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.

Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Population in largest city in South Africa was reported at 6324351 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

South Africa ZA: Population in Largest City data was reported at 9,822,625.000 Person in 2017. This records an increase from the previous number of 9,615,976.000 Person for 2016. South Africa ZA: Population in Largest City data is updated yearly, averaging 3,628,124.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9,822,625.000 Person in 2017 and a record low of 2,136,849.000 Person in 1960. South Africa ZA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Population in the largest city (% of urban population) in South Africa was reported at 14.26 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

South Africa ZA: Population in Largest City: as % of Urban Population data was reported at 26.327 % in 2017. This records an increase from the previous number of 26.291 % for 2016. South Africa ZA: Population in Largest City: as % of Urban Population data is updated yearly, averaging 23.218 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 26.327 % in 2017 and a record low of 18.806 % in 1991. South Africa ZA: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

Johannesburg was the wealthiest city in Africa as of 2021. South Africa's biggest city held 239 billion U.S. dollars in private wealth, while Cape Town followed with 131 billion U.S. dollars. The country led the ranking of wealthiest nations in Africa. The wealth value referred to assets such as cash, properties, and business interests held by individuals living in each country, less liabilities. Moreover, government funds were excluded.

In 2024, Pietermaritzburg (South Africa) ranked first in the crime index among African cities, with a rating of roughly ** index points. The six most dangerous areas on the continent were South African cities. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). South Africa’s crime situation According to the crime index ranking, ************ was the most dangerous country in Africa in 2023, followed by ***************** and ******. Murder and organized crime are particularly widespread in South Africa. In 2023, the country had one of the highest murder rates globally, registering around ** homicides per 100,000 inhabitants. Moreover, South Africa’s crime scene is also characterized by the presence of organized criminal activities, for which the country ranked third in Africa. Reflecting these high levels of crime, a survey conducted in 2023 showed that around ** percent of South Africans were worried about crime and violence in the country. Crime risks in Africa The African continent hosts some of the most dangerous places worldwide. In 2023, *********** and the ******************************** were the least peaceful countries in Africa, according to the Global Peace Index. Worldwide, they ranked fourth and fifth, respectively, behind Afghanistan, Yemen, and Syria. Terrorism is a leading type of crime perpetrated in Africa. Home to Boko Aram, Nigeria is among the countries with the highest number of terrorism-related deaths globally. Furthermore, Burkina Faso had the highest number of fatalities in the world. Human trafficking is also widespread, predominantly in West Africa. The most common forms of exploitation of victims of trafficking in persons are forced labor and sexual exploitation.

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

Abstract

The World Values Survey aims to attain a broad understanding of socio-political trends (i.e. perceptions, behaviour and expectations) among adults across the world.

Geographic coverage

National The sample was distributed as follows: 60% metropolitan (large cities with populations of 250 000+); 40% non-metropolitan (including cities, large towns, small towns, villages and rural areas)

Analysis unit

Individual

Universe

The sample included adults 16 years+ in South Africa

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample had to be representative of urban as well as rural populations. Roughly the distribution was as follows: - South Africa: 60% metropolitan (large cities with populations of 250 000+); 40% non-metropolitan (including cities, large towns, small towns, villages and rural areas).

A standard form of sampling instructions was sent to each agency to ensure uniformity in the sampling procedure. Markinor stratified the samples for each country by region, sex and community size. To this end, statistics and figures that were supplied to us by the agencies were used. However, we requested the agencies to revise these where necessary or where alternatives would be more effective. The agencies then supplied the street names for the urban starting points, and made suggestions for sampling procedures in rural areas where neither maps nor street names were available. From sample-point level, the respondent selection was done randomly according to a selection grid used by Markinor (the first two pages of the master questionnaire).

Substitution was permitted after three unsuccessful calls. Six interviews were conducted at each sample point. The male/female split was 50/50. The urban sample included all community sizes greater than 500 and the rural sample all community sizes less than 500. This is the definition of urban and rural used in South Africa.

Remarks about sampling: -Final numbers of clusters or sampling points: 500 -Sample unit from office sampling: Street Names

Mode of data collection

Face-to-face [f2f]

Research instrument

The WVS questionnaire was translated from the English questionnaire by a specialist translator The translated questionnaire was pre-tested. The pre-tests were part of the general pilots. In total 20 pilots were conducted. The English questionnaire from the University of Michigan was used to make the WVS. Extra questions were added at the end of the questionnaire. Also, country specific questions were included at the end of the questionnaire, just before the demographics.The sample was designed to be representative of the entire adult population, i.e. 18 years and older, of your country. The lower age cut-off for the sample was 16 and there was not any upper age cut-off for the sample.

Cleaning operations

Some measures of coding reliability were employed. Each questionnaire is coded against the coding frame. A minimum of 10% of each coders work is checked to ensure consistency in interpretation. If any discrepancies in interpretation are World Values Survey (1999-2004) - South Africa 2001 v.2015.04.18 discovered, a 100% check is carried out on that particular coders work. Errors were corrected individually and automatically.

