Are Disasters Disastrous to Nonprofit Organizations? Investigating the Financial Implications of Hurricane Sandy for Nonprofits

Abstract

Nonprofit organizations are sensitive to external disasters due to their high reliance on external funds and volunteers. In this study, I investigate how disasters affect the financial health of nonprofits and what factors make them more vulnerable within the context of disaster. The sample in this study includes nonprofits directly and indirectly affected by Hurricane Sandy. Using a logistic regression model, I explore if the disaster contributed to the likelihood of a nonprofit experiencing financial distress. Disaster, as an external shock, increases risks of nonprofits experiencing financial distress, especially for smaller nonprofits and nonprofits not relying on commercial revenue.

Keywords

nonprofit financial health revenue organizational characteristics community characteristics disasters impacts

Introduction

Nonprofit organizations are highly dependent on external resources, like funds and volunteers (Never, 2014). The dependency on the munificence of funders makes nonprofits more vulnerable to external circumstances, compared to both for-profit firms and public organizations that rely on taxes (Tuckman & Chang, 1991). Thus, maintaining internal and external stability is more important, yet challenging, for nonprofits, especially in turbulent environments (Prentice, 2016b). Turbulence like natural disasters, terrorist attacks, and economic recessions are unforeseen external shocks that inhibit a nonprofit’s capacity to generate resources and maintain mission-related operations. Nonprofits in disaster-affected areas may suffer physical damage to their complexes, lose essential mission-related facilities, and staff may be unable to work (Auer, 2006). Most nonprofits have limited budgetary flexibility for emergency use toward disaster response (Stys, 2011).

Nonprofits play important roles in disaster response. First, nonprofits, like human services nonprofits, are the primary service providers for vulnerable populations like people with disabilities, elderly citizens, and low-income families (Salamon, 2004). These groups need extra assistance when an emergency occurs (Stys, 2011). The homeless may need extra beds in shelters during storms, while the disabled may need assistance when power systems are destroyed. Therefore, the demand for nonprofit services increases significantly during a disaster.

Following a disaster, maintaining organizational operations and satisfying heightened service demands are key to nonprofits’ survival and mission. Financial resources are crucial for maintaining operations and delivering mission-related services (Lin & Wang, 2016). However, sustaining healthy financial conditions is challenging for nonprofits under normal conditions (Salamon, 2004), and maybe impossible in turbulent external environments (Lin & Wang, 2016). Thus, given the demand and need for effective and reliable operations following disasters, investigating how disasters affect nonprofits’ financial performance is warranted.

Prior research has investigated turbulent environments like economic downturns. However, limited inquiries have focused on natural disasters. Natural disasters are unpredictable and cause great damage to economic and social life. Given climate change trends (Easterling et al., 2000), extreme events have increased recently, like drought, hurricanes, and tornadoes, resulting in tremendous economic losses. Hurricanes Katrina (2005), Sandy (2012), and Harvey (2017) caused damage of $125 billion, $65 billion, and $125 billion, respectively (CNN library, 2018). Yet, how these events affect nonprofits’ bottom-line remains unclear. Therefore, I investigate whether natural disasters cause poor financial health within nonprofits. I first discuss the concept of financial health and factors that influences it.

Financial Health

Financial health is a complex and multidimensional concept. Existing literature interprets and measures it in different ways (Bowman, 2011; Prentice, 2016a; Zietlow, 2012). Measures include revenue volatility (Carroll & Stater, 2008), revenue growth (Keating et al., 2005; Lin & Wang, 2016), assets (De Andrés-Alonso et al., 2015; Keating et al., 2005; Trussel, 2002), and program expense (De Andrés-Alonso et al., 2015; Keating et al., 2005; Lin & Wang, 2016; Never, 2014). These measures reflect the different aspects of financial health (Bowman, 2011). Unlike economic shocks which usually attack revenues, natural disasters affect nonprofits in a more complex manner. Nonprofits may lose offices, supplies, and/or staff. Therefore, retaining the ability to provide mission-related services is a more direct way to measure a disaster’s impacts on nonprofits. Never (2014) argues that “an organization’s expenses are a more contemporaneous measure of its presence in community” (p.70). Therefore, total expense is a good measure to assess a nonprofit’s service provisioning. Expenses reflect the finances outlaid toward a mission. Cutting back on expenses means cutting back on the mission (Tuckman & Chang, 1991).

Earlier literature often uses “financial vulnerability” instead of “financial health” (Prentice, 2016b). According to Tuckman and Chang (1991), a nonprofit is considered financially vulnerable “if it is likely to cut back its service offerings immediately when it experiences a financial shock” (p.445). The authors identify four indicators of financial vulnerability: net assets, surplus, revenue concentrations, and administration cost.

The above framework developed over time as scholars explored nonprofits’ financial vulnerability. Hager (2001) tested these four indicators among art organizations. They found that these indicators were not universally applicable to all nonprofits, implying that the organizational type is another factor to be accounted for when discussing nonprofit financial vulnerability. Later, organizational size was added to this framework (Joseph, 2011; Trussel & Greenlee, 2004). Keating and their colleagues (2005) used a large sample of nonprofit financial data to examine nonprofits’ financial vulnerability using insolvency, and financial, funding disruption and program disruptions. Their results showed that reliance on commercial revenues and endowment sufficiency can reduce nonprofit financial vulnerability.

