Teenage pregnancy

Author/s: Katharine Hall
Date: July 2023


This indicator shows the number and proportion of young women aged 15 – 24 who are reported to have given birth to a child in the past year. 


Data Source

Statistics South Africa (2010-2021) General Household Survey 2009-2019. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall, Children's Institute, University of Cape Town

Notes The denominator in the above indicator is all females in the 15 – 24 year age group.

Teenage pregnancy rates are difficult to calculate directly because it is hard to determine how many pregnancies end in miscarriage, still-birth or abortion: these are not necessarily known to the respondent, or accurately reported. In the absence of reliable data on pregnancy, researchers tend to rely on childbearing data (i.e. the percentage of women in an age group who have given birth to a live child).

Despite widespread assumptions that teen pregnancy in South Africa is an escalating problem, the available data suggest that the percentage of teenage mothers is not increasing. A number of studies have suggested a levelling off and even a decrease in fertility rates among teenagers in South Africa.1 Teenage fertility rates declined after the 1996 census, and Department of Health data between 2004 and 2017 showed a consistent decline in the share of teenagers aged 15 – 19 who attended antenatal clinics.2

Fertility rates are, of course, an indicator of possible exposure to HIV. HIV prevalence rates are higher among women in their late twenties and thirties, and lower among teenagers, and the prevalence rate in the 15 – 24 age group has decreased over the past 10 years. However prevalence rates are still worryingly high: of the young pregnant women surveyed in antenatal clinics in 2017, 11.3% of those aged 15 – 19 and 21.9% of those aged 20 – 24 were HIV positive.3 For many years the majority of deaths in young mothers were caused by HIV.4 Much of the overall decline in maternal deaths since 2011 is attributed to implementation of policies to manage and prevent HIV,5 but it is still important that safe sexual behaviours are encouraged and practised.

Studies have found that early childbearing – particularly by teenagers and young women who have not completed school – has a significant impact on the education outcomes of both the mother and child, and is also associated with poorer child health and nutritional outcomes.6 For this reason is it important to delay childbearing, and to ensure that teenagers who do fall pregnant are appropriately supported. This includes ensuring that young mothers can complete their education, and that they have access to parenting support programmes and health services. Although pregnancy is a major cause of school drop-out, some research has also suggested that teenage girls who are already falling behind at school are more likely to become pregnant than those who are progressing through school at the expected rate.7 So efforts to provide educational support for girls who are not coping at school may also help to reduce teenage pregnancies.

Poverty alleviation is important for both the mother and child, but take-up of the Child Support Grant among teenage mothers is low compared with older mothers.8 This suggests that greater effort should be made to assist young mothers to obtain birth certificates to apply for GSGs. Ideally, home affairs and social security services should form part of a comprehensive maternal support service at clinics and maternity hospitals.

Since 2009 the nationally representative General Household Survey (GHS) conducted by Statistics South Africa has included questions on pregnancy and fertility. The pregnancy question asks the household respondent: “Has any female household member [between 12 – 50 years] been pregnant during the past 12 months?” For those reported to have been pregnant, a follow-up question asks about the current status of the pregnancy. This indicator calculates the number and percentage of young women who have given birth in the past year.

According to the GHS, the national childbearing rate for young women aged 15 – 24 was 7% in 2017. There has been no significant change in this rate since 2009 when the question was first asked in the survey, and the estimated number of young women giving birth in a year has remained fairly stable at around 350,000.

As would be expected, childbearing rates increase with age. Only 2% of girls aged 15 – 17 were reported to have given birth in the previous 12 months (representing 31,000 teenagers in this age group). Childbearing rates rose to 9% among 18 – 20-year-olds (125,000 when weighted), and 10% in the 21 – 24 age group (188,000). These rates have also been stable over the past decade.

1 See, for example: Jonas K, Crutzen R, van den Borne B, Sewpaul R & Reddy P (2016) Teenage pregnancy rates and associations with other health risk behaviours: A three-wave cross-sectional study among South African school-going adolescents. Reproductive Health, 13(50). DOI: 10.1186/s12978-016-0170-8;
Ardington C, Branson N & Leibbrandt M (2011) Trends in Teenage Childbearing and Schooling Outcomes for Children Born to Teens in South Africa. SALDRU Working Paper 75. Cape Town: Southern African Labour & Development Research Unit, UCT;
Makiwane M, Desmond, C Richter L & Udjo E (2006) Is the Child Support Grant Associated with an Increase in Teenage Fertility in South Africa? Evidence from National Surveys and Administrative Data. Pretoria: Human Sciences Research Council.

2 Department of Health (2004 – 2019) National Antenatal Sentinel HIV and Syphilis Prevalence Surveys 2004 –2017. Pretoria: DoH.

3 Department of Health (2019) National Antenatal Sentinel HIV and Syphilis Prevalence Surveys 2017. Pretoria: DoH

4 Ardington C, Menendez A & Mutevedzi T (2015) Early childbearing, human capital attainment and mortality risk. Economic Development and Cultural Change, 62(2): 281-317.

