IJMED November2012 121228-1

International Journal of

MASSEMERGENCIESANDDISASTERSVol. 30No. 3November2012

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International Journal of Mass Emergencies and DisastersOfficial Journal of the Research Committee on Disasters

International Sociological Association

The International Journal of Mass Emergencies and Disasters is one of theworld’s leading journals of disaster research. Establishedin March 1983, it publishes internationally refereedarticles of the highest scholarly standard. The Journal ispublished triannually during the months of March, August andNovember. It is concerned with the social and behavioralaspects of relatively sudden collective stress situationstypically referred to as disasters or mass emergencies. Allaspects of the life history of such events, both actual andthreatened, are examined including mitigation, preparedness,response, and recovery activities. These situations oftenare created by natural disaster agents, technologicalaccidents, violent intergroup conflicts, shortages of vitalresources, acute and chronic environmental threats, andother kinds of major hazards to life, health, property,well-being, and everyday routines. Although the focus of theJournal is on work dealing with the human and organizationalaspects of mass emergencies, contributions concerningmedical, biological, physical engineering, or othertechnical matters are acceptable if social and behavioralfeatures of disasters are also prominently discussed.

The Journal addresses issues of theory, research, planning,and policy. The central purpose is publication of results ofscientific research, theoretical and policy studies, andscholarly accounts of such events as floods and earthquakes,explosions and massive fires, disorderly crowds and riots,energy cut-offs and power blackouts, toxic chemicalpoisonings and nuclear radiation exposures, and similartypes of crisis-generating situations. Its audience includesspecialists within various areas of research and teachingplus people working in the field who are responsible formitigative, preparedness, response, or recovery actions.Although each issue of the Journal typically deals with avariety of relevant topics, special issues on particular

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subjects are published whenever possible to bring together aset of articles on a specific problem area. Since the Journalis intended to provide an international forum, contributionsfrom and about all sections of the world are encouraged.Priority is given to those writing with cross-cultural dataor implications. The Journal has five sections, although notall of these appear in any single issue:

•Research and Theoretical Articles: Empirical and scholarlystudies reflecting the central concerns of the Journal.

•Book and Film Reviews: Critical examinations of thewritten and visual literature on disasters and massemergencies.

•Editorial Commentaries: Brief overviews by Journal editorsand special-issue editors placing the material in a givenissue in the larger context of the state of the field.

•The Critic’s Corner: An open page presenting controversialissues and personal opinions by invited and volunteerauthors.

•Feedback from the Field: Letters to the editor and briefunrefereed commentaries on current questions and issues inthe disaster area.

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INTERNATIONAL JOURNAL OFMASS EMERGENCIES AND DISASTERS

For information regarding subscriptions, changes of address,or advertising rates, contact:

Dr. William Lovekamp, Secretary/TreasurerInternational Research Committee on DisastersPO Box 961Mattoon, IL 61938 Email: [email protected]

Rates for subscriptions to the International Journal of MassEmergencies and Disasters are set according to the subscriber’sCountry Category (according to the InternationalSociological Association) and the type of subscription—Regular, Student or Institutional.

Table 1. Subscription Rates Rates in US$ A-1yr A-4yr B-1yr B-4yr C-1yr C-4yrRegular 35.00 100.00 15.00 45.00 4.00 12.00Students 15.00 45.00 10.00 30.00 2.50 10.00Institutions

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A, B, and C refer to the ISA Country Categories shown inTable 2 at www.ijmed.org/subscribing.php. Individualsubscriptions must be prepaid. Please make checks (on a U.S.bank) payable to: Research Committee on Disasters. Invoiceand credit card orders (MasterCard and Visa) are alsoaccepted.

Electronic copies of back issues may be purchased for US$15.00 per issue. Electronic copies of individual articlesare available for US$ 2.00 per article. Requests should besent to the IRCD Secretary/Treasurer. As with subscriptions,checks for single issues or articles must be written on a

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U.S. bank and made payable to: Research Committee onDisasters; orders placed using invoices, MasterCard, andVisa are also accepted.

All manuscripts should be submitted electronically (emailwith manuscript as a .doc or .rtf attachment) to:

Thomas A. Birkland, Ph.D.Editor, International Journal of Mass Emergencies and DisastersInternational Research Committee on DisastersEmail: [email protected]

The Research Committee on Disasters of the InternationalSociological Association acknowledges with gratefulappreciation facilities and assistance provided by theUniversity of Delaware, Texas A&M University, and theUniversity of Southern California.

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INTERNATIONAL JOURNAL OFMASS EMERGENCIES AND DISASTERS

Volume 30, No. 3 November 2012

Editor Co-EditorDr. Thomas Birkland Dr. Michael K. Lindell School of Public & International Affairs HazardReduction & Recovery CenterNorth Carolina State University Texas A&M UniversityRaleigh NC 27695 College Station, TX 77843-3137+1 (919) 513-7799 +1 (979) [email protected] [email protected]

Book and Film Review EditorDr. Carla S. Prater

Hazard Reduction & Recovery CenterTexas A&M University

College Station, TX 77843-3137+1 (979) 862-3970

[email protected]

Board of the Research Committee on DisastersInternational Sociological Association

President Ex-OfficioW.G. Peacock (USA) R.W. Perry (USA)

Secretary/TreasurerW. Lovekamp (USA)

Vice President Members-at-LargeB. Porfiriev (Russia) S. Ullberg (Sweden)

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G. Poghosyan (Armenia)

H. Hirose (Japan)

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International Journal of Mass Emergencies and DisastersNovember 2012, Vol. 30, No. 3

Copyright Research Committee on DisastersInternational Sociological Association

PO Box 961, Mattoon, IL 61938 USA

ISSN 0280-7270

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INTERNATIONAL JOURNAL OF MASS EMERGENCIES AND DISASTERS

Volume 30, No. 3 November 2012

CONTENTS

ARTICLES

Reproductive Improvisation and the Virtues of Sameness: 249The Art of Reestablishing New York City’s Emergency Operations Center

Tricia WachtendorfJames M. Kendra

Forecasting Shelter Accessibility and Vehicle Availability for 275Hurricane Evacuation in Northern New Jersey Using Sample Enumeration

Devajyoti DekaJon A. Carnegie

Unidentified Bodies and Mass-Fatality Management in Haiti:301

A Case Study of the January 2010 Earthquake with a Cross-Cultural Comparison

David McEntireAbdul-Akeem SadiqKailash Gupta

Resilience of Small Scale Enterprises to Natural Disasters:328

A Study of a Flood Prone Area in BangladeshMohammad Aftab Uddin KhanAmir Mohammad Sayem

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Khan & Sayem: Enterprise Resilience in Bangladash

International Journal of Mass Emergencies and DisastersNovember 2012, Vol. 30, No. 3, pp. 328–355.

Resilience of Small Scale Enterprises to Natural Disasters:A Study of a Flood Prone Area in Bangladesh

Mohammad Aftab Uddin KhanGraduate Institute of International and Development Studies,

Geneva, Switzerlnad

Amir Mohammad Sayem Independent Researcher, Dhaka, Bangladesh

Email: [email protected]

Abstract

The study investigated the level of resilience of, and identified factors affectingresilience in, small scale enterprises. A cross sectional survey was carried out witha sample of 254 micro entrepreneurs in a subdistrict of the Sirajganj district inBangladesh. To investigate different sorts of business resilience, we developedseveral items for each scale. Results indicate that the items in each of the capital-based resiliences are reliable and valid, suggesting that the capital-basedapproach developed by Mayunga (2007) can be used to further test validity andreliability. Multivariate regression analysis revealed that several factors hadsignificant impact on different sorts of capital-based resilience. Although therewas variation in capital specific resilience, education, monthly income, numberof years of engagement in the current profession, number of employees, type ofmarket, monthly income through revenue or disposal before disaster, loanreceived prior to disaster, and perception of recovery dynamics all hadsignificant impact. The study concluded with theoretical and applied implicationsof the findings.

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mailto:[email protected]


Keywords: Resilience, small enterprises, flood, Bangladesh.

