ORI GIN AL PA PER

The dynamics among poverty, vulnerability,and resilience: evidence from coastal Bangladesh

Md. Nasif Ahsan1,2,3 • Kuniyoshi Takeuchi1

Received: 28 July 2014 / Accepted: 2 September 2015� The Author(s) 2015. This article is published with open access at Springerlink.com

Abstract The concepts of vulnerability and resilience govern contemporary natural haz-

ard-led disaster risk management approaches. However, the empirical assimilation of

socioeconomic resilience and its calibration with poverty and vulnerability are very few,

which inhibit a rational process of risk analysis and policy making. In this study, we per-

formed an empirical investigation of socioeconomic resilience to natural hazard-triggered

disasters regarding tropical cyclone affected communities in southwestern coastal Bangla-

desh. Applying the ‘Access model’ (Blaikie et al. in At risk: natural hazards, people’s vul-

nerability and disasters. 1st edn, Routledge, London, 47–74, 1994) framework, we found that

tropical Cyclone Aila invoked detrimental impacts on the communities, especially in terms of

consumption, employment and access to resources. Our empirical findings also revealed that

the poor were more vulnerable and thus suffered significantly higher financial, settlement,

and physical damage. Nonetheless, such a high degree of vulnerability did not necessarily

result in a low level of resilience since the poor households demonstrated a better ability to

withstand perturbations and stresses than their non-poor neighbors. This contravenes the ‘flip

side’ dogma conventionally assumed between vulnerability and resilience (i.e., vulnerability

is the flip side of resilience). The results further indicate that an increasing degree of risk from

tropical cyclones is likely to affect the consumption and socioeconomic status of the coastal

communities. However, we did not find any evidence to suggest that the burden of detrimental

impacts from cyclones is likely to be disproportionately borne by the poor households.

Keywords Access model � Poverty � Socioeconomic vulnerability � Socioeconomic

resilience � Natural hazards � Cyclone Aila � Bangladesh

& Md. Nasif Ahsannasif.ahsan@yahoo.com; nasif@icharm.org

1 International Centre for Water Hazard and Risk Management (UNESCO-ICHARM), Tsukuba,Ibaraki, Japan

2 National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan

3 Economics Discipline, Khulna University, Khulna 9208, Bangladesh

123

Nat HazardsDOI 10.1007/s11069-015-1950-0

1 Introduction

Natural hazard risk management frameworks have experienced a paradigm transition in

contemporary times from emergency response to an all-inclusive disaster risk management

approach (UN/ISDR 2005). The impetus for this transition is spurred through emphasizing

the formation of climate-resilient communities by enhancing the coping capacity of vul-

nerable people to the impact of natural hazards.

The definitions of vulnerability and resilience differ within and between research domains

(Birkmann et al. 2013; Gallopın 2006). Relevant disaster risk literature defines socioeco-

nomic vulnerability as susceptibility that precedes and succeeds disasters with different

intensity and subsequently affects social, economic, political, and institutional components

(Cutter et al. 2000; Finch et al. 2010; IPCC 2012; Lee 2014). Possessing a ‘mediating role,’

socioeconomic vulnerability has become a pivotal factor that exacerbates the effects and

impacts of catastrophic events in the form of climatic extreme events across the world. Such

vulnerability affects not only the local community’s sensitivity but also their coping capacity

to environmental changes (Lee 2014). Thus, the development research community opts for a

comprehensive definition of socioeconomic vulnerability as a combination of sensitivity,

exposure, and response capacity (Adger 2006; Gallopın 2006). The theme of sensitivity

revolves around the susceptibility-oriented definition of vulnerability in disaster risk litera-

ture. Sensitivity is an inherent characteristic of a system and explores the degree to which a

system is likely to be affected by an endogenous or exogenous shock (Gallopın 2006).

Exposure is the nature and degree to which a system experiences geophysical or sociopolitical

stress (Adger 2006). The response capacity of a system is its ability to manage or cope with

perturbations and stresses (Tompkins and Adger 2005).

The concept of ‘resilience’ emerged in the knowledge domain of ecology between the

late 1960s and the early 1970s. It refers to the degree of relationship among the compo-

nents in a system and a measurement of the ability of a system to absorb unanticipated

adverse states and learn to bounce back to its original state through different trajectories

(Folke 2006; Gallopın 2006; Holling 1973; Wildavsky 1988). The entry of the term ‘re-

silience’ in the disaster discourse has been treated as a new paradigm in the disaster risk

reduction concept since the World Conference on Disaster Reduction (WCDR) in 2005

(Manyena 2006). Two broad approaches are applied to explain disaster resilience in human

communities: (1) outcome (end result) and (2) process (leading to desired outcomes)

(Kaplan 1999). The former approach defines resilience as the ability of a human habitat to

apprehend, absorb, accommodate, or recover from a shock (IPCC 2012). The yardstick in

this case is the success or failure of a system to revert to a state as good as or better than

that of the pre-disaster state in the shortest possible time (DFID 2011; Gilbert 2010). The

latter approach exhibits resilience as a synergy among self-organization, experience from

‘learning by doing,’ diagnosis of information, and adaptation accordingly (Resilience

Alliance 2010). This latter approach treats resilience as a broader phenomenon than just

recovery, cincturing a system’s coping capacity to shock along with its ability to lessen

exposure to shock (Adger et al. 2011; Cutter et al. 2008b).

A thematic cross-disciplinary division with regard to vulnerability and resilience

emerges through academic debates on the innate features of their mutual links. Scholars

opining for the narrowly defined paradigm suggest that vulnerability is the flip side of

resilience (Folke et al. 2002); in other words, a high level of vulnerability attributes to a

low incidence of resilience and vice versa (Cannon 2008). However, the upholding for the

broadly defined vulnerability paradigm demits the flip-side hypothesis, advocating that

Nat Hazards

123

though a resilient system is generally less vulnerable than a non-resilient one, the relation

is likely to be asymmetric (Gallopın 2006). Hence, two alternative hypotheses are pro-

posed: either (1) resilience is a subset of vulnerability; or (2) both vulnerability and

resilience are two distinct concepts with some overlapping components (Cutter et al.

2008a; Gallopın 2006).

Manyena (2006) contrasted vulnerability and resilience on the basis of intrinsic charac-

teristics where the former belongs to the domains of physical and life sciences while the latter

belongs to the medical and social-science domains. Recently, resilience has been considered

as a more pivotal component than vulnerability in socioinstitutional dynamics (Manyena

2014; Wood et al. 2013). Gallopın (2006) chalks out two fundamental thematic demarcations

between vulnerability and resilience. First, resilience associates with the transition of states

among domains of attraction, whereas vulnerability associates with structural changes in a

system. Second, resilience is an innate property of a system which does not include exposure

to shock, while vulnerability does. Vulnerability and resilience are assumed to be interlinked

through response capacity, which is considered as a core component of vulnerability (Gal-

lopın 2006; Nelson et al. 2007). This conclusion invites further debate on whether resilience is

a subset of vulnerability or a different but overlapping concept. The solution in this case might

be the way response and adaptive capacities are defined since resilience is influenced by a

system’s adaptive and response capacities (Akter and Mallick 2013). Some scholars treat and

apply resilience and vulnerability interchangeably (Adger 2006; Smit and Wandel 2006).

This suggests that resilience is very likely to be a subset of vulnerability. Based on a time

frame, on the other hand, Turner et al. (Turner et al. 2003) defined adaptive and response

capacities as long- and short-term strategic activities, respectively. The scope of response

capacity, referring mainly to the ability just to survive, is smaller than that of adaptive

capacity. Hence, adaptive capacity implies a relatively long-term, sustainable mechanism that

can adjust sensitivity and exposure to shock for a system (Adger et al. 2011; Gallopın 2006;

Turner et al. 2003). This view demonstrates that resilience and vulnerability are two distinct

concepts having response capacity as a common component.

Debates over the vulnerability–resilience dynamics dim our understanding on distri-

bution of effects from natural hazards across different groups in the same community.

Socioeconomic vulnerability literature postulates a high degree of affinity between the

socioeconomic status and vulnerability of a household (Adger 1999, 2006; Ahsan and

Warner 2014; Ahsan 2010). At a given level of socioeconomic status, the poor and

marginalized segments of society are more likely to live in weakly constructed settlements

situated in hazard-prone locations, which makes them more exposed and sensitive to

hazard shocks. Besides, they suffer from the inability to reduce such exposure and sen-

sitivity (i.e., less adaptive capacity) by shifting to safer places or strongly constructed

settlements and consequently to cope with shocks (i.e., less response capacity) (Ahsan

2014; Akter and Mallick 2013). This exhibits a close relationship between the poverty

status and socioeconomic resilience of communities. The dynamics between socioeco-

nomic vulnerability and resilience play a key role in determining the pattern of relationship

with poverty. For instance, the ‘flip side’ dogma exhibits more vulnerable communities

such as the poor and marginalized are also less resilient. This renders that immediate

effects (e.g., loss of life and assets), short-term impacts (e.g., structural, physical, and

economic damage), and long-term impacts (i.e., less income and consumption, fewer

economic opportunities, and lower standards of living) from natural hazards are very likely

to be disproportionately borne by the poorer segment of society. If the opposite is valid,

i.e., high vulnerability does not necessarily lead to low resilience, then the poor and

Nat Hazards

123

marginalized may bear a significant share of immediate effects and short-term impacts

from natural hazards, but they may equally be able to evade long-term adverse impacts.

Data-driven empirical studies on socioeconomic resilience and its conjuncture with

poverty and socioeconomic vulnerability are very few (Cutter et al. 2008b; Gallopın 2006;

Manyena 2006). Such limited empirical studies dealing with socioeconomic resilience

have mainly focused on the issue of adaptive and response capacities (Alam and Collins

2010; Kaul and Thornton 2013; Lee 2014; Lei et al. 2014; O’Brien and O’Keefe 2010; Paul

and Routray 2011; Reams et al. 2013). Alongside, some studies have addressed particular

strategies (e.g., education, selling livestock, microfinance, and remittance) for assessing

households’ ability to recover after a hazard shock (Helgeson et al. 2013; Mohapatra et al.