Prentice (2016b) proposed against using “financial vulnerability” interchangeably with “financial health.” They claimed that financial health is a broader term to be used when assessing nonprofit financial performance. They also claimed that different environmental factors, like macroeconomics, political force, and community-based factors, must be considered when analyzing nonprofit financial health, along with specific accounting and firm measures. Nonprofit finances should be evaluated in an open, not a closed, system perspective (Prentice, 2016b). Recent studies provided evidence on the impacts of environmental factors on nonprofit financial health and added new variables to the model, like resource distribution (Paarlberg et al., 2018), minority communities (Lam & McDougle, 2016), and effects of capital campaigns (Woronkowicz, 2018). A sudden environmental shock, like a natural disaster, is another example of an environmental factor, which is less understood in nonprofit literature. This study fills this research gap by examining if natural disasters lead to nonprofits’ poor financial health and, if so, whether this impact is felt equally across nonprofits with different organizational characteristics.

Here, “financial health,” instead of “financial vulnerability,” will be used to describe nonprofit financial performance. “Financial distress,” in its literal sense, means that a nonprofit is struggling financially. Thus, “financial distress” will be used as an indicator of poor financial health.

Assessing Financial Health in Disaster Contexts

Natural disasters may negatively influence nonprofits as nonprofits may lose staff and infrastructure. Approximately 600 nonprofits were reportedly negatively influenced by Hurricane Sandy and required FEMA public assistance (HS Task Force, 2017). Nonprofits require financial resources to successfully meet increased service demands and alleviate operational losses due to natural disasters. However, substantial nonprofit revenues come from external resources, like governmental and foundational grants. These revenue sources, often with restrictions, are not readily adaptable or easily deployable in disaster responses (Stys, 2011). Therefore, the following hypothesis is developed.

Hypothesis 1: Nonprofits affected by a natural disaster are more likely to experience financial distress compared to nonprofits not affected by a natural disaster.

Pfeffer and Salancik (2003, p. 2) argued that “the key to organizational survival is the ability to acquire and maintain resources.” Organizational size is associated with a nonprofit’s staffing and funding capacities, which are vital resources for nonprofit survival. Smaller nonprofits with fewer employees and assets are more likely to have limited capacities to acquire and maintain resources in turbulent environments. Joseph (2011) studied what affected nonprofits’ fundraising abilities during the Great Recession. They showed that larger organizations (based on the number of employees and assets) have a higher capacity to raise funds and maintain sustainability compared to smaller ones. According to Lin and Wang (2016), nonprofits with limited capacities are challenged to build new, or manage multiple, funding relationships in economic downturns due to multifold accountability requirements from funders.

In disaster contexts, nonprofits may look for alternative funding sources to meet new service demands. However, smaller nonprofits may have difficulties due to limited capacity for exploring new funding sources and the lack of professional fundraising staff members during emergencies. Therefore, the following hypothesis is developed.

Hypothesis 2: Smaller nonprofits are more likely to be in financial distress than larger nonprofits when affected by a natural disaster.

Newer nonprofits may be prone to failure because they lack financial resources, social capital, and important routines for their stability and efficiency (Stinchcombe & March, 1965). For example, young nonprofits may have fewer collaborators and allies compared to well-established nonprofits. Therefore, they may lack the social capital to deal with new emergencies (Guo & Acar, 2005). Kapucu (2007) found that organizations with more ties with others are more likely to be resilient in disaster contexts, using the 9/11 terrorist attack as a case study. Thus, young nonprofits may have less capacity to withstand the shock of a disaster and meet increased demands. Therefore, the following hypothesis is developed.

Hypothesis 3: Younger nonprofits are more likely to experience financial distress than older nonprofits when affected by a natural disaster.

Natural disasters may have greater impact on certain fields of nonprofits, like homeless shelters because their service demands ramp up after disasters. Stys (2011) used homeless shelters as an example to describe the increasing demands when hurricanes strike. Shelters must increase beds for new clients because more homeless will need housing during the storm. Also, people may lose their houses after hurricanes and need places to stay for extended periods. Therefore, the following hypothesis is developed.

Hypothesis 4: Human service nonprofits are more likely to experience financial distress than other types of nonprofits when affected by a natural disaster.

Revenue diversification has complex impacts on nonprofit financial health (Hung & Hager, 2019; Qu, 2019). Some studies argued that nonprofits with diverse revenue sources have flexible financing. Therefore, their vulnerability to internal uncertainties and external dependencies decreases (Carroll & Stater, 2008; Froelich, 1999). In contrast, other studies claimed that revenue concentration helps nonprofits lower administrative and fundraising costs (Frumkin & Keating, 2011; Wicker & Breuer, 2013). In disaster contexts, since financial flexibility and autonomy are more important, nonprofits with greater revenue diversification can benefit from access to multiple revenue sources. Therefore, the following hypothesis is developed.

Hypothesis 5: Nonprofits with greater revenue diversification are less likely to be in financial distress than nonprofits with lower revenue diversification when affected by a natural disaster.

Some subsectors may benefit more from revenue diversification than others (Hung & Hager, 2019). Human services nonprofits have lower net assets compared to other subsectors (Prentice, 2016b). Therefore, they are more likely to need new funds during a natural disaster. These nonprofits with diverse revenue sources may have better capacity in searching for new funds as they are more familiar with various sources that could potentially provide much needed funding.

Hypothesis 6: Human services nonprofits with greater revenue diversification are less likely to be in financial distress than nonprofits with lower revenue diversification when affected by a natural disaster.

Different revenue streams have different characteristics, and thus, different impacts on nonprofits’ financial health (Carroll & Stater, 2008; Froelich, 1999). Donative revenue streams are associated with high administrative costs, and thus, are sensitive to environmental change. The uncertainties associated with the nature of donative revenue stream often leads to difficulties in maintaining nonprofit services during a disaster (Froelich, 1999; Grønbjerg, 1993). In contrast, commercial revenue streams tend to have greater financial stability. Therefore, the following hypothesis is developed.

