As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

This dataset is a collation of articles written by different authors on the history of South Africa during the apartheid regime (1948 to 1994). Apartheid in South Africa was the racial segregation under the all-white government of South Africa which dictated that non-white South Africans (a majority of the population) were required to live in separate areas from whites and use separate public facilities and contact between the two groups would be limited. The different racial group were physically separated according to their location, public facilities and social life.

Abstract

The Gauteng City-Region Observatory (GCRO) (based at the University of Johannesburg (UJ)) in partnership with the Gauteng Provincial Government, contracted Development Research Africa (DRA) to conduct an integrated Quality of Life/Customer Satisfaction Survey in the Gauteng City-Region (GCR). The objective of the GCRO is to assist the Gauteng Government to build Gauteng as an integrated and globally competitive region, where the economic activities of different parts of the province complement each other in consolidating Gauteng as an economic hub of Africa, and an internationally recognised global city-region. The this end, the main aim of the survey, conducted from July to October 2009, was to inform the GCRO and the Provincial Government, as well as other role-players about the perceived state of the municipalities within the GCR footprint about the quality of life of their inhabitants.

Geographic coverage

The Quality of Life Survey covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Analysis unit

Households and individuals

Universe

The Gauteng City-Region Observatory Quality of Life Survey 2009 covered all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Kind of data

Qualitative and quantitative data

Sampling procedure

For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling units within the wards being selected in the second stage and respondents selected at the third stage.

Phase 1 The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.

Phase 2 During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.

The following process of household selection was adhered to: From the starting point 20 houses were counted in a ward. However, if there were:

• 1-5 target number of interviews to be completed in a ward; 01 starting point was used; • 6-10 target number of interviews to be completed in a ward; 02 starting points were used; • 11-15 target number of interviews to be completed in the ward; 03 starting points were used; • 16-20 target number of interviews to be completed in the ward; 04 starting points were used; • 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and • 25 and above target number of interviews to be completed in a ward; 06 starting points were used

In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.

Phase 3 For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria: • Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and • 18 years of age or older • In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Gauteng City-Region Observatory and Data Research Africa (DRA) developed the quantitative evaluation tool for the survey. DRA reformatted the pre-pilot questionnaire and provided input into the layout and flow as well as question structure to ensure accurate data capturing. DRA field managers piloted the questionnaire with 30 interviews with individuals from households with different demographic characteristics . The Gauteng City-Region Observatory Quality of Life Survey 2009 questionnaire collected data on demographic details of the enumerated population (population group, gender, age, language) and on housing (dwelling type, tenure, satisfaction with dwelling, perceived quality of housing and housing allocation) as well as household services (water, sanitation, refuse, energy sources). Questions included those on migration, health (including disability), education and employment (including employment sector). Questions on community services and amenities were included, and questions on transport, leisure activities and safety and crime. Financial data was collected (including on debts, income, and social grants) and data on household assets. Finally, data on public participation and governance was also collected, and data on the perceived personal wellbeing and quality of life of respondents.

The African Research on Kidney Disease (ARK) Consortium is a multicenter partnership between Malawi, South Africa, Uganda, and the United Kingdom. The study sites for the ARK Consortium are nested within population cohorts hosted by the Malawi Epidemiology and Intervention Research Unit (MEIRU), the Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit; and the Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit, South Africa. Our primary objectives were to determine chronic kidney disease (CKD) prevalence (estimated from serum creatinine) and identify associated risk factors.

South Africa is the southernmost country in Africa. It covers an area of 1,221,037 square kilometres (471,445 square miles). South Africa has three capital cities: executive Pretoria, judicial Bloemfontein and legislative Cape Town. The largest city is Johannesburg. About 80% of South Africans are of Black African ancestry, divided among a variety of ethnic groups speaking different African languages. The remaining population consists of Africa's largest communities of European (White South Africans), Asian (Indian South Africans and Chinese South Africans), and Multiracial (Coloured South Africans) ancestry.

