This statistic shows the number of social network users in South Africa from 2017 to 2026. In 2019, there were approximately 30 million social network users in South Africa, and this figure is projected to grow to 40.77 million users in 2026.

This edition of the Abstract of Agricultural Statistics contains South African agricultural statistics of major importance that were available up to December 2017. The "Abstract" contains meaningful information on, inter alia, field crops, horticulture, livestock, important indicators and the contribution of agriculture.

This statistic shows the number of Facebook users in South Africa from 2017 to 2026. In 2026 the number of Facebook users in South Africa is expected to reach 35.92 million, up from 25.4 million users in 2020.

Abstract

This cumulative dataset contains statistics on mortality and causes of death in South Africa covering the period 1997-2017. The mortality and causes of death dataset is part of a regular series published by Stats SA, based on data collected through the civil registration system. This dataset is the most recent cumulative round in the series which began with the separately available dataset Recorded Deaths 1996.

The main objective of this dataset is to outline emerging trends and differentials in mortality by selected socio-demographic and geographic characteristics for deaths that occurred in the registered year and over time. Reliable mortality statistics, are the cornerstone of national health information systems, and are necessary for population health assessment, health policy and service planning; and programme evaluation. They are essential for studying the occurrence and distribution of health-related events, their determinants and management of related health problems. These data are particularly critical for monitoring the Sustainable Development Goals (SDGs) and Agenda 2063 which share the same goal for a high standard of living and quality of life, sound health and well-being for all and at all ages. Mortality statistics are also required for assessing the impact of non-communicable diseases (NCD's), emerging infectious diseases, injuries and natural disasters.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

This dataset is based on information on mortality and causes of death from the South African civil registration system. It covers all death notification forms from the Department of Home Affairs for deaths that occurred in 1997-2017, that reached Stats SA during the 2018/2019 processing phase.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The registration of deaths is captured using two instruments: form BI-1663 and form DHA-1663 (Notification/Register of death/stillbirth).

Data appraisal

This cumulative dataset is part of a regular series published by Stats SA and includes all previous rounds in the series (excluding Recorded Deaths 1996). Stats SA only includes one variable to classify the occupation group of the deceased (OccupationGrp) in the current round (1997-2017). Prior to 2016, Stats SA included both occupation group (OccupationGrp) and industry classification (Industry) in all previous rounds. Therefore, DataFirst has made the 1997-2015 cumulative round available as a separately downloadable dataset which includes both occupation group and industry classification of the deceased spanning the years 1997-2015.

This statistic presents the social network penetration in South Africa. As of the third quarter 2017, ** percent of the population were active with social media users. The most popular social platform was social messenger WhatsApp with a ** percent penetration rate.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage. The lowest level of geographic aggregation for the data is Province

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses a master sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household surveys that have reasonably compatible design requirement as the QLFS. The 2013 master sample is based on information collected during the 2011 population Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the master sample since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the master sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current master sample (3 324) reflects an 8,0% increase in the size of the master sample compared to the previous (2007) master sample (which had 3 080 PSUs). The larger master sample of PSUs was selected to improve the precision (smaller CVs) of the QLFS estimates.

The master sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are:urban, tribal and farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one (1) to four (4) and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

There are a number of aspects in which the 2013 version of the master sample differs from the 2007 version. In particular, the number of primary sample units increased. Mining strata were also introduced which serves to improve the efficiency of estimates relating to employment in mining. The number of geo-types was reduced from 4 to 3 while the new master sample allows for the publication of estimates of the labour market at metro level. The master sample was also adjusted Given the change in the provincial distribution of the South African population between 2001 and 2011. There was also an 8% increase in the sample size of the master sample of PSUs to improve the precision of the QLFS estimates. The sample size increased most notable in Gauteng, the Eastern Cape and KwaZulu-Natal. For more details on the differences between the two master samples please consult the section 8 (technical notes) of the QLFS 2015 Q3 release document (P0211).

From the master sample frame, the QLFS takes draws exmploying a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The primary stratification occurred at provincial, metro/non-metro, mining and geography type while the secondary strata were created within the primary strata based on the demographic and socio-economic characteristics of the population.

For each quarter of the QLFS, a ¼ of the sampled dwellings is rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings are expected to remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Face-to-face [f2f]

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Face-to-face [f2f]

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Please note that DataFirst provides versioning at dataset and file level. Revised files have new version numbers. Files that are not revised retain their original version numbers. Changes to any of the data files will result in the dataset having a new version number. Thus version numbers of files within a dataset may not match

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses a master sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household surveys that have reasonably compatible design requirement as the QLFS. The 2013 master sample is based on information collected during the 2011 population Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the master sample since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the master sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current master sample (3 324) reflects an 8,0% increase in the size of the master sample compared to the previous (2007) master sample (which had 3 080 PSUs). The larger master sample of PSUs was selected to improve the precision (smaller CVs) of the QLFS estimates.

