Demography - Children in South Africa
Demography - Children in South Africa
Author/s:  Katharine Hall
Date: April 2012
Definition
This indicator provides the total number of children living in South Africa, as well as child population numbers by province, population, age group and sex.
Data
Data Source
  • Statistics South Africa (2003 – 2011) General Household Survey 2002 – 2010. Pretoria, Cape Town: Statistics South Africa.
  • Analysis by Katharine Hall, 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.
What do the numbers tell us?

In mid-2010, South Africa’s total population was estimated at 50 million people, of whom 18.5 million were children (under 18 years). Children therefore constitute 37% of the total population. The child population has grown by about 6% (1 million) over the eight-year period from 2002 to 2010.

Exactly half of all children live in three of the nine provinces: KwaZulu-Natal (23%), Eastern Cape (14%) and Limpopo (12%). A further 18% of children live in Gauteng, a mainly metropolitan province, and 10% in the Western Cape.

It is not uncommon in South Africa for children to live separately from their biological parents, due to labour migration and care arrangements that involve extended families. The distribution of children across provinces is slightly different to that of adults, with a greater proportion of children living in provinces with large rural populations (Limpopo, the Eastern Cape and KwaZulu-Natal) and greater proportions of adults in the largely metropolitan provinces. Despite being the smallest province on the map, Gauteng accommodatesa quarter of all households and 24% of adults, but only 18% of children. This is because of the relatively large number of adult-only households in the province.

We can look at inequality by dividing all households into quintiles: five equal groups, with quintile 1 being the poorest 20% of households, quintile 2 being the next poorest and so on. Quintile 5 consists of the least-poor 20%. The income quintiles are based on total income to the household through wages and self-employment, plus total social grants to the household. Children are over-represented in poor households: 59% of children live in the poorest 40% of households. An analysis across income quintiles suggests that inequality between children has increased since 2002, when 48% of children lived in the poorest 40% of households. The proportion of children un the upper three quintiles has dropped, and children are increasingly concentrated in quintile 2 (the poorest 20-40%), rather than quintile 1. This may be the effect of social grants, the majority of which are targeted to households where children live.

There have been striking changes in the provincial child populations since 2002. While there are slight decreases in the number of children living in the Eastern Cape, Limpopo and the North West provinces, the number of children living in Gauteng has risen by 21%. This may be partly the result of in-migration of children to join existing households, or new births within the province. Either way, the increase suggests a more permanent migration pattern. An apparent increase in the child population in the Northern Cape since 2002 is very pronounced due to the relatively small population in that province.

Children are fairly equally distributed across the age groups, with on average just over one million children in each year under 18. The gender split is equal for children, while that in the adult population is slightly skewed towards females(53%).

Technical notes
This indicator refers to the number and proportion of children under the age of 18 years who live in South Africa. The proportions are calculated by dividing the number of children per category by the total number of children in the population.

The population numbers are drawn from the General Household Survey after person weights are applied. The person weights are calculated to yield the mid-year population figures for each year, as estimated by Statistics South Africa.

Strengths and limitations of the data

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 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 analysis over time, 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 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:
  • When comparing the weighted 2002 data with the ASSA2003 AIDS and Demographic model estimates, it seems that the number of children aged 0 – 9 years was under-estimated in the GHS, while the number of children aged 10 – 19 was over-estimated. The pattern is consistent for both sexes. The number of very young males aged 0 – 4 years appears to be under-estimated by 15%. Girls in this age group have been under-estimated by 15.8%. Males in the 10 – 14-year age group appear to be over-estimated by 5.7%.
  • Similarly in 2003, there was considerable under-estimation of the youngest age group (0 – 9 years) and over-estimation of the older age group (10 – 19 years). The pattern is consistent for both sexes. The results also show that the over-estimation of males (9%) in the 10 – 19-year age group is more than double the over-estimation for females in this age range (3.8%).
  • In the 2004 results, it seems that the number of children aged 7 – 12 years was over-estimated by 6%, as well as the number of persons aged 13 – 22 years. The number of very young children appeared to have been under-estimated. The patterns of over- and under-estimation appear to differ across population groups. For example, the number of White children appears to be over-estimated by 14%, while the number of Coloured persons within the 13 – 22-year age group appears to be 9% too low.
  • In 2005, the GHS weights seem to have produced an over-estimate of the number of males within each five-year age group. The extent of the overestimation is particularly severe for the 10 – 14-year age group. In contrast, the weights produce an under-estimate of the number of girls – the error seems greatest in respect of the younger age groups. These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.09 respectively for the four age groups covering children (ie 0 – 4, 5 – 9, 10 – 14 and 15 – 19 years).
  • The 2006 weighting process yielded the same results as in 2005. The one exception is that the under-estimation of females is greatest in the 5 – 9 and 15 – 19-year age groups. This results in male-to-female ratios of 1.03, 1.10, 1.11 and 1.12 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 3 – 5% while the over-estimation is in the range of 1 – 7%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.08 respectively for the four age groups covering children.
  • Overall, assuming the ASSA2003 Aids and Demographic model to be the ‘gold standard’, it appears that the GHS2008 over-estimates both male and female populations under the age of 19 years, except for 0 – 4- year-old females. The extent of over-estimation for boys is in the range 0 – 7%. It is particularly severe for boys aged 10 – 14 years. Over-estimation is in the range of 2 – 5% for girls aged five years and above. For girls aged 0 – 4 years, the ASSA2003 model suggests that these may have been under-estimated by about 1%. The GHS2008 suggests a sex ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA model and Statistics South Africa's mid-year estimates.
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 patternfor 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.
 
