As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

In 1800, the population of modern day area of South Africa was approximately 1.44 million. Like most of the continent, the population of South Africa increased gradually through most of the 19th century, reaching 4.71 million by the start of the 20th century. Beginning in the 20th century however, the population would begin to rise exponentially as industrialization, advances in medicine and health, and the spread of vaccinations allowed for lower child mortality rates and increased life expectancy among adults. The population of South Africa would continue to rise exponentially for almost a century, going from just under 5 million at the start of the 1900s to almost 45 million by 2000. However, since the early 2000s, South Africa’s population growth has slowed, the result of a significant decrease in fertility rates in the country in recent years. In 2020, South Africa is estimated to have a population of 59.31 million.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated Although South Africa’s yearly population growth has been dropping since 2013, the growth rate still stood above the world average in 2021. That year, the global population increase reached 0.94 percent, while for South Africa, the rise was 1.23 percent. The majority of the people lived in the borders of Gauteng, the smallest of the nine provinces in land area. The number of people residing there amounted to 15.9 million in 2021. Although Western Cape was the third-largest province, one of it cities, Cape Town, had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the third quarter of 2022, the unemployment rate reached close to 60 percent and 42.9 percent among people aged 15-24 and 25-34 years, respectively. In the same period, some 25 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 71.2 percent.

Geographic coverage

The 1985 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

The 1985 Census dataset contains 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

Analysis unit

The units of analysis under observation in the South African census 1985 are households and individuals

Universe

The South African census 1985 census covered the provinces of the Cape, the Orange Free State, Transvaal, and Nata and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Although the census was meant to cover all residents of the so called white areas of South Africa, in 88 areas door-to-door surveys were not possible and the population in these areas was enumerated by means of a sample survey conducted by the Human Sciences Research Council.

Mode of data collection

Face-to-face [f2f]

Research instrument

The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

Data appraisal

UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
Whites 7.6%
Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

In the fourth quarter of 2023, the labor force participation rate among Black South Africans reached over 59 percent, marking a year-on-year change of two percent compared to the fourth quarter of 2022. However, the participation rate of the labor force among Indian/Asian South Africans dropped from about 61 percent in the previous year to 58 percent.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of South Floral Park by race. It includes the distribution of the Non-Hispanic population of South Floral Park across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of South Floral Park across relevant racial categories.

Key observations

Of the Non-Hispanic population in South Floral Park, the largest racial group is Black or African American alone with a population of 855 (57.85% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the South Floral Park
• Population: The population of the racial category (for Non-Hispanic) in the South Floral Park is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of South Floral Park total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for South Floral Park Population by Race & Ethnicity. You can refer the same here

In 2018, the total number of deaths related to cancer was the highest amongst the Black African ethnicity group with 23,823 reports. The white ethnicity group followed with 9,033 reports in the same year. In each ethnicity group, there is a worrying trend of increasing deaths due to cancer between 2008 and 2018. As a point of reference, it might be useful to keep the population numbers of each ethnic group in mind.

This statistic shows the total number of students that graduated from postsecondary institutions in South Africa in 2015, by field of study and race. In 2015, a total of 27,337 African students earned a degree in education.

As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

This project used the updated 2007-2011 HSRC's master sample. Aerial photographs drawn from Google Earth were utilised to ensure that the most up-to-date information was available sample. the master sample is defined as a selection, for the purpose of repeated community or household surveys, of a probability sample of census enumeration areas throughout South Africa that are representative of the country's provincial, settlement and racial diversity. The sampling frame that was used in the design of the Master Sample was the 2001 census Enumerator Areas (EAs) from Statistics South Africa (Stats SA). The target population for this study were all people in South Africa, excluding persons in so-called special institutions (e.g. hospitals, military camps, old age homes, schools and university hostels). The EAs were used as the Primary Sampling Units (PSUs) and the Secondary Sampling Units (SSUs) were the visiting points (VPs) or households (HHs). The Ultimate Sampling Units (USUs) were the individuals eligible to be selected for the survey. Any member of the household "who slept here last night", including visitors was an eligible household member for the interview. This sampling approach was used in the 2001 census and is a standard demographic household survey procedure.