Source: Objaverse 1.0 / Sketchfab

Unemployment Rate in South Africa increased to 33.20 percent in the second quarter of 2025 from 32.90 percent in the first quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Part of the British Academy Knowledge Frontiers: International Interdisciplinary Research funding programme. Funded by the Department for Business, Energy and Industrial Strategy (BEIS). We are one of nine research projects bringing together novel, interdisciplinary ideas from across the humanities and social sciences in collaboration with the natural, medical and engineering sciences to propose solutions to international challenges past, present and future.• Theme of ‘What is a good city?’• 2-year projects with interdisciplinary and international teams• Projects “strengthen understanding of international challenges … and engage with questions concerning the relationship between expertise, public understanding and policy delivery internationally.” (British Academy)This research project investigated the challenge of food insecurity in cities as experienced by migrant communities and explored the role of traditional foods in well-being. The global population is increasingly urbanised, with Sub-Saharan Africa experiencing the fastest rate of urban population growth. South Africa is a centre for regional migration, with Johannesburg being the destination for the largest proportion of both within-country and international migrants. The project focused on two migrant groups in Johannesburg - South African rural-to-urban migrants and international regional migrants. Urban populations are dependent on food markets for daily sustenance and nutrition, hence access to affordable, acceptable and nutritious food through markets must be prioritised by cities. By identifying the drivers of food choice in urban migrant and immigrant populations around traditional foods, barriers to consumption and engaging with those involved in knowledge in urban planning and development, this project aimed to go some way towards tackling the problem of urban food insecurity and malnutrition.

Abstract

The South Africa Enterprise Survey was conducted between January and December 2007. Data from 1057 establishments in private manufacturing and services sectors were analyzed. The sample included enterprises with more than four employees (937 companies) as well as micro firms, establishments with less than 5 workers, (120 observations). The survey targeted establishments in Johannesburg, Cape Town, Port Elizabeth and Durban.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The South Africa Enterprise Survey 2007 included enterprises with more than four employees as well as micro establishments, firms with less than five workers. There are 120 micro establishments in the sample.

The sample for enterprises with more than four employees was designed using stratified random sampling with strata defined by region, sector and firm size.

Establishments located in Johannesburg, Cape Town, Port Elizabeth and Durban were interviewed.

Following the ISIC (revision 3.1) classification, the following industries were targeted: all manufacturing sectors (group D), construction (group F), retail and wholesale services (subgroups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and communications (group I), and computer and related activities (sub-group 72 of group K). For establishments with five or more full-time permanent paid employees, this universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15), Machinery and Equipment (Group D, sub-group 29), Electrical Machinery and Equipment (Group D, sub-group 31); 2. Manufacturing: Textiles (Group D, sub-group 17), Garment (Group D, sub-group 18), Leather and Footwear (Group D, sub-group 19), Paper and Paper Products (Group D, sub-group 21), Printing and Publishing (Group D, sub-group 22); 3. Manufacturing: Non-Metallic Mineral Products (Group D, sub-group 26), Basic Metals (Group D, sub-group 27), Fabricated Metal Products (Group D, sub-group 28); 4. Manufacturing: Wood and Wood Products (Group D, sub-group 20), Furniture (Group D, sub-group 36) 5. Manufacturing: Refined Petroleum Products (Group D, sub-group 23), Chemical Products (Group D, sub-group 24), Rubber and Plastics (Group D, sub-group 25) 6. Retail Trade: (Group G, sub-group 52); 7. Rest of the universe, including: • Other Manufacturing (Group D excluding sub-groups in strata 1-5); • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

The implementing agency (EEC Canada) was unable to obtain a satisfactory sample frame from South African statistical agency (STASA) or its Department of Revenue. The best alternative solution was a list obtained from the Department of Trade and Industry Companies and Intellectual Property Registration Office (CIPRO), which contained about 800000 establishments when delineating in-scope cities and industries, but which had incomplete firm characteristics necessary for stratification purposes (e.g. contact information, size). In order to determine the sample frame, EEC Canada randomly drew 9550 units and contacted them.