Introduction

Recent catastrophic events—the tsunami earthquake inJapan in 2011, the devastating earthquake in Haiti and Chilein 2010, the catastrophic flood in Pakistan in July 2010,Cyclone Nargis in Myanmar in 2008, the tropical cyclone Sidralong the coastal areas of Bangladesh in 2007, HurricaneKatrina along the U.S. Gulf Coast in 2007, and thedevastating Indian Ocean Tsunami in 2004—are stark remindersof the global significance of natural hazards and theirimpact on the socioeconomic vulnerability of populations, avulnerability aggravated in many cases by climate change.Data suggest that the number of victims rose from 198.7million in 2000 to 217.3 million in 2009 (Guha-Sapir et al.2010). Natural disasters cause billions of dollars’ worth ofdamage, a figure that varies grossly from region to region.Data from Guha-Sapir et al. (2010) indicate that, in termsof monetary value, total damage sustained by the Americaswas the highest (US$56.84 billion), followed by Asia(US$34.76 billion), Europe (US$17.70 billion), Australia andOceania (US$14.51 billion), and Africa (0.06 billion). Thesame report also reveals that Asia’s share of global damagesin 2010 (28.1%) was below its 2000 to 2009 share of 39.8%.Damage in Europe accounted for 14.3% of global reporteddamage in 2010, while Oceania’s share amounted to 11.7%,with damage in Africa accounting for only 0.05% of globaleconomic damage from natural disasters in 2010, marginallyless than its 2000 to 2009 share of 1.2%.

Disaster often results in the region or locality beingseverely affected. Tierney and Nigg 1995) found that oneconsequence of floods in the city of Des Moines was that of

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lifeline disaster. The survey indicated that flooding causedpower outages that affected 35,000 households as well as theentire downtown business district, leaving a total of300,000 residents without potable water or electric power. Atotal of 80% of the businesses in Des Moines reported beingwithout water due to the flooding. Although only some partsof the community experienced direct flood damage, damage tothe city’s water treatment and sewage facilities affectedthe entire community, and flood related electricity serviceinterruptions were extensive. Loss of critical lifelineservices, particularly water, was the main cause of businessclosure in the affected region.

Each year, small businesses in nations around the worldsuffer major losses as a direct consequence of naturaldisasters such as earthquakes, severe storms, and flooding.Evidence indicates that in most communities it is smallbusinesses that are the major employers, and that the lossescaused by disasters often result in these businesses closingdown (Alesch et al. 2001). It has been recognised thatbusinesses play an important socioeconomic role in communityfunctioning by providing products and/or services, andemployment opportunities, and represent an important sourceof revenue via the taxes they pay (Cochrane 1992). Despitethis, disaster research has thus far had a propensity tofocus on families, households, and government agencies(Burby 1998; Tierney, Lindell, and Perry 2001) rather thanbusinesses. Earlier research emphasised the need tounderstand the reason behind the failure of small businessesto devise ways to prepare for, and eventually recover from,natural disasters (Alesch et al. 2001). Of even greaterimportance, however, particularly in regard to thedevelopment of suitable protective measures, may be theresilience of such companies in the face of naturaldisaster.

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Response to disasters before, during, and after theyoccur is a matter of both hazard and disaster managementpractice and public policy at national and internationallevel. Survivors face the arduous task of rebuilding atpersonal, structural, and economic levels. In general, partof the rebuilding process is discharged via a combination offoreign and domestic humanitarian relief aid. Yet, despitetremendous effort on the part of governments, as well asinternational and national humanitarian and developmentagencies, the actual impact and proper focus of postdisaster economic recovery strategies remain somewhatuncertain from an empirical point of view. Since the 2004Indian Ocean tsunami, donors have become aware that suchstrategies should place greater emphasis on therehabilitation of the local economy (Bennett et al. 2006),thereby re-empowering beneficiaries without delay,personally and economically. The very concept of disasterresilience and livelihood recovery gained wide interestamong scholars and practitioners in the wake of the 2004tsunami, becoming popular after the adoption of the HyogoFramework for Action 2005–2015: Building the resilience of nations andcommunities to disasters (Manyena 2006).

Given that in the field of post disaster livelihood andeconomic recovery management scientific knowledge andpractical experience are both very limited, it is unlikelythat the challenges that lie ahead will be fully met in theshort term, especially in the developing world (Sahni andAriyabandu 2003; Seck 2008). Studies show that the creationand sustainable development of new microeconomic and smallbusiness activities, even more so in vulnerable developingcountries, is the result of a complex chemistry of enablingfactors, which can be delivered neither by local governmentnor foreign donor agencies alone (Runyan 2006). Suchchemistry, however, is far less attainable in the unstable,

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abnormal circumstances generated by natural disasters and/orviolent crises, the urgent need to create employment andincome generating activities as rapidly as possiblenotwithstanding (ILO 2005). Business sustainability after anatural disaster is complex and multidimensional, andseveral authors report that the same depends not only onassistance, but also on the company concerned, localcommunity systems, market structures, and the pattern ofinteractions between family members. In developing countriessmall businesses, largely family run, are often conductedfrom the family residence (Rose and Liao 2003; Rose,Oladosu, and Liao 2005; Webb et al. 2000). The greater thesupport from family and social or community networks, thegreater the resilience and long term sustainability after adisaster.

Concept of Disaster Resilience

Timmerman (1981) is credited with being the first to usethe concept of resilience in relation to hazard and disaster(Klein et al. 2003). Timmerman (1981) defines the term asthe measure of a system’s capacity, or part thereof, toabsorb and recover from hazardous events (Klein et al.2003). Subsequent to the work of Timmerman (1981), althoughnumerous definitions of the concept of resilience in thefield of hazard and disaster have emerged, no consensusamong researchers and practitioners on one common definitionhas yet been reached. The majority of authors use capacityor ability to define disaster resilience, in effect equatingthe notion of resilience with the capacity or ability ofpeople, individually or collectively, to cope with disaster(Mayunga 2007). Other authors, however, define disasterresilience in terms of the speed with which such peoplerecover from disaster, an aspect that gave rise to several

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studies being conducted (Rubin 1991; Rubin et al. 1985;Dahlhamer and Tierney 1998).

As units of analysis in disaster research,businesses/enterprises have only recently begun to bestudied (Rodriguez et al. 2007). Researchers studying theeconomic impacts of disasters have tended to focus on unitsof analysis larger than individual firms and enterprises,such as community and regional economies. Until veryrecently, very little was known regarding businessvulnerability, loss reduction measures adopted bybusinesses, disaster impacts on businesses, and business andenterprise resilience and recovery. Systematic research waslacking despite the importance of business for society(Tierney 2007). Businesses are the foundation of local,regional, and national economies; when businesses areaffected by disasters, that disruption produces direct andindirect business losses, as well as having a ripple effecton the wider economy. The destruction of or damage tobusinesses, along with disaster related closures, result injob losses, thereby negatively affecting incomes andcreating even greater challenges for households,neighbourhoods, and communities as they attempt to recoverfrom these catastrophes (Rodriguez et al. 2007). Afterdisasters, business owners and entrepreneurs, too, face ahost of challenges, including how to finance businessrecovery while simultaneously coping with damage tostructures and contents of commercial and residentialpremises (Cochrane 1992 as cited in Zhang et al. 2009).Further research on the impacts of disaster on businessunits is needed if business communities and entrepreneurs indisaster prone areas are to be better prepared for theupheaval caused by natural disasters. Although large scalestudies are useful for understanding the national andregional impacts of disasters, their level of aggregation

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obscures the differential impacts of disasters on specifictypes of businesses within the affected communities (Zhanget al. 2009). As a result, a microanalytic approach isneeded to provide guidance to community planners andbusiness owners in developing better methods for minimisingthe impacts of disaster (Zhang et al. 2009).

As commercial activities involved in themanufacture/distribution of goods and/or the provision ofservices (Zhang et al. 2009), businesses are affected in anumber of ways; direct physical damage not only tobuildings, but also to vehicles and stock (Whitney et al.2001). The outcome of any disaster in a given community isborne by businesses and the local community alike.Households, other businesses, and even government all playimportant roles as consumers and suppliers in businessoperations (Zhang et al. 2009). In addition, although smallscale- and micro businesses are usually conducted in a localarea, their operations are not necessarily confined to thelocal area; they may be engaged in servicing not only thelocal community, but also communities further afield.

