Author/s: Katharine Hall & Helen MeintjesDate: October 2016
A child-only household is defined as a household in which all members are younger than 18 years, i.e. a household consisting only of children. These households are also commonly referred to as ‘child-headed households’.
Statistics South Africa, the agency responsible for the General Household Surveys, defines a household as consisting of people who have stayed in a common dwelling for an average of at least four nights a week in the month preceding the survey. The proportion of children living in child-only households in South Africa is calculated by identifying the number of children living in households where the oldest resident is no older than 17 years, and dividing this figure by the total child population in South Africa.
The proportion of child-only households is calculated by dividing the number of households where the oldest resident is no older than 17 years, by the total number of households in South Africa.
There has been much concern within government and civil society that the number of children living in child-only households is escalating and that kinship networks are stretched to their limits. While orphaning undoubtedly places a large burden on families, there is little evidence to suggest that their capacity to care for orphans has been saturated, as commentators have feared. Rather than seeing increasing numbers of orphaned children living without adults, the vast majority of orphans live with family members, and child-headed households are not primarily the result of orphaning.
There were about 54,000 children living in a total of 45,000 child-only households across South Africa in 2014. This equates to 0.3% of all children. While children living in child-only households are rare relative to those resident in other household forms, the number of children living in this extreme situation is of concern.
Importantly, however, there has been no significant change in the proportion of children living in child-only households in the period between 2002 and 2014, nor has there been any change in the proportion of child-only households over the same period.. Predictions of rapidly increasing numbers of child-headed households as a result of HIV are at this point unrealised. An analysis of national household surveys to examine the circumstances of children in child-headed households in South Africa reveals that most children in child-only households are not orphans. These findings suggest that social phenomena other than HIV may play important roles in the formation of these households.
While it is not ideal for any child to live without an adult resident, it is positive that over half (59%) of all children living in child-only households are aged 15 years and above. Children can work legally from the age of 15, and from 16 they can obtain an identity book and receive grants on behalf of younger children. Three percent of children in child-headed households are under six years.
Research suggests that child-only households are frequently temporary arrangements, and often exist just for a short period, for example while adult migrant workers are away, or for easy access to school during term-time, or after the death of an adult and prior to other arrangements being made to care for the children (such as other adults moving in or the children moving to live with other relatives).
Over three-quarters of all children in child-only households live in three provinces: Limpopo (which accounts for 35% of children in child-only households), KwaZulu-Natal (29%) and Eastern Cape (15%). From 2002 to 2014, these provinces have consistently been home to the majority of children living in child-only households.
Relative to children in mixed-generation households, child-only households are vulnerable in a number of ways. Child-only households are predominantly clustered in the poorest 20% of households. In addition to the absence of adult members who may provide care and security, they are at risk of living in poorer conditions, with poor access to services, less (and less reliable) income, and low levels of access to social grants.
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.
2 See no. 1 above.
3Hill 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;
Meintjes H & Giese S (2006) Spinning the epidemic: The making of mythologies of orphanhood in the context of AIDS. Childhood: A Global Journal of Child Research, 13(3): 407-430.
4Statistics South Africa (2003-2013). General Household Survey 2002-2012 Metadata. Cape Town, Pretoria: Statistics South Africa