Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
Self Reported Vulnerability to Food Insecurity in a South
African Low Income Neigbourhood
Wynand C.J. Grobler
Food security is defined as a state in which all people in a household at all
times have both physical and economic access to sufficient food to meet
their dietary needs for a productive and healthy life. Household food
insecurity has been associated in the last decade with several negative
health and nutrition outcomes, and South Africa in this regard was no
exception. The question however arises: To what extent are low income
households vulnerable to food insecurity? This study analysed food
security status of households, and analyse the self- reported vulnerability
with regard to food security of these households. 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. Following the
Household Food Insecurity Access Scale, households were asked to
describe behaviors and attitudes that relate to food insecurity, also called
domains of the food insecurity experience. The study showed a high
incidence of vulnerability towards food insecurity amongst households in
this low income neighborhood. Research indicated that urban food
insecurity is a considerable challenge and that food insecure urban
households may be more vulnerable to deeper food insecurity than their
rural counterparts.
This indicates that the drivers and maybe
consequences of food insecurity in urban areas may require different
conceptual framings and policy responses from policy makers.
This paper therefore suggests the development of an explicitly urban food
policy, to lower the vulnerability of food insecure households in urban
settings.
Fields of Research: Economics, Social Science
Keywords: poverty, food security, food insecurity, social security, grants.
1. Introduction
The concept 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; World Food Summit,
1996).Three distinct variables are essential for a household to be food secure: food
availability (sufficient quantities of appropriate food), food access (individuals have
adequate incomes or other resources to purchase or barter to obtain appropriate
food), and food utilization (food properly used )(USAID,1992; Coates, Swindale &
Bilinsky, 2007). In studies by Moser (1998) and Tawodzera (2011) a fourth
dimension to food security was introduced, namely „vulnerability‟ to food security.
Factors such as unemployment and increased household size may increase the
„vulnerability‟ of households towards food insecurity (Moser, 1998). In recent times
__________________
Prof. W.C.J. Grobler, North West University (Vaal Triangle Campus ), Vanderbijlpark, 1900, South
Africa, Email: Wynand.Grobler@nwu.ac.za
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
an increasing number of people especially in Southern Africa experience food
insecurity and a growing number of households have become vulnerable to food
insecurity (Wiggins, 2003; Maunder & Wiggins, 2007; Drimie & Casale, 2009 ).
The concept of food insecurity and vulnerability is sometimes seen as synonymously
(Devereux, 2006). Food insecurity, however, may be seen as form of vulnerability to
access food, and sometimes as an outcome of vulnerability ( Du Toit & Ziervogel,
2004). The vulnerability of the poor is sometimes due to unemployment, but low
returns on their labour may result in them becoming part of the “working poor”
(Kumar & Aggarwal, 2003:5295). The implication is that heads of households who
are employed may still be vulnerable to food insecurity.
This study investigated the food security status of households, and analysed their
self-reported vulnerability with regard to food security of these households. Previous
research indicated that urban food insecurity is a considerable challenge and that
food insecure urban households may be more vulnerable to greater food insecurity
than their rural counterparts. This indicates that the drivers and perhaps
consequences of food insecurity in urban areas may require different conceptual
framings and policy responses from policy makers. The rapid urbanization and
heightened urban poverty increase the focus of food security from a rural perspective
to an urban and rural perspective.
It is thus the purpose of this study to analyse the vulnerability of heads of households
towards food insecurity, by considering the status of food security, and the socio
economic variables that may increase or decrease the vulnerability of specific
households.
The study is outlined as follows. Section 2 provides an exposition of the literature
related to food expenditure and food security, Section 3 outlines the research
methodology, measuring instrument, background of the study area, and model to
estimate socio-economic factors that impact on food security status. Section 4
presents the findings of the study, and finally a conclusion is drawn in section 5.