In South Africa, the survey included panel data collected from establishments surveyed in the 2003 Investment Climate Survey (ICS) of South Africa. That survey included establishments in the manufacturing and the rest of universe strata, distributed across Gauteng (Johannesburg), KwaZulu Natal (Durban), Western Cape (Cape Town) and Eastern Cape (Port Elizabeth) provinces.

In order to collect the largest possible set of panel data, an attempt was made to contact and survey valid establishments (579) in the panel list provided which was part of the Enterprise Survey's scope. Of the 716 establishments provided to EEC Canada from those surveyed in 2003, there were 35 doubles, 8 out-of-scope, 89 excluded from this survey by The World Bank to avoid over representing Construction in a single Residual stratum, and 5 with undefined ISIC codes. This left a total potential of 579 panel establishments. EEC Canada surveyed 231 panel establishments or 40% of the total potential panels without eliminating those establishments which had closed. Once eliminated, this percentage coverage exceeded 55%. Given the non-random nature of panel establishment selection, these establishments are not allocated probability weights in the final dataset.

In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees located in Johannesburg. The implementing agency selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all states of the survey.

First, to randomly select individual micro establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there was a high concentration of micro establishments; ii) count all micro establishments in these specific zones; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

The districts and the specific zones were selected at first according to local sources. The EEC team then went in the field to verify the sources and to count micro establishments. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

At the head office, the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select micro establishments to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

In Johannesburg, enumerators were sent to each zone with instructions how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

For complete information about sampling methodology, refusal rate and weighting please review "South Africa Enterprise Survey 2007 Implementation Report" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro

Abstract

The survey was undertaken in 2005 and conducted face-to-face interviews with a sample of 1 000 adults from Cape Town, Durban, Pretoria and Johannesburg, where ninety percent of the country's Jewish population reside.

Geographic coverage

The survey covered selected Jewish households in Cape Town, Durban, Pretoria and Johannesburg

Analysis unit

Households and individuals

Universe

The target population of the survey consists of Jewish South Africans.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire was used for the study

Abstract

The survey was conducted from 1991 to 1994 by a research team headed by Donald J. Treiman, UCLA; Sylvia N. Moeno, then in the Strategic Planning Group at Eskom, Johannesburg; and Lawrence Schlemmer, then Director of the Centre for Policy Studies, School of Business, University of the Witwatersrand, Johannesburg. Data collection was undertaken by MarkData, the in-house survey unit of the Human Sciences Research Council (HSRC), Pretoria, South Africa. The survey was funded by the U.S. National Science Foundation (SES89 12677), and by several South African agencies: the Anglo- American/De Beers Chairman's Fund, the Trust for Educational Advancement in South Africa, the Human Sciences Research Council, Johannesburg Consolidated Investments, and the Union Carbide Corporation.

Because at the time of the survey, South Africa had a policy of residential segregation by race (authorized under the the "Group Areas Act", which was abolished in June, 1991), South African surveys typically were conducted by drawing separate samples from each of the “Group Areas,” that is, areas designated for residence by Whites, Asians, Coloureds, and Blacks.1 In South Africa, interviewing usually is carried out by interviewers of the same race as interviewees. To accommodate scheduling considerations, the main survey was carried out in two parts: 3,679 interviews were conducted in White, Asian, and Coloured areas in April and May 1991 (the WAC sample) and 3,689 interviews were conducted in Black areas in August and September 1991 (the Black sample). Processing of the two parts was also carried out separately. Because there proved to be substantial under-representation of males in both samples (see below), data were collected for a supplementary sample of 749 men in May-July 1992. Finally, data were collected from a special sample of 969 rural respondents in 1993-94 (see below). In all, between 1991-94 data were collected for 9,086 respondents.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The population surveyed consisted of persons age 20 or older residing in "greater South Africa," that is, the Republic of South Africa plus the nominally independent "TVBC states": Transkei, Venda, Bophutatswana, and Ciskei.

