Community Businesses as Social Units in Post-Disaster Recovery

Abstract

Spanish

Chinese

With global climate change and rapid development in environmentally vulnerable areas, communities are increasingly looking for ways to manage, adapt to, and mitigate adverse impacts from disaster events. To help communities achieve this goal, planners must first understand how various stakeholders behave in post-disaster situations. In this paper, we use household and business surveys conducted in New York City after the 2012 Hurricane Sandy to examine recovery decisions of businesses compared to households as well as their respective use of social, financial, and institutional network resources to recover. Our findings suggest that community businesses, particularly locally owned small businesses, are not simply economic units but also play critical social roles in community functioning. Business recovery decisions are often made based on social, not purely profit-maximizing reasons. Recognizing this dual characteristic of businesses is an important step to better engaging this stakeholder group in community resilience and recovery planning processes.

Keywords

post-disaster recovery business Hurricane Sandy

Introduction

With global climate change and rapid development in environmentally vulnerable areas, planners are increasingly faced with the challenge of facilitating community resilience to withstand and recover from more frequent and severe disasters. Public participation is an important component of this community resilience planning process (Olshansky, Johnson, and Topping 2006), and businesses, especially locally owned small businesses, have been recognized as an integral part of their communities (Xiao and Van Zandt 2012). A better understanding of the needs and decision processes of this stakeholder group can help improve their resilience as well as that of the community as a whole.

In this paper, we discuss how businesses make decisions in a post-disaster context, which can aid planners in better involving them in community resilience planning both before and after a disaster. We propose that businesses, especially small businesses, are not only economic units but also social units critical to community functioning; business owners, managers, and employees are often also residents of the same community in which they work. Businesses also supply jobs, goods, and services necessary to sustain community members as well as payroll and real estate taxes critical to municipal solvency. We use this frame to ask two related questions in this paper:

Research Question 1: What factors affect business recovery decisions?

Research Question 2: How do businesses compare with households in their financial, social, and institutional capacities to recover?

This paper is organized as follows. First, we discuss how businesses function as both economic and social units of a community. Then, we review the literature on social, financial, and institutional factors that affect household and business recovery decisions. Next, we describe our research design, followed by a discussion of our findings. We end the paper with conclusions and discussions related to recovery and resilience planning.

Roles of Community Businesses

Community Businesses as Economic Units

Businesses have been traditionally viewed as economic units because they resemble establishments of production. They utilize resources such as capital, labor, land, and entrepreneurship to produce goods and services that meet certain customer demand (Pride, Hughes, and Kapoor 2014) and gain profit (Kahneman, Knetsch, and Thaler 1986). In this traditional view, households and businesses are interrelated through the market economy system. In the factor market, households provide labor, an essential factor of production, to businesses, and in exchange, they receive monetary income, such as wages and salaries. In the product market, businesses provide goods and services to households and in exchange, receive revenue (Mankiw 2006). Households, conversely, pay businesses in exchange for goods and services to fulfill their consumption needs (Mankiw 2006; Zhang, Lindell, and Prater 2009).

In a normal situation, markets can adjust to minor fluctuations in supply and demand around an equilibrium. Disasters, however, throw the system off balance. Households may be evacuated and displaced from their original locations (Bolin and Stanford 2006; Girard and Peacock 1997), creating labor shortages and a lack of customers. On the other hand, businesses can shut down due to extensive disaster damage, supplier issues, and/or transportation problems, making households lose jobs and income (Zhang, Lindell, and Prater 2009). It has been empirically shown that in a post-disaster recovery framework, household and business recovery decisions are contingent on each other and can highly influence the outcomes for community recovery (Xiao and Van Zandt 2012).

Community Businesses as Social Units

Community businesses are also social units that serve social functions within their community. This is something that has long been understood, at least anecdotally. In her classic book, The Death and Life of Great American Cities, Jane Jacobs (1968) pointed out that community businesses carry important social functions, serving as places for informal face-to-face meetings and building community ties and rapport and a source of eyes on the street for maintaining safety. Small businesses are often motivated by social norms, such as building strong relationships out of trust, reputation, and legitimacy, in addition to maintaining profitability (Sen and Cowley 2013), and their survival and success are dependent in part on the non-market relationship they share with the larger community (Kilkenny, Nalbarte, and Besser 1999).

This type of social consciousness is particularly evident during times of crises when businesses are often called on to serve noneconomic needs of their surrounding community. Local stores, for example, are known to have allowed people to charge cell phones free of charge after disasters and served as places for community meetings in addition to their normal business function (Business Civic Leadership Center 2012; Cochrane 1992). Rate of business recovery is shown to positively affect individual perceptions of coping capacity and psychological well-being (Liu et al. 2012), while businesses’ failure after disasters has been shown to delay community recovery on the whole (Carrido 2000).

Capacity Approach to Understanding Recovery Decisions

The recovery decisions of households and businesses depend on their ability to access and utilize social, financial, and institutional resources to meet their specific needs in a timely manner (referred to here as social, financial, and institutional networking capacities). A capacity approach provides a holistic understanding of recovery because it emphasizes both a household or business’s own internal ability to drive its recovery as well as the physical and institutional context in which this agency is actualized.

Social Capacity and Social Capital

Social capacity refers to the ability of a business or household to transform social assets to expedite its decision processes (Chandrasekhar and Finn 2013). It should be noted that the concepts of social capacity and social capital have typically been used to discuss individual or household decisions, whereas here we extended the concepts to also discuss the decisions of business owners/managers.

Social assets can be measured by social capital, which has been defined variously by different researchers. Kawachi et al. (1997) believes that social capital is a collective asset or public good available to all community members. In contrast, Bourdieu suggests that social capital is less a public good but more of an individual property and may be unequally distributed in society (Bourdieu 1986; Bourdieu and Wacquant 1992). More recently, social capital has been explained from the perspective of connections and linkages, specifically the processes of building trusting relationships, mutual understanding, and shared actions that bring together individuals, communities, and institutions (Loeffler et al. 2004; Putnam 2000).

Researchers have grouped social capital into three categories: bonding, bridging, and linking capitals (Aldrich and Meyer 2015; Kuhlicke et al. 2011; Mathbor 2007; Szreter and Woolcock 2004; Woolcock and Narayan 2000). Bonding social capital refers to the individual’s ties with immediate family members, neighbors, and close friends (Woolcock 2002; Woolcock and Narayan 2000). Bridging social capital is the horizontal and loose connection among people from different ethnic, geographical, and occupational backgrounds but with similar economic status and political influence (Aldrich and Meyer 2015; Woolcock 2002). Linking social capital relates to the vertical ties between a household (or business) and those in higher power positions or authority gradients in society (Aldrich and Meyer 2015; Woolcock and Narayan 2000).

