NutritionNutrition

Child hunger

Author/s: Katharine Hall & Winnie Sambu
Date: November 2019

Definition

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

Data


Data Source
Statistics South Africa (2003 - 2019) General Household Survey 2002 - 2018. 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. There are a number of ways in which access to food can be monitored. At a global level, the Food and Agricultural Association (FAO) regularly publishes estimates of the prevalence of undernourishment, which is defined as the percentage of a population without access to sufficient dietary energy needed for an active and healthy life.1

South Africa’s average undernourishment rate for the 2016 – 2018 period was calculated at 6%, an increase from an average of 4.4% that was reported for the 2002 – 2004 period. The relatively low rate of undernourishment in South Africa, compared to other countries in the region which have undernourishment rates above 20% (Botswana, Namibia and Eswatini), suggests that there is enough food to cater for the majority of the country’s population. However, distribution and accessibility constraints, coupled with high rates of poverty and inequality, mean that a substantial proportion of the country’s population is food insecure.

At the household level, one of the main indicators used to monitor food insecurity is reported hunger. 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.

In 2018, 11% of children (2.1 million) lived in households that reported child hunger. More than a third of these children (36%) are from KwaZulu-Natal, while a fifth are from Gauteng. Child hunger rates in 2018 were 19 percentage points lower than they were in 2002 when 30% of children (5.5 million) lived in households that reported child hunger. The largest declines have been in the Eastern Cape, Limpopo and Mpumalanga. One of the main contributors to this decline is the expansion of the Child Support Grant which in 2018 covered over 12 million children.2 Another is the National School Nutrition Programme, which by 2016/2017 reached over 9 million learners in approximately 20,000 schools3 (though only during term-time and excluding children who are too young to attend school).

Analysis of child hunger rates within provinces shows that child hunger rates are highest in the North West and KwaZulu-Natal provinces, affecting 19% and 18% of children living there respectively. The lowest hunger rates are in Limpopo and Eastern Cape provinces (3% and 5% respectively). Despite high poverty rates, Limpopo has always reported child hunger rates below the national average, perhaps because of its highly fertile and productive land in rural areas where most of the population lives. However, there is no clear explanation for the dramatic decline in reported hunger in the Eastern Cape. Over the period from 2002 – 2018, reported child hunger rates in that province fell from 48% (higher than any other province) to 5% (the second lowest). This is despite the fact that the Eastern Cape has the highest poverty rates in the country, with 48% of children living below the food poverty line.

There are no differences in reported child hunger across gender or age groups. However, there are significant differences across race; 12% of African children live in households that reported child hunger, compared to 7% of Coloured children and less than 1% of Indian and White children. Differences are even more pronounced across income quintiles. While 18% of children living in the poorest 20% of households experienced hunger, only one percent of children in quintile 5 (the richest 20%) lived in households that reported child hunger.

Children who suffer from hunger are at risk of various forms of malnutrition, including wasting, stunting, overweight and micronutrient deficiencies. It must be recognised that child hunger is a subjective indicator and does not capture other important aspects of food security such as dietary diversity and consumption of nutrient-dense foods, both of which are important for children’s healthy growth especially in early childhood. Children may live in households that do not report hunger but may still not have access to sufficient nutritious food and are therefore at risk of malnutrition. In 2018, approximately 30% of children who lived in households that did not report child hunger were classified as living below the food poverty line, an indicator that their households lacked the financial resources needed to meet minimum dietary requirements for children and other household members.4


1  FAO, IFAD, UNICEF, WFP & WHO (2019) The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns. Rome: FAO.

2 Hall K (2019) Income poverty and grants – Child Support Grants. Children Count website, Children’s Institute, University of Cape Town. Accessed on 24 October 2019: www.childrencount.uct.ac.za.

3 Government Gazette No. 41704, 16 June 2018;
National Treasury (2019) Estimates of National Expenditure, Vote 14 Basic Education. Pretoria: National Treasury.

4 Statistics South Africa (2019) General Household Survey 2018. Pretoria: Stats SA. Analysis Winnie Sambu.

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