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.

Cairo, in Egypt, ranked as the most populated city in Africa as of 2025, with an estimated population of over 23 million inhabitants living in Greater Cairo. Kinshasa, in Congo, and Lagos, in Nigeria, followed with some 17.8 million and 17.2 million, respectively. Among the 15 largest cities in the continent, another one, Kano, was located in Nigeria, the most populous country in Africa. Population density trends in Africa As of 2023, Africa exhibited a population density of 50.1 individuals per square kilometer. Since 2000, the population density across the continent has been experiencing a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 58.5 by the year 2030. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 627 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 45.5 percent in 2024. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating only around a third of the total population then. This trajectory is projected to continue its rise in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2024, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. As of the same year, Africa's population was estimated to expand by 2.27 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.3 percent, reaching its pinnacle at 2.63 percent in 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Actual value and historical data chart for South Africa Population In Largest City

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;

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.; ;

Johannesburg was the wealthiest city in Africa as of 2021. South Africa's biggest city held *** billion U.S. dollars in private wealth, while Cape Town followed with *** 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.

About this dataset

Context

This list ranks the 1 cities in the Major County, OK by South African population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by South African Population: This column displays the rank of city in the Major County, OK by their South African population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• South African Population: The South African population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as South African. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Major County South African Population: This tells us how much of the entire Major County, OK South African population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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.

About this dataset

Context

This list ranks the 5 cities in the Big Horn County, WY by South African population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by South African Population: This column displays the rank of city in the Big Horn County, WY by their South African population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• South African Population: The South African population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as South African. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Big Horn County South African Population: This tells us how much of the entire Big Horn County, WY South African population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

District Municipalities 2018 is a shapefile and attributes information of all the district municipalities in South Africa. In the hierarchy of local government structure, the District Municipalities are contained within Provinces, then District Municipalities contain Local Municipalities. District Municipalities 2018 was published in the Year 2018 after the municipal boundaries had minor technical adjustments. The district (Category C) municipalities are municipalities that are comprised of local (Category B) municipalities. The Metropolitan (Category A) Municipalities are municipalities with the major cities as the core (e.g. City of Johannesburg) and they are outside the District Municipalities. When the boundaries of local municipalities change and affect the boundary of district municipalities, the new district municipal boundary is generated. In the District Municipalities 2018 shapefile there are 44 District Municipalities and 8 Metropolitan Municipalities. Note that Metropolitan Municipalities are included in the District Municipalities shapefile to ensure that the layer is continuous throughout the country. If the Metropolitan Municipalities were left out, there will be void spaces in the layer.

The fastest growing city in Africa is Bujumbura, in Burundi. In 2020, this city had an estimated population of about one million. By 2035, the population of Bujumbura could increase by 123 percent and reach roughly 2.3 million people. Zinder, in Niger, had about half million inhabitants in 2020 and, with a growth rate of 118 percent, is Africa's second fastest growing city. In 2035, Zinder could have over one million residents.

As of 2021, the largest city in whole Africa is Lagos, in Nigeria. Other highly populated cities in Africa are Kinshasa, in Congo, Cairo, and Alexandria, both located in Egypt.

Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community–based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

In 2024, Pietermaritzburg in South Africa ranked first in the crime index among African cities, scoring **** index points. The six most dangerous areas on the continent were South African cities. Furthermore, Pretoria and Johannesburg followed, with a score of **** and **** points, respectively. 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). Contact crimes are common in South Africa Contact crimes in South Africa include violent crimes such as murder, attempted murder, and sexual offenses, as well as common assault and robbery. In fiscal year 2022/2023, the suburb of Johannesburg Central in the Gauteng province of South Africa had the highest number of contact crime incidents. Common assault was the main contributing type of offense to the overall number of contact crimes. Household robberies peak in certain months In South Africa, June, July, and December experienced the highest number of household robberies in 2023. June and July are the months that provide the most hours of darkness, thus allowing criminals more time to break in and enter homes without being detected easily. In December, most South Africans decide to go away on holiday, leaving their homes at risk for a potential break-in. On the other hand, only around ** percent of households affected by robbery reported it to the police in the fiscal year 2022/2023.