2012; Parvin and Shaw 2013; Razafindrabe et al. 2014). These studies consider resilience

as a segregated concept linking to neither poverty nor vulnerability. As a result, there is

hardly any comprehensive knowledge available that focuses resilience through the poverty

and vulnerability dynamics in a real-world scenario. The general outcomes obtained from

the existing studies render that the immediate, short-term, and long-term effects of natural

hazards are significant and adverse for livelihood strategies of rural poor and marginalized

households. The usage of common response strategies such as education, insurance,

remittance, or sale of livestock may help affected people to survive, yet only at a lower

welfare level for an uncertain period.

As the risk of natural hazard-led disasters has increased around the world and the new

paradigm of disaster risk reduction has emerged with an emphasis on forming resilient

societies, it is important to develop and enrich a knowledge base on the dynamics of

socioeconomic resilience and its links with poverty and vulnerability (Cutter et al. 2008b).

For a better understanding of this dynamics, three issues need to be assessed: (1) What are

patterns of resilience for different groups (e.g., poor and non-poor) in a community?; (2) What

are varying patterns of resilience for different vulnerability profiles (e.g., high to low) within a

community?; and (3) What type of policy recommendation is necessary to minimize the

difference in resilience (if any) among different groups? This paper reports an empirical case

study, which investigated these three questions by utilizing primary data collected from a

household survey in a hazard-prone and low-income community in the southwestern coastal

Bangladesh. Due to the unavailability of a well-established framework for resilience

assessment in the domain of social science, we applied a customized version of the ‘Access

model’ in this paper, which is a notable assessment model in the disaster risk domain.

Investigating socioeconomic resilience before and after a natural hazard-led disaster event,

we examined mutual links among the different components of vulnerability and resilience for

poor and non-poor groups in a community on the basis of different definitional spectrums.

To proceed with our analysis, the remainder of this paper is structured as follows:

Sect. 2 outlines a theoretical framework applied for resilience assessment; Sect. 3 presents

the context of the case study with relevant descriptions on the study location and data;

Sect. 4 presents empirical findings (results); Sect. 5 focuses on discussion of results; and

Sect. 6 concludes the paper with policy recommendations.

2 Theoretical framework

This section introduces a theoretical framework applied to resilience assessment in this

study. We, therefore, first overview several existing frameworks, followed by discussion

on the Access model.

Nat Hazards

123

2.1 Existing frameworks

Available resilience assessment frameworks differ, depending on their orientation toward

the outcome- or process-driven approach. The former approach deals with resilience in

terms of end results, while the latter approach treats resilience as a sequence of dynamic

reformations to regain the original state. For instance, the resilience assessment framework

by DFID (2011) proposes four possible reactions (i.e., outcomes) to a shock. The best case

is ‘bounce back better,’ which indicates that a household demonstrates a stronger capability

to deal with future perturbations than it did in the past. The second best case is ‘bounce

back,’ indicating a household’s capability of reinstating the pre-disaster status. The third

best case, ‘recover, but worse than before,’ refers to less capacity, compared with the pre-

disaster condition; and finally, ‘collapse’ indicates the worst case where a household

suffers substantial deterioration in its capacity to cope with future perturbations. Likewise,

FAO (2013) portraits resilience as outcome through a number of socioeconomic variables

namely access to income, food, basic services, assets, and social safety-nets together with

stability of adaptive capacity. By assigning weights to these factors, a composite weighted

index is obtained which provides a ‘resilience score’ for a specific location. Following this

approach, FAO compares the resilience scores among different locations.

The process-driven approach encapsulates the dynamism of resilience in terms of its

operational definition. So far, the most notable process-oriented approach denoting resi-

lience is the disaster resilience of place (DROP) model suggested by Cutter et al. (2008b).

This model considers and contrasts the attributes of a system (e.g., household or com-

munity) in different time periods (i.e., pre- vs. post-disaster). The pre-disaster attributes,

treated as intrinsic vulnerability, are assumed to be the static benchmarks of a household

which are governed by three types of determinant that are social, structural, and envi-

ronmental. The structural determinants focus a household’s sensitivity, whereas the

environmental determinants imply its exposure. The social determinants estimate the

degree of sensitivity and response capacity. The post-disaster attributes explore the

dynamic benchmarks of resilience through factors such as hazard maps, evacuation routes,

and early warnings (altogether constitute adaptive capacity).

The ‘4 Rs’ model suggested by Forgette et al. (2008) portraits resilience by measuring a

household’s capacity on the basis of risk recognition, resistance, redundancy, and rapidity.

Risk recognition postulates the degree to which a household can detect the risk likelihood

of a disaster. Resistance is the robustness of the structural, environmental, and socioeco-

nomic attributes of a system to withstand perturbations. Redundancy is the extent to which

structural, environmental, and socioeconomic statuses permit substitutes or resources for

the replacement of critical commodities (e.g., food, logistic supply, and credit), and

rapidity is the span of time utilized by individuals and groups in a community to reach

internal and external support (e.g., the time to reach relief supports).

In line with ‘4 Rs’ model, the MOVE framework which is also a process-oriented

approach focuses the nexuses among vulnerability, risk, and social responses (Birkmann

et al. 2013). Considering resilience as societal response capacity to perturbation and stress

by utilizing the common-pool resources, the definitional spectrum of the MOVE frame-

work includes pre-disaster risk reduction, coping capacity during disaster, and post-disaster

response measures by the at-risk community with a focus of learning from the past

experience(s) and accordingly applying those lessons to handle future hazard events

(Birkmann et al. 2013).

Nat Hazards

123

The frameworks mentioned in the above discussion establish a paradigm where pre- and

post-disaster situations lie between two extremes, and the DROP model (Cutter et al.

2008b), the resilience index framework (FAO 2013), and the 4 Rs model (Forgette et al.

2008) take respective positions somewhere ‘in-between’ of the two poles. In addition,

resilience in the MOVE framework (Birkmann et al. Birkmann et al. 2013) only deals with

pre-disaster features and post-disaster response (not recovery). Thus, no model addresses

the full paradigm of all the existing scientifically accepted approaches on disaster resi-

lience. Therefore, we opt for the Access model, introduced first by Blaikie et al. (1994) and

further developed by Wisner et al. (2004) since it accommodates a wider spectrum of

resilience dynamics. We customized the Access model to apply it for better understanding

of socioeconomic resilience to perturbations and stresses (i.e., natural hazard impacts).

2.2 Access model

The Access model is developed by Blaikie et al. (1994) and upgraded further by Wisner

et al. (2004, 2012) and related to the pressure and release (PAR) model, which is a political

economy approach to address disaster impacts. The PAR model postulates that disaster risk

is formed by the interaction (known as the pressure point) between the progression of

vulnerability (root cause, dynamic pressure, and unsafe conditions) and hazard. However,

the PAR model does not provide a detailed analysis of a scenario at the pressure point. The

Access model deals with the details of what takes place at the pressure point between

catastrophic events and expected immediate, together with short- and long-term social

processes. Hence, this model presents how households’ resilience in a community is

affected by difference in access to economic or political resources (e.g., income/con-

sumption, employment, acquaintance with local elites such as community leaders and

people with political power) required maintaining a steady livelihood or normal state.

Resource accessibility is the key challenge for households to recover their livelihoods,

make themselves stable, increase their resilience to hazard shocks, and gain the capacity to

restitute livelihoods to the previous normal state.

We divided our customized Access model into five major phases in accordance with a

common logic used in a conventional disaster management cycle: pre-disaster normal state,

adaptive capacity, resistance, response and recovery, and post-disaster quasi-normal state

(Fig. 1). The pre-disaster normal state (also known as the pre-disaster steady state) is the

features indicating the ‘initial (original) state of well-being’ at time t with the livelihood of

households given exposure, sensitivity, and response capacity to natural hazards (e.g.,

cyclones, floods). The iterative features of livelihood are altogether suggested by repeated

cycles denoting livelihood decisions, and each on one box, arranged in the diagram behind

each other, implies cyclic decision-making during different time periods (at time t and

t ? 1, respectively). The sequence of adaptive capacity and resistance resembles the tra-

jectory that operates the transition between normal and disaster states. Pre- and post-

disaster states are differentiated by a threshold level, which consists of a simultaneous

decrease in both Access-profile and Access-qualification, where Access-profile is viewed

in terms of resource-access phenomenon such as the degree of access to sanitation, water

and electricity, structure of settlement, and non-land asset portfolio; while Access-quali-

fication implies socioeconomic welfare phenomena such as household consumption and

employment opportunity (Blaikie et al. 1994; Wisner et al. 2004). Crossing this threshold

invokes the transition to disaster at time t ? 1. Beyond the transition to the disaster phase,

the response and recovery functions by households commence, and thus, the post-disaster

quasi-normal state (also known as post-disaster steady state) is obtained, which is

Nat Hazards

123

temporary and inferior to the initial state of well-being. Successful adoption of necessary

disaster risk reduction actions by households may necessarily lead them to bounce back to

their pre-disaster normal state at t ? 1; otherwise, their livelihood is very likely to be

collapsed, and eventually, the households would encounter a new cycle of another disaster

at time t ? 1. Social relation and the structure of domination in this customized model lead

to social integration (e.g., social capital) and acquaintance with local elites, respectively.

Different components and subcomponents during time t and t ? 1 in Fig. 1 are assumed to

be interlinked.

