Housing & services

Overcrowding

Author/s: Katharine Hall
Date: January 2024

Definition

Children are defined as living in over-crowded dwellings when there is a ratio of more than two people per room (excluding bathrooms but including kitchen and living room). The over-crowding ratio is obtained by dividing the total number of household members by the total number of rooms occupied by the household. Thus, a dwelling with two bedrooms, a kitchen and sitting-room would be defined as over-crowded if there were more than eight people living in it.

Data

Data Source

Statistics South Africa (2003 - 2023) General Household Survey 2002 - 2022. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall, Children’s Institute, University of Cape Town.

Notes

Children are defined as persons aged 0 – 17 years.
Population numbers have been rounded off to the nearest thousand.
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-points differ. CIs are represented in the bar graphs by vertical lines at the top of each bar.

What do the numbers tell us?

The UN Committee on Economic, Social and Cultural Rights defines “habitability” as one of the criteria for adequate housing.¹Overcrowding is a problem because it can undermine children’s needs and rights. For instance, it is difficult for school children to do homework if other household members want to sleep or watch television. Children’s right to privacy can be infringed if they do not have space to wash or change in private. The right to health can be infringed as communicable diseases spread more easily in overcrowded conditions, and young children are particularly susceptible to the spread of disease. Overcrowding also places children at greater risk of sexual abuse, especially where boys and girls have to share beds, or children have to share beds with adults.

Overcrowding makes it difficult to target services and programmes to households effectively – for instance, urban households are entitled to six kilolitres of free water, but this household-level allocation discriminates against overcrowded households because it does not take account of household size.

In 2018, 3.5 million children lived in overcrowded households. This represents 18% of the child population – much higher than the share of adults living in crowded conditions (10%).

Overcrowding is associated with housing type: 57% of children who stay in informal dwellings also live in overcrowded conditions, compared with 27% of children in traditional dwellings and 13% of children in formal housing.

Young children are significantly more likely than older children to live in overcrowded conditions. Twenty-one percent of children below six years live in crowded households, compared to 17% of children aged 6 – 11, and 15% of children over 12 years.

There is a strong racial bias in children’s housing conditions. While 19% of African and 21% of Coloured children live in crowded conditions, less than 1% of Indian and White children live in overcrowded households. Children in the poorest 20% of households are more likely to be living in overcrowded conditions (22%) than children in the richest 20% of households (5%).

The average household size has gradually decreased from 4.5 at the time of the 1996 population census, to around 3.4 in 2018, indicating a trend towards smaller households. This is related to the rapid growth in single-person households where adults live alone: there are nearly 17 million households in South Africa, of which 22% (around 3.7 million) are households where one person lives alone. The reduction in average household size has also been linked to the provision of small subsidy houses and the splitting of households into smaller units. Households in which children live are larger than the national average, although they have also declined in size over time. The mean household size for adult-only households is 1.7, while the mean household size for households with children is 4.9.²

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.

2 Own analysis of General Household Survey 2018.

Technical notes

There is no standard measure of overcrowding in South Africa, but there are many international definitions. The definition used here is derived from the UN-HABITAT definition, which is a maximum of two people per habitable room. ‘Habitable’ rooms exclude bathrooms and toilets. The data are taken from the General Household Survey, which records the total number of people in a household as well as the total number of rooms occupied (excluding bathrooms and toilets).

This indicator is based on the ratio of total household size to the total number of rooms occupied, excluding bathrooms. However in 2020 the questions about different room types was dropped, and instead a one-shot question was used, which asked how many rooms in total the household occupied.This was because the questionnaire was shortened to allow for a shorter telephonic interview during lockdown. For purposes of this indicator in 2020, the total number of rooms is estimated as the reported total less one if the household occupies more than one room in total and has a toilet/bathroom inside the dwelling.

For purposes of measuring and monitoring persistent racial inequality, population groups are defined as 'African', 'Coloured', 'Indian', and 'White'.

Strengths and limitations of the data

The numbers 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