Rose (2007) argues that there are two aspects of businessresilience: static and dynamic. Rose interprets staticresilience as essentially making the best of the resourcesavailable at a given point in time, which affects the time-path of the economy. Although static resilience has greatpotential to reduce losses incurred in a disaster in astraightforward and inexpensive manner, this aspect hasusually been overlooked in favour of its dynamic resiliencecounterpart, which focuses on the speed of recovery andwhich dominates most of the engineering-based literature onthe subject. He further demonstrates that dynamic resilienceis more complex from an economic standpoint, and moreexpensive. While no less important, dynamic resilience isgiven less attention in this paper in order to compensate

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for the neglect of static resilience in the literature to-date. He further distinguishes two more types of businessresilience: inherent and adaptive (Rose 2007). Inherentresilience refers to the ordinary ability to deal withcrises (e.g., the ability of individual rms to substitutefiother inputs for those curtailed by an external shock, orthe ability of markets to reallocate resources in responseto price signals). Adaptive resilience, in contrast, refersto the ability in crises to maintain function on the basisof ingenuity or extra effort (e.g., increasing inputsubstitution opportunities in individual business operationsor strengthening the market by providing information tomatch suppliers with customers).

Resilience is, of late, being considered in the light ofthe capital approach (Tierney 2006) to include the fivemajor forms of capital: social, economic, physical, human,and natural. As the literature suggests, the notion ofcapital aligns very well with the concept of sustainability(Smith et al. 2001), which is related, and often linked, tothe concept of disaster resilience (Tobin 1999; Brown andKulig 1996/1997). The essence of using the capital approachis that capital consists of those components necessary forthe development of a sustainable community economy. Theconventional wisdom here is that the more economicopportunities the community has, the more potential itpossesses for reducing the impacts of disaster, hence, themore resilient the community. Healthy businesses aresynonymous with economically healthy communities, and it isthis mutually beneficial relationship that could account forfirms in more economically developed communities’potentially functioning well, post disaster, compared withfirms in communities where economic development isrelatively poor.

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Community resilience is a complex process due to thedynamic interaction between people, communities, societies,and the environment. There are currently many conceptualframeworks being proposed to measure this concept: (Brownand Kulig 1996/1997; Tobin 1999; Adger 2000; Buckle 2006;Foster 2006; Tierney 2006). Generally, most of theseframeworks conceptualise disaster resilience in the sameway, focusing as they do, similarly, on factors that couldreduce vulnerability and increase community resilience. Suchfactors include economic resources, assets and skills,information and knowledge, support and supportive networks,access to services, and shared community values. Onelimitation of most of these frameworks is that they onlytend to focus on one, or at most, a few dimensions ofdisaster resilience and do not adequately take a broaderview of the concept (Mayunga 2007). The capital-basedapproach is not new in the field of hazard and disaster; ithas been widely applied in sustainable development andpoverty alleviation programmes (DFID 1999). Mayunga (2007)proposes a new framework, one that focuses on five communitycomponents—social, economic, physical, human, and naturalcapital.

Putnam (1995) defines the concept of social capital as afeature of social organisations such as networks, norms, andsocial trusts that facilitate coordination and cooperationfor mutual benefit. In the context of community resilience,it reflects the fact that community ties and networks arebeneficial because they allow individuals to draw on thesocial resources in their communities, and increase thelikelihood that such communities will be able to adequatelyaddress their collective concerns (Green and Haines 2002).Economic capital denotes financial resources that people usein order to earn their livelihood. It includes savings,income, investments, and credit. The contribution of

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economic capital to building community resilience isstraightforward in the sense that it increases the abilityand capacity of individuals, groups, and communities toabsorb the impacts of disasters and accelerates process ofrecovery (Mayunga 2007). Physical capital refers to thebuilt environment, comprising residential housing, publicbuildings, business/industrial premises, dams and levees,and shelters. It also includes lifelines such aselectricity, water, and telecommunication, and criticalinfrastructure such as hospitals, schools, fire and policestations, and nursing homes. Economists define the conceptof human capital as the capability, both innate and derivedor accumulated, embodied in the working age population thatallows it to work productively with other forms of capitalto sustain economic production (Smith et al. 2001). The termnatural capital refers to natural resources, such as water,minerals and oil, land on which to live and work, and theecosystems that keep water and air clean, and the climatestable (Smith et al. 2001).

Disaster Research in Bangladesh

Disaster research in Bangladesh has been conducted usingsix major approaches: geographical, behavioural, structural,historical-structural, sociological, and anthropological(Nasreen 2004). Past research on natural disasters (such asfloods, river bank erosion, earthquakes, and cyclones) inBangladesh has followed the geo-anthropological approach ofthe Chicago-Colorado-Clark-Toronto School of Natural HazardStudies associated with White (1964, 1974) and Burton et al.(1993). Falling within the first school of thought (i.e.,the geographical approach) described by Alexander (2004),disaster response studies (Islam 1974; Alam 1990) deal withpeople's behaviour; their perceptions, attitudes, beliefs,

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values, responses, and personalities. They are concernedwith discovering people's choices, behaviour, andadjustments to disaster, that is, how they viewed thecatastrophe and how they perceived alternative opportunitiesavailable to them for coping with such event (Nasreen 2004).

Although the geographical perspective, concerning amongothers floods, cyclones, riverbank erosion, earthquakes, andarsenicosis, is much discussed, some of the writings doembrace the social impact that disaster has on people, andhow they cope. While again following the geographicalapproach to disaster (Ahsan and Khatun 2004), a recentpublication focuses on the aspect of gender. Using thebehavioural approach, Hossain et al. (1987) examine whetherrural people in flood free and flood prone areas adoptdifferent survival strategies, and while they also focus onthe responses of rural people in general, they do notparticularly focus on the responses of women. Shaw (1989),highlighting the problems of poor women in a relief camp inDhaka city, notes how it is women who bear the social burdenof shame when forced to live among strangers, and drawsattention to the difficulties they face when trying tomaintain parda during floods. In their study on riverbankerosion and floods, Rahman and Haque (1988) argue thatpeople's ability to adjust to catastrophe should be viewedas an extension of existing social and natural systems.

Pioneering disaster research (Nasreen 2004) based on thesociological approach provides a detailed picture ofdisaster as experienced by rural households. It focuses onthe pre-, during-, and post disaster activities performed bymen and women during floods. The author argues that whereasdisaster affects both sexes, the burden of coping withfloods falls heaviest on women. When floods occur, men inrural areas lose their jobs, leaving women to shoulder theresponsibility of maintaining the family. Nasreen (2004)

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argues that not only do poor rural women ordinarily havevery few options at their disposal to deal with theirproblems, but their role in times of disaster is complicatedeven further, encompassing as it does a complete range ofsocioeconomic activities: bearing and raising children,collecting food, fuel, water, fodder, and buildingmaterials, and safeguarding household belongings. Women alsorepresent a productive potential (i.e., daily labour inconstruction activities, home based gardening, small scaletrade and sales, producing handicrafts, etc.), none of whichwas earlier acknowledged (Brouwer 2007).

Floods in Bangladesh and Belkutchi

The unique geomorphological and climatic conditions ofthe country have made Bangladesh vulnerable to monsoonriverine floods. Since its independence in 1971, the countryexperienced floods of different magnitudes in 1971, 1974,1978, 1984, 1986, 1987, 1988, 1989, 1991, 1993, 1995, 1996,1997, 1998, 1999, and 2000 (Asiatic Society of Bangladesh,2003) and more recently in 2004 and 2007. Unlike the normalfloods, which cover large parts of the country for severaldays or weeks during July and August, the floods in 1998lasted until mid September in many areas, killing hundredsof people and destroying roads, houses, crops, and otherassets.

In late June 2004, heavy monsoon rains swelled the watersof the Meghna River, which reached its peak in early July.The Jamuna and Padma Rivers also burst their banks in earlyJuly due to heavy rains in the north of the country, causingflash floods in the northern and west-central districts. Thefloods spread, eventually impacting Dhaka and other centraldistricts, and adversely affected the economy by damaginginfrastructure, reducing economic growth, and upsetting the

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macroeconomic balances. Preliminary analysis shows that as aresult of the flood the economic growth of fiscal year (FY)2005 (July 2004 to June 2005), which was earlier projectedto be about 6%, would likely decline to about 5% from 5.5%in FY2004 (Asian Development Bank 2004). Agriculture,particularly the crop, livestock, and poultry subsectors,and small and medium scale industries, were the mostadversely affected in the central-north part of the country,notably the Sirajganj district because of its geophysicallocation (Asian Development Bank 2004).

