The effect of education on household food security in two informal urban settlements in Kenya: a longitudinal analysis
and urban health issues. Caroline has published extensively in peer-
reviewed journal articles
The effect of education on household food security in two informal urban settlements in Kenya: a longitudinal analysis
Maurice Mutisya 0 1 2 4 5
Moses W. Ngware 0 1 2 4 5
Caroline W. Kabiru 0 1 2 4 5
Ngianga-bakwin Kandala 0 1 2 4 5
0 Department of Population Health, Luxembourg Institute of Health , 1A-B, rue Thomas Edison, L-1445 Strassen , Luxembourg
1 School of Public Health, University of the Witwatersrand , Johannesburg , South Africa
2 African Population and Health Research Center , Nairobi , Kenya
3 Maurice Mutisya
4 Department of Mathematics and Information Sciences, Faculty of Engineering and Environment, Northumbria University , Newcastle upon Tyne , UK
5 Dr. Ngianga-bakwin Kandala is a Professor of Biostatistics in N o r t h u m b r i a U n i v e r s i t y , Newcastle upon Tyne and Head o f H e a l t h E c o n o m i c s i n Luxembourg Institute of Health. H e a l s o h o l d s a v i s i t i n g Professor position at the Division o f E p i d e m i o l o g y a n d Biostatistics, School of Public H e a l t h , U n i v e r s i t y o f Witwatersrand , Johannesburg , South Africa , and at the Division of Health Sciences, Warwick University, UK where he previously worked between 2006 and 2015. Prior to joining Warwick, he worked as a Medical Statistician at King's College London, a Research Fellow at the University of Southampton, a Mellon Foundation fellow at University of Montreal, Canada, the University of Munich , LMU
Poverty and food insecurity continue to feature prominently in the global agenda, with particularly close attention being paid to the determinants of food insecurity. However, the effect of education is mixed and remains understudied in low income countries. Using longitudinal data collected between 2007 and 2012 in Kenya, we investigated the effect of household education attainment on food security among poor urban households. Household food security was constructed from a set of four key items while education was the average years of schooling for individuals aged 18 years and above in a household. To determine the association between education attainment and food security, we fitted a random effects generalised ordered probit model. The prevalence of severe food insecurity ranged from 49 % in 2008 to 35 % in 2012. The ordered probit results showed a significant effect of education on food security. The probability of being food insecure decreased by 0.019 for a unit increase in the average years of schooling for a given household. The effect of education, remained significant even after controlling for household wealth index, a more proximate determinant of food security in a cash-based economy such as the urban slums. The findings highlight the need to focus on the food security status of the urban poor. Specifically, results suggest the need for programs aimed at reducing food insecurity among the urban poor and enhancing household livelihoods. In addition, investment in the education of the slum households may, in the long term, contribute to reduction in the prevalence of food insecurity.
Food security; Slums; Nairobi Kenya; Urban poor; Education attainment
The World Food Summit of 1996, defined food security as
Bwhen all people at all times have physical, social and
economic access to sufficient, safe and nutritious food that meets
their dietary needs and food preferences for an active and
(World Food Summit 1996)
. This definition
encompasses the four dimensions of food security: access,
availability, utilization and stability, which are necessary for a
household to be categorized as food secure
Food security is a human right, yet close to 11.3 % (805
million) of the world population remains food insecure
(FAO et al. 2014)
. Globally there has been a modest decline
in food insecurity; however, the decline has been
disproportionate. The Sub-Saharan Africa (SSA) region still has the
highest prevalence of undernourishment with one in every
four people in the region being food insecure
(FAO et al.
. Between 2000–2002 and 2012–2014, while the
proportion of undernourished population in SSA and Kenya
reduced to 23.8 % and 24.3 %, the absolute number of people
increased by 214.1 million and 10.8 million respectively,
telling a different story
(FAO et al. 2014)
. In the slums of Nairobi,
more than half of the population is severely food insecure
(Faye et al. 2011)
Many studies on food insecurity have tended to focus on
rural populations. However, new challenges to achieving food
security in the world are emerging. Among the many is rapid
urbanization occurring in developing countries with
unparalleled economic growth. This growth in most cases has led to
the growth of an urban poor population living in slums. The
growth of slums coupled with limited urban agriculture,
means that the urban poor population is increasingly
becoming vulnerable to food insecurity. In SSA, poverty, food
insecurity, child mortality and malnutrition have for long been
widely viewed as problems affecting rural populations
de Poel et al. 2007; Fotso 2006, 2007)
. However, with the
growing population of the urban poor, the urban advantage
is disappearing (Fotso 2007). While the rural population
remains relevant and of interest to researchers, the growing
urban population, especially the urban poor is increasingly
becoming of interest too for it poses risks to achieving social,
economic and health development in low and middle income
(Ravallion et al. 2007)
. In these countries, rapid
urbanization poses unanticipated challenges, one of which is
food insecurity. Agricultural production in rural areas has
declined over the years as a result of high prices of inputs,
climatic conditions and low returns on agricultural investments,
and this is hypothesized to drive rural to urban migration
(Hove et al. 2013)
. Food production in many low income
countries remains largely rural and small scale. Therefore,
urbanization not only leads to a reduction in workforce and
necessary skills in rural areas, but also a decline in food
production. The situation is complicated by the fact that in SSA, it
is the ‘urbanisation of poverty’, unlike in high income
countries where urbanization was associated with diversification
and transformation from agriculture to manufacturing and
(Obeng-Odoom 2010; Otto 2008;
Ravallion 2002; Ravallion et al. 2007)
. In SSA, urban
agriculture remains mainly informal, sometimes outlawed and is not
integrated in urban planning, making it unsustainable
(Martellozzo et al. 2014). Furthermore, those moving to the
cities are largely food consuming and in search of
employment and are therefore more likely to be exposed to food
(United Nations 2004)
, yet there remains a dearth
of evidence on the drivers of food security in this population.
