Aspirations and food security in rural Ethiopia
Aspirations and food security in rural Ethiopia
Daniel Ayalew Mekonnen 0
Nicolas Gerber 0
0 Center for Development Research (ZEF), University of Bonn , Walter-Flex-Str. 3, D-53113 Bonn , Germany
Despite some improvements in recent years, poverty and food insecurity remain widespread and the main challenges in Ethiopia. Much of the empirical literature focuses on identifying the resource-related constraints for farmers to achieve food security and move out of poverty, with little attention paid to 'internal' or psychological factors such as aspirations. Using individual and household data collected in rural Ethiopia, we examined if aspirations were strongly associated with well-being outcomes, in our case food security, as posited in the theoretical framework of aspirations failure. We found that aspirations of the household head were positively and strongly associated with various triangulating measures of household food security including per-capita calorie consumption, the food consumption score (FCS), the household dietary diversity score (HDDS), and negatively associated with the household food insecurity access scale (HFIAS). In contrast, results suggest that the aspirations of the spouse of the household head are negatively associated with per-capita calorie consumption and FCS. We discuss the channels through which aspirations may affect food security and the avenues for future research.
Aspirations; Farm households; Food security; Ethiopia
Despite some improvements in recent years, poverty and food
insecurity remain widespread and the main challenges in
Ethiopia. These challenges are further exacerbated by climatic
shocks such as failure of rainfall, which adversely affect
agriculture and allied activities, the main livelihood activities for
the rural population.1 Following the failure of rainfall during
the 2015 agricultural seasons, estimates suggest that about
10.1 million people required emergency food assistance as
of December 2015
. Poverty persistence had
long been recognised as a major contributing factor for the
continuing vulnerability of the food insecure groups and this
has led the government, jointly with development partners, to
implement a social safety net program (PSNP) since 2005.
This program aims at Bsmoothing consumption, reducing risks
the poor face and protecting their assets^
2012, the PSNP reached over 7.6 million people and the
program is complemented by a household asset building program
(HABP), which provides food insecure households with
financial services and technical support to strengthen their
production systems by diversifying income sources, and
increasing productive assets so as to improve their productivity
(World Bank 2013)
Notwithstanding the potential benefits associated with
policies such as the PSNP, the alleged benefits can be realized
only under a set of conditions. For example, the recent
weather-related shocks highlight the level of vulnerability of
the poor despite such programs. In addition, while earlier
evaluations of the PSNP
(e.g. Gilligan et al. 2009; Berhane
et al. 2011, 2014; Coll-Black et al. 2011)
found some positive
1 According to the Central Statistics Agency of Ethiopia (CSA), the rural
population is estimated to constitute about 83% of the total which is estimated at
87,952, 000 as of July 2014. http://www.csa.gov.et/ (accessed Nov 17, 2015).
impact of the program on food security, asset holdings and
income growth, there is little evidence of graduation.2 These
studies attribute the lack of graduation, among other factors, to
limited efficiency in program implementation, higher food
prices and the nature of the program, i.e. targeting households,
which are both poor and food insecure. Yet, what is missing in
these studies (and in the broader empirical literature on the
determinants of food security) is the importance of
psychological factors or ‘internal’ constraints, such as low aspirations.
However, internal constraints are also important for they could
reinforce external constraints (or material deprivations) and
this may lead to a self-sustaining trap of poverty and low
levels of proactivity
(Appadurai 2004; Ray 2006; Dalton
et al. 2016)
. This is because poverty limits the people’s
‘capacity’ to aspire (Appadurai 2004) by creating mental models
that uniquely diminish the significance of some features of the
environment while magnifying others
(Bernard et al. 2008)
Nonetheless, whether aspiration failure is a cause or a
consequence of poverty is inconclusive. For example,
viewed poverty as both a result of and a cause of a failure of
Dalton et al. (2016)
, on the other hand,
theoretically showed that while both the rich and the poor may share
the same preferences and also behavioural bias in setting
aspirations, poverty exacerbates the effects of this behavioural
bias leading to behavioural poverty traps and the failure of
aspirations, ultimately affecting their choices of effort.
Aspirations can be understood as assumed targets or
(Payne et al. 1980)
, hence they are motivators of
effort. This may be reflected, for example, in terms of creating
opportunities or exploiting available ones
Bernard et al. 2008)
, which in turn may lead to achieving
better well-being outcomes such as food security. In this
regard, in rural Ethiopia, Bernard et al. (2011) and
found evidence that low aspirations are
correlated with low demand for long-term loans and low use of
such loans for long-term investments. Elsewhere, other
studies have found strong correlations between aspirations and:
expenditures on agricultural inputs, yields, and savings
(Kosec et al. 2012)
, savings choices and health-seeking
(Ghosal et al. 2013)
, private school enrolment
et al. 2013)
, educational outcomes
(Serneels and Dercon
, dropout behaviour
(Goux et al. 2014)
aspirations and educational attainment of adolescent girls
et al. 2012)
, and risky choice behaviour
(Payne et al. 1981)
Further, based on a randomized control,
Bernard et al. (2014)
showed that aspirations can be altered, implying that a
potential role for policy exists. Despite the growing literature
linking aspirations and effort or forward-looking behaviour
in general, the effect of aspirations on food security remains
2 BGraduation^ is a situation where a household can meet its food needs for all
12 months and is able to withstand modest shocks in the absence of the PSNP
largely unexplored. This study, motivated by the related
literature, contributes to filling the gap in the literature by testing
the hypothesis that aspirations are correlated with household
food security in rural Ethiopia.
