Khan & Sayem: Enterprise Resilience in Bangladash
International Journal of Mass Emergencies and Disasters November 2012, Vol. 30, No. 3, pp. 328–356.
Resilience of Small Scale Enterprises to Natural Disasters: A Study of a Flood Prone Area in Bangladesh
Mohammad Aftab Uddin Khan International Federation of Red Cross and Red Crescent Societies Graduate Institute of International and Development Studies, Geneva, Switzerland
Amir Mohammad Sayem Independent Researcher, Dhaka, Bangladesh
Email: [email protected]
Abstract
The study investigated the level of resilience of, and identified factors affecting resilience in, small scale enterprises. A cross sectional survey was carried out with a sample of 254 micro entrepreneurs in a subdistrict of the Sirajganj district in Bangladesh. To investigate different sorts of business resilience, we developed several items for each scale. Results indicate that the items in each of the capital-based resiliences are reliable and valid, suggesting that the capital-based approach developed by Mayunga (2007) can be used to further test validity and reliability. Multivariate regression analysis revealed that several factors had significant impact on different sorts of capital-based resilience. Although there was variation in capital specific resilience, education, monthly income, number of years of engagement in the current profession, number of employees, type of market, monthly income through revenue or disposal before disaster, loan received prior to disaster, and perception of recovery dynamics all had significant impact. The study concluded with theoretical and applied implications of the findings.
Keywords: Resilience, small enterprises, flood, Bangladesh.
Introduction
Recent catastrophic events—the tsunami earthquake in Japan in 2011, the devastating earthquake in Haiti and Chile in 2010, the catastrophic flood in Pakistan in July 2010, Cyclone Nargis in Myanmar in 2008, the tropical cyclone Sidr along the coastal areas of Bangladesh in 2007, Hurricane Katrina along the U.S. Gulf Coast in 2007, and the devastating Indian Ocean Tsunami in 2004—are stark reminders of the global significance of natural hazards and their impact on the socioeconomic vulnerability of
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Khan & Sayem: Enterprise Resilience in Bangladash populations, a vulnerability aggravated in many cases by climate change. Data suggest that the number of victims rose from 198.7 million in 2000 to 217.3 million in 2009 (Guha-Sapir et al. 2010). Natural disasters cause billions of dollars’ worth of damage, a figure that varies grossly from region to region. Data from Guha-Sapir et al. (2010) indicate that, in terms of monetary value, total damage sustained by the Americas was the highest (US$56.84 billion), followed by Asia (US$34.76 billion), Europe (US$17.70 billion), Australia and Oceania (US$14.51 billion), and Africa (0.06 billion). The same report also reveals that Asia’s share of global damages in 2010 (28.1%) was below its 2000 to 2009 share of 39.8%. Damage in Europe accounted for 14.3% of global reported damage in 2010, while Oceania’s share amounted to 11.7%, with damage in Africa accounting for only 0.05% of global economic damage from natural disasters in 2010, marginally less than its 2000 to 2009 share of 1.2%. Disaster often results in the region or locality being severely affected. Tierney and Nigg 1995) found that one consequence of floods in the city of Des Moines was that of lifeline disaster. The survey indicated that flooding caused power outages that affected 35,000 households as well as the entire downtown business district, leaving a total of 300,000 residents without potable water or electric power. A total of 80% of the businesses in Des Moines reported being without water due to the flooding. Although only some parts of the community experienced direct flood damage, damage to the city’s water treatment and sewage facilities affected the entire community, and flood related electricity service interruptions were extensive. Loss of critical lifeline services, particularly water, was the main cause of business closure in the affected region. Each year, small businesses in nations around the world suffer major losses as a direct consequence of natural disasters such as earthquakes, severe storms, and flooding. Evidence indicates that in most communities it is small businesses that are the major employers, and that the losses caused by disasters often result in these businesses closing (Alesch, Holly, Mittler and Nagy 2001). It has been recognised that businesses play an important socioeconomic role in community functioning by providing products and/or services, and employment opportunities, and represent an important source of revenue via the taxes they pay (Cochrane 1992). Despite this, disaster research has thus far had a propensity to focus on families, households, and government agencies (Burby 1998; Tierney, Lindell, and Perry 2001) rather than businesses. Earlier research emphasised the need to understand the reason behind the failure of small businesses to devise ways to prepare for, and eventually recover from, natural disasters (Alesch et al. 2001). Of even greater importance, however, particularly in regard to the development of suitable protective measures, may be the resilience of such companies in the face of natural disaster. Response to disasters before, during, and after they occur is a matter of both hazard and disaster management practice and public policy at national and international level. Survivors face the arduous task of rebuilding at personal, structural, and economic levels.
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In general, part of the rebuilding process is discharged via a combination of foreign and domestic humanitarian relief aid. Yet, despite tremendous effort on the part of governments, as well as international and national humanitarian and development agencies, the actual impact and proper focus of post disaster economic recovery strategies remain somewhat uncertain from an empirical point of view. Since the 2004 Indian Ocean tsunami, donors have become aware that such strategies should place greater emphasis on the rehabilitation of the local economy (Bennett et al. 2006), thereby re- empowering beneficiaries without delay, personally and economically. The very concept of disaster resilience and livelihood recovery gained wide interest among scholars and practitioners in the wake of the 2004 tsunami, becoming popular after the adoption of the Hyogo Framework for Action 2005–2015: Building the resilience of nations and communities to disasters (Manyena 2006). Given that in the field of post disaster livelihood and economic recovery management scientific knowledge and practical experience are both very limited, it is unlikely that the challenges that lie ahead will be fully met in the short term, especially in the developing world (Sahni and Ariyabandu 2003; Seck 2008). Studies show that the creation and sustainable development of new microeconomic and small business activities, even more so in vulnerable developing countries, is the result of a complex chemistry of enabling factors, which can be delivered neither by local government nor foreign donor agencies alone (Runyan 2006). Such chemistry, however, is far less attainable in the unstable, abnormal circumstances generated by natural disasters and/or violent crises, the urgent need to create employment and income generating activities as rapidly as possible notwithstanding (ILO 2005). Business sustainability after a natural disaster is complex and multidimensional, and several authors report that the same depends not only on assistance, but also on the company concerned, local community systems, market structures, and the pattern of interactions between family members. In developing countries small businesses, largely family run, are often conducted from the family residence (Rose and Liao 2003; Rose, Oladosu, and Liao 2005; Webb et al. 2000). The greater the support from family and social or community networks, the greater the resilience and long term sustainability after a disaster.
