Teenage pregnancy

Author/s: Katharine Hall
Date: October 2016


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


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Data Source

Statistics South Africa (2010-2015) General Household Survey 2009-2014. 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 years age group.

Teenage pregnancy rates are difficult to calculate directly because it is hard to determine how many pregnancies end in miscarriage, stillbirth 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 proportion 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 2012 showed no increase in the share of teenagers aged 15 – 19 who attended ante-natal 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 ante-natal clinics in 2012, 12% in the 15 –19-age group and 24% of those aged 20 – 24 were HIV positive.3 There is a strong association between early childbearing and maternal mortality, and the majority of deaths in young mothers are caused by AIDS.4 It is important that safe sexual behaviour is 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.5 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.6 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.7 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 a question on pregnancy. The 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 proportion of young women who have given birth in the past year.

According to the GHS the national child-bearing rate for young women aged 15 – 24 was 7% in 2014. There had been no significant change in this rate since 2009 and the estimated number of young women giving birth in a year has remained stable at between 350,000 and 380,000.

As would be expected, child-bearing rates increase with age. Less than three percent of girls aged 15 – 17 were reported to have given birth in the past 12 months (representing just under 40,000 teenagers in this age group). Child-bearing rates increased to 9% among 18 – 20-year-olds (137,000 when weighted), and 10% in the 21 – 24 age group (202,000). These rates have also been stable over the five-year period that the GHS has included this question.

The numerator for this indicator is the number of young women aged 15 – 24 years, who had ever been given birth to a live child, and the denominator is the number of all females aged 15 – 24 years.

The data are derived from the General Household Survey4, 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 current master sample was used for the first time in 2004, meaning that, for longitudinal analysis, 2002 and 2003 may not be easily comparable with later years as they are based on a different sampling frame. From 2006, the sample was stratified first by province and then by district council. Prior to 2006, the sample was stratified by province and then by urban and rural area. The change in stratification could affect the interpretation of results generated by these surveys when they are compared over time.
Provincial boundary changes
Provincial boundary changes occurred between 2002 and 2007, and slightly affect the provincial populations. Comparisons on provincial level should therefore be treated with some caution. The sample and reporting are based on the old provincial boundaries as defined in 2001 and do not represent the new boundaries as defined in December 2005.


Person and household weights are provided by Stats SA and are applied in Children Count – Abantwana Babalulekile analyses to give estimates at the provincial and national levels. Survey data are prone to sampling and reporting error. Some of the errors are difficult to estimate, while others can be identified. One way of checking for errors is by comparing the survey results with trusted estimates from elsewhere. Such a comparison can give an estimate of the robustness of the survey estimates. The GHS weights are derived from Stats SA’s mid-year population estimates. For this project, weighted GHS population numbers were compared with population projections from the Actuarial Society of South Africa’s ASSA2008 AIDS and Demographic model. 

Analyses of the ten surveys from 2002 to 2011 suggest that some over- and under-estimation may have occurred in the weighting process: 

§  When comparing the weighted 2002 data with the ASSA2008 AIDS and Demographic model estimates, it seems that the number of children was under-estimated by 5% overall. The most severe under-estimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield under-estimations of 15% and 16% respectively. The next age group (5 – 9 years) is also under-estimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14-year age group, although boys are still under-estimated by around 1% and girls by 3%. In contrast, the weighted data yield over-estimates of boys and girls in the upper age group (15 – 17 years), with the GHS over-counting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal male-to-female ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively. 

§  Similarly in 2003, there was considerable under-estimation of the youngest age groups (0 – 4 years and 5 – 9 years) and over-estimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are under-estimated by as much as 16%, with under-estimates for babies below two years in the range 19 – 30%. The results also show that the over-estimation of males in the 15 – 17-year age group (9%) is much more severe than the over-estimation for females in this age range (1.4%), resulting in a male-to-female ratio of 1.09 in this age group, compared with ratios around 1.02 in the younger age groups. 

