Housing and Services - Urban-rural distribution

Housing and Services - Urban-rural distribution Definition
This indicator shows the number and proportion of children living in urban or rural areas, as reported by Statistics South Africa. Information on the whereabouts of children helps to shed light on child mobility and urbanisation, and can inform spatial targeting.The indicator was not unavailable for some years, when Statistics South Africa did not report the urban-rural variable due to controversy around area classification.
Data
What do the numbers tell us?
Location is one of the seven elements of adequate housing identified by the UN Committee on Economic, Social and Cultural Rights. The General Household Survey captures information on all household members, making it possible to look at the distribution of children in urban and non-urban households and compare this to the adult distribution. Nearly half of South Africa’s children (44%) lived in rural households in 2014 – equivalent to 8.2 million children. Looking back over a decade, there seems to be a slight shift in the distribution of children towards urban areas: in 2002, 47% of children were found in urban households, and this increased to 56% by 2014. A consistent pattern over the years is that children are more likely than adults to live in rural areas: In 2014, 68% of the adult population were urban, compared with 56% of children. There are marked provincial differences in the rural and urban distribution of the child population. This is related to the distribution of cities in South Africa, and the legacy of apartheid spatial arrangements, where women, children and older people in particular were relegated to the former homelands. The Eastern Cape, KwaZulu-Natal and Limpopo provinces alone are home to about three-quarters (74%) of all rural children in South Africa. KwaZulu-Natal has the largest child population in numeric terms, with 2.5 million (61%) of its child population being classified as rural. The province with the highest proportion of rural children is Limpopo, where only 11% of children live in urban areas. Proportionately more children (41%) live in the former homelands, compared with adults (28%), while 59% of adults live in urban formal areas, compared with 48% of children. Eight percent of children live in urban informal areas, and the remaining 3% live in “formal rural” areas – these being mainly commercial farming areas. Over 99% of children living in the former homeland areas are African. Technical notes
Although the urban–non-urban variable was always used in the sampling procedure, it was not reported by Statistics South Africa between 2004 and 2010, due to controversy around the definition of area types. The area type variable is part of the stratified sample design, and the weights that are applied effectively impose on the data the urban–rural split that is estimated by a demographic model. Therefore the distribution of urban and rural households reflects the estimated size of urban and rural populations, and is not a statistical finding of the survey itself. The distinction between urban and rural is described by Statistics South Africa as “rather fluid”, and some areas have been reclassified in the past few years. This is mostly because the ‘semi-urban’ category was dispensed with in the 2001 Census, resulting in a slightly more inclusive ‘urban’ classification which, for example, now includes informal settlements on the urban periphery. Statistics South Africa only reported area type for the years 2002-2004 and 2010. For 2008 we use data from the National Income Dynamics Study. Strengths and limitations of the data
^{2} The sample is based on the enumeration areas established during the Census demarcation phase and therefore covers all parts of the country. The sample of 30,000 dwelling units ensures a representative sample when stratifying by province. The resulting estimates should be representative of the total population of South Africa. Person and household weights are provided by Statistics South Africa and are applied in Children Count – Abantwana Babalulekile analyses to give representative estimates at the provincial and national levels.When comparing the weighted 2004 data with the ASSA2003 Aids and Demographic model estimates, it seems that the number of children aged 7 – 12 years was over-estimated by 6%. 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.
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 stratificationA new 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 sample frame.
Provincial boundary changesProvincial boundary changes occurred since 2002, and may 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.
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 DisaggregationStatistics 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.
NIDS is the first national panel survey to be conducted in South Africa. The baseline survey or first “wave” of data collection was undertaken in 2008, with subsequent waves planned at intervals of two years. In the first wave, data were obtained for every member of each sampled household, and these individuals became the permanent sample members or panel – even if they were children or babies. Subsequent waves endeavour to return not only to the original households, but also to each original household member, even if members have moved out of the household. The advantage of a panel survey is that it enables longitudinal analysis of the variables or outcomes under study, while effectively controlling for variation in individual characteristics. Such surveys are “invaluable in promoting understanding of who is making progress in society and who is not and, importantly, what factors are driving these dynamics” ^{.3}The NIDS sample was drawn from the master sample developed by Statistics South Africa for the QLFS and other national surveys. NIDS uses a much smaller sample. The realised sample in 2008 was 7,305 households with 28,255 individuals after an original targeted sample of 9600 households with the aim of achieving a sample of 8000 households. This can be compared to the targeted 30,000 households and approximately 100,000 individuals in the GHS and QLFS. Nevertheless, NIDS is still nationally representative in the first wave. The sample of 400 primary sampling units is a subset of the master sample, and users are cautioned against disaggregating to provincial level as the sample was not designed to be representative at the level of province. However, wave 1 of the panel survey yields plausible statistics on most socio-demographic indicators for children, even at provincial level. This has been ascertained by comparing a range of child-centred variables derived from the GHS and NIDS for the same year. As household composition changes over time, subsequent waves of NIDS will no longer be nationally representative. References and Related Links
^{2} Statistics South Africa (2014). General Household Survey 2013 Metadata. Cape Town, Pretoria: Statistics South Africa.^{3 }Leibbrandt M, Woolard I & de Villers L (2009) Methodology: Report on NIDS Wave 1. Technical Paper No.1. Cape Town: Southern African Labour & Development Research Unit (SALDRU), UCT. Available: www.nids.uct.ac.za/home. | ||||||