Hypothesis 7: Commercial nonprofits are less likely to be in financial distress than nonprofits relying on other sources of revenue when affected by a natural disaster.

Surplus, or net income, is the amount of revenue left over after expenses (Tuckman & Chang, 1991). More surplus means higher flexibility in nonprofits’ finances. They can better cope with a sudden surge of financial demands by either using their surplus directly or exploiting it to generate new revenues. Thus, nonprofits with a higher surplus are less dependent on external environments and have a higher ability to acquire resources. In contrast, if a nonprofit has negative or no surplus, it may have very little or no cash to spend on new service demands or repairing damaged facilities during/after a disaster. Therefore, the following hypothesis is developed.

Hypothesis 8: Nonprofits with a larger surplus are less likely to be in financial distress than nonprofits with a smaller surplus when affected by a natural disaster.

Equity is the annual surplus of total assets less total liability (Tuckman & Chang, 1991). According to Tuckman and Chang (1991), equity can help nonprofits in four ways when they experience external shocks. First, nonprofits can use equity to borrow funds from the capital markets. Second, unrestricted liquid equity can be converted to cash to offset revenue shocks. Third, when an external shock causes substantial losses that cannot be offset by unrestricted liquid funds, unrestricted illiquid funds can be sold to meet the nonprofit’s needs. Fourth, after an external shock, nonprofits can increase services that use restricted funds. Therefore, after being affected by a natural disaster, nonprofits with large equity can satisfy their financial needs by expensing the unrestricted funds and borrowing from capital markets. Therefore, the following hypothesis is developed.

Hypothesis 9: Nonprofits with greater equity are less likely to be in financial distress than nonprofits with lower equity when affected by a natural disaster.

Data and Method

Sample Selection

This study evaluates a natural disaster’s effects (Hurricane Sandy or “Sandy”) on nonprofit financial performance. Sandy struck New York State and its surrounding regions in 2012. Sandy has been chosen, given the scale and severity of its impact, and the unique characteristics of New York State. Sandy was the fourth most damaging hurricane in U.S. history. Thousands of nonprofits in this region were affected, given their location and mission-related activities (Amadeo, 2018).

This study uses the National Center for Charitable Statistics’ (NCCS) Core PC files. These files contain tax return information of public charities, under sections of 501(c)(3), which are required to file Form 990. The data used here are from 2011 to 2013, assuming that the aftermath of Sandy extended beyond its landfall. Adding unaffected regional nonprofits helps evaluate if the impact on nonprofit finances is disaster related or due to other factors. To reduce measurement errors commonly documented in 990 data, the data set was deep-cleaned. The cleaning procedure is shown in Table 1. There are 15,747 and 42,450 observations in disaster- and non-disaster-affected areas, respectively, for a combined 58,197 observations in the final data set.

Table 1.

Sample Selection and Data Cleaning Summary.

Sample selection	n
Beginning sample size in 2011 NCCS core file	381,035
Less nonprofits not located in New York State	−352,243
Beginning sample size in 2012 NCCS core file	373,358
Less nonprofits not located in New York State	−344,694
Beginning Sample size in 2013 NCCS core file	382,401
Less nonprofit not located in New York State	−353,004
Sample size in combined file	86,853
Less nonprofits with data errors (negative revenues, expense, or contributions)	−9,941
Less nonprofits if their fiscal year and the year of NCCS core file is not constant^a	−18177
Less nonprofit with incorrectly calculated equity ratio surplus (outliers)	−538
Final Sample size	58,197

Note. NCCS = National Center for Charitable Statistics.

Outliers removed are defined as observations that fall out of the 3-sigma range of the sample distribution. Details about 3-sigma can be found here.

Dependent Variables

Financial distress

The dependent variable is nonprofits’ financial distress, measured by a binomial indicator indicating if an organization cut its total expense by at least 10%, 20%, or 30% in the following year. The expense cut ratio is the difference between a nonprofit’s total expense in 2012 and 2013 divided by its 2012 baseline total expense. This ratio is encoded to a binary variable (0/1) using thresholds of 10%, 20%, and 30% reductions. Throughout this study, I adopt a naming convention that an expense cut ratio of 10% indicates light distress, 20% indicates medium distress, and 30% indicates heavy distress.

Independent Variables

Independent variable in Model 1: Disaster

The independent variable is whether a nonprofit was Sandy affected. This is measured by a binomial indicator indicating if an organization is located within a designated county declared by FEMA (2012) for individual and public assistance following Sandy. FEMA-designated cities are Bronx, Kings, Nassau, New York, Orange, Putnam, Queens, Richmond, Rockland, Suffolk, Sullivan, Ulster, and Westchester. The organizations’ locations were identified using the NCCS core data field named “city.” Sandy-affected counties were coded as 1, as opposed to 0 for unaffected regions.

Independent variables in Model 2

Model 2 includes multiple independent variables that are also included as control variables in the Model 1. (a) Organization size is indicated by a natural logarithm of an organization’s total assets (Prentice, 2016b). A logarithm conversion was used because assets across the sample will be highly right-skewed without transformation due to its large scale (Benoit, 2011). (b) Revenue diversification is derived from the Hirschman–Herfindahl index (HHI), which is widely used in nonprofit research (Carroll & Stater, 2008; Hager, 2001; Keating et al., 2005; Prentice, 2016b). HHI compares the nonprofit’s revenue obtained from each source (contributions, commercial revenue, and investment) within its total revenue. A revenue diversification score close to 1 indicates that the revenue of a nonprofit is equally distributed among the three sources.