Falling under the Rajshahi division and with an area of2,497.92 km2, the Sirajganj district is bound by the Bogradistrict to the north, the Pabna district to the south, theTangail and Jamalpur districts to the east, and the Pabna,Natore, and Bogra districts to the west. The main rivers arethe Jamuna, the Baral, the Ichamati, the Karatoa, and thePhuljuri. As a result, it is one of the most flood proneareas in Bangladesh. About ten per cent of the area of theChalan Beel wetland is located in the Tarash upazila1 ofthis district. Mostly, however, it is the River Jamuna thatpasses through here. Main industries/occupations includeagriculture 35.49%, commerce 11.98%, handicraft 5.59%,service 5.49%, agricultural labour 21.45%, wage earninglabour 5.77%, industrial labour 2.78%, and others 11.45%(Banglapedia, undated). The district is affected by bothnormal and flash floods almost every year. As a result,people suffer from different flood related consequences,such as migration to other cities, temporary or permanentclosure of business activities, and crop losses(Banglapedia, undated).

1 The districts are divided into subdistricts called Upazila or Thana inBangladesh ; which are similar to the county subdivisions found in some western countries. The upazilas are the second lowest tier of regional administration in Bangladesh.

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Within the Sirajganj district, the Belkutchi subdistrict,one of the most flood prone, was selected as the study areadue to its low lying position and location to the side ofthe mighty River Jamuna. Since 2000, devastating floodsoccurred in 2004 that inundated more than half of the areasof this subdistrict. Although the magnitude was to someextent less than normal, the floods in mid July 2007inundated major parts of this subdistrict. As the people inthis area are predominantly agricultural farmers and ownersof small firms, in times of severe floodingproduction/trading is usually halted, causing great economichardship and impacting directly on the ability of suchbusinesses to survive. In a number of cases, owners areunable to resume business immediately after the disaster. Asin the case of the 2004 and 2007 floods, even those who domanage to do so are most often obliged to borrow money fromformal (donor agencies, money lending agencies, NGOs, etc.)and informal (relatives, friends, and community members)networks in order to re-establish themselves.

Despite being of importance, there has been very littleor no academic or policy research done on disaster inducedsmall business vulnerability in Bangladesh. This studyfocuses on the resilience to floods of small scale and microenterprises in the Belkutchi subdistrict and, morespecifically, attempts to investigate the level ofresilience of, and identify the factors affecting resiliencein, small scale enterprises. In order to understand businessresilience, this study applies the proposed capital-basedcommunity resilience framework developed by Mayunga (2007),consisting of five capital-based disaster resilienceelements. We choose to apply community resilience sincebusiness functions within community and community isconsidered to affect business; a community with social,financial, human, physical, and natural capital is therefore

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expected to support business to an even greater extent. Ourspecific hypotheses were: 1) educated business owners have ahigher capital-based resilience; 2) the higher the monthlyincome, the higher the capital-based resilience; 3) thegreater the number of years engaged in the currentprofession, the higher the capital-based resilience; 4) thegreater the number of employees, the higher the capital-based resilience; 5) firms oriented towards regionalproduction have a higher tendency for capital-basedresilience; 6) the higher the monthly income throughrevenue, or the higher the monthly disposable income, pre-disaster, the higher the capital-based resilience; 7) thebigger the loan received prior to disaster, the higher thecapital-based resilience; and 8) the greater the perceptionof recovery measures, the higher the capital-basedresilience among owners of small scale firms.

Methods

Area of Study

This study was carried out in the Belkutchi subdistrictof the Sirajganj district. The Belkutchi subdistrict islocated to the side of the River Jamuna. The communicationssystem is good, as is the road network, which conveys buses,auto rickshaws, and rickshaws. The majority of people,though, prefer to get around on foot. In the rainy season,however, when flooding is most common, not least due to theproximity of the Jamuna, the boat becomes the preferred modeof transport. More importantly, being mostly char2 land,2 Low lying area frequently inundated by flood. Chars in Bangladesh canbe considered a 'by-product' of the hydro-morphological dynamics of itsrivers. The Irrigation Support Project for Asia and the Near East(ISPAN) study indicates that the chars that are not eroded in the firstfour years of their emergence could be used for either cultivation or

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Belkutchi is an underdeveloped part of the country.Education here is similar to that in other areas, that is,education is divided into three levels; primary, secondary,and tertiary. Around half of the people in this area havesome form of formal and informal education, withpredominance in male education. The area’s natural resourcesinclude arable land, rivers, ponds, a variety of trees, anda variety of fish. Although most of the arable land can becultivated two or three times a year, erosion has reducedthe amount of cultivable land—land that is mostly low lyingand prone to flooding in the rainy season. There is,however, some land in higher areas that can be cultivatedall year round. The second important sector comprisesbusinesses oriented towards trade and production.Historically, Belkutchi is famous for its production oftextiles, produced using both hand and power looms.

Respondents

Taking part in the study were 254 small scaleentrepreneurs of both sexes, doing business in variousfields: groceries, power looms, tea stalls, spice grinding,carpentering, blacksmithing, repairing electronics, and thewholesale selling of raw materials, among others. Smallscale enterprises in the area are of two types: sole andpartnership, and are usually oriented towards production,retail, and service. To some extent, microentrepreneursreceived training in business as well as in disastermitigation from local NGOs, government training agencies,etc. It must be noted that as there were no secondary dataregarding disaster impact available at company level, and asthe business owners were assumed to be sufficiently reliable

settlement by the end of these four years.

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respondents, the study canvassed them in order to obtainmuch-needed primary data.

While most respondents (65%) had received formaleducation, more than one-third of them (35%) had not. Themajority of the firms (57.9%) had one employee, with a meanof 2.98 (SD = 3.63) employees. Employers’ average monthlyincome was 1,527.95 Bangladeshi Taka (BDT) (SD=3,467.81),whereas the majority had no income (59.1%), followed by<3,000 and >3000 BDT being earned by 28.7% and 12.2% of therespondents, respectively. For 66.5% of the firms, themarket for the product was local, whereas 33.5% enjoyed aregional market. On average, the firms had been inproduction for 7.35 years (SD=4.81). Specifically, more thanone-third of the firms (35.4%) had been in production for 5to 6 years, followed by 7 to 8, 9 to 10, <4, and >10 yearsin production or 23.2%, 16.9%, 16.5%, and 7.9%,respectively. On average, revenue or disposable income,before disaster, was BDT8064.37 (SD=7,288.06). More thantwo-fifths of the respondents (45.7%) had monthly incomesranging from BDT5001 to BDT7500, followed by BDT7,501 toBDT10,000 (24.0%), < BDT5,000 (22.0%), and > BDT10,000(8.3%). On average, respondents received BDT1,929.13(SD=5,864.31). Specifically, the majority of the respondentsreceived > BDT5,000, whereas only 16.3% of the respondentsreceived <BDT5,000 prior to disaster. The average totalscore on the items measuring respondents’ perceptionsregarding recovery from disaster was 21.87 out of 40.

Sampling Technique

Sample selection was done by a multistage procedure. Inthe first stage, one union was randomly selected from theBelkutchi subdistrict. In the second, a systematic randomsampling technique was used to identify small scale

27


entrepreneurs, which systematic sampling consisted ofselecting every kth sampling unit of the population afterrandom selection of the first sampling unit (Frankfort-Nachmias and Nachmias 1996; Bryman 2008). Selection of thefirst small scale entrepreneurs was determined by randomprocess. First, one small scale entrepreneur was selectedrandomly from ten small scale entrepreneurs. The secondsample was selected with an interval of five; that is tosay, if initially the card holding the number 3 wasselected, then the rest of the samples were selected on thebasis of the equal interval of five, i.e., 3+5=8, 13, 18,23, and so on. A lottery system was used to select the firstsample. Interviewers visited small-scale entrepreneurs asper the number assigned by the study team.

