Mauritius had the highest population density level in Africa as of 2023, with nearly *** inhabitants per square kilometer. The country has also one of the smallest territories on the continent, which contributes to the high density. As a matter of fact, the majority of African countries with the largest concentration of people per square kilometer have the smallest geographical area as well. The exception is Nigeria, which ranks among the largest territorial countries in Africa and is very densely populated at the same time. After all, Nigeria has also the largest population on the continent.

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 Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 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. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

The average for 2021 based on 53 countries was 112 people per square km. The highest value was in Mauritius: 634 people per square km and the lowest value was in Namibia: 3 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

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.

The dataset is a zip file that contains 28 cloud optimized tiff files that cover the continent of Africa. Each of the 28 files represents a region or area - these are not divided by country. These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe. To create the high-resolution maps, machine learning techniques are used to identify buildings from commercially available satellite images then general population estimates are overlaid based on publicly available census data and other population statistics. The resulting maps are the most detailed and actionable tools available for aid and research organizations.

South Africa ZA: Population Density: People per Square Km data was reported at 46.754 Person/sq km in 2017. This records an increase from the previous number of 46.176 Person/sq km for 2016. South Africa ZA: Population Density: People per Square Km data is updated yearly, averaging 30.287 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 46.754 Person/sq km in 2017 and a record low of 14.773 Person/sq km in 1961. South Africa ZA: Population Density: People per Square Km 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.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Central African Republic: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

Central African Republic CF: Population Density: People per Square Km data was reported at 8.183 Person/sq km in 2022. This records a decrease from the previous number of 8.206 Person/sq km for 2021. Central African Republic CF: Population Density: People per Square Km data is updated yearly, averaging 4.833 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 8.206 Person/sq km in 2021 and a record low of 2.784 Person/sq km in 1961. Central African Republic CF: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

This feature contain two layers, 1 depicts the up-to-date COVID-19 cases for Nigeria by states and the 2 shows population density of Nigeria by LGAs. These were superimposed on each other for easy comparison. Data sources include NCDC, WHO, and Africa Geoportal. The COVID-19 data is updated at least once per day, following NCDC update timeline. This layer is specifically designed for a COVID-19 monitoring dashboard found here. This layer is created and maintained by DR. NKEKI F. N. and his team (Eugene .A. Atakpiri and Akinde .N. Kolawole) to Support NCDC to fight against the spread of COVID-19 in Nigeria. This layer is opened to the public and free to share. Contact Info: Phone: +23408063131159Email: nkekifndidi@gmail.com Phone: +2348117643525

Email: nkekifndidi@gmail.com Phone: +2348117643525

Email: nkekifndidi@gmail.com

Phone: +2348117643525

Email: nkekifndidi@gmail.com

The African two-wheeler market, valued at approximately $5 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) exceeding 4% from 2025 to 2033. This surge is driven by several key factors. Increasing urbanization and a burgeoning young population fuel demand for affordable and efficient personal transportation. Improving infrastructure in certain regions, coupled with rising disposable incomes, particularly within the middle class, further enhances market accessibility and purchasing power. The prevalence of motorcycles and scooters as primary modes of commuting, especially in densely populated areas and underdeveloped transportation networks, significantly contributes to market expansion. Furthermore, the growing adoption of financing options and attractive financing schemes offered by manufacturers are making two-wheelers more accessible to a wider segment of the population. However, challenges remain, including inconsistent economic growth across different African nations, fluctuating fuel prices potentially impacting affordability, and the ongoing need for improved road safety infrastructure and regulations. Growth is segmented by propulsion type, with gasoline-powered two-wheelers currently dominating the market. However, the emergence of electric two-wheelers is gaining traction, driven by environmental concerns, government incentives in select countries, and technological advancements resulting in increased battery efficiency and reduced costs. Key players like KTM, Bajaj Auto, Yamaha, Hero MotoCorp, and Honda are strategically focusing on expanding their presence across the continent through localized production, partnerships, and the introduction of models tailored to the specific needs and preferences of African consumers. The diverse market landscape presents opportunities for both established global manufacturers and emerging local brands to capture significant market share. Regional variations in growth are expected, with countries like Nigeria, South Africa, and Egypt leading the market based on their larger populations and higher levels of economic development. This comprehensive report provides an in-depth analysis of the burgeoning Africa two-wheeler market, covering the period from 2019 to 2033. With a base year of 2025 and a forecast period spanning 2025-2033, this study offers crucial insights into market size (in million units), growth drivers, challenges, and future trends. The report leverages data from the historical period (2019-2024) to provide a robust foundation for future projections. Key players like Bajaj Auto Ltd, Yamaha Motor Company Limited, Hero MotoCorp Ltd, and KTM Motorcycles are analyzed, along with emerging trends in motorcycle sales in Africa and the impact of evolving African motorcycle regulations. Recent developments include: September 2023: KTM India launched the two all-new, single-cylinder Duke 390 and 250 motorcycles priced at INR 310,520 and INR 239,000 respectively.July 2023: Harley-Davidson spinoff LiveWire Unveils Its Second Motorcycle – and It Can Hit 103 MPH.July 2023: Hero Motocorp and Harley-Davidson launched their co-developed premium motorcycle – the Harley-Davidson X440 in India from a starting price of INR 229 thousand and going to INR 269 thousand.. Key drivers for this market are: Rapid Urbanization and Demand for Convinient Transportation. Potential restraints include: Traffic Congestion in Major Cities. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