2. Literature Review
Households in South Africa, urban and rural, are net purchasers of food, and depend
to a large extent on paid employment to ensure accessibility to food(Hendriks &
Maunder, 2006; Du Toit, 2005, Maxwell & Slatter, 2003;, Chambers & Conway,
1992). Vulnerability refers to exposure to contingencies as well as stress and
difficulty in coping with them, and can be divided into external stressors to which an
individual or household is subjected, and internal stressors which forms part of one‟s
lack of defence, meaning a lack of means to cope without damaging loss
(Chambers, 1989).According to Mc Carthy et al. (2001) external vulnerability is
shaped by socio-economic factors influencing a household. Internal vulnerability
concerns the ability of the households to respond to stressors (Bohle,2001). This
study focuses on the external vulnerability of households. Moser (1998) views
vulnerability as the “insecurity in the well-being of individuals, households or
communities in the face of a changing environment”. These changes include
unemployment and changing household size. Moser and Satterthwaite (2008)
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
indicate that environmental changes that increase vulnerability include economic,
social, and political factors. Tawodzera (2011) in his study found a significant
relationship between low income, unemployment, and vulnerability to food insecurity.
Hart (2009) argues that households with livelihoods that do not enable accumulation
of the assets required to cope with shocks in the external environment, increase their
vulnerability to food insecurity. Food security may become the outcome of
vulnerability (Du Toit & Ziervogel, 2004). Devereux (2009) indicates that food
insecurity interventions needs to be based on an understanding of the causes of
insecurity.
Crush et al. (2012) found that the mean food insecurity score (HFIAS Score) in
Johannesburg, South Africa are 4.7, and that 80% of the households in the study
experience some degree of food insecurity.
3.1 Area of study
The study was conducted in Bophelong, a low-income neighbourhood in southern
Gauteng, South Africa. It is 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 Methodology
Sample and data collection
A stratified sample of participants was 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. Trained fieldworkers
administered the questionnaires. They were required 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 informed that participation in the
survey was voluntary. 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 usable questionnaires
were used for the analyses in this study.
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 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 nine questions included in the Household Food
Insecurity Access Scale (HFIAS) are shown in Table 1.
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
No
1
2
3
4
5
6
7
8
9
Table 1: Household Food Insecurity Access Scale Questions
Occurrence Questions
In the past four weeks did you worry that your household would not have
enough food?
In the past four weeks, were you or any household member not able to eat the
kinds of food you preferred because of a lack of resources?
In the past four weeks, did you or any household member have to eat a limited
variety of foods due to a lack of resources?
In the past four weeks, did you or any household member have to eat some
foods that you really did not want to eat because of a lack of resources to
obtain other types of food?
In the past four weeks, did you or any household member have to eat a
smaller meal than you felt you needed because there was not enough food?
In the past four weeks, did you or any member have to eat fewer meals in a
day because there was not enough food?
In the past four weeks, was there ever no food to eat of any kind in your
household because of lack of resources to get food?
In the past four weeks, did you or any household member go to sleep at night
hungry because there was not enough food?
In the past four weeks, did you or any household member go a whole day and
night without eating anything because there was not enough food?r
Respondents were requested to answer Yes or No to the nine questions, and
indicate how often this happened using the following responses: rarely (once or twice
in the past four weeks), sometimes (three to ten times in the past four weeks) or
often (more than ten times in the past four weeks). Four types of indicators can then
be calculated namely: household food insecurity access related conditions („yes‟
answer to question 7, and response 3 to question 7), household food insecurity
access domains („yes‟ to question 2,3 and 4), food insecurity access scale score (
Sum of the frequency-of-occurrence during the past four weeks for the 9 food
insecurity-related conditions ,0 to 27, where 27 indicate highest insecurity), and
household food insecurity access prevalence (HFIAP) (the HFIAP indicator
categorizes households into four levels: food secure, mildly food insecure,
moderately food insecure, and severely food insecure.
The next section outlines the model used to analyse the socio-economic
characteristics on social grant recipients‟ household food security status.
Model
Multiple regression was used to determine the effects of socio-economic
characteristics, on household food security status. Food security determined by the
HFIAS score was used, where a higher score represents a more severe food
insecure household.
The multiple regression 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)
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
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. Results and Discussion
4.1 Demographic characteristics of the respondents
Table 2 below shows the descriptive statistics of the sample ( N=295). The average
household size is 4.49, with a minimum of 1 member and a maximum of 17
members per household. The average age of the head of the household is 49.60
years with a minimum age of 18 and maximum age of 99 years. The average
number of years schooling of the head of the household is 10.76 years, and the
average income per household R 3253.05. The average employed persons per
household are 0.807 with a maximum of 4 and minimum of 0. The average HFIAS
score per household is 12.18.