Data Collection Procedure

Data were collected using a semi structured questionnaireprimarily prepared on the basis of available literature andin line with study objectives. Later, it was pilot tested onfive potential respondents. The pilot test was carried outin order to ascertain the suitability of the questionnaireand/or whether any further additions were necessary, and todirect the sequence of the questionnaire. Insights from thepilot test were incorporated into the finalisedquestionnaire, which was then used to collect quantitativedata. Data were collected via one-to-one interviews. Beforecollecting the data, several preparatory tasks, such asidentifying interviewers and training them, were undertaken.

First, two interviewers were selected, based on theirprior experience in data collection in the area of study.Interviewers were then trained and after completion of thetraining five pretests were carried out in the presence ofthe researcher in order to observe whether the interviewer

28


was able to elicit the information accurately. Theresearcher supervised the entire data collection process andin some cases re-interviewed households in order to verifythe data collected by the interviewer. Due informed consentwas obtained prior to interviews being conducted;interviewers explained the objective of the study andidentified the researcher, and the rights of respondents toparticipate, or not, in the study. Affirmative responses onthe part of respondents signalled the start of theinterviews. Although interviewers approached 270 potentialrespondents altogether, 16 owners of small scale firmsdeclined to participate on the grounds that they did nothave the time. Interviews took four months, from Septemberto December 2010.

Measurement of Variables Measurement of Dependent Variables

Capital-based resilience was measured by Mayunga’s (2007)capital-based approach—social, financial, physical, human,and natural capital bases of resilience. Based on histheoretical framework, we developed several items for eachof the capitals. Social capital-based resilience wasmeasured using seven items (presented in Table 1).

29


Table 1. Mean and Standard Deviation (SD) of DifferentCapital-based Resiliences

Scale Items Mean SDDependent Variable I: Social Capital Number of local voluntary organisations that come to assist during a disaster 5.13 2.93 Number of times in the last three years you joined a local community project or working bee 1.16 1.27 Number of times you and your close family and other relatives with whom you do not live exchanged practical help or advice 1.67 1.00 Number of times you have been part of a project to organise a new service in your local community 0.41 0.68 Number of times you and your family exchange practical help or advice 3.03 1.05 Number of times you and your neighbours exchange practical help or advice 1.06 0.79 Number of times during the last one year you and your current work mates or associates exchanged practical help or advice 0.93 1.07Dependent Variable II: Financial Capital The amount (BDT) of per capita household income 2532.9

2 3603.04 The total amount (BDT) of income of your enterprise per year 64427.

95 25710.88 The amount (BDT) of savings you/your firm have in the bank 16657.

48 32988.87 The total economic value (in BDT) of your total property 73062.

99155966.4

3 The total economic value (BDT) of your firm property 81974.

41 47056.48 How much money (BDT) have you invested in your firm 65872.

05 42434.38 How many members of your family work in your business 1.08 0.46 How frequently in a year do you take a loan from the bank 1.61 1.89 The amount of money (BDT) you have saved in the bank to tackle any unexpected events like natural disasters

8108.27 20167.70

Dependent Variable III: Physical Capital The distance (in km) between your home and your business enterprise 1.17 0.97 The number of schools within a three square km of your business enterprise 10.86 3.18 The number of other public buildings/shelters within a three square km of your business enterprise 2.83 1.67 The number of houses/establishments that you have for your business 1.10 0.96 The average number of hours in a day you get an electricity supply during the disaster 7.25 6.03

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The amount of water you get during a disaster (n=9) 6.78 4.89 The number of hours telephone or mobile service remains operational during the disaster 13.06 3.08 The number of hospitals within a three square km of your business 3.05 1.54 The distance between the main road and your business enterprise 1.76 1.01Dependent Variable IV: Human Capital The number of formally trained employers 0.57 0.65 The average number of training opportunities the employers experienced 0.84 0.52 The number of formally educated employers 0.77 0.78 The number of employers who possess a tertiary level of schooling 0.11 0.35 The number of workers who dropped out of school before age 15 2.00 2.94 How many workers are within working age (older than 18 to younger than or equal to 60) 2.87 3.64 The average number of hours in a day your firm employees can work without being exhausted 8.47 1.76 The number of workers with previous experience in tackling disaster 0.27 0.63 How many workers, collectively, live in one (single) room 0.67 0.68 How many workers have access to (public/private) transport 0.42 0.85Dependent Variable V: Natural Capital What is the total amount of land you possess 0.85 1.75 How many trees are there on the land occupied by your firm 10.34 8.13 What is the extent of the land on which your firm is located 1.24 2.06

Financial capital-based resilience was measured using 10items; however, as all respondents did not respond to oneitem (i.e. the amount you invested in other businesses), itwas deleted from the final analysis (e.g., the amount ofmoney invested in other business or income generatingactivities). Eight items were used to measure physicalcapital-based resilience; however, as two items had nouniversal response, they were deleted from the finalanalysis (e.g., “The number of shelters within three squarekm from your business entity” and “The amount of water youget during disaster”). Human capital-based resilience wasmeasured using 10 items, while natural capital was measuredusing 3 items.

Measurement of Independent Variables31


In total, eight independent variables were used toinvestigate their association with dependent variables (seeTable 2). The independent variables included theparticipant’s education, number of years of engagement inthe current profession, participant’s monthly income,monthly income through revenue or disposal prior todisaster, total number of employees, types of markets,amount of loan received prior to disaster, and perception ofrecovery dynamics. The participant’s education was measuredby dichotomous measures (e.g., 0 = no and 1 = yes). Monthlyincome in BDT was measured as the total average income fromthe business as well as other sources. Monthly incomethrough revenue or disposal prior to disaster was measuredby the amount of average monthly income in BDT throughrevenue or disposal before disaster. The number of years ofengagement in the current profession was measured incompleted years.

Table 2. Variable DefinitionsVariables Coding schemeEducation 0=No

1=Yes Monthly income IntervalNumber of years engaged with the currentbusiness

Interval

Number of employees IntervalType of market 0=Local production

oriented1=Regional productionoriented

Monthly income before disaster through revenue or disposal

Interval

Loan received prior to disaster IntervalPerception of recovery dynamics Index of eight items Social Capital Index of seven items Financial Capital Index of ten items Physical Capital Index of eight itemsHuman Capital Index of ten items

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Natural Capital Index three items

The number of employees was measured by the total numberof employees of the firm at the time of the survey. Thetypes of markets were measured by dichotomous categories(e.g., 0=local production oriented and 1=regional productionoriented). The amount of loan received prior to disaster wasmeasured by the total amount of loan (in BDT) taken beforedisaster to run the business.

Respondents’ perception of recovery dynamics was measuredusing eight items (see Table 3). Each item had a ratingscale of 1 (fully agree) to 5 (fully disagree). Therespondents rated each item within this score. A total scorewas obtained by summing the item scores. To assess theirreliability as a single combined score of perception,Cronbach’s alpha was calculated. As the obtained value ofCronbach’s alpha (.80) was greater than the conventionalthreshold of .70, all the items were used as an index ofperception of recovery dynamics.

Table 3. Perception of Recovery from Disaster Items of recovery perception Mean SD

1. A community with disaster proof houses (i.e., fencingaround the house, building house in high land etc. )should recover more quickly than a community with alltraditional buildings/houses

3.25 1.229

2. A community with all high income households shouldrecover more quickly than a community will all low incomehouseholds

3.32 0.953

3. A community with all large businesses should recovermore quickly than a community with all small businesses

3.26 1.024

4. A community with all export-oriented businesses shouldrecover more quickly than a community with all local-oriented businesses

3.48 0.915

5. All lifeline mitigations should hasten recovery times 3.76 0.791

6. Mitigating transportation should hasten recovery more 4.05 0.91

33


than mitigating other lifelines 97. All planning and response measures should hastenrecovery times

3.34 0.943

8. Agents should be less likely to fail or leave as moremitigation and planning measures are taken

3.95 1.011

Notes: The responses ranged on five points scale items: 1=stronglyagree; 2=partially agree; 3=neutral; 4=disagree; 5=completely disagree

Data Processing and Analysis

After data collection, data editing was done in twostages—one in the field and another just before data entry.During the field interview, each questionnaire was checkedafter data collection to identify any inconsistencies and/ormissing information. In this regard, one interviewer crosschecked the questionnaire of another interviewer. In casesof inconsistency, interviewers were asked to re-interview orre-collect the information so that the information providedby the respondents would be accurate. Only five interviewsneeded to be redone in order to collect information that wasmissing from the first interview. At the second stage,collected data were edited just before performing dataentry. This time, the researcher edited the data to verifythat there was no missing response or inconsistency.