Previous studies on socioeconomic vulnerability revealed mutual links among poverty,

exposure, sensitivity, response, and adaptive capacity. The core message from these links

is that the poor are more likely to be vulnerable (in compliance with the narrow and broad

spectral definition of vulnerability) and less likely to be prepared (Brouwer et al. 2007; Lei

et al. 2014). However, the mutual links that contribute to sketch the pre-disaster features to

post-disaster status have not been addressed yet. In this context, we inclined our attention

to the said pre–post issues in the current study. Therefore, we intended to investigate the

following three propositions in particular. First, a higher degree of exposure and sensitivity

associated with inadequate adaptive capacity is likely to expedite higher damage (propo-

sition 1). Second, households that suffer less damage (i.e., high resistance) possess better

Thre

shol

d

The trigger event (cyclone)

Structure of domination

Social relations

Exposure

Sensitivity Resistance Adaptive capacity Response

Poverty

Poverty-led unsafe condition

Change in Access Profile

Change in Access Qualification

Transition to disaster

Structure of domination

Social relations

Post-disaster quasi normal state (Success/failure to adopt actions for

disaster risk reduction)

Collapse of livelihood

Iteration of new disaster

time

= t

time

= t+

1

Pre-disaster normal state

Response and

recovery

Household livelihood

Fig. 1 Access model for assessing socioeconomic resilience toward natural hazard-led disasters [adaptedand customized from Blaikie et al. (1994); Wisner et al. (2004)]

Nat Hazards

123

absorptive capacity (i.e., high response capacity) (proposition 2). Lastly, resistance,

response, and absorptive capacities are the pivotal elements of recovery (proposition 3).

3 Materials and methods

3.1 Study area profile

The spatial focus of our case study is a tropical cyclone affected coastal community,

Koyra, which is a subdistrict of Khulna district, located in the southwestern coastal

Bangladesh (Fig. 2). This subdistrict is located at the southern side of Khulna, having an

area of about 1800 sq. km. The administrative setup of Koyra consists of seven union

parishads,1 72 mouzas2, and 131 villages (Banglapedia 2006). Bangladesh, a plain and

low-lying delta facing northern Indian Ocean, is ranked as one of the most vulnerable

countries to extreme meteorological events (e.g., cyclone) (Germanwatch 2014). Forming

an about 600-km coastline, the southern part of Bangladesh shares its boundary with the

Bay of Bengal. The coastal zone occupies about 30 % of Bangladesh and is a habitat for

one-third of the total population of the country. The different geophysical settings of the

coastal zone provide diversified employment opportunities for the coastal communities

whose residents are mostly poor and live in structurally weak settlements (BBS 2011;

PDO-ICZMP 2003). Within the span of the last five decades, this coastal zone has suffered

from at least 14 strong cyclones with storm surges, three of which (Cyclones Bhola in

1970, Gorky in 1991, and Sidr in 2007) were calamitous (Alam and Collins 2010).

Cyclones Bhola and Gorky were the most devastating tropical cyclones in the history of

high-speed wind disasters in Bangladesh (EM-DAT 2014).

Koyra comprises of flat land having natural ground slopes and is surrounded by the

Sundarbans, the largest mangrove forest and a UNESCO world heritage site. The geo-

graphical location of Koyra belongs to an immature deltaic slope, which is a habitat for

around 195 thousand people (BBS 2010). Aquaculture and traditional agricultural farming

(e.g., cropping) are the notable sources of livelihood here. People living near and along the

border of the Sundarbans are the poorest and depend on resources from this mangrove

forest for their livelihood, such as woodlot collection, honey and wax harvesting,

extraction of ‘nipa palm’ as fuel wood, and ecotourism (Harun-or-Rashid et al. 2009;

Sadath and Krott 2012). The concerned agency of the Bangladesh government (forest

department) reserves the right to authorize public access to this forest consisting of

reserved and non-reserved forest areas. Every year for a certain period, the agency does not

authorize public access even to the non-reserved part.

Within less than 2 years after super cyclone Sidr, the southwestern coastal belt was hit

again by a Category I (wind speed 119–153 km/h) tropical cyclone, Aila, with a wind

velocity of 120 km/h. The associated storm surges were three meters higher than that of the

normal tide. Most coastal districts (11 out of 19) were severely affected. The economic

damage due to this cyclone was about US$ 170 million, including 190 deaths, 7000

injuries, and more than 100,000 livestock deaths (UNDP 2010). The Ministry of Disaster

Management and Relief of the Bangladesh government along with local and international

NGOs conducted response and recovery activities in the post-Aila period. Food, cash,

potable drinking water, and essential medicines along with necessary non-food items were

1 Lowest tier of Local Government in Bangladesh.2 Clusters.

Nat Hazards

123

distributed for affected people as emergency relief supplies. The majority of the supplies

were distributed through government-operated schemes like Vulnerability Group Feeding

(VGF), Vulnerable Group Development (VGD), and Cash for Work (CFW).

3.2 Data collection

With a view to realizing the study objective, we designed a cross-sectional household

survey approach through a random experimental framework to make ‘pre versus post’ and

‘poor versus non-poor’ comparisons. The household survey was conducted 7 months (in

December 2009 and January 2010) after the holocaust made by Cyclone Aila in May 2009.

Fig. 2 Location of the study area [prepared with the data provided by the GIS unit of the Local Governmentand Engineering Department (LGED) of the Government of Peoples Republic of Bangladesh (2010)]

Nat Hazards

123

Data were collected from all seven unions of the Koyra subdistrict, and three villages were

randomly selected from each union (Fig. 2). The previous year of Cyclone Aila (i.e., 2008)

was treated as the base year for the ‘pre versus post’ comparison. Information on the base

year was collected during the household survey by applying retrospective recalling, which

is a widely used method in social-science research. We applied a standard difference-in-

difference estimation approach in this paper to compare cross-sectional welfare outcomes

(1) between the poor and non-poor and (2) before and after the cyclone event.

We designed an analysis plan with three stages. First, the connection between vulner-

ability- and resilience components (propositions 1 and 2) was assessed by applying linear

correlation, parametric, and non-parametric testing tools. Second, these propositions were

compared cross-sectionally between the poor and non-poor to understand whether they

would vary significantly and/or systematically.3 For the ‘pre versus post’ comparison

(proposition 3), a number of deterministic models were estimated. For models, a general

difference-in-difference approach was followed. We considered Ct,t?1 and At,t?1 as the

major threshold indicators for Access-qualification and Access-profile, respectively. Sub-

sequently, Xt,t?1, Yt,t?1, and Zt,t?1 were variable sets representing resistance, response, and

adaptive capacities, respectively (see details in Table 1). u was a vector of the unobserved

characteristics of households, having impact on the threshold indicators. e was treated as

idiosyncratic error. We controlled unobservable heterogeneity biases by assuming them as

time invariants. Hence, these were controlled with fixed baseline household characteristics

such as age, occupation, religion, education, and location. We adopted the following

general form (Eqs. 1 and 2) of difference-in-difference specification for the consumption-

and asset-profile-growth equations. We used consumption since the income of the

respondent households was found very volatile, especially in the post-cyclone period.

DCt;tþ1 ¼ ac þ bcXðcÞt;tþ1þ ccYðcÞt;tþ1

þ dcZðcÞt;tþ1þ uc þ eðcÞt;tþ1

ð1Þ

DAt;tþ1 ¼ aa þ baXðaÞt;tþ1þ caYðaÞt;tþ1

þ daZðaÞt;tþ1þ ua þ eðaÞt;tþ1

ð2Þ

where DCt,t?1 and DAt,t?1 denote consumption growth (i.e., the difference in a household’s

yearly consumption between the pre- and post-cyclone periods) and asset-profile growth

(i.e., difference between the pre- and post-cyclone periods in monetary value of all non-

land assets owned by the households for a year), respectively; a denotes constant; b, c, and

d are coefficients to be estimated for consumption and asset profile, accordingly. Thus, a

positive value of DCt,t?1 indicates a higher consumption level at the pre-cyclone normal

state (i.e., pre-disaster period), while a negative value denotes a higher consumption at the

post-cyclone quasi-normal state (i.e., post-disaster period). This same rule is also appli-

cable to DAt,t?1. Both consumption and asset profile are measured in 2009–2010 US

dollars.

The household survey using structured questionnaire was conducted for 420 households

(20 from each village and thus 60 from each union). The local university’s senior

undergraduate students along with local experts were deployed for this questionnaire

survey after intensive training through a weeklong workshop to ensure uniformity in the

surveying process. Due to the unavailability of household lists from local government

offices, the ‘random walk’ methodology (WHO 2011) was applied to choose the road-

direction from the central marketplace of the concerned localities (commonly known as

3 This implies the power of a repetitive-measures design. In this case, we divided entire sample into twogroups (poor and non-poor) where ‘systematically’ refers to the effect size (i.e., power) of the repetitivemeasure, which is shown by point-biserial (r). For a detailed explanation, see Field (2005).

Nat Hazards

123

Table 1 Components and associated indicators of vulnerability and resistance

Components Indicators Measurements Source forindicators

Sensitivity Sex ratio Female–male ratio in household Chambers andConway(1992)

Natural resourcedependency

Dependency of household on naturalsources (fisheries, agriculture) for theirlivelihood

Lee (2014)

Dependency ratio Number of children (0–14 years) andelderly (60? years) in household

Cutter et al.(2008b)

House type Material used for constructing the housebefore cyclone Aila

(a: mud; b: bamboo; c: wood; d: straw; e:dry nipa palm; f: concrete; g: tin/tally)

Location of cyclone center Distance to the nearest cyclone center fromhousehold’s location (km)

Exposure Distance from the erodedriver

Distance calculated using GPS coordinatesof household’s location

Brower et al.(2007)

Responsecapacity

Relief requirement, rapidityof reaching to relief andrehabilitation aid

Households required with emergency reliefas external aid (food, shelter, medicalsupport) after the cyclone

Forgette et al.(2008)

Time elapsed to reach emergency reliefs(days)

Households received housing materials asrehabilitation aid

Adaptivecapacity

Hazard identification andrecognition

Household participated in disasterpreparedness training before the cyclone

Forgette et al.(2008); Leiet al. (2014)Household’s understanding of early

warning message

Early warning received by the household

Literacy Schooling years of the household head Demurger andFournier(2011)

Microfinance Household borrowed money after thecyclone

Marincioniet al. (2013)

Social capital Living duration within the samecommunity

Ahsan andWarner(2014)

Safety net Household is a member of any GO/NGO-operated safety-net program

Acquaintance with localelitesa

Connection or affinity with local elites Pelling andHigh (2005)

Resistance Economic damage Value of financial damage (in US$) Forgette et al.(2008)Structural damage Settlement (house) damage (in %)

Physical damage Number of household members died orinjured due to the cyclone

a In this study, the concept of local elite refers to community leaders (e.g., teachers, chief of local mosquecommittee) and people with political power (e.g., village chairman, political leader)

Nat Hazards

123

Hut in Bengali), where every 20th household along the randomly chosen road was

approached for a face-to-face interview. A set of rules of thumb suggested by the United

Nations Statistical Division (UN 2008) was followed for this survey. The questionnaire

was structured through an iterative process where the first draft was prepared after seven

focus group discussions and additional discussions with local experts including local

government officials, NGO workers, priests (Imam for the Muslims and Purohit for the

Hindus), and teachers from schools and colleges. Having conducted two subsequent trial

runs in the study location, the questionnaire was finalized. The final version contained 32

main questions with one general section and two specific sections. The general section

consisted of ten main questions on basic socioeconomic and sociodemographic information

of households (income, consumption, asset portfolio, settlement conditions, utilities, and

sanitation). The specific sections consisted of 22 main questions with a set of recall-type

questions on the economic status and living standard in the pre-Aila period. Each survey

took around thirty minutes to complete. Household respondents were also enquired on their

physical and economic perturbations made by Aila and the pre- and post-strategies applied

to cope with the effects and impacts of this catastrophe. They were also asked about the

nature and extent of assistance offered by government and non-government agencies.

