Nonprofit Arts Organizations: Debt Ratio Does Not Influence Donations

Abstract

Due to increased competition for scarce resources, scholars and practitioners have been devoting more attention to identifying the factors that drive private contributions to nonprofit organizations in recent years. This study aims to investigate whether capital structure decisions made by nonprofit managers have an impact on future contributions from individual donors. More specifically, it asks whether debt is associated with a reduction in future financial support. This study relies on data derived from the DataArts Cultural Data Profile to answer this question. It utilizes a log-log model where the dependent variable is defined as total private contributions in the current period. Results indicate that an increase in the interest expense to total expense ratio is associated with a decrease in future contributions. A nonprofit’s debt to assets ratio, however, does not have a statistically significant impact on future contributions.

Keywords

capital structure nonprofit donations philanthropy debt

Introduction

There are more than 1.5 million tax-exempt organizations in the United States, including approximately 1.2 million public charities and private foundations (National Center for Charitable Statistics, 2015). The nonprofit sector contributed an estimated US$905.9 billion to the U.S. economy in 2013, comprising 5.4% of the country’s GDP (McKeever, 2015). In 2015, charitable donations totaled US$375.25 billion (2.1% of GDP), with 71% coming from individuals rather than corporations; (McKeever, 2015). More specifically, the arts, culture, and humanities subsector—the focus of this article—gained an additional 7% in contributions from the previous year (6.8% inflation adjusted) receiving a total of US$17.07 billion from individuals (McKeever, 2015). Financial support from individuals is critical for nonprofits; they devote substantial time and resources courting individual donors. Given that the ability to attract new donors (and maintain existing donor relationships) is so vital to the survival of nonprofit organizations, understanding the determinants of charitable giving is a topic of significant importance and relevance (Balsam & Harris, 2014). This article examines the question of whether the use of debt by nonprofit arts organizations influences future charitable donations from individuals.

In the short term, nonprofits borrow to cover temporarily inadequate cash flows so they can continue to operate (Bowman, 2015). For example, many nonprofits maintain a line of credit (LOC) with a local bank that allows them to continue operating when cash flow is negative. Indeed, a LOC is one of the most valuable tools a nonprofit can have to get over the many bumps and challenges along the road to mission fulfillment. But there are other tools available to nonprofits in the short term. As Bowman (2015) argues, past-due trade debt is an implicit method of borrowing often overlooked in the literature—yet it is very costly. Vendors typically give a nonprofit 30 days to pay a bill in full. Many vendors also provide a prompt payment discount, but if a nonprofit fails to pay on time, vendors may charge late fees, some as punitive as 18% per year (Bowman, 2015).

In the long term, nonprofits looking to finance capital projects can utilize large reserves of existing wealth or accumulated net assets, embark on a capital campaign to generate large reserves, issue debt, or use a combination of debt and wealth (Calabrese, 2011). A capital campaign might be a promising option for a nonprofit with access to wealthy donors, but capital campaigns require extensive time commitments—3 to 5 years in some cases (Linzer & Linzer, 2007). If a capital campaign is not feasible, and if a nonprofit does not have considerable net assets, then debt financing becomes the only available option. Although the benefits of borrowing for nonprofit organizations are easy to understand, Yetman (2007) argues, “what is typically less well understood are the costs and ramifications of borrowing” (p. 244). For example, whereas gifts have the benefit of not having to be repaid, borrowing increases long-term debt, carries significant risk, and has the potential to cause financial distress (Bowman, 2015). Debt burdens an organization with “an increased and constant drain on net cash flow for many years” (Bowman, 2015). Furthermore, in the event that the organization has to default on its loan, any assets used as collateral are forfeited.

From a financial management perspective, debt financing may be appropriate for nonprofit organizations because it can help smooth lumpy capital expenditures and, unlike pay-as-you-go financing, does not require a significant cash outlay at the start of a project or at the time of purchase of a capital asset (Denison, 2009). Although most nonprofit organizations have manageable debt burdens, Yetman (2007) observes, “nonprofit use of debt is pervasive and nonprofits are sophisticated users of debt instruments” (p. 244). Although nonprofits continue to use the more traditional types of debt (e.g., loans and mortgages), increasingly they are experimenting with more sophisticated instruments such as tax-exempt leases (Yetman, 2007).