Some concepts and measurements of food (in)security
Food security is a complex concept and its definition
continues to evolve. The latest definition that refined the one
adopted in the 1996 World Food Summit states that Bfood
security (is) a situation that exists when 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 healthy life^
Jones et al. (2013)
, this definition addresses
concerns related to: the inequitable distribution of food not
only within countries but also within households, the ability
to acquire socially and culturally acceptable food and the ways
in which to acquire it, and the macro- and micronutrient
composition of the food. Food insecurity on the other hand is a
state Bwhen people do not have adequate physical, social or
economic access to food^ as defined above
The multiple approaches and tools used for assessing food
security reflect the complexity of the operationalization of the
concept. For example, in some cases the concept of food
insecurity is used interchangeably with nutrition insecurity even
though nutrition security requires food security along with
Bcare, health and hygiene practices^
(Jones et al. 2013)
Undernutrition, which is Bcaused by undernourishment –
defined as a level of food intake insufficient to meet dietary
(FAO, IFAD and WFP 2015)
, is also
often used to measure food and nutrition security.
et al. (2013)
showed the different
overlapping concepts (see Fig. 1). They can be apprehended by
various food and nutrition security measurements, their
choice best dictated by a clear understanding of their
underlying constructs and the identification of their intended
(Jones et al. 2013)
Fig. 1 Overlapping concepts within the context of food and nutrition
security. The figure is from
Jones et al. (2013)
Used with permission from the International Food Policy Research
Empirical evidence on the state of food (in)security
and their determinants
Existing studies on food security largely ignore the role of
aspirations. Hence, the main hypothesis of this study is
motivated by the related literature reviewed in the introduction.
The literature briefly reviewed in this section is intended to
guide our choice of confounding factors to be included in the
analysis, in order to better isolate the relationship between
aspirations and food security in rural Ethiopia.
To begin with, the latest report on the State of Food
Insecurity in the World
(FAO, IFAD and WFP 2015)
the number of people undernourished in 2014–16 at 795
million or 10.9% of the total, a reduction from 18.6% in 1990–92.
The report notes that the vast majority of the hungry (780
million people) live in the developing world and the overall
share of the hungry currently stands at 12.9% of the total
population. The same report estimates that the share of people
in Ethiopia who are undernourished in 2014–16 is 32%, a
reduction from 74.8% in 1990–92. According to the report,
this improvement in Ethiopia could be attributed to several
interlinked factors including the high GDP growth rate the
country has been experiencing in recent years and the existing
social protection program (PSNP). This assertion echoes other
studies such as those of the
World Bank (2016
), Berhane et al.
(2011, 2014) and
Dorosh and Rashid (2012)
. According to the
World Bank (2016
), for example, real GDP growth in the
country averaged 10.9% between 2004 and 2014 and a
significant part of this growth came from agriculture. If this is
indeed the case, the reduction in the number of
undernourished is not surprising for the majority of the people in
Ethiopia depend on agriculture, a sector which has been found
to have a high growth poverty elasticity
and Demery 2007)
, and poverty is arguably one of the main
determinants of food and nutrition insecurity. In this context,
estimated that a 1% increase in agricultural per
capita value added in Ethiopia would result in a 1 % decline in
the poverty level of rural households.
Although the concept of food and nutrition security (FNS)
is evolving, rigorous studies on the determinants of FNS in
Ethiopia at the national level are largely lacking. A brief
review of available studies, which are mainly limited to smaller
geographic areas and often associated with project
evaluations, sheds some light regarding one or other domains of food
security. In this context,
Asenso-Okyere et al. (2013)
example, studied the determinants of food security in selected
agro-pastoral communities in south-eastern Ethiopia. Using
availability of food in the household as proxy indicator for
food security, they found that the most significant factors
affecting household food security were: the educational level of
the spouse and that of the household head, size of farm land,
availability of household assets including livestock, peace and
security. Beside household endowments such as land
et al. 2005)
and proximity to food markets
reports that livelihood
diversification strategies such as livestock rearing, growing cash
crops, and engagement in trading are important factors for
achieving household food security.