Over 62 % of the respondents in our sample could neither read nor write. About 80 % of

the sampled households were Muslim. More than 73 % were found to be living below the

upper poverty line4 before the cyclone. The households living below the poverty line were

significantly more likely to have illiterate heads, to have a significantly larger household

size, and to have a significantly higher dependency and female–male ratios. A substantial

portion of households under the poverty line were involved in informal sectors as day

laborers and were significantly less likely to have access to electricity and sanitation.

4 Results

This section consisting of four subsections presents the results of this study. The first subsection

enacts a brief discussion on empirical findings on mutual dynamics among poverty, the pre-

cyclone normal state, and adaptive capacity. The remaining subsections explicate empirical

findings associated with key mutual links of our interest, which are structured in Sect. 2.2.

Table 1 provides a summary of indicators used to quantify the vulnerability and resilience.

4.1 Poverty and adaptive capacity in the pre-cyclone normal state

Table 2 presents test results (Chi-squared, mean difference test) for the key indicators of

the pre-cyclone normal state (i.e., the pre-disaster period) and adaptation between the poor

and non-poor. The empirical findings suggest that sensitivity, exposure, and adaptive

capacity were more likely to be associated with poverty, which is consistent with the

results documented in relevant socioeconomic vulnerability literature. The poor were more

likely to reside in structurally weak settlements (non-concrete houses made of bamboo,

mud-platform, straw, dried nipa palm), near the exposed zones (i.e., close to eroded rivers),

and relatively distant locations from cyclone centers. These households possessed a higher

4 The poverty line was calculated in 2005 (accordingly adjusted for 2008–09) by applying the Cost of BasicNeed (CBN) consumption as a poverty threshold value, which was US$ 202/capita/year in 2008–09 (BBS2005, 2010, 2011). The CBN consumption consists of both food and non-food items required for main-taining a minimum living standard.

Nat Hazards

123

female–male ratios and were significantly and systematically more dependent on natural

resources for their livelihood. They were less likely to participate in disaster preparedness

training and to receive early warnings for any upcoming hazard. All these features pos-

tulate the unsafe living conditions of the poor. However, the poor were significantly

quicker than non-poor households in reaching emergency reliefs (response capacity).

Table 2 Poverty, pre-cyclone normal state, and adaptive capacity (N = 420)

Components Indicators Poora Non-poora

Test statistics (p value)(effect sized)

Sensitivity Households lived in strong (i.e.,concrete) settlements (%)

62 76 7.65b (p\ 0.006) (0.14d)

Female–male ratio in thehousehold

1.17 0.64 7.11c (p\ 0.000) (0.33d)

Dependency ratio 0.38 0.36 1.001c (p\ 0.317) (0.05d)

Distance from the nearest cyclonecenter (km)

3.48 2.99 3.53c (p\ 0.000) (0.17d)

Households depend on naturalsources (e.g., fishery, forestry,and agriculture) for theirlivelihoods (%)

84.75 43.20 75.81b (p\ 0.000) (0.45d)

Exposure Distance from the eroded river(km)

2.2 3.94 15.26c (p\ 0.000) (0.60d)

Responsecapacity

Households required withemergency relief as external aid(food, shelter, medical support)(%)

82 62 20.07b (p\ 0.000) (0.22d)

Time elapsed to reach emergencyreliefs (days)

2.4 3.31 10.13c (p\ 0.000) (0.44d)

Households received housingmaterials as rehabilitation aid(%)

70.85 60.8 4.06b (p\ 0.044) (0.10d)

Adaptive capacity Households participated inpreparedness training beforeCyclone Aila (%)

12.88 91.2 233.20b (p\ 0.000) (0.75d)

Early warning received by thehouseholds (%)

8.14 84.4 241.45b (p\ 0.000) (0.76d)

Schooling years of the householdhead

3.92 5.94 6.17c (p\ 0.000) (0.29d)

Households borrowed credit aftercyclone (%)

89.15 33.6 136.27b (p\ 0.000) (0.57d)

Living duration within samecommunity (years)

37.72 42.12 2.86c (p\ 0.000) (0.14d)

Member of any GO/NGO-operatedsafety-net program (%)

88.47 20 189.48b (p\ 0.000) (0.67d)

Households’ acquaintance withlocal elites (%)

23.3 79.8 124.31b (p\ 0.036) (0.58d)

a Households below and above of the poverty threshold before Cyclone Ailab Chi-squared statisticsc z-statistics for mean difference testd Point-biserial (r) where 0.2, 0.5, and 0.8 refer to small but not trivial, medium, and high effect size,respectively (Field 2005)

Nat Hazards

123

Being from a poor household community also significantly increased the likelihood of

availing emergency relief supplies (excluding food) (all reliefs: z = 3.09, p\ 0.003; food:

z = 0.75, p\ 0.454).

4.2 Nexus among exposure, sensitivity, adaptive capacity, and resistance

Proposition 1 A higher degree of exposure and sensitivity associated with inadequate

adaptive capacity is likely to expedite higher level of damage.

As anticipated, financial, settlement, and physical damages were directly and signifi-

cantly correlated with sensitivity and exposure. On average, the households residing in

weak settlements (i.e., non-concrete houses) suffered significantly higher levels of damage

than those residing in strong settlements (i.e., concrete houses) (Table 3). At the same time,

the former group of households were significantly and systematically more likely to incur

fatality and/or physical injury and higher financial damage than those of the latter group

(Table 3). The poor households (of the pre-cyclone normal state) incurred significantly

higher relative financial damage than the non-poor group due to the cyclone-led disaster

event (z = 6.58, p\ 0.001, effect size = 0.31). Although no statistically significant and

systematic relationship was found in the number of dependent members between the poor

and non-poor households, the poor were significantly more likely to experience a higher

number of fatalities or physical injuries (z = 7.37, p\ 0.000, effect size = 0.34). This

might be due to their settlement locations since the proximity to the exposed zones (i.e.,

near eroded rivers) exhibited a statistically significant inverse correlation with physical,

financial, and settlement damage. The households living farther from the exposed zones

Table 3 Relation between sensitivity and resistance (N = 420)

Financialdamagea,e (US$)

Settlementdamage (%)

Physical damage (nos. ofinjury or death)

Strong settlement (i.e., concrete houses) 155 58.92 0.67

Weak settlement (i.e., houses made bymud, bamboo, and straw)

182 66.08 0.82

Mean difference test statisticsb (p value)(effect sizec)

21.45(p\ 0.000)(0.73c)

3.91(p\ 0.001)(0.19c)

1.93 (p\ 0.05) (0.10c)

Near to cyclone center (less than 2 km) 151.83 48.32 0.45

Away from cyclone center (more than2 km)

167.18 68.58 0.86

Mean difference test statisticsb (p value)(effect sizec)

5.21 (p\ 0.001)(0.25c)

13.07p\ 0.000)(0.54c)

5.46 (p\ 0.000) (0.26c)

Number of dependent members 0.07d (p\ 0.138)

Distance to the nearest cyclone center(km)

0.03d (p\ 0.573)

a Five observations containing outlier values for financial damage were excluded from datasetb z-statisticsc Point-biserial (r) for effect sized Pearson’s correlation coefficiente Calculated for all concerned non-land assets

Nat Hazards

123

were likely to incur significantly lower physical damage (i.e., death or injury) (r = -0.22,

p\ 0.000). The degree of financial damage (r = -0.26, p\ 0.001) was also significantly

lower for the households residing farther from the exposed zones. In the same way, the

association between the degree of settlement damage and the distance from the exposed

zones showed a significantly inverse relationship (r = -0.49, p\ 0.001), suggesting that

households located away from an exposed zone were likely to experience significantly

lower structural damage for their settlements.

Participation of households in disaster preparedness training exhibited a significant

negative correlation with financial (r = -0.24, p\ 0.000), settlement (r = -0.60,

p\ 0.000), and physical (r = -0.29, p\ 0.000) damage, respectively. Furthermore, a

statistically significant inverse relationship was obtained between the likelihood of phys-

ical injury and the failure to seek refuge in a cyclone center (z = 11.69, p\ 0.000, effect

size = 0.50). This suggests that the households having participated in preparedness

training were able to avoid higher financial, settlement, and physical damage; however,

when they failed to seek refuge in a cyclone center due to space insufficiency, they were

more likely to experience a higher number of death or injury.