There are four reasons why nonprofits should use debt financing: to smooth short-term working capital needs, to purchase facilities and equipment, to take advantage of a business opportunity or when experiencing unanticipated cash need, and finally, to refinance existing debt (Yetman, 2007). Although “debt is crucial for capital investment” (Calabrese, 2011, p. 121), a nonprofit’s use of debt may have other unintended consequences. For example, if donors use debt to expand their existing programs, this could encourage or “crowd in” additional donors (Calabrese & Grizzle, 2012). However, if donors have a preference for “funding current output rather than past output,” the use of debt might displace or “crowd out” current donors (Calabrese & Grizzle, 2012, p. 222). If donors believe that the organization may no longer be a going concern due to excessive debt use whether perceived or actual, this might also drive them away (Calabrese & Grizzle, 2012).

Unlike the corporate literature, to the best of my knowledge, only one prior study (Calabrese & Grizzle, 2012) has examined directly the impact of outstanding debt on future donations. Calabrese and Grizzle (2012) find that increased borrowing reduces subsequent donations from individuals. They use data from the National Center on Charitable Statistics (NCCS), and although their sample includes the entire universe of nonprofit organizations filing the IRS 990 form, their sample is limited to the years 1998 to 2003. A more recent study (Yan & Sloan, 2016) with a focus on the impact of higher than median employee compensation on donations includes a measure of outstanding debt as a control variable in the model. Yan and Sloan (2016) find that long-term debt has no obvious correlation with donations. However, 67.4% of the organizations in their sample had no recorded donations. Ashley and Faulk (2010) in their research on the impact of financial efficiency ratios in the nonprofit grants marketplace find that an organization’s debt ratio is negatively related to grant amounts awarded. However, they use a data set comprising a sample of 2,669 grants distributed by 72 foundations to 1,328 nonprofit organizations in Georgia.

Although these prior studies make important initial steps, many questions remain regarding the costs and consequences of outstanding debt. This study aims to fill this gap in the literature. More broadly, this study builds on prior research that examines donor responsiveness to financial disclosure information. The empirical analysis finds that a high interest expense ratio is associated with a decrease in future contributions from private donors.

The rest of the article proceeds as follows: The first section provides background information and summarizes prior research with a primary focus on how financial disclosure information influences nonprofit charitable donations. The next section presents the hypotheses, methodology, analyses, results, and study limitations. The last section puts the results in context and frames the contribution of this article to the field.

The Use of Financial Disclosure Information by Individual Donors

The nonprofit literature indicates that donors—especially large donors—rely almost exclusively on financial indicators obtained from 990 forms or financial statements to evaluate the effectiveness or efficiency of the charities they support (Parsons, 2003; Tinkelman, 2004; Tinkelman & Mankaney, 2007). Since the seminal paper by Weisbrod and Dominguez (1986) was published, numerous studies presented by scholars such as Tinkelman (1998); Tinkelman (1999); Greenlee and Brown (1999); Frumkin and Kim (2001); Marudas (2004); Tinkelman (2004); Tinkelman and Mankaney (2007); Marudas, Hahn, and Fred (2014); and others have examined the link between reported financial results and subsequent donations. None of these studies explicitly claims that all donors directly evaluate financial information before making a donation, but each study does find a positive relationship between certain financial ratios and individual donations (Trussel & Parsons, 2008). Even if donors do not rely directly on financial reports to make their financial decisions, they may use recommendations from watchdog agencies such as the Better Business Bureau’s Wise Giving Alliance (Trussel & Parsons, 2008), and many of the ratios used by watchdog agencies are actually calculated using financial information from the IRS 990 form. This study argues that, regardless of whether donors independently evaluate leverage information or use leverage information provided by a watchdog agency or large donor, debt load may have a negative relationship with future donations.