Definition

This indicator shows the number and proportion of children living in urban or rural areas, as reported by Statistics South Africa. Information on the whereabouts of children helps to shed light on child mobility and urbanisation, and can inform spatial targeting.The indicator was not unavailable for some years, when Statistics South Africa did not report the urban-rural variable due to controversy around area classification.

Commentary

Location is one of the seven elements of adequate housing identified by the UN Committee on Economic, Social and Cultural Rights.^{1} Residential areas should ideally be situated close to work opportunities, clinics, police stations, schools and child-care facilities. In a country with a large rural population, this means that services and facilities need to be well distributed, even in areas which are not densely populated. In South Africa, service provision and resources in rural areas lag far behind urban areas.

The General Household Survey captures information on all household members, making it possible to look at the distribution of children in urban and non-urban households and compare this to the adult distribution. Nearly half of South Africa’s children (44%) lived in rural households in 2014 – equivalent to 8.2 million children. Looking back over a decade, there seems to be a slight shift in the distribution of children towards urban areas: in 2002, 47% of children were found in urban households, and this increased to 56% by 2014.

A consistent pattern over the years is that children are more likely than adults to live in rural areas: In 2014, 68% of the adult population were urban, compared with 56% of children.

There are marked provincial differences in the rural and urban distribution of the child population. This is related to the distribution of cities in South Africa, and the legacy of apartheid spatial arrangements, where women, children and older people in particular were relegated to the former homelands. The Eastern Cape, KwaZulu-Natal and Limpopo provinces alone are home to about three-quarters (74%) of all rural children in South Africa. KwaZulu-Natal has the largest child population in numeric terms, with 2.5 million (61%) of its child population being classified as rural. The province with the highest proportion of rural children is Limpopo, where only 11% of children live in urban areas. Proportionately more children (41%) live in the former homelands, compared with adults (28%), while 59% of adults live in urban formal areas, compared with 48% of children. Eight percent of children live in urban informal areas, and the remaining 3% live in “formal rural” areas – these being mainly commercial farming areas. Over 99% of children living in the former homeland areas are African.

Children living in the Gauteng and Western Cape are almost entirely urban-based (98% and 96% respectively). These provinces historically have large urban populations. The greatest provincial increase in the urban child population has been in the Free State, where the proportion of children living in urban areas increased from 66% of the child population in 2002 to 84% in 2014. In the Eastern Cape, the urban child population has increased by over 15 percentage points, signifying a possible urban trend.

