Demography - Orphanhood
Demography - Orphanhood
Author/s:  Katharine Hall & Helen Meintjes
Date: October 2016
Definition

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:

  • A maternal orphan is a child whose mother has died but whose father is alive;
  • A paternal orphan is a child whose father has died but whose mother is alive;
  • A double orphan is a child whose mother and father have both died.

The total number of orphans is the sum of maternal, paternal and double orphans.This definition differs from those commonly used by United Nations agencies and the Actuarial Society of South Africa (ASSA), where the definition of maternal and paternal orphans includes children who are double orphans. As the orphan definitions used here are mutually exclusive and additive, the figures differ from orphan estimates provided by the ASSA models.

Data
Data Source Statistics South Africa (2003 – 2015) General Household Survey 2002 – 2014. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall & Winnie Sambu, Children’s Institute, University of Cape Town.
Notes
  1. Children are defined as persons aged 0 – 17 years.
  2. Population numbers have been rounded off to the nearest thousand.
  3. Sample surveys are always subject to error, and the proportions simply reflect the mid-point of a possible range. The confidence intervals (CIs) indicate the reliability of the estimate at the 95% level. This means that, if independent samples were repeatedly taken from the same population, we would expect the proportion to lie between upper and lower bounds of the CI 95% of the time. The wider the CI, the more uncertain the proportion. Where CIs overlap for different sub-populations or time periods we cannot be sure that there is a real difference in the proportion, even if the mid-point proportions differ. CIs are represented in the bar graphs by vertical lines at the top of each bar.
What do the numbers tell us?
In 2014, there were approximately 3 million orphans in South Africa. This includes children without a living biological mother, father or both parents, and is equivalent to 16% of all children in South Africa.

The total number of orphans increased by 28% between 2002 and 2010, with 840,000 more orphaned children in 2010 than in 2002. However the rate of increase in orphaning has slowed in recent years, with a drop-off in the number of orphans since 2010/2011.

Orphan numbers do not indicate the nature or extent of care that children are receiving. 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 maternal orphans are at risk of poorer outcomes than paternal orphans – for example, in relation to education.

The vast majority (around 60%) of all orphans in South Africa are paternal orphans (with living mothers). In 2014, 3% of children were maternal orphans with living fathers, 10% were paternal orphans with living mothers, and a further 4% were recorded as double orphans. This means that 14% of children in South Africa did not have a living biological father and 7% did not have a living biological mother.  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 (1.8%, or 330,000 children have fathers whose vital status is reported to be “unknown”, compared with 0.4% or 70,000 children whose mothers’ status is unknown).

The number and proportion of double orphans more than doubled between 2002 and 2011 (from approximately 361,000 to 952,000), translating to an increase of three percentage points in double orphans in South Africa (2002: 2%; 2011: 5%). Since 2012, there has been a gradual decrease in the number of double orphans, and as at 2014, 653,000  children lived in households where both parents were dead. Despite the recent decreases, the number of double orphans is still high, and likely to be as a result of AIDS. Four provinces carry particularly large burdens of care for double orphans: In KwaZulu-Natal, Eastern Cape and Mpumalanga  5% of children have lost both parents and 6% of children in the Free State have lost both parents .

Throughout the period 2002 – 2014, roughly half of all orphans in South Africa have been located in KwaZulu-Natal and the Eastern Cape. KwaZulu-Natal has the largest child population and the highest orphan numbers, with 21% of children in that province recorded as orphans who have lost a mother, a father or both parents. Orphaning rates in the Eastern Cape and the Free State are similarly high, at 20% in both provinces. The lowest orphaning rates are in the Western Cape (7% of children have lost at least one parent) and Gauteng (12%). 

The poorest households carry the greatest burden of care for orphans. Close to half (46%) of all orphans are resident in the poorest 20% of households. Around a fifth of children in the poorest 20% of households are orphans, compared with the richest 20% where total orphaning rates are around 5%.

The likelihood of orphaning increases with age. Across all age groups, the main form of orphaning is paternal orphaning, which increases from 4% in children under 6 years, to 16% among children aged 12 - 17. While 2% of children under 6 years have lost their mothers, this increases to 12% in children aged 12-17 years.