There is no consistency among scholars regarding which ratios to include in donation models. Some scholars have used fundraising expenses as a proxy for the information available to donors and find a positive association between fundraising expenses and donations (Tinkelman, 1999; Weisbrod & Dominguez, 1986). Marudas (2004) finds a negative relationship between donations and net assets to total revenue ratio, indicating nonprofit organizations that save revenue, rather than reinvest in programming, receive fewer donations. A more recent study finds that as the price to raise a dollar (the inefficiency of fundraising efforts) increases, total individual donations fall (Marudas et al., 2014). Because donors are unable to directly observe an organization’s reputation, they have relied on variables such as age and size as proxies (Tinkelman, 1999). They find that not only do donors view new organizations more skeptically than older ones (Tinkelman, 1999; Weisbrod & Dominguez, 1986), but smaller organizations tend to have less capacity to produce quality reports (Tinkelman, 1999). Findings related to age have been mixed, however (Jacobs & Marudas, 2009; Tinkelman, 1999; Weisbrod & Dominguez, 1986). Prior research confirms that organizational efficiency is associated with the ability to attract donations (Greenlee & Brown, 1999; Jacobs & Marudas, 2009; Tinkelman & Mankaney, 2007); another study finds wealthier organizations receive more donations on average than poorer organizations, all else equal (Marudas et al., 2014). Trussel and Parsons (2008) find evidence that financial stability and reputation are positively related to donations, whereas higher price and higher administrative costs are inversely related to donations.

Scholars also find that the effects of financial disclosure information on donations vary substantially across subsectors within the U.S. nonprofit sector, suggesting the importance of testing industry-specific samples (Jacobs & Marudas, 2009; Marudas, 2004; Marudas & Jacobs, 2004). For arts nonprofits, the focus of this study, donation price, administrative efficiency, organizational age, and government support have no significant effect on donations (Jacobs & Marudas, 2009). However, wealth has a large significant negative effect on donations, whereas program service revenue has a small, significant, negative effect on donations (Jacobs & Marudas, 2009).

Background and Hypotheses

The debt overhang theory, first formalized by Myers (1977), argues that debt increases in a corporation can cause equity holders to underinvest in profitable projects. Magnus, Smith, and Wheeler (2003) argue that the debt overhang theory can be applied in the nonprofit context in a unique way: A nonprofit organization’s use of debt “could crowd out individual donations and hence charitable output that the contributions would fund” (Magnus et al., 2003, p. 14). Donors become concerned that any additional contributions will be used to pay for debts already incurred rather than current program provision (Bowman, 2002). Similarly, Yetman (2007) argues that donors may prefer their contributions be spent on providing current programs rather than on debt service for past program provision.

There are three main explanations in the literature for how debt may affect future donations. Bowman (2002) posits “donations reduce the need for leverage, and donors are likely to be increasingly uncomfortable as leverage increases” (p. 306). Donors are more inclined to give charitably “when they expect to reap a fair share of the rewards such as witnessing an expansion of community benefits” (Magnus et al., 2003, p. 14). If a nonprofit organization is overwhelmed by its debt obligations, and if the donations are fungible, then donors may not see these benefits and will be less inclined to make a donation (Magnus et al., 2003). Calabrese and Grizzle (2012) provide a second explanation to justify why debt might have an impact on donations and argue that as leverage increases, nonprofits might have to shift resources to debt service obligation that would otherwise go to fundraising and marketing. This argument is not unreasonable as scholars find a positive association between fundraising expenses and donations (Tinkelman, 1999; Weisbrod & Dominguez, 1986). That is, as an organization increases spending for fundraising and marketing, donations should increase. This article posits that the opposite is also true: As an organization reduces spending on fundraising and marketing and reallocates this money for debt service, contributions should fall. The third reason why increased leverage may cause donations to decrease is because donors prefer to fund organizations that are a going concern (Parsons, 2003). This principle is based on the assumption that a nonprofit will remain in operation for the foreseeable future. A high debt ratio might signal to donors that a nonprofit is a risky investment and potentially insolvent, thereby crowding out future donations (Calabrese and Grizzle, 2012). However, particularly if most of the debt is unsecured and is in the form of accounts payable and grants payable, for example, donors might view the organization as simply having a short-term liquidity problem; they may be more inclined to increase their donations to help alleviate the organization’s short-term liquidity issues (Calabrese and Grizzle, 2012).

The research question proposed here is as follows:

Research Question 1: Does a nonprofit organization’s debt load have an impact on the future direct contributions from individuals?

The discussion in the preceding paragraphs suggests the impact of debt load on donations is rather ambiguous and depends on how donors view a highly leveraged nonprofit organization. The following hypotheses are tested here:

Hypothesis 1: A nonprofit organization’s debt load will positively affect future donations, all else equal. Donors will view the organization as having a temporary liquidity problem and will increase donations to help alleviate the short-term problem.