Concept of Disaster Resilience
Timmerman (1981) is credited with being the first to use the concept of resilience in relation to hazard and disaster (Klein et al. 2003). Timmerman (1981) defines the term as the measure of a system’s capacity, or part thereof, to absorb and recover from hazardous events (Klein et al. 2003). Subsequent to the work of Timmerman (1981), although numerous definitions of the concept of resilience in the field of hazard and disaster have emerged, no consensus among researchers and practitioners on one common definition has yet been reached. The majority of authors use capacity or ability to define disaster
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Khan & Sayem: Enterprise Resilience in Bangladash resilience, in effect equating the notion of resilience with the capacity or ability of people, individually or collectively, to cope with disaster (Mayunga 2007). Other authors, however, define disaster resilience in terms of the speed with which such people recover from disaster, an aspect that gave rise to several 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 be studied (Rodriguez et al. 2007). Researchers studying the economic impacts of disasters have tended to focus on units of analysis larger than individual firms and enterprises, such as community and regional economies. Until very recently, very little was known regarding business vulnerability, loss reduction measures adopted by businesses, disaster impacts on businesses, and business and enterprise resilience and recovery. Systematic research was lacking despite the importance of business for society (Tierney 2007). Businesses are the foundation of local, regional, and national economies; when businesses are affected by disasters, that disruption produces direct and indirect business losses, as well as having a ripple effect on the wider economy. The destruction of or damage to businesses, along with disaster related closures, result in job losses, thereby negatively affecting incomes and creating even greater challenges for households, neighbourhoods, and communities as they attempt to recover from these catastrophes (Rodriguez et al. 2007). After disasters, business owners and entrepreneurs, too, face a host of challenges, including how to finance business recovery while simultaneously coping with damage to structures and contents of commercial and residential premises (Cochrane 1992 as cited in Zhang et al. 2009). Further research on the impacts of disaster on business units is needed if business communities and entrepreneurs in disaster prone areas are to be better prepared for the upheaval caused by natural disasters. Although large scale studies are useful for understanding the national and regional impacts of disasters, their level of aggregation obscures the differential impacts of disasters on specific types of businesses within the affected communities (Zhang et al. 2009). As a result, a microanalytic approach is needed to provide guidance to community planners and business owners in developing better methods for minimising the impacts of disaster (Zhang et al. 2009). As commercial activities involved in the manufacture/distribution of goods and/or the provision of services (Zhang et al. 2009), businesses are affected in a number of ways; direct physical damage not only to buildings, but also to vehicles and stock (Whitney et al. 2001). The outcome of any disaster in a given community is borne by businesses and the local community alike. Households, other businesses, and even government all play important roles as consumers and suppliers in business operations (Zhang et al. 2009). In addition, although small scale- and micro businesses are usually conducted in a local area, their operations are not necessarily confined to the local area; they may be engaged in servicing not only the local community, but also communities further afield.
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Rose (2007) argues that there are two aspects of business resilience: static and dynamic. Rose interprets static resilience as essentially making the best of the resources available at a given point in time, which affects the time-path of the economy. Although static resilience has great potential to reduce losses incurred in a disaster in a straightforward and inexpensive manner, this aspect has usually been overlooked in favour of its dynamic resilience counterpart, which focuses on the speed of recovery and which dominates most of the engineering-based literature on the subject. He further demonstrates that dynamic resilience is more complex from an economic standpoint, and more expensive. While no less important, dynamic resilience is given less attention in this paper in order to compensate for the neglect of static resilience in the literature to- date. He further distinguishes two more types of business resilience: inherent and adaptive (Rose 2007). Inherent resilience refers to the ordinary ability to deal with crises (e.g., the ability of individual firms to substitute other inputs for those curtailed by an external shock, or the ability of markets to reallocate resources in response to price signals). Adaptive resilience, in contrast, refers to the ability in crises to maintain function on the basis of ingenuity or extra effort (e.g., increasing input substitution opportunities in individual business operations or strengthening the market by providing information to match suppliers with customers). Resilience is, of late, being considered in the light of the capital approach (Tierney 2006) to include the five major forms of capital: social, economic, physical, human, and natural. As the literature suggests, the notion of capital aligns very well with the concept of sustainability (Smith et al. 2001), which is related, and often linked, to the concept of disaster resilience (Tobin 1999; Brown and Kulig 1996/1997). The essence of using the capital approach is that capital consists of those components necessary for the development of a sustainable community economy. The conventional wisdom here is that the more economic opportunities the community has, the more potential it possesses for reducing the impacts of disaster, hence, the more resilient the community. Healthy businesses are synonymous with economically healthy communities, and it is this mutually beneficial relationship that could account for firms in more economically developed communities’ potentially functioning well, post disaster, compared with firms in communities where economic development is relatively poor. Community resilience is a complex process due to the dynamic interaction between people, communities, societies, and the environment. There are currently many conceptual frameworks being proposed to measure this concept: (Brown and Kulig 1996/1997; Tobin 1999; Adger 2000; Buckle 2006; Foster 2006; Tierney 2006). Generally, most of these frameworks conceptualise disaster resilience in the same way, focusing as they do, similarly, on factors that could reduce vulnerability and increase community resilience. Such factors include economic resources, assets and skills, information and knowledge, support and supportive networks, access to services, and shared community values. One limitation of most of these frameworks is that they only
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Khan & Sayem: Enterprise Resilience in Bangladash tend to focus on one, or at most, a few dimensions of disaster resilience and do not adequately take a broader view of the concept (Mayunga 2007). The capital-based approach is not new in the field of hazard and disaster; it has been widely applied in sustainable development and poverty alleviation programmes (DFID 1999). Mayunga (2007) proposes a new framework, one that focuses on five community components— social, economic, physical, human, and natural capital. Putnam (1995) defines the concept of social capital as a feature of social organisations such as networks, norms, and social trusts that facilitate coordination and cooperation for mutual benefit. In the context of community resilience, it reflects the fact that community ties and networks are beneficial because they allow individuals to draw on the social resources in their communities, and increase the likelihood that such communities will be able to adequately address their collective concerns (Green and Haines 2002). Economic capital denotes financial resources that people use in order to earn their livelihood. It includes savings, income, investments, and credit. The contribution of economic capital to building community resilience is straightforward in the sense that it increases the ability and capacity of individuals, groups, and communities to absorb the impacts of disasters and accelerates process of recovery (Mayunga 2007). Physical capital refers to the built environment, comprising residential housing, public buildings, business/industrial premises, dams and levees, and shelters. It also includes lifelines such as electricity, water, and telecommunication, and critical infrastructure such as hospitals, schools, fire and police stations, and nursing homes. Economists define the concept of human capital as the capability, both innate and derived or accumulated, embodied in the working age population that allows it to work productively with other forms of capital to sustain economic production (Smith et al. 2001). The term natural capital refers to natural resources, such as water, minerals and oil, land on which to live and work, and the ecosystems that keep water and air clean, and the climate stable (Smith et al. 2001).