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).
References and Related Links
1. Statistics South Africa (2003-2011). General Household Survey 2002-2010 Metadata. Cape Town, Pretoria: Statistics South Africa
Author: Katharine Hall

Definition
This indicator provides the total number of children living in South Africa, as well as child population numbers by province, population, age group and sex.
Commentary

In mid-2010, South Africa’s total population was estimated at 50 million people, of whom 18.5 million were children (under 18 years). Children therefore constitute 37% of the total population. The child population has grown by about 6% (1 million) over the eight-year period from 2002 to 2010.

Exactly half of all children live in three of the nine provinces: KwaZulu-Natal (23%), Eastern Cape (14%) and Limpopo (12%). A further 18% of children live in Gauteng, a mainly metropolitan province, and 10% in the Western Cape.

It is not uncommon in South Africa for children to live separately from their biological parents, due to labour migration and care arrangements that involve extended families. The distribution of children across provinces is slightly different to that of adults, with a greater proportion of children living in provinces with large rural populations (Limpopo, the Eastern Cape and KwaZulu-Natal) and greater proportions of adults in the largely metropolitan provinces. Despite being the smallest province on the map, Gauteng accommodatesa quarter of all households and 24% of adults, but only 18% of children. This is because of the relatively large number of adult-only households in the province.

We can look at inequality by dividing all households into quintiles: five equal groups, with quintile 1 being the poorest 20% of households, quintile 2 being the next poorest and so on. Quintile 5 consists of the least-poor 20%. The income quintiles are based on total income to the household through wages and self-employment, plus total social grants to the household. Children are over-represented in poor households: 59% of children live in the poorest 40% of households. An analysis across income quintiles suggests that inequality between children has increased since 2002, when 48% of children lived in the poorest 40% of households. The proportion of children un the upper three quintiles has dropped, and children are increasingly concentrated in quintile 2 (the poorest 20-40%), rather than quintile 1. This may be the effect of social grants, the majority of which are targeted to households where children live.

There have been striking changes in the provincial child populations since 2002. While there are slight decreases in the number of children living in the Eastern Cape, Limpopo and the North West provinces, the number of children living in Gauteng has risen by 21%. This may be partly the result of in-migration of children to join existing households, or new births within the province. Either way, the increase suggests a more permanent migration pattern. An apparent increase in the child population in the Northern Cape since 2002 is very pronounced due to the relatively small population in that province.

Children are fairly equally distributed across the age groups, with on average just over one million children in each year under 18. The gender split is equal for children, while that in the adult population is slightly skewed towards females(53%).

Strengths and limitations of the data

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 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 analysis over time, 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 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:
  • When comparing the weighted 2002 data with the ASSA2003 AIDS and Demographic model estimates, it seems that the number of children aged 0 – 9 years was under-estimated in the GHS, while the number of children aged 10 – 19 was over-estimated. The pattern is consistent for both sexes. The number of very young males aged 0 – 4 years appears to be under-estimated by 15%. Girls in this age group have been under-estimated by 15.8%. Males in the 10 – 14-year age group appear to be over-estimated by 5.7%.
  • Similarly in 2003, there was considerable under-estimation of the youngest age group (0 – 9 years) and over-estimation of the older age group (10 – 19 years). The pattern is consistent for both sexes. The results also show that the over-estimation of males (9%) in the 10 – 19-year age group is more than double the over-estimation for females in this age range (3.8%).
  • In the 2004 results, it seems that the number of children aged 7 – 12 years was over-estimated by 6%, as well as the number of persons aged 13 – 22 years. The number of very young children appeared to have been under-estimated. The patterns of over- and under-estimation appear to differ across population groups. For example, the number of White children appears to be over-estimated by 14%, while the number of Coloured persons within the 13 – 22-year age group appears to be 9% too low.
  • In 2005, the GHS weights seem to have produced an over-estimate of the number of males within each five-year age group. The extent of the overestimation is particularly severe for the 10 – 14-year age group. In contrast, the weights produce an under-estimate of the number of girls – the error seems greatest in respect of the younger age groups. These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.09 respectively for the four age groups covering children (ie 0 – 4, 5 – 9, 10 – 14 and 15 – 19 years).
  • The 2006 weighting process yielded the same results as in 2005. The one exception is that the under-estimation of females is greatest in the 5 – 9 and 15 – 19-year age groups. This results in male-to-female ratios of 1.03, 1.10, 1.11 and 1.12 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 3 – 5% while the over-estimation is in the range of 1 – 7%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.08 respectively for the four age groups covering children.
  • Overall, assuming the ASSA2003 Aids and Demographic model to be the ‘gold standard’, it appears that the GHS2008 over-estimates both male and female populations under the age of 19 years, except for 0 – 4- year-old females. The extent of over-estimation for boys is in the range 0 – 7%. It is particularly severe for boys aged 10 – 14 years. Over-estimation is in the range of 2 – 5% for girls aged five years and above. For girls aged 0 – 4 years, the ASSA2003 model suggests that these may have been under-estimated by about 1%. The GHS2008 suggests a sex ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA model and Statistics South Africa's mid-year estimates.
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 patternfor 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.
 
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).
Technical notes
This indicator refers to the number and proportion of children under the age of 18 years who live in South Africa. The proportions are calculated by dividing the number of children per category by the total number of children in the population.

The population numbers are drawn from the General Household Survey after person weights are applied. The person weights are calculated to yield the mid-year population figures for each year, as estimated by Statistics South Africa.

References
1. Statistics South Africa (2003-2011). General Household Survey 2002-2010 Metadata. Cape Town, Pretoria: Statistics South Africa
Department of International Development UK Children's Institute