In the context of community post-disaster recovery, social capital can function as a key engine of recovery (Aldrich and Meyer 2015; Mathbor 2007). For example, households and businesses often use interpersonal ties and linkages to bond together to address collective concerns (Mayunga 2007). In turn, the need to maintain these ties and linkages can encourage households and businesses to also build back in the same community (Chamlee-Wright and Storr 2011; Nakagawa and Shaw 2004). In fact, communities with more trust, civic engagement, and stronger networks are often better able to bounce back after a crisis than fragmented, isolated ones (Aldrich 2008).

Financial Capacity

A business or household’s financial capacity refers to its ability to mobilize economic and financial resources for recovery. This includes support from formal and informal financial institutions, nonprofit and community-based organizations (Andrew et al. 2013; Quarantelli 1999; Welsh and Esnard 2009); availability of and access to livelihood, employment, and education opportunities (Ingram et al. 2006; Mileti 1999; Quercia and Bates 2002); and the availability of goods and services in its vicinity (Arlikatti et al. 2010). Household members’ age, gender, wealth, education, and livelihood profiles are often internal indicators of the household’s financial capacity to recover. For example, elderly and women-headed, low-income, immigrant, and racial and ethnic minority households often have lower financial capacity to recover because of preexisting economic and educational inequalities (Morrow 1999; Tierney 2006). In addition, delayed restoration of livelihood (Nakabayashi 1990) and the lack of diversified livelihood skills (Hunter and David 2011) can also decrease a household’s financial capacity to recover from disasters.

Like households, numerous financial factors influence a business’s recovery decisions, many of which are internal to its operation, such as its line of business, financial status, and business owner or manager’s experience or entrepreneurial skills (Sydnor et al. 2017). Financially stronger businesses are more likely to make the decision to return to operation than those that are already on the decline pre-disaster (Alesch et al. 2001; Webb, Tierney, and Dahlhamer 2002). Business characteristics, such as size and property ownership (i.e., rent vs. own), reflect financial ability to operate and make a profit; small businesses and renters are more likely to suffer losses and cease operations after a disaster (Chang and Falit-Baiamonte 2002; Wasileski, Rodríguez, and Diaz 2011). A business owner or manager’s entrepreneurial skill is also critical to recovery. Alesch et al. (2001) reported that businesses whose owners made decisions to adapt to new and often changed circumstances after a disaster were likely to do better than nonadaptive businesses. Business owners’ education levels and management background (i.e., years served as business owner or manager) may affect their entrepreneurial skills. Other social characteristics of business owners, such as minority status, age, and gender, also are factors that may affect business recovery decisions.

Institutional Networking Capacity

A business or household’s institutional networking capacity refers to its ability to connect and collaborate with other institutions around it, such as government and nongovernment organizations, to obtain external aid and support for recovery. Once again, a household’s institutional networking capacity is mediated by its demographic, social, and political standing. Aid distribution has been shown to marginalize racial and ethnic minorities and favor the politically connected (Aldrich 2011), while language fluency and cultural differences, particularly in immigrant communities, have been shown to pose additional barriers to obtaining recovery aid (Fothergill, Maestas, and Darlington 1999).

Institutional network capacity also affects a business’s decision to reopen or close shop after a disaster. Franchise businesses, for instance, are likely to receive corporate assistance that allows them to bounce back more rapidly (Xiao and Van Zandt 2012). Membership in a local business association, such as a Chamber of Commerce, can be a source of critical information to address recovery needs (Lacho, Bradley, and Cusack 2006). Financial assistance is also critical to recovery success, but the specific fine-grained structure of recovery loans and aid programs is even more critical. In some cases, accepting loans or assistance for business recovery may even be counterproductive. For example, Dahlhamer and Tierney’s (1998) study of business recovery after the Northridge, California, earthquake found that businesses that used more aid sources were less likely to report positive recovery outcomes. Graham (2007) reported that many businesses in New York City were locked into their no longer profitable locations after the September 11 terrorist attack in 2001 because they took funding assistance from the Lower Manhattan Initiative (LMI), which required them to remain in their place of business for at least one year.

Research Gap

While our knowledge of post-disaster recovery of households and businesses is growing, there are still gaps to be addressed. Critically, there is a need for research that examines the social aspects of business recovery in addition to its traditional economic ones. Little is known about the extent to which business decisions are driven by nonmarket forces such as the owner’s personal ties to a neighborhood or even sentimental reasons. Moreover, because businesses and households are rarely studied in combination, we do not know how businesses differ from households in their recovery capacities and decisions. This lack of understanding in turn creates a communication barrier that can inhibit participation by businesses in community recovery planning. Our paper helps remove this barrier by providing a comparative perspective of businesses and households recovery decisions and capacities, thereby enabling the development of shared goals, values, and visions for community recovery as a whole.

Research Design

Hurricane Sandy and Its Impacts

Hurricane Sandy made landfall on the Northeast coast of the United States on October 29, 2012, and by most emerging estimates will become one of the costliest disaster events in the history of the United States, with costs in New York and New Jersey alone topping $70 billion. In parts of New York City, winds gusted over 70 miles per hour, and storm surges exceeded 13 feet. Impacts were widespread and affected almost every aspect of life in the city. At least forty-three New York City residents were killed by the storm, and 900,000 business and residential customers were without power in the immediate aftermath (City of New York 2013). New York’s transit system, the country’s largest, suffered massive damage. Seven of fourteen subway tunnels connecting Manhattan with the outer boroughs of Brooklyn and Queens were completely flooded, as were two vehicular tunnels and multiple bus and subway train storage yards. Subway service was suspended completely for 72 hours, with repairs on some lines taking weeks longer (City of New York 2013). More than five years later, major capital renovations or repairs to the transit network prompted by Sandy remain. Across the affected region, recovery has been relatively swift, but many households and businesses will never regain the economic status they had attained before the storm.

