 0% 14% 31% 2% 1% 9% Colbyn Valley 0% 21% 0% 0% 0% 0% 38% 11% 0% 0% 7% Constantia Park 1% 28% 2% 2% 1% 2% 12% 17% 3% 2% 7% Danville 2% 33% 3% 2% 2% 0% 12% 21% 12% 2% 9% Daspoort 4% 30% 17% 8% 5% 3% 15% 19% 13% 5% 12% De Wilgers 2% 36% 5% 3% 2% 1% 11% 14% 2% 1% 8% Deerness 2% 48% 58% 0% 0% 0% 8% 11% 0% 2% 13% Derdepoort 15% 47% 6% 0% 0% 9% 21% 50% 4% 0% 15% Derdepoort Park 6% 44% 6% 0% 0% 0% 4% 38% 17% 6% 12% Doornpoort 2% 18% 3% 1% 3% 1% 6% 19% 3% 1% 6% Dorandia 1% 32% 4% 2% 1% 1% 11% 15% 5% 1% 7% East Lynne 1% 25% 11% 1% 1% 1% 10% 18% 6% 1% 8% Eastmead 4% 26% 4% 4% 4% 4% 8% 14% 3% 0% 7% Eastwood 7% 37% 1% 0% 1% 1% 15% 16% 4% 0% 8% Eersterus 10% 38% 12% 7% 3% 0% 22% 24% 21% 3% 14% Ekklesia 2% 26% 5% 2% 2% 2% 8% 20% 4% 0% 7% Elandsfontein 9% 43% 66% 70% 9% 74% 26% 30% 11% 4% 34% Elandspoort 3% 38% 13% 1% 3% 1% 25% 25% 16% 1% 12% Elarduspark 3% 34% 3% 1% 1% 0% 8% 19% 2% 1% 7% Eloffsdal 4% 30% 2% 1% 2% 0% 16% 13% 7% 2% 8% Erasmuskloof 3% 36% 3% 1% 2% 1% 8% 15% 2% 2% 7% Erasmuspark 0% 0% 100% 100% 100% 100% 0% 83% 17% 100% 60% Erasmusrand 1% 32% 5% 1% 1% 0% 10% 17% 1% 1% 7% Faerie Glen 2% 32% 3% 2% 1% 0% 7% 13% 2% 1% 6% Florapark 12% 37% 0% 0% 0% 0% 0% 50% 0% 0% 10% Florauna 4% 33% 2% 1% 0% 0% 9% 13% 3% 0% 7% Garsfontein 1% 33% 2% 1% 1% 0% 7% 15% 2% 0% 6% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