Specifically, the effect of education attainment on food
security among the urban poor has not been closely examined.
Several studies, the majority outside SSA, have explored
the mechanism through which education and food security
influence each other
(Das and Sahoo 2012; Amali 2012;
USDA Economic Research Service 2014; Bashir and
Schilizzi 2013; Reimers and Klasen 2013; Gebre 2012;
Oluwatayo 2009; De Muro and Burchi 2007)
. The findings
are, however, mixed with some showing a negative effect of
education on agricultural production
majority found a positive association
(Bashir et al. 2012; De
Muro and Burchi 2007; Faye et al. 2011; Birhane et al. 2014)
Much of the research evidence on the effect of education on
food security is based on rural populations. The existing
evidence points to a two way causal relationship between food
security and education
. First, food security has
an effect on education and health. Food insecurity, especially
during the early years of growth, leads to malnutrition among
children; malnutrition is associated with poor cognitive
growth and low educational achievement and the effects
may extend to later life
(Black et al. 2013)
. Secondly, and
the focus of this study, is the effect of education on food
security among the urban poor. The human capital theories
posit that human capital is a major determinant of production
and later life chances of success e.g. employment and earnings
. These theories postulate that education, a
measure of human capital, is associated with both productivity and
efficiency. Education has direct and wider returns to
individual and immediate members of their family and society at
large in terms of increased income, improved health and better
(McMahon 2009; Psacharopoulos and
. Education is indeed considered a key
determinant of social mobility, by moving individuals and
households out of poverty.
The mechanisms through which education influences food
security differ, depending on the context, including urban
versus rural. In the rural context, education influences food
security through access to information on best agricultural
production, nutrition and sanitation; increased efficiency, hence
increased production and better decision making as well as the
pride that comes with education
(De Muro and Burchi 2007;
Bashir and Schilizzi 2013)
. While these mechanisms may also
apply among urban households, the pathways differ. In the
urban context, the effect of education is through proxies such
as employment, household income and decision making.
These proxies have effects on the access, utilization and
availability dimensions of food security. Increased years of
schooling are associated with better employment opportunities,
working efficiency, better decision making and increased
(Bashir and Schilizzi 2013; Gebre 2012)
. It is
estimated that 90 % of the food consumed by the urban
population is purchased and that poor households spend more
than 50 % of their income on food and are more vulnerable
to food price increases
(Ruel and Garrett 2004; FAO et al.
. Given this, individuals and households with higher
levels of education can be said to be more likely to be food
secure because of their increased purchasing power
and Schilizzi 2013)
. While this is true, labour participation
among the urban poor is mainly in the informal sector with
returns that can barely meet their daily needs. For instance, in
Nairobi’s informal settlements, the main source of income
is employment with 52 % of the persons aged above
18 years either in fulltime salaried employment or in
casual employment but 27 % are economically inactive
(Emina et al. 2011)
Urban food security is of programmatic importance to
policy makers in low and middle income countries that are
characterized by urban poverty and low rates of food production,
high food prices and unemployment. Indeed, food security is a
driver for sustainable development, yet there remains a
paucity of information on the key drivers of food security especially
among the urban poor. Specifically, the effect of household
education remains understudied. The existing evidence on
determinants of food security has several methodological
limitations. First, most existing studies are cross-sectional in
nature and thus limited in the extent to which causal inferences
can be made and fail to capture intra-household dynamics over
time. Second, many studies categorize food security as a
dichotomy and only utilize the household head level of
education, and thus may fail to capture important nuances in
household food security. Third, many studies focus on rural
contexts, yet the benefits and the mechanisms through which
education influences food security in the rural context varies
from that of the urban poor households. Our study addresses
these limitations and expands our understanding of urban
dynamics of food insecurity by analysing the relationships
between household food insecurity and education, taking
cognizance of the cumulative educational attainment of household
and wealth status. The study uses longitudinal data collected
between 2007 and 2012 in two urban informal settlements and
employs robust analytical techniques to determine the
prevalence of food insecurity among the urban poor and to examine
the effect of education on household food security.