Disaster Research in Bangladesh
Disaster research in Bangladesh has been conducted using six major approaches: geographical, behavioural, structural, historical-structural, sociological, and anthropological (Nasreen 2004). Past research on natural disasters (such as floods, river bank erosion, earthquakes, and cyclones) in Bangladesh has followed the geo- anthropological approach of the Chicago-Colorado-Clark-Toronto School of Natural Hazard Studies 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 with people's behaviour; their perceptions, attitudes, beliefs, values, responses, and personalities. They are concerned with discovering people's choices, behaviour, and adjustments to disaster,
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Khan & Sayem: Enterprise Resilience in Bangladash that is, how they viewed the catastrophe and how they perceived alternative opportunities available to them for coping with such event (Nasreen 2004). Although the geographical perspective, concerning among others floods, cyclones, riverbank erosion, earthquakes, and arsenicosis, is much discussed, some of the writings do embrace the social impact that disaster has on people, and how they cope. While again following the geographical approach to disaster (Ahsan and Khatun 2004), a recent publication focuses on the aspect of gender. Using the behavioural approach, Hossain et al. (1987) examine whether rural people in flood free and flood prone areas adopt different survival strategies, and while they also focus on the responses of rural people in general, they do not particularly focus on the responses of women. Shaw (1989), highlighting the problems of poor women in a relief camp in Dhaka city, notes how it is women who bear the social burden of shame when forced to live among strangers, and draws attention to the difficulties they face when trying to maintain parda during floods. In their study on riverbank erosion and floods, Rahman and Haque (1988) argue that people's ability to adjust to catastrophe should be viewed as an extension of existing social and natural systems. Pioneering disaster research (Nasreen 2004) based on the sociological approach provides a detailed picture of disaster as experienced by rural households. It focuses on the pre-, during-, and post disaster activities performed by men and women during floods. The author argues that whereas disaster affects both sexes, the burden of coping with floods falls heaviest on women. When floods occur, men in rural areas lose their jobs, leaving women to shoulder the responsibility of maintaining the family. Nasreen (2004) argues that not only do poor rural women ordinarily have very few options at their disposal to deal with their problems, but their role in times of disaster is complicated even further, encompassing as it does a complete range of socioeconomic activities: bearing and raising children, collecting food, fuel, water, fodder, and building materials, and safeguarding household belongings. Women also represent a productive potential (i.e., daily labour in construction activities, home based gardening, small scale trade and sales, producing handicrafts, etc.), none of which was earlier acknowledged (Brouwer 2007).
Floods in Bangladesh and Belkutchi
The unique geomorphological and climatic conditions of the country have made Bangladesh vulnerable to monsoon riverine floods. Since its independence in 1971, the country experienced 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 normal floods, which cover large parts of the country for several days or weeks during July and August, the floods in 1998 lasted until mid September in many areas, killing hundreds of people and destroying roads, houses, crops, and other assets.
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Khan & Sayem: Enterprise Resilience in Bangladash
In late June 2004, heavy monsoon rains swelled the waters of the Meghna River, which reached its peak in early July. The Jamuna and Padma Rivers also burst their banks in early July due to heavy rains in the north of the country, causing flash floods in the northern and west-central districts. The floods spread, eventually impacting Dhaka and other central districts, and adversely affected the economy by damaging infrastructure, reducing economic growth, and upsetting macroeconomic balances. Preliminary analysis shows that, as a result of the flood, the economic growth of fiscal year (FY) 2005 (July 2004 to June 2005), which was earlier projected to 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 most affected in the central-north part of the country, notably the Sirajganj district because of its geophysical location (Asian Development Bank 2004). Falling under the Rajshahi division and with an area of 2,497.92 km2, the Sirajganj district is bound by the Bogra district to the north, the Pabna district to the south, the Tangail and Jamalpur districts to the east, and the Pabna, Natore, and Bogra districts to the west. The main rivers are the Jamuna, the Baral, the Ichamati, the Karatoa, and the Phuljuri. As a result, it is one of the most flood prone areas in Bangladesh. About ten per cent of the area of the Chalan Beel wetland is located in this district’s Tarash upazila (a subdistrict that is similar to the county subdivisions found in some western countriesand is Bangladesh’s second lowest tier of regional administration). Mostly, however, it is the River Jamuna that passes through here. Main industries/occupations include agriculture 35.49%, commerce 11.98%, handicraft 5.59%, service 5.49%, agricultural labour 21.45%, wage earning labour 5.77%, industrial labour 2.78%, and others 11.45% (Banglapedia, undated). The district is affected by both normal and flash floods almost every year. As a result, people suffer from different flood related consequences, such as migration to other cities, temporary or permanent closure of business activities, and crop losses (Banglapedia, undated). Within the Sirajganj district, the Belkutchi subdistrict, one of the most flood prone, was selected as the study area due to its low lying position and location to the side of the mighty River Jamuna. Since 2000, devastating floods occurred in 2004 that inundated more than half of the areas of this subdistrict. Although the magnitude was to some extent less than normal, the floods in mid July 2007 inundated major parts of this subdistrict. As the people in this area are predominantly agricultural farmers and owners of small firms, in times of severe flooding production/trading is usually halted, causing great economic hardship and impacting directly on the ability of such businesses to survive. In a number of cases, owners are unable to resume business immediately after the disaster. As in the case of the 2004 and 2007 floods, even those who do manage to do so are most often obliged to borrow money from formal (donor agencies, money lending agencies, NGOs, etc.) and informal (relatives, friends, and community members) networks in order to re- establish themselves.