R e v e n u e D i v e r s i f i c a t i o n = \frac{1 - Σ_{i = 1}^{3} R_{i}^{2}}{2 / 3}

(c) Other independent variables are age, surplus, human service nonprofits, and commercial/total revenue. The operationalization of these variables is shown in Table 2.

Table 2.

Variable Operationalization.

Name	Formula	NCCS Core data fields
Financial distress	(Total expense in 2012 − total expense in 2013)/total expense in 2013	exps (2012), exps (2013)
Disaster	In disaster area coded as 1	city, state, zip5
Size	Natural logarithm of the total assets	ass_eoy
Age	Fiscal year − start year	ruledate, fisyr
Human Services	If NTEE code is P, coded as 1	ntee1
Revenue diversification	HHI	cont, progrev, invinc, netrent, salesecn, saleothn, fundinc, grprof, othinc, secur, spevtg
Commercial/total revenue	Commercial revenue/total revenue	cont, progrev, invinc, netrent, salesecn, saleothn, fundinc, grprof, othinc, secur, spevtg
Surplus	(Total revenue − total expense)/total revenue	totrev, exps
Equity ratio	Net assets/total revenues	fundbal, totrev
Poverty rates	U.S. Census data	U.S. Census
Density	Number of nonprofits in a county divided by population multiply 1,000	U.S. Census
Urban status	Depend on whether a county had 50,000 or more individuals	U.S. Census
Blau Index (BI)	1−Ʃ (organization share of nonprofit expenditure in county)² = 1− $Σ {(\frac{e x p e n s e_{i}}{t o t a l e x p e n s e_{j}})}^{2}$ (i = nonprofits; j = counties)	exps, fips

Note. NCCS = National Center for Charitable Statistics; NTEE = National Taxonomy of Exempt Entities; HHI = Hirschman–Herfindahl index.

Control Variables

The nonprofit market has a great influence on nonprofit financial health. Thus, this study includes four control variables at the county level to indicate this market’s impact on nonprofit financial health. These variables were selected based on Paarlberg et al. (2018). The first control variable is poverty rates, which were obtained from the U.S. census. Higher poverty rates mean higher demands of services and fewer community resources that can be provided to nonprofits. The second control variable, nonprofit density, measures the number of nonprofits per 1,000 people in a county, and quantifies sector size at the county level. The third control variable, urban status, indicates whether a county has 50,000 or more individuals according to the definition given by Census Bureau. The fourth control variable, Blau Index (BI), indicates whether nonprofit expenses are evenly distributed in a county. BI’s formula is shown in Table 2. BI is confined between 0 to 1. Higher BI means more even expense distribution, suggesting that more intense competition among nonprofits for resources.

Model Specification

This study uses a logistic regression model to explore the relationship between natural disasters and nonprofits’ financial health. This model is chosen because the dependent variable “financial distress” was measured as a binary variable. Financial distress is tested using three different thresholds (namely 10%—light distress, 20%—medium distress, and 30%—heavy distress).

This study’s first part (Model 1) tests whether a nonprofit’s financial health is associated with natural disaster (first hypothesis) while controlling for all other model variables. This analysis includes both disaster and non-disaster-affected organizations. This model includes an error term to account for the additional factors that may contribute to nonprofit financial health.

To exclude the impact of environmental factors on nonprofit financial health (i.e., the financial stress is induced by the disaster, not other factors intrinsic to the geographical difference between disaster- and non-disaster-affected areas), Model 1 is run twice. The first run is a baseline model using data from 2011 (predisaster year), while the second is based on data from 2012 (disaster year).

\begin{matrix} l n (\frac{f i n a n c i a l d i s t r e s s}{1 - f i n a n c i a l d i s t r e s s}) = β_{0} + β_{1} d i s a s t e r + β_{2} a g e \\ + β_{3} s i z e + β_{4} h u m a n s e r v i c e \\ + β_{5} r e v e n u e d i v e r s i f i c a t i o n \\ + β_{6} c o m m e r c i a l / t o t a l r e v e n u e \\ + β_{7} s u r p l u s + β_{8} e q u i t y \\ + β_{9} p o v e r t y r a t e s + β_{10} d e n s i t y \\ + β_{11} u r b a n + β_{12} B l a u I n d e x + μ \end{matrix}

(Model 1)

This study’s second part (Model 2) uses only nonprofits in disaster-affected areas to test the remaining hypotheses.

\begin{matrix} l n (\frac{f i n a n c i a l d i s t r e s s}{1 - f i n a n c i a l d i s t r e s s}) = β_{0} + β_{1} a g e + β_{2} s i z e \\ + β_{3} h u m a n s e r v i c e \\ + β_{4} r e v e n u e d i v e r s i f i c a t i o n \\ + β_{5} c o m m e r c i a l / t o t a l r e v e n u e \\ + β_{6} s u r p l u s + β_{7} e q u i t y \\ + β_{8} p o v e r t y r a t e s + β_{9} d e n s i t y \\ + β_{10} u r b a n + β_{11} B l a u I n d e x + μ \end{matrix}

(Model 2)

Results

Table 3 presents the variable descriptive statistics. About 27% nonprofits in the sample are in disaster-affected areas, 11% are from human services subsector, and 64% rely on commercial revenue. On average, nonprofits in this sample are approximately 20 years old.

Table 3.

Descriptive Statistics.