After completion of the editing, the data were enteredusing SPSS (Statistical Package for Social Science) version12.0 and the data were analysed in three stages. In thefirst stage, univariate analysis was performed by frequencyanalysis for the variables. In the second stage, bivariaterelationships to each dependent variable were calculated viaPearson correlation coefficients. In total, eightindependent variables and five dependent variables were usedto perform bivariate analyses (see Table 4). The independentvariables were selected on the basis of relevance to thedependent variables. It should be noted that items of each

34


of the capital-based resiliences were measured usingdifferent parameters, which made it impossible to analyseraw data. To use them as a single measure, data wereweighted or smoothed by exponential method. This procedurewas applied to all the capital-based resilience measures. Inaddition, in order to assess the internal consistencyreliability of each capital-based resilience measure,Cronbach’s alpha was calculated for each of them. Cronbach’salpha was calculated for the items in the social, financial,physical, human, and natural capital-based resiliencemeasures. The resulting alpha values were 0.76, 0.81, 0.82,0.79, 0.77, and 0.87, respectively. Cronbach’s alpha wasmore than 0.70 for all the resilience measures, suggestingthat each capital-based resilience measure was adequate foruse as a single index.

In the third stage, multivariate linear regressionanalysis was performed because all the dependent andindependent variables were measured at interval level,except for education and market type. However, these twovariables dichotomized so they could be treated as intervaldata and included in the regression analyses. Eachregression analysis was checked for multicollinearity amongthe independent variables by inspecting the zero ordercorrelation coefficients to verify that no coefficientexceeded 0.50. Each of the dependent variables was regressedonto all of the independent variables using the entermethod. All the analyses were performed using SPSS version12.0. Regression analysis results (B, Std Error, Beta and pvalue) are presented in Table 5.

Results

Social Capital-based Resilience

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The mean weighted score for social capital-basedresilience was 13.11 (SD = 3.28) (not shown). As indicatedin Table 4, the Pearson correlation coefficients revealedthat the number of years in engagement in the currentprofession had a significant, positive correlation withsocial capital-based resilience to disaster (r = 0.42, p =0.000). The number of employees also had a significantpositive correlation with social capital-based resilience todisaster (r = .38, p = .000). In addition, positiverelationships were noted in relation to monthly incomethrough revenue or disposal before disaster (r = .18, p =.003) and the amount of loan received prior to disaster (r =.25, p = .000). Owners’ perceptions

36


Table 4. Zero-order Correlation Coefficients Among Dependent and Independent VariablesMIN YEC NOE TYM IRD LPD PRD WSC WFC WPC WHC WNC

EDU .04 .12 .09 .12 .04 .01 .12 -.04 -.04 .00 -.24*** -.18**

MIN .24*** .14* .06 .14* -.01 -.10 .08 .09 .03 .11 .22**

YEC .41*** -.03 .24*** .02 -.12 .42*** .42*** -.06 .35*** .34***

NOE -.08 .27*** -.09 -.07 .38*** .40*** .00 .34*** .29***

TYM -.06 .03 .13* -.07 -.15* -.49*** -.18** -.19**

IRD .02 -.05 .18** .20** .11 .14* .26***

LPD -.07 .25*** .15* -.13* .22*** .17**

PRD -.36*** -.38*** -.41*** -.34*** -.38***

WSC .60*** .06 .45*** .60***

WFC .11 .53*** .58***

WPC .09 .16**

WHC .49***

Notes: EDU=education, MIN=monthly income, YEC= number of years of engagement with the current profession;NOE=number of employees, TYM=type of market, IRD=monthly Income before disaster through revenue or disposal,LPD=loan received prior to disaster, PRD=perception of recovery dynamics, WSC=weighted social capital,WFC=weighted financial capital, WPC=weighted physical capital, WHC=weighted human capital, and WNC=weightednatural capital.

Table 5. Linear Regression Analysis of Capital-based Resiliences to DisasterSocial Capital Financial Capital Physical Capital Human Capital Natural Capital

BStd.Error

Beta B Std.Error Beta B

Std.Error

Beta BStd.Error

Beta BStd.Error

Beta

(Constant)

17.93**

* 1.31 479810.29*

**40626.2

3 69.82*** 3.50 22.65**

* 1.62 17.37**

* 1.49

EDU -.45 .35 -.07 -11463.18 11056.75 -.05 2.25* .94 .12 -

2.14*** .44 -.25 -.74 .40 -.10

MIN -2.64 .00 -.05 N/A N/A N/A 6.16 .00 .04 1.01 .00 .02 3.06 .00 .05YEC .19*** .04 .28 5785.96*** 1202.12 .27 -.27* .10 -.14 .20*** .05 .24 .17*** .04 .23NOE .25*** .05 .27 7617.60*** 1608.15 .27 -.15 .14 -.06 .29*** .06 .26 .15* .06 .16TYM .04 .36 .01 -16616.19 11118.5 -.08 -8.62*** .95 -.45 -.85 .44 -.10 -.10 .41 -.01

37

Khan & Sayem: Enterprise Resilience in Bangladash 4

IRD 1.15 .00 .03 .46 .59 .04 .00* .00 .11 -3.61 .00 -.00 -4.22 .00 -.01LPD .00*** .00 .25 2.65** .91 .15 .00** .00 -.15 .00*** .00 .23 .00** .00 .17PRD -.31*** .06 -.29 -1027.81*** 1776.44 -.30 -1.16*** .15 -.39 -.31*** .07 -.23 -.29*** .07 -.26

Multiple R .62 .62 .65 .62 .50

R2 .39 .38 .42 .37 .25Note: N/A= Since firm owners monthly income was incorporated into financial capital-based resilience, we dropped it from analysis of financial capital-based resilience.

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of recovery dynamics appeared to have a significant negativerelationship with social capital-based resilience todisaster (r = -.36, p = .000).

Multivariate linear regression analysis found thatindependent variables explained 39% of variance in socialcapital-based resilience to disaster (Table 5). Both theeducation and monthly income of business owners appeared tohave a negative impact on social capital-based resilience;however, the result was not statistically significant. Firmowners with more years of engagement in the currentprofession had higher social capital-based resilience todisaster ( = .28, p = .000). A positive association wasalso noted in relation to the number of employees and theamount of loan received before disaster to run the business.The higher the number of employees, the higher the socialcapital-based resilience ( = .27, p = .000); similarly, thehigher the amount of loan received prior to disaster, thehigher the social capital-based resilience ( = .25, p =.000). A higher score in perception of recovery dynamics,however, appeared to have lower social capital-basedresilience ( = -.29, p = .000).

Financial Capital-based Resilience

The mean weighted score for financial capital-basedresilience was 315,989.08 (SD = 102,644.67) (not shown). Asindicated in Table 4, the Pearson correlation coefficientsrevealed that several independent variables had asignificant relationship with financial capital-basedresilience to disaster. Firm owners’ engagement in thecurrent profession for a longer period of time had asignificant positive correlation with financial capital-based resilience to disaster (r = .42, p = .000), suggestingthat the higher the years of engagement in the current

39


profession, the higher the financial capital-basedresilience to disaster. Likewise, the higher the number ofemployees, the higher the financial capital-based resilienceto disaster (r = .40, p = .000). In addition, a positivecorrelation was noted in relation to monthly income throughrevenue or disposal before disaster (r = .20, p = .001) andthe amount of loan received prior to disaster (r = .15, p =.019). Owners with regional production orientedmicroenterprises had lower financial capital-basedresilience compared with local production orientedmicroenterprises (r = -.15, p = .019). A negative relationwas also noted between financial capital-based resilience todisaster and perception of recovery dynamics (r = -.38, p =.000).

Multivariate linear regression analysis found thatindependent variables explained 38% of variance in financialcapital-based resilience to disaster (Table 5). Businessowners with a higher number of years engaged in business hadhigher financial capital-based resilience to disaster ( =.27, p = .000). In addition, firm owners with a highernumber of employees had higher financial capital-basedresilience to disaster ( = .27, p = .000); similarly,higher amounts of loan taken prior to disaster had higherfinancial capital-based resilience ( = .15, p = .004). Onthe other hand, a negative association was noted betweenperception related to recovery dynamics and financialcapital-based resilience to disaster ( = -.30, p = .000).