216 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Pretoria (continued) 210 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation General Kemp Heuwel 3% 33% 0% 0% 0% 0% 7% 29% 6% 7% 9% Gezina 1% 41% 2% 1% 1% 0% 11% 14% 6% 0% 8% Groenkloof 1% 42% 2% 1% 3% 1% 26% 17% 3% 2% 10% Hatfield 4% 64% 5% 2% 5% 1% 64% 6% 2% 2% 15% Hazelwood 0% 22% 0% 2% 2% 0% 9% 9% 2% 0% 5% Hermanstad 2% 39% 4% 1% 1% 0% 14% 13% 11% 1% 9% Hillcrest 4% 69% 5% 2% 37% 0% 73% 8% 1% 0% 20% Hornsoord 10% 32% 7% 48% 46% 72% 52% 36% 10% 5% 32% Jan Niemand Park 4% 32% 1% 2% 2% 0% 15% 22% 8% 2% 9% Kenley AH 0% 5% 11% 0% 0% 5% 11% 15% 0% 0% 5% Kilner Park 2% 36% 4% 0% 1% 0% 10% 13% 2% 2% 7% Kirkney 50% 22% 33% 11% 0% 89% 22% 42% 5% 10% 28% Koedoespoort Industrial 0% 50% 50% 0% 50% 100% 100% 56% 29% 0% 43% Kwaggasrand 2% 28% 2% 1% 2% 0% 10% 20% 9% 0% 8% Kwaggasrant 80% 20% 100% 100% 80% 100% 90% 62% 0% 9% 64% La Montagne 1% 39% 3% 0% 1% 0% 7% 11% 3% 0% 7% Les Marais 1% 37% 11% 1% 2% 0% 10% 12% 6% 1% 8% Lindo Park 3% 21% 2% 2% 1% 0% 9% 23% 12% 3% 8% Loeka Villa 16% 38% 30% 59% 26% 77% 44% 32% 13% 5% 34% Lotus Gardens 7% 36% 2% 2% 2% 1% 25% 27% 25% 1% 13% Lukasrand 3% 48% 2% 0% 5% 0% 5% 19% 2% 17% 10% Lydiana 5% 35% 14% 0% 2% 0% 11% 14% 2% 0% 8% Lynnwood 3% 37% 3% 0% 1% 1% 14% 16% 2% 1% 8% Lynnwood Glen 1% 38% 3% 1% 1% 3% 13% 16% 1% 1% 8% Lynnwood Manor 1% 33% 3% 2% 1% 1% 10% 15% 1% 2% 7% Lynnwood Park 0% 29% 0% 2% 0% 0% 21% 20% 1% 2% 7% Lynnwood Ridge 1% 31% 3% 1% 2% 2% 8% 14% 2% 1% 6% Lyttelton 0% 23% 7% 2% 0% 0% 8% 24% 10% 9% 8% Magalieskruin 2% 25% 2% 3% 1% 6% 13% 18% 2% 3% 7% Maroelana 2% 60% 4% 2% 0% 4% 6% 13% 3% 0% 9% Mayville 13% 31% 3% 1% 1% 0% 9% 15% 4% 0% 8% Menlo Park 3% 37% 3% 1% 2% 1% 12% 20% 1% 2% 8% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

217 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Pretoria (continued) 211 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Menlyn 0% 30% 8% 2% 0% 0% 8% 14% 6% 0% 7% Meyerspark 2% 25% 2% 1% 3% 1% 12% 16% 2% 0% 6% Montana 5% 21% 4% 2% 2% 2% 8% 16% 2% 0% 6% Montana AH 0% 27% 1% 4% 20% 13% 13% 20% 2% 0% 10% Montana Ext 2 0% 22% 0% 0% 0% 22% 5% 19% 0% 0% 7% Montana Park 0% 24% 3% 1% 2% 1% 10% 20% 1% 0% 6% Montana Park Exts 1% 30% 1% 1% 2% 1% 9% 14% 2% 0% 6% Montana Tuine 1% 29% 1% 1% 1% 0% 6% 15% 5% 0% 6% Monument Park 2% 32% 3% 2% 2% 0% 13% 17% 2% 1% 7% Moregloed 2% 30% 2% 2% 1% 1% 9% 17% 4% 1% 7% Moreletapark 4% 29% 5% 3% 2% 1% 9% 17% 2% 2% 7% Mountain View 2% 27% 2% 2% 1% 1% 13% 17% 7% 0% 7% Muckleneuk 3% 44% 7% 1% 2% 1% 15% 12% 8% 1% 9% Murrayfield 3% 32% 6% 1% 1% 1% 9% 15% 2% 0% 7% New Muckleneuk 4% 34% 10% 1% 0% 0% 12% 16% 2% 1% 8% Newlands 3% 29% 2% 1% 2% 0% 8% 14% 3% 1% 6% Onderstepoort 0% 51% 5% 0% 3% 0% 60% 8% 1% 0% 13% Parktown Estate 2% 33% 8% 0% 0% 0% 13% 16% 2% 0% 7% Phillip Nel Park 3% 47% 3% 1% 2% 1% 17% 9% 6% 1% 9% Pretoria CBD 1% 47% 5% 1% 1% 1% 25% 10% 12% 4% 11% Pretoria Gardens 3% 38% 7% 1% 2% 0% 13% 18% 6% 1% 9% Pretoria Industrial 0% 32% 2% 0% 2% 1% 57% 15% 6% 2% 12% Pretoria North 2% 33% 4% 1% 2% 1% 12% 15% 6% 1% 8% Pretoria University & Schools 0% 22% 2% 0% 0% 0% 6% 7% 1% 0% 4% Pretoria University Sports Grounds 0% 43% 0% 0% 0% 0% 0% 3% 0% 0% 5% Pretoria West 1% 37% 5% 4% 3% 3% 13% 20% 13% 6% 11% Pretoriuspark Ext 1 5% 39% 7% 4% 4% 4% 7% 22% 5% 3% 10% Prinshof 3% 80% 4% 1% 1% 0% 71% 19% 2% 4% 19% Proclamation Hill 5% 38% 2% 2% 1% 0% 10% 18% 9% 2% 9% Pumulani AH 32% 34% 37% 25% 23% 42% 28% 34% 15% 2% 27% Queenswood 2% 36% 4% 1% 1% 1% 11% 14% 2% 1% 7% Radio Uitkyk 0% 16% 0% 0% 0% 0% 13% 25% 2% 3% 6% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

