Np Finance

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Community variation in the financial health of nonprofit human service organizations: An

examination of organizational and contextual effects

Marcus Lam

Columbia University

School of Social Work

Lindsey McDougle

Rutgers University-Newark

School of Public Affairs and Administration

ABSTRCT Nonprofit human service organizations (HSO) provide vital services to communities. Yet

studies show that the density of these nonprofits varies from one community to the next. Studies

also show that there are often fewer quantities of these nonprofits located in vulnerable

communities. Findings such as these have led to concerns regarding the ability of nonprofit HSO

to meet community needs. However, in this article, we posit that organizational density is a

limited indicator of the sector’s ability to provide services, and suggest that financial health is a

more relevant indicator. We model six measures of financial capacity and sustainability as

conceptualized by Bowman (2011) and examine relationships between these measures to

indicators of community vulnerability. Results indicate that variation exists in four of our six

outcome measures (equity ratio, months of spending, mark up, and months of liquidity), and that

contextual effects (e.g., being located in a minority or low-mobility community) partially explain

these variances.

Article Accepted. To appear in forthcoming edition of the journal: “NONPROFIT AND VOLUNTARY SECTOR

QUARTERLY.” Posted in accordance with Sage Publication’s author’s permissions policy

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Nonprofit human service organizations (HSO) have a considerable responsibility for

maintaining America’s social safety net. In many communities, these nonprofits are one of the

primary vehicles for implementing and administering social and welfare programming intended

to promote economic stability and upward mobility among vulnerable populations (Allard, 2009;

Lam et al., 2013; Lam & Grusky, 2015). Despite their importance to communities, the density of

nonprofit HSO tends to differ significantly from one community to the next. While one might

expect to find more nonprofit HSO located in high poverty neighborhoods (given their

orientation to serve the poor) research suggests that these nonprofits are often less prevalent in

low-income areas (Allard, 2009; Grønbjerg & Paarlberg, 2001; Joassart-Marcelli & Wolch,

2003) and are more likely to locate in areas with higher public expenditures or a higher density

of similar organizations (Gronbjerg & Paalberg, 1994; Bielefed & Murdoch, 2004).

Although an understanding of variation in locational dynamics of HSO (and the factors

that influence such variation) is certainly important, few studies have examined whether

community variation exists in the financial health of these organizations. Yet, financial health is

a fundamental requirement for organizational stability, as it allows organizations to operate more

effectively (Bowman, 2011; Calabrese, 2013; DeVita & Fleming, 2001). Indeed, service

provision comprises more than just merely existing; and, to simply know that an organization

exists is no indication of service delivery capability (Marwell & Gullickson, 2013). It has long

been recognized that a critical component of effective service delivery is the proper management

of financial resources (Lohmann, 1980). Therefore, it may be possible that a small handful of

well managed and financially secure nonprofit HSO will be more effective at providing services

in a community than a large number of financially vulnerable nonprofits. Thus, the purpose of

this study is to contribute to the literature on nonprofit geography by addressing the following

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two research questions: (1) Is there community variation in the financial health of nonprofit

HSO? (2) If variation does exist, to what extent is this variation explained by either

organizational (for example, assets, age, and revenue structure) or community-level differences

(for example, differences in resources and demand for services based on organizational

location)?

We operationalize our organizational outcome of interest—financial health—using

Bowman’s (2011) six measures of financial capacity and sustainability. Together, the six

measures capture what Bowman considers to be the three broad objectives of nonprofit financial

health. Specifically, the long-term financial objective is to maintain programs and services and is

measured by the equity ratio (ER) and return on assets (RA); the short-term objective is to

maintain resiliency to sudden shocks in the resource and funding environment and is measured

by months of spending (MS) and mark up (MU); and the current-term objective relates to the

ability of an organization to pay bills on time and is measured by months of liquidity (ML) and

change in months of liquidity (CML).