Kind of data

Sample survey data

Sampling procedure

The population surveyed consisted of persons age 20 or older residing in "greater South Africa," that is, the Republic of South Africa plus the-at the time-nominally independent "TVBC states": Transkei, Venda, Bophutatswana, and Ceskei.2 The sample, as designed, was a complex area probability sample, in which sampling categories and the number of cases to be interviewed were determined by the researchers, households were to be selected within areas at random with probability proportionate to size, and adults within households were to be selected at random, but in such a way that the probability that males would fall into the sample was twice the probability that females would fall into the sample. The sample was designed so that, suitably weighted, it could be regarded as representative of the entire adult population of "greater South Africa." Further information on sampling is in the survey codebook provided with the data.

Mode of data collection

Face-to-face [f2f]

Data appraisal

In addition to the codebook the documentation for this survey included three reports on the field work. The first report, Social Stratification Report, provided information on the initial WAC sample but has not been located. This report contains, among other information, a copy of the English language questionnaire used for the WAC sample and responses to open-ended questions in the WAC sample. The second report, Fieldwork Report - Project: Social Stratification, provided information on the Black sample, the special samples, and the supplementary sample of males but has not been located.

Interviews were conducted with multiple stakeholders in South Africa so as to investigate barriers and opportunities for energy services delivery to informal settlements in the country during the 2010s, although account was also taken of the historical and political context that impacts on energy delivery in South Africa. The interviews were conducted in South Africa, and took place in Cape Town, Johannesburg, and Polokwane. The interviews are with multiple categories of stakeholders, namely: 1.) the electricity supply industry; 2.) the national government; 3.) the provincial government; 4.) the municipal government; 5.) academics; and 6.) NGO/civil society actors. The range of interviewee categories was aimed at constructing a rounded and in-depth qualitative picture of barriers and opportunities for energy service delivery in situations of housing and settlement informality.Energy is a critical enabler of development. Energy transitions, involving changes to both systems of energy supply and demand, are fundamental processes behind the development of human societies and are driven by technical, economic, political and social factors. Historical specificities and geography influence the character of energy transitions. In a world that is experiencing unprecedented urban growth, modern urbanised societies are highly dependent on energy. By 2030, more than 50% of people in developing countries are expected to live in cities, which is a figure set to grow to 66% by 2050. This urbanisation trend is even more prominent in South Africa, where 64% of its population already live in urban areas and is expected to rise to 70% by 2030. South African cities are highly dependent on energy, and access to and the provision of energy services affects urban energy transitions. Furthermore, access to affordable and reliable energy services is fundamental to reducing poverty and advancing economic growth. In response to this, many cities in South Africa and beyond have adopted sustainable energy provision strategies and solutions as a way of promoting economic development and greening of urban economies. However, Sustainable Energy Africa (SEA)'s State of the Energy in South African Cities report (2015) identifies that much remains to be done in order to transform South African cities towards a more sustainable urban energy profile, which is in turn aimed at improving welfare, supporting economic activity, creating 'green collar' and other jobs, and reducing carbon emissions. The project's focus on urban energy transitions is therefore both timely and necessary. Cities in South Africa are notable for their central role in the governance of energy. Municipalities are constitutionally mandated to serve as electricity distributors and are responsible for maintaining infrastructure, providing new connections and setting minimum service level standards as well as pricing and subsidies levels for poor consumers. Therefore, municipalities have become major actors in urban energy infrastructures. Nonetheless, systemic change is hampered by: a.) the lack of integrated energy strategies; b.) the declining performance of energy supply networks in South Africa; c.) the high carbon intensity of South Africa's energy supply, at a time when South Africa is actively seeking to decarbonize the economy; d.) a stalled level of electrification in certain poor urban areas in South African cities; and e.) the continued prevalence of energy poverty, even in grid-connected South African urban households. A key issue is the continued prevalence of a focus on energy supply, as opposed to the broader and more complex notion of energy services. It is clear that municipal processes and systems will have to change in order for energy transitions to occur. This project investigates the dynamics and co-evolution of municipal processes so as to create pathways to new, greener and fairer urban energy configurations. The project establishes a dialogue between work on socio-technical transitions and on energy geographies to analyze and identify energy transition pathways towards municipal-scale energy services regimes. The project's embeddedness in ongoing urban energy transition work will provide an evidence-base for co-designing pathways for energy services provision in South Africa's cities, alongside exploring opportunities in new energy configurations for transformations to urban green economies. This research project consists of SA research partners (the University of Cape Town's Energy Research Centre) and UK partners (King's College London; the University of Manchester; Plymouth University and the University of Sussex), together with the local energy transition expertise of Sustainable Energy Africa. Semi-structured qualitative interviews were carried out, with a mixture of face-to-face individual interviews, and interviews of pairs of respondents on occasions when both interviewees worked in the same office or unit. Due to the nature of the research topic and of the universe of potential interviewees, purposive sampling was utilised so as to select interviewees from across a range of interviewee categories (the electricity supply industry (4 interviews), national government (5 interviews), provincial government (3 interviews), municipal government (10 interviews), academics (7 interviews), and NGO/civil society actors (11 interviews)). All interviews were conducted in South Africa, and in English.