Physical Capital-based Resilience

The mean weighted score for physical capital-basedresilience was 41.41 (SD = 9.01) (not shown). As indicatedin Table 4, the Pearson correlation coefficients revealedthat several independent variables had a significant

40


relationship with physical capital-based resilience todisaster. Firm owners with regional production orientedfirms had significant, higher physical capital-basedresilience compared with firm owners with local productionoriented microenterprises (r = -.49, p = .000). In addition,the amount of loan received prior to disaster (r = -.13, p =.047) and perception of recovery dynamics (r = -.41, p =.000) had a significant, negative correlation with physicalcapital-based resilience to disaster. No other variablesappeared to have any significant correlation with physicalcapital-based resilience.

Multivariate linear regression analysis indicated thatindependent variables explained 42% of the variance inphysical capital-based resilience to disaster (Table 5). Sixof the independent variables appeared to have a significantimpact on physical capital-based resilience to disaster. Incontrast to their uneducated counterparts, educated firmowners had a higher tendency to higher physical capital-based resilience to disaster ( = .12, p = .018). A similarpositive association appeared between physical capital-basedresilience to disaster and monthly income through revenue ordisposal prior to disaster ( = .11, p = .040). On the otherhand, four variables appeared to have a significant,negative impact on physical capital-based resilience todisaster. Firm owners with more years of engagement in thecurrent profession had lower physical capital-basedresilience to disaster ( = -.14, p = .011). Similarly,market type ( = -.45, p = .000), the amount of loanreceived prior to disaster ( = -.15, p = .003), andperception related to recovery dynamics ( = -.39, p = .000)had a significant, negative impact on physical capital-basedresilience to disaster.

Human Capital-based Resilience

41


The mean weighted score for human capital-basedresilience was 16.81 (SD = 4.02) (not shown). As indicatedin Table 4, the Pearson correlation coefficients revealedthat several independent variables had a significantrelationship with human capital-based resilience todisaster. Education had a significant negative correlationwith human capital-based resilience to disaster (r = -.24, p= .000), suggesting that educated micro entrepreneurs hadlower human capital-based resilience compared with their noneducated counterparts. Owners of regional productionoriented microenterprises had lower human capital-basedresilience (r = -.18, p = .005), whereas perception ofrecovery dynamics had a significant, lower human capital-based resilience to disaster (r = -.34, p = .000). On theother hand, the number of years engaged in the currentprofession was positively correlated with human capital-based resilience to disaster (r = .35, p = .000). Inaddition, the number of employees (r = .34, p = .000),monthly income through revenue or disposal before disaster(r = .14, p = .025), and the amount of loan received priorto disaster (r = .22, p = .000) had a significant, positivecorrelation with human capital-based resilience to disaster.

Multivariate regression analysis found that independentvariables explained 37% of the variance in human capital-based resilience to disaster (Table 5). Five independentvariables, mainly, explained human capital-based resilienceto disaster. Education appeared to have a negative impact onhuman capital-based resilience to disaster ( = -.25, p =.000). A similar, negative impact on human capital-basedresilience to disaster was noted with regard to perceptionsrelated to recovery dynamics ( = -.23, p = .000). Firmowners who had a greater number of years engaged in thecurrent profession had higher human capital-based resilience

42


( = .24, p = .000). Positive association was also notedbetween human capital-based resilience to disaster and notonly the number of employees ( = .26, p = .000), but alsothe amount of loan taken before disaster ( = .23, p =.000).

Natural Capital-based Resilience

The mean weighted score for natural capital-basedresilience was 12.51 (SD = 3.38) (not shown). As indicatedin Table 4, the Pearson correlation coefficients revealedthat several independent variables had a significantrelationship with financial loss due to disaster. Educationappeared to have a significant, negative correlation withnatural capital-based resilience to disaster (r = -.18, p =.004), suggesting that educated owners had lower naturalcapital-based resilience compared with uneducated owners. Inaddition, market type (r = -.19, p = .003) and perception ofrecovery dynamics (r = -.38, p = .000) had a significant,negative correlation with natural capital-based resilienceto disaster. On the other hand, owners’ monthly income had asignificant, positive correlation with natural capital-basedresilience (r = .22, p = .001). Additionally, number ofyears of engagement with the current profession (r = .34, p= .000), number of employees (r = .29, p = .000), monthlyincome before disaster through revenue or disposal (r = .26,p = .000) and loan received prior to disaster (r = .17, p =.002) had significant positive correlations with naturalcapital based resilience.

Multivariate regression analysis found that independentvariables explained 25% of the variance in natural capital-based resilience to disaster (Table 5). There was a negativeimpact on natural capital-based resilience to disaster forperception related to recovery dynamics ( = -.26, p =

43


.000). On the other hand, a positive association manifesteditself between natural capital-based resilience to disasterand the number of years of engagement in the currentprofession ( = .23, p = .000), between natural capital-based resilience to disaster and the number of employees (

= .16, p = .012), and between natural capital-basedresilience to disaster and the amount of loan received priorto disaster ( = .17, p = .002).

Discussion

The study attempted to investigate business resilience tonatural disasters, specifically floods in Bangladesh, interms of social, financial, physical, human, and naturalcapital. To investigate different kinds of businessresilience, we developed several items for each scale.Results indicate that the items of each of the capital-basedresiliences are valid and reliable, suggesting that theCapital-Based Approach developed by Mayunga (2007) can beused to further test validity and reliability. Recentresearch suggests community development theory hasdemonstrated that success and sustainability depend on theability of a community to appreciate, access, and utilisethe major forms of capital (Beeton 2006). However, capitalas a concept has not been acknowledged, nor regarded as acentral focus in understanding and assessing communitydisaster resilience. As is hoped, our capital-basedresilience findings may yet serve as an important tool forunderstanding community-level disaster resilience.

The level of education of the respondents had a mixedeffect on business resilience, in that education had apositive effect on physical capital-based resilience, but anegative effect on humancapital-based resilience. In anotherstudy (Danes et al. 2009), education had no significant

44


impact on post disaster family firm resilience. Relativelyspeaking, positive associations with physical capital-basedresilience may be due to an enhanced ability to choose abetter place for setting up a small firm, a place lesslikely to be inundated by floods and where premises alreadyexist (e.g., schools and other public places or buildingsnot usually inundated) from which to conduct businesstemporarily until such time as the situation returns tonormal. Positive associations with physical capital-basedresilience may be due also to an increased ability to betterpredict potential future disaster. Unexpected findings inthe field of human capital-based resilience may be theresult of other factors, such as less human capital (e.g.,no workers with previous experience in tackling disasters, alack of formal training among employees, and a lack offormal education among workers), which together counteractany positive influence hat the owners’ level of educationmight have on their business.

Our study established that the greater the number ofyears of engagement in the current profession, the higherthe resilience, except in the case of physical capital-basedresilience, which had a negative association. Severalstudies showed that, often, firms that experienced disasterssurvived tough economic times not necessarily because theywere well-managed, but because of family fortitude (Hammond2003). Positive association may be the outcome of severalfactors, such as a relatively higher level of experience inboth the linear (smooth) and curvilinear (ups and downs)progression of business, an owner’s lasting patience,effective and pragmatic planning to minimise any risk ofclosure of the firm while simultaneously maximising itsoperation, hands-on understanding of how to restart abusiness immediately after disaster, and a solid businessnetwork from which support is expected in times of need.

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Conversely, ‘liability of newness’ as experienced by acompany newly involved in business activities may contributenot insignificantly to any lower resilience on the part ofsuch company. Previous studies also support the liability ofnewness argument (Carroll and Delacrok 1982; Carroll and Huo1986; Freeman et al. 1983), which contends that neworganizations need to invest time and effort to establishnew roles and encourage their members to socialize(Stinchcombe 1965), if they are to compete successfully withestablished firms for custom, both old and new (Singh andLumsden 1990).