Background Literature

The density of nonprofit organizations often differs significantly from one community to

the next (Allard, 2009; Grønbjerg & Paarlberg, 2001; Jossart-Marcelli & Wolch, 2003). More

affluent communities often have ample nonprofit resources and highly diverse nonprofit

landscapes (Bielefeld, 2000; Wolch & Geiger, 1983; Wolpert 1993), while lower-income

communities often have fewer nonprofits and civic institutions (Grønbjerg & Paarlberg, 2001;

Joassart-Marcelli & Wolch, 2003). In many areas these differences have resulted in unequal

access to nonprofit services for those in need, particularly for those residing in low-income

communities. Allard (2009), for instance, found that relatively high poverty neighborhoods had

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fewer quantities of nonprofit social service providers and that individuals residing in these

neighborhoods had limited access to the providers in their communities.

Although findings such as this certainly raise concerns regarding the density of nonprofits

in low-income communities, the mere presence of nonprofits tells us little about the ability that

these organizations have to effectively act as service providers. In fact, Marwell and Gullickson

(2012) examined the distribution of government contracts across New York City, and reasoned

that “…the actual number of government dollars allocated within a given neighborhood is a

better measure of service availability than the existence or number of NPOs in that

neighborhood” (p. 325). Following this line of reasoning we posit that more so than examining

access to, or even the availability of, organizations in a given community, the management of

financial resources will be a more critical component of effective service delivery in determining

an organization’s capacity to serve its community (Bowman, 2011; Lohman, 1980; Finkler,

Purtell, Calabrese, & Smith, 2013).

Financial management can be defined as a set of inter-related activities that plan for,

control, appropriate, and report on the use of resources (usually monetary) to advance

organizational goals (Finkler et al., 2013; Lohmann, 1980; Sanchez-Myers, 2008). Financial

management includes many functions, such as resource acquisition, allocation, and reporting that

are critical to maintaining an organization’s financial health (Sanchez-Myers, 2008). Other

functions include having adequate liquidity to respond to unexpected contingencies, or

maintaining long-term solvency by not incurring large amounts of debt relative to assets.

Without adequate financial resources through proper financial management, nonprofit HSO are

strained, both, in their ability to meet the needs of socially and economically vulnerable

populations (Reed, Lally, & Quiett, 2003) and to survive economic shocks or disruptions in

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revenue streams (Bowman, 2011). A lack of adequate financial resources can also lead to

financial bankruptcy (Clemenson & Sellers, 2013)—which could ultimately end the supply of

critically needed services to many communities.

It is, therefore, important that we understand the extent to which geographic variation

exists in the financial health of nonprofit HSO —particularly given the important role they play

in service provision to vulnerable communities. Despite this importance, few studies have

examined contextual explanations for variation in nonprofit financial health. The existing

research on nonprofit financial health and performance has generally focused on using

organizational-level variables (e.g., revenue concentration, debt levels, administrative expenses,

or percentage of public funding) to predict amounts of nonprofit operating reserves (Calabrese,

2013), measures of financial vulnerability (Tuckman & Chang, 1991), or nonprofit failure rates

(Hager, 2001; Walker & McCarthy, 2010). Marwell & Gullickson’s (2013) study on the spatial

allocation of government contracts in New York City is the exception but does not explore

variability in nonprofit financial health explicitly. In this study, they find partial evidence that

government dollars are distributed to nonprofits that service neighborhoods most in need.

Factors Influencing Variation in Nonprofit Financial Health

To understand why there may (or may not) be community variation in nonprofit financial

health, we draw broadly from the organizational ecology literature. This body of literature

highlights the effects of environmental factors on organizational outcomes such as organizational

survival (Walker & McCarthy, 2010) or organizational processes such as strategy formulation

(Hillman, Withers, & Collins, 2009). In particular, Walker and McCarthy (2010) argue that

community based organizations (CBOs) located in “resource-deprived local environments”

operate in a “vicious cycle” in which resources required for survival are often lacking. CBOs

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therefore, must devote more efforts to grassroots fundraising or building extra-local institutional

legitimacy to survive (Walker & McCarthy, 2010). In short, organizations are affected by

elements of their external environment and must engage in reciprocal transactions with this

environment in order to operate effectively (Walker & McCarthy, 2010; Hillman et al., 2009).