The conceptual framework guiding this study is shown in
Fig. 1. Drawing on existing literature and the conceptual
framework of malnutrition, we postulate that household food security
is directly influenced by household education attainment
(Feinstein et al. 2006; Pieters et al. 2013)
. In addition, we
postulate that education also has indirect effects on food security by
influencing income (proxied by household wealth index)
(Botha 2010). In this regard, household wealth mediates the
effect of household level of education on food security
. The direct effect of education can therefore be
said to be the net effect of education on food security and can be
referred to as the impact controlling for other covariates, hence
(Victora et al. 1997; Baron and Kenny 1986)
The framework also takes into consideration other factors
that may influence food security by affecting either the
acquisition of food or its utilization. First, we consider the
availability of resources to support household food production such as
(Pieters et al. 2013)
. The decision to include this variable
is informed by the authors’ understanding of the local context
that most households in Nairobi’s informal settlements own
land in their rural homes, which they use for food production.
We also consider the other household factors that have been
documented in the literature to have direct effects on
household food security: household demographic characteristics
(e.g. the age and gender of the household head); duration of
stay in the slum, area of residence, household size and
dependency ratio, as well as household shocks
(Bashir and Schilizzi
. Duration of stay can be considered as an indirect
measure of coping. Households that have stayed for longer period
have experience in navigating the challenges of being in the
slums. The literature on food security shows that social
networks provide strong support for households
(Martin et al.
. Households that have stayed in the slum for a long time
can be thought to have established networks and connections
through which they can negotiate when faced with challenges
such as being food insecure. Moreover, shocks experienced by
households increase their vulnerability and may expose
households to food insecurity by the shifting of resources to
address them, leading to loss of household resources
(Feinstein et al. 2006)
In this study we use panel data collected between 2007 and
2012 from the Nairobi Urban Health and Demographic
Surveillance System (NUHDSS). The NUHDSS is a health
and demographic surveillance system situated in two informal
settlements, Korogocho and Viwandani, Nairobi, Kenya. The
NUHDSS collects data on vital events (deaths, births and
migration), household characteristics and health information
from every household in the surveillance area every 4 months.
By 2012, the NUHDSS had a midyear population of about 67,
800 individuals in about 27,300 households. The selection of
the study sites was based on two datasets: the 1999 Kenya
housing and population census and the 1997 Kenya welfare
monitoring survey. The 49 locations in Nairobi county were
ranked according to the proportion of population categorized
as poor and put into five groups
(Ngware et al. 2012)
focus was poor locations and therefore those ranked as rich
were excluded. The two selected sites, Korogocho and
Viwandani, were ranked in the poorest quintile, were in
positions 44 and 48, respectively, and had comparable features
with other slum settlements.
In 2006, the DSS operationalized a household
characteristics and amenities module that was administered once a year.
The module contained items that collect rich data on
household socio-economics, amenities, household shocks and
food security. The module targeted all households enumerated
in the two study sites. In this study, we drew on the data
gathered using this module. The overall response rate on
questions relating to household food security was 93 %. We
Outcome: Household Food
Head gender and age
Other household characteristics:
shocks, size, duration of stay, food
production, respondent, dependency
restricted our analysis to households with at least one child
aged 5 years or younger. This is because one of the key
measures of household food security is child hunger, which was
only collected from households with children below 5 years.
The final sample for analysis included a cumulative number of
56,344 records from 23,549 unique households. At least 50 %
of the households had a minimum of 3 records and only 18 %
had one record.
Measurement of food security
The primary outcome was household food security, a latent
variable assessed using a single indicator derived from
(Bicklel et al. 2000)
. As noted by
Faye et al.
, the items were a subset of those identified in the
Radimer et al. (1992)
to measuring hunger and food
security and mainly included insufficiency of the quantity of
household food, access to food, anxiety over its availability
and child and adult hunger. Three of the items were part of a
household hunger scale and have been validated to not only
measure but also monitor food insecurity in low income
(Deitchler et al. 2010)
. The items were administered once
every year between the months of September and December
to all the households in the two study sites. The items assessed
food insecurity over a recall period of 30 days (about 4 weeks)
and attracted three response options (1 = Often true (more than
10 times); 2 = Sometimes true (3 to 10 times); 3 = Never true
(0 to 2 times))
(Leroy et al. 2015)
a. Household had enough food during the last 30 days
(insufficient food quantity)
b. In the last 30 days, the food that the household bought was
finished and there was no money to obtain more (food
c. During the past 30 days, children in the household failed
to eat for a whole day/slept hungry because there was not
enough money for food (child hunger).
d. During the past 30 days, you or other adult(s) in your
household failed to eat for a whole day because there
was not enough food (adult hunger).