Variables	Observation	M	SD	Min	Max
Light distress (10%)	19,896	.293	.455	0	1
Medium distress (20%)	19,896	.223	.416	0	1
Heavy distress (30%)	19,896	.185	.388	0	1
Disaster	19,431	.273	.446	0	1
Size	19,117	12.197	2.642	0.015	22.500
Age	19,379	19.836	17.486	0	94.940
Human service	19,431	.117	.321	0	1
Revenue diversification	11,797	.743	.287	0.001	1
Commercial/total revenue	19,431	.637	.481	0	1
Surplus	19,044	−.316	6.625	−499.500	1
Equity ratio	19,044	2.720	11.375	−223.900	240.785
Poverty rates	19,425	30.774	12.312	1	51
Density	19,425	1.951	1.385	0.557	6.897
Urban	19,431	.966	.182	0	1
Blau Index (BI)	19,431	.898	.113	0.295	0.980

Table 4 shows financially distressed nonprofits’ distribution across disaster/non-disaster-affected areas. More nonprofits in disaster-affected areas experienced financial distress compared to those in non-disaster areas. Approximately 17% experienced heavy financial distress (threshold = 30%) in disaster-affected areas, compared to 15% in non-disaster-affected areas. The same trend was observed for light and medium distress.

Table 4.

Financial Distressed Nonprofits’ Distribution in the New York State.

		Non-disaster area	Disaster area
Light distress (10%)	Non-financial distress nonprofits	8,222	2,999
	Financial distress nonprofits	3,111	1,191
	Total	11,333	4,190
	Percentage of financial distress	27.45%	28.42%
Medium distress (20%)	Non-financial distress nonprofits	9,183	3,276
	Financial distress nonprofits	2,150	914
	Total	11,333	4,190
	Percentage of financial distress	18.97%	21.81%
Heavy distress (30%)	Non-financial distress nonprofits	9,677	3,459
	Financial distress nonprofits	1,656	731
	Total	11,333	4,190
	Percentage of financial distress	14.61%	17.45%

Based on descriptive data, nonprofits in disaster-affected areas experienced more severe financial distress than those in non-disaster-affected areas. More nonprofits in disaster-affected areas experienced this financial distress. Is the difference between nonprofits in disaster- and non-disaster-affected areas caused by the disaster? What organizational characteristics or financial ratios lead to financial distress in disaster contexts? These questions were explored using a logistic regression model.

Tables 5 and 6 show variable correlations in Models 1 and 2, respectively. Tables 7 and 8 show the logistic regression models’ results. The results provide mixed support for the hypotheses. Model 1’s results (Table 7, the year 2012, N = 9686) indicate that nonprofits in disaster-affected areas experienced more financial distress compared to those elsewhere, affirming Hypothesis 1. When a 10% threshold (light distress) was used to distinguish financially distressed nonprofits, results show that the log odds of financial distress were 0.145 higher for Sandy-affected nonprofits, as compared to those not affected. The result is statistically significant at the 0.05 level. Similarly, on average, the log odds of financial distress are 0.227 and 0.218 higher for Sandy-affected nonprofits, as compared to those that were not when using 20% (medium distress) and 30% thresholds (heavy distress), respectively. These results are statistically significant at the 0.01 and 0.05 levels, respectively.

Table 5.

Model 1 Variable Correlation Table.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. Light distress	1.000	—	—	—	—	—	—	—	—	—	—	—	—	—
2. Medium distress	0.832***	1.000	—	—	—	—	—	—	—	—	—	—	—	—
3. Heavy distress	0.739***	0.889***	1.000	—	—	—	—	—	—	—	—	—	—	—
4. Disaster	0.010	0.032***	0.035***	1.000	—	—	—	—	—	—	—	—	—	—
5. Equity ratio	0.021**	0.027***	0.028***	−0.011	1.000	—	—	—	—	—	—	—	—	—
6. Surplus	−0.051***	−0.061***	−0.070***	−0.008	−0.260***	1.000	—	—	—	—	—	—	—	—
7. Size	−0.191***	−0.202***	−0.187***	0.072***	0.082***	0.034***	1.000	—	—	—	—	—	—	—
8. Age	−0.070***	−0.092***	−0.092***	−0.010	0.018	0.014	0.367***	1.000	—	—	—	—	—	—
9. Revenue diversification	0.030**	0.036***	0.039***	0.013	−0.024**	−0.011	−0.184***	−0.238***	1.000	—	—	—	—	—
10. Human service	−0.024**	−0.032***	−0.027***	−0.047***	−0.025***	−0.002	0.055***	0.073***	0.003	1.000	—	—	—	—
11. Commercial/total revenue	0.014	0.013	0.009	−0.125***	−0.055***	−0.015**	−0.268***	−0.101***	0.009	−0.024***	1.000	—	—	—
12. Urban	0.000	0.003	0.005	0.115***	−0.021**	−0.004	−0.019**	−0.024**	0.070***	0.003	−0.014	1.000	—	—
13. Poverty rate	0.005	0.003	0.005	0.114***	−0.004	−0.008	−0.020**	−0.006	0.050***	−0.004	−0.017*	0.096***	1.000	—
14. Density	0.013	0.024**	0.023*	0.617***	0.008	−0.004	0.073***	0.027***	−0.041***	−0.044***	−0.093***	0.020**	0.121***	1.000
15. Blau Index (BI)	−0.008	0.010	0.013	0.384***	−0.026***	−0.005	0.008	−0.014*	0.043***	−0.035***	−0.053***	0.248***	0.381***	0.252***

p < .05. . **p < .01. ***p < .001.

Table 6.

Model 2 Variable Correlation Table.