5 Department of Health (2018) Saving Mothers 2014 – 2016: Seventh triennial report on confidential enquiries into maternal deaths in South Africa: Short report. Pretoria: DOH

6 Branson N, Ardington C & Leibbrandt M (2015) Health outcomes of children born to teen mothers in Cape Town, South Africa. Economic Development and Cultural Change, 63(3): 589-616;
Ardington et al, 2015 (above)
Ardington et al, 2011 (above).

7 Timæus I & Moultrie T (2015) Trends in childbearing and educational attainment in South Africa. Studies in Family Planning, 46(2): 143-160.

8 Makiwane M (2010) The Child Support Grant and teenage childbearing in South Africa. Development Southern Africa, 27(2): 193-204;
Kesho Consulting and Business Solutions (2006) Report on Incentive Structures of Social Assistance Grants in South Africa. Report commissioned by Department of Social Development, Pretoria;
Makiwane et al, 2006 (above).

The numerator for this indicator is the number of young women aged 15 – 24 years, who are reported to have given birth to a live child in the past year, and the denominator is the number of all females aged 15 – 24 years.

This question was only introduced in the General Household Survey in 2009.

The numbers are derived from the General Household Survey, a multi-purpose annual survey conducted by the national statistical agency, Statistics South Africa, to collect information on a range of topics from households in the country’s nine provinces. The survey uses a sample of 30,000 households. These are drawn from Census enumeration areas using multi-stage stratified sampling and probability proportional to size principles. The resulting estimates should be representative of all households in South Africa.

The GHS sample consists of households and does not cover other collective institutionalised living-quarters such as boarding schools, orphanages, students’ hostels, old-age homes, hospitals, prisons, military barracks and workers’ hostels. These exclusions should not have a noticeable impact on the findings in respect of children.

Changes in sample frame and stratification
The sample design for the 2015 GHS was based on a master sample that was designed in 2013 as a general purpose sampling frame to be used for all Stats SA household-based surveys. The same master sample is shared by the GHS, the Quarterly Labour Force Survey, the Living Conditions Survey and the Income and Expenditure Survey. The 2013 master sample is based on information collected during the 2011 population census. The previous master sample for the GHS was used for the first time in 2008, and the one before that in 2004. These again differed from the master sample used in the first two years of the GHS: 2002 and 2003. Thus there have been four different sampling frames during the 14-year history of the annual GHS, with the changes occurring in 2004, 2008 and 2013. In addition, there have been changes in the method of stratification over the years. These changes could compromise comparability across iterations of the survey to some extent, although it is common practice to use the GHS for longitudinal monitoring and many of the official trend analyses are drawn from this survey.

Person and household weights are provided by Stats SA and are applied in Children Count analyses to give estimates at the provincial and national levels. The GHS weights are derived from Stats SA’s mid-year population estimates. The population estimates are based on a model that is revised from time to time when it is possible to calibrate the population model to larger population surveys (such as the Community Survey) or to census data.

In 2013, Stats SA revised the demographic model to produce a new series of mid-year population estimates. The 2013 model drew on the 2011 census (along with vital registration, antenatal and other administrative data) but was a “smoothed” model that did not mimic the unusual shape of the age distribution found in the census. The results of the 2011 census were initially questioned because it seemed to over-count children in the 0 – 4 age group and under-count children in the 4 – 14-year group.

The 2013 model was used to adjust the benchmarking for all previous GHS data sets, which were re-released with the revised population weights by Stats SA, and was still used to calculate weights for the GHS up to and including 2015, even though it is now known that the mid-year population estimates on which the weights are based are incorrect. All the Children Count indicators were re-analysed retrospectively, using the revised weights provided by Stats SA, based on the 2013 model. The estimates are therefore comparable over the period 2002 to 2015. The revised weights particularly affected estimates for the years 2002 – 2007.

It is now thought that the fertility rates recorded in the 2011 population census may have been an accurate reflection of recent trends, with an unexplained upswing in fertility around 2009 after which fertility rates declined gradually. Similar patterns were found in the vital registration data as more births were reported retrospectively to the Department of Home Affairs, and in administrative data from schools, compiled by the Department of Basic Education. In effect, this means that there may be more children in South Africa than appear from the analyses presented in these analyses, where we have applied weights based on a model that it is now known to be inaccurate.

Statistics South Africa suggests caution when attempting to interpret data generated at low level disaggregation. The population estimates are benchmarked at the national level in terms of age, sex and population group while at provincial level, benchmarking is by population group only. This could mean that estimates derived from any further disaggregation of the provincial data below the population group may not be robust enough.

Reporting error
Error may be present due to the methodology used, i.e. the questionnaire is administered to only one respondent in the household who is expected to provide information about all other members of the household. Not all respondents will have accurate information about all children in the household. In instances where the respondent did not or could not provide an answer, this was recorded as “unspecified” (no response) or “don’t know” (the respondent stated that they didn’t know the answer).

For more information on the methods of the General Household Survey, see the metadata for the respective survey years, available on Nesstar  or DataFirst