Around 73 % households that incurred financial, settlement, or physical damage bor-

rowed credit from microfinance organizations. The lender households were acquainted

with local NGO officials, and at least 57 % of them took credit even during the pre-disaster

period. Local elites played key pseudo-roles in the decision-making process of NGOs and

local governments. A statistically significant difference was observed between the likeli-

hood of borrowing credit and the degree of financial (z = 4.72, p\ 0.000, effect

size = 0.23), settlement (z = 10.26, p\ 0.000, effect size = 0.49), and physical

(z = 5.09, p\ 0.001, effect size = 0.24) damage, respectively. In addition, the likelihood

of borrowing credit varied significantly and systematically in terms of the pre-cyclone

consumption level (z = 10.83, p\ 0.000, effect size = 0.47) and the (non-land) asset

profile (z = 5.50, p\ 0.000, effect size = 0.26) of households.

4.3 Nexus between resistance and response capacity

Proposition 2 Households that suffer less damage possess better absorptive capacity.

We examined response capacity by applying two yardsticks: the necessity and rapidity

to reach emergency relief supplies (i.e., external aid). The lower or zero necessity of

emergency relief during the post-catastrophe indicates a higher internal response capacity.

Relief dependency during an emergency does not necessarily demonstrate deficiency in

response to capacity as long as such relief (i.e., aid) can be reached within a reasonable

time frame (i.e., rapidity). About 76 % of the respondents required emergency relief in any

form to cope with the immediate devastation of Cyclone Aila. As anticipated, the

households that suffered a significantly less degrees of financial, settlement, and physical

damage were able to respond to the catastrophe through a mobilization of their internal

assets or resources. Such households mostly belonged to the non-poor group (see Table 2).

Due to sociopolitical as well as economical biases, together with damaged physical

infrastructure (e.g., roads and river networks), the distribution of emergency relief supplies

varied within and between administrative boundaries (e.g., unions and villages). Areas with

damaged road networks deterred emergency relief efforts from reaching to affected

households. Controlling for the proximity to the exposed zones and cyclone centers, the

time needed to reach emergency relief supplies showed a significant negative correlation

with financial, settlement, and physical damage (financial: r = -0.19, p\ 0.004;

Nat Hazards

123

settlement: r = -0.29, p\ 0.001; physical: r = -0.17, p\ 0.000). A similar trend was

obtained for the households that received housing materials as rehabilitation aid. However,

no significant difference was observed in receiving rehabilitation aid between the house-

hold groups that suffered proportionately more (70 % and above) and less (below 70 %)

settlement damage (z = 1.36, p\ 0.177). Involvement in any program, either operated by

local government or NGOs, escalated the likelihood of receiving rehabilitation aid (Chi-

squared = 3.16, p\ 0.078). These empirical findings suggest that a mutual link between

resistance and response is not convincing enough to conclude that a low level of resistance

does not cause a low level of response capacity.

4.4 Nexus among resistance, response capacity, absorptive capacity,and recovery

Proposition 3 Resistance, response, and absorptive capacities are the pivotal elements

of recovery.

This subsection deals with the deterministic association of resistance, response, and

absorptive capacities with recovery from the perspective of Access-qualification and

Access-profile thresholds. We first made a contrast of Access-qualification and Access-

profile threshold conditions between the pre-cyclone normal state and the post-cyclone

quasi-normal state. We then identified the major drivers for these thresholds through

regression results.

4.4.1 Access-qualification and Access-profile thresholds

For this study, we opted for a set of household-level socioeconomic features as determi-

nants of the Access-qualification and Access-profile thresholds. These determinants are

likely to vary in accordance with locational contexts. In this study, we considered con-

sumption, poverty status, and employment status as determinants of the Access-qualifi-

cation threshold. Simultaneously, for the Access-profile threshold, we considered multiple

factors of settlement structure, land possession, access to pure drinking water, sanitation,

and electricity. Table 4 compares their pre- and post-cyclone situations.

It is evident that Cyclone Aila had detrimental effects on the capability of the house-

holds in terms of poverty, total, and per capita consumption levels. The proportion of the

households below the poverty line escalated from 70 to 79 % after the catastrophic event.

Interestingly, the poor suffered a significantly and systematically lower dispersion in yearly

average consumption (US$ 48.09) than the non-poor (US$ 328.54) in the post-cyclone

period, compared with the pre-cyclone period (z = 5.76, p\ 0.000, effect size = 0.27).

Similarly in per capita consumption, the poor experienced a significantly and systemati-

cally lower dispersion (US$ 11.06) than the non-poor (US$ 86.16) between the same

periods (z = 8.09, p\ 0.000, effect size = 0.37). No significant difference was observed

in consumption dispersion between the households whose heads became unemployed after

the cyclone and those whose heads maintained their employment (z = 1.05, p\ 0.294,

effect size = 0.05).

The percentage of the households that possessed a piece of land (either self-owned or

leased) for income generation decreased significantly and systematically after the cyclone.

A very small improvement was noted in terms of settlement conditions; less than 2 % of

the weak settlements were reconstructed with rehabilitation materials in the post-cyclone

period. Considering settlement resilience, a significant and systematic difference was

Nat Hazards

123

observed between the poor and non-poor households (Chi-squared = 70.51, p\ 0.001,

effect size = 0.17). However, the households exhibiting a higher settlement resilience

significantly and systematically suffered a lower dispersion in yearly average consumption

(US$ 31.38) compared to those whose settlement condition remained weak in the post-

event period in contrast to the pre-event period (z = 5.99, p\ 0.000, effect size = 0.28).

The households’ accessibility to sanitation, pure drinking water, and electricity diminished

significantly and systematically after the catastrophe. The degrees of access to sanitation

and clean water sources were associated significantly and systematically, suggesting that

the households that suffered less access to clean water sources were more likely to have

poor access to sanitation (Chi-squared = 39.44, p\ 0.000, effect size = 0.12). The

households with insufficient access to sanitation experienced significantly and systemati-

cally higher settlement damage (z = 28.53, p\ 0.000, effect size = 0.82). Although no

significant association was observed between poverty status and access to pure drinking

water sources (Chi-squared = 1.63, p\ 0.203, effect size = 0.03), the poor were more

likely to suffer significantly and systematically higher settlement damage due to the

cyclone event (Chi-squared = 111.47, p\ 0.000, effect size = 0.16). The households

acquainted with local NGOs were significantly and systematically more likely to overcome

their limited accessibility to clean water in the post-cyclone period (Chi-square = 17.65,

p\ 0.000, effect size = 0.43).

4.4.2 Major drivers behind change

This section deals with regression results. Both Eqs. (1) and (2) had the same set of

regressors; therefore, the concerned estimations might encounter endogeneity problem.

Hence, the seemingly unrelated regression (SUR) method was applied to estimate both the

equations, where the correlation between residuals of these equations was found very weak

and also not significant (correlation: 0.064; Breusch–Pagan test of independence:

Table 4 Access-qualification and Access-profile thresholds before and after Cyclone Aila (N = 420)

Determinants Before(2008)

After(2009)

Test statistics (p value) (effectsize)

Access-qualification

Households below poverty line (%) 70.24 79.29 160.49a (p\ 0.000) (0.10c)

Yearly average household consumption(US$)

887.00 755.45 5.70b (p\ 0.000) (0.26c)

Per capita consumption (US$) 185.52 152.12 7.33b (p\ 0.000) (0.39c)

Unemployment (%) 12.86 45.00 75.74a (p\ 0.000) (0.35c)

Access-profile

Households possessed either self-owned orleased land for income generation (%)

83.19 61.67 16.33a(p\ 0.001) (0.42c)

Weak settlements (%) 65.24 63.81 10.45a (p\ 0.001) (0.12c)

Access to sanitation (%) 71.67 36.90 19.97a (p\ 0.000) (0.35c)

Access to pure drinking-water source (%) 77.38 25.71 5.06a (p\ 0.024) (0.52c)

Access to electricity (%) 25.71 21.19 297.31a (p\ 0.000) (0.15c)

a Chi-square statisticsb Z-statistics for mean difference testc Point-biserial (r) for effect size

Nat Hazards

123

v2(1) = 1.718, p\ 0.192). Furthermore, the cross-equation constraint was also found not

significant (v2(1) = 0.42, p\ 0.519). Thus, we ran SUR without imposing any restriction,

and the obtained results are presented in Table 5. In the following part, we first focused on

the relationship between consumption growth (i.e., difference in households’ total con-

sumption between the pre- and post-cyclone states) and a set of indicators representing

resistance, response, and adaptive capacity together with some fixed characteristics. We

then focused on the relationship between asset-profile growth (i.e., difference in non-land

asset values between the pre- and post-cyclone states) and the same set of indicators.

Out of the resistance indicators, settlement damage (i.e., the structural damage of

houses) exhibited a statistically significant positive5 relationship with both consumption

growth and (non-land) asset-profile growth, indicating households that incurred settlement

damage suffered significantly lower consumption and asset profile in the post-cyclone

quasi-normal state, compared with the pre-cyclone normal state. No significant difference

was observed between consumption growth and asset-profile growth in the case where

either a male or female household member(s) suffered physical injury or death. The mean

coefficient of financial damage was significantly different than zero for consumption

growth with a positive sign indicating lower consumption during the post-cyclone period

for the households. For response capacity, the households that delayed reaching emergency

relief experienced a significantly lower consumption in post-cyclone situation on average,

and other factors held constant. However, no significant difference was observed between

response time and asset-profile growth. Among the set of adaptive capacity components,

the associated coefficients of preparedness and acquaintance with local elites suggested

that the households that participated in preparedness training and were acquainted with

local elites could avoid significantly lower consumption and asset profile aftermath the

cyclone event. Interestingly, the households that took credit could maintain a significantly

higher level of asset profile in the post-cyclone period. The coefficients of the other

components of adaptive capacity (safety-net membership and social capital) were not

significantly different than zero. Having conducted the propensity score matching6 to

estimate difference in difference for consumption growth between the poor and non-poor

households, we observed that the poor could consume significantly more (by US$ 38 on

yearly average) during the post-cyclone period than the non-poor. However, we did not

find similar evidence for asset-profile growth.