218 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Pretoria (continued) 212 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Rietfontein 3% 32% 3% 1% 2% 1% 10% 14% 3% 1% 7% Rietondale 0% 37% 2% 0% 0% 0% 11% 17% 3% 1% 7% Rietvalleirand 1% 38% 2% 1% 1% 3% 2% 7% 4% 0% 6% Rietvlei Nature Reserve 0% 7% 45% 0% 4% 4% 0% 42% 9% 0% 11% Riviera 1% 39% 7% 2% 1% 0% 17% 13% 3% 1% 8% Roseville 1% 18% 15% 2% 0% 2% 6% 15% 6% 1% 7% Salvokop 7% 27% 2% 7% 3% 0% 25% 20% 8% 10% 11% Scientia 0% 40% 0% 0% 0% 0% 20% 53% 10% 0% 12% Silverton 3% 28% 8% 1% 2% 1% 11% 19% 4% 2% 8% Sinoville 1% 31% 1% 0% 0% 1% 10% 15% 3% 0% 6% Sinoville Ext 3% 23% 1% 0% 1% 1% 8% 14% 3% 1% 5% Skanskop Sterrewag 0% 37% 1% 2% 1% 0% 3% 11% 2% 0% 6% Suiderberg 3% 25% 2% 1% 1% 2% 11% 18% 4% 2% 7% Sunnyside 1% 46% 4% 0% 1% 3% 21% 10% 8% 1% 10% Technikon Rant 60% 0% 40% 40% 40% 40% 20% 62% 7% 0% 31% Thaba-Tshwane 2% 31% 9% 1% 1% 1% 7% 22% 8% 3% 8% Tileba 4% 28% 4% 2% 2% 0% 8% 15% 3% 1% 7% Trevenna 2% 46% 6% 1% 1% 0% 26% 8% 11% 0% 10% Val de Grace 2% 36% 4% 1% 1% 1% 14% 16% 3% 0% 8% Valhalla 5% 35% 3% 1% 1% 0% 18% 18% 4% 2% 9% Villieria 2% 34% 5% 1% 2% 1% 9% 15% 3% 0% 7% Waltloo 0% 13% 0% 5% 2% 0% 8% 22% 6% 0% 6% Wapadrand 2% 29% 2% 2% 2% 2% 11% 15% 2% 3% 7% Waterkloof 2% 39% 4% 0% 1% 0% 14% 20% 2% 1% 8% Waterkloof AH 12% 18% 11% 17% 16% 67% 22% 28% 2% 1% 19% Waterkloof Glen 3% 32% 4% 0% 1% 1% 15% 17% 2% 1% 8% Waterkloof Heights 3% 33% 9% 4% 1% 0% 14% 21% 3% 2% 9% Waterkloof Park 2% 15% 10% 4% 2% 2% 4% 19% 1% 2% 6% Waterkloof Ridge 2% 33% 13% 1% 1% 0% 13% 19% 2% 1% 9% Waverley 3% 27% 2% 1% 1% 0% 11% 17% 2% 1% 7% Weavind Park 2% 50% 3% 0% 2% 2% 10% 13% 2% 0% 8% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