Hypothesis 2: A nonprofit organization’s debt load will negatively affect future donations, all else equal. Donors will view highly leveraged organizations as no longer being a going concern and will withdraw their financial support.

Hypothesis 3: A nonprofit organization’s debt load will have no impact on future donations, all else equal.

Data, Method, and Sample Selection Process

Data

The DataArts Cultural Data Profile (CDP) is the emerging national standard for data collection in the arts and cultural nonprofit subsector. In early 2013—when the DataArts data were obtained for this study—there were about 30,000 observations from more than 9,300 organizations over multiple years across 11 states and the District of Columbia. The current study limits the sample to only 501c3 public charities with NTEE code “A,” which includes organizations whose missions relate to arts, culture, and/or the humanities, such as symphony orchestras, art museums, theaters, and professional societies and associations. Approximately, 3,200 organizations fit these criteria for nonprofit arts organizations and are used for the analyses. This study limits analysis to observations reported for fiscal years 2009, 2010, 2011, and 2012 because the number of observations was larger in these years than in years prior.

The DataArts data are neither a random sample nor a comprehensive selection of all arts organizations. Rather, the data set is comprised of self-selected arts organizations that either participate voluntarily or are required to file the form to apply for a grant from a DataArts-affiliated funder. Nonprofit organizations create a data profile with DataArts using their program and operational data. Organizations typically complete the data profile after one fiscal year is closed and when a board-approved audit or review becomes available. Participating organizations are encouraged to report information derived from audited financial statements or board-approved year-end financial statements. A DataArts CDP profile includes basic organizational information; a wide range of financial information, such as revenues, expenses, assets, liabilities, net assets, investments, and loans; and nonfinancial information, such as number of contributors, sources of contributions, attendance, program activity, staffing, community engagement activities, space, and pricing. Financial information is derived from a wide variety of sources such as audits, reviews, the IRS Form 990, or board-approved year-end financial statements. After nonprofit organizations enter their own information into the DataArts database, DataArts staff reviews it for consistency and accuracy.

Model Specifications

The analysis in this article is largely based on the work of Marudas, Hahn, and Fred (2014). Marudas et al. (2014) argue that a majority of prior studies omit variables known to affect donations and include only one measure of inefficiency (i.e., PRICE); they subsequently suffer from misspecification due to omitting correlated factors.

The basic model tested here can be specified as

{lnDON}_{i t} = β_{0} + β_{1} l {nADMIN}_{i t - 1} + β_{2} {lnWEALTH}_{i t} + β_{3} {lnFUND}_{i t - 1} + β_{4} {lnAGE}_{i t} + β_{5} {lnASSTS}_{i t} + β_{6} {lnGOVT}_{i t} + β_{7} {lnPREV}_{i t} + β_{8} {lnFR}_{i t - 1} + {YEAR}_{t} + e_{i},

where

lnDON_it = the natural logarithm of the dollar amount of direct contributions received by the nonprofit from individuals during the year

lnADMIN_{it − 1} = the natural logarithm of administrative expenses/total expenses in the prior year

lnWEALTH_it = the natural logarithm of net assets − permanently restricted net assets / total expenses − fundraising expenses

lnFUND_{it − 1} = the natural logarithm of the nonprofit’s total fundraising expenses in the prior year/total donations in the prior year

lnFR_{it − 1} = the natural logarithm of the nonprofit’s total fundraising expenses in the prior year

lnAGE_it = the natural logarithm of the number of years the nonprofit has been registered with the IRS

lnASSTS_it = the natural logarithm of total assets at the beginning of the year

lnGOVT_it = the natural logarithm of government support

lnPREV_it-= the natural logarithm of program service revenue.

The debt to assets ratio (total liabilities/total assets) is a common metric used in the literature to describe a nonprofit’s debt load (Yetman, 2007) and is added to the basic model to test the impact of leverage on future donations. This ratio provides mangers “with a relative assessment of how much of the organization’s assets are financed with debt” (Yetman, 2007, p. 245). The impacts of two other measures of leverage are also tested here: a secured debt to assets ratio (e.g., credit line, mortgages, other loans, and notes) and the ratio of interest expense to total expense, which “tells a manager how much of the current cash flow for expenses is being consumed by interest payments” (Yetman, 2007, p. 245).