Table 2: Descriptive Statistics of Sample (N=295)
Variable
N
Minimum
Maximum Mean
HH Size
Age Head
Education Head
Total Income
Employed persons in
household (number)
HFIAS Score
295
295
295
295
295
1
18
2
100
0
17
99
17
16000
4
4.49
49.60
10.76
3253.05
.807
Standard
Deviation
2.05
13.21
4.99
3033.81
.853
295
0
27
12.18
6.97
Table 3 shows the gender distribution, employment status and marital status of
households in the sample. A total of 95 households are headed by males and 200
households by females. With regard to employment, 180 heads of households are
unemployed and 115 employed.
Table 3: Frequency Distribution of Households ( N=295)
Description
Frequency
Percent
Male
95
32.20
Female
200
67.80
Unemployed
180
61.02
Employed
115
38.98
4.2 Food security status of respondents
Table 4 shows that a total of 7.46% of households are food secure, while 92.54 %
are mild, moderate or severe food insecure. A total of 23.73% of households are
moderately food insecure, while 57.63 percent of the households are severe food
insecure. Of the severe food insecure category a total of 78.6 % indicated that
sometimes they “go to sleep at night hungry because there was not enough food, of
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
which 15.2 % of the category severe food insecure indicated that it happen on a
regular base.
Table 4: Food security status of grant recipient households
Food Insecurity Category
Number of
%
Households
Food secure
22
Mild food insecure
7.46
33
11.19
Moderately food insecure
70
23.73
Severe food insecure
170
57.63
Total
295
100.00
The household food insecurity access prevalence (HFIAP) distribution is given below
in table 5. The highest number of „yes‟ responses were indicated with regard to
anxiety levels, with regard to food insecurity. This means anxiety about, not enough
food to eat, and concerns around the quality of food consumed. A high number of
responses were also recorded for questions 3,4,5 and 6 measuring the quality or
adequacy of food intake. A total of 141 „yes‟ responses were recorded for question 7:
“In the past four weeks, was there ever no food to eat of any kind in your household
because of lack of resources to get food?” A total of 112 „ yes‟ responses were
recoded for question 8 : “In the past four weeks, did you or any household member
go to sleep at night hungry because there was not enough food? The most
vulnerable group of 109 respondents indicated „yes‟ to question 9: “In the past four
weeks, did you or any household member go a whole day and night without eating
anything because there was not enough food?”
Table 5: Household Food Insecurity Access Prevalence ( HFIAP)
Question
Frequency
Number of
Rarely
Sometimes
Often
‘yes’
1
2
3
responses
1a
228
19.0(8.3%)
152(66.7%)
57(25%)
2a
231
17(7.4%)
173(74.9%)
41(17.7%)
3a
231
18(7.8%)
178(77.1%)
35(15.2%)
4a
232
24(10.3%)
177(76.3%)
31(13.4%)
5a
223
17(7.6%)
177(79.4%)
29(13.0%)
6a
217
15(6.9%)
173(79.7%)
29(13.4%)
7a
141
7(5.0%)
116(82.3%)
18(12.8%)
8a
112
7(6.3%)
88(78.6%)
17(15.2%)
9a
109
8(7.3%)
88(80.7%)
13(11.9%)
Food insecurity Category
Food Secure
Mildly Food Insecure
Moderately food insecure
Severely food insecure
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
A comparison of descriptive statistics of food insecure and food secure households
provided in Table 6, shows that the average household size of food insecure
households of 4.52 are slightly higher than the household size of 4.40 of food secure
households. The age of the head of food insecure households are 49.42 years,
compared to 50.40 years of heads of households of the food secure households.
The total average income of food-insecure households is significantly lower at R
1408.84, compared to an average total income of R 3253.05 for food secure
households. Income, age and household size may be important external factors
impacting on the vulnerability of food insecure households.
Table 6: Descriptive Statistics Comparison between Food Secure and Food
Insecure Households
Variable
N
Min. Max.
