inequality are a concern to both developing and developed nations across the
world (Melamed, 2014; Shepherd et al.,
2014). These problems are more serious in developing countries like Ethiopia
(UNDP, 2013). Poverty is so widespread and prevalent in Ethiopia that the
country is amongst the poorest nations in the world (UNDP, 2013).
government claimed that poverty and inequality are declining in all regions
(MoFED, 2013). However, empirical literature shows that poverty, in general,
and chronic and transitory poverty, in particular, have long history in rural
areas and show growing trends (Adugna, 2012).
For sustained fight against rural poverty and inequality; and
for realization of poverty and inequality free Ethiopia, there is a need to
design pro-poor and location specific poverty and inequality reduction policies
and strategies in the country generally, and this is an on-going effort since
fifteen years ago.
But, for ease of intervention
and to succeed in the near future, that entails deep assessment of not only the
dynamics and extent of rural poverty, but also identification of who the poor are
and how long will it take, on average, for the poor households to exit poverty
provided that the per capita consumption of the poor increases by a positive
rate per year. Moreover, factors which are antidotes of poverty and aggravating
poverty need to be understood and substantiated both spatially and temporally.
It is obvious that it is hardly possible to use poverty
assessment results carried out elsewhere in the country for other areas due to
the fact that the country is differentiated with diverse socio-economic
settings, and agro-climatic zones. Even, regional poverty analysis results are
seldom used by other districts as the households may differ in their
socio-cultural contexts and livelihood strategies being pursued.
inevitably calls for the need to go deep into the analysis of the average time
that will take for the poor to exit poverty and the determinants of rural
poverty at the household level so that it will support the on-going poverty
reduction programme of the country ? GTP the so-called PASDEP II (MoFED,
the need to analyze farmer households’ vulnerability to poverty and the income
inequality that prevails, critical assessment is required in the rural areas of
are no recent studies on the aspects in this area. And those studies carried
out in the past with special focus on the poverty did not include the
vulnerability and inequality.
is the motive behind the need to analyze rural peoples’ vulnerability to poverty
at a household level.
this study is deemed to fill the existing knowledge gap concerning the extent
of rural poverty, the average time required to exit poverty and its
Hence, one safely
concludes that a critical analysis of poverty and inequality of opportunities
in pastoralist and agro-pastoralist areas is scanty.
that have been conducted in different disciplines on related topics
predominantly focus on poverty profile and determinants of poverty without
considering the vulnerability and inequality prospects.
Climate variability will have profound
effects on agriculture and food security; expose farm households to shocks and
affects their resilience capacity. Farm households in local communities are
vulnerable to the threat of climate change and variability; though, their
contribution to GHGs emission is little (IPCC, 2014b). The most
vulnerable are poor and marginalized farm households who are highly reliant on
agriculture (Pettengell, 2010) consume of
their subsistence agricultural production (Lobell and Burke, 2010) and market
dependent in the time of hungry seasons (Devereux, 2016).
Tackling ex post poverty and ex ante vulnerability enhances farm households’ resilience capacity
(Alinovi et al., 2009). However, the future incidence of climate
variability on poverty and resilience of farm households remains unpredictable.
Poor farm households have too little
access to infrastructure and low public services, limited access to diversified
income sources and inadequate support from government and civil society (Issa et al., 2014). Any shock that
happens to agriculture highly threatens food security, livelihood and the
resilience status of the food insecure households most. Many farm households
are stuck in chronic food insecurity associated with structural problems of
availability, access and/or utilization (Upton et al., 2015) exposed and vulnerable to climatic shocks (Pettengell, 2010) to disproportionate distress and hunger
unlikely to be untangled (HLPE, 2012). These troubles enlarge the vulnerable group of
farm households to ex ante food
insecurity (Yaro, 2013) and create weak resilient environment.