For the fixed initial household characteristics, we obtained some interesting results. The

coefficients of the (non-land) asset indicators (ownership of cattle and a mobile phone)

significantly influenced the households’ consumption and asset-profile growth. While cattle

ownership significantly contributed to maintaining a higher consumption level (i.e., less or

no consumption shock) for the households, mobile phone ownership indulged them to

maintain a lower consumption level (i.e., higher consumption shock) after the cyclone. The

reason behind these opposite scenarios is likely to be the disproportionate maintenance

cost. The households with relatively better educated heads suffered significantly less

consumption and asset profile aftermath the cyclone. Again, the households with more

dependent members could ensure a significantly higher asset profile in the post-cyclone

5 As mentioned in Sect. 3.2, a positive value of consumption growth indicates a higher consumption level inthe pre-cyclone period (normal state), while a negative value denotes higher consumption in the post-cyclone period (quasi-normal state). This same rule is also applicable to asset-profile growth.6 Propensity scores in regions of common support were estimated where the average treatment effect ontreated (ATT) estimation using the radius method (100 replications) provided a value of -37.968 with at-statistic of 2.525 and a bias-corrected 95 % confidence interval of -55.8526 to -15.0008.

Nat Hazards

123

Table 5 Seemingly unrelated regression (SUR) results for drivers of household consumption- and asset-profile growth

Variable name Variable description Eq. (1)DCt;tþ1

Eq. (2)DAt;tþ1

Coefficients (SE)

Indicators of resistance (Xt,t?1)

Financial damagea Monetary value of total damagefor non-land asset (in US$)

0.023* (0.011) 0.329 (0.578)

Settlement damage House structure damage (%) 0.539***(0.245)

3.508**(2.804)

Injured or dead (female) Number of female householdmembers injured or dead

-1.556 (4.981) 11.6 (17.1)

Injured or dead (male) Number of male householdmembers injured or dead

-0.541 (4.497) -55.65(154.2)

Response capacity (Yt,t?1)

Response time Time elapsed to reach emergencyrelief (in days)

9.328***(3.136)

15.8 (10.8)

Adaptive capacity (Zt,t?1)

Preparedness Household’s participation inemergency preparednesstraining before cycloneAila = 1, otherwise = 0

-43.78***(6.035)

-147.80**(27.8)

Acquaintance with local elites Household’s connection withlocal elites = 1, otherwise = 0

-7.720* (6.05) -184.9**(46.3)

Credit Household loaned money afterthe cyclone = 1,otherwise = 0

-6.304 (7.186) -55.3**(46.9)

Safety-net member Household is a member of anyGO/NGO-operated safety-netprogram = 1, otherwise = 0

7.741 (7.560) 179.0 (260.3)

Social capital Living duration of householdwith current community (years)

-0.0424(0.332)

0.724 (11.44)

Fixed initial household characteristics at reference level (u)

Religion Muslim = 1, otherwise = 0 1.206 (6.676) 229.9 (664.1)

Age Age of household head (years) -0.0687(0.377)

10.16 (12.97)

Education Schooling of household head(years)

1.798**(0.910)

109.3***(31.29)

Dependents Number of dependentmember(s) (age below 5 andover 60 years)

-3.279 (4.360) -36.7***(12.29)

Exposed zone (river) proximity Location of household within twokilometers from erodedriver = 1, otherwise = 0

-3.068 (2.436) -166.8**(83.44)

Self-employedb Household head is self-employed = 1, otherwise = 0

-2.111 (8.028) -62.99 (276.5)

Day laborerb Household head is daylaborer = 1, otherwise = 0

-4.791 (6.896) -19.81(237.5)

Cattle (non-land asset indicator 1) Household owned cattle = 1,otherwise = 0

-19.80*(10.70)

-710.8*(368.5)

Nat Hazards

123

period although no such findings were obtained for consumption growth. Regardless of

ages of dependent members during evacuation, they usually carried light assets (jewelry,

radios, or precious metallic belongings) as well as cattle, which relegated them from

suffering an asset shock aftermath the cyclone. In addition, households that lived near to

eroded river (i.e., exposed zone) were likely to maintain significantly higher asset profile in

post-cyclone period only, which was possibly due to their successful access with relief

supplies and rehabilitation aid. Religion, age, self-employment, and day labor had sig-

nificant influence on neither consumption nor asset-profile growth.

Table 6 presents results obtained from Models 1 and 2, postulating a similar difference-

in-difference estimation approaches conducted in Eqs. (1) and (2), where unemployment

and land loss are used as dependent variables instead of consumption and asset profile;

these two variables are assumed to be the major drivers of changes in Access-qualification

and Access-profile, respectively. The dependent variable in Model 1 in Table 6 is unem-

ployment, coded as 0 if the household head was employed before and after the cyclone, and

as 1 if he had been employed before the cyclone but became unemployed after the event.

The dependent variable in Model 2 is land loss, coded as 0 if the household suffered no loss

of land before and after the cyclone, and as 1 if and only if it incurred loss of land

aftermath the cyclone.

Similar to the findings from the consumption growth model (Table 5), one of the

outcomes of Model 1 in Table 6 showed a positive relationship between the loss of (non-

land) assets (fishery and livestock) and the likelihood of becoming unemployed in the post-

cyclone period. Also similar to both consumption and asset-profile growth models, the

households that maintained acquaintance with local elites were less likely to be unem-

ployed after the catastrophe. Again, consistent with the consumption growth model, better

educated household heads were more likely to lose job after the catastrophe, which is

supported by the fact that day laborers were less likely to be unemployed in the same

period because they were more flexible to switch over different employment opportunities

than self-employed and wage-paid individuals. For instance, day laborers working in

shrimp farms (known as Gher) can switch to honey collection or agricultural (e.g., crop)

contract workers, while self-employed and wage-paid individuals are tied to a specific type

of employment. Unlike the consumption growth model, the households with unemployed

heads were more likely to borrow money in the post-cyclone situation.

Table 5 continued

Variable name Variable description Eq. (1)DCt;tþ1

Eq. (2)DAt;tþ1

Coefficients (SE)

Mobile phone (non-land assetindicator 2)

Household head owned a mobilephone = 1, otherwise = 0

10.71* (5.652) 198.3* (174.3)

Constant 109.8***(33.38)

118** (96.0)

Number of observations (N) 415 415

R2 0.308 0.231

Standard errors in parentheses

*** p\ 0.01; ** p\ 0.05; * p\ 0.1a Five observations containing outlier values for financial damage were excluded from datasetb Reference level category is wage-paid labor

Nat Hazards

123

Table 6 Major drivers behind change in unemployment and land loss

Variable name Variable description Model 1Unemploymenta

Model 2Land lossb,d

Coefficients (SE)

Indicators of resistance (Xt,t?1)

Fishery Loss of fishery = 1,otherwise = 0

2.683*** (0.686) 1.277*** (0.358)

Livestock Loss of livestock = 1,otherwise = 0

2.655*** (0.683) 0.259 (0.349)

Settlement damage House structure damage (%) – -0.0134 (0.0113)

Injured or dead Number of householdmember(s) injured or dead

0.0205 (0.362) 2.106*** (0.347)

Adaptive capacity (Zt,t?1)

Social capital Living duration of householdwith current community(years)

0.0400 (0.0182) –

Acquaintance withlocal elites

Household’s connection withlocal elites = 1,otherwise = 0

-0.509** (0.323) 0.0170 (0.0111)

Credit Household loaned moneyafter the cyclone = 1,otherwise = 0

1.536*** (0.584) 0.364* (0.268)

Safety-net member Household is a member ofany GO/NGO-operatedsafety-net program = 1,otherwise = 0

-0.863 (0.923) 0.681 (0.864)

Fixed initial household characteristics at reference level (u)

Religion Muslim = 1, otherwise = 0 0.276 (0.646) 0.576 (0.381)

Education Schooling of household-head(years)

0.197* (0.111) -0.0553 (0.0798)

Exposed zone (river)proximity

Location of household within2 km from erodedriver = 1, otherwise = 0

-0.889* (0.478) 1.638*** (0.386)

Day laborerc Household-head is daylaborer = 1, otherwise = 0

-4.614*** (1.386) 0.263 (0.433)

Constant -5.575*** (1.609) -2.012** (0.901)

Model fit statistics

Number ofobservations (N)

189 259

Percentage correctlyclassified

87.59 73.35

Pseudo R-squared 0.6075 0.3755

Likelihood-ratioChi-square

140.50, df = 11 162.39, df = 11

*** p\ 0.01; ** p\ 0.05; * p\ 0.1a 1 = employed before cyclone, unemployed after; 0 = employed both before and after cycloneb 1 = loss of land (owned or leased) for cultivation after cyclone; 0 = otherwisec Reference level category is wage-paid labord Piece of land either self-owned or leased by the household for purpose of income generation

Nat Hazards

123

Model 2 in Table 6 shows the association between the loss of land and the same set of

indicators used in Model 1, where the only exception is that only settlement damage and

social capital were not included in Models 1 and 2, respectively. The households that

suffered land loss were also more likely to lose fishery since small fish farms (Gher) in the

study location were generally formed by using self-owned or leased land. Unlike in the

consumption and asset-profile growth models, the households that experienced land loss

were more likely to suffer physical injury and/or death of family members. In this context,

the location of the households might be an important factor since the results suggested a

significantly positive association between the loss of land and the proximity of household

to the eroded rivers (within 2 km). The households affected by land loss were significantly

more inclined to use loans after the cyclone to cope with consumption shock.

5 Discussion

The poverty–vulnerability dynamics may be differently perceived on the basis of the

definitional spectrum of vulnerability. Applying the narrow definitional spectrum (i.e.,

vulnerability is the degree of susceptibility/sensitivity to an external shock), we found

convincing evidence in favor of the hypothesis that the poor were more susceptible to

natural hazards such as tropical cyclones than the non-poor since the poor resided in weak

settlements, located relatively farther away from the nearest cyclone centers, lived near to

the exposed zones, and depended more on natural resources for their livelihoods. With the

wide definitional spectrum incorporating exposure, sensitivity, and response capacity as the

core components of vulnerability, the poverty–vulnerability dynamics exhibited rather

weak evidence. Despite the fact that the poor households were significantly more exposed

to risk posed by tropical cyclones, since most of them lived in the exposed areas (near the

eroded rivers), their response capacity to post-cyclone situations by reaching emergency

relief was significantly quicker than the non-poor. The poor households could avail all

aftershock emergency relief supplies except food, whose distribution was substantially

influenced by local elites. Acquaintance with officials from local government offices and/or

NGOs helped them to receive post-cyclone rehabilitation aid and subsequently restore

settlements along with necessary support (e.g., access to clean water); however, the

decision-making process of these institutions was influenced substantially by local elites.