Spatial analysis at different levels can help understand spatial variation of human immunodeficiency virus (HIV) infection, disease drivers, and targeted interventions. Combining spatial analysis and the evaluation of the determinants of the HIV burden in Southern African countries is essential for a better understanding of the disease dynamics in high-burden settings.The study countries were selected based on the availability of demographic and health surveys (DHS) and corresponding geographic coordinates. We used multivariable regression to evaluate the determinants of HIV burden and assessed the presence and nature of HIV spatial autocorrelation in six Southern African countries.The overall prevalence of HIV for each country varied between 11.3% in Zambia and 22.4% in South Africa. The HIV prevalence rate was higher among female respondents in all six countries. There were reductions in prevalence estimates in most countries yearly from 2011 to 2020. The hotspot cluster findings show that the major cities in each country are the key sites of high HIV burden. Compared with female respondents, the odds of being HIV positive were lesser among the male respondents. The probability of HIV infection was higher among those who had sexually transmitted infections (STI) in the last 12 months, divorced and widowed individuals, and women aged 25 years and older.Our research findings show that analysis of survey data could provide reasonable estimates of the wide-ranging spatial structure of the HIV epidemic in Southern African countries. Key determinants such as individuals who are divorced, middle-aged women, and people who recently treated STIs, should be the focus of HIV prevention and control interventions. The spatial distribution of high-burden areas for HIV in the selected countries was more pronounced in the major cities. Interventions should also be focused on locations identified as hotspot clusters.

Background: To optimally allocate limited health resources in responding to the HIV epidemic, South Africa has undertaken to generate local epidemiological profiles identifying high disease burden areas. Central to achieving this, is the need for readily available quality health data linked to both large and small geographic areas. South Africa has relied on national population-based surveys: the Household HIV Survey and the National Antenatal Sentinel HIV and Syphilis Prevalence Survey (ANC) amongst others for such data for informing policy decisions. However, these surveys are conducted approximately every 2 and 3 years creating a gap in data and evidence required for policy. At subnational levels, timely decisions are required with frequent course corrections in the interim. Routinely collected HIV testing data at public health facilities have the potential to provide this much needed information, as a proxy measure of HIV prevalence in the population, when survey data is not available. The South African District health information system (DHIS) contains aggregated routine health data from public health facilities which is used in this article.Methods: Using spatial interpolation methods we combine three “types” of data: (1) 2015 gridded high-resolution population data, (2) age-structure data as defined in South Africa mid-year population estimates, 2015; and (3) georeferenced health facilities HIV-testing data from DHIS for individuals (15–49 years old) who tested in health care facilities in the district in 2015 to delineate high HIV disease burden areas using density surface of either HIV positivity and/or number of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facility locations and compared with the real observed values calculating the residuals. Lower residuals means the Inverse Weighted Distance (IDW) interpolator provided reliable prediction at unknown locations. Results were adjusted to provincial published HIV estimates and aggregated to municipalities. Uncertainty measures map at municipalities is provided. Data on major cities and roads networks was only included for orientation and better visualization of the high burden areas.Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, iLembe and uMngundgudlovu; and around major cities and national routes.Conclusion: The methods provide accurate estimates of the local HIV burden at the municipality level. Areas with high population density have high numbers of PLHIV. The analysis puts into the hand of decision makers a tool that they can use to generate evidence for HIV programming. The method allows decision makers to routinely update and use facility level data in understanding the local epidemic.

The local (Category B) municipalities are municipalities that are contained within a district (Category C) municipalities. The Metropolitan (Category A) Municipalities are municipalities with the major cities as the core (e.g. City of Johannesburg) and they are outside the District Municipalities. When the boundaries of local municipalities change and affect the boundary of district municipalities, the new district municipal boundary is generated. In the Local Municipalities 2018 shapefile there are 205 Local Municipalities and 8 Metropolitan Municipalities.

Abstract

This study covers Cape Town, one of four African cities surved between 2013 and 2019 by the African Center for Cities. The African Center for cities is based at the University of Cape Town and is a partner of the Hungry Cities Partnership (HCP).