Mean
Std Dev
Food
HH Size
240
1
17
4.52
2.11
insecure
Age Head
240
18
99
49.42
12.69
Total Income
240 100 16000 2968.54
2834.38
HFIAS Score
240
10
27
14.38
5.66
Food
HH Size
55
1
9
4.40
1.78
Secure
Age Head
55
26
80
50.40
15.38
Total Income
55 850 14370 3253.05
3553.11
HFIAS Score
55
0
27
12.18
6.97
4.3 Analyses of the Determinants of Food Security Status of Households
Table 7 shows results from the multiple regression on the determinants of food
security status score (HFIAS Score as dependent variable). The coefficient for the
gender of the head of the household is positive (0, if male and 1, if female in the
model) meaning that female-headed households have a higher food insecurity score,
but this was not statistical significant in the model.
Table 7: Determinants of food security score
Std.
95% Confidence
Determinant
Coef.
Err
t
P>|z|
Interval
dY/dX
Gender of head
1.859
1.047
1.77
.077
-.203
3.920
1.858
HH size
.841
.211
3.98
.000*
.425
1.256
.841
Age of head
Marital status of
head
Employment
Status
Number of
employed persons
in household
.155
.022
7.01
.000*
.111
.1992
.156
2.52
1.038
2.43
.016**
.483
4.568
2.53
1.55
1.212
1.28
.200
-.829
3.943
0
-1.51
.689
-2.19
.029**
-2.876
-.154
-1.51
Total Income
-.0006
.0001
-4.22
.000*
-.0009
.0003
-.0006
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
Number of Observations = 295
Prob>F = 0.000
R-squared = 0.727
Adj. R-squared =0.721
Significant at 1% level = *
Significant at 5% level = **
The coefficient for household size is positive, and statistically significant at one
percent level, meaning that a larger household may experience a higher degree of
food insecurity. The age of the head of the household is positive related to a higher
food insecurity score, and was significant at the one percent level. Marital status (0, if
un-married, and 1 if married) and number of employed persons in the household
were significant at the five percent level. Total income received per household was
significant at one percent level, with a negative coefficient, meaning that a higher
income reduces the food insecurity score. The F-value were statistically significant at
the 1% level. (Sig=0.00; p< .0005) in the model. The adjusted R 2 indicates that
approximately 72% of the variance in food insecure score of households can be
explained primarily by household size, gender. age, and number of employed
persons in the household. In terms of vulnerability this external factors can be seen
as the most significant factors contributing towards food insecurity.
Considering the marginal effect in the model, female headed households have a
18.58 percent higher change to be food insecure. Households with more numbers
per households do have a 8.4 percent higher change to be food insecure, while more
employed persons per household reduces the change of being food insecure by
15.10 percent.
5. Conclusion
The objective of this study was to determine the extent of vulnerability to food
insecurity in a low-income neighbourhood. The socio-economic determinants that
contribute to food insecurity were analysed. A total of 23.73% of households are
moderately food insecure, while 57.63 percent of the households are severe food
insecure A total of 141 „yes‟ responses were recorded for the question: “In the past
four weeks, was there ever no food to eat of any kind in your household because of
lack of resources to get food?”, while a total of 112 „ yes‟ responses were recoded for
the question: “In the past four weeks, did you or any household member go to sleep
at night hungry because there was not enough food?”. The most vulnerable group of
109 respondents indicated „yes‟ to the question:“In the past four weeks, did you or
any household member go a whole day and night without eating anything because
there was not enough food?”.
A comparison of between food insecure and food secure households, showed that
the average household size of food insecure households are slightly higher than the
household size of food secure households. The total average income of foodinsecure households are significantly lower, compared to an average total income of
food secure households.
Household size, age of the head of the household, marital status, number of
employed persons in the household, and total income received per household were
Proceedings of Global Business Research Conference
7-8 November 2013, Hotel Himalaya, Kathmandu, Nepal, ISBN: 978-1-922069-35-1
statistical significant contributors explaining food insecurity, and can be considered
as factors contributing to the vulnerability of food insecure households. Considering
the marginal effect in the model, female headed households have a higher change to
be food insecure and are more vulnerable towards food insecurity. Households with
more numbers per households do have a higher change to be food insecure, while
more employed persons per household reduces the change of being food insecure
and vulnerability.
Urbanization, unemployment, and poverty increases the vulnerability of households
to be food insecure. It is evident that low overall income, unemployment and
increased density remain challenges to policymakers. 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, gender of the head of the household, and low-income levels. The lack of
sufficient income (employment) is a significant predictor of food insecurity. 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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