Administrative efficiency—defined as administrative expenses/total expenses—is included in the model and has been shown to have a negative relationship with donations (Jacobs & Marudas, 2009; Tinkelman & Mankaney, 2007). Organizational wealth, operationalized as (net assets − permanently restricted net assets) / (total expenses − fundraising expenses) is also included in the model. The literature on organizational wealth is ambiguous. Marudas (2015) notes donors may view wealthier organizations as more financially sound, such that wealth positively affects donations, or they could view them as being less needy, in which case wealth negatively affects charitable donations. For a subsample of arts nonprofits, however, Marudas (2015) finds wealth does not have a statistically significant impact on donations.

Fundraising expenses is included as a proxy for information available to donors because numerous studies find a positive association between fundraising expenses and donations (Frumkin & Kim, 2001; Tinkelman, 1999; Weisbrod & Dominguez, 1986). Following prior studies, age is measured as the natural logarithm of the number of years the organization has been registered with the IRS; it functions as a proxy for an organization’s name recognition and reputation (Trussel & Parsons, 2008). Although results related to the age coefficient are also mixed, this study posits that reputation matters quite a bit for arts organizations; thus, it will be positively associated with donations. According to Trussel and Parsons (2008), organizational size represents a nonprofit’s ability to succeed in fulfilling its mission and attract revenues, including donations. Here, it is measured as the natural logarithm of total assets at the beginning of the year. Larger organizations are expected to attract more donations.

Program service revenue is revenue generated by charging a provision fee and is expected to have a positive relationship with donations. Trussel and Parsons (2008) argue that the service that is being provided by the nonprofit requires market discipline and sends a quality signal to the market because recipients are capable of evaluating the quality of the service.

This study contends that government support will be positively associated with donations because nonprofits receiving government grants are subject to increased reporting and auditing requirements (Trussel & Parsons, 2008). Donors may perceive government oversight will improve the nonprofit’s quality of service or product provided (Trussel & Parsons, 2008). The “cost to raise a dollar” is measured as the natural logarithm of total fundraising expenses in the prior year divided by total donations in the prior year. This ratio provides an indication of the cost of generating donations and addresses the efficiency and effectiveness of fundraising (Trussel & Parsons, 2008). As the “cost to raise a dollar” increases, donations are expected to decrease.

Table 1 summarizes the sample selection and data cleaning process.

Table 1.

Summary of Sample Selection and Data Cleaning.

Total sample size	35,579
Less: organizations not designated 501c3	1,945
Less: observations with obvious data errors (such as negative assets and liabilities, no administrative expenses, negative expenses)	18,808
Less: observations lost when creating lagged variables Note: The use of lagged variables in the model significantly reduces sample size.	10,276
Less: duplicates in terms of year and organization ID	18
Less: organizations not classified as National Taxonomy of Exempt Entities (NTEE) Code “A” or with NTEE code missing	435
Less: organizations not reporting on the accrual basis	438
Less: observations in years 2005, 2006, 2007	227
Less: organizations missing variables used in the analysis	231
Final sample size	3,201

Because log (0) is unidentified, sample size is further reduced in a log-log model.

Table 2 provides a brief description of the selected variables and the expected direction of their impact on the dependent variable (sum of total contributions received from individuals, foundations, corporations, and board members).

Table 2.

Description of Variables.

	Variable	Operation	Expected direction
Dependent Variable	Contributions	Total direct public support (contributions, gifts, grants, bequests received from the public) in the current period
Independent Variabless measuring debt	Total debt ratio	Total liabilities/total assets	?
Independent Variabless measuring debt	Secured debt ratio	Total credit line, mortgage, other loan/notes payable/total assets	?
	Interest expense ratio	Total interest expense/total expenses	?
Other Iindependent Variables	Admin_cost	Administrative expenses/total expenses	−
	Wealth	(Net assets − permanently restricted net assets) / (total expenses − fundraising expenses)	±
	Fundraising expenses	Total fundraising expenses	+
	Cost to raise US$1	(Total fundraising expenses in the prior year) / (total donations in the prior year)	−
	Age	Number of years the organization has been registered with the Internal Revenue Service (IRS)	+
	Size (assets)		+
	Govt_support	Total government support	+
	Program_rev	Program service revenue	+

Following Frumkin and Kim (2001) and Tinkelman and Mankaney (2007), several outcome variables are lagged to ensure they are predetermined and to help mitigate any potential endogeneity problems. The dependent variable is regressed using ordinary least squares (OLS) regression. Organization and year-fixed effects are included to capture the time-invariant heterogeneity within organizations and to control for macrolevel time-varying shocks, such as recession. In addition, the heteroskedasticity correction in STATA is performed and robust standard errors are reported in Table 4 (Tinkelman & Neely, 2010).