	1	2	3	4	5	6	7	8	9	10	11	12
1. Light distress	1.000	—	—	—	—	—	—	—	—	—	—	—
2. Medium distress	0.838***	1.000	—	—	—	—	—	—	—	—	—	—
3. Heavy distress	0.730***	0.870***	1.00	—	—	—	—	—	—	—	—	—
4. Equity ratio	0.003	−0.004	−0.003	1.000	—	—	—	—	—	—	—	—
5. Surplus	−0.065***	−0.074***	−0.083***	−0.354***	1.000	—	—	—	—	—	—	—
6. Size	−0.192***	−0.219***	−0.207***	0.086***	0.036*	1.00	—	—	—	—	—	—
7. Age	−0.067***	−0.086***	−0.091***	0.033*	0.026	0.371***	1.000	—	—	—	—	—
8. Revenue diversification	0.010	0.041	0.049**	−0.014	−0.007	−0.212***	−0.293***	1.000	—	—	—	—
9. Human service	−0.003	−0.022	−0.019	−0.014	0.014	0.026	0.061***	0.020	1.000	—	—	—
10. Commercial/total revenue	−0.002	0.010	0.014	−0.037**	−0.011	−0.287***	−0.131***	−0.022	−0.066***	1.000	—	—
11. Poverty rates	−0.059***	−0.067***	−0.062***	−0.021	0.031*	0.029*	0.007	0.086***	0.087***	0.098***	1.000	—
12. Density	0.047**	0.052***	0.045**	0.017	−0.015	−0.029*	−0.009	−0.082***	−0.086***	−0.099***	−0.946***	1.000
13. Blau Index (BI)	0.021	0.021	0.012	−0.004	0.019	−0.025	−0.007	−0.058***	−0.068***	−0.083***	−0.723***	0.886***

Note.. *p < .05. **p < .01.***p < .001.

Table 7.

Logistic Regression Models’ Results of Financial Distress.

	2011			2012
	Model 1a	Model 1b	Model 1c	Model 1a	Model 1b	Model 1c
	10% threshold	20% threshold	30% threshold	10% threshold	20% threshold	30% threshold
Disaster	0.025 (0.076)	0.102 (0.092)	0.155 (0.102)	0.145* (0.071)	0.227** (0.083)	0.218* (0.092)
Size	−0.161*** (0.012)	−0.201*** (0.014)	−0.213*** (0.015)	−0.170*** (0.012)	−0.204*** (0.014)	−0.203*** (0.016)
Age	−0.002 (0.002)	−0.005** (0.002)	−0.005* (0.002)	−0.003 (0.002)	−0.006** (0.002)	−0.008*** (0.002)
Human service	0.091 (0.221)	−0.022 (0.286)	−0.406 (0.362)	0.098 (0.210)	0.094 (0.256)	0.227 (0.281)
Revenue diversification	0.047 (0.097)	0.181 (0.119)	0.258 (0.134)	−0.026 (0.095)	−0.030 (0.113)	0.009 (0.126)
Human service × revenue diversification	−0.449 (0.275)	−0.319 (0.350)	−0.0547 (0.435)	−0.191 (0.262)	−0.250 (0.317)	−0.316 (0.347)
Commercial/total revenue	−0.492*** (0.055)	−0.507*** (0.067)	−0.623*** (0.077)	−0.348*** (0.053)	−0.480*** (0.064)	−0.501*** (0.072)
Surplus	−0.137*** (0.031)	−0.127*** (0.028)	−0.071** (0.024)	−0.122*** (0.028)	−0.112*** (0.026)	−0.0979*** (0.025)
Equity ratio	−0.002 (0.003)	0.003 (0.003)	0.008** (0.003)	0.00713** (0.003)	0.00843** (0.003)	0.00699* (0.003)
Poverty rates	−0.001 (0.002)	−0.001 (0.003)	0.002 (0.003)	0.000 (0.002)	−0.003 (0.003)	−0.002 (0.003)
Density	0.053* (0.022)	0.026 (0.027)	0.040 (0.0230)	0.033 (0.022)	0.059* (0.025)	0.055 (0.028)
Urban	0.0905 (0.152)	0.174 (0.192)	0.106 (0.212)	−0.255 (0.138)	−0.359* (0.160)	−0.225 (0.184)
Blau Index (BI)	−0.472 (0.337)	−0.326 (0.410)	−0.707 (0.457)	−0.362 (0.266)	−0.109 (0.320)	−0.248 (0.355)
Constant	1.471*** (0.329)	1.151** (0.401)	1.204** (0.444)	1.692*** (0.292)	1.632*** (0.344)	1.291*** (0.383)
Likelihood ratio	424.46***	456.90***	440.55***	424.46***	456.90***	440.55***
R ²	.043	.060	.068	.043	.060	.068
N	8,886	8,886	8,886	9,686	9,686	9,686

Note. *p < .05. **p < .01. ***p < .001.

In contrast, the baseline model (Table 7, year 2011, N = 8,886) reveals that the coefficients of the variable disaster are not statistically significant using any of the three thresholds (10%, 20%, or 30%), suggesting that the financial distress observed in 2012 is truly a result of Hurricane Sandy. In summary, results of Model 1 support Hypothesis 1 that disaster, as an environmental factor, leads to nonprofit financial distress.

I can perform a deeper dive into the control variables of Model 1 in year 2012 (disaster year). Organizational size is statistically significant using all three thresholds, indicating that size is associated with all levels of financial distress. Age is statistically significant while using the 20% and 30% thresholds, indicating that age is negatively associated with medium and deep financial distress. Contrary to expectations, equity ratios are positively associated with financial distress (p < .05), despite a relatively minor effect. Contrary to equity ratios, surplus and commercial revenue help reduce the likelihood of financial distress (p < .001). Among the four environmental variables, only urban and density are statistically significantly associated with medium financial distress. Nonprofits in urban areas are less likely to experience financial distress while geographical sparsity decreases a nonprofit’s risk of experiencing financial distress.