The boundaries of this external environment, however, are relative to the specific

activities that these organizations engage in. For example, service and program activities (which

are often more localized and specialized) will generally have a smaller geographic boundary than

fundraising and revenue generating activities (which often extend far beyond an organization’s

area of operation). Bielefeld, Murdoch, and Waddell (1997), for example, found the highest

concentration of nonprofit organizations located within a one-mile radius of block groups with

high minority populations, average age, and ethnic heterogeneity. The concentration of

nonprofits decreased as the geographic boundary extended to a three- and five-mile radius. They

concluded, therefore, that “...distance is an important factor in nonprofit activity” (p. 217).

Interestingly, Bielefeld, Murdoch, and Waddell (1997) also found a higher concentration

of nonprofits located within a one-mile radius of areas that had higher average per capita income

values. This, they argued, suggested that nonprofits may also “…desire…to be closest to the

people most likely to contribute to the organization or to pay for services” (p. 217). There exists

additional empirical support for the idea that nonprofits locate in resource-rich areas where

access to capital is more abundant rather than in economically distressed areas (see for example,

Zakour & Gillespie, 1998; Marsh, 1995; Bielefeld, 2000; Esparza, 2009; for exceptions,

however, see Corbin, 1999).

One question that arises from the observation that nonprofits tend to select into particular

types of environments is whether the neighborhood environment is associated with nonprofit

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financial health. Local conditions may affect financial health in two important ways. First,

nonprofit HSO located in vulnerable communities may serve clients who have limited economic

resources, but who also have higher service needs. This may lead to higher program costs for

these organizations. Secondly, human services nonprofits serving vulnerable communities may

have higher fundraising expenses given that fee income, or other income generating activities,

from clients with limited economic resources may be difficult to sustain. Thus, higher overall

expenses may lead to lower net assets and lower long-term financial capacity and sustainability.

We, therefore, propose the following two hypotheses:

H1: Spatial variation in the financial health of human services nonprofits can

largely be explained by neighborhood attributes, and

H2: Human services nonprofits located in vulnerable communities will have lower

financial health compared to those not located in vulnerable communities—that

is, financial health will be negatively correlated with measures of community

vulnerability.

Overview of Study Site

This study focuses on the financial health of human services nonprofits across ZIP codes

in San Diego County, California.1 San Diego is an area highly variable in several dimensions

believed to influence nonprofit functioning (see for instance, Bielefeld, 2000; Bielefeld &

Murdoch, 2004). Indeed, Bielefeld (2000) found that although San Diego has considerably more

amenity-type nonprofits (e.g., arts, cultural, and educational nonprofits) than other areas, it is a

region that directs a relatively large percentage of gifts and grants to nonprofit human services

providers—potentially an indication of the extent to which human services nonprofits in the

county are relied upon to meet a variety of social and welfare needs.

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Data and Methods

Sample

The financial health data used in this study was drawn from IRS Form 990, which

contains self-reported financial data on nonprofit operations (such as balance sheets, operating

statements, and statements of functional expenses). We focus on 501(c)(3) nonprofits classified

by the National Taxonomy for Exempt Entities (NTEE) as engaging in the following primary

activities: crime and legal; employment and job; food, agriculture, and nutrition; housing and

shelter; human service—multipurpose; public safety; recreation, sports, leisure, and athletics;

and youth development.

We purchased organizational-level datasets from the National Center for Charitable

Statistics (NCCS) for nonprofit HSO in San Diego County for fiscal years 2005, 2006, and 2007.

The combined sample for all three years was 2,343 organizations. We followed data cleaning

procedures outlined in Bowman, Tuckman, and Young (2012). In particular, we excluded group

filers, 990EZ filers, non-accrual accounting method filers, non-SFAS 117 filers, and non-active

organizations.2 We also excluded nonprofits without three years of data.3 Finally, we excluded

nonprofits with values on the dependent variables outside of a relevant range. Specifically, these

included nonprofits with equity ratios (net assets/total assets) above one).4 Our final sample

included 222 human services nonprofits for the three fiscal years under study.

