Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Food Security of Social Grant Recipients in a Low Income
Neighborhood in South Africa
Wynand C.J. Grobler
The origins of social security in South Africa can be traced back to attempts by
the previous apartheid government to create a welfare state for whites during
the apartheid era. Since the first democratic election in South Africa in 1994,
the total number of social security system beneficiaries increased from 2.4
million in 1998 to 12.4 million in 2008. Recent research has confirmed that the
various social grants are well targeted at the poor and that they have a
significant impact on poverty. However, the question arises: To what extent
does the social security system ensure food security for households in poorer
areas? This study analysed food security of households receiving government
grants. A quantitative research method was deployed, and a stratified random
sample of 295 questionnaires was administered in the township of Bophelong,
a low-income neighbourhood in southern Gauteng, South Africa. Using the
Household Food Insecurity Access Scale, households were classified into
food secure and insecure. The total amount of social grants received per
household, household size, and the age of the household head were found to
exert a strong positive impact on food security. Conclusions include whether
households who receive social grants, differ significantly from households who
do not receive social grants, in terms of food security.
Fields of Research: Economics, Social Science
Keywords: poverty, food security, food insecurity, social security, grants.
1. Introduction
During the 1996 World Food Summit in November 1996, heads of state signed the Rome
Declaration on World Food Security, re-affirming “the right of everyone to have access to safe
and nutritious food, consistent with the right to adequate food, and the fundamental right of
everyone to be free from hunger” (FAO, 1996). During the debate preceding the declaration,
food insecurity was identified as both cause and effect of poverty and slow growth. Despite
this, in 2010, more than 900 million people across the world were still insecure; this is against
a doubling of world food production in the last decade (FAO, 2010). The South African
government Constitution, Section 27, states that “everyone has the right to ... sufficient food”
and that the state must take reasonable legislative and other measures, within its available
resources, to achieve this. Against this background, the South African government developed
the Integrated Food Security Strategy (IFSS) in 2002. The National Planning Commission, in
2011, identified food security as a “key shaping force” for South Africa (NPC, 2011). In a study
by the African Food Security Urban Network (AFSUN) amongst 6500 households in South
Africa, using the Household Food Insecurity Access Scale (HFIAS), 77 percent of households
were found to be moderately or severely food insecure (Frayne et al., 2010:43).
________________________________________________________________________
Prof. W.C.J. Grobler, North West University, Vanderbijlpark, South Africa, Email: Wynand.Grobler@nwu.ac.za
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
In a study by Battersby (2012:1), food insecurity was identified as increasingly urban, with a
lack of focused policies to address food insecurity in urban settings.
Food availability, however, is not the only condition for food security if households or
individuals do not have financial or productive resources to acquire food (Migotto et al.,
2006:3). Against this background, numerous studies refer to the use of social security
schemes to improve food security by improving food access, or by providing households with
income to purchase food (Adato & Basset, 2012; ; Cook & Frank, 2007; Miller et al., 2011).
Food insecurity in South Africa is not viewed as a failure to produce enough food nationally,
but rather as a failure to provide adequate cash to purchase food at the household level
(Manyamba et al., 2012). The origins of social security in South Africa can be traced back to
attempts by the previous apartheid government to create a welfare state for whites in South
Africa during the apartheid era. Since the first democratic election in South Africa in 1994, the
total number of social security system beneficiaries increased from 2.4 million in 1998 to 12.4
million in 2008. Research has confirmed that the various social grants are well targeted at the
poor, and that they have a significant impact on poverty (Samson et al., 2004). The question,
however, arises: To what extent does the social security system ensure food security of
households in poorer areas, especially those households who receive social grants.
Thus, the objectives of the research reported here were: Firstly, to determine the extent of food
insecurity of social grant recipients in a low income neighborhood, and second, to analyse the
socio-economic determinants that contribute to food insecurity of social grant recipients in a
low-income neighborhood. This research aimed to increase the general understanding of food
insecurity in an urban setting, in order to improve interventions to solve food insecurity
amongst food insecure households.