Results

Descriptive Statistics

Table 3 provides descriptive statistics for the dependent and independent variables in the sample. To increase the accuracy of the results, the sample includes only organizations that reported financial information using the accrual basis of accounting (1% of the observations are dropped). The analytic results also exclude organizations with obvious data errors, for example, negative assets and liabilities and negative expenses.

Table 3.

Descriptive Statistics.

Variable	Observation	M	SD	Median	Minimum	Maximum
Total donations	3,201	US$1,860,996	US$6,252,069	US$380,000	US$1,233	US$172,000,000
Age	3,201	45.42	32.88	36	6	270
Total assets	3,201	US$20,800,000	US$110,000,000	US$1,300,000	US$3,249	US$3,320,000,000
Government support	3,201	US$450,688	US$2,199,258	US$72,700	US$850	US$505,000,000
Program revenue	3,201	US$3,027,049	US$14,800,000	US$440,000	US$1	US$599,000,000
Administrative expenses ratio	3,201	0.26	0.12	0.24	0.03	0.88
Fundraising efficiency	3,201	0.43	1.10	0.24	0.001	35.89
Wealth	3,201	1.75	3.08	0.84	0.001	89.15
Fundraising expenses	3,201	US$384,276	US$1,008,303	US$96,348	US$137	US$15,000,000
Total debt ratio	3,201	0.22	0.21	0.14	0.00	0.99
Secured debt ratio	3,201	0.08	0.15	0.00	0.00	0.93
Unsecured debt ratio	3,201	0.13	0.16	0.07	0.00	0.98
Interest expense ratio	3,201	0.01	0.02	0.00	0.00	0.25

Overall, private donations average US$1.86 million during the study period, but there is a great degree of variance in this figure. Total donations range from as little as US$1,233 to close to US$172 million, which supports the use of logged variables throughout the analysis. On average, nonprofits in the sample devote approximately 26% of total expenses to administrative functions and have existed for approximately 45 years. There is significant variation in the size of nonprofit organizations. Organizations report average total end of year assets of US$20.8 million. On average, organizations in the sample spend US$0.43 to raise US$1. However, there is significant variation in this variable—organizations spend between less than a penny and US$36 to raise US$1 of donations. There is also significant variation in the overall debt ratio of the organizations in the sample. On average, the total debt ratio is 0.21; however, this ranges from 0 (i.e., organizations that have no debt or liabilities outstanding) to 0.99 (i.e., organizations that have US$0.99 of assets for every US$1 of liabilities owed to others). With regard to the measure of secured debt, more than half (i.e., 55%) have no outstanding secured debt.

Regression Results

The results of the regression estimation are presented in Table 4. The correlation matrix of all variables used (see the appendix) and the variance inflation factor (VIF) test indicates the model does not suffer from multicollinearity.

Table 4.

Regression Results for Model Predicting the Influence of Debt Load on Contributions.