In 2024, Tunis was the city with the largest population in Tunisia, with around 693,000 inhabitants. The cities of Sfax and Sousse followed with approximately 277,000 and 164,000 people, respectively. Other highly populated areas were Sousse, Kairouan, and Bizerte. Rapid urbanization in Tunisia and across Africa The Tunisian population is mostly concentrated in cities. In recent years, increasingly more people have migrated from rural areas to urban centers. In fact, having undergone rapid socio-economic and infrastructural development, cities generally offer a higher standard of living and more employment opportunities to the population. As a result, the share of people living in urban areas in the country has increased steadily, reaching almost 70 percent in 2021. Tunisia has one of the highest urbanization rates in Africa. On the continent, the most urbanized countries are Gabon and Libya, which record rates above 80 percent. In general, urbanization is increasing rapidly across Africa and is forecast to grow further in the coming years to reach 722 million people by 2026. A slowdown in population growth In 2023, the total population of Tunisia amounted to around 12 million. The number of inhabitants has risen in the last decade and is forecast to keep growing in the coming years, with the country’s population reaching 12.8 million people by 2030 and almost 14 million by 2050. Nevertheless, population growth has generally been declining in Tunisia. Decreasing natality and a high mortality rate are some factors contributing to this slowdown. For instance, the number of births dropped from 226,000 in 2014 to 173,000 in 2020. Moreover, the country has the highest death rate in the Maghreb region after Mauritania.

SETTLEMENT EXTENTS:The Tanzania Settlement Extents Version 01.01. database are polygons representing areas where there is likely a human settlement based on the presence of buildings detected in satellite imagery. Settlement extents are not meant to represent the boundaries of an administrative unit or locality. A single settlement extent may be made up of multiple localities, especially in urban areas. Each settlement extent has an associated population estimate. Provided is information on the common operational boundary that the extent fully resides within along with their associated place codes (PCodes)The data are in geodatabase format and consist of a single-feature class combining built-up areas (BUA), small settlement areas (SSA), and hamlets (hamlets). A built-up area (BUA) is generally an area of urbanization with moderately-to-densely-spaced buildings and a visible grid of streets and blocks. Built up areas are characterized as polygons containing 13 or more buildings across an area greater than or equal to 400,000 square meters. A small settlement (SSA) is a settled area of permanently inhabited structures and compounds of roughly a few hundred to a few thousand inhabitants. The housing pattern in SSAs is an assemblage of family compounds adjoining other similar habitations. Small settlement areas are characterized as polygons containing 50 or more buildings across an area less than 400,000 square meters. A hamlet is a collection of several compounds or sleeping houses in isolation from small settlements or urban areas. Hamlets are characterized as polygons containing between 1 and 49 buildings across an area less than 400,000 square meters. Extent: The country's Admin Level 0 Boundaries. The overall extent of the layer is limited to the overall extent of the building footprint data set and may not reflect the extent of official administrative boundaries. Coordinate system: GCS WGS 1984.For full methodological details please explore data release statement available for download here. POPULATION ATTRIBUTES:The associated population estimates for the Settlement Extents datasets are derived from two WorldPop high resolution data sources. (1) The WorldPop Top-down constrained population estimates 2020 (Population) uses, for each country, the highest admin level official population totals of the 2000 and 2010 census rounds, that are publicly available and can be mapped to associated boundaries, and projects them to 2020. These projected values then disaggregated statistically to 100x100m resolution using a set of detailed geospatial datasets to disaggregate them to grid cell-based counts. The estimates are constrained to settlements based on the satellite-derived building footprint data from Maxar/ecopia for the 51 African countries, and based on a built settlement growth model of WorldPop for the remaining countries.(2) The Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution) population estimates (Pop_UN_adj) recognises that the United Nations produce their own estimates of national population totals. WorldPop, in order to provide flexibility to users, adjusted the number of people per pixel of its top-down constrained population estimates nationally to match the corresponding official United Nations population estimates (i.e. 2019 Revision of World Population Prospects).For more information about WorldPop’s methods, see: https://www.worldpop.org/methods/populations and https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained"Population Counts / Constrained Individual countries 2020 (100m resolution)" & "Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution)" derived from WorldPop.org.