The next section will provide a literature review of food insecurity and socio-economic
determinants of food insecurity. Following that will be an explanation of the research
methodology followed in the study, an overview of the study area, and social security in South
Africa. Finally, the empirical findings will be discussed and a conclusion drawn.
2. Literature Review
Food security can be defined as a state in which all people, at all times, have both physical
and economic access to sufficient food to meet their dietary needs for a productive and healthy
life. (USAID, 1992). Conceptualising food security has evolved over time, together with an
understanding of poverty, and since the World Food Conference in 1974, the debate
surrounding food insecurity has shifted from the national level to the household level (Maxwell,
1996). Several studies (Radimer et al., 1990; Radimer et al., 1992; Hamilton et al., 1997)
provided insight into the ways households may experience food insecurity by way of access,
namely feelings of uncertainty or anxiety over food, perceptions that food is of insufficient
quantity, perceptions that food is of insufficient quality, reported reductions of food intake,
reported consequences of reduced food intake, and feelings of shame for resorting to socially
unacceptable means to obtain food. To measure food insecurity presents many challenges,
and the measurement and assessment methodologies can differ considerably in the field of
qualitative as well as quantitative methods (Migotto et al., 2006). The study of Migotto et al.
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
(2006), identifies five general types of methodologies, measures of undernourishment,
measures of food intake, measures of nutritional status, measures of access to food in terms
of income, and finally, measures of vulnerability. Vulnerability in this regard is often measured
through qualitative survey questions or self-assessment surveys measuring food insecurity.
According to Kneppel et al. (2009), researchers, in developing a measurement tool for
household food insecurity, most often adapted a version of the Cornell/Radimer measuring
tool, or developed a tool based on research on how households experience food insecurity,
where both methods have produced valid or accurate measurements. Based on this, the US
Agency for International Development (USAID) funded Food and Nutritional Technical
Assistance (FANTA) project has developed the Household Food Insecurity Access Scale
(HFIAS), a measuring instrument that can be used cross-culturally. In this study, the reported
research is based on a self-assessment of food insecurity using the Household Food
Insecurity Access Scale (HFIAS) of the FANTA project. In recent years, the measurement of
food insecurity in South Africa includes the 1995 Income and Expenditure survey, which found
an urban food poverty rate of 27 percent and a rural food poverty rate of 54 percent, the
national Food Consumption Survey of 1999, which found food insecurity of 42 percent in urban
areas, compared to 62 percent in rural areas, and the South African Social Attitudes Survey of
2008, which found 20.5 percent urban food insecurity and 33.1 percent rural food insecurity
(Rose & Charlton, 2002; Labadarios et al., 2011). In low income developing countries, it was
found in 12 out of 18 samples, that food insecurity in urban areas was the same or higher than
in rural areas (Ahmed et al., 2007).
With regard to expected relation of explanatory variables with food insecurity, in earlier studies
positive relations were found with age of the household head (Obamiro et al., 2003: Babatunde
et al., 2007; Amaza et al., 2006), female-headed households (Knueppel et al., 2009; Joshi and
Maharjan, 2011; Mutuonotzo, 2006; Amaza et al., 2006), family size (Babatunde et al., 2007;
Mutunotzo, 2006; Amaza et al., 2006),and dependency ratio (WFP, 2001), while negative
relations were found with level of education (Haile et al., 2005 ) and income level (Davis et al.,
1983). In a recent study of Arene and Anyaeji (2010), only two variables were found to be
important in explaining food security status of households. They are income and the age of the
household head.
With regard to social security and its impact on food security, researchers concluded that cash
transfers, for example, improve food security by improving food access and providing
households with income to purchase food (Reilly et al., 1999). Much of the literature on the
impact of cash transfers on food security found increased spending on food by grant recipients
(Fiszbein et al., 2008; Gertler, 2005; Maluccio & Flores, 2005). In South Africa, Booysen & Van
Der Berg (2005) found that income grant recipients used the social grant primarily to pay for
food. Several other studies (Lagarde, Haines & Palmer, 2008; Dufflo, 2000; Miller, Tsoka &
Reichert, 2007) found positive impacts of social grants on food security. Despite these
findings, questions still arise whether social grants must, for example, only be targeted to
female-headed households.