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Despite being of importance, there has been very little or no academic or policy research done on disaster induced small business vulnerability in Bangladesh. This study focuses on the resilience to floods of small scale and micro enterprises in the Belkutchi subdistrict and, more specifically, attempts to investigate the level of resilience of, and identify the factors affecting resilience in, small scale enterprises. In order to understand business resilience, this study applies the proposed capital-based community resilience framework developed by Mayunga (2007), consisting of five capital-based disaster resilience elements. We choose to apply community resilience since business functions within community and community is considered to affect business; a community with social, financial, human, physical, and natural capital is therefore expected to support business to an even greater extent. Our specific hypotheses were: 1) educated business owners have a higher capital-based resilience; 2) the higher the monthly income, the higher the capital-based resilience; 3) the greater the number of years engaged in the current profession, the higher the capital-based resilience; 4) the greater the number of employees, the higher the capital-based resilience; 5) firms oriented towards regional production have a higher tendency for capital-based resilience; 6) the higher the monthly income through revenue, or the higher the monthly disposable income, pre-disaster, the higher the capital-based resilience; 7) the bigger the loan received prior to disaster, the higher the capital-based resilience; and 8) the greater the perception of recovery measures, the higher the capital-based resilience among owners of small scale firms.
Methods
Area of Study
This study was carried out in the Belkutchi subdistrict of the Sirajganj district. The Belkutchi subdistrict is located on the River Jamuna and has a good communications system and a good road network that 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 the proximity of the Jamuna, the boat becomes the preferred mode of transport. More importantly, being mostly char (low lying area frequently inundated by flood), 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 have some form of formal and informal education, with predominance in male education. The area’s natural resources include arable land, rivers, ponds, a variety of trees, and a variety of fish. Although most of the arable land can be cultivated two or three times a year, erosion has reduced the amount of cultivable land—land that is mostly low lying and prone to flooding in the rainy season. There is, however, some land in higher areas that can be cultivated all year round. The second important sector comprises businesses oriented
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Khan & Sayem: Enterprise Resilience in Bangladash towards trade and production. Historically, Belkutchi is famous for its production of textiles, produced using both hand and power looms.
Respondents
Taking part in the study were 254 small scale entrepreneurs of both sexes, doing business in various fields: groceries, power looms, tea stalls, spice grinding, carpentering, blacksmithing, repairing electronics, and the wholesale selling of raw materials, among others. Small scale enterprises in the area are of two types: sole and partnership, and are usually oriented towards production, retail, and service. To some extent, microentrepreneurs received training in business as well as in disaster mitigation from local NGOs, government training agencies, etc. It must be noted that as there were no secondary data regarding disaster impact available at company level, and as the business owners were assumed to be sufficiently reliable respondents, the study canvassed them in order to obtain much-needed primary data. While most respondents (65%) had received formal education, 35% had not. The majority of the firms (57.9%) had one employee, with a mean of 2.98 (SD = 3.63) employees. Employers’ average monthly income 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 the respondents, respectively. For 66.5% of the firms, the market for the product was local, whereas 33.5% enjoyed a regional market. On average, the firms had been in production for 7.35 years (SD=4.81). Specifically, more than one-third of the firms (35.4%) had been in production for 5 to 6 years, followed by 7 to 8, 9 to 10, <4, and >10 years in 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 than two-fifths of the respondents (45.7%) had monthly incomes ranging from BDT5001 to BDT7500, followed by BDT7,501 to BDT10,000 (24.0%), < DT5,000 (22.0%), and > BDT10,000 (8.3%). On average, respondents received BDT1,929.13 (SD=5,864.31). The majority of the respondents received > BDT5,000, whereas only 16.3% of the respondents received Sampling Technique Sample selection was done by a multistage procedure. In the first stage, one union was randomly selected from the Belkutchi subdistrict. In the second, a systematic random sampling technique was used to identify small scale entrepreneurs, which systematic sampling consisted of selecting every kth sampling unit of the population after random selection of the first sampling unit (Frankfort-Nachmias and Nachmias 1996; Bryman 337 Khan & Sayem: Enterprise Resilience in Bangladash 2008). Selection of the first small scale entrepreneurs was determined by random process. First, one small scale entrepreneur was selected randomly from ten small scale entrepreneurs. The second sample was selected with an interval of five; that is to say, if initially the card holding the number 3 was selected, then the rest of the samples were selected on the basis 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 first sample. Interviewers visited small-scale entrepreneurs as per the number assigned by the study team. Data Collection Procedure Data were collected using a semi structured questionnaire primarily prepared on the basis of available literature and in line with study objectives. Later, it was pilot tested on five potential respondents. The pilot test was carried out in order to ascertain the suitability of the questionnaire and/or whether any further additions were necessary, and to direct the sequence of the questionnaire. Insights from the pilot test were incorporated into the finalised questionnaire, which was then used to collect quantitative data. Data were collected via one-to-one interviews. Before collecting the data, several preparatory tasks, such as identifying interviewers and training them, were undertaken. First, two interviewers were selected, based on their prior experience in data collection in the area of study. Interviewers were then trained and after completion of the training five pretests were carried out in the presence of the researcher in order to observe whether the interviewer was able to elicit the information accurately. The researcher supervised the entire data collection process and in some cases re-interviewed households in order to verify the data collected by the interviewer. Due informed consent was obtained prior to interviews being conducted; interviewers explained the objective of the study and identified the researcher, and the rights of respondents to participate, or not, in the study. Affirmative responses on the part of respondents signalled the start of the interviews. Although interviewers approached 270 potential respondents altogether, 16 owners of small scale firms declined to participate on the grounds that they did not have the time. Interviews took four months, from September to 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 his theoretical framework, we developed several items for each of the capitals. Social capital-based resilience was measured using seven items (presented in Table 1). 