As of 2023, the total population of Africa was over 1.48 billion. The number of inhabitants on the continent increased annually from 2000 onwards. In comparison, the total population was around 831 million in 2000. According to forecasts, Africa will experience impressive population growth in the coming years and will close the gap with the Asian population by 2100. Over 200 million people in Nigeria Nigeria is the most populous country in Africa. In 2025, the country’s population exceeded 237 million people. Ethiopia followed with a population of around 135 million, while Egypt ranked third, accounting for approximately 118 million individuals. Other leading African countries in terms of population were the Democratic Republic of the Congo, Tanzania, South Africa, and Kenya. Additionally, Niger, the Democratic Republic of Congo, and Chad recorded the highest population growth rate on the continent in 2023, with the number of residents rising by over 3.08 percent compared to the previous year. On the other hand, the populations of Tunisia and Eswatini registered a growth rate below 0.85 percent, while for Mauritius and Seychelles, it was negative. Drivers for population growth Several factors have driven Africa’s population growth. For instance, the annual number of births on the continent has risen constantly over the years, jumping from nearly 32 million in 2000 to almost 46 million in 2023. Moreover, despite the constant decline in the number of births per woman, the continent’s fertility rate has remained considerably above the global average. Each woman in Africa had an average of over four children throughout her reproductive years as of 2023, compared to a world rate of around two births per woman. At the same time, improved health and living conditions contributed to decreasing mortality rate and increasing life expectancy in recent years, driving population growth.

This statistic shows the total population of Sub-Saharan Africa from 2014 to 2024. Sub-Saharan Africa includes all countries south of the Sahara desert. In 2024, the total population of Sub-Saharan Africa amounted to approximately 1.29 billion inhabitants.

South Africa ZA: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data was reported at 37.102 % in 2017. This records an increase from the previous number of 36.958 % for 2016. South Africa ZA: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data is updated yearly, averaging 26.647 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 37.102 % in 2017 and a record low of 25.848 % in 1960. South Africa ZA: Population in Urban Agglomerations of More Than 1 Million: as % of Total 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 urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2000 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; Weighted Average;

South Africa ZA: Population: Growth data was reported at 1.245 % in 2017. This records a decrease from the previous number of 1.301 % for 2016. South Africa ZA: Population: Growth data is updated yearly, averaging 2.282 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.794 % in 1972 and a record low of 1.047 % in 2008. South Africa ZA: Population: Growth 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. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

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.

South Africa ZA: Population: as % of Total: Male: Aged 15-64 data was reported at 66.071 % in 2017. This records an increase from the previous number of 65.988 % for 2016. South Africa ZA: Population: as % of Total: Male: Aged 15-64 data is updated yearly, averaging 56.838 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 66.071 % in 2017 and a record low of 54.429 % in 1966. South Africa ZA: Population: as % of Total: Male: Aged 15-64 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. Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average;

This statistic depicts the average annual change rate of urban population in North Africa in 2015, by country. During the surveyed time period, the urban population of Mauritania increased by *** percent annually on average.

This statistic shows a projection of global populations that had no access to electricity in 2009 and 2016, with a forecast to 2030, broken down by region. By 2030, it is estimated that some 602 million people in Sub-Saharan Africa will not have access to electricity, an increase from the 588 million people without access in 2016.

South Africa ZA: Rural Population Growth data was reported at -0.235 % in 2017. This records a decrease from the previous number of -0.168 % for 2016. South Africa ZA: Rural Population Growth data is updated yearly, averaging 1.217 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.679 % in 1972 and a record low of -0.329 % in 2008. South Africa ZA: Rural Population Growth 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. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

In 2025, around ***** million people in Africa were living in extreme poverty, with the poverty threshold at **** U.S. dollars a day. The number of poor people on the continent dropped slightly compared to the previous year. Poverty in Africa is expected to decline slightly in the coming years, even in the face of a growing population. The number of inhabitants living below the extreme poverty line would decrease to around *** million by 2030.