SETTLEMENT EXTENTS:The Djibouti Settlement Extents Version 01.01. database are polygons representing areas where there is likely a humansettlement based on the presence of buildings detected in satellite imagery. Settlement extents are not meant to represent the boundaries of an administrative unit or locality. A single settlement extent may be made up of multiple localities, especially in urban areas. Each settlement extent has an associated population estimate. Provided is information on the common operational boundary that the extent fully resides within along with their associated place codes (PCodes)The data are in geodatabase format and consist of a single-feature class combining built-up areas (BUA), small settlement areas (SSA), and hamlets (hamlets). A built-up area (BUA) is generally an area of urbanization with moderately-to-densely-spaced buildings and a visible grid of streets and blocks. Built up areas are characterized as polygons containing 13 or more buildings across an area greater than or equal to 400,000 square meters. A small settlement (SSA) is a settled area of permanently inhabited structures and compounds of roughly a few hundred to a few thousand inhabitants. The housing pattern in SSAs is an assemblage of family compounds adjoining other similar habitations. Small settlement areas are characterized as polygonscontaining 50 or more buildings across an area less than 400,000 square meters. A hamlet is a collection of several compounds or sleeping houses in isolation from small settlements or urban areas. Hamlets are characterized as polygons containing between 1 and 49 buildings across an area less than 400,000 square meters. Extent: The country's Admin Level 0 Boundaries. The overall extent of the layer is limited to the overall extent of the building footprint data set and may not reflect the extent of official administrative boundaries. Coordinate system: GCS WGS 1984.For full methodological details please explore data release statement available for download here. POPULATION ATTRIBUTES:The associated population estimates for the Settlement Extents datasets are derived from two WorldPop high resolution data sources. (1) The WorldPop Top-down constrained population estimates 2020 (Population) uses, for each country, the highest admin level official population totals of the 2000 and 2010 census rounds, that are publicly available and can be mapped to associated boundaries, and projects them to 2020. These projected values then disaggregated statistically to 100x100m resolution using a set of detailed geospatial datasets to disaggregate them to grid cell-based counts. The estimates are constrained to settlements based on the satellite-derived building footprint data from Maxar/ecopia for the 51 African countries, andbased on a built settlement growth model of WorldPop for the remaining countries.(2) The Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution) population estimates (Pop_UN_adj) recognises that the United Nations produce their own estimates of national population totals. WorldPop, in order to provide flexibility to users, adjusted the number of people per pixel of its top-down constrained population estimates nationally to match the corresponding official United Nations population estimates (i.e. 2019 Revision of World Population Prospects).For more information about WorldPop’s methods, see: https://www.worldpop.org/methods/populationsand https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained"Population Counts / Constrained Individual countries 2020 (100m resolution)" & "Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution)" derived from WorldPop.org

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of African households in rural Eastern Cape and African and Coloured households in urban Western Cape.

Kind of data

Survey data

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or coloured EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Statssa) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Coloured)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly-selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban coloured (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban coloured households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least 100

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.

The lack of medical services in West Africa represents a serious issue in sanitary emergency. As of **********, different West African countries counted less than a doctor every 10,000 inhabitants. Especially, Sierra Leone had ***** physicians per 100,000 individuals, the lowest density of medical doctors in West Africa. Moreover, Burkina Faso was estimated to have only ** ventilators in the whole country for a population of almost ********** people.

The average number of doctors across the OECD countries in 2019 equaled to ** per 10,000 inhabitants. The member countries of OECD are mostly high-income countries, whereas Nigeria is an emerging economy and it belongs to countries with lower middle-incomes.

Niger had the highest birth rate in the world in 2024, with a birth rate of 46.6 births per 1,000 inhabitants. Angola, Benin, Mali, and Uganda followed. Except for Afghanistan, all 20 countries with the highest birth rates in the world were located in Sub-Saharan Africa. High infant mortality The reasons behind the high birth rates in many Sub-Saharan African countries are manyfold, but a major reason is that infant mortality remains high on the continent, despite decreasing steadily over the past decades, resulting in high birth rates to counter death rates. Moreover, many nations in Sub-Saharan Africa are highly reliant on small-scale farming, meaning that more hands are of importance. Additionally, polygamy is not uncommon in the region, and having many children is often seen as a symbol of status. Fastest-growing populations As the high fertility rates coincide with decreasing death rates, countries in Sub-Saharan Africa have the highest population growth rates in the world. As a result, Africa's population is forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.