In 2023, the crude birth rate in live births per 1,000 inhabitants in South Africa was 18.78. Between 1960 and 2023, the figure dropped by 21.04, though the decline followed an uneven course rather than a steady trajectory.

In 1925, the crude birth rate in South Africa was just under 49 births per thousand people, meaning that almost five percent of the population was born in that year. This figure would follow the country’s trends in fertility, remaining largely unchanged until the 1950s when, following the implementation of apartheid rule in the country in 1948, declines in fertility from the government's family planning programs would lead to the birth rate's rapid decline. Apart from a brief pause in the early-1980s, births rates would decline throughout the second half of the 20th century, falling to just under 24 births per thousand people by 2000. The crude birth rate would see a brief increase in the early 2000s, largely attributed to a diversion of healthcare funding away from contraceptives to funding for treatments for the HIV/AIDS epidemic in the country, but since then, birth rates have resumed their decline, and in 2020, it is estimated that South Africa had a birth rate just under 21 births for every thousand people.

South Africa ZA: Fertility Rate: Total: Births per Woman data was reported at 2.458 Ratio in 2016. This records a decrease from the previous number of 2.485 Ratio for 2015. South Africa ZA: Fertility Rate: Total: Births per Woman data is updated yearly, averaging 3.924 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 6.041 Ratio in 1960 and a record low of 2.458 Ratio in 2016. South Africa ZA: Fertility Rate: Total: Births per Woman 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: Health Statistics. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.; ; (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; Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children.

South Africa ZA: Birth Rate: Crude: per 1000 People data was reported at 20.981 Ratio in 2016. This records a decrease from the previous number of 21.297 Ratio for 2015. South Africa ZA: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 30.616 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 40.222 Ratio in 1960 and a record low of 20.981 Ratio in 2016. South Africa ZA: Birth Rate: Crude: per 1000 People 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. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (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;

The total fertility rate represents the average number of children that a woman will have over the course of their reproductive years. In South Africa in the early-1920s, the average woman would have 6.5 children over the course of their reproductive years, a rate that would remain fairly constant until 1950. From this point until 2005, South Africa’s fertility rate would drop consistently, and would reach 2.9 children per woman by the beginning of the 21st century. There was a slight increase in fertility in 2005, largely attributed to a diversion of healthcare funding away from contraceptives to funding for treatments for the HIV/AIDS epidemic in the country, however, the fertility rate would again decrease in the years following this. In 2020, the total fertility rate for South Africa is estimated to be just 2.41 children per woman, a rate much lower than most other Sub-Saharan countries.

South Africa ZA: UCB Projection: Crude Birth Rate: per 1000 Persons data was reported at 13.500 NA in 2050. This records a decrease from the previous number of 13.600 NA for 2049. South Africa ZA: UCB Projection: Crude Birth Rate: per 1000 Persons data is updated yearly, averaging 20.050 NA from Jun 1985 (Median) to 2050, with 66 observations. The data reached an all-time high of 33.600 NA in 1985 and a record low of 13.500 NA in 2050. South Africa ZA: UCB Projection: Crude Birth Rate: per 1000 Persons data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s South Africa – Table ZA.US Census Bureau: Demographic Projection.

In 2024, the fertility rate in Africa was *** children per woman. The average number of newborn infants per woman on the continent decreased compared to 2000, when women had approximately **** children throughout their reproductive years. By 2030, fertility in Africa is projected to decline to around *** births per woman, yet it will remain high. The highest fertility rate worldwide Despite its gradually declining rate, fertility in Africa is the highest in the world. In 2023, the average fertility rate on the continent stood at **** children per woman, compared to a global average of **** births per woman. In contrast, Europe and North America were the continents with the lowest proportion of newborns, each registering a fertility rate below two children per woman. Additionally, Africa records the highest fertility rate among the young female population aged 15 to 19 years. In 2022, West and Central Africa had an adolescent fertility rate of nearly *** children per 1,000 girls, the highest value worldwide. Lower fertility in Northern Africa Fertility levels vary significantly across Africa. In 2023, Somalia, Chad, Niger, the Democratic Republic of Congo, and the Central African Republic were the countries with the highest fertility rates on the continent. In those countries, women had an average of over *** children in their reproductive years. The number of adolescent girls giving birth also differed within Africa. For instance, the adolescent fertility rate in North Africa stood at around **** children per 1,000 young women in 2023. On the other hand, Sub-Saharan Africa registered a higher rate of approximately **** children per 1,000 girls as of the same year. In general, higher poverty levels, inadequate social and health conditions, and increased infant mortality are some main drivers of higher fertility rates.

In 2023, the total fertility rate in children per woman in South Africa was 2.22. Between 1960 and 2023, the figure dropped by 3.89, though the decline followed an uneven course rather than a steady trajectory.