Food insecurity is also affected by seasonality or by
irregular shocks such as weather events, deaths or conflicts
. Based on a household survey data from 15 villages in
Dercon and Krishnan (2000)
, for example,
found that the body mass index, a widely used indicator of
FNS, of adults in poor households as opposed to richer
households, was affected by idiosyncratic agricultural shocks. Food
insecurity may be chronic or transitory, depending on the
frequency of such shocks
(Jones et al. 2013)
. In response to
temporary shocks, households may resort to the sale of assets
and other coping strategies which may, in turn, lead to more
severe shocks, failed returns on investments, and an eventual
fall into a state of chronic food insecurity
(Jones et al. 2013)
In the event of such shocks, food aid through different
modalities is the often used policy response. In this context, a few
studies have examined the importance of food aid programs
following drought or harvest failures on food security in
(e.g. Yamano et al. 2005; Quisumbing 2003; and
Gilligan and Hoddinott 2007)
. These studies found positive
impact of such transfers on consumption or child nutrition
outcomes, but Gilligan and Hoddinott (2007) also uncovered
some evidence of food aid dependency. In addition, even the
achieved positive effects were considered to be short term as
the country continued to suffer from food insecurity even in
good harvest years
(Clay et al. 1999)
. It is this realization that
led to the policy shift from such Bad hoc responses^ to the
more planned and systematic approach of the PSNP
In general, the presence of widespread food insecurity in
Ethiopia is argued to be the result of several factors including
recurrent drought and heavy reliance on nature, use of
backward agricultural technologies (or low input – low output
production systems), and inappropriate agricultural policies
in the past
von Braun and
) more broadly classified the major factors
of food crisis in the country as: population pressure,
production failures, marketing failures, and policy, institutional, and
A household survey was conducted between January and
March 2014 in Ethiopia. We re-interviewed an existing
sample of agricultural households surveyed in 2006 and again in
2010 in Oromia region under an NGO project promoting
Income and Wealth are measured in terms of Ethiopian Birr (The official exchange rate during the time of the survey was 1 USD = 19 ETB), Children’s
Education in terms of grades/years of education; and, Social Status in terms of the percentage of people in that village that ask for the individual’s advice
on some important decisions
agricultural innovations, which ended in 2010. The original
survey used a mix of purposive and random sampling
procedures to select 390 households from three study sites (i.e. 130
households per site)
(Aredo et al. 2008)
. The primary
sampling unit (i.e. study site) consisted of three pairs of
neighboring districts or woredas, namely Bakko and Sibu-Sire, Lume
and Adaa, and Hettosa and Tiyyo. The districts were chosen
based on the density of cultivation of the major crop (maize,
teff or wheat) and on the presence of active farmers’
cooperatives. In the second stage, kebeles (sub-districts) with active
farmers’ cooperatives were purposively selected. Finally,
using the participating households within a cooperative as
the sampling frame, households were randomly selected.
Our survey covered 379 households. Between one and
three households in each district dropped out of our survey
for various reasons, including death, relocation to another area
or unavailability for the survey interview. Nevertheless, when
compared against the full sample, the households that dropped
out of the survey did not show any statistically significant
baseline difference with regards to key indicators such as
income, wealth, and landholdings (results not reported but
available upon request). Further, due to a (non-systematic) problem
of missing data on some indicators, the number of
observations in the regression analyses (at household level) varied
between 374 and 375. Also, about 10% of households in the
sample were female (single) headed, and they dropped out of
some specifications that control for the characteristics of both
the household head and the spouse. As a result, the number of
observations for some specifications was 302.
In addition to the basic socio-economic indicators, the
survey collected information on individual aspirations and future
expectations on four indicators including: income, wealth,
social status and children’s education. The survey also collected
information about the weight each respondent attached to each
of the four indicators.3 Following
Beaman et al. (2012)
3 Appendix presents a detailed description of the measurement of aspirations
including the survey instrument, the theory underlying the indicators used and
the construction of the aggregate index, and the data collection.
Bernard and Taffesse (2014)
, the aspirations level was
calculated using an aggregate index based on respondents’ answers
to questions about their aspirations in the four dimensions.
The aggregate aspirations index was then used to classify
individuals into low-aspirations and high-aspirations status
relative to the district average.
The calculation of the aggregate aspirations index (Ai) can
be represented as:
Ai ¼ ∑n4¼1
is the aspired outcome of individual i on dimension n
(income, assets, education, or social status).
is the average aspired outcome in district d for outcome
is the standard deviation of aspired outcomes in district
d for outcome n.
is the weight individual i places on dimension n.
Table 1 presents the level of aspirations of the respondents by
gender along with the corresponding mean comparison tests.
In general, males revealed higher level of aspirations in all
dimensions as well as the aggregate index, and the mean
differences were statistically significant. Table 2 gives the
descriptive statistics on the weights respondents attached to each
of the four dimensions of aspirations. In a decreasing order,
the average weight respondents gave was 30% to Income,
26% to Children’s Education, 24% to Assets, and 20% to
Social Status. In some cases, Children’s Education gets the
maximum possible weight, which also exhibits the highest
dispersion in the data followed by the weight respondents
attach to Income.4
Food security, as discussed before, is a broad and
complex concept and we try to capture its multidimensionality
(i.e. availability, access, utilization and stability) by employing
widely used indicators. We constructed triangulating
measures of food (in)security including per-capita calorie
consumption, food consumption score (FCS), household
dietary diversity score (HDDS), household food insecurity
access scale (HFIAS), and the incidence of inadequate
food supply in the household in the previous 12 months.