The reliability of the items was assessed using Cronbach’s
alpha and found to be 0.75, which was within the acceptable
(Tavakol and Dennick 2011)
. Responses to the
questions were dichotomized as described by
Faye et al. (2011)
(Ballard et al. 2011)
. Thus for items ‘b’, ‘c’ and ‘d’, those
responding as either often or sometimes true were categorized
as ‘1’ otherwise ‘0’ ; while for item ‘a’, the response was
negated, and those who responded to never true (did not have
enough food) were coded as ‘1’, else ‘0’. Using a similar
approach described by
Deitchler et al. (2010)
, we calculated
a sum of the item scores; scores ranged from zero to four.
Households that did not experience any of the situations (score
of zero) were categorized as food secure; households that
experienced either one or two (score of one or two) were
categorized as moderately food insecure, those that experienced at
least three (score of above two) were coded as severely food
Measurement of household educational attainment
The main independent variable is the average household
educational attainment (AHEA) measured by average number of
schooling years within a household for adult members aged
18 years and above. That is the total number of schooling
years divided by the number of individuals in the household
aged 18 years and above. The variable is continuous and large
values indicate higher education attainment for that
Measure of other independent variables
Household wealth index score This is a composite measure,
calculated using principle component analysis (PCA). In the
PCA model, both household amenities and asset ownership
measures were included. The household amenities variables
were main material of the wall, floor and roof, main source of
drinking water, and the main type of toilet. The assets included
ownership of a car, motor bike, and bicycle or radio, gas
cooker, sewing machine, bed and mobile phone among others.
Household shocks were determined by whether the
household had, in the past year, experienced any household shocks,
with the key ones being fire, mugging, floods, death, theft,
eviction, demolition and illness. Households that reported
experiencing a particular shock were coded as ‘yes’ and
Food production This is captured by whether the household
grew crops and how the produce was utilized. The variable
takes three categories: 1 = Household did not grow any crops;
2 = household grew crops and used the entire produce for food;
and 3 = Household grew and partly used and also sold them.
Dependency burden We used the conventional definition of
dependency to calculate this indicator. That is, the indicator
expresses the proportion of people aged either below 15 or
above 64 years within a household as a fraction of the number
aged between 15 and 64 years.
Household size This is the number of people in a household.
The variable is treated as continuous.
Duration of stay This is the number of years that the
household has stayed/lived in the study sites.
Other variables Household age, maintained as the actual age
of the household head; study site coded as either Korogocho
or Viwandani and gender of the household head as either male
or female and year of observation.
We employed both descriptive and inferential data analysis
techniques. The descriptive statistics included frequencies,
means, proportions and percentages. ANOVA was used to
determine whether there was an association between food
security status and the continuous variables.
To determine the relationship between household
education attainment and food insecurity status among households
living in informal settlements, we used a random effects
generalized ordered probit model.
In eqs. 1 to 6, we describe the model. Let the outcome, food
security ‘y’ have ‘j’ categories that have a natural ordering,
y j ¼ 1; 2; …:: j; f j > 2g
it ¼ X 0itβ þ ∝i þ εit
Considering the observed food security status to have an
underlying latent variable y*, then y* ¼ X 0iβ þ εi where X' is a
vector of variable(s) that conditions the outcome; yi (observed
food security status) and its underlying variable y* can be said
to associate in such a way that:
y ¼ j if and only if k j−1 ≤ y* ¼ X 0iβ þ εi ≤ k j
Where j = 1 , 2 … . j and kj are the thresholds (often
referred to as cut points) to be estimated. The cut points partition
the latent variable y* into j categories, since the observed
variable is categorical and ordered with an assumption of parallel
lines. In this type of model, the cut points can be flexible and
allowed to vary, such that:
kij ¼ k0 j þ X 0iδ j
Pudney and Shields (2000)
, allowing the
thresholds not to be fixed but vary, based on the conditioning
covariates (X 0iδ jÞ, then household heterogeneity is accounted
for in the threshold. By so doing, the response probability and
the cumulative distribution function is given by:
pfyi ≤ jjX ig ¼ k j−1 < y* ≤ k jjX i
¼ F k0j þ X 0iδ j−X 0iβ
¼ F k0 j−X 0iβ j
where βj = β − δj, which allows a separate set of coefficients
for each of the categories; this in essence implies that
observable individual heterogeneity in the cut points and mean of the
regression are assessed.
Since the response variable has j categories (which are
more than two), unlike in a multinomial regression which
compares pairwise, ordered probit, j − 1 binary response
models or equations are estimated. The j − 1 equations are
estimated sequentially such that the first model, category 1 is
compared with category 2 up to j; the second equation
compares category 1 and 2 versus 3 up to j, with the last model
comparing category 1 to j − 1 against the jth category. In the
current study, the response variable has three categories
(1 = Food secure; 2 = Moderately food insecure and
3 = Food insecure), thus two models were estimated. The first
model compares food secure households versus combined
categories of moderate and severely food insecure households
(model 1), and the second compares food secure and moderate
food insecure against the food insecure households (model 2).