lndonations	Basic model			Total debt ratio			Secured debt ratio			Interest expense ratio
lndonations	Coefficient	Robust SE	p > t	Coefficient	Robust SE	p > t	Coefficient	Robust SE	p > t	Coefficient	Robust SE	p > t
ln_totaldebtratio_lag				0.017	0.013	.186
ln_secureddebtratio_lag							0.015	0.015	.344
ln_interestexpenseratio_lag										***−0.027	0.010	.008
ln_adminratiolag	***−0.098	0.028	.001	**−0.076	0.039	.051	−0.081	0.054	.132	−0.077	0.052	.136
lnwealth	***−0.092	0.013	.000	***−0.102	0.019	.000	***−0.095	0.028	.001	***−0.093	0.025	.000
ln_fundraising_donationslag	***−0.730	0.018	.000	***−0.737	0.025	.000	***−0.744	0.033	.000	***−0.779	0.032	.000
ln_fundraisinglag	***0.821	0.016	.000	***0.806	0.023	.000	***0.837	0.032	.000	***0.810	0.028	.000
ln_age	***−0.068	0.020	.001	***−0.092	0.028	.001	−0.046	0.036	.206	**−0.081	0.035	.023
ln_assets	***0.168	0.018	.000	***0.191	0.024	.000	***0.150	0.033	.000	***0.172	0.032	.000
ln_govt	−0.001	0.009	.900	−0.008	0.013	.543	−0.004	0.017	.803	−0.005	0.017	.755
ln_progrevenue	***−0.033	0.010	.001	***−0.035	0.013	.008	−0.022	0.019	.260	−0.018	0.021	.390
yr2008	**0.121	0.058	.035
yr2009	***−0.209	0.040	.000	***−0.249	0.059	.000	***−0.315	0.088	.000	***−0.283	0.079	.000
yr2010	−0.013	0.030	.665	0.019	0.041	.645	−0.008	0.055	.889	−0.001	0.054	.984
yr2011	0.035	0.025	.167	0.042	0.034	.217	0.001	0.048	.989	0.001	0.046	.837
Constant	***0.589	0.111	.000	***0.665	0.154	.000	***0.501	0.182	.006	**0.359	0.186	.054
	Number of observations = 3,201R² = .875Prob > F = .000			Number of observations = 1,778R² =.876Prob > F = .000			Number of observations = 829R² = .892Prob > F = .000			Number of observation = 908R² = .890Prob > F = .000

Significant at 10%. **Significant at 5%. ***Significant at 1%.

The overall model is significant at the .001 confidence level, and the R² indicates that the model explains more than 80% of the variation in donations for this sample of arts nonprofits. The independent variables suggested by the current framework explain a significant amount of the variation in donations.

The interest expense ratio is negatively associated with donations and is the only measure of debt load with a statistically significant association. This suggests a crowding-out effect: Increased interest expense reduces contributions to arts nonprofits. The results indicate that a 10% increase in the interest expense to total expense ratio is associated with a 0.27% decrease in donations, all else equal; this result is small but not insignificant.¹ The coefficients of the other two measures of debt do not have a statistically significant impact on donations.

The results for the control variables are generally consistent with the literature—with some exceptions. The positive sign on the size variable is expected, because a larger asset base is predicted to be positively associated with donations. This result supports prior findings that the strength of an organization’s financial capacity, measured most commonly by the size of its asset base, will affect donations. Organizations with a larger asset base have greater financial capacity, which is thought to motivate more donations because the donors will perceive the organization as sustainable for the foreseeable future.

The prevailing assumption in the nonprofit sector is that administrative expenses divert funds from programs, so donors view high administrative expenses negatively (Tinkelman & Mankaney, 2007). This study finds some support for this assumption: Administrative expenses have a negative and statistically significant impact on donations to arts nonprofits. Government support has no association with future donations, however, a result consistent with earlier studies of arts nonprofits that find the coefficient on government revenue to be statistically insignificant (see, for example, Jacobs & Marudas, 2009; Marudas, 2004).

In recent years, there has been increasing concern for commercialized and professionalized nonprofits in the sector as a whole. The current analysis also finds support for prior studies that find increased program revenue reduces donations, suggesting that as nonprofits become more commercialized, donors are less willing to provide support. As expected, nonprofits that spend more on fundraising and marketing are rewarded with more donations. This is consistent with a number of previous studies that find a positive association between fundraising expenses and donations (Tinkelman, 1999; Weisbrod & Dominguez, 1986). Finally, all other things being equal, on average, older organizations receive fewer donations when taking the debt load into consideration. Weisbrod and Dominguez (1986) argue that an organization’s age may approximate its ability to establish a stock of goodwill or trust among potential donors. Thus, donors should be more supportive of older organizations because they have more stability in operation and are more trustworthy. The results here suggest that donors to arts and culture-related nonprofits prioritize supporting relatively young organizations that are more likely to be innovative over older more established organizations.

Study Limitations

This research has three primary limitations. First, it examines nonprofits in the United States and is country specific. Second, it examines a unique subgroup within the U.S. nonprofit sector (arts nonprofits), and therefore, results in other groups, such as human service nonprofits, may be very different. Third, this is a small, self-selected sample of arts nonprofit organizations, not a study of the entire population of arts nonprofit organizations. The results obtained here should, therefore, be interpreted with caution and should not be generalized across all nonprofit organizations and in countries outside the United States.