The sample was designed with two main explicit strata, the provinces and the geography types (geotype) of the EA. In the 2001 census, the four geotypes were urban formal, urban informal, rural formal (including commercial farms) and tribal areas (rural informal) (i.e. the deep rural areas). In the formal urban areas, race was used as a third stratification variable. What this means is that the Master Sample was designed to allow reporting of results (i.e. reporting domain) at a provincial, geotype and race level. A reporting domain is defined as that domain at which estimates of a population characteristic or variable should be of an acceptable precision for the presentation of survey results. A visiting point is defined as a separate (non-vacant) residential stand, address, structure, and flat in a block of flats or homestead. The 2001 estimate of visiting points was used as the Measure of Size (MOS) in the drawing of the sample. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then preceded with the interview.

All people in the households, resident at the visiting point were invited to participate in the study. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. These PSUs were allocated to each of the explicit strata. With a view to obtaining an approximately self-weighting sample of visiting points (i.e. SSUs), (a) the EAs were drawn with probability proportional to the size of the EA using the 2001 estimate of the number of visiting points in the EA database as a measure of size (MOS) and (b) to draw an equal number of visiting points (i.e. SSUs) from each drawn EA. An acceptable precision of estimates per reporting domain requires that a sample of sufficient size be drawn from each of the reporting domains. Consequently, a cluster of 15 VP was systematically selected on the aerial photography produced for each of the EAs in the master sample. Since it is not possible to determine on an aerial photograph whether a 'dwelling unit' is indeed a residential structure or whether it was occupied (i.e. people sleeping there), it was decided to form clusters of 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 0-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children.

The sample size estimate for the 2012 survey was guided by the (1) requirement for measuring change over time in order to detect a change in HIV prevalence of 5 percentage points in each of the main reporting domains, namely gender, age-group, race, locality type, and province (5% level of significance, 80% power, two-sided test), and (2) the requirement of an acceptable precision of estimates per reporting domain; that is, to be able to estimate HIV prevalence in each of the main reporting domains with a precision level of less than ± 4%, which is equivalent to the expected width of the 95% confidence interval (z-score at the 95% level for two-sided test). A design effect of 2 was assumed.

Overall, a total of 3...

This project used the updated 2007-2011 HSRC's master sample. Aerial photographs drawn from Google Earth were utilised to ensure that the most up-to-date information was available sample. the master sample is defined as a selection, for the purpose of repeated community or household surveys, of a probability sample of census enumeration areas throughout South Africa that are representative of the country's provincial, settlement and racial diversity. The sampling frame that was used in the design of the Master Sample was the 2001 census Enumerator Areas (EAs) from Statistics South Africa (Stats SA). The target population for this study were all people in South Africa, excluding persons in so-called special institutions (e.g. hospitals, military camps, old age homes, schools and university hostels). The EAs were used as the Primary Sampling Units (PSUs) and the Secondary Sampling Units (SSUs) were the visiting points (VPs) or households (HHs). The Ultimate Sampling Units (USUs) were the individuals eligible to be selected for the survey. Any member of the household "who slept here last night", including visitors was an eligible household member for the interview. This sampling approach was used in the 2001 census and is a standard demographic household survey procedure.

The sample was designed with two main explicit strata, the provinces and the geography types (geotype) of the EA. In the 2001 census, the four geotypes were urban formal, urban informal, rural formal (including commercial farms) and tribal areas (rural informal) (i.e. the deep rural areas). In the formal urban areas, race was used as a third stratification variable. What this means is that the Master Sample was designed to allow reporting of results (i.e. reporting domain) at a provincial, geotype and race level. A reporting domain is defined as that domain at which estimates of a population characteristic or variable should be of an acceptable precision for the presentation of survey results. A visiting point is defined as a separate (non-vacant) residential stand, address, structure, and flat in a block of flats or homestead. The 2001 estimate of visiting points was used as the Measure of Size (MOS) in the drawing of the sample. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then preceded with the interview.

All people in the households, resident at the visiting point were invited to participate in the study. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. These PSUs were allocated to each of the explicit strata. With a view to obtaining an approximately self-weighting sample of visiting points (i.e. SSUs), (a) the EAs were drawn with probability proportional to the size of the EA using the 2001 estimate of the number of visiting points in the EA database as a measure of size (MOS) and (b) to draw an equal number of visiting points (i.e. SSUs) from each drawn EA. An acceptable precision of estimates per reporting domain requires that a sample of sufficient size be drawn from each of the reporting domains. Consequently, a cluster of 15 VP was systematically selected on the aerial photography produced for each of the EAs in the master sample. Since it is not possible to determine on an aerial photograph whether a 'dwelling unit' is indeed a residential structure or whether it was occupied (i.e. people sleeping there), it was decided to form clusters of 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 0-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children.