219 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Pretoria (continued) Saulsville Soshanguve Part Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Weskoppies 0% 15% 4% 0% 0% 0% 2% 15% 3% 0% 4% Wespark 2% 27% 2% 2% 2% 1% 12% 21% 11% 3% 8% Willow Glen AH 4% 31% 3% 3% 4% 3% 33% 19% 3% 2% 10% Willow Park 0% 38% 0% 0% 0% 6% 27% 22% 0% 0% 9% Willow Park AH 7% 31% 1% 9% 6% 4% 18% 43% 7% 1% 13% Willowbrae AH 8% 24% 30% 27% 0% 36% 53% 30% 5% 5% 22% Wingate Park 1% 23% 4% 2% 2% 1% 9% 18% 2% 1% 6% Wolmer 3% 31% 3% 5% 3% 3% 22% 21% 13% 4% 11% Wonderboom 3% 36% 5% 1% 1% 1% 16% 15% 2% 1% 8% Wonderboom Aerodrome 0% 19% 0% 0% 0% 0% 0% 5% 0% 0% 2% Wonderboom AH 0% 17% 3% 0% 3% 39% 12% 19% 1% 0% 9% Wonderboom Nature Reserve 0% 0% 0% 0% 0% 0% 50% 0% 0% 100% 15% Wonderboom South 1% 40% 2% 1% 1% 0% 8% 13% 4% 0% 7% Woodhill 1% 25% 1% 4% 0% 6% 11% 22% 2% 0% 7% Zwartkop Nature Reserve 0% 0% 0% 0% 0% 0% 0% 0% 0% Saulsville SP 16% 27% 6% 3% 4% 12% 43% 29% 36% 4% 18% Jeffersville 92% 36% 56% 14% 95% 3% 45% 36% 39% 7% 42% Mchenguville 14% 40% 7% 3% 6% 2% 43% 35% 35% 4% 19% Phumolong 96% 26% 49% 20% 98% 12% 48% 36% 35% 1% 42% Saulsville 93% 33% 97% 98% 99% 19% 52% 32% 39% 10% 57% Vergenoeg 95% 32% 68% 55% 98% 15% 50% 34% 40% 7% 49% Itumeleng 97% 37% 99% 88% 99% 96% 52% 53% 44% 1% 67% Soshanguve 1A 95% 29% 47% 50% 87% 46% 47% 39% 35% 3% 48% Soshanguve A 3% 50% 1% 9% 1% 0% 56% 33% 43% 0% 20% Soshanguve AA 4% 32% 2% 9% 1% 1% 28% 36% 29% 0% 14% Soshanguve BB 8% 33% 2% 5% 10% 1% 14% 27% 17% 0% 12% Soshanguve CC 0% 16% 0% 1% 0% 0% 7% 27% 21% 0% 7% Soshanguve DD 35% 29% 24% 35% 35% 23% 26% 36% 21% 0% 26% Soshanguve East 4% 45% 1% 9% 1% 0% 29% 28% 13% 7% 14% Soshanguve F 1% 41% 4% 3% 1% 0% 30% 31% 29% 1% 14% Soshanguve FF 45% 27% 2% 27% 50% 1% 36% 38% 27% 2% 26% Soshanguve G 31% 37% 32% 34% 29% 28% 34% 35% 34% 1% 29% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

