Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
Socio Economic Determinants of Access to Basic Necessities:
The Case of a Low Income Neighbourhood in South Africa
Wynand C.J. Grobler
Post-apartheid South Africa has achieved significant political, transformational and increment al
improvements in basic social services. However, povert y and economic inequalit y have increased,
mak ing pro-poor socio economic growt h one of the greatest challenges facing South Africa.
Studies on the ext ent of povert y in S outh Af rica show that almost half of its population live in
povert y. One of the measures that can be used to measure the extent of poverty is the Lived
Poverty Index(LPI), which is an experiential measure consisting of a series of survey questions
that measure how frequently people actually go without basic necessities during the cours e of a
year. However, the question arises: To what extent do socio economic factors determine access to
basic necessities? To ans wer this question a quantitative res earch approach was used to collect
data from a stratified random sample of 295 households in Bophelong, a low income
neighbourhood in South Africa.The study focused on access to basic necessities, such as water,
medicine, electricity, food, cash income and fuel for cook ing.
The Lived Poverty Index (LPI) raw score were used to measure the extent of poverty of
households. Multiple regressions were used to determine the effects of socio-economic
characteristics, on the Lived Poverty Index. The Lived Poverty Index was considered as the
outcome variable and socio economic variables as predictors. The study concluded that access to
basic necessities and its resultant effects remain a challenge to polic y mak ers. Hence, there may
be an urgent need for the development of a more c omprehensive strategy, focusing on urban
areas in South Africa to increase access to basic necessities.
Field of Research: Economics, Social Sciences
Keywords: Poverty, Socio Economics, Social Security, Grants, Lived Poverty, Basic
Necessities.
1. Introduction
The Lived Poverty Index (LPI) can be defined as an experiential measuring instrument
consisting of survey questions measuring how frequently people go without basic necessities
such as water, medicine, food, cash income, fuel for cooking, and electricity. (Dulani, Mattes
and Logan, 2013). Recent studies on Lived Poverty indicated that almost 20 percent of
people in Africa still experience deprivation of basic needs like food, clean water and medical
treatment, while in Southern Africa the lack to basic needs range from 50 percent to 94
percent of households(Mattes, 2008; Dulani, Mattes and Logan, 2013) .
Post-apartheid South Africa has achieved significant political transformation and incremental
improvements in access to basic social services. However, poverty and economic inequality
have increased, making pro-poor socio-economic growth one of the greatest challenges
facing South African policy makers. Against this background a pro-poor policy framework has
been adopted by the South African Government, increasing the share o f government
expenditure on social services in the form of social grants. The success of social security
towards reducing poverty amongst the poor are well documented (Samson et al., 2004;
Ardington and Lund 1995; Case and Deaton, 1996). Accordingly 76 percent of government
_________________________________________________________________________
Prof. W.C.J. Grobler, North West University, Vaal Campus, Vanderbijlpark, 1900, South A frica, email:
Wynand.Grobler@nwu.ac.za
Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
spending on social grants in South Africa is received by the poorest 40 percent of the
population, while social grants increased the share of to tal income of the poor households
from 4.7 percent to 7.8 percent of total income in South Africa (Van der Berg, Lekezwa and
Siebrits, 2008).
In order to eradicate poverty and to ensure sustainable economic growth, the key
developmental goal of all governments around the world has been to eradicate extreme
hunger and poverty, ensuring access to basic needs such as clean water, access to food,
and medical treatment (World Bank, 2007). One of the measures that can be used to
measure the extent of poverty is the Lived Poverty Index (LPI). The question however arises:
To what extent do socio economic characteristics of a household determine access to these
basic necessities?
This study aimed at examining the socio economic factors that may impact on access to
basic necessities such as clean water, food, medical treatment, electricity, cash income, and
fuel for cooking. The study was conducted in Bophelong, a low-income neighbourhood in
Southern Gauteng, South Africa. The key research question was: To what extent do socio
economic factors like gender, age, employment status, number of years schooling and social
grant income determine access to basic needs.
The outline of the study is as follows: Section 2 discusses the relevant literature to poverty,
access to basic needs and the experience of households towards lived poverty. Section 3
discusses the background to the study area, research methodology and model. Section 4
presents the findings with regard to socio economic determinants to access of basic needs.