Equation 4 does not account for random effects since
households are observed over time; therefore, substituting in
Eq. 4 with a mean of zero and constant variance as described
(Pfarr et al. 2011)
where εit|Xi is normally distributed with a mean of 0 and a
constant variance δ2 (such that rho (p)= δ2/(1+ δ2) allowing
household effects (observed heterogeneity) to be estimated by
cut points that are varying and random effects (unobserved
heterogeneity) that occur due to the repeated nature of the
data. Eq. 5, response probability and the cumulative
distribution function, is given by:
pfyi ≤ jjX ig ¼ F k0j þ X 0iδ j−X 0iβ j−∝i
One key assumption of ordered models is that the estimated
parameters are parallel and the same for the different estimated
models. In this case, the assumption is such that the parameter
estimates for model 1 which compares food secure households
against combined categories of moderate and food insecure
households are the same to those of model 2 which compares
food secure and moderately food insecure against the food
insecure households. This assumption may not hold. One
advantage of the random effects generalized ordered probit
model is that this assumption can be relaxed. This is achieved by
estimating an unrestricted model and carrying out a sequential
global Wald tests, which tests the parallel line assumption
(Pfarr et al. 2011)
. Several models are estimated when
restricting for the variables that have the highest probability
until variables that have probability scores of the set
significance level (in our case below 0.05) remain. The series of
estimations depend on the number of variables that do not
violate the parallel line assumption.
Based on our conceptual framework (Fig. 1) and the
Victora et al. (1997)
on the role of conceptual
frameworks in epidemiological analysis, we fitted 1) a model with
only the outcome and the main explanatory variable,
household educational attainment; 2) a model with household
educational attainment and wealth index; in this model the
mediating effect of the wealth index was isolated; and 3) a model
with household educational attainment and wealth index
controlling for other variables. In the last, the estimated
coefficients for education represent what is not mediated by the
wealth index and other possible covariates in the model.
Thereafter, we estimated the marginal effects in order to
measure the probability of being food secure given a unit increase
in household education attainment and wealth score holding
other covariates at mean. We analysed data in STATA 13 using
the REGOPROB2 command
(Pfarr et al. 2010)
Table 1 shows the demographic characteristics of households
by household food security status. The prevalence of severe
food insecurity in the two urban informal settlements of the
study was 42 % with a greater proportion of severely food
insecure households observed in Korogocho (66 %) than
Viwandani (15 %). Household food security was significantly
associated with household educational attainment at bivariate
level. The average household educational attainment was 8.29
schooling years, which is almost equivalent to the primary
level of education. The average household educational
attainment decreased with food insecurity status – was highest
among the food secure households and lowest among the food
insecure households. ANOVA showed a significant difference
in the means of household educational attainment between the
three food security statuses. The social economic status of a
household was measured by a wealth index score. High scores
indicated high household social economic status. Severely
food insecure households had lower wealth index scores.
Severe food insecurity was highest (42 %) among
households that did not grow any crops and lowest (18 %) among
those households that grew crops for household consumption.
There was no difference in the proportion of severely food
insecure households between households that did not grow
any crops and those that grew crops both for household
consumption and other purposes (49 %). Results also showed
variations in food security status over time. Specifically, between
2007 and 2009, around 48 % of households were severely food
insecure compared with 36 % in 2010. A slight increase was
observed in 2012 with 39 % of households categorized as
severely food insecure. Food insecurity was highest between
2007 and 2009 with a gradual decrease thereafter. The high
levels of food insecurity observed in the earlier years may be
associated with the political turmoil witnessed in Kenya after
the 2007 general elections and a drought that began in 2008.
Tables 2 and 3 shows the independent effect of education,
wealth and year of observation on food insecurity from a
random effects generalized ordered probit model and the
effect of educational attainment controlling for wealth index
score, respectively. In the bivariate models, the household
average years of schooling and wealth status variables were
not constrained since they all violated the parallel line
assumption. Model 1 compares food secure households (category 1)
to combined categories of the moderate (category 2) and
severely (category 3) food insecure households. Model 2
compares the categories of food secure or moderate insecure
households with severely food insecure households (1 and 2
versus 3). For Model 1, a negative coefficient indicates a
higher probability of being categorised as food secure
(category 1) while a positive coefficient indicates an increased
probability of being moderately or severely food insecure;
similarly Model 2 shows a negative coefficient if the
household is categorized as food secure or moderately insecure.
In both models in Table 2, the coefficient for household
educational attainment is negative and highly significant.
