Household Vulnerability in Dynamic Environments: Frameworks for
Quantifying Vulnerability to Extreme Climatic Events
Andrew Dougill, Evan Fraser, Matt Chadwick and Mark Reed
Outline
Introduction: The Research Challenge
Many communities, especially the rural poor in the developing world, depend directly
on natural resources for their livelihood (Rennie and Singh, 1996). Therefore for their
livelihoods to be sustainable there is a fundamental need that their use of natural
resources is sustainable. Consequently, any assessment of household sustainability
needs to include both social and environmental analysis. Methods to achieve this
integrated analysis increasingly use sustainable rural livelihoods analytical approaches
(e.g. Scoones, 1998; Carney, 1998), which have developed from participatory work of
development economists such as Sen (1980) and Chambers (1987, 1997).
The methodological frameworks provided by livelihoods studies (e.g. Figure 1)
display the threats to livelihood sustainability, arising from external shocks and trends
(either environmental or socio-economic), but mediated by the “vulnerability context”
of affected households. Difficulties however arise in using these largely qualitative
frameworks in unpacking this vulnerability context box into a quantifiable assessment
of household vulnerability to external shocks. This failing of recommended
development research approaches (World Bank, 2001; DFID, 2002 etc.) is
particularly apparent when examining household vulnerability to climate change (CC
vulnerability refs), notably the shock imposed on households by extreme events, such
as either droughts or floods.
Figure 1: The DFID Sustainable Livelihoods Framework (DFID, 2002)
Much existing literature on vulnerability to climate extremes, especially droughts (e.g.
Davies, 1996; del Ninno et al., 2001; Gray, 2002) describes community ‘coping
strategies’ by using in-depth social survey methods, similar to those of livelihoods
analysis. These approaches are good at producing lists of potential management
options assigned after a major event, but struggle to quantify household vulnerability
before an event. This paper provides temporal analysis of the information gained
using such livelihoods analysis methods before-, during- and after- both a major
drought in Southern Africa and an extreme flood in rural Bangladesh. We aim to use
this information to demonstrate that households not only react to, and cope with, these
extreme events, but that they also anticipate (and plan for) the effects of droughts or
floods. This anticipation, or ‘preparedness’, is a key element in affecting household
vulnerability to such climatic shocks and is therefore a component that should always
be addressed in livelihoods analysis in dynamic environments, such as drylands or
floodplains. We aim to discuss the manner in which analysis of household
preparations for extreme events can be used as part of vulnerability assessments that
provide a useful sustainability indicator at a household level.
Purpose of this Paper
This paper will contribute to debates on vulnerability to climate change by discussing
the various ways in which household level sustainable livelihoods analysis can be
used to provide both qualitative and quantitative information on how households
anticipate and react to extreme climatic events. A research framework that focuses
on environmental resilience and socio-economic resilience is formulated and
discussed in terms of its ability to predict household vulnerability and therefore better
inform policy decisions on institutional support to drought- and flood-prone areas.
Discussions are based on the temporal analysis of livelihoods information taken from
two distinct case studies (Southern African drought of 1998/99 and Bangladeshi
floods of 1998) to examine if methodological issues are common across different
climatic shocks.
In achieving integrated socio-environmental analysis, improved models of
environmental change for dynamic environments are essential. For example, models
highlighting the non-equilibrium functioning of dryland environments are used (e.g.
Behnke et al., 1993; Walker and Abel, 2002). We argue that research frameworks
need to provide understanding of how extreme events affect not only the natural
resource base, but also social capital asset changes, which influence people’s longterm adaptation mechanisms and socio-economic resilience. This paper aims to
evaluate how sustainable livelihoods analysis can be used in conjunction with
theoretical concepts, such as the Panarchy framework proposed by Gunderson and
Holling (2002), to better understand the factors which influence (and could quantify)
household sustainability. This builds on the Global Environmental Change paper by
Fraser et al. (2003) that has outlined the data required to assess ‘environmental
sensitivity’ and ‘social resilience’ by providing case study analysis based on how
livelihoods analytical approaches can provide information on these variables. The
temporal analysis (before, during and after extreme events) enables us to examine
how changes in communities, due to long-term environmental changes, policy
changes and/or market changes can affect household vulnerability to changing
climatic conditions.
Objectives
The general aims of this paper are met by investigation of the following research
objectives:

To evaluate the ability of, and problems with, ‘static’ sustainable livelihoods
analytical approaches to quantify vulnerability to extreme climatic events, as
required for a quantifiable assessment of household sustainability.

To analyse the extra information available from ‘dynamic’ assessments of
environmental sensitivity and social resilience from analysis of detailed case
studies through time including, pre-, during- and post- an extreme climatic
event for a drought event in Southern Africa and a flood event in Bangladesh.

To examine whether the Panarchy framework of Gunderson and Holling
(2002) offers a methodological tool capable of improving assessments of
household vulnerability to extreme climatic events.