The master sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are:urban, tribal and farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one (1) to four (4) and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

There are a number of aspects in which the 2013 version of the master sample differs from the 2007 version. In particular, the number of primary sample units increased. Mining strata were also introduced which serves to improve the efficiency of estimates relating to employment in mining. The number of geo-types was reduced from 4 to 3 while the new master sample allows for the publication of estimates of the labour market at metro level. The master sample was also adjusted Given the change in the provincial distribution of the South African population between 2001 and 2011. There was also an 8% increase in the sample size of the master sample of PSUs to improve the precision of the QLFS estimates. The sample size increased most notable in Gauteng, the Eastern Cape and KwaZulu-Natal. For more details on the differences between the two master samples please consult the section 8 (technical notes) of the QLFS 2015 Q3 release document (P0211).

From the master sample frame, the QLFS takes draws exmploying a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The primary stratification occurred at provincial, metro/non-metro, mining and geography type while the secondary strata were created within the primary strata based on the demographic and socio-economic characteristics of the population.

For each quarter of the QLFS, a ¼ of the sampled dwellings is rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings are expected to remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Face-to-face [f2f]

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.; ;

South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.520 % in 2017. South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.520 % from Dec 2017 (Median) to 2017, with 1 observations. South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 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: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

As of 2022, most South African companies reported that marketing was managing their social media platforms in recent years. Nevertheless, it registered a noticeable decrease after 2020. Some ** percent of account managers were Individuals in 2022, which was ** percent less than the previous year. Moreover, ** percent of the companies had agencies managing their social media accounts.

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;

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Quarterly: average based on 3 monthly data points

Sampling procedure

Sample size: 275968

Abstract

The VCS series is a countrywide household-based survey that has three main objectives: • Provide information about the dynamics of crime from the perspective of households and the victims of crime • Explore public perceptions of the activities of the police, prosecutors, courts and correctional services in the prevention of crime and victimization • Provide complimentary data on the level of crime within South Africa in addition to the statistics published annually by the South African Police Service.

Geographic coverage

National coverage.

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers' hostels. The survey does not cover other collective living quarters such as students' hostels, old-age homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalized and non-military persons or households in South Africa.

Kind of data

Sample survey data [ssd]

Sampling procedure

VCS 2016/2017 uses a Master Sample frame which has been developed as a general-purpose household survey frame that can be used by other Stats SA household-based surveys. VCS 2016/2017 collection was based on the Stats SA 2013 Master Sample. This Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample. There are 3 324 primary sampling units (PSUs) in the Master Sample with an expected sample of approximately 33 000 dwelling units (DUs). The updating of the Master Sample as compared to previous VCSs is expected to improve the precision of statistical estimates.

The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed based on the questions used in the International Crime Victim Survey (ICVS), previous VOCSs (both conducted by ISS and Stats SA) with modifications in some instances. The Stats SA questionnaire design standard for household surveys was also used as a normative reference. In order to minimize fieldworker and capturing errors, the questionnaire was largely pre-coded. Sections 10 to 20 of the questionnaire represent household crimes for which a proxy respondent (preferably head of the household or acting head of household) answered on behalf of the household. All analysis done in this report that included demographic variables was done using the demographic characteristics of the household head or proxy. Section 21 to 28 of this questionnaire required that an individual be selected using the birthday section method to respond to questions classified as individual crimes. This methodology selects an individual who is 16 years or older, whose birthday was first to follow the survey date.

In the VOCS 2016/17 questionnaire, respondents were asked what they thought could be the motive for perpetrators committing crime. This question was asked differently in 2016/17 as compared to the previous years. Users are advised to use caution when these responses across the series.

Data appraisal

Comparability:

Prior to 2014/2015, VOCS respondents were asked about their crime-related experiences in the previous calendar year, but since 2014/15 VCS changed to a Continuous Data Collection (CDC) method. In this data collection method, respondents were interviewed on a rolling basis over the course of a year and asked about crime experienced in the 12 months prior to the interview. As a result of this, the victimization experiences reported by respondents interviewed in a period of 12 months relate to a broader span of 23 months.

As of 2022, the total number of students in South Africa amounted to **** million. Of those, most were in KwaZulu-Natal and Gauteng throughout the period under review. For both provinces, the number of learners reached **** million and **** million that year, respectively. On the other hand, Northern Cape had the lowest count of students.