Analysis unit

Woman, Birth, Child, Birth, Man, Household Member

Universe

Women age 15-49, Births, Children age 0-4, Men age 15-59, All persons

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: National Department of Health (NDoH) [South Africa], Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC), and ICF.

SAMPLE UNIT: Woman SAMPLE SIZE: 8514

SAMPLE UNIT: Birth SAMPLE SIZE: 14144

SAMPLE UNIT: Child SAMPLE SIZE: 3548

SAMPLE UNIT: Man SAMPLE SIZE: 3618

SAMPLE UNIT: Member SAMPLE SIZE: 38850

Mode of data collection

Face-to-face [f2f]

South Africa administrative levels 0 (country), 1 (province), 2 (district), and 3 (local municipality) population statistics.

REFERENCE YEAR: 2016

These CSV files are suitable for database or GIS linkage to the South Africa - Subnational Administrative Boundaries shapefiles.

NMCDC Copy of Living Atlas map. Source: https://www.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8fIllustration by Brian BrenemanThis layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2016-2020ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: March 17, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

Abstract

The primary objective of the South Africa Demographic and Health Survey (SADHS) 2016 is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the SADHS 2016 collected information on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of contraceptives; breastfeeding practices; nutrition; childhood and maternal mortality; maternal health, including antenatal and postnatal care; key aspects of child health, including immunisation coverage and prevalence and treatment of acute respiratory infection (ARI), fever, and diarrhoea; potential exposure to the risk of HIV infection; coverage of HIV counselling and testing (HCT); and physical and sexual violence against women. Another critical objective of the SADHS 2016 is to provide estimates of health and behaviour indicators for adults age 15 and older, including use of tobacco, alcohol, and codeine-containing medications. In addition, the SADHS 2016 provides estimates of the prevalence of anaemia among children age 6-59 months and adults age 15 and older, and the prevalence of hypertension, anaemia, high HbA1c levels (an indicator of diabetes), and HIV among adults age 15 and older.

The information collected through the SADHS 2016 is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the SADHS 2016 is the Statistics South Africa Master Sample Frame (MSF), which was created using Census 2011 enumeration areas (EAs). In the MSF, EAs of manageable size were treated as primary sampling units (PSUs), whereas small neighbouring EAs were pooled together to form new PSUs, and large EAs were split into conceptual PSUs. The frame contains information about the geographic type (urban, traditional, or farm) and the estimated number of residential dwelling units (DUs) in each PSU. The sampling convention used by Stats SA is DUs. One or more households may be located in any given DU; recent surveys have found 1.03 households per DU on average.

Administratively, South Africa is divided into nine provinces. The sample for the SADHS 2016 was designed to provide estimates of key indicators for the country as a whole, for urban and non-urban areas separately, and for each of the nine provinces in South Africa. To ensure that the survey precision is comparable across provinces, PSUs were allocated by a power allocation rather than a proportional allocation. Each province was stratified into urban, farm, and traditional areas, yielding 26 sampling strata.

The SADHS 2016 followed a stratified two-stage sample design with a probability proportional to size sampling of PSUs at the first stage and systematic sampling of DUs at the second stage. The Census 2011 DU count was used as the PSU measure of size. A total of 750 PSUs were selected from the 26 sampling strata, yielding 468 selected PSUs in urban areas, 224 PSUs in traditional areas, and 58 PSUs in farm areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used in the SADHS 2016: the Household Questionnaire, the individual Woman’s Questionnaire, the individual Man’s Questionnaire, the Caregiver’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to South Africa. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After the preparation of the questionnaires in English, the questionnaires were translated into South Africa’s 10 other official languages. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files for the SADHS 2016 were transferred via the IFSS to the Stats SA head office in Pretoria, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by a core group of four people; secondary editing was completed by 11 people. All persons involved in data processing took part in the main fieldwork training, and they were supervised by senior staff from Stats SA with support from ICF. Data editing was accomplished using CSPro software. Secondary editing was initiated in October 2016 and completed in February 2017. Checking inconsistencies in dates of immunisations was aided by the digital images of the immunisation page of the Road-to-Health booklet that had been collected on the tablet by fieldworkers at the time of the interview for that purpose.