We captured intra-household food allocations based on the
information we collected by asking whether all household
members ate the same diet, and whether each of them ate a
more- or less- diversified diet and how many times a day,
by age categories.
The measurement of food consumption using kilocalories
(such as per-capita calorie consumption) is referred to as the
Bgold standard^ to measure food security but its
implementation is challenging for it requires the collection of detailed food
intake data, which is time consuming
. This study,
benefitted from the availability of such information in the data,
which also helped triangulate the result from other indicators.
One of the alternative tools for measuring food security is the
WFP’s (2008) FCS, which measures the frequency of
consumption of different food groups consumed by a household
during the 7 days before the survey. In this approach, different
food items are first categorized into 9 main groups and a food
consumption score is then calculated using weights assigned to
each food group.5 Using FCS cut-offs which have been
validated based on data collected from households in different
(e.g. Wiesmann et al. 2009)
, this technique categorises
4 Descriptive statistics of other variables used in the study are presented in
Table 8 in the Appendix.
5 The 9 main food groups and the given corresponding weights (in
parentheses) include- Main staples: cereals, starchy tubers and roots (2); Pulses:
legumes and nuts (3); Meat and fish: beef, goat, poultry, pork, eggs and fish (4);
Vegetables (including green leaves) (1); Fruits (1); Oil: oils, fats and butter
(0.5); Milk: milk, yogurt and other diary (4); and Sugar: sugar and sugar
products, honey (0.5). For details including calculation steps, see WFP (2008).
households into three food security groups: poor, borderline
A related composite measure is the HDDS, which reflects
the average household dietary diversity and proxies for
household’s food access
(Swindale and Bilinsky 2006)
differs from FCS as it does not attach any weight among different
food items and also does not take into account the frequency
of consumption of a certain food. Further, it often uses a 24-h
recall period, which is shorter than the seven-day recall used in
FCS. The average HDDS was calculated based on whether
anyone in the household consumed any of the 12 types of
food groups.6 To examine household food access, the
resulting HDDS was compared among income groups such
On the other hand, household food insecurity could also be
measured using the HFIAS, which captures the household’s
food insecurity (in terms of access), including the frequency of
occurrence of the event in the 4 weeks prior to the survey
(Coates et al. 2007)
. In this measure, three dimensions of
occurrence of food insecurity are captured: Banxiety and uncertainty
about the household food supply; insufficient quality (includes
variety and preferences of the type of food); and, insufficient
food intake and its physical consequences^
(Coates et al. 2007:
. The HFIAS is then calculated by summing over the
frequency-of-occurrence of food insecurity-related
conditions with higher values indicating severe food insecurity.
Following the recommended cut-offs
(Coates et al. 2007)
households were then categorised into 4 levels of household
food insecurity: food secure, mild, moderately and severely
food insecure. Next, we provided empirical evidence on the
level of household food (in)security among the study
households using the indicators discussed above.
Based on the direct responses by the household head (and/
or the spouse), the data suggested that only about 7% of
households did not have enough food in the previous
12 months. In terms of intra-household food allocations,
under-five children had, on average, 4 meals per-day by
comparison to 3 meals eaten by other household members.
Further, about 83% of households reported that all household
members ate roughly the same diet while the remaining
reported that children ate more diverse foods.
Based on the recommended cut-offs of food (in)security
measurements such as FCS and HFIAS, the data suggest that
the share of households in the sample who were food insecure
lay between 7 and 10% (see Table 3).7 However, when we
6 These food groups include: cereals; root and tubers; vegetables; fruits; meat,
poultry offal; eggs; fish and sea food; pulses/legumes/nuts; milk and milk
products; Oil/fats; Sugar/honey; miscellaneous. HDDS is then calculated
Swindale and Bilinsky (2006)
7 In this study, we merged the food insecure categories (i.e. the poor and
borderline in FCS and Calorie consumption, and the mildly, moderately and
severely food insecure categories for HFIAS) since the number of observations
for each were too small for meaningful inference. We thank one of the
reviewers for this useful suggestion.
investigated calorie consumption using 2100 kcal per person
per day as dietary energy requirement,8,9 the share of
households that can be considered food insecure increased to 27%
(Table 3). These figures may seem great underestimates of the
level of food insecurity by the country standard since FAO,
IFAD and WFP’s (2014) estimate puts the share of people
undernourished in 2012–14 at 35%. However, we offer two
reasons for our results: (1) our sample households were drawn
from relatively well-off districts in terms of average land
holdings and agricultural potential, and (2) data were collected
immediately after harvest. These two factors may tend to
overemphasize the availability of food in the sample households.
Nonetheless, availability of food does not necessarily
guarantee access to- and utilisation of- food and by extension overall
food security. To capture the household’s food access, we
cross-tabulated one measure of diet quality (HDDS) against
per-capita food expenditure terciles. According to Fig. 2, the
average diet diversity increases with the increase in
expenditure. Further, consumption of food groups such as fruits,
meats, and eggs vary greatly by income group with
progressive increase. For example, the share of households that
consume fruits, meats, and eggs for the lowest expenditure group
is 13%, 21%, and 33%, respectively while corresponding
figures for each food group by the middle expenditure group are
roughly twice, and that by the top expenditure group are
roughly thrice (Fig. 2).