I further narrow down our sample to only examine nonprofits that reside in the disaster-affected areas (N = 2,984) and use this newly reduced sample in Model 2. Data in Model 2 are from year 2012 (disaster year), and the results are displayed in Table 8. Two control variables—urban and density—are removed in Model 2. Urban is removed because all nonprofits in this model are in urban areas. Density is removed due to its multicollinearity with BI (correlation = 0.886, see Table 6) and high variance inflation factor (VIF = 40.17). After removing Density, the mean VIF of Model 2 decreased from 8.31 to 1.42. Hypotheses 2 and 7 are supported. Results suggest that organizational size is negatively associated with financial distress using all three thresholds (p < .001), meaning larger organizations are less likely to experience financial distress during a disaster (Hypothesis 2). Results reveal that nonprofits heavily dependent on commercial revenue are less likely to experience financial distress after a disaster (p < .001). The effect sizes are −0.368, −0.412, and −0.487 when using 10%, 20%, and 30% as thresholds, respectively. Hypothesis 8 is supported when using 30% as threshold (p < .05), suggesting that nonprofits with larger surplus are less likely to experience heavy financial distress during a disaster.

Table 8.

Logistic Regression Models’ Results of Financial Distress (Disaster Area Only).

	Model 2a	Model 2b	Model 2c
	10% threshold	20% threshold	30% threshold
Size	−0.171***	−0.212***	−0.201***
Size	(0.0201)	(0.0226)	(0.0247)
Age	−0.000943	−0.00146	−0.00369
Age	(0.00262)	(0.00302)	(0.00342)
Human service	−0.00233	−0.545	−0.333
Human service	(0.424)	(0.556)	(0.611)
Revenue diversification	−0.268	−0.0720	0.0387
Revenue diversification	(0.166)	(0.189)	(0.212)
Human service × revenue diversification	0.175	0.617	0.440
Human service × revenue diversification	(0.512)	(0.650)	(0.712)
Commercial/total revenue	−0.368***	−0.412***	−0.487***
Commercial/total revenue	(0.0986)	(0.114)	(0.130)
Surplus	−0.0266	−0.0339	−0.0340*
Surplus	(0.0174)	(0.0187)	(0.0164)
Equity ratio	0.000388	−0.00197	−0.00618
Equity ratio	(0.00474)	(0.00537)	(0.00583)
Poverty rates	−0.254	−0.267	−0.262
Poverty rates	(0.346)	(0.410)	(0.435)
Blau Index (BI)	−50.32	−48.49	−47.59
Blau Index (BI)	(78.37)	(92.80)	(98.52)
Constant	58.66	57.34	55.79
Constant	(87.37)	(103.5)	(109.8)
Likelihood ratio	137.60***	173.99***	151.17***
R ²	.041	.061	.062
N	2,984	2,984	2,984

Note. *p < .05. ***p < .001.

The results of this analysis do not support Hypotheses 3, 4, 5, 6, and 9. The finding indicates that nonprofits, regardless of organizational age or subsector, are all financially vulnerable in natural disaster contexts. The results fail to indicate a positive relationship between revenue diversification and financial distress, suggesting that a nonprofit’s revenue diversification does not significantly affect their financial distress following a disaster. The interaction effect between human services nonprofit and revenue diversification is not statistically significant, meaning that to the extent of our study, revenue diversification is not associated with human services nonprofits’ financial distress. Equity ratio fails to demonstrate a statistically significant relationship with financial distress, indicating that nonprofits with various equity do not perform differently in disaster contexts. Contrary to the results of most recent studies, this study does not support the impacts of communities’ characteristics and market structures on financial health in natural disaster contexts.

Discussion

In this study, I investigated whether disasters affect nonprofit financial health and what organizational characteristics and financial ratios lead to poor nonprofit financial health within disaster contexts. The main finding is that disasters, as an external shock, contribute to the likelihood of nonprofit financial distress. This is consistent with the argument of open-system perspectives (Prentice, 2016b), which stated that nonprofits’ financial performance is determined by their internal and external environments. Previous studies tested this perspective in economic recession settings (Prentice, 2016b). This study expands this perspective and evaluates it in disaster contexts, while providing useful information for practitioners like nonprofit managers and stakeholders (like donors and policymakers). As disasters lead to poor financial health, nonprofit managers must make preparation plans for natural disasters. Donors and policymakers also must provide support for nonprofits following disasters as they often contribute to disaster response. With increased demand amid a financially taxing environment, nonprofits may be unduly stressed to provide services and fulfill their missions at such critical times of community need.

Although natural disasters may affect all nonprofits, the impacts may vary across nonprofits. This study found that smaller nonprofits are more likely to experience financial distress. This finding is consistent with existing nonprofit financial health literature (Joseph, 2011; Lin & Wang, 2016; Trussel & Greenlee, 2004). Therefore, smaller nonprofits should be aware of natural disaster-induced financial pressures. In addition, this study found that nonprofits relying on commercial revenue, compared to those relying on other revenue sources, are less vulnerable in disaster contexts. Therefore, nonprofits relying on noncommercial revenues should be more alert and well prepared for natural disasters. Furthermore, this study found that natural disasters influence nonprofits regardless of their age and subsectors.