§  In the 2004 results, all child age groups seem to have been under-estimated, with the under-estimate being more severe in the upper age group (15 – 17 years). This is the result of severe under-estimation in the number of girls, which outweighs the slight over-estimation of boys in all age groups. Girls are under-estimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while over-estimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in male-to-female ratios of 1.10, 1.11, 1.12 and 1.14 for the four age groups. 

§  In 2005, the GHS weights seem to have produced an over-estimate of the number of males and an under-estimate of the number of females within each five-year age group. The extent of under-estimation for girls (by 7% overall) exceeds that of the over-estimation for boys (at 2% overall). These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.13 respectively for the four age groups covering children. 

§  The 2006 weighting process yields different patterns from other years when compared to population estimates for the same year derived from ASSA2008, in that it yielded an under-estimation of both females and males. The under-estimation of females is greatest in the 0 – 4 and 5 – 9-year age groups, while the under-estimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14-year age group. This results in male-to-female ratios of 1.09, 0.99, 0.96 and 1.00 respectively for the four age groups covering children. 

§  The 2007 weighting process produced an over-estimation for boys and an under-estimation for girls. The under-estimation of females is in the range of 4 – 8% while the over-estimation for boys is in the range of 1 – 5%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.06 respectively for the four age groups covering children. 

§  In 2008, the GHS weighted population numbers when compared with ASSA2008 over-estimated the number of boys aged 10 and over, in the range of 3% for the 10 – 14 age group, and 8% for the 15 – 17 age group. The total weighted number of girls is similar to the ASSA population estimate for girls, but this belies an under-estimate of female babies below two years (by 7 – 8%), and an over-estimate of young teenage girls. The GHS 2008 suggests a male-to-female ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA2008 model. 

§  A comparison of the GHS and ASSA for 2009 suggests a continuation of the general pattern from previous years, which is that GHS weights result in an under-estimation of children in the 0 – 4 age group (especially infants), and an over-estimate of older children. In 2009 the under-estimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHS-weighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The male-to-female ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHS-derived ratio is higher, at 1.08, compared to 1.00 in ASSA. 

§  In 2010, the GHS weights again produce an underestimation of children in the 0 – 4 age group and an over-estimate of children aged 15 – 17 years. For the middle age groups, and for the child age group as a whole, there is less than 1% difference in the estimates from the two sources. For the 0 – 4 age group the under-estimate is lower than previously, at 2%, but for the oldest age group there is an over-estimate of 5%. The male-to-female ratios are similar across the two sources, although the ratio is 1.00 for all but the 0 – 4 age group in ASSA as against 1.01 for the youngest age group in ASSA and for all age groups in the GHS. 

§  A comparison of the GHS2011 to ASSA2008 (projected to 2011) suggests an under-estimation of children below two years and an over-estimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The under-estimation is particularly pronounced for babies under a year, at 8%. The male-to-female ratio for all children under 17 is 1.00 in ASSA, and 1.01 in the GHS.  

The apparent discrepancies in the ten years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. From 2005 to 2008, consistently distorted male- to-female ratios means that the total estimates for certain characteristics would be somewhat slanted toward the male pattern. This effect is reduced from 2009, where more even ratios are produced, in line with the modelled estimates. A similar slanting will occur where the pattern for 10 – 14-year-olds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups. 

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, ie 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).

1See, for example, 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 No 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.

2Department of Health (2004 – 2013) National Antenatal Sentinel HIV and Syphilis Prevalence Surveys 2004 –2012. Pretoria: DoH.

3See no. 6 above. [DOH 2004-2013]

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

5Branson 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;

       See no. 8 above; [Ardington et al – Early childbearing]

       See no. 5 (Ardington et al 2011) above. 

6Timæus I & Moultrie T (2015) Trends in childbearing and educational attainment in South Africa. Studies in Family Planning, 46(2): 143-160.

7Makiwane 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;

         See no. 5 (Makiwane et al, 2006) above.

8Statistics South Africa (2003-2014). General Household Survey 2002-2013 Metadata. Cape Town, Pretoria: Statistics South Africa.