Demography - Orphanhood
An orphan is defined as a child under the age of 18 years whose mother, father, or both biological parents have died (including those whose living status is reported as unknown, but excluding those whose living status is unspecified). For the purpose of this indicator, we define orphans in three mutually exclusive categories:
What do the numbers tell us?
The 2010 General Household Survey indicates that there were approximately 3.84 million orphans in South Africa. This includes children without a living biological mother, father or both parents, and is equivalent to 21% of all children in South Africa. The total number of orphans has increased substantially, with 845,000 more orphaned children in 2010 than in 2002. This is an increase of 28% in the number of orphaned children since 2002.
Orphan numbers do not say anything about the nature or extent of care that children are receiving: Child-rearing in South Africa has long been characterised by the presence of multiple caregivers and the involvement of broad kinship networks in the lives of children both with and without living parents. It is important to disaggregate the total orphan figures because the death of one parent may have different implications for children than the death of both parents. In particular, it seems that children who are maternally orphaned are slightly more at risk of poorer outcomes than paternal orphans – for example, in relation to education.1
In 2010, 17% of children in South Africa did not have a living biological father, while 8% did not have a living biological mother. Of the children without biological mothers, 3.6% (658,000 children) were recorded to be maternal orphans with living fathers; and a further 4.8% (885,000) were recorded as double orphans. In other words, the vast majority (60%) of all orphans in South Africa are paternal orphans (with living mothers). The numbers of paternal orphans are high because of the higher mortality rates of men in South Africa, as well as the frequent absence of fathers in their children’s lives (1.3% or 244,000 children have fathers whose vital status is reported to be “unknown”).
The figures illustrate notable increases in the number and proportion of double orphans over a nine-year period: The number of children who have lost both a mother and a father has more than doubled since 2002 (from approximately 350,000 to 885,000), indicating an increase of nearly three percentage points in double orphans in South Africa (2002: 2.0%; 2010: 4.8%). These increases are likely to be driven primarily by the AIDS pandemic. Three provinces carry particularly large burdens of care for double orphans: 7% of children living in KwaZulu-Natal and the Free State have lost both parents, and 6% of children in the Eastern Cape are double orphans.
Throughout the period 2002 – 2010, roughly half of all orphans in South Africa have been resident in only two of the country’s nine provinces: Kwazulu-Natal and the Eastern Cape. KwaZulu-Natal has the largest population and the highest orphan numbers, with 27% of children in that province recorded as orphans who have lost either a mother, a father or both parents. Orphaning rates in the Eastern Cape are similarly high, at 26%, followed by the Free State, at 24%. The lowest orphaning rates are in the Western Cape (10% of children have lost at least one parent) and Gauteng (15%).
Children are more likely to be orphaned as they get older. In 2010, 80% of all child orphans were of school-going age (between 7 and 17-years-old) and half were 12 years or more.
Orphaning is associated with poverty in that orphaning rates are higher for poor children than for relatively well-off children. Around 25% of children in the poorest 40% of households are orphans, compared with the richest 20% where total orphaning rates have remained well below 10% over the last decade.
The definition used here differs from that commonly used by the UN agencies as well as the Actuarial Society of South Africa (ASSA). The definition of maternal and paternal orphan employed by these institutions includes children who are double orphans: for instance, all children who have lost a mother (whether or not their father is alive) are included in their measure of maternal orphans. Using those definitions, maternal. paternal and double orphan numbers add up to more than the total number of orphans.
Because the orphan definitions used here are mutually exclusive and additive, the figures differ from orphan estimates provided by the ASSA models. This is particularly striking in the instance of maternal orphans, estimated by the ASSA model to total 1.7 million children in 2007 – of whom 500,000 are estimated to be double orphans. The GHS represents a cross-sectional survey at a single point in time, while the ASSA model is a modeling approach that calibrates to mortality and the antenatal HIV survey data. In spite of these differences, the orphan estimates are consistent over time, and the estimates of total orphan numbers similar.
Strengths and limitations of the data
The data 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 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 Statistics South Africa 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. For this project, GHS data were compared with estimates from the Statistics South Africa’s mid-year estimates, as well as the Actuarial Society of South Africa’s ASSA2003 AIDS and Demographic model.
Analyses of the seven surveys from 2002 to 2008 suggest that over- and under-estimation may have occurred in the weighting process:
The apparent discrepancies in the seven years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. Since 2005 the male and female patterns vary in respect of a particular characteristic, which means that the total estimate for this characteristic will be somewhat slanted toward the male pattern. 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).
References and Related Links
1 See for example: Ardington C (2007) Orphanhood and schooling in South Africa: Trends in the vulnerability of orphans between 1993 and 2005. Cape Town: South African Labour Department Research Unit, University of Cape Town; Case A, Paxson C & Ableidinger J (2004) Orphans in Africa: parental death, poverty and school enrollment. Demography, 41(3): 483-508; Cluver L, Gardner F & Operario D (2007) Psychological distress amongst AIDS-orphaned children in urban South Africa. Journal of Child and Psychology and Psychiatry and Allied Disciplines, 48(8): 755-763; Beegle K, De Weerdt J & Dercon S (2010) Orphanhood and human capital destruction: Is there persistence into adulthood? Demography, 47(1): 163-180.
2 Statistics South Africa (2003-2009). General Household Survey 2002-2008 Metadata. Cape Town, Pretoria: Statistics South Africa
Ardington C (2007) Orphanhood and schooling in South Africa: Trends in the vulnerability of orphans between 1993 and 2005. Cape Town: South African Labour Department Research Unit, University of Cape Town.
Bray R (2003) Predicting the social consequences of orphanhood in South Africa. African Journal of AIDS Research, 2(1), 39-55.
Case A, Paxson C & Ableidinger J (2004) Orphans in Africa: parental death, poverty and school enrollment. Demography, 41(3): 483-508.
Cluver L, Gardner F & Operario D (2007) Psychological distress amongst AIDS-orphaned children in urban South Africa. Journal of Child and Psychology and Psychiatry and Allied Disciplines, 48(8): 755-763.
Grassly NC, Lewis JJ, Mahy M, Walker N & Timæus IM (2004) Comparison of household-survey estimates with projections of mortality and orphan numbers in sub-Saharan Africa in the era of HIV/AIDS. Population Studies, 58(2): 207-217.
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.
Monasch R & Boerma J (2004) Orphanhood and childcare patterns in sub-Saharan Africa: an analysis of national surveys from 40 countries. AIDS, 18 (suppl 2), S55-S65.
UNICEF & UNAIDS (2004) The framework for the protection, care and support of orphans and vulnerable children living in a world with HIV and AIDS. New York: UNICEF & UNAIDS. Available: www.unicef.org/aids/files/Framework_English.pdf
UNICEF, UNAIDS Secretariat & World Health Organisation (2007) Children and AIDS: A stocktaking report. Geneva: UNICEF, UNAIDS Secretariat & World Health Organisation. Available: www.unicef.org/aids/files/FINAL_STOCKTAKING_REPORT(1).pdf