This study found that financial ratios (equity ratio, surplus, commercial/total revenue, and revenue diversification) and community characteristics are not significant predictors of financial distress in disaster contexts. Therefore, this study suggests that nonprofits must develop emergency and recovery plans for natural disasters regardless of their revenue source distribution. In addition, this study corroborates previous findings that indicate mixed results about revenue diversification’s impact on nonprofits’ financial capacity. Revenue diversification did not improve nonprofits’ financial situation during and immediately following disasters. However, using HHI to measure nonprofit revenue might be problematic. Recent studies pointed out that revenue diversification benefits are more likely to be achieved through optimizing revenue source mix according to a nonprofit’s specific situation, instead of simply adding a revenue stream (Hung & Hager, 2019; Qu, 2019). This study also did not have significant findings on the role of equity in preventing nonprofit financial distress in disaster contexts. This might be because most nonprofits may not have sufficient available financial reserves for disaster response. Due to nonprofits limited financial reserve, extra support from all sources, including individuals, government, and charitable foundations are needed within disaster contexts.

Comparing Models 1 (all nonprofits) and 2 (only nonprofits in disaster-affected areas), this study found that factors, like equity ratio, urban, and density, associated with financial distress in Model 1 are not statistically significant in Model 2. This suggests that better resource environments may not help reduce nonprofit financial distress in disaster-affected areas. Therefore, disaster influences nonprofit financial health regardless of internal financial status, external communities’ characteristics, and market structures.

Limitations

Natural disasters vary in scale and magnitude, and influence nonprofits differently. This study uses Hurricane Sandy to study the impact of natural disasters on nonprofit financial health as Sandy has more generalities compared to others. First, the disaster affected areas within New York State, a relatively wealthy area in the United States where nonprofits are likely to have more resources. Nonprofits in resource-constrained areas are more likely to experience financial distress under a similar disaster. Second, New York State (compared to Texas or Louisiana) is rarely influenced by hurricanes. As hurricanes are less expected, nonprofits may not have sufficient experience and preparedness. Therefore, these results can be more easily generalized to other types of unforeseeable natural disasters like earthquakes.

This study uses secondary data from the IRS 990 returns provided by NCSS. However, some scholars critique their reliability (Tinkelman & Mankaney, 2007; Trussel & Parsons, 2007). Although the data set was cleaned to deal with the drawbacks of 990 data, it may be necessary to test these hypotheses in similar settings using different data sets. Moreover, Sandy occurred in the late fall of 2012. However, this study relies on year-end data and full-year 990 returns. Therefore, 990 returns may not encompass all factors, such as end-of-day results, like a stock price would indicate. Furthermore, nonprofits have different filing points dependent on if they file based on tax or fiscal years which creates noise in the analysis. Although 990 returns can only provide a limited perspective on the implications of disasters for nonprofits, their perspective holds merit since the data are available on a large-scale and consistently reported according to the form’s prompts. Third, this study uses full-year 990 returns, but Sandy occurs on a specific day. Fourth, nonprofits that did not file tax returns in 2012 and 2013 are not included in this sample. Therefore, this study has a survivor’s bias because nonprofits that did not survive throughout this difficult period are excluded. Finally, some variables, such as administration or fundraising cost, may affect nonprofit financial health, but these are not reported in the NCCS Core PC files so could not be included in this analysis. Future studies should address those challenges and improve this model’s predictive ability.

Conclusion

In this study, I explored whether disasters lead to nonprofit financial distress and what factors compromise nonprofit financial health in disaster contexts. I found that disasters, as external factors, make nonprofits vulnerable to financial distress. However, large nonprofits and nonprofits relying on commercial revenue are less likely to experience financial distress. Financial ratios that predict nonprofit vulnerability in economic recession settings do not affect the likelihood of financial distress for disaster-affected nonprofits.

This study contributes to nonprofit financial health literature by exploring it in a new setting: natural disasters. Financial ratios and community characteristics that predict poor financial health in a stable environment do not predict poor financial health in disaster settings. Nonprofits’ poor financial health following disasters is associated with organizational characteristics, like reliance on commercial revenue and size, outside the organization’s control. In addition, this study supports previous findings that nonprofit financial health should be studied from an open-system perspective and that environmental factors do influence nonprofit financial health. Finally, the study suggests that nonprofit managers should view natural disasters as a risk to nonprofits and make emergency plans for natural disasters. Stakeholders like governments and donors should provide support to nonprofits following disasters. The COVID-19 pandemic presents a previously unimagined external shock to the nonprofit sector, and the implications of this study may be informative for nonprofits affected by the pandemic. While the pandemic may be of a different nature compared to Hurricane Sandy, it cast similar influences on nonprofits and can cause changes in service needs (McMullin & Raggo, 2020), interrupt regular operations, and result in the loss of staff and volunteers as they are personally affected by the disaster (Kim & Mason, 2020). This study’s results indicate that smaller nonprofits may be more vulnerable than larger nonprofits, and that organizations reliant on donations may be affected differently than nonprofits reliant on commercial revenues. For those that support the sector, such as funders and infrastructure organizations, they may want to consider how to tailor their responses and supports as environmental shocks such as a pandemic do not have one-size-fits-all impacts. In future research, the impact of external shocks on nonprofit financial health should be tested in other disaster contexts. Moreover, factors that could help nonprofits quickly recover from natural disasters need to be studied.

Footnotes

Acknowledgements

The author would like to thank Amanda Stewart, Richard Clerkin, Jeffrey Diebold, Peter Frumkin, and the three anonymous reviewers for their feedbacks on the earlier drafts of this article.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Xintong Chen

Author Biography

Xintong Chen is an assistant teaching professor in the Public Administration Department at North Carolina State University. Her research interests include nonprofit management, disaster response, and diversity, equity, and inclusion.

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