220 Municipality Main-place name Sub-place name City of Tshwane Metro (continued) Soshanguve Part 1 (continued) 214 Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Soshanguve GG 6% 28% 2% 8% 22% 0% 26% 38% 22% 0% 15% Soshanguve H 11% 49% 15% 8% 6% 3% 56% 32% 21% 1% 20% Soshanguve HH 62% 32% 48% 57% 75% 47% 47% 42% 27% 2% 44% Soshanguve JJ 43% 30% 27% 33% 74% 23% 42% 45% 37% 0% 36% Soshanguve K 2% 39% 3% 2% 1% 1% 56% 22% 18% 1% 15% Soshanguve L 18% 41% 1% 5% 12% 0% 40% 29% 23% 9% 18% Soshanguve LL 59% 34% 8% 13% 42% 1% 40% 36% 27% 1% 26% Soshanguve M 3% 29% 3% 8% 1% 2% 19% 30% 23% 3% 12% Soshanguve NN 92% 32% 29% 37% 100% 28% 50% 48% 39% 1% 45% Soshanguve P 83% 34% 2% 25% 98% 4% 51% 46% 34% 1% 38% Soshanguve PP 100% 30% 100% 100% 100% 100% 78% 45% 45% 0% 70% Soshanguve R 38% 30% 10% 21% 98% 9% 45% 45% 27% 1% 32% Soshanguve RR 91% 27% 40% 57% 99% 24% 45% 43% 27% 2% 46% Soshanguve S 81% 50% 4% 23% 95% 3% 49% 43% 36% 1% 39% Soshanguve South 96% 61% 0% 8% 65% 85% 49% 44% 48% 0% 45% Soshanguve SS 86% 39% 21% 18% 62% 5% 46% 42% 37% 1% 36% Soshanguve T 93% 38% 53% 61% 98% 44% 53% 44% 33% 2% 52% Soshanguve TT 60% 43% 2% 23% 46% 1% 53% 45% 38% 8% 32% Soshanguve UU 2% 37% 1% 2% 0% 0% 11% 30% 20% 0% 10% Soshanguve V 77% 35% 39% 40% 98% 28% 49% 44% 34% 1% 45% Soshanguve VV Soshanguve W 64% 41% 3% 16% 82% 3% 43% 38% 30% 0% 32% Soshanguve WW 45% 31% 8% 9% 56% 1% 25% 35% 30% 1% 24% Soshanguve X 84% 37% 28% 43% 99% 28% 50% 45% 29% 1% 44% Soshanguve XX 1% 31% 2% 4% 1% 0% 12% 26% 24% 0% 10% Soshanguve Y 90% 43% 34% 39% 98% 33% 43% 42% 20% 0% 44% Soshanguve YY Tswaing 100% 25% 96% 100% 96% 83% 79% 68% 51% 7% 71% Tswaing Nature Reserve 32% 28% 88% 92% 88% 96% 100% 68% 49% 0% 64% Temba Part1 SP 10% 35% 14% 44% 0% 35% 14% 88% 1% 7% 25% Temba Part 1 Temba 99% 30% 99% 99% 92% 99% 59% 38% 32% 2% 65% Kungwini Bronkhorstspruit Bronkhorstspruit SP 1% 32% 2% 3% 2% 9% 14% 23% 6% 1% 9% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty

221 Municipality Main-place name Sub-place name Kungwini (continued) Merafong City Bronkhorstspruit (continued) Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- type: Informal household sal income ment source Cultura Park 0% 41% 0% 16% 0% 29% 25% 17% 1% 4% 13% Riamar Park 1% 26% 3% 4% 2% 1% 17% 23% 9% 1% 9% Wageenbietjieskop SH 9% 28% 37% 42% 44% 95% 62% 43% 15% 0% 37% Ekandustria Ekandustria SP 0% 0% 0% 0% 0% 75% 0% 63% 0% 0% 14% Bashewa AH 15% 20% 11% 28% 32% 83% 40% 32% 6% 6% 27% Bronkhorstspruit NU 35% 21% 40% 45% 51% 93% 40% 48% 12% 3% 39% Cullinan NU 22% 17% 41% 43% 50% 97% 28% 36% 12% 0% 35% Donkerhoek SH 8% 35% 76% 16% 45% 100% 45% 29% 10% 9% 37% Doornkloof SH 11% 26% 3% 13% 17% 77% 19% 31% 9% 1% 21% Kungwini Part 2 Mooiplaas SH 8% 27% 16% 24% 28% 99% 26% 29% 7% 9% 27% Olympus AH 4% 21% 12% 27% 16% 97% 59% 35% 8% 11% 29% Shere AH 8% 16% 12% 19% 7% 70% 20% 25% 9% 2% 19% Tierpoort AH 26% 22% 34% 32% 31% 97% 53% 51% 10% 4% 36% Tweedracht AH 20% 25% 4% 52% 66% 99% 39% 48% 12% 14% 38% Valtaki AH 40% 22% 11% 47% 65% 99% 61% 59% 7% 5% 42% Mooikloof 1% 25% 5% 3% 1% 3% 8% 26% 1% 0% 7% Pretoria Mooikloof SH 23% 13% 20% 16% 36% 98% 29% 37% 8% 0% 28% Silver Lakes 1% 38% 0% 0% 1% 0% 11% 21% 2% 2% 8% Rethabiseng SP 16% 47% 20% 17% 10% 10% 58% 55% 26% 1% 26% Rethabiseng Rethabiseng 100% 36% 98% 98% 98% 98% 75% 57% 39% 2% 70% Rethabiseng Ext 1 10% 41% 0% 5% 4% 0% 64% 44% 37% 4% 21% Rethabiseng Ext 2 100% 43% 96% 98% 100% 100% 77% 51% 35% 0% 70% Sehlakwana Sehlakwana SP 39% 59% 92% 13% 98% 99% 80% 64% 18% 0% 56% Zithobeni SP 4% 33% 4% 15% 5% 8% 38% 44% 36% 1% 19% Zithobeni 89% 33% 62% 27% 58% 79% 60% 51% 30% 8% 50% Zithobeni Zithobeni Ext 100% 34% 99% 99% 100% 100% 57% 60% 30% 5% 68% Zithobeni Ext 1 89% 36% 61% 47% 62% 70% 55% 58% 34% 5% 52% Zithobeni Ext 2 8% 37% 2% 16% 2% 3% 54% 51% 30% 1% 20% Zithobeni Unit S 4% 44% 0% 61% 0% 47% 54% 54% 34% 0% 30% Blybank Blybank SP 0% 27% 2% 2% 3% 3% 11% 28% 23% 8% 11% Blyvooruitzicht Blyvooruitzicht SP 0% 1% 3% 0% 0% 5% 58% 44% 13% 0% 12% Northdene 2% 12% 1% 0% 1% 0% 14% 39% 23% 2% 9% 215