Finally, we triangulated relations among the different food
(in)security indicators used in this study. Pairwise correlation
of per-capita calorie consumption, FCS, HDDS, HFIAS and
per-capita food expenditure suggests that all except HFIAS
score were statistically significantly correlated with each other
8 The cut-off point, as the minimum caloric requirement, used by official
reports in Ethiopia is 2200 kcal
(See MoFED 2013)
. If we were to use that
cut off point, the number of food insecure groups would rise to 32%. However,
we used 2100 kcal cut-off to keep consistency with the internationally used
measures and in line with other indicators employed in this study.
9 The calorie value of foods consumed in the household was calculated using
FAO’s calorie conversion factors. Calorie/gmhttp://www.fao.org/docrep/003
/X6877E/X6877E20.htm. Calorie consumption thresholds were based on
Wiesmann et al. (2009)
(i.e. p < 0.01) (Table 4). Note, however, that since households
draw their calories mainly from cereals, the correlation
coefficients of FCS and HDDS with per-capita calorie consumption
are relatively low (i.e. less than 0.3). Yet, as expected, there was
a high correlation coefficient between FCS and HDDS since
both indicators reflect the diversity of foods consumed. HFIAS
score is also statistically significantly correlated with FCS and
per-capita food expenditure (i.e. at p < 0.1 and p < 0.05
respectively), though the correlation is low. The latter can be
explained by the different nature of the self-reported HFIAS,
which may also reflect tastes, preferences and traditions.
One of the preliminary approaches to explore the possible
links between household food security and aspirations is to
examine the share of people with low aspirations that belong in
each food (in)security profile across indicators. Table 5 presents
such descriptive statistics, differentiated by the aspirations of the
household head and of the spouse.10 According to Table 5, in all
three food (in)security indicators, the share of household heads
with low aspirations is greater in households that are considered
Bfood insecure^ than those of Bfood secure.^ With the exception
of calorie consumption indicator, the same is also true for the
spouses. For example, among households who are considered
Bfood insecure^ based on FCS, the share of household heads
with low aspirations is 56% while the corresponding figure for
spouses is 75%. In terms of HFIAS, the corresponding figures
for household heads and spouses are 57% and 72%,
respectively. These bivariate results seem to indicate that there could be
some correlations between aspirations and household food
security. Further, Table 5 implies that it may be useful to control
for the aspirations status of both the household head and of the
spouse while studying household food security correlates using
multivariate analysis, which we do next.
Estimation and results
The food security status (y) of the jth household can be
expressed in the following function:
y j ¼ f ðA; I ; H ; CÞ
Where A represents the aspirations status (of the household
head and of the spouse), I denotes other characteristics of the
household head and of the spouse, H and C respectively
denote other household and community level characteristics. As
opposed to the assumption behind unitary household models
where preferences (or decision making) of the household is
often proxied by the preferences of the head of the household,
in this study we assumed joint decision making by the two
10 We focus on FCS, HFIAS and per capita calorie consumption because (a)
Per-capita food expenditure is not used as a FS measure in this study but as an
indirect measure of food access and (b) the HDDS does not have standard
cutoffs and is best used in relation to other indicators.
spouses and hence the food security of the household was
determined by the characteristics of both the head of the
household and of the spouse, in combination with other
household and community characteristics including district
fixed effects. We estimated a series of an ordinary least
squares (OLS) models relating indicators of household food
(in)security with aspirations of the household head and of the
spouse, and a wide range of other potential determinants
which were selected based on their importance in the food
security literature reviewed in this study. Yet, our purpose
remained to see if aspirations of the two spouses, given other
factors, were strong correlates of household food security
without necessarily claiming causal relations. This is because
results might still be confounded by unobserved
householdspecific heterogeneity, which we could not account for since
we only have cross-sectional observations on the main
variables of interest (i.e. aspirations and food security). Further,
aspirations are formed based on own outcomes in the past as
well as present
and they tend to change in the light
of new experiences (including shocks), choices and
(Leavy and Smith 2010)
. We attempted to control for such
dynamics using indicators for experience of shocks, change of
income over time, and present level income and wealth
indicators since they may also have a bearing on food security. In
addition, since the same level of shocks may be perceived
differently by different people, depending on their wealth
status and demographic characteristics, controlling for such
factors would help to tease out the correlations between the two
main variables of interest better (i.e. food security and
aspirations). Two-way or reverse causation (i.e. higher food security
may lead to higher aspirations) is also another concern in the
study of a causal link, which cannot be tackled with
crosssectional data. Nonetheless, it is believed that if properly
executed an exploratory study such as this one is in itself a strong
contribution. Angrist and Pischke (2009) even argue that
Bcorrelation can sometimes provide pretty good evidence of
a causal relation^ (p.113).