However, no conclusive evidence was obtained to suggest that local elites were likely to

influence the distribution process of post-cyclone relief supplies, yet affinity with them

eventually helped the poor to become employed (e.g., in informal wage-paid jobs). Thus,

the response capacity of the poor households might be distorted by elite influence;

nonetheless, such distortion did not invite any systematic discrimination against the poor.

Depending on the definitional spectrums, the poverty–resilience dynamics also differ in

the same manner as poverty–vulnerability dynamics. From the perspective of the outcome-

oriented definition, our empirical results exhibited that poor households are more resilient

than non-poor because the poor were more able to restore the pre-cyclone normal state

from the post-cyclone quasi-normal state due to the following reasons: (1) the poor

maintained a relatively higher consumption level aftermath the cyclone event; (2) day

laborers, who were typically belonged to the poorer segment of society, were likely to

maintain their employment in the post-cyclone; (3) the poor households were significantly

more likely to access to credit and rehabilitation aid than the non-poor; and (4) by utilizing

Nat Hazards

123

these types of support, the poor were likely to construct stronger settlements (i.e., houses)

in the post-cyclone quasi-normal state.

In the realm of the process-oriented definition, the positive association with the pov-

erty–resilience dynamics seemed to be slightly weaker due to obvious distinctions between

the poor and non-poor households on ‘hazard identification and recognition,’ an indicator

of adaptive capacity. The poor households were found less prepared in terms of partici-

pating in preparedness training and receiving early warnings. We observed a significantly

negative impact of no preparedness training on the degree of financial, settlement, and

physical damage. This finding demonstrates a substantial (indirect) negative effect on

socioeconomic resilience.

Regardless of the definitional spectrums, our empirical results did not produce any

conclusive evidence in favor of the flip-side hypothesis for vulnerability (i.e., vulnerability

is the flip side of resilience). In light of the narrow definitional spectrum, vulnerability and

resilience seem to possess a substantial portion in common. Despite the fact that sensitivity

unambiguously contributed to high financial, settlement, and physical damage; it was not

necessarily manifested through lower resilience. The empirical results postulated that both

financial and settlement damages contributed to a significantly lower level of consumption

in the post-cyclone quasi-normal state (which is also true for asset profile in some cases). In

addition, the households that incurred loss of (non-land) assets were also more likely to

become unemployed due to losing equipment related to their occupations (e.g., fishing gear

or boats). However, the poor households maintained a significantly higher consumption

level (ATT = -37.968; t = 3.487), suggesting that they could avoid post-cyclone con-

sumption shock. These facts indicated the evidence that the poor households learnt lessons

from previous experiences and accordingly adopted necessary preventive actions to avoid

any future loss.

Evidence supporting the flip-side hypothesis of vulnerability lacks credibility further

when the definitional spectrum of vulnerability is widened. We observed an interesting

impact of cyclone exposure on post-cyclone employment status and consumption growth.

From one side, the households living far from the exposed zones were less likely to be

unemployed after the cyclone event. From the other side, the households living close to the

exposed zones could maintain a significantly higher level of asset profile after the cyclone.

These findings resemble to Sapountzaki’s (2012) axiom on the vulnerability–resilience

dynamics, where resilience is structured as a process of vulnerability re-arrangement and a

function of unevenly distributed opportunities among communities.

6 Concluding remarks and policy recommendations

Throughout this study, we investigated and explored mutual links among poverty, vul-

nerability, and resilience. The main objective of this study was to enrich our understanding

of the dynamics underpinning poverty, vulnerability, and resilience with a view to bridging

the existing knowledge gap on resilience diversity among households. In alignment with

the findings of existing literature on disaster risk domain, the empirical findings from our

study suggest that tropical cyclones significantly exacerbated adverse immediate effects as

well as medium- and long-term impacts on coastal people’s lives and livelihoods in terms

of consumption, employment, and access to basic utilities like clean water and sanitation.

An established economic theory on consumption postulates that consumption is a function

of income (Friedman 1957; Keynes 1936). Hence, based on the empirical findings on

Nat Hazards

123

consumption growth (Table 5) in our study, we can conclude that the coastal communities

were very likely to suffer an income shock aftermath the cyclone, which was reflected

through their consumption growth. The loss of (non-land) assets used for income gener-

ation, human capital shock, and proximity to the exposed zones were the pivotal factors to

explain the resilience diversity among households. Despite the fact that the poor were more

vulnerable to hazard shocks and severely affected by higher financial, settlement, and

physical damage, they showed a comparatively better ability to respond to, cope with, and

recover from shock than did the non-poor. These findings exhibit that increasing risk from

tropical cyclones is likely to affect the income opportunity and living standards of coastal

residents. However, we did not find any evidence that the incidence of these adverse

impacts is likely to be disproportionately borne by poor households.

Four key policy recommendations can be made based on the findings of this study. First,

the current level of social safety-net programs operated by the government and/or non-

government organizations (NGOs) does not appear to be optimally effective for the poor to

get rid of the poverty trap. Thus, the scope of existing social safety-nets needs to be

expanded to make people less dependent on natural resources for their livelihood. At the

same time, soft credit schemes with minimal interest rates for the haves-not should be

enhanced, especially for the frictional-unemployed and self-employed ones to help them to

avoid being unemployed for an indefinite period of time. Second, the existing hazard

preparedness training programs appear to systematically exclude poor households.

Therefore, the scope and effectiveness of the preparedness programs should be improved

by reaching out to poor households, enhancing the current capacity along with the asso-

ciated utilities in cyclone centers, arranging evacuation drills at least once a year, and

providing necessary logistic supports to smoothen evacuation process for households with

elderly members and children living far from their nearest cyclone centers. Thirdly, the

means of the early warning systems in the study area are very limited. However, mobile

phones are commonly owned by a good number of households in coastal communities.

Thus, voice messages in the local dialect might be effective if disseminated by the gov-

ernment through mobile phone operators to the coastal areas prior to upcoming hazards.

Simultaneously, loud speakers in mosques can also be used for this purpose. Finally, the

post-cyclone emergency relief and rehabilitation programs seem to be well focused.

However, the insufficiency of relief supplies relative to the demand for them appears to

exasperate competition, thereby making room for local social elites to influence the dis-

tribution process. An effective way of eliminating such influence could be rational pre-

estimation of actual demand for relief supplies and allocation in addition to strict moni-

toring of their distribution.

Acknowledgments The data collection was financially supported by the Executive Committee ofWageningen University, the Netherlands. We are particularly grateful for the support provided by Md. FirozAhmed and Rezwanul Haque of Economics Discipline of Khulna University, Bangladesh during datacollection. We thank Nahid Morshed for his help in map preparation. We also thank the anonymousreviewers for their valuable comments and suggestions to improve this article. The usual disclaimer applies.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Inter-national License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons license, and indicate if changes were made.

Nat Hazards

123

References

Adger WN (1999) Social vulnerability to climate change and extremes in coastal Vietnam. World Dev27:249–269

Adger WN (2006) Vulnerability. Glob Environ Change 16:268–281. doi:10.1016/j.gloenvcha.2006.02.006Adger WN, Brown K, Nelson DR et al (2011) Resilience implications of policy responses to climate change.

Wiley Interdiscip Rev: Clim Change 2:757–766. doi:10.1002/wcc.133Ahsan NM (2010) Climate change and socioeconomic vulnerability: experiences and lessons from south-

western coastal Bangladesh. Wageningen University LibraryAhsan MN (2014) Effects of livelihood strategies on mangrove-forest resource: do the consumption

behaviour of households jeopardise the forest resource base? Manag Environ Qual: Int J 25:696–711Ahsan MN, Warner J (2014) The socioeconomic vulnerability index: a pragmatic approach for assessing

climate change led risks—a case study in the south-western coastal Bangladesh. Int J Disaster RiskReduct 8:32–49. doi:10.1016/j.ijdrr.2013.12.009

Akter S, Mallick B (2013) The poverty–vulnerability–resilience nexus: evidence from Bangladesh. EcolEcon 96:114–124. doi:10.1016/j.ecolecon.2013.10.008

Alam E, Collins AE (2010) Cyclone disaster vulnerability and response experiences in coastal Bangladesh.Disasters 34:931–954. doi:10.1111/j.1467-7717.2010.01176.x

Banglapedia (2006) Koyra Upazila. National Encyclopedia of Bangladesh. Asiatic Society. Dhaka http://en.banglapedia.org/index.php?title=Koyra_Upazila. Accessed 19 March 2012

BBS (Bangladesh Bureau of Statistics) (2005) Household Income and Expenditure Survey (HIES) 2005.Government of the People’s Republic of Bangladesh, Dhaka

BBS (Bangladesh Bureau of Statistics) (2010) Household Income Expenditure Survey (HIES) 2010. Ban-gladesh Bureau of statistics; ministry of planning, Government of the People’s Republic of Bangla-desh. http://www.bbs.gov.bd/WebTestApplication/userfiles/Image/HIES-10/Chapter-06.pdf. Accessed22 Jan 2014

BBS (Bangladesh Bureau of Statistics) (2011) Population and Housing Census (Preliminary results, July2011). Bangladesh bureau of statistics; ministry of planning, Government of Peoples’ Republic ofBangladesh. http://203.112.218.66/WebTestApplication/userfiles/Image/BBS/Socio_Economic.pdf.Accessed 22 Jan 2014

Birkmann J, Cardona OD, Carreno ML et al (2013) Framing vulnerability, risk and societal responses: theMOVE framework. Nat Hazards 67:193–211. doi:10.1007/s11069-013-0558-5

Blaikie P, Cannon T, Davis I, Wisner B (1994) Access to resources and coping in adversity. At risk: naturalhazards, people’s vulnerability and disasters, 1st edn. Routledge, London, pp 47–74