Section 5 draws a conclusion and makes some recommendations to policy makers.
2. Literature Review
Although the World Bank aimed in 1990 with Millennium Development Goals (MDG) to halve
the degree of poverty by 2015, the number of people in Sub-Saharan Africa who live below
the poverty line of 1 US $ per day increased since 1990. The number of people in subSaharan Africa experiencing hunger, malnutrition and a lack of access to basic needs has
increased from 1990 (Foeken and Owuor, 2008; Armstrong, Lekezwa and Siebrits, 2008). In
this regard South Africa was no exception and the proportion of people living in poverty in
South Africa has not changed significantly in the last decade, while households in poverty
sunk deeper into poverty (Schwabe, 2004; Bhorat, Van der Westhuizen and Cassim, 2009).
Poverty means different things to different people and no universal definition of the
phenomenon exists (Bhorat et al., 2001; Ngwane, Yadavilli and Steffens, 2001). Consensus,
however, exists that poverty means the inability of individuals or households to attain at least
an acceptable minimum standard of living with access to resources such as income and
health facilities. (Rosalina et al, 2007; Ngwane et al., 2001). Poverty, in the context of this
study, refers to the deprivation of access to clean water, food, income, and resources like
electricity. It can be measured in two ways, namely looking at the number of poor people, or
developing an index to measure the degree of poverty (Hargreaves et al., 2005; Ngwane et
al., 2001). Several studies in this regard considered the standard of living as a criterion for
poverty (Bhorat et al., 2001; Hargreaves et al., 2005; Ngwane et al., 2001).
The definition of poverty used by the World Summit on Social Development in 1995,
Copenhagen, is multidimensional, including lack of income and the lack of productive
resources to ensure a sustainable livelihood. This includes hunger, malnutrition, ill health,
lack of basic services, increased morbidity and mortality from illnesses, inadequate housing,
Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
unsafe environments and social discrimination. Absolute poverty in this context is defined as
a condition of severe deprivation of basic human needs, including food, safe drinking water,
sanitation facilities, health, shelter, education and information. Sen (1999) indicate that one’s
standard of living lies in living itself, the experience of shortages of basic necessities.
The Afro barometer (Dulani, Mattes and Logan, 2013) used the Lived Poverty Index (LPI) to
measure the experience of households towards the lack of access to basic needs, to
determine the extent of poverty amongst households. It consists of a series of survey
questions that measure how frequently people actually go without basic necessities during
the course of a year.
Socio economic factors however may determine access to basic needs. In a recent study by
Nishimwe-Niyimbanira, Sekhampu and Muzindutsi (2014) it was found that female-headed
households more frequently experience a lack of access to basic necessities. Numerous
other studies concluded that female-headed households are poorer than male-headed
households (Haddad et al., 1996; Buvinic and Gupta, 1997; Ray, 2000, Budlender, 1997;
Dungamaro, 2008; Duflo, 2003; Lund, 2006). Some studies found a positive relationship
between income and poverty (Booysen, 2003; Samson et al., 2004; Barrientos and Lloydsherlock, 2002). Higher income secures food security and food access as a basic need
(Reilly et al., 1999; Meng, Florkowski and Kolavalli, 2012). Socio-demographic factors such
as age, gender, marital status, education were significantly correlated with increased access
to food (Meng, Florkowski and Kolavalii, 2012; Jolly, Awauah, Fialor, Agyemang, Kagochi
and Binns, 2008).
Employment status significantly correlated with access to food, and basic needs (Hendriks
and Maunder, 2006; Du Toit, 2005; Maxwell and Slatter, 2003). Tawodzera (2011) found a
significant correlation between low income, unemployment and lack of access to food.
Furthermore, several studies found a significant relationship between receiving social grants
and access to basic needs (Samson et al., 2004, Van den Berg et al., 2005; Ardington and
Lund, 1995, Case and Deaton, 1996).