Conclusion

Prior literature has examined factors influencing individual charitable donations given to nonprofits, as well as the factors influencing how nonprofits finance capital/asset acquisitions. This study examines something considerably different and asks whether donors are influenced by leverage decisions made by arts nonprofits. Carroll and Stater (2009) argue that the debt ratio “provides insight into the ability of an organization to meet its financial obligations” (p. 954) with greater values indicating “less financial flexibility due to a higher proportion of debt to assets and a negative relationship with donations” (p. 954). Our results provide some support for the Carroll and Stater (2009) argument: Donations will decline as an organization increases the amount of cash flow used to pay interest expense on outstanding debt.

According to the annual report on charitable giving released by Giving USA, Americans donated an estimated US$358.38 billion to charity in 2014—the highest total ever in the report’s 60-year history. Therefore, how donors allocate their contributions is an important question because of the dollar amount individuals contribute to nonprofits each year. As Tinkelman (2006) asserts, “All things equal a donor would prefer that as much of his or her donation as possible is devoted to program spending and not diverted to administrative or fundraising spending” (p. 441). This study supports his argument: Increased leverage is associated with a decrease in donations, suggesting that donors may withdraw support for nonprofits that use debt to finance capital acquisitions and those that may use donations to service their debt instead of funding programs.

For nonprofit organizations, the benefits of borrowing are many and easy to understand (Yetman, 2007). However, the costs and consequences of borrowing for nonprofit organizations, although a topic of great consequence, is understudied in the nonprofit literature—especially when compared with the corporate literature—thus, it has been more difficult to understand. When a nonprofit decides to take on debt, it promises to allocate a proportion of its future cash stream to servicing this debt (i.e., paying off the interest and principal while foregoing the opportunity to provide services to charitable beneficiaries; Yetman, 2007). Current debt can be viewed as a substitute for future donations and can effectively crowd out future charitable donations (Yetman, 2007). This study finds some evidence for the crowding out of future donations: As a nonprofit’s interest expense to total expense ratio increases, future donations decrease. It appears that individuals who contribute to arts and cultural nonprofit organizations do not attach the same value to the benefits and costs of past projects (as do managers inside the organization). These donors, therefore, are not as willing “to cover principal and interest payments as they would be to cover current and future investments in charitable projects” (Yetman, 2007, p. 258).

Footnotes

Appendix

Correlation Matrix of All Variables.

	1	2	3	4	5	6	7	8	9	10
lndonations	1.0000
ln_interestexpenseratio_lag	.0717*	1.0000
ln_adminratiolag	−.0401*	.1216*	1.000
Lnwealth	.2797*	.2892*	.2201*	1.0000
ln_fundraisingdonationslag	−.1697*	.0109	.2407*	.0269	1.0000
ln_fundraisinglag	.2514*	.1227*	.0897*	.2744*	.1169*	1.0000
ln_age	.7701*	.2765*	.0489*	.6380*	.0480*	.4342*	1.0000
ln_assets	.5268*	.0322	−.0801*	.2080*	.0694*	.2157*	.5784*	1.0000*
ln_govt	.5614*	.0654*	−.0070	.1540*	.0618*	.2727*	.5474*	.4355*	1.0000
ln_progrevenue	.6344*	.1136*	−.0910*	.2296*	.0972*	.3748*	.7625*	.4477*	.5129*	1.0000

Note. Mean variance inflation factor = 3.08.

p < .05.

Acknowledgements

DataArts is a nonprofit organization that empowers the arts and cultural sector with high-quality data and resources to strengthen the sector’s vitality, performance, and public impact.

Author’s Note

Any interpretation of the data is that of the author, not DataArts. For more information, visit .

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.

Notes

Author Biography

Cleopatra Charles, PhD, is an associate professor of public and nonprofit budgeting and financial management in the School of Public Affairs and Administration at Rutgers University-Newark.

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Nonprofit Arts Organizations: Debt Ratio Does Not Influence Donations—Interest Expense Ratio Does

Abstract

Keywords

Introduction

The Use of Financial Disclosure Information by Individual Donors

Background and Hypotheses

Data, Method, and Sample Selection Process

Data

Model Specifications

Results

Descriptive Statistics

Regression Results

Study Limitations

Conclusion

Footnotes

Appendix

Acknowledgements

Author’s Note

Declaration of Conflicting Interests

Funding

Notes

Author Biography

References