IntroductionLow and middle income countries bear the majority burden of self-harm, yet there is a paucity of evidence detailing risk-factors for self-harm in these populations. This study aims to identify environmental, socio-economic and demographic household-level risk factors for self-harm in five impoverished urban communities in Johannesburg, South Africa.MethodsAnnual serial cross-sectional surveys were undertaken in five impoverished urban communities in Johannesburg for the Health, Environment and Development (HEAD) study. Logistic regression analysis using the HEAD study data (2006–2011) was conducted to identify household-level risk factors associated with self-harm (defined as a self-reported case of a fatal or non-fatal suicide attempt) within the household during the preceding year. Stepwise multivariate logistic regression analysis was employed to identify factors associated with self-harm.ResultsA total of 2 795 household interviews were conducted from 2006 to 2011. There was no significant trend in self-harm over time. Results from the final model showed that self-harm was significantly associated with households exposed to a violent crime during the past year (Adjusted Odds Ratio (AOR) 5.72; 95% CI 1.64–19.97); that have a member suffering from a chronic medical condition (AOR 8.95; 95% 2.39–33.56) and households exposed to indoor smoking (AOR 4.39; CI 95% 1.14–16.47).ConclusionThis study provides evidence on household risk factors of self-harm in settings of urban poverty and has highlighted the potential for a more cost-effective approach to identifying those at risk of self-harm based on household level factors.

Abstract

The Gauteng-City Region Observatory (GCRO) commissioned Data World to conduct its Second Quality of Life Survey, with surveys being conducted in second half of 2011.

The Gauteng City-Region Observatory (GCRO) was established in 2008 as a partnership between the University of Johannesburg (UJ), the University of the Witwatersrand, Johannesburg (Wits) and the Gauteng Provincial Government (GPG), with local government in Gauteng also represented. The objective of the GCRO is to inform and assist the various spheres of the Gauteng government in building and maintaining the province as an integrated and globally competitive region.

The Second Quality of Life Survey must comprehensively represent the whole of Gauteng, which consists of 10 municipalities, which in turn covers 508 wards. Data World was contracted to undertake 15000 surveys across this sphere. Among the main aims of the Quality of Life Survey, is to inform the GCRO as well as provincial government and other relevant parties with regards to the perceived states of the municipalities within Gauteng, with focus on the quality of the lives of people who live within these municipalities.

Geographic coverage

The Gauteng City-Region Observatory Quality of Life Survey 2011 covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Analysis unit

The units of analysis in theGauteng City-Region Observatory (GCRO) Quality of Life Survey are households and individuals

Universe

The Gauteng City-Region Observatory Quality of Life Survey 2009 covered all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling units within the wards being selected in the second stage and respondents selected at the third stage.