The next section outlines the methodology followed in this study, background of the study
area, and the background of social security in South Africa.
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
3. Research Methodology and Data
3.1 Background of the study area
The study was conducted in Bophelong, a low-income neighbourhood in southern Gauteng,
South Africa and a former black township, established in 1955. The population in Bophelong is
estimated at 37,779, and the number of households is estimated at 12,352. The size of
Bophelong is approximately nine square kilometers, with a shopping complex and informal
shops and markets. A study by Slabbert and Sekhampu (2009) revealed that 66.3 percent of
the residents of Bophelong are poor, and the unemployment rate is 62.2 percent in the area. In
a study by Dubhilela,(2011) it was found that female-headed households have a shortfall of 53
percent of their income to be placed on their respective poverty line.
3.2 Background of Social Security in South Africa
The origins of social security in South Africa can be traced back to attempts by the previous
apartheid government to create a welfare state for whites in South Africa.. Since the first
democratic election in South Africa in 1994, the total number of social security system
beneficiaries increased from 2.4 million in 1998 to 12.4 million in 2008 (Van Der Berg, Siebrits
& Lekezwa, 2011). Projections of the National Treasury (2008) indicated that 66.6 percent of
grants paid would have been child support grants, 17.9 percent old age pensions, and 11.4
percent disability grants. The remainder of grants are war veteran grants, grants in aid, foster
care and care dependency.
3.2 Methodology
Sample and data collection
A stratified sample of participants were drawn from the semi-urban area located in southern
Gauteng, South Africa, in order to reflect on their perceptions on food insecurity and their
socio-economic background. A self-administered, on-site survey via a structured questionnaire
was used in data collection. Fieldworkers, who attended a training session, conducted the
interviews. Fieldworkers had to be proficient in English and one or more of the African
languages in order to explain the purpose of the study, as well as questions in the survey.
Participants were under no obligation to participate in the survey.
Every second household was sampled in each street, and both male and female respondents,
as head of the household, were chosen for the study. Of the 295 questionnaires administered,
a total of 118 questionnaires were used for the analyses in this study (respondents who
receive social grants).
Measuring instrument
A nine-item food insecurity scale, developed by USAID’S FANTA project, was used to
measure food insecurity. The measurement instrument follows a progression, beginning with
anxiety about food supply, followed by questions about the quality of food, then questions on
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
the quantity of food consumed, and the last questions on going to sleep hungry, or going all
day and night without eating (Deitchler, Ballard, Swindale & Coates, 2010). The HFIAS score
calculated is a continuous measure of the degree of food insecurity (access) in the household
in the past four weeks (30 days), adding up to a maximum score for a household of 27, and a
minimum of zero. The higher the score, the more food insecure a household will be. A
Household Food Insecurity Access category is then calculated for each household in terms of
food secure, mildly food insecure, moderately food insecure, and severely food insecure. For
the purpose of the binary logistic regression analyses, the categories of food secure and mildly
food insecure were grouped together (category 1 and category 2) and considered as food
secure households. The next section outlines the model used to analyse the socio-economic
characteristics on social grant recipients’ household food security status.
Model
Binary logistic regression was used to determine the effects of socio-economic characteristics,
on social grant recipients’ household food security status. Households who receive more than
20 percent of their income from social grants were considered as grant recipient households. A
binary response function (food secure and food insecure) was specified and estimated by the
logistic procedure. In this case, where the endogenous variable is dichotomous (households
who are food secure and those who are food insecure), the binary logistic specification can be
used (Arene, 2010).
Food security determined by the HFIAS was used, where one represents food secure
households and zero represents food insecure households.
