NutritionNutrition

Child hunger

Author/s: Katharine Hall & Winnie Sambu
Date: November 2018

Definition

This indicator shows the number and proportion of children living in households where children were reported to go hungry “sometimes”, “often” or “always” because there wasn’t enough food. 

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, UCT.
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.

Section 28(1)(c) of the Bill of Rights in the Constitution gives every child the right to basic nutrition. The fulfilment of this right depends on children's access to sufficient food. This indicator shows the number and proportion of children living in households where children are reported to go hungry “sometimes”, “often” or “always” because there isn’t enough food. Child hunger is emotive and subjective, and this is likely to undermine the reliability of estimates on the extent and frequency of reported hunger, but it is assumed that variation and reporting error will be reasonably consistent so that it is possible to monitor trends from year to year.

The government has introduced a number of programmes to alleviate income poverty and to reduce hunger, malnutrition and food insecurity, yet 2.3 million children (12%) lived in households where child hunger was reported in 2017. There was a significant drop in reported child hunger, from 30% of children in 2002 to 16% in 2006. Since then the rate has remained fairly consistent, suggesting that despite the expansion of social grants, school feeding schemes and other efforts to combat hunger amongst children, many households remain vulnerable to food insecurity. South Africa therefore has some way to go if it is to achieve the Sustainable Development Goal target of ending hunger by 2030.1 

There are large disparities between provinces and population groups. Provinces with relatively large numbers of children and high rates of child hunger are the KwaZulu-Natal (18%), North West (16%), Free State (15%), Mpumalanga (14%), and the Western Cape (11%). Together these provinces have over 1.6 million children living in households that report having insufficient food for children. The Northern Cape has the highest percentage of children living in households where there was child hunger, though the province has the lowest child population in the country. The Eastern Cape has had the largest decrease between 2002 and 2017, with reported child hunger being reduced by 41 percentage points over the 16-year-period from 48% to 7%. Limpopo has a large rural child population with high rates of unemployment and income poverty, yet child hunger has remained well below the national average, reported at 3% in 2017.

Hunger, like income poverty and household unemployment, is most likely to be found among African children. In 2017, some 2.2 million African children lived in households that reported child hunger. This equates to 13% of the total African child population. Eight percent of Coloured children were reported to live in households where there was child hunger, while the hunger rates for Indian was 4% and White children below 1%.

Although social grants are targeted to the poorest households and are associated with improved nutritional outcomes, child hunger is still most prevalent in the poorest households: 19% of children in the poorest quintile go hungry sometimes, compared with less than 1% in the wealthiest quintile. The differences in child hunger rates across income quintiles are statistically significant.

There are no significant differences in reported child hunger across age groups. However, more than 820,000 children young than five years old are reported to have experienced child hunger, signalling a risk of under-nutrition. Young children are particularly vulnerable to prolonged lack of food, which increases their risk of stunting. Inadequate food intake compromises children’s growth, health and development, increases their risk of infection, and contributes to malnutrition.

It should be remembered that this is a household-level variable, and so reflects children living in households where children are reported to go hungry often or sometimes; it does not reflect the allocation of food within households. The indicator also doesn’t reflect the quality of food, including dietary diversity, which has been found to affect the nutritional status of children under five years.



1 United Nations Economic and Social Council (2017) Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. E/CN.3/2017/2, Annex III. Revised list of global Sustainable Development Goal indicators. New York: United Nations.
The General Household Survey asks: “In the past 12 months, did any child in this household go hungry because there wasn’t enough food?” Those children living in households where the respondent answered “sometimes”, “often” or “always” are included as children experiencing hunger. "Not applicable” and unspecified responses were excluded from the analysis, and the distribution of valid responses was applied to the total child population.

The ‘hunger’ question in the General Household Survey provides notoriously weak data. Child hunger is emotive, subjective and estimates of frequency unreliable – particularly since the presence and frequency of ‘child hunger’ is reported by one adult in the household. It is assumed, however, that reporting error will be similar in each year of data collection, so that it is possible to report trends even if proportions for a single year are questionable. For this indicator the 5-point scale is collapsed into a dichotomous (yes/no) variable.

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  or DataFirst