Data and Sampling

This study focuses on the recovery of businesses and households in the New York City boroughs of Brooklyn, Queens, and Staten Island that were directly and substantially affected by the storm (Figure 1). Our study was limited to these three areas of the city because they each faced widespread damage from Sandy. The fourth borough (The Bronx) experienced minimal damage, and the fifth (Manhattan) experienced damage primarily in the downtown financial district, which is such a unique location (e.g., dense urban form, unmatched access to financial resources, low percentage of residents, and type of firms that predominate) that we felt it would not generate findings that could be widely transferable to other contexts. To develop a study area, we first overlaid the USGS Hurricane Sandy storm surge map (U.S. Geological Survey 2013) with zip codes in the aforementioned three boroughs to extract zip code areas submerged by storm surge. Then we purchased random samples of households and businesses in these zip codes from InfoUSA, a data provider. The study area selection is at the zip code level because it is the finest geographic unit that the household and business samples can be drawn and sold by the data provider. InfoUSA drew simple random samples of households and businesses from its master listing of all households and businesses in these flooded zip codes. In our sample of 2,000 households, 64.0 percent of them are from Brooklyn, 19.5 percent from Staten Island, and 16.5 percent from Queens. For the business sample, 56.7 percent are from Brooklyn, 16.4 percent from Staten Island, and 26.9 percent from Queens.

Figure 1.

Study area.

This paper is primarily based on self-administered random sample surveys from 2013, sent to 2,000 households and 2,000 businesses in the study areas. The survey targeted adult members of households and owners or managers of businesses who can reasonably be expected to know or be engaged in recovery decision processes for the household or business. Their responses were assumed to be representative of the interests, actions, and decisions of the household or business, respectively. The survey collected the information on damage experienced, sources of recovery finance, social connections to place, knowledge of institutions and networking, issued of enduring concern over time, and demographic information of the respondent. This last component was used as an indicator of the social, cultural, and economic status of the household or business in its community. Within each of the three selected boroughs, three specific middle-income coastal neighborhoods that experienced some of the most intensive damage from Sandy (City of New York 2013) were selected for more in-depth analysis: Red Hook in Brooklyn, The Rockaways in Queens, and Midland Beach in Staten Island. In addition to site visits to understand the physical context, extent of damage, and recovery context, we interviewed fifteen informants in these neighborhoods (residents, business owners, or both) to gain more nuanced insights on the dynamics of their recovery decision making.

A total of 200 households and 75 businesses responded to the surveys. To examine potential self-selection bias, we compared the demographics of household survey respondents to those of New York City as reported by 2011–2013 American Community Survey three-year estimates (Table 1). Our household survey respondents tend to be more educated and older and use English as primary language at home. People from all races were fairly well represented in the responded survey, with slightly higher percentage of Caucasians and lower percentages of African Americans and Asian/Pacific Islander. The employment status of survey respondents was on par with that of New York City.

Table 1.

Comparison of Demographics of Household Survey Respondents to New York City.

Household Demography	New York City	Survey Respondents
Race
Caucasian (not Hispanic)	45.8	62.3
African American	26.2	17.8
Asian/Pacific Islander	14.4	3.7
Native American	0.9	1.1
Ethnicity, Hispanic	28.8	7.9
English, primary language	50.9	85.1
Education level
High school or less	44.5	14.2
College or more	32.5	67.5
Age of respondent (years)	35.7	55.0
Employment status	56.7	59.1

Source: U.S. Census Bureau, 2011–2013 three-year American Community Survey.

We also compared the sectoral mix of businesses who responded to the survey to the entire business sample (Table 2). Our business respondents came from all sectors except for mining, which was but 0.2 percent of the full sample in the first place. Across the board, comparing the survey respondents to the full sample, the percentage differences of all sectors were less than 4.5 percent. As such, the survey respondents were fairly representative of the entire business sample in sectoral mix.

Table 2.

Percentage of Businesses by Sector: Full Sample Versus Survey Respondents.

Sector	Full Sample	Survey Respondents	Difference
Agriculture, forestry, and fishing	1.0	5.3	4.3
Construction	10.7	6.7	−4.0
Finance, insurance, and real estate	8.7	12.0	3.4
Manufacturing	7.8	10.7	2.9
Mining	0.2	0.0	−0.2
Retail trade	32.7	29.3	−3.3
Services	25.9	29.3	3.5
Transportation, communications, electric, gas, and sanitary services	6.9	2.7	−4.2
Wholesale trade	6.4	4.0	−2.4
Total	100.0	100.0	0.0

Analytical Methods

In this study, we compare businesses and households in their recovery decisions and capacities by analyzing survey data where households and businesses represent two independent random samples. To examine whether the two samples are significantly different from each other, we used two-sample t test for continuous variables such as percentage of recovery funds from various sources. For nominal variables such as whether insurance was purchased by the household or business, we used a chi-square test.

Findings

Demographics Comparison

Before we compare businesses to households in their damage, recovery decisions, and capacities, we first compare the differences in demographics of the survey respondents. As shown in Table 3, the business and household survey respondents differ significantly in racial composition and age. Compared to the household sample, Caucasians were overrepresented and African Americans underrepresented in the business sample. About 76.4 percent of business owners/managers were Caucasian, and 62.3 percent of household survey respondents were Caucasian, a difference of 14.1 percent. Age wise, there were higher proportions of business owners/managers in the age groups 50 to 59 and 60 to 69 and much lower proportions in the <30, 30 to 39, and above 70 age groups. We do see age and racial differences in business-owning/managing and non–business-owning/managing groups.

Table 3.

Comparison of Demographics of Business and Household Survey Respondents.

	Business	Household	Difference
Race and ethnicity
Caucasian (not Hispanic)	76.4	62.3	14.1
African American	4.2	17.8	−13.6
Hispanic	5.6	7.9	−2.3
Asian/Pacific Islander	8.3	3.7	4.7
Native American	0.0	1.1	−1.1
Two or more race	5.6	3.7	1.9
Other	0.0	3.7	−3.7
Total	100.0	100.0
df = 6; χ² = 15.00; p = .02
Educational attainment
Less than high school	4.1	3.6	0.5
High school graduate/GED	13.5	10.7	2.8
Some college/vocational school	16.2	18.3	−2.1
College/vocational school graduate	37.8	32.0	5.9
Graduate school degree	28.4	35.5	−7.2
Total	100.0	100.0
df = 4; χ² = 1.92; p = .75
Age
<30	1.4	11.2	−9.8
30–39	5.5	11.7	−6.2
40–49	19.2	15.4	3.7
50–59	34.3	23.4	10.9
60–69	34.3	23.4	10.9
⩾70	5.5	14.9	−9.4
Total	100.0	100.0
df = 5; χ² = 16.92; p = .00

There appeared to be no significant difference between business and household survey respondents in terms of educational attainment. Compared to household survey respondents, there were higher proportions of business owners/managers achieving the education levels of less than high school, high school degree, or college degree. But such differences were not statistically significant.