The logistic model is specified as:
Y= bₒ + b₁X₁ + b₂X₂ +b₃X₃ +b₄X₄ + b₅X₅ + b₆X₆ + b₇X₇
Where Y = Food security status (1, if the household is food secure; 0, if the household is food
insecure)
X₁ =Gender of household head (male = 0; female =1)
X₂ =Household size (number of dependents)
X₃ =Total grant income
X₄ =Age of household head (years)
X₅ =Marital status of head of household (0, if single; 1, if married or live together)
X₆ =Education of head of household (no of years in a school)
X₇ =Employment status of head of household (0, if unemployed; 1, if employed)
4. Interpretation and Findings
4.1 Demographic Characteristics of the Respondents
The sample data were based on responses from the head of grant recipient households, and a
total of 118 grant recipient households were analysed. Table 1 provides the descriptive
statistics for the sampled population. The number of household members per household varied
from one to 17 members, with an average household size of 4.86. The average age of the
head of the household was 54.08 years, with a minimum of 13 years and maximum of 99
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
years old. Significant variations in household income were recorded, with the lowest monthly
income R280, the highest monthly income R14370, and an average monthly income of
R2326.61 per household. The average number of years of schooling of the heads of
households was 9.84 years, with a minimum years of schooling two years, and a maximum of
17 years (10 years of schooling are equal to mid-secondary school level).This shows that, on
average, the respondents in the sample have a very low average education. High deviations
with regard to age of the head of the household and total income were recorded, 15.236 and
2063.58 respectively.
Table 1: Descriptive Statistics of Grant Recipient Households
Variable
N
Minimum
Maximum
Mean
Standard
Deviation
HH size
118
1
17
4.86
2.1519
Age of head
118
13
99
54.08
15.236
118
2
17
9.84
5.2925
118
280
14370
2326.61
2063.58
Education of
head
Total income
Of the total grant recipient households of 118, a total of 90 heads of households (68.64
percent) are unemployed, and 28 heads of households (23.73 percent) are employed. A total
of 22 households are male-headed households, and 96 are female-headed households.
4.2 Food Security Status of Social Grant Recipients
The analyses with regard to the food security status of the total sample showed that only 7.46
percent of households are food secure. Households who are mildly food insecure are 9.49
percent, while 25.42 percent of the households are moderately food insecure, and 57.63
percent are severely food insecure. The food security status of grant recipient households is
shown in Table 2. When comparing the food security status of grant recipient households with
non-grant recipient households, it shows that 49.15 percent of grant recipient households are
severely food insecure, compared to 63.28 percent of non-grant recipient households, who are
severely food insecure. If the categories, food secure and mild food insecure are considered to
represent the food secure households, 16.95 percent of grant recipient households are food
secure and 83.05 percent are food insecure, compared to 12.94 percent of non-grant recipient
households who are food secure and 87.06 percent of non-grant recipient households who are
food insecure. Table 2 thus shows that grant recipient households, on average, are more food
secure than non-grant recipient households.
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 2: Food Security Status of Grant Recipient Households
Food
Insecurity
Category
Grant Recipient
Households
Number
%
Non Grant Recipient
Households
Number
%
Total
Number
%
Food secure
Mild food
insecure
Moderately
food
insecure
Severe food
insecure
Total
9
7.63
13
7.34
22
7.46
11
9.32
17
9.60
28
9.49
40
33.90
35
19.77
75
25.42
58
49.15
112
63.28
170
57.63
118
100.00
177
100.00
295
100.00
4.3 Analyses of the Determinants of Food Security Status of Households
Table 3 shows a comparison of descriptive statistics between food secure and food insecure
households. Female-headed households represent 66.10 percent of the food insecure
households, compared to 15.25 percent of male-headed households. In a study, Manyamba et
al. (2012) suggests that groups most vulnerable to food insecurity in South Africa are the rural
poor, female-headed households, disabled and the elderly. Table 3 shows that the average
household size of food insecure households is 4.97 members, compared to 4.41 members for
food secure households. The average age of the head of the household for food insecure
households is 53 years, compared to 59 years for food secure households. The reason for this
may be that older people qualify for old age pension, and with the higher income they may
have a higher probability to be food secure. The average years of schooling for food insecure
households is 10.26 years, compared to 8.04 years of schooling for food secure households.
This implies that there may be no significant difference in employability for somebody with
eight or 10 years schooling, since it is still mid-secondary school level. In terms of employment
status of the head of the household, 21.88 percent of heads of households of the food
insecure group are employed, compared to the 31.81 percent employed of the food secure
group, suggesting that more heads of households are employed in the food secure group.