Results and discussion
We have shown in a bivariate context that aspirations and food
security are positively correlated. In this section, we examine
if that relationship still holds and whether the correlation is
statistically significant after controlling for other potential
determinants of the four pillars of household food security,
namely, availability, access, utilization, and stability of food.
Following the existing literature and data availability, we used
per-capita calorie consumption, FCS, HDDS and HFIAS as
measures of food (in)security.
Per-capita calorie consumption per day
Per-capita monthly food expenditure
* p < 0.10, ** p < 0.05, *** p < 0.01
Per-capita calorie consumption
a Corresponding statistics do not include female headed households
which account for about 10% of the total
Tables 6 presents a summary of the main correlates of food
(in)security, based on a unitary household model framework.
Results suggest that aspirations are indeed strongly associated
with household food (in)security in three out of the four
indicators (i.e. per-capita calorie consumption, HDDS and
HFIAS). For example, according to Column 1, a standard
deviation increase in the aspirations level of the household
head is associated with a (323 × 0.61) = 197 cal per-capita
per-day increase in household consumption.11 This is roughly
an increase of a (197/3035) = 6.5% over the mean calories
consumption per capita per day. Also, the coefficient estimate
further increases when we control for income and indicators of
various shocks experience (Column 2). Similarly, according to
Columns 5 and 7 respectively, a standard deviation increase in
the aspirations index of the household head was associated
with a (0.21× 0.61) = 0.13 points increase in HDDS, and a
(−0.30 × 0.61) = 0.18 points decrease in HFIAS (recall that
unlike other indicators, HFIAS actually measures food
insecurity).12 In reference to the corresponding mean outcomes,
these are roughly a (0.13/8.68) = 1.5% increase in HDDS, and
a (0.18/0.48) = 38% decrease in HFIAS.
Next we tested if the aspirations and other characteristics of
the spouse of the household head were also important
correlates of household food security, given the aspirations and
other characteristic of the household head and other controls
(see Table 7). It is assumed that the head of the household and
the spouse often talk to each other and exchange information.
11 The mean and standard deviation of the aspirations index of the household
head are 0.158 and 0.61, respectively.
12 The mean values, corresponding to Table 6, of the per-capita calorie
consumption, FCS, HDDS, and HFIAS are 3035, 71, 8.6, and 0.5, respectively.
Yet, in specifications where the characteristics of both the household head and
the spouse are considered (i.e. Table 7), female headed households drop out
from the analysis. In that case, the corresponding mean values of the household
per-capita calorie consumption, FCS, HDDS, and HFIAS are respectively
2997, 71.4, 8.68, and 0.48.
This may lead the two spouses to individually update their
(Ray 2006; Leavy and Smith 2010)
and this in turn
may influence their decision-making in the household. Thus,
we controlled for such feedback (or interactions) effect using
the interaction term of the aspirations of the two spouses.
According to Table 7, the aspirations index of the spouse is
negatively associated with the household per capita calorie
consumption, at 10% level of statistical significance (Columns 1
and 2). While the negative relationship may seem surprising, it
may also indicate the existence of trade-offs between current
consumption and savings (or forward-looking) behaviour. That
is, with the increase in aspirations, the spouse of the household
head may tend to cut back on current consumption and save for
future investment or consumption smoothening. Similarly, we
found that the interaction term of the aspirations of the
household head and the spouse was negatively and statistically
significantly correlated with FCS (Column 4). In this specification,
we reject the null hypothesis of the joint test of significance of
a) the aspirations of the spouse and the interaction term,13 and
of b) the aspirations of the head and the spouse and their
interaction term14 (Column 4).15 These results may suggest that the
aspirations of the two members of the same households have
different effects on household food security.
The strong and positive correlations between aspirations of
the household head and food security indicators should be put
into context, as explained next. As motivators, aspirations
may affect food security through different channels. First,
aspirations may improve households’ forward looking behavior
(Bernard et al. 2014)
and this may involve reducing risk by
diversifying livelihood strategies (e.g. by engaging in
nonfarm income generating activities) which may lead to
improved food security (e.g. through improved purchasing
power or economic access). Secondly, aspirations may motivate
households to reduce their risk aversion (Payne et al. 1981)
and this may lead to investment in agricultural innovations
(e.g. Mekonnen and Gerber 2016)
, major determinants of
agricultural productivity, which in turn may determine some
aspects of food security (such as food availability and
stability). Thirdly, farming in Ethiopia is a labor intensive sector
and productivity may depend on the physical fitness of farm
labor, which in turn is determined by the health status and
consumption of foods that provide the necessary nutrients
and adequate calories
(Asenso-Okyere et al. 2011)
. In this
context, aspirations may motivate households to consume
more diversified and dietary foods and make other
investments that would improve their health and nutrition status,
linked to at least one aspect of food security (e.g. utilisation).
In contrast, aspirations may also enhance savings behaviour.
