Author/s: Katharine Hall & Helen Meintjes Date: October 2016
This indicator shows the number and proportion of children in South Africa who are living in the same household/dwelling with both their biological parents, with their mother only; with their father only; or who are not living with either of their biological parents.
Many children in South Africa do not live consistently in the same dwelling as their biological parents. This is a long-established feature of childhoods in South Africa, and is related to many factors including historic population control, labour migration, poverty, housing and educational opportunities, low marriage rates and cultural practice. It is common for relatives to play a substantial role in child-rearing. Many children experience a sequence of different caregivers, are raised without fathers, or live in different households to their biological siblings.
Virtually all children live with at least one adult, and the vast majority live in households where there are two or more co-resident adults. This indicator examines co-residence between children and their biological parents specifically. Although many children live with just one of their biological parents (usually the mother), this does not mean that the mother is a “single parent” as she is not necessarily the only adult caregiver in the household. In most cases, there are other adult household members such as aunts, uncles and grandparents, who may contribute to the care of children.
The proportion of children living with both parents decreased from 39% in 2002 to 35% in 2014. Forty-one percent of all children – 7.5 million children – live with their mothers but not with their fathers. Only 4% of children live in households where their fathers are present and their mothers absent. Twenty-one percent do not have either of their biological parents living with them. This does not necessarily mean that they are orphaned: in most cases (83%), children without any co-resident parents have at least one parent who is alive but living elsewhere.
There is some provincial variation in these patterns. In the Western Cape and Gauteng, the proportion of children living with both parents is significantly higher than the national average, with around half of children resident with both parents (56% and 55% respectively). Similarly, the number of children living with neither parent is low in these two provinces (6% and 10%). In contrast, over a third of children (34%) in the Eastern Cape live with neither parent. These patterns are consistent from 2002 to 2014.
Children in the poorest 20% of households are least likely to live with both parents: only 17% have both parents living with them, compared with 76% of children in the least-poor 20% of households.
Less than one third (29%) of African children live with both their parents, while the vast majority of Indian and White children (84% and 78% respectively) are resident with both biological parents. Almost a quarter of all African children do not live with either parent and a further 44% of African children live with their mothers but without their fathers. These figures are striking for the way in which they suggest the limited presence of biological fathers in the domestic lives of large numbers of African children.
The data are derived from the General Household Survey1, 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 a two-stage stratified design with probability proportional to size sampling of primary sampling units (PSUs) and systematic sampling of dwelling units from the sampled PSUs. The resulting weighted estimates are 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 GHS 2009 was based on a master sample that was originally designed for the Quarterly Labour Force Survey (QLFS) and was used for the first time for the GHS in 2008. The same master sample is shared by the GHS, the QLFS, the Living Conditions Survey and the Income and Expenditure Surveys, The previous master sample for the GHS was used for the first time in 2004, which again differed from the master sample used in the first two years of the GHS: 2002 and 2003. Thus there have been three different sampling frames during the eight-year history of the annual GHS, with the changes occurring in 2004 and 2008. In addition, there have been changes in the method of stratification over the years. These changes would 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.
Provincial boundary changes
Provincial boundary changes occurred between 2002 and 2007, and slightly affect the provincial populations. 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.
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).
For more information on the methods of the General Household Survey, see the metadata for the respective survey years, available on Nesstar - http://interactive.statssa.gov.za:8282/webview/
1 Statistics South Africa (2003-2014). General Household Survey 2002-2013 Metadata. Cape Town, Pretoria: Statistics South Africa
Hall K & Wright G (2010) A profile of children living in South Africa in 2008. Studies in Economics and Econometrics, 34(3): 25-43.
Hill C, Hosegood V & Newell M-L (2008) Children's care and living arrangements in a high HIV prevalence area in rural South Africa. Vulnerable Children and Youth Studies, 3(1): 65-77.
Hosegood V, Floyd S, Marston M, Hill C, McGrath N, Isingo R, Crampin A, & Zaba B (2007) The effects of high HIV prevalence on orphanhood and living arrangements of children in Malawi, Tanzania, and South Africa. Population Studies, 61(3): 327-336.
Madhavan S, & Schatz EJ (2007) Coping with change: Household structure and composition in rural South Africa. Scandinavian Journal of Public Health, 35(Suppl 69): 85-93.
Ford K, & Hosegood V (2005) AIDS mortality and the mobility of children in KwaZulu Natal, South Africa. Demography, 42(4): 757-768.
Madhavan S (2004) Fosterage patterns in the age of AIDS: Continuity and change. Social Science & Medicine, 58(7): 1443-1454.
South African Law Reform Commission (2002) The parent / child relationship. In: Discussion Paper 103 (Project 110) Review of the Child Care Act.