Interestingly, and to underscore our point about businesses acting as social units, from our informant interviews, we found a large number of business owners/managers in our study were also long-time residents of the communities within which their business is located, which may lend a degree of communality to their recovery decision making.

Damage Comparison

We asked the business and household survey respondents to report their damage level on a scale from 0 to 10, with 0 representing no damage and 10 standing for extremely severe damage (see Table 4 for a summary of damages). Business owners/managers were asked to report both damage to their own homes as well as their businesses. As the data show, business owners/managers suffered double impacts from Hurricane Sandy. Business and household survey respondents reported similar levels of damage to their dwellings (2.68 and 2.74 out of 10, respectively) and neighborhood (5.75 and 5.64 out of 10, respectively), though business owners/managers had more damage to personal possessions inside their dwelling than other households. None of these differences were statistically significant, indicating similar levels of household impacts among businesses owners/managers and other households.

Table 4.

Average Reported Damages to Business and Household.

	Business		Household		Difference	t Test
	Mean	Standard Deviation	Mean	Standard Deviation	Difference	p Value
Damage to household
Dwelling (primary residence)	2.7	3.5	2.7	3.6	−0.1	.87
Personal possessions inside dwelling	3.0	3.8	2.5	3.7	0.5	.64
The neighborhood	5.8	3.7	5.6	3.7	0.1	.94
Damage to business
This business	4.4	4.0
Other businesses in the same area	5.4	4.0
Business building	3.1	3.7
Inventory	4.0	4.5
Machinery/equipment	3.8	4.3

Note: All damages were reported on a scale from 0 to 10, with 0 representing no damage and 10 standing for extremely severe damage.

However, business owners/managers also had to deal with business impacts, which made their recovery more difficult. Business owners/managers reported moderate damages to their business (4.38 on average). The average damage to business building, inventory, and machinery/equipment were 3.11, 3.99, and 3.77, respectively. The overall business damage was 4.38 out of 10. Business owners/managers reported higher damage (5.37 out of 10) to other businesses in the same area compared to their own business damage, which could be interpreted as business owners/managers downplaying their own losses or our survey did not capture responses from businesses with higher damage. Nevertheless, as these results illustrate, during recovery, many business owners/managers have to deal with both household and business damages simultaneously, making both recoveries more difficult because of the added financial costs as well as costs in time and less tangible factors such as physical exertion and emotional stress.

Recovery Decisions and Reasons

As part of the survey, we also asked whether the household or business would prefer to stay and recover in the same neighborhood or move to another place. About 88.0 percent of households and 94.4 percent of businesses said that they would stay and recover in situ. For households, the reasons are presented in Table 5, which shows that a mixture of community attachment, economic reasons, and sentimental reasons affected households’ recovery decisions. Social ties, a type of community attachment, were reported as the top reason, with 37.3 percent of household survey respondents saying that they stayed because they wished to stay with family and 29.0 percent reporting they stayed due to the closeness to friends. Attachment to the amenities in the neighborhood, such as access to parks and waterfronts, was as frequently reported as social ties with friends (29.0 percent). Economic reasons were also very important. Of households, 30.2 percent said that they stayed because they could not afford rents in other places, while 24.3 percent of households stated that they stayed due to convenient job locations and also the recovery of services and stores in the neighborhood. This finding echoes previous research that has showed household recovery decisions are dependent on business recovery (Xiao and Van Zandt 2012).

Table 5.

Reasons for Households and Businesses to Recover in the Same Neighborhood.

Reasons to Stay	Percentage of Respondents
Households
Wish to stay close to family	37.3
Comparable housing in other neighborhoods is too expensive	30.2
Wish to stay close to friends	29.0
Access to amenities such as parks, waterfront, and so on	29.0
Job is located here or in nearby neighborhood	24.3
All services and stores I need have reopened	24.3
Others	23.7
Sentimental reasons	14.2
Schools or day care facilities are nearby and don’t want to look for others	10.7
Owe the bank more than our house is worth	7.7
In a long-term lease	1.8
Businesses
Established customer base	52.2
Strong personal ties with the community	41.8
Strong business ties with the community	38.8
Established business network	29.9
Friendly business environment	29.9
Cannot afford to move to other locations	25.4
Available workforce and other reasons	17.9
Return to status quo requires less effort than looking for alternative locations	14.9
No alternative location is available for this business	14.9

Similarly, business recovery decisions were also affected by business owner/managers’ community attachment and economic reasons. Only 14.9 percent of business owners/managers said their businesses stayed because no alternative locations were available, and 25.4 percent of business owners/managers said that they could not afford to move. About 14.9 percent of business owners/managers reported that staying required less effort than moving to a new location. Thus, businesses appear to have had the option and ability of moving to other places, raising the question of why they chose to stay and recover in situ. The top reasons are all related to business owner/managers’ community attachment. Over half of business owners/managers (52.2 percent) reported that their businesses stayed because of an established customer base, while 41.8 percent reported that the business owner/managers’ personal ties with the community were an important reason, followed by their business ties (38.8 percent). About 30 percent of business owners/managers mentioned established business network and friendly business environment. In contrast, availability of a workforce was a minor issue, with only 17.9 percent of businesses staying due to workforce issues.

Social Capacity

We also asked the business and household survey respondents to report their frequencies of interactions with social networks before Sandy and how helpful these networks were to recovery. Responses were reported on a scale from 0 (never) to 10 (very frequent) for frequency of interaction and 0 (not helpful) to 10 (extremely helpful) for helpfulness.

Data suggest that compared to household survey respondents, business owners/managers had more contacts with all levels of social groups before Sandy (Table 6). Before Sandy, businesses owners/managers generally interacted more frequently with all categories of social groups, including neighbors (6.1 for businesses and 5.7 for households), friends and family (7.9 for businesses and 7.3 for households), neighborhood organizations (5.1 for businesses and 3.5 for households), local community board (2.9 for businesses and 1.1 for households), and city government, state government, and elected representatives (0.9, 1.0, 1.3 for businesses and 0.9, 0.5, 1.1 for households), except for federal emergency management agencies. Households interacted slightly more frequently with federal emergency management entities (0.5 for businesses and 0.6 for households). Notably, moving up the hierarchy from immediate social ties (friends, family, and neighbors) to institutional ties (local, city, state, and federal governments), it is apparent that the frequency decreased drastically for both household members and business owners/managers.