Sampling error estimates

The error margins for this survey can be calculated by taking the following factors into account: - all samples were random (as opposed to quota-controlled) - the sample size per country (or segment being analysed) - the substitution rate per country (or segment being analysed) - the rates were recorded on CARD 1; col. 805 of the questionnaire. From the substitution rate, the response rate can be calculated.

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.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

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.

ZA：最大城市人口在12-01-2017达9,822,625.000人，相较于12-01-2016的9,615,976.000人有所增长。ZA：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为3,628,124.500人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达9,822,625.000人，而历史最低值则出现于12-01-1960，为2,136,849.000人。CEIC提供的ZA：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的南非 – 表 ZA.世界银行：人口和城市化进程统计。

Abstract

The Measuring Living Standards in Cities (MLSC) survey is a new instrument designed to enhance understanding of cities in Africa and support evidence based policy design. The instrument was developed under the World Bank's Spatial Development of African Cities Program, and was piloted in Dar es Salaam (Tanzania) and Durban (South Africa) over the course of 2014/15.

Geographic coverage

The survey covered households in Durban, South Africa.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The total nominal sample of 2400 households in Durban was, selected in four stages rather than two. These were: (i) selection of 200 EAs with probability proportional to size; (ii) large EAs were segmented into area units of roughly the same size (using GIS data), and one segments was selected randomly with equal probability; (iii) following listing of buildings, 15 were selected using systematic equal probability sampling; (iv) households in the 15 selected buildings were listed so that 12 households could then be selected per EA by systematic equal probability sampling. This approach reduced the need to enter as many buildings as would otherwise have been necessary, without reducing the representativeness of the sample.

For further details on sampling strategy, see Survey Methodology section of World Measuring Living Standards within Cities report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

The refusal and non-completion rate was 41 percent.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the Khayelitsha and Mitchell's Plain areas of Cape Town, South Africa.

Analysis unit

The unit of analysis for this survey includes households and individuals.

Universe

The survey covers the African and Coloured populations of the Khayelitsha and Mitchell's Plain areas of Cape Town.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to represent all adults (18 years of age and older) in the Mitchell’s Plain Magisterial district. As discussed above, the most cost-efficient method of interviewing residents of such a large area is to use a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For our clusters of households, we relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. These Enumerator Areas are neighbourhoods of roughly 50 to 200 households. They are drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell’s Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS). The measure of size being the number of households in each Enumerator Area as measured by the 1996 Population Census. This method was chosen as it provides the most efficient way to obtain equal subsample sizes across two stages of selection, i.e. we are able to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available through this site under Other Study Materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire: Was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter a separate set of questions covering education, health and work status was included.

The adult questionnaire: Was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.

The smart cities market will rise from USD 848.0 Billion in 2025 to USD 5,647.6 Billion by 2035, at a 28.2% CAGR, driven by urban growth and smart technologies.

Market Size and Growth: The hospitality industry in South Africa is experiencing steady growth, reaching a market size of USD 1.36 billion in 2025. It is projected to continue expanding at a CAGR of 4.43% from 2025 to 2033, driven by factors such as increasing tourism, government initiatives, and growing demand for luxury accommodations. The industry is segmented into various types, including chain hotels, independent hotels, service apartments, budget and economy hotels, mid and upper mid-scale hotels, and luxury hotels. Key Trends and Challenges: The hospitality industry in South Africa is influenced by several trends, including the rise of online booking platforms, the growing popularity of eco-tourism, and the increasing focus on personalized experiences. However, the industry also faces challenges, such as rising costs, competition from the informal sector, and the impact of economic downturns. The major companies operating in the market include Hilton Worldwide Holdings Inc., InterContinental Hotels Group, Marriott International Inc., and Accor SA. Market concentration is relatively low, with a diverse range of players in the market and no single dominant competitor. Recent developments include: In March 2022, Kasada announced the purchase of the Cap Grace Hotel in Cape Town, South Africa. Kasada's hotel acquisition marks the company's first foray into the South African hotel operator market. It also helps Kasada's strategy of expanding into all major cities in Sub-Saharan Africa., In May 2022, Millat Investments took over the iconic Winston Hotel in Rosebank, Johannesburg, South Africa, adding another key property to its rapidly expanding hospitality portfolio. The purchase of the Winston property comes on the heels of the successful Africa Travel Indaba in Durban, an event aimed at reviving tourism to South Africa and the continent following the global pandemic lockdown.. Key drivers for this market are: Rising Tourism in the United Arab Emirates Bolsters the Growth in Hospitality Sector, The Rise in the Mice Industry in the United Arab Emirates Drives the Hospitality Sector. Potential restraints include: High Rentals in the United Arab Emirates Pose a Restraint to the Hospitality Sector. Notable trends are: Growth in Tourism Sector in South Africa is Expected to Outpace Hospitality Industry.