United States BED: sa: Employment: Birth Rate data was reported at 0.700 % in Mar 2018. This stayed constant from the previous number of 0.700 % for Dec 2017. United States BED: sa: Employment: Birth Rate data is updated quarterly, averaging 0.900 % from Sep 1992 (Median) to Mar 2018, with 103 observations. The data reached an all-time high of 1.400 % in Mar 1993 and a record low of 0.700 % in Mar 2018. United States BED: sa: Employment: Birth Rate data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G043: Business Employment Dynamics.

In 2023, the crude birth rate in South Africa decreased by 0.3 live births per 1,000 inhabitants (-1.57 percent) compared to 2022. This marks the lowest rate during the observed period. The crude birth rate is the annual number of live births divided by the total population, expressed per 1,000 people.Find more statistics on other topics about South Africa with key insights such as total fertility rate, fertility rate of women aged between 15 and 19 years old, and infant mortality rate.

ZA: Total Fertility Rate: Children per Woman data was reported at 2.220 Person in 2023. This records a decrease from the previous number of 2.230 Person for 2022. ZA: Total Fertility Rate: Children per Woman data is updated yearly, averaging 2.440 Person from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 3.720 Person in 1990 and a record low of 2.220 Person in 2023. ZA: Total Fertility Rate: Children per Woman data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Africa – Table ZA.OECD.GGI: Social: Demography: Non OECD Member: Annual.

South Africa ZA: UCB Projection: Fertility Rate: per Woman data was reported at 1.840 NA in 2050. This records a decrease from the previous number of 1.850 NA for 2049. South Africa ZA: UCB Projection: Fertility Rate: per Woman data is updated yearly, averaging 2.275 NA from Jun 1985 (Median) to 2050, with 66 observations. The data reached an all-time high of 4.050 NA in 1985 and a record low of 1.840 NA in 2050. South Africa ZA: UCB Projection: Fertility Rate: per Woman data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s South Africa – Table ZA.US Census Bureau: Demographic Projection.

South Africa ZA: Completeness of Birth Registration data was reported at 85.000 % in 2012. This records a decrease from the previous number of 92.000 % for 2008. South Africa ZA: Completeness of Birth Registration data is updated yearly, averaging 85.000 % from Dec 2006 (Median) to 2012, with 3 observations. The data reached an all-time high of 92.000 % in 2008 and a record low of 78.000 % in 2006. South Africa ZA: Completeness of Birth Registration 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. Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.; ; UNICEF's State of the World's Children based mostly on household surveys and ministry of health data.; Weighted average;

In 2023, the births per one thousand women in the age group between 15 and 19 in South Africa were *****. Between 1960 and 2023, the figure dropped by *****, though the decline followed an uneven course rather than a steady trajectory.

Saudi Arabia SA: Fertility Rate: Total: Births per Woman data was reported at 2.532 Ratio in 2016. This records a decrease from the previous number of 2.579 Ratio for 2015. Saudi Arabia SA: Fertility Rate: Total: Births per Woman data is updated yearly, averaging 6.194 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 7.313 Ratio in 1975 and a record low of 2.532 Ratio in 2016. Saudi Arabia SA: Fertility Rate: Total: Births per Woman data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Health Statistics. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.; ; (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; Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children.

Saudi Arabia SA: Birth Rate: Crude: per 1000 People data was reported at 19.562 Ratio in 2016. This records a decrease from the previous number of 19.921 Ratio for 2015. Saudi Arabia SA: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 37.785 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 47.577 Ratio in 1960 and a record low of 19.562 Ratio in 2016. Saudi Arabia SA: Birth Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (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;

As of 2022, almost 245 thousand births out of the total births registered in South Africa occurred among mothers between the ages of 30 and 34 years. Women older than this age group showed a decreasing number of birth registrations.

South Africa ZA: Mortality Rate: Under-5: per 1000 Live Births data was reported at 43.300 Ratio in 2016. This records a decrease from the previous number of 44.100 Ratio for 2015. South Africa ZA: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 66.000 Ratio from Dec 1974 (Median) to 2016, with 43 observations. The data reached an all-time high of 125.500 Ratio in 1974 and a record low of 43.300 Ratio in 2016. South Africa ZA: Mortality Rate: Under-5: per 1000 Live Births 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. Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

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.

Saudi Arabia SA: UCB Projection: Crude Birth Rate: per 1000 Persons data was reported at 10.800 NA in 2050. This records a decrease from the previous number of 10.900 NA for 2049. Saudi Arabia SA: UCB Projection: Crude Birth Rate: per 1000 Persons data is updated yearly, averaging 14.200 NA from Jun 1992 (Median) to 2050, with 59 observations. The data reached an all-time high of 34.000 NA in 1992 and a record low of 10.800 NA in 2050. Saudi Arabia SA: UCB Projection: Crude Birth Rate: per 1000 Persons data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Saudi Arabia – Table SA.US Census Bureau: Demographic Projection.