222 Municipality Main-place name Sub-place name Merafong City (continued) Blyvooruitzicht (continued) Carletonville Dwelling Femaleheaded dispoholployerty Refuse House- Unem- Pov- Water Electricittioracing Sanita- Illite- Crowd- type: Informal household sal income ment source Southdene 1% 22% 1% 2% 3% 1% 20% 26% 19% 1% 10% The Hill 0% 17% 1% 0% 3% 3% 12% 30% 14% 3% 8% The Village 1% 11% 1% 0% 1% 0% 11% 27% 7% 4% 7% Carletonville SP 2% 29% 2% 1% 0% 4% 24% 19% 7% 0% 9% Carletonville Central 1% 32% 2% 1% 1% 0% 18% 21% 10% 1% 9% Carletonville Ext 1 1% 38% 2% 0% 1% 0% 18% 22% 17% 4% 10% Carletonville Ext 10 2% 35% 5% 0% 4% 1% 5% 24% 13% 0% 9% Carletonville Ext 2 0% 33% 0% 0% 0% 2% 16% 27% 12% 6% 10% Carletonville Ext 3 0% 32% 1% 2% 2% 0% 21% 19% 16% 0% 9% Carletonville Ext 4 2% 29% 1% 0% 1% 1% 21% 24% 8% 5% 9% Carletonville Ext 5 1% 19% 1% 1% 1% 0% 11% 20% 15% 4% 7% Carletonville Ext 6 60% 0% 0% 5% 5% 0% 10% 48% 17% 43% 19% Carletonville Ext 8 2% 23% 2% 0% 0% 1% 13% 22% 8% 0% 7% Carletonville Ext 9 3% 24% 9% 1% 1% 1% 17% 22% 7% 1% 9% Deelkraal SP 1% 50% 1% 1% 0% 0% 16% 26% 14% 1% 11% Deelkraal Deelkraal Gold Mine 1% 8% 0% 0% 1% 18% 8% 44% 4% 0% 8% Doornfontein Doornfontein SP 0% 1% 0% 0% 2% 8% 3% 47% 2% 1% 6% East Driefontein Mine SP 10% 0% 1% 0% 1% 63% 10% 52% 4% 1% 14% East Driefontein Mine East Village 1% 26% 1% 0% 0% 0% 24% 24% 13% 1% 9% West Village 0% 42% 0% 0% 0% 0% 35% 29% 4% 0% 11% Elands Ridge Elands Ridge SP 1% 12% 1% 0% 1% 0% 10% 29% 7% 4% 7% Khutsong SP 25% 52% 3% 18% 2% 0% 56% 41% 35% 2% 23% Cross Roads 98% 58% 97% 98% 99% 3% 78% 47% 18% 0% 60% Hani 98% 58% 97% 97% 97% 46% 67% 44% 38% 3% 65% Khutsong 98% 38% 99% 99% 78% 2% 73% 57% 57% 7% 61% Khutsong B 99% 71% 99% 99% 93% 1% 74% 47% 20% 0% 60% Khutsong Khutsong Ext 1 6% 25% 1% 2% 1% 0% 29% 32% 33% 0% 13% Khutsong Ext 2 30% 41% 2% 5% 2% 1% 51% 44% 33% 0% 21% Khutsong Ext 3 70% 61% 6% 7% 0% 1% 64% 49% 37% 0% 29% Khutsong Ext 6 19% 35% 3% 15% 3% 0% 44% 32% 34% 0% 18% Khutsong South 4% 48% 3% 5% 1% 0% 64% 44% 31% 1% 20% Mandela 96% 43% 94% 99% 98% 64% 61% 68% 30% 1% 65% 216