3. Research Method and Design
Sample
This geographical area covered by the study is Bophelong, a former black low income area
which is approximately 70 kilometres south of Johannesburg in the Gauteng Province, South
Africa. The neighbourhood is part of the Emfuleni Municipal area, and can be defined as an
urban neighbourhood. The estimated population size is 37779 and the number of households
is estimated at 12 352. A stratified sample of participants was drawn from the area in order to
reflect on their perceptions on access to basic necessities.
Instrument
An questionnaire was designed to collect date from the identified sample. The questionnaire
was divided into a section asking questions on socio demographic factors such as age,
gender and employment status. while the second part of the questionnaire adapted from
Mattes et al (2002) contained questions about the lived poverty index, measuring the level of
access to basic necessities by a household. As part of the lived poverty index respondents
were asked “ How often in the past year did you or you family gone without enough food to
eat, enough clean water, medicine or medical treatment, electricity, enough fuel to cook food
Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
or without cash income.. The respondents could choose between 1 (never) to always (5).
The Lived Poverty Index (LPI), a six item scale was used to measure the extent of poverty
per household. The measurement instrument measures the access to food, access to clean
water, access to medicine or medical treatment, access to electricity, access to cash income
and access to fuel for cooking purposes. In the questionnaire respondents were asked “Over
the past year, how often, if ever have you and your family gone without: enough food to eat,
enough clean water, medicine or medical treatment, electricity, enough fuel to cook and cash
income. Respondents could answer never (1), just once or twice (2), several times (3), many
times (4) and always (5). A minimum raw score of 6, indicate no poverty, while a maximum
score of 30, indicate severe poverty. Multiple regression modelling was then used to
determine the impact of socio economic factors on the LPI raw score.
Principal Component Analysis
The Bartlett’s Test of Sphericity and the Kaiser-Meyer-Olkin (KMO) measure of sampling
adequacy was conducted to establish whether the data was suitable for exploratory factor
analysis. Both these tests (KMO = 0.724; Bartlett’s Test of Sphericity (sig) = 0.00) were
found to be acceptable (Kaiser, 1974). Principal Component Analysis (PCA) was then
conducted on the data. Using a minimum eigenvalue of 1, the PCA extracted one component
of Lived Poverty Index (LPI) with an Eigen value of 2.4 explaining 40.56 percent of the
variance was extracted Cronbach’s Alpha test was used to determine the reliability of the
questionnaire. A Cronbach’s Alpha of 0.811 the Lived Poverty Index (LPI) was achieved.
According to Pallant (2013) a Cronbach’s Alpha value of greater than 0.6 indicate that the
questionnaire can be regarded as reliable, hence the the LPI can be considered as a reliable
measure of poverty. The implication of this is that all six variables can be classified in 1
component as shown below in table 1.
Table 1: Component matrix: Reasons for poverty
Enough food to eat
Enough clean water
Medicine or Medical treatment
Access to electricity
Enough fuel to cook
Access to cash income
Component 1
0.832
0.074
0.106
0.688
0.738
0.841
Model
Multiple regressions was used to determine the effect of socio economic factors such as
gender, age, income, employment status, number of years schooling , grant income on Lived
Poverty. The Lived Poverty Index (LPI) raw score were used as dependent variable and
socio economic factors as predictors.
The regression model is given as:
Yi= β0 + β1Xi1 + β2 Xi2 + β3 Xi3 + β4 Xi4 + β5 Xi5 + β6 Xi6 + β7 Xi7 + βεi
Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
Where Y = Lived Poverty Index (LPI) Raw Score
Xi1 = Gender (Male =1, Female=0)
Xi2 = Household Size
Xi3 = Age of Head of Household
Xi4= Employment Status (Employed = 1, Unemployed =0)
Xi5= Number of Years Schooling
Xi6 = Income of Household
Xi7= Grant income
βεi = Difference between the predictor and observed value of Y.
4. Findings and Interpretation
Demographic Characteristics
Table 2 provides an illustration of the demographic statistics of the sample. The average
household size in the sample is 4.49 with a minimum of 1 and a maximum of 17 members in
a household. The average age of the head of the household in the sample is 49.60 years
with a minimum age of 18 and maximum age of 99 years. The average number of years
schooling are 10.76 (equal to secondary school level) years with a maximum of 17 years
(equal to post graduate level). The average income of households in the sample is R
3253.05 with a minimum of R 100 and a maximum income of R 16000 per mo nth. The
average grant income received from government by households in the sample is R 1008.95
with a maximum grant income received from government per household of R 12 290 per
month.