Brouwer R, Akter S, Brander L, Haque E (2007) Socioeconomic vulnerability and adaptation to environ-mental risk: a case study of climate change and flooding in Bangladesh. Risk Anal 27:313–319

Cannon T (2008) Reducing people’s vulnerability to natural hazards communities and resilience. ResearchPaper No. 2008/34, Helsinki

Chambers R, Conway G (1992) Sustainable rural livelihoods: practical concepts for the 21st century. IDS,Brighton Report No. 296

Cutter SL, Mitchell JT, Scott MS (2000) Revealing the vulnerability of people and places: a case study ofGeorgetown County, South Carolina. Ann Assoc Am Geogr 90:713–737

Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E, Webb J (2008a) Community and regionalresilience: Perspectives from hazards, disasters, and emergency management. http://www.resilientus.org/wp-content/uploads/2013/03/FINAL_CUTTER_9-25-08_1223482309.pdf. Accessed 3 Mar 2014

Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E, Webb J (2008b) A place-based model forunderstanding community resilience to natural disasters. Glob Environ Change 18:598–606

Demurger S, Fournier M (2011) Poverty and firewood consumption: a case study of rural households innorthern China. China Econ Rev 22:512–523. doi:10.1016/j.chieco.2010.09.009

DFID (2011) Defining disaster resilience: a DFID approach paper. Department for international develop-ment. http://www.gov.uk/government/uploads/system/uploads/attachment_data/file/186874/defining-disaster-resilience-approach-paper.pdf. Accessed 3 Mar 2014

EM-DAT (2014) Top 10 natural disasters in Bangladesh from 1900–2014. The international disasterdatabase, centre for research on the epidemiology of disasters (CRED). http://www.emdat.be/result-country-profile. Accessed 25 Feb 2014

FAO (2013) Measuring resilience: a concept note on the resilience tool. Food and Agricultural Organization.http://www.fao.org/docrep/013/al920e/al920e00.pdf. Accessed 2 June 2014

Field A (2005) Comparing two means. In: Discovering statistics with SPSS. 2nd edn. Sage, London,pp 296–304

Nat Hazards

123

Finch C, Emrich CT, Cutter SL (2010) Disaster disparities and differential recovery in New Orleans. PopulEnviron 31:179–202. doi:10.1007/s11111-009-0099-8

Folke C (2006) Resilience: the emergence of a perspective for social-ecological systems analyses. GlobEnviron Change 16:253–267. doi:10.1016/j.gloenvcha.2006.04.002

Folke C, Carpenter S, Elmqvist T, Gunderson L, Holling CS, Walker B (2002) Resilience and sustainabledevelopment: building adaptive capacity in a world of transformations. Ambio 31:437–440

Forgette RG, Hilliard MR, Van Boening MV, Combs TE, Easson GL, Vogt DP (2008) Socio-economicresilience and dynamic micro-economic analysis for a large-scale catastrophe. http://www.serri.org/publications/Documents/Ole%20Miss%20Project%2080038%20-%20Final%20Report%20-%2011%20September%202011%20(Forgette).pdf. Accessed 3 Mar 2014

Friedman M (1957) A theory of the consumption. Princeton University Press, New JerseyGallopın GC (2006) Linkages between vulnerability, resilience, and adaptive capacity. Glob Environ

Change 16:293–303Germanwatch (2014) Global climate risk index 2013. Germanwatch. http://germanwatch.org/en/download/

7170.pdf. Accessed 27 Mar 2014Gilbert SW (2010) Disaster resilience: a guide to the literature. US Department of Commerce, National

Institute of Standards and Technology (NIST), Special Publication. http://www.nist.gov/manuscript-publication-search.cfm?pub_id=906887. Accessed 21 Dec 2013

Harun-or-Rashid S, Biswas SR, Backer R, Kruse M (2009) Mangrove community recovery potential aftercatastrophic disturbances in Bangladesh. For Ecol Manage 257:923–930

Helgeson JF, Dietz S, Hochrainer-Stigler S (2013) Vulnerability to weather disasters: the choice of copingstrategies in rural Uganda. Ecol Soc. doi:10.5751/ES-05390-180202

Holling CS (1973) Resilience and stability of ecological systems. Annu Rev Ecol Syst 4:1–23IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation:

special report of the intergovernmental panel on climate change. http://ipcc-wg2.gov/SREX/report/.Accessed 2 Mar 2014

Kaplan HB (1999) Toward an understanding of resilience: a critical review of definitions and models. In:Glantz MD, Jhonson JL (eds) Resilience and development: positive life adaptations. Kluwer, NewYork, pp 17–83

Kaul V, Thornton TF (2013) Resilience and adaptation to extremes in a changing Himalayan environment.Reg Environ Change. doi:10.1007/s10113-013-0526-3

Keynes J (1936) The general theory of employment interest and money. Macmillan, LondonLee YJ (2014) Social vulnerability indicators as a sustainable planning tool. Environ Impact Assess Rev

44:31–42. doi:10.1016/j.eiar.2013.08.002Lei Y, Wang J, Yue Y, Zhou H, Yin W (2014) Rethinking the relationships of vulnerability, resilience, and

adaptation from a disaster risk perspective. Nat Hazards 70:609–627. doi:10.1007/s11069-013-0831-7LGED (2010) Sub-district level shape files. GIS division, local government engineering department,

Government of the People’s Republic of Bangladesh, DhakaManyena SB (2006) The concept of resilience revisited. Disasters 30:433–450Manyena SB (2014) Disaster resilience: a question of ‘multiple faces’ and ‘multiple spaces’? Int J Disas

Risk Reduct 8:1–9. doi:10.1016/j.ijdrr.2013.12.010Marincioni F, Appiotti F, Pusceddu A, Byrne K (2013) Enhancing resistance and resilience to disasters with

microfinance: parallels with ecological trophic systems. Int J Disaster Risk Reduct 4:52–62. doi:10.1016/j.ijdrr.2013.01.001

Mohapatra S, Joseph G, Ratha D (2012) Remittances and natural disasters: ex-post response and contri-bution to ex-ante preparedness. Environ Dev Sustainability 14:365–387. doi:10.1007/s10668-011-9330-8

Nelson DR, Adger WN, Brown K (2007) Adaptation to environmental change: contributions of a resilienceframework. Annu Rev Environ Resour 32:395

O’Brien G, O’Keefe P (2010) Resilient responses to climate change and variability: a challenge for publicpolicy. Int J Public Policy 6:369–385

Parvin GA, Shaw R (2013) Microfinance institutions and a coastal community’s disaster risk reduction,response, and recovery process: a case study of Hatiya, Bangladesh. Disasters 37:165–184

Paul SK, Routray JK (2011) Household response to cyclone and induced surge in coastal Bangladesh:coping strategies and explanatory variables. Nat Hazards 57:477–499

PDO-ICZMP (2003) Delineation of the coastal zone vol working paper No. WP005. Project director office-integrated coastal zone management project, Government of the People’s Republic of Bangladesh,Dhaka

Pelling M, High C (2005) Understanding adaptation: what can social capital offer assessments of adaptivecapacity? Glob Environ Change 15:308–319

Nat Hazards

123

Razafindrabe BHN, Kada R, Arima M, Inoue S (2014) Analyzing flood risk and related impacts to urbancommunities in central Vietnam. Mitig Adapt Strategy Glob Change 19:177–198. doi:10.1007/s11027-012-9433-z

Reams MA, Lam NSN, Cale TM, Hinton CM (2013) Applying a community resilience framework toexamine household emergency planning and exposure-reducing behavior among residents of Louisi-ana’s Industrial Corridor. J Emerg Manag 11:107–120

Resilience Alliance (2010) Assessing resilience in social-ecological systems: workbook for practitioners(vol 2). Resilience Alliance. http://www.resalliance.org/index.php/resilience_assessment. Accessed 27May 2014

Sadath MN, Krott M (2012) Identifying policy change: analytical program analysis: an example of twodecades of forest policy in Bangladesh. For Pol Econ 25:93–99. doi:10.1016/j.forpol.2012.07.013

Sapountzaki K (2012) Vulnerability management by means of resilience. Nat Hazards 60:1267–1285.doi:10.1007/s11069-011-9908-3

Smit B, Wandel J (2006) Adaptation, adaptive capacity and vulnerability. Glob Environ Change 16:282–292Tompkins EL, Adger NW (2005) Defining response capacity to enhance climate change policy. Environ Sci

Pol 8:562–571Turner BL, Kasperson RE, Matson PA et al (2003) A framework for vulnerability analysis in sustainability

science. Proc Natl Acad Sci 100:8074–8079UN (2008) Designing household survey samples: practical guidelines vol 98. Statistical Division, United

NationsUN/ISDR (2005) Hyogo framework for action 2005–2015. United Nations International Strategy for Disaster

Reduction. http://www.unisdr.org/2005/wcdr/intergover/official-doc/L-docs/Hyogo-framework-for-action-english.pdf. Accessed 16 January 2013

UNDP (2010) Cyclone Aila: UN joint multi-sector assessment and response framework. United NationsDevelopment Program, Bangladesh, Dhaka

WHO (2011) Tuberculosis prevalence surveys: a handbook. World Health Organization. http://www.who.int/tb/publications/2010/limebook20110311prepubcopy.pdf. Accessed 29 May 2012

Wildavsky AB (1988) Searching for safety, vol 10. Transaction Publishers, NJWisner B, Blaikie P, Cannon T, Davis I (2004) Access to resources and coping in adversity. At risk: natural

hazards, people’s vulnerability and disasters, 2nd edn. Routledge, London, pp 87–124Wisner B, Gaillard J, Kelman I (2012) The Routledge handbook of hazards and disaster risk reduction.

Routledge, LondonWood LJ, Boruff BJ, Smith HM (2013) When disaster strikes… how communities cope and adapt: a social

capital perspective. In: Johnson CD (ed) Social capital: theory, measurement and outcomes. NovaScience Publishers Inc, New York, pp 141–169

Nat Hazards

123