This shows that the probability of being moderately or
severely food insecure decreases with increased level of education –
simply higher educational attainment has a positive effect on
Social-demographic characteristics’ association with household food security status, Nairobi informal settlements, 2007 to 2012, n = 56,344
+ = Overall prevalence of food (in)security for the entire sample; ++ = Mean and standard deviation reported; CI = Confidence Intervals;
HH = Household; HHH = Household Head; AHEA = Average Household Educational Attainment; * chi square test for categorical variables and
ANOVA for the continuous variables; P = P-Value
food security. The bivariate results for the year variable are
mixed. First, the years 2009, 2011 and 2012 did not violate the
parallel line assumption and were therefore constrained hence
similar coefficients in the two models. In essence, this means
that the constrained dummy variables have the same effect
across the ordered categories of the outcome variable.
Secondly, 2008 and 2009 have positive coefficients
while the other years have negative coefficients. Compared
to 2007, households in 2008 and 2009 had increased
probability of being food insecure; thereafter, the probability
of being moderately or severely food insecure decreased.
This result suggests substantial improvements in food security
The household wealth index in Table 2, shows that wealth
index score (higher wealth score indicates improved wealth
status) was negatively associated with food insecurity. That is,
the severity of food insecurity decreased with increased
wealth scores. In order to assess the mediating effect of
household wealth on the estimated effect of education level, a model
with both variables was fitted (Table 3). When both variables
were included, none fitted the parallel assumption. Although
the education coefficient remained statistically significant, it
was slightly attenuated after controlling for wealth index
score. This suggests a mediating effect of wealth index,
though not so strong as to eliminate the significant effect of
Table 4 presents results on the effect of educational
attainment on food security controlling for other known determinants
(full model). Household educational attainment is a significant
Table 3 Education level and HH
wealth index results of a random
effects generalized ordered probit
model (n = 56,344)
determinant of household food security, even after controlling
for other covariates that were thought to be significantly
associated with food security. The coefficient for the education
attainment variable remained negative and statistically
significant. Unlike the results in Table 2, the effect of education on
food security in the full model is constrained. The constrained
effect of education implies that households with high education
attainment had increased probability of being food secure. That
is the probability of being food insecure decreased by 0.019
given a unit increment (one year increment) in household
education attainment. Fig. 2 shows the predicted marginal effect of
security given a unit increase in household education
attainment holding other covariates in the full model at means. The
probability of food security significantly increased with
increased education attainment: likewise, the probability of being
food insecure significantly decreased with increased average
years of schooling in a household.
Household social economic status, measured by wealth
index score showed a negative relationship between increased
* P < 0.05; ** P < 0.01; Coef = Coefficient; CI = Confidence Intervals; AHEA = Average Household Educational
Attainment; Variable dummies in bold do not violate the parallel line assumption
Model 1: 1 vs 2 & 3 food security status
Model 2: 1 & 2 vs 3 food security status
Educational attainment and wealth
Wealth index score −0.183**
95 % CI
**P < 0.01; Coef = Coefficient; CI = Confidence Intervals; AHEA = Average Household Educational Attainment
Estimation results of a random effects generalized ordered probit model (n = 56,344)
wealth score and food insecurity. This relationship is
illustrated in Fig. 3. The predicted marginal effects for the food secure
shows a positive linear association between wealth score and
predicted probability of being food secure. Conversely, the
incidence of food insecurity decreased with increased wealth
score. The household education and wealth scores marginal
effects were stratified by study sites. Though we observed a
difference in the magnitude of the predicted marginal effects
the directions remained the same.
Duration of stay, health shocks and gender of the
household head were constrained. The coefficient for duration of
stay was negative and significant implying that households
that had lived in the study site for a longer period had a higher
probability of being food secure. Male- headed households
had insignificantly reduced (0.023) probability of being food
insecure compared with female- headed households.
The variables that were not constrained in the model mean
that they violated the parallel line assumption. The effect of
these variables on food security varied between model 1 and
model 2 as their coefficients differed in magnitude between
the two models. These non-constrained variables contributed
substantially to the heterogeneity observed in household food
security. In some instances, the significance of the
nonconstrained variables varied between model 1 and 2 of
The dependency burden within the household is a strong
predictor of food security. The probability of a household
being food insecure increased with increased dependency
burden. That is, a unit increase in dependency burden was
associated with 0.096 and 0.125 increase in the probability of
being food insecure in model 1 and 2, respectively. In terms
of the study site, households in Viwandani had higher
probability of being food secure than those in Korogocho. The year
variable was included in order to examine changes in food
Fig. 3 Household wealth score
predictive margins with 95%
insecurity over the period of observation as well as control
for unobserved time effects that may be occasioned by
changes that are not directly measured in the data (e.g., political
environment and climatic variation). The results of the year
variable mirror those of the bivariate models presented in
Table 2. The years 2008 and 2009 showed increased food
insecurity status compared to 2007. From 2010, the
magnitude of the coefficients for model 2 are large and negative as
compared to those of model 1. This shows an increased
probability of being food secure or moderately food insecure from
2010 compared to 2007. This is an indication of improved
household food security status, though borderline over the
period of observation.