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 3: Food Secure and Food Insecure Households
Gender
Food Secure
Households
3.39%
15.25%
Male
Female
Household size
Average age of
head of household
Years schooling of
head of household
Employed head of
household
Food Insecure
Households
15.25%
66.10%
4.41 members
4.97 members
59 years
53 years
8.04 years
10.26 years
31.81%
21.88%
Table 4 shows results from the logistic regression on the determinants of food security status
of grant recipient households. The coefficient for the gender of the head of the household is
negative (0, if male and 1, if female in the model) meaning that female-headed households
have a lower probability to be food secure, but this was not statistical significant in the model.
The coefficient for household size is negative, and statistical significant at five percent level of
significance, meaning that more members in a household size lower the probability of being
food secure.
Table 4: Determinants of Food Security Status of Grant Recipient Households
Determinant
Coef.
Std.
Err
Z
P>|z|
95% Confidence
Interval
Gender of head
-.111
.733
-0.15
0.879
-1.548
1.325
-
HH size
-.289
.145
-2.00
0.046⃰⃰ ⃰
-.572
-.005
-.0388
Age of head
Marital status of
head
.0062
.119
0.52
0.606
-.017
.297
-
-.504
.724
-0.70
0.486
-1.92
.915
-
dY/dX
Education of head -.132
Employment
Status
1.11
.051
-2.58
0.010⃰⃰
-.233
-.032
-.0178
.689
1.62
0.105⃰⃰ ⃰
-.234
2.46
.1502
Grant Income
.000
1.94
0.052⃰⃰ ⃰
-2.91e-06
.0005
-
.0003
Employment status was significant only at the 10 percent level; with a negative coefficient,
meaning that to be unemployed increases the probability to be food insecure. The coefficient
of the years of schooling of the head of the household was negative. This may be attributed to
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
the fact that there may be no significant difference in employability for somebody with eight or
10 years schooling, since it is still mid-secondary school level. The coefficient for the size of
grant income is positive, and statistical significant at the 10 percent level, meaning that a
higher grant income increase the probability of being food secure. Considering the marginal
effect, it is shown that household size, if increased by one member, lowers the probability to be
food secure by 3.88 percent, ceteris paribus. It is also indicated that if the head of the
household finds employment, it will increase the probability of being food secure by 15.02
percent, ceteris paribus. No statistical significance was found with regard to the gender and
marital status of the head of the household. The model containing all the variables was
significant, indicating that the model was able to distinguish between the various explanatory
variables used in the model with a p value of the chi two of 0.000.
5. Conclusion
The objective of this study was to determine the extent of food insecurity of social grant
recipients in a low-income neighborhood. The socio-economic determinants that contribute to
food insecurity were analysed. The HFIAS measuring tool was used to measure food security
status of social grant recipients. Data from the sample of 295 households, of which 118
receive grants, were analysed. The results of the analyses show that a high percentage (83.05
percent) of households who receive grants are food insecure. Urbanisation and resultant
effects of unemployment, poverty, and ultimately food insecurity, remain a challenge to
policymakers. It is evident that social grants alone do not solve problems with regard to food
insecurity, as low overall income, unemployment and increased density remain challenges to
policymakers. Using binary logistic regression results in this study indicate that household size
and employment are significant contributors to food security status in low-income areas.
Although the study could not find female-headed households as significant in the model,
descriptive statistics showed that 66.10 percent of households are food insecure. With this in
mind, it could be argued that policymakers should consider targeting female-headed
households as part of social security.
Closing the income gap between rich and poor should be seen as a key objective to ensure
even distribution of income, to improve food security and reduce poverty. From a policy
perspective, the problem of food security can be attributed to socio-economic factors such as
family size, low educational levels, gender of the head of the household, and low-income
levels. The lack of sufficient income (employment) is a significant predictor of food security. It
may be important for policy makers to understand the impact of different socio-economic
factors on food security. There may be an urgent need for the development of a more
comprehensive food security strategy, focusing on urban as well as rural areas in South Africa.
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