13 With an F (2, 269) = 6.93 and Prob > F = 0.0012.
14 With an F(3, 269) = 4.63 and Prob > F = 0.0036.
15 Similarly, with F(3, 269) = 4.53 and Prob > F = 0.0041, we reject the null
hypothesis of the joint test of significance of the aspirations of the head and of
the spouse and their interaction term of the specification in Column 2.
Correlation of aspirations of the household head and other factors with household food (in)security
Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1
a Binary (1/0)
b AE = adult equivalent
When that happens, household savings may come at the
expense of current consumption and hence the food security of
the household may decrease, as the negative coefficient of the
aspirations of the spouse (Columns 1 and 2) and the
interaction term (Column 4) in Table 7 seem to suggest. Yet it is
important to note that despite the wide range of control
variables including wealth indicators, present income as well as
income growth in the past
(i.e. between 2006 and 2010)
study does not establish causal inference.
Beyond aspirations, and following the literature, other
results of Table 6 focus on relevant ‘external’ constraints. We
find that resource endowments such as household assets, land
and livestock holdings are positively correlated with some of
the food security indicators. This is because wealth avails
Correlation of aspirations of the household head and that of the spouse and household food (in)security
Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1
a The Spouse’s other characteristics include age and education categories similar to that of the household head listed in Table 6, and a dummy for
Spouse’s participation in business or wage labor. Of these, only the dummy for BSpouse education: 8+^ is statistically significant (Columns 3–6)
b AE = adult equivalent
c Other controls include those listed in Table 6
some form of coping mechanism to the household in the event
of abrupt changes that threaten their food security.
Remoteness of the household from the market and asphalt
road is also negatively associated with food security
(Columns 4, 5 & 6), which is consistent with the findings of
other studies such as that of
Abay and Hirvonen (2016)
report that proximity to food markets improves consumption
of more diverse diets and better child nutrition outcomes in
northern Ethiopia. This is because access to roads and markets
determine accessibility and stability of food. Results also
suggest that female headed households are more likely to be food
insecure (Columns 5 & 6). Further, results suggest that having
1-to-4 years of education (by comparison to no education) by
the household head (Columns 4–6) is negatively correlated
with dietary diversity of the household. This may be attributed
to measurement error. Otherwise, the result is surprising since
it is likely that individuals with some level of education may
have better knowledge of the benefit of dietary diversity (e.g.
Strauss and Thomas (1995)
for a survey of the literature).
The incidence of illness of a household member other than the
head and spouse (columns 4 and 6) are positively correlated
with FCS and HDDS. One potential explanation is that ill
people are fed with a more diverse set of foods in order to
speed up recovery.16 Lastly, most of these results hold when
we further include the characteristics of the spouse of the
household head (Table 7, results not reported). If anything,
we find that by comparison to no education, having education
level higher than 8th grade by the spouse of the household
head is associated with improved household food security (i.e.
FCS and HDDS, results not reported but available on request).
This is in line with expectations as education enhances the
nutrition knowledge of the main care giver (often wives in
Summary and conclusions
This study empirically examines whether aspirations are
important correlates of food security in rural Ethiopia. We
established robust evidence by employing several objective
as well as subjective measures of food (in)security that also
reflect the multi-dimensionality of the concept. Descriptive
statistics suggest that, by comparison, the proportion of people
with low-aspirations is higher among food insecure
households than that of food secure, and this is true for most of
We used regressions to relate each food (in)security
outcome against the aspirations indicator and other potential
drivers including human capital and the household’s access
to: natural capital, physical capital, financial capital, roads,
markets; and the household’s experience of various shocks.
To account for the unobserved factors common to all
residents in each study site, we controlled for district fixed
effects. The main finding of the study, which is robust
across outcome indicators, is that the aspirations of the
household head are strongly associated with household
food security in rural Ethiopia. In contrast, we find that
the aspirations of the spouse are negatively associated with
some indicators of household food security. This perhaps
suggests the existence of trade-offs between current
consumption and savings behaviour which may compromise
the current level of household food security.
Despite the cross-sectional nature of the data used in this
study, which is the major limitation for unobserved household
characteristics, these might affect both aspirations and food
security or present the possibility of reverse causation. The
robustness of findings across various indicators and
specifications, however, suggest that aspirations of the household head
are indeed strong correlates of household food security. Yet, it
is important to note that we have controlled for present income
and assets, the change in income in the past
(i.e. between 2006
, and a wide range of other factors, which might
affect both variables of interest (i.e. aspirations and household
food security). This perhaps might help minimise the
influence of the error term that would result from the unobserved
heterogeneities. Nonetheless, the study also has other
limitations. The survey, which this study mainly relies upon,
covered an existing sample of farm households which had been
interviewed by other organizations in the past. The original
survey used a mix of purposive and random sampling
procedures from study sites, which have high agricultural potential.
This might limit the external validity of the study. Given those
caveats, however, most of the findings of this study are in line
with theory and some other empirical studies. Further research
on the linkages between aspirations and food security could
have strong policy implications if they could establish the
direction of the linkage and the potential channels. To that
end, data on aspirations and their determinants, as well as on
food security and its determinants, cutting across time and
households would be necessary.