Response rate

A total of 15,292 households were selected for the sample, of which 13,288 were occupied. Of the occupied households, 11,083 were successfully interviewed, yielding a response rate of 83%.

In the interviewed households, 9,878 eligible women age 15-49 were identified for individual interviews; interviews were completed with 8,514 women, yielding a response rate of 86%. In the subsample of households selected for the male survey, 4,952 eligible men age 15-59 were identified and 3,618 were successfully interviewed, yielding a response rate of 73%. In this same subsample, 12,717 eligible adults age 15 and older were identified and 10,336 were successfully interviewed with the adult health module, yielding a response rate of 81%. Response rates were consistently lower in urban areas than in nonurban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the SADHS 2016 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the SADHS 2016 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the SADHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings

See details of the data quality tables in Appendix C of the survey final report.

South Africa ZA: Population: Male: Ages 20-24: % of Male Population data was reported at 9.321 % in 2017. This records a decrease from the previous number of 9.449 % for 2016. South Africa ZA: Population: Male: Ages 20-24: % of Male Population data is updated yearly, averaging 9.135 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 10.411 % in 2007 and a record low of 8.061 % in 1969. South Africa ZA: Population: Male: Ages 20-24: % of Male 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. Male population between the ages 20 to 24 as a percentage of the total male population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

Abstract

The Community Survey is a nationally representative, large-scale household survey which is designed to provide information on the extent of poor households in South Africa, their access to services, and levels of unemployment, at national, provincial and municipal levels. The main objectives of the survey are: 1. To fill data gaps between national population and housing censuses 2. To provide estimates at lower geographical levels than existing household surveys 3. To build capacities for the next census round 4. To provide inputs to the mid-year population projections.

Geographic coverage

The survey covered the whole of South Africa.

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tw0 was the selection of dwelling units. Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

Mode of data collection

Face-to-face [f2f]

Research instrument

The CS 2016 questionnaire consisted of six main sections, 11 sub-sections and a total of 225 questions. A first draft of the paper questionnaire was developed in February 2015 and various versions were reviewed and updated thereafter based on discussions with stakeholders. The target population of the survey was all persons in the sampled dwelling who were present on the reference night (i.e. the night between 6 and 7 March 2016). The final CAPI questionnaire was made up of three person rosters. One roster was utilised for the person information, one roster for emigration and one roster for mortality.

Data appraisal

The Community Survey 2016 data was released in 2017. There are four data files. These are files for households, persons, mortality, and emigration. The emigration file is currently not available. Statistics SA has not provided an explanation for the missing file. DataFirst is working to obtain this file, and will add the data file to the dataset we publish once we have it.

The Community Survey 2016 is also missing employment and income data. Data on employment type and employment status data was collected with questions 3.7.6 - 3.7.6.24 of the questionnaire. Income data was collected with questions 3.7.7. - 3.7.7.4. According to Statistics SA, the data from these questions was not released because changes in collection methodologies resulted in this data not being comparable with the employment and income data in the Quarterly Labour Force Survey.

In a study conducted between 2016 and 2018, *** in ** Africans declared to have heard about climate change. Mauritius, Malawi, and Uganda registered the highest percentages of respondents who stated to have heard about climate change. Over ** percent of people from Mauritius had heard about climate change, the highest share among ** surveyed African countries. Africa is particularly vulnerable to climate change. A large part of the population lives below the poverty line and its livelihood depends on activities extremely sensitive to climate change and weather conditions.

The statistic shows the most population distribution of mobile service subscribers in the Middle East and North Africa region, by country and by service type, in the second quarter of 2016. At that time, ** percent of the population in the MENA region was subscribed to mobile internet services.