Phase 1 The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.

Phase 2 During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.

The following process of household selection was adhered to: From the starting point 20 houses were counted in a ward. However, if there were: • 1-5 target number of interviews to be completed in a ward; 01 starting point was used; • 6-10 target number of interviews to be completed in a ward; 02 starting points were used; • 11-15 target number of interviews to be completed in the ward; 03 starting points were used; • 16-20 target number of interviews to be completed in the ward; 04 starting points were used; • 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and • 25 and above target number of interviews to be completed in a ward; 06 starting points were used In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.

Phase 3 For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria: • Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and • 18 years of age or older • In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument (questionnaire) which was used was provided by the GCRO. The instrument was similar to the questionnaire used for the initial Quality of Life survey, with new questions being added only where questions from the previous survey were removed. This was done with the intention of keeping the duration of the survey the same as the initial one. The survey instrument was a 20 page questionnaire, broken up into 12 sections. The bulk of the possible answers were pre-defined, such that most of the survey could be answered using a combination of tick-boxes or by writing down a number answer from a predefined set. To this end there are not many open - ended questions in the survey.

The survey instrument was reformatted by Data World to ensure optimal flow, as well as to cater for the technology platform which was used to conduct the surveys.

Data appraisal

The survey company, Data Research Africa, utilised a range of quality control measures during fieldwork for the survey. In the field, fieldworkers checked completed questionnaire schedules immediately after interviews to ensure that all questions were answered and relevant skips were followed. The checked questionnaires were then handed to field or office managers who, whilst in field, performed a second quality check on each questionnaire. They focused on skip patterns, as well as on ensuring that answers corresponded with previous responses and followed a logical process.

This statistic shows the top ten largest municipalities in South Africa as of 2016. Johannesburg had the largest population of South African municipalities in 2016, with nearly 5 million inhabitants.

The South African commercial real estate market, valued at $9.28 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 7.63% from 2025 to 2033. This expansion is fueled by several key drivers. Increased urbanization and population growth in major cities like Johannesburg, Cape Town, and Durban are creating a surge in demand for office, retail, and industrial spaces. Furthermore, a burgeoning tourism sector and associated hospitality investments are contributing significantly to the market's positive trajectory. The ongoing development of logistics infrastructure to support growing e-commerce activity also plays a crucial role. While potential economic uncertainties and fluctuations in interest rates could pose challenges, the overall market outlook remains positive, underpinned by the continued strength of the South African economy and targeted investments in key sectors. Strong performance is expected across all segments, with the office and logistics sectors likely to see particularly substantial gains due to increasing corporate activity and supply chain optimization strategies respectively. The diverse portfolio of established and emerging property developers in South Africa, including major players like Growthpoint Properties and Amdec Group, further underscores the market's dynamic nature and competitive landscape. The segmentation of the market reveals strong growth potential within specific areas. The substantial investment in modernizing existing commercial infrastructure in Johannesburg and Cape Town will drive significant growth. Furthermore, the expansion of retail spaces in rapidly growing suburban areas will cater to evolving consumer preferences and boost market value in those regions. However, challenges remain, including the need for continued infrastructure development to support sustainable growth in key areas, particularly in logistics and transportation networks. While the overall market exhibits positive momentum, proactive strategies focused on addressing these factors will be crucial to ensure sustained, long-term growth. Careful risk management by investors and developers regarding economic volatility will be critical in navigating potential headwinds. Recent developments include: November 2023: WeWork South Africa announced that it was accelerating its expansion plans as the rise in popularity of hybrid work saw a boost in demand for flexible office spaces.September 2023: Instant Group, a flexible workspace marketplace, acquired property advisor PSA to broaden its reach and grow its business across Africa.. Key drivers for this market are: 4., Urbanization and Population Growth4.; Foreign Direct Investments. Potential restraints include: 4., Urbanization and Population Growth4.; Foreign Direct Investments. Notable trends are: Increasing Demand for Office Space in South Africa.