Acknowledgements The research leading to these results received
funding from the European Union’s Seventh Framework Programme
FP7/2007–2011 under Grant Agreement n° 290,693 FOODSECURE.
The authors are very thankful to Joachim von Braun and Alemayehu
Seyoum Taffesse for valuable comments and support during the research
work. Finally, we thank the anonymous reviewers who helped improve
the text substantially. Any remaining mistakes and inconsistencies are
entirely the responsibility of the authors.
Compliance with ethical standards
16 We thank the anonymous reviewer for suggesting this as one of the possible
Conflict of interest The authors declare that they have no conflict of
Appendix: measuring aspirations
Individuals may set different goals in life, which makes
aspirations multidimensional. Aspirations are also
dynamic in that they tend to change in the light of new
experiences, choices and information
(Leavy and Smith
. Further, since aspirations are attitudinal in nature,
measurement errors could easily arise due to
Banchoring, wording and scale dependence; respondent role
playing and instability over time or over respondents’
(Bernard and Taffesse 2014. p.190)
this backdrop, however, what is suggested in the
literature is that useful information could still be collected as
long as extra care is taken during the design and
implementation of surveys. For this study, we employed the
survey instrument developed and tested for validity and
Bernard and Taffesse (2014)
. To capture
aspirations in four dimensions, the survey asks individuals a
series of five questions regarding their income, wealth, social
status and children’s education. Specifically, the questions
(1) BWhat is the level of […] you have at present?^
(2) BWhat is the level of […] that you would like to
(3) BWhat is the level of […] that you think you will reach
within ten years?^
(4) BWhat is the maximum level of […] that a person can
have in your village?^
(5) BWhat is the minimum level of […] that a person can
have in your village?^
The questions regarding own current level, village
maximum and village minimum are intended to serve
as benchmarks against which respondents would state
their aspired level. The question on the expected level
is intended to guide respondents in differentiating their
aspirations from their expectations. Further, to maintain
data quality, we executed careful preparations before the
survey. For example, highly experienced enumerators
were recruited and trained. The survey questionnaire
was translated into local language and both the English
and Amharic versions were made available to the
enumerators. In addition, as part of the training, we
conducted both mock interviews among enumerators and
fieldtested the questionnaire. Further, to ensure that
respondents understood the questions and did not state their
simple wishes when asked their aspirations,17 special
17 Simple wishes are different from aspirations for the latter entails action to
(Bernard et al. 2014)
care was applied during interviews, including probing
and checking for consistency across responses. For
example, after further clarification of the concept and
definitions, respondents were allowed to change their
responses if they thought that they had given Bincorrect^
On the calculation of the aspirations index: Since
each dimension of aspirations may mean different things
for different people
(Leavy and Smith 2010)
, the weight
or relative importance respondents place on each of the
four dimensions was captured as explained next. First,
respondents were given 20 beans and a piece of paper
that pictured four squares. Each square was labelled with
one of the four dimensions of the aspiration measures
(i.e. income, wealth, social status or children’s
education). Then, respondents were asked to distribute all the
20 beans in the four squares according to their own
assessment of the dimension’s significance for them. The
instructions were clear. For example, it was explained
that no bean in a square means the respondent does not
attach any importance to that particular indicator and,
many beans in a square means the respondent attaches
a significant importance to it. In what follows, we
explain how the subjective weights given by the
respondents were used in the calculation of an aggregate
As noted earlier, individuals aspire to achieve
different things depending on their experiences and
information set. Hence, relying on any single indicator may not
suffice to measure aspirations. Yet, the four indicators
are believed to be strongly correlated with each other
and many other targets a person might want to achieve
in their life. In this context, an aggregate index constructed
from the four dimensions is believed to capture a broad
array of life targets and serve as a strong proxy for
aspirations. Hence, we calculated the aggregate index
Beaman et al. (2012)
using Eq. 1. The index was constructed
by first normalising18 each dimension (i.e. by removing
the average level for individuals in the same district, and
then dividing this difference by the standard deviation for
individuals in the same district) and multiplying the result
by the weight each individual gives to each of the four
indicators. Summing across the weighted average of the
four normalized outcomes provides an aggregate
18 Since attitudinal measures such as aspirations are likely to be measured with
errors, normalization would help smooth out errors at the individual level.
Further, normalization also makes individual indicators unit free, a prerequisite
All data are from our 2014 survey, except when otherwise specified. n simply reflects sample size of the spouse
a Binary outcome
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Daniel Ayalew Mekonnen is a
postdoctoral researcher at the
Department of Economic and
Technological Change, Center
f o r D e v e l o p m e n t R e s e a r c h
(ZEF), University of Bonn.
Before conducting his doctoral
studies, he had over 6 years of
research experience working with
the International Food Policy
Research Institute in Addis
A b a b a , t h e M D G s / P o v e r t y
A n a l y s i s a n d M o n i t o r i n g
Section of the UN ECA, and the
Ethiopia Ministry of Finance and
Economic Development. His previous research examined the different
aspects of agricultural and food policy issues including the provision of
agricultural extension and health services and their linkages with
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