338 Khan & Sayem: Enterprise Resilience in Bangladash Table 1. Mean and Standard Deviation (SD) of Different Capital-based Resiliences Scale Items Mean SD Dependent 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 1.07 Dependent Variable II: Financial Capital The amount (BDT) of per capita household income 2532.92 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 73062.99 155966.43 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 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 1.01 Dependent 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 0.85 Dependent 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 2.06 339 Khan & Sayem: Enterprise Resilience in Bangladash Financial capital-based resilience was measured using 10 items; however, as all respondents did not respond to one item (i.e. the amount you invested in other businesses), it was deleted from the final analysis (e.g., the amount of money invested in other business or income generating activities). Eight items were used to measure physical capital-based resilience; however, as two items had no universal response, they were deleted from the final analysis (e.g., “The number of shelters within three square km from your business entity” and “The amount of water you get during disaster”). Human capital-based resilience was measured using 10 items, while natural capital was measured using 3 items. Measurement of Independent Variables In total, eight independent variables were used to investigate their association with dependent variables (see Table 2). The independent variables included the participant’s education, number of years of engagement in the current profession, participant’s monthly income, monthly income through revenue or disposal prior to disaster, total number of employees, types of markets, amount of loan received prior to disaster, and perception of recovery dynamics. The participant’s education was measured by dichotomous measures (e.g., 0 = no and 1 = yes). Monthly income in BDT was measured as the total average income from the business as well as other sources. Monthly income through revenue or disposal prior to disaster was measured by the amount of average monthly income in BDT through revenue or disposal before disaster. The number of years of engagement in the current profession was measured in completed years. Table 2. Variable Definitions Variables Coding scheme Education 0=No 1=Yes Monthly income Interval Number of years engaged with the current business Interval Number of employees Interval Type of market 0=Local production oriented 1=Regional production oriented Monthly income before disaster through revenue or Interval disposal Loan received prior to disaster Interval Perception of recovery dynamics Index of eight items Social Capital Index of seven items Financial Capital Index of ten items Physical Capital Index of eight items Human Capital Index of ten items Natural Capital Index three items The number of employees was measured by the total number of employees of the firm at the time of the survey. The types of markets were measured by dichotomous 340 Khan & Sayem: Enterprise Resilience in Bangladash categories (e.g., 0=local production oriented and 1=regional production oriented). The amount of loan received prior to disaster was measured by the total amount of loan (in BDT) taken before disaster to run the business. Respondents’ perception of recovery dynamics was measured using eight items (see Table 3). Each item had a rating scale of 1 (fully agree) to 5 (fully disagree). The respondents rated each item within this score. A total score was obtained by summing the item scores. To assess their reliability as a single combined score of perception, Cronbach’s alpha was calculated. As the obtained value of Cronbach’s alpha (.80) was greater than the conventional threshold of .70, all the items were used as an index of perception 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., fencing around the house, 3.25 1.229 building house in high land etc. ) should recover more quickly than a community with all traditional buildings/houses 2. A community with all high income households should recover more 3.32 0.953 quickly than a community will all low income households 3. A community with all large businesses should recover more quickly than 3.26 1.024 a community with all small businesses 4. A community with all export-oriented businesses should recover more 3.48 0.915 quickly than a community with all local-oriented businesses 5. All lifeline mitigations should hasten recovery times 3.76 0.791 6. Mitigating transportation should hasten recovery more than mitigating 4.05 0.919 other lifelines 7. All planning and response measures should hasten recovery times 3.34 0.943 8. Agents should be less likely to fail or leave as more mitigation and 3.95 1.011 planning measures are taken Notes: The responses ranged on five points scale items: 1=strongly agree; 2=partially agree; 3=neutral; 4=disagree; 5=completely disagree Data Processing and Analysis After datacollection, data editing was done in two stages—one in the field and another just before data entry. During the field interview, each questionnaire was checked after data collection to identify any inconsistencies and/or missing information. In this regard, one interviewer cross checked the questionnaire of another interviewer. In cases of inconsistency, interviewers were asked to re-interview or re-collect the information so that the information provided by the respondents would be accurate. Only five interviews needed to be redone in order to collect information that was missing from the first interview. At the second stage, collected data were edited just before performing data entry. This time, the researcher edited the data to verify that there was no missing response or inconsistency. 341 Khan & Sayem: Enterprise Resilience in Bangladash After completion of the editing, the data were entered using SPSS for Windows 12.0 version and the data were analysed in three stages. In the first stage, univariate analysis was performed by frequency analysis for the variables. In the second stage, bivariate relationships to each dependent variable were calculated via Pearson correlation coefficients. In total, eight independent variables and five dependent variables were used to perform bivariate analyses (see Table 4). The independent variables were selected on the basis of relevance to the dependent variables. It should be noted that items of each of the capital-based resiliences were measured using different parameters, which made it impossible to analyse raw data. To use them as a single measure, data were weighted or smoothed by exponential method. This procedure was applied to all the capital-based resilience measures. In addition, in order to assess the internal consistency reliability of each capital-based resilience measure, Cronbach’s alpha was calculated for each of them. Cronbach’s alpha was calculated for the items in the social, financial, physical, human, and natural capital-based resilience measures. The resulting alpha values were 0.76, 0.81, 0.82, 0.79, 0.77, and 0.87, respectively. Cronbach’s alpha was more than 0.70 for all the resilience measures, suggesting that each capital-based resilience measure was adequate for use as a single index. In the third stage, multivariate linear regression analysis was performed because all the dependent and independent variables were measured at interval level, except for education and market type. However, these two variables dichotomized so they could be treated as interval data and included in the regression analyses. Each regression analysis was checked for multicollinearity among the independent variables by inspecting the zero order correlation coefficients to verify that no coefficient exceeded 0.50. Each of the dependent variables was regressed onto all of the independent variables using the enter method. All the analyses were performed using SPSS 12. Regression analysis results (b, Std Error, β and p value) are presented in Table 5. Results Social Capital-based Resilience The mean weighted score for social capital-based resilience was 13.11 (SD = 3.28) (not shown). As indicated in Table 4, the Pearson correlation coefficients revealed that the number of years in engagement in the current profession had a significant, positive correlation with social capital-based resilience to disaster (r = 0.42, p = 0.000). The number of employees also had a significant positive correlation with social capital-based resilience to disaster (r = .38, p = .000). In addition, positive relationships were noted in relation to monthly income through revenue or disposal before disaster (r = .18, p = .003) and the amount of loan received prior to disaster (r = .25, p = .000). Owners’ perceptions 342 Khan & Sayem: Enterprise Resilience in Bangladash Table 4. Zero-order Correlation Coefficients Among Dependent and Independent Variables MIN 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=weighted natural capital. Table 5. Linear Regression Analysis of Capital-based Resiliences to Disaster Social Capital Financial Capital Physical Capital Human Capital Natural Capital Std. Std. Std. Std. b b Std. Error b b b Error β β Error β Error β Error β (Constant) 17.93*** 1.31 479810.29*** 40626.23 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 .05 YEC .19*** .04 .28 5785.96*** 1202.12 .27 -.27* .10 -.14 .20*** .05 .24 .17*** .04 .23 NOE .25*** .05 .27 7617.60*** 1608.15 .27 -.15 .14 -.06 .29*** .06 .26 .15* .06 .16 TYM .04 .36 .01 -16616.19 11118.54 -.08 -8.62*** .95 -.45 -.85 .44 -.10 -.10 .41 -.01 IRD 1.15 .00 .03 .46 .59 .04 .00* .00 .11 -3.61 .00 -.00 -4.22 .00 -.01 LPD .00*** .00 .25 2.65** .91 .15 .00** .00 -.15 .00*** .00 .23 .00** .00 .17 PRD -.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 .25 Note: N/A= Since firm owners monthly income was incorporated into financial capital-based resilience, we dropped it from analysis of financial capital-based resilience. 343 Khan & Sayem: Enterprise Resilience in Bangladash of recovery dynamics appeared to have a significant negative relationship with social capital-based resilience to disaster (r = -.36, p = .000). Multivariate linear regression analysis found that independent variables explained 39% of variance in social capital-based resilience to disaster (Table 5). Both the education and monthly income of business owners appeared to have a negative impact on social capital-based resilience; however, the result was not statistically significant. Firm owners with more years of engagement in the current profession had higher social capital-based resilience to disaster (β = .28, p = .000). A positive association was also noted in relation to the number of employees and the amount of loan received before disaster to run the business. The higher the number of employees, the higher the social capital-based resilience (β = .27, p = .000); similarly, the higher the amount of loan received prior to disaster, the higher the social capital-based resilience (β = .25, p = .000). A higher score in perception of recovery dynamics, however, appeared to have lower social capital-based resilience (β = -.29, p = .000). Financial Capital-based Resilience The mean weighted score for financial capital-based resilience was 315,989.08 (SD = 102,644.67) (not shown). As indicated in Table 4, the Pearson correlation coefficients revealed that several independent variables had a significant relationship with financial capital-based resilience to disaster. Firm owners’ engagement in the current profession for a longer period of time had a significant positive correlation with financial capital- based resilience to disaster (r = .42, p = .000), suggesting that the higher the years of engagement in the current profession, the higher the financial capital-based resilience to disaster. Likewise, the higher the number of employees, the higher the financial capital- based resilience to disaster (r = .40, p = .000). In addition, a positive correlation was noted in relation to monthly income through revenue or disposal before disaster (r = .20, p = .001) and the amount of loan received prior to disaster (r = .15, p = .019). Owners with regional production oriented microenterprises had lower financial capital-based resilience compared with local production oriented microenterprises (r = -.15, p = .019). A negative relation was also noted between financial capital-based resilience to disaster and perception of recovery dynamics (r = -.38, p = .000). Multivariate linear regression analysis found that independent variables explained 38% of variance in financial capital-based resilience to disaster (Table 5). Business owners with a higher number of years engaged in business had higher financial capital- based resilience to disaster (β = .27, p = .000). In addition, firm owners with a higher number of employees had higher financial capital-based resilience to disaster (β = .27, p = .000); similarly, higher amounts of loan taken prior to disaster had higher financial capital-based resilience (β = .15, p = .004). On the other hand, a negative association was 344 Khan & Sayem: Enterprise Resilience in Bangladash noted between perception related to recovery dynamics and financial capital-based resilience to disaster (β = -.30, p = .000). Physical Capital-based Resilience The mean weighted score for physical capital-based resilience was 41.41 (SD = 9.01) (not shown). As indicated in Table 4, the Pearson correlation coefficients revealed that several independent variables had a significant relationship with physical capital-based resilience to disaster. Firm owners with regional production oriented firms had significant, higher physical capital-based resilience compared with firm owners with local production oriented 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 physical capital-based resilience to disaster. No other variables appeared to have any significant correlation with physical capital-based resilience. Multivariate linear regression analysis indicated that independent variables explained 42% of the variance in physical capital-based resilience to disaster (Table 5). Six of the independent variables appeared to have a significant impact on physical capital-based resilience to disaster. In contrast to their uneducated counterparts, educated firm owners had a higher tendency to higher physical capital-based resilience to disaster (β = .12, p = .018). A similar positive association appeared between physical capital-based resilience to disaster and monthly income through revenue or disposal prior to disaster (β = .11, p = .040). On the other hand, four variables appeared to have a significant, negative impact on physical capital-based resilience to disaster. Firm owners with more years of engagement in the current profession had lower physical capital-based resilience to disaster (β = -.14, p = .011). Similarly, market type (β = -.45, p = .000), the amount of loan received prior to disaster (β = -.15, p = .003), and perception related to recovery dynamics (β = -.39, p = .000) had a significant, negative impact on physical capital-based resilience to disaster. Human Capital-based Resilience The mean weighted score for human capital-based resilience was 16.81 (SD = 4.02) (not shown). As indicated in Table 4, the Pearson correlation coefficients revealed that several independent variables had a significant relationship with human capital-based resilience to disaster. Education had a significant negative correlation with human capital-based resilience to disaster (r = -.24, p = .000), suggesting that educated micro entrepreneurs had lower human capital-based resilience compared with their non educated counterparts. Owners of regional production oriented microenterprises had lower human capital-based resilience (r = -.18, p = .005), whereas perception of recovery 345 Khan & Sayem: Enterprise Resilience in Bangladash dynamics had a significant, lower human capital-based resilience to disaster (r = -.34, p = .000). On the other hand, the number of years engaged in the current profession was positively correlated with human capital-based resilience to disaster (r = .35, p = .000). In addition, 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 prior to disaster (r = .22, p = .000) had a significant, positive correlation with human capital- based resilience to disaster. Multivariate regression analysis found that independent variables explained 37% of the variance in human capital-based resilience to disaster (Table 5). Five independent variables, mainly, explained human capital-based resilience to disaster. Education appeared to have a negative impact on human capital-based resilience to disaster (β = - .25, p = .000). A similar, negative impact on human capital-based resilience to disaster was noted with regard to perceptions related to recovery dynamics (β = -.23, p = .000). Firm owners who had a greater number of years engaged in the current profession had higher human capital-based resilience (β = .24, p = .000). Positive association was also noted between human capital-based resilience to disaster and not only the number of employees (β = .26, p = .000), but also the amount of loan taken before disaster (β = .23, p = .000). Natural Capital-based Resilience The mean weighted score for natural capital-based resilience was 12.51 (SD = 3.38) (not shown). As indicated in Table 4, the Pearson correlation coefficients revealed that several independent variables had a significant relationship with financial loss due to disaster. Education appeared to have a significant, negative correlation with natural capital-based resilience to disaster (r = -.18, p = .004), suggesting that educated owners had lower natural capital-based resilience compared with uneducated owners. In addition, market type (r = -.19, p = .003) and perception of recovery dynamics (r = -.38, p = .000) had a significant, negative correlation with natural capital-based resilience to disaster. On the other hand, owners’ monthly income had a significant, positive correlation with natural capital-based resilience (r = .22, p = .001). Additionally, number of years of engagement with the current profession (r = .34, p = .000), number of employees (r = .29, p = .000), monthly income 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 natural capital-based resilience. Multivariate regression analysis found that independent variables explained 25% of the variance in natural capital-based resilience to disaster (Table 5). There was a negative impact on natural capital-based resilience to disaster for perception related to recovery dynamics (β = -.26, p = .000). On the other hand, a positive association manifested itself between natural capital-based resilience to disaster and the number of years of engagement in the current profession (β = .23, p = .000), between natural capital-based 346 Khan & Sayem: Enterprise Resilience in Bangladash resilience to disaster and the number of employees (β = .16, p = .012), and between natural capital-based resilience to disaster and the amount of loan received prior to disaster (β = .17, p = .002). Discussion The study attempted to investigate business resilience to natural disasters, specifically floods in Bangladesh, in terms of social, financial, physical, human, and natural capital. To investigate different kinds of business resilience, we developed several items for each scale. Results indicate that the items of each of the capital-based resiliences are valid and reliable, suggesting that the Capital-Based Approach developed by Mayunga (2007) can be used to further test validity and reliability. Recent research suggests community development theory has demonstrated that success and sustainability depend on the ability of a community to appreciate, access, and utilise the major forms of capital (Beeton 2006). However, capital as a concept has not been acknowledged, nor regarded as a central focus in understanding and assessing community disaster resilience. As is hoped, our capital-based resilience findings may yet serve as an important tool for understanding community-level disaster resilience. The level of education of the respondents had a mixed effect on business resilience, in that education had a positive effect on physical capital-based resilience, but a negative effect on humancapital-based resilience. In another study (Danes et al. 2009), education had no significant impact on post disaster family firm resilience. Relatively speaking, positive associations with physical capital-based resilience may be due to an enhanced ability to choose a better place for setting up a small firm, a place less likely to be inundated by floods and where premises already exist (e.g., schools and other public places or buildings not usually inundated) from which to conduct business temporarily until such time as the situation returns to normal. Positive associations with physical capital-based resilience may be due also to an increased ability to better predict potential future disaster. Unexpected findings in the field of human capital-based resilience may be the result of other factors, such as less human capital (e.g., no workers with previous experience in tackling disasters, a lack of formal training among employees, and a lack of formal education among workers), which together counteract any positive influence hat the owners’ level of education might have on their business. Our study established that the greater the number of years of engagement in the current profession, the higher the resilience, except in the case of physical capital-based resilience, which had a negative association. Several studies showed that, often, firms that experienced disasters survived tough economic times not necessarily because they were well-managed, but because of family fortitude (Hammond 2003). Positive association may be the outcome of several factors, such as a relatively higher level of experience in both the linear (smooth) and curvilinear (ups and downs) progression of business, an 347 Khan & Sayem: Enterprise Resilience in Bangladash owner’s lasting patience, effective and pragmatic planning to minimise any risk of closure of the firm while simultaneously maximising its operation, hands-on understanding of how to restart a business immediately after disaster, and a solid business network from which support is expected in times of need. Conversely, ‘liability of newness’ as experienced by a company newly involved in business activities may contribute not insignificantly to any lower resilience on the part of such company. Previous studies also support the liability of newness argument (Carroll 1983; Carroll and Delacrok 1982; Carroll and Huo 1986; Freeman et al. 1983), which contends that new organizations need to invest time and effort to establish new roles and encourage their members to socialize (Stinchcombe 1965), if they are to compete successfully with established firms for custom, both old and new (Singh and Lumsden 1990). The study revealed that the higher the number of employees, the higher the capital- based resilience. In another study (Danes et al. 2009), it was shown that the size of the firm or the number of employees had a significant association with business resilience. The larger the firm, the greater the complexity of relationships, which in turn affects the resilience of family run businesses (Bryant and Zick 2005). Firms with numerous staff have several advantages. First, they have the manpower necessary to remove to safety important goods and materials before flooding occurs, Second, they are likely to have workers with prior experience in tackling natural disasters. Third, in moments of crisis, they can rely on financial help from their staff. Fourth, they are able to take advantage of the division of labour to speed up recovery works. In addition, having a full staff complement is an indication that the firm has a high turnover (at least to a level where it can afford 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 for the business as it tends to have fewer resources and limited access to credit compared with medium or large enterprises (Dahlhamer and