Table 6.

Frequency of Interaction with Social Groups before Hurricane Sandy.

	Business	Household	Difference	Chi-Square
	Business	Household	Difference	p Value	Significance
Your neighbors	6.1	5.7	0.4	.72
Friends and family	7.9	7.3	0.6	.28
Neighborhood organizations	5.1	3.5	1.5	.04	**
Local community board	2.9	1.1	1.8	.00	***
City government	0.9	0.8	0.1	.34
New York State government	1.0	0.5	0.5	.01	***
Federal emergency management	0.5	0.6	−0.1	.07	*
Elected representatives	1.3	1.1	0.2	.81
Business specific
Your friends in business	7.3
Suppliers	7.1
Customers	7.7
Trade or merchant associations	4.1

Note: Scores were out of 10, with 0 for no interaction and 10 for very frequent interaction.

p = .10. **p = .05. ***p = .01.

Moreover, business owners/managers had very frequent contacts before Sandy with business-specific social networks, such as friends in business, suppliers, and customers. These frequencies were rated at 7.3, 7.1, and 7.7, respectively, higher than all of the other social contacts except for friends and family. Business owners/managers’ contacts with trade or merchant associations were quite frequent (an average score of 4.1 out of 10), higher than business owners/managers’ contacts with all government agencies.

However, compared to households, business owners/managers were less likely to report being helped by their social connections during recovery from Sandy (Table 7). This was true for all levels of social networks except local community boards. In New York City, 59 appointed neighborhood-based community boards are recognized in the city charter and, though lacking legislative power, hold significant influence in city council and city agency decision making. In general, for both households and businesses, immediate social ties at arm’s reach (friends and family, neighbors, and neighborhood organizations) were reported to be more helpful than more distant institutional ties (i.e., with local community board, elected representatives, and city, state, and federal governments). Businesses, however, received significant help from business-specific contacts like friends in business, suppliers, and customers (5.1, 4.2, and 5.1, respectively). The helpfulness of business-specific contacts was on par with that of business owner/managers’ immediate social ties. Trade and merchant associations were said to be somewhat helpful (average score of 2.1), which was less helpful than their local community board but more helpful than all other government entities.

Table 7.

Helpfulness of Social Groups in Recovery from Sandy.

	Business	Household	Difference	Chi-Square
	Business	Household	Difference	p Value
Your neighbors	4.7	4.9	−0.3	.57
Friends and family	5.4	6.3	−0.9	.39
Neighborhood organizations	3.9	4.0	−0.1	.24
Local community board	2.6	2.0	0.6	.68
City government	2.0	2.1	−0.2	.28
New York State government	1.5	1.5	−0.1	.63
Federal emergency management	1.8	3.3	−1.4	.29
Elected representatives	1.6	1.9	−0.3	.86
Business-specific contact
Your friends in business	5.1
Suppliers	4.2
Customers	5.1
Trade or merchant associations	2.1

Note: Scores out of 10, with 0 for not helpful and 10 for extremely helpful. None of the differences are statistically significant.

Financial and Institutional Capacities

Finally, we asked the household and business survey respondents to list the financial sources utilized in household and business recovery (see Table 8 for details). Financial sources included money from internal funds (personal savings and cashing out retirement account for both household members and business owners/managers and business revenue for businesses) and external funds (money from family and friends, credit cards, insurance payouts, bank loans, donations, government assistance, etc.).

Table 8.

Percentage of Money from Various Financial Sources Utilized in Recovery.

	Business		Household		Difference	t Test
	Mean	Standard Deviation	Mean	Standard Deviation	Difference	p Value	Significance
Own personal savings	48.6	39.7	36.7	38.3	11.9	.10	*
Cash out retirement account (401k, etc.)	0.0	0.0	5.5	20.3	−5.5	.02	**
Money from friends and family	1.6	7.3	10.2	23.9	−8.6	.01	***
Credit card or bought on credit from vendor	6.7	14.3	9.2	21.0	−2.5	.45
Insurance reimbursement	6.4	19.5	20.6	33.0	−14.2	.00	***
Commercial bank loans	0.6	3.2	1.0	5.4	−0.4	.65
Donations	1.1	4.8	3.3	16.6	−2.2	.29
Local, state, and federal government assistance	9.8	23.9	13.4	26.6	−3.6	.45
Business revenue	24.8	39.3
Corporate assistance	0.2	1.4

p = .10. **p = .05. ***p = .01.

Businesses primarily relied on their own internal funds for recovery. To finance business recovery, nearly half (48.6 percent) of the money came from the owners’ personal savings and about a quarter (24.8 percent) from business revenue. On average, these two sources comprised up to three-quarters of businesses’ recovery funds. In contrast, just over 40 percent of households’ recovery money came from internal sources. Compared to households, businesses received significantly less from friends and family (only 1.6 percent of the total money compared to 10.2 percent for households) and depended significantly less on insurance payouts (6.4 percent compared to 20.6 percent for households).

The ability to receive government assistance and aid is a proxy for measuring institutional capacity. We found that assistance from local, state, and federal governments for households comprised 13.4 percent of total funds but only 9.8 percent for businesses.

To further understand institutional networking capacities, we examined households’ and business owners/managers’ understanding of government assistance programs (Table 9). Because Hurricane Sandy was declared a federal disaster in the State of New York, the Federal Emergency Management Agency (FEMA), US Department of Housing and Urban Development (HUD), and US Small Business Administration (SBA) activated disaster assistance programs to help households and businesses with recovery. We asked respondents to rate their understanding of procedures to apply for governmental assistance programs on a scale from 0 to 10, with 0 representing not familiar at all and 10 meaning extremely familiar.

Table 9.

Understanding of Procedures to Apply for Governmental Assistance before and after Sandy.

	Business	Household	Difference	Chi-Square
	Business	Household	Difference	p Value	Significance
Personal assistance before Sandy	0.9	2.5	−1.6	.00	***
Business assistance before Sandy	0.8
Personal assistance after Sandy	3.4	4.6	−1.2	.12
Business assistance after Sandy	3.1

Note: Scores out of 10, with 0 for not familiar at all and 10 for extremely familiar.

***

p = .01.