223 Municipality Main-place name Sub-place name Merafong City (continued) West Rand NU Water source Dwelling type: Informal Femaleheaded household Electricity Sanitation Phila Park 99% 52% 96% 99% 97% 4% 78% 44% 62% 0% 63% Khutsong (continued) Revonia 99% 58% 98% 98% 99% 11% 76% 49% 38% 0% 63% Slovo 98% 47% 99% 98% 98% 19% 73% 56% 33% 6% 63% Sonderwater 87% 57% 98% 97% 86% 7% 78% 49% 36% 4% 60% Letsatsing Letsatsing SP 5% 9% 1% 0% 6% 0% 13% 34% 31% 1% 10% Oberholzer NU 37% 15% 29% 41% 39% 58% 39% 58% 19% 5% 34% Merafong City Part 1 Randfontein NU 21% 15% 35% 40% 51% 66% 50% 58% 15% 3% 35% Watersedge SH 15% 24% 18% 46% 47% 56% 51% 41% 14% 4% 32% Oberholzer SP 3% 27% 3% 1% 1% 4% 17% 18% 6% 1% 8% Oberholzer Oberholzer Ext 1 1% 25% 9% 0% 1% 0% 12% 13% 5% 1% 7% Oberholzer Ext 2 7% 36% 0% 5% 0% 0% 21% 23% 1% 0% 9% Phomolong Phomolong SP 1% 8% 0% 1% 0% 2% 8% 39% 26% 20% 11% Welverdiend Welverdiend SP 5% 22% 6% 9% 7% 7% 22% 27% 9% 0% 11% Welverdiend SH 0% 7% 0% 35% 35% 21% 35% 35% 9% 0% 18% Westdriefontein Westdriefontein SP 3% 1% 3% 2% 3% 31% 16% 52% 3% 2% 12% Western Deep Levels Mine Western Deep Levels Mine SP 14% 2% 2% 1% 2% 3% 5% 45% 4% 0% 8% Brits NU 29% 32% 5% 11% 4% 92% 20% 35% 16% 2% 25% West Rand NU Krugersdorp NU 12% 20% 19% 34% 40% 95% 37% 54% 9% 10% 33% Pretoria NU 29% 22% 17% 38% 47% 98% 30% 43% 13% 4% 34% Refuse disposal Household income Illiteracy Unemployment Crowding Poverty 217

224 218

225 APPENDIX 3 ALLOCATION OF MAIN-PLACE MIGRATION DATA TO SUB PLACES : STEPWISE REGRESSION FOR NUMBER OF IN-MIGRANTS ( ) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1.897(a) (b) a Predictors: (Constant), POP2001 b Predictors: (Constant), POP2001, UNEMPL ANOVA(c) Model Sum of Squares df Mean Square F Sig. 1 Regression (a) Residual Total Regression (b) Residual Total a Predictors: (Constant), POP2001 b Predictors: (Constant), POP2001, UNEMPL c Dependent Variable: IN96_01 Coefficients(a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) POP (Constant) POP UNEMPL a Dependent Variable: IN96_01 219