Address Confirmation and Identification of Satellite Location

To confirm the address of each organization (as reported on the organization’s Form 990)

and to identify all associated service branches and satellite locations, we conducted a verification

process, which occurred from June to August 2010. Verification methods consisted of queries of

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several sources—including organizational websites (if available), Guidestar listings, local public

directories, and other administrative data sources (such as the California Secretary of State’s

registry of charitable organizations, and the California Department of Public Health). Upon

verification, if ZIP code information listed on the (original) Form 990 was incorrect (or missing),

the information was corrected. When location information on the Form 990 differed from

information provided by any of these sources the nonprofit was contacted directly. Deference

was given to information provided by the contact, as it was assumed that an individual currently

working for (or, volunteering with) a nonprofit would provide more accurate information.

Using these same methods, attempts were also made to identify nonprofits with multiple

service locations. Of the 222 nonprofit HSO in our sample, twenty-three were identified as

having satellite offices.5 The ZIP codes for each satellite location was identified using the same

procedures outlined above to confirm organizational addresses.

To incorporate demographic data from the ZIP codes of satellite offices into our model,

we calculated the weighted average of demographic indicators based on the number of satellite

offices in each respective ZIP code. For example, if an organization’s Form 990 address was in

ZIP code 90001—where 30% of the households were in poverty—and had two satellite offices

located in ZIP code 90002—where 60% of the households were in poverty—and also had one

satellite office located in ZIP code 90003—where 10% of the households were in poverty—then

the value for households in poverty used for this organization was a weighted average calculated

as (.30+.60+.60+.10)/4=.40. We then created a new ZIP code identifier for each organization

with satellite offices where demographic indicators were averaged. This artificially increased the

number of ZIP codes from 66 (without incorporating service locations) to 85 (by incorporating

service locations).

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Dependent Variables

We operationalized financial health using six measures of financial capacity and

sustainability suggested by Bowman (2011). These six measures are organized into three

categories: long-term, short-term, and current-term financial objectives (Table 1). The six

financial health measures were first calculated separately for each fiscal year, and then averaged

across the three years (2005-2007). The final averaged values were used as our outcome

variable(s).

<<Table 1>>

Long-Term Financial Health. Long-term financial capacity was measured by the equity

ratio (ER). A low ER indicates that creditors have claim to a high proportion of an organization’s

assets, and that the organization has limited financial capacity. Long-term financial sustainability

was measured by the return on assets (RA). Negative values indicate that an organization is not

covering its expenses. Low positive values indicate that an organization is only marginally

covering its expenses.

Short-Term Financial Health. Short-term financial capacity was measured by months

of spending (MS)—also referred to as unrestricted operating reserves. Short-term financial

sustainability was measured by mark up (MU). Positive values on this measure indicate that an

organization is maintaining or building reserves. High and negative values indicate that an

organization is depleting reserves. An adequate MU percentage depends on an organization’s

assets and is measured by the “status quo mark up” (SQMU). Thus, an organization with large

assets will require larger reserves in order to meet asset replacement and repair needs (Bowman,

2011).

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Current-Term Financial Health. Current-term financial capacity was measured by

months of liquidity (ML). In general, an organization should maintain between one to two ML

(Bowman, 2011). Current term financial sustainability was measured by the change in ML and

was calculated as the difference between the end of year ML and the beginning of year ML,

divided by spending on operations.

Independent Variables

Community Vulnerability. Measures of community vulnerability were obtained from

San Diego Association of Government (SANDAG), and included indicators used to identify

“communities of concern” in the San Diego region. As described by SANDAG, communities of

concern are vulnerable or disadvantaged areas of the county that require greater attention and

resources in order to promote social equity and environmental justice. These include: (1) low-

income, (2) minority, (3) low-mobility, and (4) low-engagement communities.