Both businesses and households lacked understanding of these assistance programs before Hurricane Sandy. Business owners/managers reported 0.9 out of 10 as an average score for understanding of personal assistance and 0.8 out of 10 for understanding of business assistance, illustrating that they had almost no idea at all about applying for these programs before Sandy. Households showed slightly better understanding of these government assistance programs, with an average score of 2.5 out of 10. Both businesses and households improved their understandings after the disaster. After Hurricane Sandy, business owners/managers reported 3.4 on understanding of personal assistance and 3.1 on understanding of business assistance while households’ understanding of personal assistance increased to 4.6. Overall, businesses lagged behind households in their understanding of federal government assistance programs both before and after Hurricane Sandy.

Conclusions and Discussions

In this article, we argue that planners should treat businesses as both economic and social units in terms of how they are engaged in community planning, particularly—but not limited to—the disaster recovery context. Based on our comparison of households’ and businesses’ recovery decisions and capacities after Hurricane Sandy, we found strong evidence to support this view: First, business owners and managers are oftentimes also households who reside in the community affected by the disaster. The housing damage reported by business owners and managers was on par with the damage to non–business-managing households. As such, many business owners and managers are faced simultaneously with business and household recovery, making both their business and household more vulnerable to poor recovery outcomes.

Second, for businesses, we found strong evidence of place attachment in the business survey. Our survey showed that alternative locations were available for most of the businesses (only 15 percent reported lack of alternative locations), most of the businesses could afford to move to other locations (only about a quarter of businesses could not afford the move), and over 85 percent felt that returning to the status quo required at least as much effort as looking for alternative locations. And yet, over 95 percent of businesses chose to recover in situ for a number of stated place-based reasons, such as having an established customer base, the business owner/managers’ strong personal ties with the community, their strong local business ties and networks, and the existence of a friendly business environment. To a large extent, business recovery decisions were influenced by business owners’ social ties and community attachment, illustrating that business recovery decisions are not purely profit driven and indeed have a strong social component.

A comparison of household and business recovery further clarified these social characteristics of businesses. Similarities include a comparable use of social, financial, and institutional capacities by households and businesses alike to drive their post-disaster recovery. Both business owners/managers and household members interacted with a range of social entities, from friends and family, to neighbors and neighborhood organizations, to all levels of government and elected representatives. Apart from the use of retirement accounts (none of the businesses in our sample used this method) and business-specific funds (including business revenue and corporate assistance), businesses and households depended on the same types of sources to finance recovery, from personal savings to money from friends and family, credit card debt, donations, insurance, commercial bank loans, and government assistance. Second, for both business owners/managers and household members, the frequency of interaction with social groups as well as their helpfulness for recovery decreased as the ties to those groups became more distant. And third, both business owners/managers and households increased their institutional knowledge after the event. More specifically, both improved their understandings of the procedures to obtain government assistance after hurricane Sandy.

There are also some differences in capacities between businesses and households: First, business owners/managers maintained more frequent contact with all levels of social groups (except emergency management agencies) but paradoxically, were also less likely to report these groups as having been helpful for recovery. This finding indicates that businesses feel the urgency to seek aid from all quarters keenly but are unable to obtain it either because formal aid sources for businesses are scarce, those sources come with too many constraints (e.g., SBA loans), or they are unable to raise the substantial amounts of capital needed for recovery through informal and immediate sources.

Second, to a large extent, businesses were self-dependent in recovery. On average, three-quarters of businesses’ recovery funds came from internal sources, including owners’ personal savings and business revenue. In contrast, households’ recovery funds came from more diverse sources, with a ratio between internal and external sources of about four to six. Finally, compared to businesses, a higher percentage of household recovery funds came from government assistance, and household members had a better understanding of the procedures to obtain federal government aid than business owners and managers both before and after the disaster event. Both of these findings are possibly a reflection of the insufficiency of formal aid generally available to businesses for disaster recovery, which is a point for further consideration in recovery governance.

Additionally, because businesses are integral parts of the communities in which they are located, they are important stakeholders in their communities’ long-term recovery planning and decision-making processes. After Sandy, for instance, both New York City and New York State conducted participatory recovery and resilience planning efforts in storm-damaged neighborhoods in order to prioritize spending the more than $8.6 billion in HUD Community Development Block Grant-Disaster Recovery (CDBG-DR) funds allocated to the city and state (Finn, Chandrasekhar, and Xiao 2016). These survey findings suggest multiple reasons that could hinder the participation of business owners and managers in community post-disaster recovery planning processes. First, lack of time prevents them from dedicated participation and engagement during a community recovery planning process. Business owners and managers often suffer from double impacts—both to their homes and businesses—and therefore, their time is split between household and business recoveries. Moreover, business owners and managers may tend to be more stressed and frustrated during recovery because they spend personal savings to finance recovery as opposed to getting external aid, and they must not only physically rebuild their place of business but also restart the stalled process of doing business, such as contacting customers that may have found new sources for goods or hiring new employees. Although businesses reportedly had more frequent contacts with a variety of social groups, they were less likely to report such social groups being helpful to their recovery, probably because businesses are profit-oriented and therefore are less likely to receive assistance. Instead, friends in business, suppliers, and customers appeared to be very important for business recovery. This finding indicates there may be a difference in the social capital accrued through these diverse sources and merits additional study.

There are a few limitations of this study. First of all, the demographics of the household survey respondents do not perfectly align with those of New York City residents; therefore, limitations can rise from the self-selection bias. Moreover, in the business survey, we did not differentiate responses from owners and managers. Managers can be limited in their decision making on issues such as relocation. For instance, it is possible that franchise managers may want to relocate the business after a disaster, but franchise restrictions may prevent it. This study did not capture such nuances of business decision making after disasters.

Nevertheless, this study illustrates some of the ways that businesses are both similar to and different from (and sometimes the same as) households in terms of how they make recovery decisions. We urge planners to recognize the roles and limitations of businesses in community resilience planning. When dealing with business stakeholders, planners should recognize their dual characteristics not only as economic units that chase profits but also as social units with established community ties and community attachment. Such a framing can help planners facilitate a greater shared understanding of holistic community recovery between these seemingly different community stakeholders.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This material is based on work supported by the National Science Foundation under Grant Nos. 1333132, 1559664, 1333155, and 1335109. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

ORCID iD

Donovan Finn

Author Biographies

Yu Xiao is an associate professor in the Toulan School of Urban Studies and Planning at Portland State University. The main area of her research deals with community resilience with a focus on local and regional economic sustainability and resiliency.