Tierney 1998). Consequently, small scale- and micro enterprises usually cannot afford to undertake preparedness and mitigation measures, such as purchasing business interruption and hazard insurance (Alesch et al. 1993). The finding that businesses oriented towards regional, rather than local, production had a negative impact on physical capital-based resilience is rather surprising, not least because it is expected that regional oriented firms be better equipped in terms of financial capacity, human resources, and their own physical set up. The situation can perhaps be ascribed to firms focusing on regional production having developed relatively few local networks. It may also be that buyers, in particular, themselves obliged to guarantee their customers a continuous supply of product, cease doing business with suppliers unable to respect delivery times, be it due to the closure of firms, delayed production, or problems maintaining an uninterrupted supply of the goods in question. By contrast, close familial and social relationships ensure that businesses oriented towards local production have the advantage of being able to rely on support from local buyers and suppliers. In a number 348 Khan & Sayem: Enterprise Resilience in Bangladash of cases, these enterprises count as investors’ relatives as well as neighbours, who then acquire partnership status. Higher monthly income through revenue, or higher monthly disposable income, before disaster, had a positive association with physical capital. Higher income has several advantages; it fosters, positively, business activities both in times of crisis and non-crisis. Higher income is likely to result in higher savings; a business able to rely on its own financial resources during a crisis is better positioned to continue its activities post crisis. In another study (Danes et al. 2009), a positive association with resilience was demonstrated in those cases where the family business was headed by a female. An increase in resilience is also more probable in times of stability; higher gross income is likely to mean greater financial resources at one’s disposal (Danes et al. 2009). However, the presence of frequent financial problems or stress may lead to the development of a repertoire of responses to disruption, thus also increasing resilience (Jang 2005). The financial condition of a business, pre-disaster, also affects business recovery. Evidence suggests that marginal or in financial trouble prior to disaster have difficulty in recovering (Durkin 1984, cited in Dahlhamer and Tierney 1998). Findings revealed that the greater the loan received prior to disaster, the greater the business resilience. Pre disaster loans or financial assistance increases a firm’s income, diversifies its asset bases and sources of livelihood, provides support for housing improvements and building reserves necessary for running the business in case of emergency, and provides risk management alternatives and solutions that increase the ability of the affected entrepreneurs to cope. Nigg (1995) argues that recovery involves more than the reconstruction of the built environment. Evidence suggests that while there is no universal agreement as yet on the ultimate impact of microfinance or financial assistance for poverty alleviation, the role of microfinance in reducing vulnerability to, and from, disaster is generally accepted (UNISDR 2005). Unexpectedly, this study found that the higher the score in perceiving recovery, the lower the resilience in a whole range of capital-based resiliences. Disaster is what its victims and respondents perceive it to be: it is the sum of many personal catastrophes, but with a certain gestalt as it represents a shock to the social organism (Alexander 2000). One would expect entrepreneurs who perceive potential disaster to be better capable of mitigating such disaster with a planned approach. The opposite findings in this study, however, may be an indication that perception does not necessarily equate to essential, supportive structures such as financial capacity, housing facilities alternative to those currently being enjoyed by the business, and group solidarity to better tackle potential disaster. In settings like Belkutchi, it is not possible to earmark sufficient funds in preparation for tackling disaster since most of the small scale entrepreneurs there operate on extremely limited budgets. Although this study has several very important indications, especially for community resilience based on different capitals, it also has several limitations. First, some of the 349 Khan & Sayem: Enterprise Resilience in Bangladash respondents were reluctant to give of their time when initially approached by the interviewer. When approached later, although they were more forthcoming their responses were hurried and largely perfunctory, ostensibly due to business commitments, to the extent that interviewers deemed five of the respondents unsuitable, and they were eventually dropped from the final analysis. Second, capital-based resilience was measured using the five “capital-based resilience” capitals proposed by Mayunga (2007) despite the lack of an already developed scale. Items were selected based on the components used by Mayunga in his proposed model. As a result, in this study, items of each of financial, social, human, physical, and natural capital were developed anew. Although this study showed the items to be reliable, such scales require further reliability testing so as 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 recall events and related issues immediately post disaster. The longer the recall period, the less accurate the data proves to be on less salient events (Pierret 2001). As it was expected that recall bias would probably be a threat to the generalizability of the findings, in order to minimise such bias interviewers were instructed to ask a question several times so that participants could recall the events as well as related issues with maximum accuracy. Implications Despite its several limitations, this study has both theoretical and applied implications. To our knowledge, there has been no study conducted on the capital-based framework covering social-, financial-, physical-, human-, and natural capital suggested by Mayunga (2007). Our findings clearly indicate that resilience can indeed be understood from a capital point of view. We suggest further study to verify whether scale items developed using Mayunga’s framework can be used in other settings. This study has several applied implications. First, the number of years of engagement in business had a generally positive impact on business resilience to disaster. While there is no valid substitute for the hands-on experience gained by actively running a business, properly focused and effectively developed training provides an alternative route to better understanding how to ensure a firm functions effectively and how to tackle unexpected events like disaster. Second, markets oriented towards regional production have less resilience and are therefore more likely to encounter serious problems. This is an important issue and one that, with a view to further improvement, merits consideration. Understanding why such markets have lower resilience and how such resilience may be increased constitutes an initial step towards remedying the situation. An excellent second step may be the undertaking and subsequent implementation, of future studies and their recommendations, respectively, particularly if these studies are of a qualitative nature. 350 Khan & Sayem: Enterprise Resilience in Bangladash Finally, as this study indicates, loans received prior to disaster increase all types of a firm’s resilience to disaster except physical capital-based resilience. The usual practice is for different organisations to come to the assistance of small scale businesses post disaster, at a time when owners can do very little to cope with the impact of the event. 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