To propose ‘best-practice’ methodological approaches that can build on
sustainable livelihoods approaches by providing more detailed assessments of
the ‘vulnerability context’ in terms of assessments of social resilience and
environmental resilience to extreme climatic events.
Methodological Framework
Sustainable livelihoods analytical approaches (based on Scoones, 1998 etc.) were
repeated three times in each case study region. This includes analysis before, during
and after the significant 1998 drought event in Southern Africa when many crops
failed in semi-arid mixed farming regions (Dougill et al., 2002); and studies before,
during and after the 1998 Bangladesh flood when the equivalent of 68 % of the
country was inundated by flood waters (DMB, 1999). Assessments were all
conducted at the household level as most people experience and respond to extreme
climatic events as members of a household first and foremost. Only by improved
understanding household vulnerability to extreme events can better community, subnational and national sustainability assessments and policy support be provided. In
developing world rural situations the key element in defining a household is the
sharing of assets of which the key asset in terms of livelihood security is food, and
therefore we define a household as a unit where food consumption is shared.
Details of each case study methods and reasons for methodological adaptations
through time.
Analysis approaches –
 Assessments of inter-household differences in each case and analysis of
potential explanations in terms of capital assets;
 Evaluation of changing livelihood interview responses through time in relation
to extreme rainfall events to assess whether these methods capture simply
information on household ‘coping strategies’ or more detailed information on
social resilience to external shocks.
 Identification of how different forms of support by key stakeholder groups
(village, Government, NGO and aid institutions) affect household decisionmaking.

Application of Panarchy framework and its components of system wealth,
connectedness and diversity to rural households to examine if this can provide
a more robust measure of household vulnerability to extreme rainfall events.
A range of scholars have proposed preliminary ways of combining qualitative and
quantitative data that are social, economic and biophysical in nature. Homer-Dixon’s
process tracing, Hollings Panarchy, Alcamo’s security diagrams, Kasperson’s regions
at risk framework. Although the details of these approaches vary, there are a number
of similarlities. First, all suggest that these are complex adaptive systems that evolve
through time. Any framework must, therefore, be dynamic allowing for comparisons
across time. Second, all differentiate between endogamous conditions (i.e. local
economic factors and biophysical indicators) and exogamous forces (such as
fluctuations in the global market or climatic variation). Third, all stress the need to
include social variables and environmental factors.
In light of these data we propose to arrange case study data based on two “meta”
variables: socio-economic resilience and biophysical resilience (Figure 2). We
loosely define socio-economic resilience as the ability of a household to find
alternative livelihoods without suffering undue affects in the event that an exogamous
environmental change causes the primary livelihood option to be disrupted. We
define biophysical resilience as the ability of a local ecosystem to maintain structural
functioning conditions in the face of exogamous change.
To assess socio-economic resilience we base our analysis on the livelihoods literature,
which itself finds its roots in entitlements theory and ethnography, and use household
level data, generated through a participatory bottom up framework to understand how
changes leading in livelihoods promote or reduce socio-economic resilience.
To assess environmental resilience, we will assess the range of acceptable
environmental disturbances (degradation indicators, precipitation levels, wind erosion
etc.) and Hollings characteristics of a vulnerable landscape (connectivity of the
landscape, biological diversity and biomass).
Figure 2 Preliminary framework to aggregate household level data
for regional analysis into two overarching categories: socioeconomic resilience (defined as the ability of the social system to
withstand change) and environmental resilience (defined as the
ability of the environmental system to withstand changes).
Case Studies

South Africa / Botswana – mixed farming systems research – Dougill et al.,
2002; Twyman et al., 2004 and a few subsequent views on disseminating
messages to policy-makers etc. Livelihoods analysis of Twyman et al. (2004)
in 1998 (pre-drought), followed by environmental analysis and follow-up
livelihoods interviews in 1999 (during drought) and 2000 (post drought) from
Dougill et al. (2002).

Botswana / Namibia rangelands – pastoral systems analysis – various Reed
and Dougill bits with specific focus on the problems of quantification for
individuals and generalising up to community and district level from this.
Initial livelihoods analysis in 2000/01 () and follow-up household interviews
in 2002 () and 2003 ().

Bangladesh rural communities and adaptive strategies to the 1998 flood using
livelihoods interview approaches in 1998 (pre and during-flood) and 1999
(post-drought) (based on key bits of Chadwick, 2004 PhD thesis – Matt this to
me looks like just a summary overview of key findings from your main study
villages – doesn’t need to include too much detail if you’re saving this for
your own publication!?).
Discussion
Spatial dimension - Key issue is whether improved household vulnerability
assessments can be scaled up to community, regional and national levels?
Temporal dimension – can we add arrows to Figure 2 style diagram to show how
policy factors (e.g. fencing of Sn Af rangelands; support for groundnut cropping in
SA; Flood Action Plan in B’desh) can reduce both environmental and social
resilience. Therefore should be able to use such a framework to evaluate the impact
of institutional support on household vulnerability to extreme events.