Housing & servicesHousing & services

Urban-rural distribution

Author/s: Katharine Hall
Date: November 2018

Definition

This indicator shows the number and share of children living in urban and rural areas. Information on the whereabouts of children helps to shed light on child mobility and urbanisation, and can inform spatial targeting. The data were not available for some years when Statistics South Africa did not report the urban-rural variable due to controversy around area classification. Some years are therefore missing from the trend.

Data


Data Source Statistics South Africa (2003 - 2018) General Household Survey 2002 - 2017. Pretoria: Stats SA.
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. In this instance it does not make sense to provide confidence intervals because the size of the urban and rural population is imposed on the data, rather than estimated by the survey. The data is weighted to accord with the mid-term estimates for that year. These are calculated through demographic modelling which is itself subject to error.
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 that 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 (43%) lived in rural households in 2017 – equivalent to 8.5 million children. Looking back over a decade, there seems to be a slight shift in the distribution of children towards urban areas: in 2002, 48% of children were found in urban households, and this increased to 57% by 2017.

A consistent pattern over the years is that children are more likely than adults to live in rural areas: In 2017, 69% of the adult population was urban, compared with 57% 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’s 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 (72%) of all rural children in South Africa. KwaZulu-Natal has the largest child population in numeric terms, with 2.6 million (62%) of its child population being classified as rural. The province with the highest proportion of rural children is Limpopo, where only 16% of children live in urban areas. Proportionately more children (39%) live in the former homelands, compared with adults (28%). More than 99% of children living in the former homeland areas are African.

In 2017, children living in the Gauteng and Western Cape are almost entirely urban (96% and 94% respectively). These provinces historically have large urban populations. The urban child population in Gauteng alone has grown by over 1.1 million since 2002 and the urban child population in the Western Cape has grown by 430,000. These increases would be partly the result of urban births, but also partly the result of movement within the province and migration from other provinces. Other provinces that have experienced a marked growth in the urban share of the child population are the Eastern Cape, Free State and North West.
Rural areas, and particularly the former homelands, have much poorer populations.

Nearly two-thirds of children in the poorest income quintile live in rural areas compared with 10% in the richest quintile. In other words, within the poorest part of the population, it is mainly rural households that care for children – even though many of these children may have parents who live and work in urban areas.

The inequalities also remain strongly racialised. More than 90% of White, Coloured and Indian children are urban, compared with 51% of African children. There are no statistically significant differences in child population in urban and rural areas across age groups.


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.

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 data for most years 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 sample design for the 2015 GHS was based on a master sample that was designed in 2013 as a general purpose sampling frame to be used for all Stats SA household-based surveys. The same master sample is shared by the GHS, the Quarterly Labour Force Survey, the Living Conditions Survey and the Income and Expenditure Survey. The 2013 master sample is based on information collected during the 2011 population census. The previous master sample for the GHS was used for the first time in 2008, and the one before that in 2004. These again differed from the master sample used in the first two years of the GHS: 2002 and 2003. Thus there have been four different sampling frames during the 14-year history of the annual GHS, with the changes occurring in 2004, 2008 and 2013. In addition, there have been changes in the method of stratification over the years. These changes could compromise comparability across iterations of the survey to some extent, although it is common practice to use the GHS for longitudinal monitoring and many of the official trend analyses are drawn from this survey.

Weights
Person and household weights are provided by Stats SA and are applied in Children Count analyses to give estimates at the provincial and national levels. The GHS weights are derived from Stats SA’s mid-year population estimates. The population estimates are based on a model that is revised from time to time when it is possible to calibrate the population model to larger population surveys (such as the Community Survey) or to census data.

In 2013, Stats SA revised the demographic model to produce a new series of mid-year population estimates. The 2013 model drew on the 2011 census (along with vital registration, antenatal and other administrative data) but was a “smoothed” model that did not mimic the unusual shape of the age distribution found in the census. The results of the 2011 census were initially questioned because it seemed to over-count children in the 0 – 4 age group and under-count children in the 4 – 14-year group.

The 2013 model was used to adjust the benchmarking for all previous GHS data sets, which were re-released with the revised population weights by Stats SA, and was still used to calculate weights for the GHS up to and including 2015, even though it is now known that the mid-year population estimates on which the weights are based are incorrect. All the Children Count indicators were re-analysed retrospectively, using the revised weights provided by Stats SA, based on the 2013 model. The estimates are therefore comparable over the period 2002 to 2015. The revised weights particularly affected estimates for the years 2002 – 2007.

It is now thought that the fertility rates recorded in the 2011 population census may have been an accurate reflection of recent trends, with an unexplained upswing in fertility around 2009 after which fertility rates declined gradually. Similar patterns were found in the vital registration data as more births were reported retrospectively to the Department of Home Affairs, and in administrative data from schools, compiled by the Department of Basic Education. In effect, this means that there may be more children in South Africa than appear from the analyses presented in these analyses, where we have applied weights based on a model that it is now known to be inaccurate.

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, i.e. 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).

For more information on the methods of the General Household Survey, see the metadata for the respective survey years, available on Nesstar http://interactive.statssa.gov.za:8282/webview/ or DataFirst www.datafirst.uct.ac.za

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”.2
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.


2 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.