Other non-constrained variables that were significantly
associated with food security were shocks happening within
households during the year, household food production and
household head’s age and whether a birth happened within the
household that year. Households that had experienced shocks
and households that grew crops were more likely to be food
insecure compared with households experiencing no shocks
or that did not grow crops. However, households that grew
crops for household consumption were significantly more
likely to be food secure or moderately food insecure. A
positive association between household head age and the
likelihood of food insecurity was observed, though only
significant for model 2 of Table 4. Further, households
that had experienced a birth during the year of observation
were likely to be either food secure or moderately food
insecure compared to households not experiencing any birth
within the year.
In order to check for robustness of the results in Table 4 and
due to the unbalanced nature of the data, we fitted a full model
using balanced data. The robustness was checked using
households that had 3 consecutive observations as well as
those with data for all the years of observation. The coefficient
for the education attainment remained negative and highly
significant. Other than the shocks which became insignificant,
in the balanced sample, all the other variables behaved in a
similar manner to the unbalanced sample. Few incidences of
the shocks included in the analysis were reported (see
Table 1), and since the balanced samples were smaller than
the original sample, the number of shocks were reduced and
we think this could explain the change in significance.
The influence of unobserved heterogeneity in the status of
food security for households over the period was measured by
rho, which is the correlation of the error terms. The
unobserved heterogeneity decreased from 30 % in the model that
only included education (Table 2) to 24 % after including
household wealth index (Table 3) and to 9 % in the full model
(Table 4). This shows that the variables included in the full
model significantly reduced the unobserved effects, and hence
accounted for much of the variation of observed household
The aim of this study was to investigate the effect of
household educational attainment on food security. Using
longitudinal data spanning 6 years from households in two informal
settlements in Nairobi, we observed high levels of food
insecurity – only 27 % of households were food secure. Levels of
food insecurity were nearly threefold that of the national
average of 26 % in 2013
(Dietz et al. 2014)
and consistent with
previous studies conducted in the same context
(Faye et al.
2011; Kimani-Murage et al. 2014)
and in other contexts
(Musyoka et al. 2010; Birhane et al. 2014)
. Urban agriculture
is limited and urban households, including the poor, depend
on food supplies from rural areas and the ability to pay in a
(Kimani-Murage et al. 2014)
. The first
three years were characterized by high levels of food
insecurity and coincided with the post-election period in 2008 that
was marked by political turmoil and the hunger strikes of
2007–2008 due to increased world food prices and inflation.
During the same period, Kenya experienced a prolonged spell
of drought that led to a decline in food production.
Dietz et al.
, also found high levels of food insecurity two years
after the 2007 elections. The effects of the violence may have
had both an economic impact on households, reducing their
ability to pay, as well as an effect on food production in the
country, due to internal displacement and destruction of farm
properties, resulting in reduced food supplies. This coupled
with drought which was associated with a decline in food
production made matters worse.
the agricultural sector recorded negative growth and that
maize production during the post-election period and
immediately thereafter decreased by 15 to 20 %. This led to
increased inflation, with the most hard hit being those of low
socio-economic status. Qualitative narratives also reported the
state of food security as being severe during the crises that
affected the economy
(Kimani-Murage et al. 2014)
Moreover, in Kenya, droughts are characterized by loss of
livestock, especially among the pastoral communities, and a
decline in food production which affects supply and hence
increases prices. By so doing, drought undermines household
livelihoods exposing them to poverty and food insecurity.
Results of the full model showed that increased household
education attainment was associated with an increased
probability of being food secure. This is consistent with other
studies that showed food security to be associated with the level of
(De Muro and Burchi 2007; Bashir et al. 2012; Faye
et al. 2011)
and in contrast to those of Garrett and Ruel (1999)
who found no significant association between education and
urban and rural food security in Mozambique. Although this
relationship was expected, we anticipated after controlling for
household wealth index, the effect to diminish
We hypothesised that the effect of education on food security
is mainly through income in the urban context. Income in this
case is a more proximate factor, since urban households
mainly depend on out of pocket purchases for food
Programme 2009; Musyoka et al. 2010)
. Education is a key
factor in food access, production and utilization. Moreover,
education is associated with better job opportunities and
provides households with the knowledge of how to meet health
and nutritional needs of their families. These opportunities
provided by education such as better employment imply
increased disposable income for households. Thus, education
was expected to exert its effect through a wealth index, which
was a proxy measure for household income. Although the
household wealth index mediated the effect of household
education attainment, it did not eliminate it entirely. This
suggests that education, irrespective of household wealth status,
has an independent effect on food security in an urban poor
context. The independent effects could be through other
unobserved characteristics that relate to education such as
household decisions and resource allocation, which in turn
determine household food security.