The HCP studies include household data on food insecurity, household food purchasing dynamics, nutritional discounting taking place in households, foods consumed and multidimensional measures of poverty. The household data is complimented with household member data and food retailer (vendor) data, including infomation on vendor employees.

The Hungry Cities Partnership is an international network of cities and city-based partner organizations which focuses on the relationships between rapid urbanization, informality, inclusive growth and urban food systems in the Global South.

Geographic coverage

The household sample is deisgned to be representative of the city of Cape Town.

Analysis unit

Households and individuals

Universe

Households and Vendors in Cape Town.

Kind of data

Sample survey data

Sampling procedure

Household sampling: the sample for the 2013 Food Security Study was designed to be two-stage and stratified, using a random probability sample of 2,500 Cape Town households .Enumeration areas were taken from Statistics SAs master lists and used as the primary sampling unit. Households were the secondard sampling unit. Strafitication was done by income group of the household. Some areas were over-sampled to improve accuracy. In each of the drawn EAs, six households were systematically selected, with the exception of the EAs in DuNoon (where 10 households were systematically selected). Starting points were allocated to ensure coverage of the entire EA. The household was defined by everyone who regularly "ate from the same pot".

Vendor sampling: The survey team documentation reads as follows: A strategy of maximum variation sampling was used to ensure a mix of commercial, formal residential, informal residential, mixed formal and informal residential, and industrial retail sites. In these areas, the main street served as the primary site of research. Informal food vending businesses were selected randomly. In total, 1,018 food vendors were interviewed over a three-week period.

For more on sampling see the study documentation.

Sampling deviation

In cases, xenophobic violence made vendor interviews dangerous in some areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two questionnaires per city, a household questionnaire and a vendor questionnaire. The household questionnaire has a subsection for household members (persons), and the vendor quesitonnaire has a subsection for employees. Answers to these subsections are supplied in separete datafiles, which can be matched to (merged with) the questoinnaire as necessary.

Vendor surveys were administered to the person directly responsible for the running of the business using handheld tablets. The household survey was administered to a senior adult member of the household, someone who could speak for the household.

Note that for the household questionnaire, the question 8 section changed slightly for Cape Town, in that the answers are not stored in 'wide' format like the other cities. Rather, if a respondent provided more than one answer, additional variables were created. This is why the dataset has less variables and the question 8 section looks different. Only up to three locations were recorded in section 8, even if the repondent mentioned more than 3 sources of food.

Cleaning operations

Datafiles were received by DataFirst in SPSS (.sav) and Excel (.xlsx) format. Variables had to be named and variable labels were taken from question text. Variables were named accoriding to question number and subject matter, in a hierachical fasion.

An effort was made to keep question numbers consistent across cities where the same questions were asked for the 2013-2019 surveys. For the vendor data, Cape Town, Maputo and Nairobi had almost identical questionnaires and so the question numbers were naturally the same across these cities (harmonized). For the household data, Maputo, Nairobi and Windhoek were similar and could be harmonized. This means users could try stack these datafiles. The Cape Town household questionnaire was more different to the others, and variable names would required adjusting to match with the other cities.

Missing values of 97, 98, and 99 were converted to -97, -98 and -99. There were some question numbers wrong in the vendor data questionnaires (typos) that were corrected.

Data appraisal

It seems that there is slight mismatch between the Cape Town household questionnaire provided and the lists in the datafile, for an example see the question 15 income sources.

In the Cape Town household data, data was not collected for the quetion 10.c and 10.d, about crops and time to travel to crops.

In general, the lists change subtly between cities, for example the lists of foods in question 8 of the household data. As such the user should take caution when comparing across cities, and refer to the questionnaires. When the lists differed, list item letters (a-z) were left in the variable name as a second way for the user to check that the data match the questionnaire in the expected way. In Cape Town an answer to questions 15a and b "support from relatives" was captured although it does not reflect in the questionnaire.

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.

Study population and frequency distribution of HIV cases among selected Southern African countries.

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