There is a considerable
uncertainty surrounding future food security (HLPE 2012; Tendall et al., 2015) due to vulnerability of farm households to climate related
shocks (Capaldo et al., 2010; Opiyo et al.,
which show temporal as well as spatial variations (Issa et al., 2014). Land fertility decline; inundation, erratic rainfall, drought
and flooding are the most influential factor (Pettengell, 2010) associated with smaller landholding and subsistence return from
agriculture (Yishak et al., 2014) are the major contributors to food insecurity.
insecurity, more than any other factors, determines vulnerability to climate
variability and limits resilience capacity of the farm households. First, food
insecure households tend to rely on climate-sensitive resources as the basis of
their livelihoods. As highlighted by the Assessment (2005) poor households depend on natural resources for survival; but
the natural resources are already being substantially degraded and increasingly
being affected by changes in the climate exposed them to vulnerability to food
Temesgen et al. (2009) examined farmers vulnerability to climate shocks in
the Nile Basin of Ethiopia based on diverse socio-economic and environmental
settings. They found that farmers living in lowland zones are relatively more
vulnerable to extreme climate events than farmers living in the other
agro-ecological zones. However, Gutu et al. (2012) examined vulnerability of farm households in North
Shewa zone, Ethiopia, using integrated vulnerability approach and the result
shows that farmers living in the highland areas are vulnerable to natural
shocks compared to those living in the lowland areas. Altitude
difference is not the only or even the most influential factor of farm
households to climate variability. This
different result indicates that there is a need for further examination and
research to bridge the gap. How vulnerable to food insecurity are felt
depends upon how their ability to resilient to these shocks.
Therefore, a separate
analysis is important specifically at farm household level. In the same vein,
this study delves to assess food security, vulnerability and resilience of farm
households to climate variability and their linkages in West Shewa zone of
Analyses of household vulnerability are becoming
increasingly prominent in the development economics literature. In large part,
this has reflected the emerging understanding that, in order to reduce poverty,
policymakers need information on both the current incidence of poverty and also
the magnitude of the threat of poverty, measured ex-ante (Calvo and
Dercon, 2005). Such a perspective implicitly recognises that poverty is a
stochastic phenomenon. To that end, while the current incidence of poverty is a
critical indicator of wellbeing it provides limited foresight into future
poverty. Rather, whether a household is likely to fall into poverty in the
future is also determined by its exposure to a variety of different shocks as
well as its ability to effectively cope in the face of shocks.
Yet little empirical work has been done that examines the
vulnerability of households in Melanesian countries, such as Vanuatu and the
Solomon Islands to experiencing povetry. This is despite the fact that both
countries are renowned for being acutely vulnerable to economic and environmental
shocks at a national level.
Using unique cross-sectional data of household
characteristics, households’ experiences of shocks and their responses to
shocks, this paper addresses this gap. It estimates the ex-ante risk
that households in Vanuatu and the Solomon Islands will, if currently non-poor,
experience poverty one period ahead, or if currently poor, remain in poverty.
It follows an approach to estimating vulnerability originally devised by
Chaudhuri, et al. (2002) that has been widely used in a number of
developing country contexts when only cross-sectional data are available.
The paper makes a number of important contributions. By
combining empirical survey data with sophisticated modelling techniques it
provides a recent and detailed account of vulnerability in Melanesia. It also
makes a contribution to the vulnerability literature more broadly by estimating
households’ vulnerability to a broader measure of poverty; specifically
multidimensional poverty, drawing on Alkire and Foster’s (2011a) approach for
calculating a Multidimensional Poverty Index (MPI). It therefore builds on Jha
and Dang (2010) who relied on aggregate household income and expenditure data
for Papua New Guinea (PNG) from 1996. The use of the MPI as a proxy for
wellbeing, rather than more conventional monetary metrics like consumption,
reflects the inherent limitations on relying solely on monetary metrics of
wellbeing in Melanesia.
Also, to the extent that it identifies those households that are
likely to be poor in the future, the paper is of particular interest to
policymakers interested in designing social protection policies in Melanesia.