We interpret the study findings bearing in mind some
limitations. Unlike previous studies which mostly used the
education level of the household head, our measure of education
was the average years of schooling which captures the human
capital of a household. One issue with this approach is reverse
causality whereby educational attainment within the
household is also a result of household wealth and food security
(Filmer and Pritchett 1999; Belachew et al. 2011)
. While this
is true, about 85 % of the population surveyed was aged below
39 years with about 50 % of the population aged between 15
and 29 years. This implies that most of the households are
young and are most likely to have young children. Our
educational attainment indicator included individuals aged
18 years and above, minimizing the effects of reverse
causality. Data on household incomes and expenditures, which may
be better measures of household wealth than the asset and
amenities index were not available. Moreover, though
individuals were mainly in informal settlements, information on
employment was not available. In this study we controlled for the
household wealth index score, a proxy measure for wealth
status, to minimize any nuances that could be attributed to
income and employment. The longitudinal nature of the data,
large sample size as well the analytical approach adopted are
strengths of the study.
The current study extends the body of knowledge by
analyzing data from multiple years using random effects ordered
probit models that take into account unobserved
heterogeneities and time effects. Moreover, use of household average
years of educational attainment means that the human capital
of the households was better measured than in most of the
previous studies. With the large datasets, one can confidently
interpret the parameter estimates. One key issue with the
generalized ordered probit models is that prior information or
knowledge of the theories that underlie the violation of the
parallel line assumption may not be available in case of
outcomes with more than two categories. In this case, one does
not know in advance which variables violate or do not violate
the assumption in an ordered outcome. This however is
overcome by the auto fitting options that are provided for this type
of analysis as described by
Pfarr et al. (2011)
. The auto fitting
provides a robust analytical approach to identifying such
variables that do not violate the assumption by sequential
modelling and using the global Wald tests; this is an assumption,
which many previous studies avoided by dichotomising the
food security indicator.
Conclusion and way forward
Prevalence of food insecurity among the urban poor is high.
However, even among the urban poor, disparities exist in food
security status. In this study, households with low educational
attainment were more likely to be food insecure than those
with at least some basic education. The urban poor face unique
challenges that are different from those faced by their rural
counterparts. Therefore, effective strategies to address the
vulnerability of the slum residents to food insecurity in the short
term are warranted. However, investments in education may
have long-term dividends in efforts to reduce food insecurity.
This is in line with the capability approach, which recognises
the role played by education in increasing capabilities of
people and by extension households. Education alone, may,
however, not reduce the severity of food insecurity if other
economic opportunities, such as employment, are not available,
calling for a systems approach.
Acknowledgments We acknowledge the valuable contributions of staff
of NUHDSS at the African Population and Health Research Center
(APHRC) who collected the data used in this study and the University
of Witwatersrand. We would also like to thank the anonymous reviewers
for their valuable comments. This research was supported by the
Consortium for Advanced Research Training in Africa (CARTA).
CARTA is jointly led by the African Population and Health Research
Center and the University of Witwatersrand and funded by the
Wellcome Trust (UK) (Grant No: 087547/Z/08/Z), the Department for
International Development (DfID) under the Development Partnerships
in Higher Education (DelPHE), the Carnegie Corporation of New York
(Grant No: B 8606), the Ford Foundation (Grant No: 1100-0399),
Google. Org (Grant No: 191994), Sida (Grant No: 54100029) and
MacArthur Foundation Grant No: 10-95915-000-INP^.
Compliance with ethical standards
Conflicts of interest The authors declare no conflicts of interest.
Ethical clearance and informed consent The study ethical clearance
was granted by the Kenya Medical Research Institute (KEMRI). In
addition, ethical clearance was obtained from the University of
Witwatersrand, Human Research Ethics Committee and AMREF
Kenya to use the data for PhD work. Informed consent was obtained from
all individual participants included in the study. All procedures performed
in studies involving human participants were in accordance with the
ethical standards of the institutional and/or national research committees
and with the 1964 Helsinki declaration and its later amendments or
comparable ethical standards.
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Dr. Moses W. Ngware is a se
nior research scientist in APHRC
and Honorary Senior Lecturer,
S c h o o l o f P u b l i c H e a l t h ,
University of Witwatersrand,
South Africa. He provides the
leadership in education research
at APHRC. Ngware holds a PhD
in Economics of Education from
Egerton University, Kenya. He
joined APHRC in 2007 prior to
which he was a Policy Analyst at
the Kenya Institute for Public
Policy Research and Analysis
and also served as a Senior
Lecturer and Chairman of the Department of Education Administration
and Planning at Egerton University, Kenya. He is a DAAD scholar and
has published several working papers, occasional reports and
peerreviewed journal articles. Ngware is driven by success and passion to
influence change that can lead to improvements in social outcomes,
particularly among marginalized populations in sub Saharan Africa.
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(JKUAT) , Kenya. He has over 8 years' experience in data management, and analysis of both qualitative and quantitative data, with a particular interest in mixed methods. Maurice has a special interest in understanding the linkages between education and health and has authored and coauthored a number of articles on Education and Health in international peer reviewed journals. He is driven by the need to contribute to the development of marginalized people and also the need to influence polices that can lead to improvement of their wellbeing