Rural areas, and particularly the former homelands, are known to have much poorer populations. Children in the poorest income quintile are more likely to be living in rural areas (66%) than those in the richest quintile (9%). These inequalities also remain strongly racialised. Over 90% of White, Coloured and Indian children are urban, compared with 49% of African children.

Strengths and limitations of the data

__General Household Survey__

When comparing the weighted 2004 data with the ASSA2003 Aids and Demographic model estimates, it seems that the number of children aged 7 – 12 years was over-estimated by 6%. 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.

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.

A new 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 sample frame.

Provincial boundary changes occurred since 2002, and may 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.

**Weights***
*Person and household weights are provided by Stats SA and are applied in

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.

__National Income Dynamics Study (NIDS)
__

NIDS is the first national panel survey to be conducted in South Africa. The baseline survey or first “wave” of data collection was undertaken in 2008, with subsequent waves planned at intervals of two years. In the first wave, data were obtained for every member of each sampled household, and these individuals became the permanent sample members or panel – even if they were children or babies. Subsequent waves endeavour to return not only to the original households, but also to each original household member, even if members have moved out of the household. The advantage of a panel survey is that it enables longitudinal analysis of the variables or outcomes under study, while effectively controlling for variation in individual characteristics. Such surveys are “invaluable in promoting understanding of who is making progress in society and who is not and, importantly, what factors are driving these dynamics”

The NIDS sample was drawn from the master sample developed by Statistics South Africa for the QLFS and other national surveys. NIDS uses a much smaller sample. The realised sample in 2008 was 7,305 households with 28,255 individuals after an original targeted sample of 9600 households with the aim of achieving a sample of 8000 households. This can be compared to the targeted 30,000 households and approximately 100,000 individuals in the GHS and QLFS. Nevertheless, NIDS is still nationally representative in the first wave. The sample of 400 primary sampling units is a subset of the master sample, and users are cautioned against disaggregating to provincial level as the sample was not designed to be representative at the level of province. However, wave 1 of the panel survey yields plausible statistics on most socio-demographic indicators for children, even at provincial level. This has been ascertained by comparing a range of child-centred variables derived from the GHS and NIDS for the same year. As household composition changes over time, subsequent waves of NIDS will no longer be nationally representative.

Technical notes

Although the urban–non-urban variable was always used in the sampling procedure, it was not reported by Statistics South Africa between 2004 and 2010, due to controversy around the definition of area types. The area type variable is part of the stratified sample design, and the weights that are applied effectively impose on the data the urban–rural split that is estimated by a demographic model. Therefore the distribution of urban and rural households reflects the estimated size of urban and rural populations, and is not a statistical finding of the survey itself.

The distinction between urban and rural is described by Statistics South Africa as “rather fluid”, and some areas have been reclassified in the past few years. This is mostly because the ‘semi-urban’ category was dispensed with in the 2001 Census, resulting in a slightly more inclusive ‘urban’ classification which, for example, now includes informal settlements on the urban periphery.

Statistics South Africa only reported area type for the years 2002-2004 and 2010. For 2008 we use data from the National Income Dynamics Study.

The distinction between urban and rural is described by Statistics South Africa as “rather fluid”, and some areas have been reclassified in the past few years. This is mostly because the ‘semi-urban’ category was dispensed with in the 2001 Census, resulting in a slightly more inclusive ‘urban’ classification which, for example, now includes informal settlements on the urban periphery.

Statistics South Africa only reported area type for the years 2002-2004 and 2010. For 2008 we use data from the National Income Dynamics Study.

References

^{1}Office of the United Nations High Commissioner for Human Rights (1991) *The Right to Adequate Housing (art.11 (1)): 13/12/91. CESCR General Comment 4*. Geneva: United Nations.