Technical notes
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. 

Weights
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.

Disaggregation

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.
References and Related Links

1Ardington C & Leibrandt M (2010) Orphanhood and schooling in South Africa: Trends in the vulnerabilityof orphans between 1993 and 2005. Economic Development and Cultural Change, 58(3): 507-536;

  Case A, Paxson C & Ableidinger J (2004) Orphans in Africa: Parental death, poverty and  school enrollment. Demography, 41(3): 483-508.

Author: Katharine Hall & Helen Meintjes

Definition

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:

  • A maternal orphan is a child whose mother has died but whose father is alive;
  • A paternal orphan is a child whose father has died but whose mother is alive;
  • A double orphan is a child whose mother and father have both died.

The total number of orphans is the sum of maternal, paternal and double orphans.This definition differs from those commonly used by United Nations agencies and the Actuarial Society of South Africa (ASSA), where the definition of maternal and paternal orphans includes children who are double orphans. As the orphan definitions used here are mutually exclusive and additive, the figures differ from orphan estimates provided by the ASSA models.

Commentary
In 2014, there were approximately 3 million orphans in South Africa. This includes children without a living biological mother, father or both parents, and is equivalent to 16% of all children in South Africa.

The total number of orphans increased by 28% between 2002 and 2010, with 840,000 more orphaned children in 2010 than in 2002. However the rate of increase in orphaning has slowed in recent years, with a drop-off in the number of orphans since 2010/2011.

Orphan numbers do not indicate the nature or extent of care that children are receiving. 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 maternal orphans are at risk of poorer outcomes than paternal orphans – for example, in relation to education.

The vast majority (around 60%) of all orphans in South Africa are paternal orphans (with living mothers). In 2014, 3% of children were maternal orphans with living fathers, 10% were paternal orphans with living mothers, and a further 4% were recorded as double orphans. This means that 14% of children in South Africa did not have a living biological father and 7% did not have a living biological mother.  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 (1.8%, or 330,000 children have fathers whose vital status is reported to be “unknown”, compared with 0.4% or 70,000 children whose mothers’ status is unknown).

The number and proportion of double orphans more than doubled between 2002 and 2011 (from approximately 361,000 to 952,000), translating to an increase of three percentage points in double orphans in South Africa (2002: 2%; 2011: 5%). Since 2012, there has been a gradual decrease in the number of double orphans, and as at 2014, 653,000  children lived in households where both parents were dead. Despite the recent decreases, the number of double orphans is still high, and likely to be as a result of AIDS. Four provinces carry particularly large burdens of care for double orphans: In KwaZulu-Natal, Eastern Cape and Mpumalanga  5% of children have lost both parents and 6% of children in the Free State have lost both parents .

Throughout the period 2002 – 2014, roughly half of all orphans in South Africa have been located in KwaZulu-Natal and the Eastern Cape. KwaZulu-Natal has the largest child population and the highest orphan numbers, with 21% of children in that province recorded as orphans who have lost a mother, a father or both parents. Orphaning rates in the Eastern Cape and the Free State are similarly high, at 20% in both provinces. The lowest orphaning rates are in the Western Cape (7% of children have lost at least one parent) and Gauteng (12%). 

The poorest households carry the greatest burden of care for orphans. Close to half (46%) of all orphans are resident in the poorest 20% of households. Around a fifth of children in the poorest 20% of households are orphans, compared with the richest 20% where total orphaning rates are around 5%.

The likelihood of orphaning increases with age. Across all age groups, the main form of orphaning is paternal orphaning, which increases from 4% in children under 6 years, to 16% among children aged 12 - 17. While 2% of children under 6 years have lost their mothers, this increases to 12% in children aged 12-17 years.

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. 

Weights
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.

Disaggregation

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.
Technical notes
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.
References

1Ardington C & Leibrandt M (2010) Orphanhood and schooling in South Africa: Trends in the vulnerabilityof orphans between 1993 and 2005. Economic Development and Cultural Change, 58(3): 507-536;

  Case A, Paxson C & Ableidinger J (2004) Orphans in Africa: Parental death, poverty and  school enrollment. Demography, 41(3): 483-508.