Table 2 Demographic statistics of Sample (N=295)
Variable
Household Size
Age Head
Number of Years
Schooling
Total Income
Total
Grant
income
N
Min
Max
Mean
295
295
295
1
18
2
17
99
17
4.49
49.60
10.76
295
295
100
0
16000
12290
3253.05
1008.95
Standard
Deviation
2.05
13.21
4.99
3033.81
1976.41
Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
Table 3 shows the descriptive statistics of the Lived Poverty Index items. The mean score for
enough to eat is 2.3220 indicating a mean between just once or twice and several times. The
highest mean were recorded for access to electricity with 2.7288 followed by enough fuel to
cook of 2.6837.
Table 3: Descriptive statistics of the Lived Poverty Index
Variable
N
Minimum
Maximum
Mean
Enough food to eat
Enough clean water
Medicine/Medical
treatment
Electricity
Enough fuel to cook
Cash income
295
295
295
1
1
1
5
6
5
2.3220
1.4694
1.6655
Standard
Deviation
0.987
0.884
0.817
295
295
295
1
1
1
5
5
5
2.7288
2.6837
2.6633
1.234
1.239
1.132
Determinants of Poverty
Table 4 shows the results of the multiple regression model. The predictors in the model are,
household size, gender, employment status, number of years schooling of the head of the
household, income of the household and social grant income received by households. The
dependent variable in the model is the Lived Poverty Index (LPI) raw score. A higher Lived
Poverty Index raw score means the household have less access to basic necessities like
food, clean water, electricity, medicine, cash income and fuel for cooking purposes. The F
value in the model was statistical significant at the 1 percent level (Sig 0.000; P=0.005).
Table 4: Determinants of Poverty- Lived Poverty Index
Unstandardised Std Error
B
(Constant)
15.150
.811
Gender
-.042
.256
HH Size
.031
.071
AgeHead
-.015
.012
Employmentstatus
-4.788
.383
Yearsschooling
-.064
.037
Income
.000
.000
Grant
-1.401
.370
2
Adjusted R = 0.632

* Significant at the 0.01 level

** Significant at the 0.05 level

*** Significant at the 0.1 level
F Value significant at 0.01 level
Β
T
Sig.
-.006
.017
-.048
-.626
-.073
.166
-.181
18.684
-.164
.444
-1.205
-12.493
-1.712
4.341
-3.784
0.870
0.658
.229
.000*
.088***
.000*
.000*
Proceedings of 10th Asian Business Research Conference
6 - 7 October 2014, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-62-7
Employment status of the head of the household were found statistical significant at the 1
percent level in the model. Total income and social grant income were found statistical
significant at the 1 percent level, while number of years schooling were statistical significant
at the 10 percent level. Gender, household size and the age of the household head were
insignificant in the model. Employment status (β= 0.626) contributed most to the model
followed by income and social grant income. The adjusted R2 value showed that
approximately 63.2 percent in the variance of Lived Poverty can be explained by employment
status of the head of the household, number of years schooling, income and social grant
income of households.
5. Conclusion and Recommendations
The aim of this study was to determine access to basic needs of households in a low income
neighbourhood. The Lived Poverty Index (LPI) was used to measure the access to basic
necessities as this can be used as a measure of poverty. Secondly, socio economic factors
as determinants to access to basic necessities were analysed.
The analyses showed that employment status, income and specifically social grant income
were statistical significant contributors to increased access to basic necessities. Households
with sufficient income, where the head of the household are employed are positively
associated with increased access to clean water, food, electricity, medical treatment and fuel
for cooking purposes. This is a further proof that policy makers via social gra nts positively
increase access to basic necessities.
It is therefore suggested that policy makers consider socio economic determinants in the
design of a comprehensive strategy to increase access to basic necessities in urban areas.
Policy makers should be be aware of the negative consequences of unemployment towards
access to basic necessities. Access to basic necessities is not only about providing basic
government services, but also about ensuring access via employment and income.
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