The study revealed that the higher the number ofemployees, the higher the capital-based resilience. Inanother study (Danes et al. 2009), it was shown that thesize of the firm or the number of employees had asignificant association with business resilience. The largerthe firm, the greater the complexity of relationships, whichin turn affects the resilience of family run businesses(Bryant and Zick 2005). Firms with numerous staff haveseveral advantages. First, they have the manpower necessaryto remove to safety important goods and materials beforeflooding occurs, Second, they are likely to have workerswith prior experience in tackling natural disasters. Third,in moments of crisis, they can rely on financial help fromtheir staff. Fourth, they are able to take advantage of thedivision of labour to speed up recovery works. In addition,having a full staff complement is an indication that thefirm has a high turnover (at least to a level where it canafford the payroll), substantial savings and fixed assets,and an increased ability to acquire bank loans. Conversely,that an enterprise is very small constitutes a liability forthe business as it tends to have fewer resources and limitedaccess to credit compared with medium or large enterprises(Dahlhamer and Tierney 1998). Consequently, small scale- and

46


micro enterprises usually cannot afford to undertakepreparedness and mitigation measures, such as purchasingbusiness interruption and hazard insurance (Alesch et al.1993).

The finding that businesses oriented towards regional,rather than local, production had a negative impact onphysical capital-based resilience is rather surprising, notleast because it is expected that regional oriented firms bebetter equipped in terms of financial capacity, humanresources, and their own physical set up. The situation canperhaps be ascribed to firms focusing on regional productionhaving developed relatively few local networks. It may alsobe that buyers, in particular, themselves obliged toguarantee their customers a continuous supply of product,cease doing business with suppliers unable to respectdelivery times, be it due to the closure of firms, delayedproduction, or problems maintaining an uninterrupted supplyof the goods in question. By contrast, close familial andsocial relationships ensure that businesses oriented towardslocal production have the advantage of being able to rely onsupport from local buyers and suppliers. In a number ofcases, these enterprises count as investors’ relatives aswell as neighbours, who then acquire partnership status.

Higher monthly income through revenue, or higher monthlydisposable income, before disaster, had a positiveassociation with physical capital. Higher income has severaladvantages; it fosters, positively, business activities bothin times of crisis and non-crisis. Higher income is likelyto result in higher savings; a business able to rely on itsown financial resources during a crisis is better positionedto continue its activities post crisis. In another study(Danes et al. 2009), a positive association with resiliencewas demonstrated in those cases where the family businesswas headed by a female. An increase in resilience is also

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more probable in times of stability; higher gross income islikely to mean greater financial resources at one’s disposal(Danes et al. 2009). However, the presence of frequentfinancial problems or stress may lead to the development ofa repertoire of responses to disruption, thus alsoincreasing resilience (Jang 2005). The financial conditionof a business, pre-disaster, also affects business recovery.Evidence suggests that marginal or in financial troubleprior to disaster have difficulty in recovering (Durkin1984, cited in Dahlhamer and Tierney 1998).

Findings revealed that the greater the loan receivedprior to disaster, the greater the business resilience. Predisaster loans or financial assistance increases a firm’sincome, diversifies its asset bases and sources oflivelihood, provides support for housing improvements andbuilding reserves necessary for running the business in caseof emergency, and provides risk management alternatives andsolutions that increase the ability of the affectedentrepreneurs to cope. Nigg (1995) argues that recoveryinvolves more than the reconstruction of the builtenvironment. Evidence suggests that while there is nouniversal agreement as yet on the ultimate impact ofmicrofinance or financial assistance for povertyalleviation, the role of microfinance in reducingvulnerability to, and from, disaster is generally accepted(UNISDR 2005).

Unexpectedly, this study found that the higher the scorein perceiving recovery, the lower the resilience in a wholerange of capital-based resiliences. Disaster is what itsvictims and respondents perceive it to be: it is the sum ofmany personal catastrophes, but with a certain gestalt as itrepresents a shock to the social organism (Alexander 2000).One would expect entrepreneurs who perceive potentialdisaster to be better capable of mitigating such disaster

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with a planned approach. The opposite findings in thisstudy, however, may be an indication that perception doesnot necessarily equate to essential, supportive structuressuch as financial capacity, housing facilities alternativeto those currently being enjoyed by the business, and groupsolidarity to better tackle potential disaster. In settingslike Belkutchi, it is not possible to earmark sufficientfunds in preparation for tackling disaster since most of thesmall scale entrepreneurs there operate on extremely limitedbudgets.

Although this study has several very importantindications, especially for community resilience based ondifferent capitals, it also has several limitations. First,some of the respondents were reluctant to give of their timewhen initially approached by the interviewer. Whenapproached later, although they were more forthcoming theirresponses were hurried and largely perfunctory, ostensiblydue to business commitments, to the extent that interviewersdeemed five of the respondents unsuitable, and they wereeventually dropped from the final analysis. Second, capital-based resilience was measured using the five “capital-basedresilience” capitals proposed by Mayunga (2007) despite thelack of an already developed scale. Items were selectedbased on the components used by Mayunga in his proposedmodel. As a result, in this study, items of each offinancial, social, human, physical, and natural capital weredeveloped anew. Although this study showed the items to bereliable, such scales require further reliability testing soas to become the established scale for measuring capital-based disaster resilience. Finally, as was expected,participant response revealed a certain recall bias.Respondents are mostly likely to be able to cite and recallevents and related issues immediately post disaster. Thelonger the recall period, the less accurate the data proves

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to be on less salient events (Pierret 2001). As it wasexpected that recall bias would probably be a threat to thegeneralizability of the findings, in order to minimise suchbias interviewers were instructed to ask a question severaltimes so that participants could recall the events as wellas related issues with maximum accuracy.

Implications

Despite its several limitations, this study has boththeoretical and applied implications. To our knowledge,there has been no study conducted on the capital-basedframework covering social-, financial-, physical-, human-,and natural capital suggested by Mayunga (2007). Ourfindings clearly indicate that resilience can indeed beunderstood from a capital point of view. We suggest furtherstudy to verify whether scale items developed usingMayunga’s framework can be used in other settings.

This study has several applied implications. First, thenumber of years of engagement in business had a generallypositive impact on business resilience to disaster. Whilethere is no valid substitute for the hands-on experiencegained by actively running a business, properly focused andeffectively developed training provides an alternative routeto better understanding how to ensure a firm functionseffectively and how to tackle unexpected events likedisaster. Second, markets oriented towards regionalproduction have less resilience and are therefore morelikely to encounter serious problems. This is an importantissue and one which, with a view to further improvement,merits consideration. Understanding why such markets havelower resilience and how such resilience may be increasedconstitutes an initial step towards remedying the situation.An excellent second step may be the undertaking and

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subsequent implementation, of future studies and theirrecommendations, respectively, particularly if these studiesare of a qualitative nature.

Finally, as this study indicates, loans received prior todisaster increase all types of a firm’s resilience todisaster except physical capital-based resilience. The usualpractice is for different organisations to come to theassistance of small scale businesses post disaster, at atime when owners can do very little to cope with the impactof the event. Our suggestion, therefore, would be to provideloan facilities to such enterprises prior to disaster. Inthis regard, it may be feasible for studies to be undertakento identify those businesses deserving of financial aid, andin what amount. Proper scientific information, however, isneeded in this instance in order to predict accurately anypotential disaster, floods in particular, in the study area.

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The Natural Hazards Center at the University of Colorado atBoulder plays a vital role in reducing the risks posed bynatural, technological, and human-induced hazards. Foralmost thirty years the Hazards Center has served as anational and international clearinghouse of knowledgeconcerning the social science and policy aspects of hazardsand we continue to advocate sustainability,interdisciplinary partnerships, and an all-hazards approachto the management of extreme events. Our basic goal is tostrengthen communication among researchers, practitioners,policy makers, and other concerned individuals, and we focuson the collection and sharing of research and experiencerelated to disaster mitigation, preparedness, response, andrecovery. The Center publishes a wide variety ofpublications, often freely available on our Web site,ranging from works-in-progress to peer-reviewed studies,with the intent of reaching as wide an audience as possible.These publications include the Natural Hazards Observer, DisasterResearch (e-newsletter), the Natural Hazards Review (incooperation with the American Society of Civil Engineers),full-length research studies (monographs), working papers,

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and special publications such as Beyond September 11: An Accountof Post-Disaster Research. Information about these publicationsas well as other Hazards Center projects, such as theDisaster Grads listserv for students interested in hazardsand disasters, our extensive library and its onlinesearchable database HazLit, and the Quick Response ResearchProgram, is available on the Web at:

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