Kai Wu is a PhD student in the Department of Landscape Architecture and Urban Planning at Texas A&M University. Her research focuses on community resilience and post-disaster recovery.

Donovan Finn is an assistant professor in School of Marine and Atmospheric Sciences and Sustainability Studies Program at Stony Brook University. His current research focuses primarily on sustainability and resiliency planning in the New York region and the role of public participation in effective planning outcomes.

Divya Chandrasekhar is an assistant professor in the Department of City & Metropolitan Planning at the University of Utah. Her research focuses on how communities recover from disasters and other environmental changes and the role of policy and planning in this process.

References

Aldrich

Daniel P.

2008. “The Crucial Role of Civil Society in Disaster Recovery and Japan’s Preparedness for Emergencies.” Japan aktuell 3:81–96.

Aldrich

Daniel P.

2011. “The Externalities of Social Capital: Post-Tsunami Recovery in Southeast India.” Journal of Civil Society 8:81–99.

Aldrich

Daniel P.

Meyer

Michelle A

. 2015. “Social Capital and Community Resilience.” American Behavioral Scientist 59:254–69.

Alesch

Daniel J.

Holly

James N.

Mittler

Elliott

Nagy

Robert

. 2001. Organizations at Risk: What Happens When Small Businesses and Not-For-Profits Encounter Natural Disasters. Alexandria, VA: Public Entity Risk Institute.

Andrew

Simon A.

Arlikatti

Sudha

Long

Laurie C.

Kendra

James M

. 2013. “The Effect of Housing Assistance Arrangements on Household Recovery: An Empirical Test of Donor-Assisted and Owner-Driven Approaches.” Journal of Housing and the Built Environment 28:17–34.

Arlikatti

Sudha

Peacock

Walter Gillis

Prater

Carla S.

Grover

Himanshu

Gnana Sekar

Arul S

. 2010. “Assessing the Impact of the Indian Ocean Tsunami on Households: A Modified Domestic Assets Index Approach.” Disasters 34:705–31.

Bolin

Robert

Stanford

Lois

. 2006. The Northridge Earthquake: Vulnerability and Disaster. New York: Routledge.

Bourdieu

Pierre

. 1986. “The Forms of Capital.” In Handbook of Theory and Research for the Sociology of Education, edited by Richardson

, 241–58. Westport, CT: Greenwood Press.

Bourdieu

Pierre

Wacquant

Loïc J. D

. 1992. An Invitation to Reflexive Sociology. Chicago: University of Chicago Press.

10.

Business Civic Leadership Center. 2012. “The Role of Business in Disaster Response.” Accessed May 29, 2012 (https://www.uschamberfoundation.org/promising-practice/role-business-disaster-response-report).

11.

Carrido

Mary L.

2000. “An International Disaster Recovery Business Alliance.” Natural Hazards Review 1:50–55.

12.

Chamlee-Wright

Emily

Storr

Virgil Henry

. 2011. “Social Capital as Collective Narratives and Post-Disaster Community Recovery.” The sociological Review 59:266–82.

13.

Chandrasekhar

Divya

Finn

Donovan

. 2013. Influence of Household Recovery Capacity and Urgency on Post-Disaster Relocation: A Case Study of the Rockaways, New York After Hurricane Sandy. Boulder CO: Natural Hazards Center,.

14.

Chang

Stephanie E.

Falit-Baiamonte

Anthony

. 2002. “Disaster Vulnerability of Businesses in the 2001 Nisqually Earthquake.” Global Environmental Change Part B: Environmental Hazards 4:59–71.

15.

City of New York. 2013. A Stronger, More Resilient New York. New York: New York City Special Initiative for Rebuilding and Resiliency (SIRR).

16.

Cochrane

Harold C.

1992. “Overview of Economic Research on Earthquake Consequences.” In The Economic Consequences of a Catastrophic Earthquake, 100–11. Washington, DC: National Academies Press.

17.

Dahlhamer

James M.

Tierney

Kathleen J

. 1998. “Rebounding from Disruptive Events: Business Recovery Following the Northridge Earthquake.” Sociological Spectrum 18:121–41

18.

Finn

Donovan

Chandrasekhar

Divya

Xiao

. 2016. “Planning for Resilience in the New York Metro Region after Superstorm Sandy.” In Spatial Planning and Resilience Following Disasters: International and Comparative Perspectives, edited by Ubaura

S. Greiving

Tesliar

, 117–35. Bristol: Policy Press.

19.

Fothergill

Alice

Maestas

Enrique G. M.

Darlington

JoAnne DeRouen

. 1999. “Race, Ethnicity and Disasters in the United States: A Review of the Literature.” Disasters 23:156–73.

20.

Girard

Chris

Peacock

Walter G

. 1997. “Ethnicity and Segregation: Post-Hurricane Relocation.” In Hurricane Andrew: Ethnicity, Gender and the Sociology of Disasters, edited by Peacock

W. G.

Gladwin

Morrow

B. H.

, 191–205. New York: Routledge.

21.

Graham

Leigh T.

2007. “Permanently Failing Organizations? Small Business Recovery after September 11, 2001.” Economic Development Quarterly 21:299–314.

22.

Hunter

Lori M.

David

Emmanuel

. 2011. “Climate Change and Migration: Considering the Gender Dimensions.” In Migration and Climate Change, edited by Piguet

Pécoud

Guchteneire

P. F. A.

, 306–30. Cambridge, UK: Cambridge University Press.

23.

Ingram

Jane C.

Franco

Guillermo

Rumbaitis-del Rio

Cristina

Khazai

Bjian

. 2006. “Post-Disaster Recovery Dilemmas: Challenges in Balancing Short-Term and long-Term Needs for Vulnerability Reduction.” Environmental Science & Policy 9:607–13.

24.

Jacobs

Jane

. 1968. The Death and Life of Great American Cities. New York: Random House.

25.

Kahneman

Daniel

Knetsch

Jack L.

Thaler

Richard H

. 1986. “Fairness and the Assumptions of Economics.” Journal of Business 59:S285–300.

26.

Kawachi

Ichiro

Kennedy

Bruce P.

Lochner

Kimberly

Prothrow-Stith

Deborah

. 1997. “Social Capital, Income Inequality, and Mortality.” American Journal of Public Health 87:1491–8.

27.

