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: 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;

Population Projection, 2016 - Nigeria

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.; ;

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]

Population, land area and population density data from the years 2011 and 2016

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.

Population Projections by state, 2007-2016, Source: Nigeria Bureau of Statistics, 2016, https://nigerianstat.gov.ng/resource/POPULATION%20PROJECTION%20Nigeria%20sgfn.xls

South Africa ZA: Population: as % of Total: Female: Aged 15-64 data was reported at 65.285 % in 2017. This records an increase from the previous number of 65.252 % for 2016. South Africa ZA: Population: as % of Total: Female: Aged 15-64 data is updated yearly, averaging 56.847 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 65.285 % in 2017 and a record low of 53.429 % in 1964. South Africa ZA: Population: as % of Total: Female: 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. Female population between the ages 15 to 64 as a percentage of the total female 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; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

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.

Population Projection, 2016 - Nigeria

This is a webmap that displays the population density by state of the country Nigeria as at 2016. It showcases a visual, easy-to-understand display of the difference in population density among the different states using a graduated colour scheme. The population density is calculated by dividing the states total population by the are of its landmass in m².

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.

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.

As of 2022, South Africa's population reached over 60.6 million inhabitants, roughly 460,000 more than in the previous year. Between the reflected period there was annual population increase, despite a slight decrease in 2006.

This app offers an interactive legend allowing users a more holistic experience with the 2016 Nigeria Population Density Map. In this app, unlike the web map, users can interact with the legend. By clicking on categories defined in the legend, they can focus on particular categories/ranges that are more relevant to them.

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: Population: Male: Ages 30-34: % of Male Population data was reported at 8.941 % in 2017. This records an increase from the previous number of 8.898 % for 2016. South Africa ZA: Population: Male: Ages 30-34: % of Male Population data is updated yearly, averaging 6.976 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 8.941 % in 2017 and a record low of 6.181 % in 1979. South Africa ZA: Population: Male: Ages 30-34: % 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 30 to 34 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.; ;

If you would like to view a straightforward comparison between the Population density (by State) of Nigeria as at 2006 and 2016, this is just for you.

This web app showcases a simple and at-a-glance comparison between the Population density of Nigeria in 2006 and 2016. It features side-by-side, two individual web apps that display the population density, by state, for each corresponding year (2006, 2016). The population density was calculated by dividing the states total population by the area of its landmass in m². Within the app, there are easy-to-use navigation tools that have been configured to help users better access its features. Examples of these include the zoom tool, Expand tool, synced pop-ups, legend and many more. Clicking on any state on either map enables its pop-up from which you can access that particular states population details. One wonderful feature of this app is that popups for the 2 maps are synced! This means that clicking on a state in one map to get its pop-up details, will effect the same in the second map. (How cool is that!) Don't hesitate to leave comment about your experience with this web app, as well as suggestions on what can be done to make it even better.Thank you!

Abstract

The South Africa Demographic and Health Survey 2016 (SADHS 2016) is the third DHS conducted in South Africa and follows surveys carried out in 1998 and 2003. The SADHS 2016 was designed to provide up-to-date information on key indicators needed to track progress in South Africa’s health programmes.

Geographic coverage

The survey was designed to provide representative estimates for main demographic and health indicators for the country as a whole, for urban and non-urban areas separately, and for each of the nine provinces in South Africa: Western Cape, Eastern Cape, Northern Cape, Free State, KwaZulu-Natal, North West, Gauteng, Mpumalanga, and Limpopo.

Analysis unit

Households and individuals

Universe

The South African Demographic and Health Survey (SADHS) covered the population living in private households in the country.

Kind of data

Sample survey data

Sampling procedure

The sample for the SADHS 2016 is a stratified sample selected in two stages from the Master Sampling Frame. Stratification was achieved by separating each province into urban, traditional, and farm areas. In total, 26 sampling strata were created (since there are no traditional areas in Western Cape). Samples were selected independently in each sampling stratum by a two-stage selection. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels within a given sampling stratum by sorting the sampling frame according to administrative units at different levels in each stratum and using probability proportional to size selection at the first stage of sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used in the SADHS 2016. Interviewers used tablet computers to record responses during interviews.

Response rate

Of the total 972 PSUs that were selected, fieldwork was not implemented in three PSUs due to concerns about the safety of the interviewers and the questionnaires for another three PSUs were lost in transit. The data file contains information for a total of 966 PSUs. A total of 12,860 households was selected for the sample and 12,247 were successfully interviewed. The shortfall is primarily due to refusals and to dwellings that were vacant or in which the inhabitants had left for an extended period at the time they were visited by interviewing teams.

Of the 12,638 households occupied 97 percent were successfully interviewed. In these households, 12,327 women were identified as eligible for the individual women's interview (15-49) and interviews were completed with 11,735 or 95 percent of them. In the one half of the households that were selected for inclusion in the adult health survey 14,928 eligible adults age 15 and over were identified of which 13,827 or 93 percent were interviewed. The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was about 2 percent.

Sampling error estimates

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.