This statistic presents the leading banks registered in South Africa in 2017, by assets. In that year, the Standard Bank of South Africa Limited was the largest domestic South African bank, with **** trillion Rand in total assets.

Abstract

The Victims of Crime Survey (VCS) is a countrywide household-based survey which collects data on the prevalence of particular kinds of crime within South Africa. The survey includes information on victimisation experienced by individuals and households and their perspectives on community responses to crime. Therefore, VCS data can be used for research in the development of policies and strategies for crime prevention and public safety and education programmes. Statistics South Africa (StatsSA) conducted its first VCS in 1998. Following the VCS 1998, victims surveys were conducted by the Institute for Security Studies (ISS). Since 2011, StatsSA began conducting an annual collection of the VCS as a source of information on crime in South Africa. The main objectives of the survey are to:

• Provide information about the dynamics of crime from the perspective of households and the victims of crime.

• Explore public perceptions of the activities of the police, prosecutors, courts and correctional services in the prevention of crime and victimisation.

• Provide complimentary data on the level of crime within South Africa in addition to the statistics published annually by the South African Police Service.

NOTE: The VCS 2017/18 is the eighth and final release in the collection and is comparable to the new Governance Public Safety and Justice Survey (GPSJS). In April 2018, StatsSA launched the GPSJS in response to the need for standardised international reporting standards on governance and access to justice that are recommended by the SDGs, ShaSA and Agenda 2063. In compliance with these standards, Stats SA has discontinued separate publication of the VCS and rather incorporated it within the new GPSJS series. Therefore, VCS 2017/18 represents the final separate release of the series and all subsequent VCS series can be extracted from the GPSJS series (i.e. VCS 2018/19 is contained within GPSJS 2018/19).

Geographic coverage

The survey has national coverage.

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers' hostels. The survey does not cover other collective living quarters such as students' hostels, old-age homes, hospitals, prisons and military barracks, and is therefore only representative of non-institutionalised and non-military persons or households in South Africa.

Kind of data

Sample survey data

Sampling procedure

VCS 2017/2018 uses a Master Sample frame which has been developed as a general-purpose household survey frame that can be used by other Stats SA household-based surveys. VCS 2017/2018 collection was based on the Stats SA 2013 Master Sample. This Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample. There are 3 324 primary sampling units (PSUs) in the Master Sample with an expected sample of approximately 33 000 dwelling units (DUs).The number of PSUs in the current Master Sample (3 324) reflects an 8,0% increase in the size of the Master Sample compared to the previous Master Sample (based on the 2001 Census which had 3 080 PSUs). The updating of the Master Sample as compared to previous VCSs is expected to improve the precision of statistical estimates.

The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

Mode of data collection

Face-to-face [f2f]

Research instrument

The VCS 2017/18 questionnaire was based on the questionnaires used in the International Crime Victim Survey (ICVS) and previous VOCSs conducted by the Institute for Security Studies (ISS) and Statistics SA.

Sections 10 to 20 of the questionnaire relate to household crimes. A proxy respondent (preferably head of the household or acting head of household) answered on behalf of the household. Section 21 to 28 of the questionnaire about crimes on individuals were asked of a household member who was selected using the birthday section method. This methodology selects an individual who is 16 years or older, whose birthday is soonest after the survey date.

Data appraisal

Comparability:

Prior to 2014/2015, VOCS respondents were asked about their crime-related experiences in the previous calendar year, but since 2014/15 VCS changed to a Continuous Data Collection (CDC) method. In this data collection method, respondents were interviewed on a rolling basis over the course of a year and asked about crime experienced in the 12 months prior to the interview. As a result of this, the victimisation experiences reported by respondents interviewed in a period of 12 months relate to a broader span of 23 months.

The VCS 2017/18 is comparable to all previous VCSs iin that several questions have remained unchanged over time. Where possible, it was generally indicated in the report. Additionally, the VCS 2017/18 is the last before VCS became incorproated into a broader survey called the GPSJS. The change to the surveys will likely cause some comparability issues going forward beyond 2018.

Metadata: There is an error in the SSA published metadata, which incorrectly states that the survey was designed with 3080 PSUs. The survey was designed with 3324 PSUs.

South Africa ZA: Population: as % of Total: Male: Aged 0-14 data was reported at 29.783 % in 2017. This records a decrease from the previous number of 29.966 % for 2016. South Africa ZA: Population: as % of Total: Male: Aged 0-14 data is updated yearly, averaging 40.295 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 42.281 % in 1966 and a record low of 29.783 % in 2017. South Africa ZA: Population: as % of Total: Male: Aged 0-14 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 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average;