As of 2022, the number of households in South Africa increased and amounted to approximately 18.48 million, roughly 530,000 more than in the previous year. Between 2002 and 2022, the number of families in South Africa grew by around 65 percent. Looking at the number of households from a regional perspective , the Gauteng province (includes the city of Johannesburg) has the bulk of households, with almost 5.6 million residences. Although Gauteng is the smallest region in the country, it is highly urbanized and houses most of the population.

Households headed by women

The number of households headed by women averaged around 42 percent. Rural areas such as the Eastern Cape and Limpopo had a higher proportion of women in charge of their family unit. Urbanized regions, namely Gauteng and the Western Cape, were more likely to be headed by men.

Languages spoken in households

The most spoken language within and outside of South African households was isiZulu, with around 25 percent of the population utilizing it. The English language was the second most common language spoken outside of households, with a share of roughly 17 percent. However, within households, individuals preferred to speak other official languages such as isiXhosa and Afrikaans. South Africa has a diverse range of cultures, and language plays a crucial role in preserving these cultures.

In 2020, the Consortium for Advanced Research Training in Africa celebrated its ten years recruiting fellows. As part of the CARTA@10 celebrations, the CARTA Secretariat organized a scientific conference, in an online video conferencing format, on December 3-4, 2020. The conference brought together CARTA fellows and graduates to present and discuss their research in health systems and non-communicable diseases, ageing, sexual and reproductive health rights, infectious diseases, and immunization.

This book of abstracts and the videos that support it were edited and put together by Rita Karoki, Gloria Chemutai and Marta Vicente-Crespo in their roles as members of the CARTA Secretariat, which is hosted at the African Population and Health Research Center in Nairobi, Kenya. Marta Vicente-Crespo is also an honorary senior lecturer at the University of the Witwatersrand in Johannesburg, South Africa.

CARTA was formed in 2008 to support the development of a vibrant African academy able to lead world-class multidisciplinary research that impacts positively on public and population health. The Consortium goals are to create networks of locally-trained internationally recognized scholars; enhance the capacity of African universities to lead globally competitive research and training programs; and build vibrant, viable, sustainable multidisciplinary research hubs at Africa-based universities. CARTA offers a well thought out approach to rebuild and to strengthen the capacity of African universities to produce world-class researchers, research leaders, and scholars. CARTA is a collaboration jointly led by the African Population and Health Research Center (APHRC), Kenya, and the University of the Witwatersrand (Wits), South Africa. CARTA's Mission is to build high-level capacity for population and public health-related research in Africa. The CARTA partner institutions are: a. African Universities - Makerere University (Uganda); Obafemi Awolowo University (Nigeria); University of Ibadan (Nigeria); University of Rwanda (Rwanda); University of Malawi (Malawi); Moi University (Kenya); University of Nairobi (Kenya); and University of the Witwatersrand (South Africa). b. African Research Institutes - African Population and Health Research Center (APHRC; Kenya); KEMRI/Wellcome Trust Research Program (Kenya); Ifakara Health Institute (IHI; Tanzania) and Agincourt Population and Health Unit (South Africa). c. Non-African Institutions - Canadian Coalition for Global Health Research (CCGHR) (Canada), Swiss Tropical and Public Health Institute (Swiss TPH) (Switzerland), University of Gothenburg (Sweden), Umeå University (Sweden), University of Warwick (UK), Brown University (USA), University of Bergen (Norway), and ESE:O and University of Chile (Chile)

Carta Website: cartafrica.org

CARTA is funded by the Carnegie Corporation of New York (Grant No. G-16-54067 & G-19-57145), Sida (Grant No. 54100113), Uppsala Monitoring Centre and the DELTAS Africa Initiative (Grant No: 107768/Z/15/Z). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (UK) and the UK government.