The sample size estimate for the 2012 survey was guided by the (1) requirement for measuring change over time in order to detect a change in HIV prevalence of 5 percentage points in each of the main reporting domains, namely gender, age-group, race, locality type, and province (5% level of significance, 80% power, two-sided test), and (2) the requirement of an acceptable precision of estimates per reporting domain; that is, to be able to estimate HIV prevalence in each of the main reporting domains with a precision level of less than ± 4%, which is equivalent to the expected width of the 95% confidence interval (z-score at the 95% level for two-sided test). A design effect of 2 was assumed.

Overall, a total of 3...

In 2023, 24.4 percent of South Carolina residents were Black or African American. A further 63.6 percent of the population were white, and 7 percent of South Carolina residents were of two or more races in that same year.

Description: The data set contains the data of the parents or guardians of children aged 0 to 11 years. Some of the questions included were the child's biographical data, health status and health questions, male circumcision, education of the child on life issues, infant and child feeding practices as well as school attendance and immunisation records. The data set contains 275 variables and 9667 cases. Refer to the user guide for information regarding guidance relating to data analysis.

Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5% From the total of 38431 (89.5%) individuals who completed the interview, 2295 (5.3%) refused to be interviewed, 2224(5.2%) were absent from the household and 2224 (5.2%) were classified as missing/other.

South African population, 2 years and older from urban formal, urban informal, rural formal (farms), rural informal (tribal area) settlements.

South African population, 2 years and older from urban formal, urban informal, rural formal (farms), rural informal (tribal area) settlements.

Description: In the combined data set four individual data sets were combined, guardians for children to 11 years, children 12 to 14 years, youths and adults 15 years and older and individual's information from the visiting point data set. The data set contains information on: biographical data, media, communication and norms, knowledge and perceptions of HIV/AIDS, male circumcision, sexual debut, partners and partner characteristics, condoms, vulnerability, HIV testing, alcohol and substance use, general perceptions about government, health and violence in the community. The data set contains 917 variables and 44029 cases. Subsequent to the dissemination of version 1 of the Combined data set the skip patterns for the Adult and Child data sets were corrected and updated in the Combined data set which is disseminated as Version 2.

Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5%

As of 2022, just over 55 percent of all men in South Africa were classified as single, which was only a slightly larger rate compared to the almost 49 percent of females among the South African adult population. At 10.2 percent, however, women made up a noticeably larger percentage of widows compared to their male counterparts at only 2.7 percent.

Description: The guardian data of the SABSSM 2005 study covers information from the parents or care givers of children 2 - 11 years on matters ranging from biographical information of the child and parent/guardian, the child's home environment, care and protection, sources of information on HIV and AIDS, media impact and the health status of the child. The data set contains 165 variables and 5260 cases.

Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the world. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the second in a series of household surveys conducted by the Human Sciences Research Council (HSRC), that allow for tracking of HIV and associated determinants over time using the same methodology used in the 2002 survey, thus making it the first national-level repeat survey. The interval of three years allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The survey provides the first nationally representative HIV incidence estimates. The study key objectives were to: Determine HIV prevalence and incidence as well as viral load in the population; Gather data to inform modelling of the epidemic; Identify risky behaviours that predispose the South African population to HIV infection; examine social, behavioural and cultural determinants of HIV; explore the reach of HIV/AIDS communication and the relationship of communication to response; assess the relationship between mental health and HIV/AIDS and establish a baseline; assess public perceptions of South Africans with respect to the provision of anti-retroviral (ARV) therapy for prevention of mother-to-child transmission and for treating people living with HIV/AIDS; understand public perceptions regarding aspects of HIV vaccines; and investigate the extent of the use of hormonal contraception and its relationship to HIV infection. In the 10 584 valid visiting points that agreed to participate in the survey, 24 236 individuals were eligible for interviews and 23 275 completed the interview. Of the 24 236 individuals, 15 851 agreed to HIV testing and were anonymously linked to the behavioural interviews. The household response rate was 84.1 % and the overall response rate for HIV testing was 55 %.

This statistic shows the total population of South Africa from 2013 to 2023 by gender. In 2023, South Africa's female population amounted to approximately 32.46 million, while the male population amounted to approximately 30.75 million inhabitants.

All persons living in occupied households (HHs) were eligible to participate.