Kilkenny

Maureen

Nalbarte

Laura

Besser

Terry

. 1999. “Reciprocated Community Support and Small Town-small Business Success.” Entrepreneurship & Regional Development 11(3): 231–46.

28.

Kuhlicke

Christian

Steinführer

Annett

Begg

Chloe

Bianchizza

Chiara

Bründl

Michael

Buchecker

Matthias

, et al. 2011. “Perspectives on Social Capacity Building for Natural Hazards: Outlining an Emerging Field of Research and Practice in Europe.” Environmental Science & Policy 14:804–14.

29.

Lacho

Kenneth J.

Bradley

Don B.

III Cusack

Michael

. 2006. “Business Nonprofits: Helping Small Businesses in New Orleans Survive Katrina.” The Entrepreneurial Executive 11:55–68.

30.

Liu

Chuanlan

Black

William C.

Lawrence

Frances C.

Garrison

M. E. Betsy

. 2012. “Post-Disaster Coping and Recovery: The Role of Perceived Changes in the Retail Facilities.” Journal of Business Research 65:641–47.

31.

Loeffler

D. N.

Christiansen

D. C.

Tracy

M. B.

Secret

M. C.

Ersing

R. L.

Fairchild

S. R.

Sutphen

. 2004. “Social Capital for Social Work: Toward a Definition and Conceptual Framework.” Social Development Issues 26(2/3):22–38.

32.

Mankiw

2006. Principles of Microeconomics (Vol. 10). Boston: Cengage Learning.

33.

Mathbor

Golam M.

2007. “Enhancement of Community Preparedness for Natural Disasters: The Role of Social Work in Building Social Capital for Sustainable Disaster Relief and Management.” International Social Work 50:357–69.

34.

Mayunga

Joseph S.

2007. “Understanding and Applying the Concept of Community Disaster Resilience: A Capital-Based Approach.” Summer Academy for Social Vulnerability and Resilience Building 1:16.

35.

Mileti

Dennis

. 1999. Disasters by Design: A Reassessment of Natural Hazards in the United States. Washington, DC: Joseph Henry Press.

36.

Morrow

Betty Hearn

. 1999. “Identifying and Mapping Community Vulnerability.” Disasters 23:1–18.

37.

Nakabayashi

Itsuki

. 1990. “Recovery of Livelihood Following Disasters in Present Japan.” Geographical Reports of Tokyo Metropolitan University 25:249–60.

38.

Nakagawa

Yuko

Shaw

Rajib

. 2004. “Social Capital: A Missing Link to Disaster Recovery.” International Journal of Mass Emergencies and Disasters 22:5–34.

39.

Olshansky

Robert B.

Johnson

Laurie A.

Topping

Kenneth C

. 2006. “Rebuilding Communities Following Disaster: Lessons from Kobe and Los Angeles.” Built Environment 32(4): 354–74.

40.

Pride

William M.

Hughes

Robert J.

Kapoor

Jack R

. 2014. Foundations of Business. Boston: Cengage Learning.

41.

Putnam

Robert D.

2000. “Bowling Alone: America’s Declining Social Capital.” In Culture and Politics, edited by Crothers

Lockhart

, 223–34. New York: Palgrave Macmillan.

42.

Quarantelli

Enrico Louis

. 1999. “The Disaster Recovery Process: What We Know and do Not Know from Research.” http://udspace.udel.edu/handle/19716/309.

43.

Quercia

Roberto G.

Bates

Lisa K

. 2002. The Neglect of America’s Housing: Consequences and Policy Responses. Millennial Housing Commission. http://govinfo.library.unt.edu/mhc/papers/quercia.doc.

44.

Sen

Suman

Cowley

James

. 2013. “The Relevance of Stakeholder Theory and Social Capital Theory in the Context of CSR in SMEs: An AUSTRALIAN PERSPECTIVE.” Journal of Business Ethics 118:413–27.

45.

Sydnor

Sandra

Niehm

Linda

Lee

Yoon

Marshall

Maria

Schrank

Holly

. 2017. “Analysis of Post-Disaster Damage and Disruptive Impacts on the Operating Status of Small Businesses after Hurricane Katrina.” Natural Hazards 85:1637–63.

46.

Szreter

Simon

Woolcock

Michael

. 2004. “Health by Association? Social Capital, Social Theory, and the Political Economy of Public Health.” International Journal of epidemiology 33:650–67.

47.

Tierney

Kathleen

. 2006. “Social Inequality, Hazards, and Disasters.” In Risk and Disaster: Lessons from Hurricane Katrina, edited by Daniels

R. J.

Ketti

D. F.

Kun

, 109–28. Philadelphia: University of Philadelphia Press.

48.

U.S. Geological Survey. 2013. “The Hurricane Sandy Storm Tide Mapper.” Accessed January 17, 2013, http://water.usgs.gov/floods/events/2012/sandy/sandymapper.html.

49.

Wasileski

Gabriela

Rodríguez

Havidán

Diaz

Walter

. 2011. “Business Closure and Relocation: A Comparative Analysis of the Loma Prieta Earthquake and Hurricane Andrew.” Disasters 35:102–29.

50.

Webb

Gary R.

Tierney

Kathleen J.

Dahlhamer

James M

. 2002. “Predicting long-Term Business Recovery from Disaster: A Comparison of the Loma Prieta Earthquake and Hurricane Andrew.” Global Environmental Change Part B: Environmental Hazards 4:45–58.

51.

Welsh

Mark G.

Esnard

Ann-Margaret

. 2009. “Closing Gaps in Local Housing Recovery Planning for Disadvantaged Displaced Households.” Cityscape 11:195–212.

52.

Woolcock

Michael

. 2002. “Social Capital in Theory and Practice: Where Do We Stand.” In Social Capital and Economic Development: Well-Being in Developing Countries, edited by Isham

Kelly

Ramaswamy

, 18–39. Cheltenham, UK: Edward Elgar.

53.

Woolcock

Michael

Narayan

Deepa

. 2000. “Social Capital: Implications for Development Theory, Research, and Policy.” The World Bank Research Observer 15:225–49.

54.

Xiao

Van Zandt

Shannon

. 2012. “Building Community Resiliency: Spatial Links between Household and Business Post-Disaster Return.” Urban Studies 49:2523–42.

55.

Zhang

Yang

Lindell

Michael K.

Prater

Carla S

. 2009. “Vulnerability of Community Businesses to Environmental Disasters.” Disasters 33:38–57.