226 Excluded Variables(c) Model Variable Beta In t Sig. Partial Correlation Collinearity Statistics Tolerance 1 EDUCAT -.102(a) UNEMPL -.118(a) INFORMAL -.051(a) ELECTR -.014(a) WATER -.005(a) REFUSE -.003(a) SANITAT -.015(a) INCOME -.079(a) FEM_HHDS -.012(a) INDEX -.046(a) EDUCAT -.070(b) INFORMAL -.029(b) ELECTR.056(b) WATER.051(b) REFUSE.000(b) SANITAT.032(b) INCOME.014(b) FEM_HHDS.072(b) INDEX.022(b) a Predictors in the Model: (Constant), POP2001 b Predictors in the Model: (Constant), POP2001, UNEMPL c Dependent Variable: IN96_01 220

227 APPENDIX 4 ALLOCATION OF MAIN-PLACE MIGRATION DATA TO SUB PLACES : STEPWISE REGRESSION FOR NUMBER OF OUT-MIGRANTS ( ) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1.953(a) (b) a Predictors: (Constant), POP2001 b Predictors: (Constant), POP2001, UNEMPL ANOVA(c) Model Sum of Squares df Mean Square F Sig. 1 Regression (a) Residual Total Regression (b) Residual Total a Predictors: (Constant), POP2001 b Predictors: (Constant), POP2001, UNEMPL c Dependent Variable: OUT96_01 Coefficients(a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) POP (Constant) POP UNEMPL a Dependent Variable: OUT96_01 221

228 Excluded Variables(c) Model Variable Beta In t Sig. Partial Correlation Collinearity Statistics Tolerance 1 EDUCAT -.064(a) UNEMPL -.115(a) INFORMAL -.044(a) ELECTR -.028(a) WATER -.015(a) REFUSE.011(a) SANITAT -.028(a) INCOME -.086(a) FEM_HHDS -.032(a) INDEX -.048(a) EDUCAT -.029(b) INFORMAL -.023(b) ELECTR.037(b) WATER.039(b) REFUSE.013(b) SANITAT.017(b) INCOME -.004(b) FEM_HHDS.043(b) INDEX.018(b) a Predictors in the Model: (Constant), POP2001 b Predictors in the Model: (Constant), POP2001, UNEMPL c Dependent Variable: OUT96_01 222

229 APPENDIX 5 COMBINED POVERTY AND MIGRATION INDEX In Table 16 the combined poverty and migration indices for the Gauteng sub places are given by municipality and main place. A separate analysis showed that Kekana Gardens (Nokeng tsa Taemane), Temba (Temba Part 1, City of Tshwane), Tshepisong (City of Johannesburg), Nooitgedacht (City of Johannesburg), Lindelani Village (Ekurhuleni Metro), Benoni Non-urban (Ekurhuleni Metro), Heidelberg Non-urban (Ekurhuleni Metro), Bronkhorstspruit Non-urban (Ekurhuleni Metro), Tshepisong Sub-place (Tshepisong, City of Johannesburg), and Nellmapius Ext 4 (Nellmapius, City of Tshwane) had combined poverty indices in excess of 1000 (i.e. more than 10 per cent). Maps 28 to 40 show the combined poverty and migration for sub-places in the various Gauteng municipalities. 223

230 Map 28 Combined poverty and migration for sub-places : City of Johannesburg Metro 224

231 Map 29 Combined poverty and migration for sub-places : City of Tshwane Metro 225

232 Map 30 Combined poverty and migration for sub-places : Ekurhuleni Metro 226

233 Map 31 Combined poverty and migration for sub-places : Emfuleni 227

234 Map 32 Combined poverty and migration for sub-places : Kungwini 228

235 Map 33 Combined poverty and migration for sub-places : Lesedi 229

236 Map 34 Combined poverty and migration for sub-places : Merafong City 230

237 Map 35 Combined poverty and migration for sub-places : Midvaal 231

238 Map 36 Combined poverty and migration for sub-places : Mogale City and West Rand (Non-urban) 232

239 Map 37 Combined poverty and migration for sub-places : Nokeng tsa Taemane 233

240 Map 39 Combined poverty and migration for sub-places : Randfontein 234

241 Map 40 Combined poverty and migration for sub-places : Westonaria 235