Low-income communities are those where 33% or more of households in the area have

an annual income of less than $30,000.6 Minority communities are those where 65% or more of

the population is non-White. Low-mobility communities are those where 25% or more of

households in the area have no automobile available for immediate use, and/or 25% or more of

the population is disabled, and/or 20% or more of the population is aged seventy-five or older.

Finally, low engagement communities are those where 20% or more of households in the area do

not speak English as a primary language, and/or 20% or more of the population in the area is

aged twenty-five and older has less than a high school education. The data used to generate each

of these four community types was obtained from SANDAG annual population estimates for

2005, 2006, and 2007, as well as the 2000 US Census of the population. Each community

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indicator was collapsed into a dichotomous variable. Descriptive statistics for these variables are

presented in Table 2.7

Organizational-Level Covariates. We controlled for several organizational-level

covariates. First, older and larger organizations are more likely to have greater financial stability

(Hager, Galaskiewicz, Bielefeld, & Pins, 1996). Therefore, we controlled for organizational age,

organizational assets, and presence of an endowment. Organizational age was measured in years

from the date when the organization first received its 501(c)(3) tax-exempt status through

January 1, 2008. Organizational assets were calculated as the log of the dollar value of the

organization’s current and non-current term assets. Presence of an endowment was measured as a

dichotomous variable. We also controlled for revenue concentration given that organizations

with higher revenue diversity are likely to have greater sophistication in securing and managing

their finances (Carroll & Stater, 2009).

Analysis Procedure

Given the nested structure of our data (organizations nested within ZIP codes) we use a

two level random intercept model to determine the degree of variance in our outcome variables

attributable to differences at the organizational level (age, assets, and endowments), and the

variation attributable to differences based on where the organization is located (ZIP code level

variables). Although ZIP codes are demographically diverse geographic areas, within these areas

service needs as well as resource capacities may be similar and affect organizational operations

in a similar manner. Therefore, it is reasonable to expect that some of the variance in our

outcome variables will result from effects due to ZIP code level characteristics (in addition to

organizational characteristics).

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We estimated a two-level random intercepts model for each outcome variable. Random

intercept models, also referred to as multilevel models, are appropriate for nested data. One of

the hallmarks of these models is to partition the residual term into “within group” (i.e.,

organizations clustered within ZIP codes or level 1) and “between group” (i.e., between ZIP

codes or level 2) variance (see Raudenbush & Bryk, 2002; Seltzer, 1994; Diez-Roux, 2000).

Thus, each ZIP code will have a unique intercept value based on the ZIP code residual or random

effects estimate.

Random intercept models account for within group dependence of the error estimates

rather than assume independence of errors, as in OLS regression and thus more accurately

estimate standard errors (Raudenbush & Bryk, 2002; and Seltzer, 1994). We estimate the

following mixed model:

Yij=+ B1(X1ij)+…+Bm(Xmij)+ 01jX01j+…njXnj +(Uj+rij)

rij~N(O,2)

Uj~N(0,)

Where =average value of outcome after controlling for model covariates; B=organizational

level covariates; =ZIP code level covariates; r=organizational level residual within ZIP codes;

U=ZIP code level residual; =variance of U; 2=variance of r (Seltzer, 1994; Raudenbush &

Bryk, 2002).

Given that our outcome variables are measured in different metrics (the ER, RA, MU,

and CML are proportions while the MS and ML are counts) we collapse the variables into

quintiles and model them as ordinal. In doing so, we are able to employ a linear regression

approach for our multilevel models (rather than a Poisson regression for the count variables or a

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specialized regression approach for the proportions) and treat the outcome as quintiles for ease of

interpretation.8

All organizational-level covariates (at level 1) were grand mean centered.9 Analyses were

conducted using the ‘xtmixed’ command with robust standard errors in Stata (v.12). Results were

confirmed with the HLM software package.

Results

Summary Statistics

Table 2 provides summary statistics for each of the variables used in the analysis. Each

quintile contains approximately twenty percent of the sample (or, 44 observations). Values of

each outcome variable are presented by quintile (20th, 40th, 60th, 80th, and maximum) along with

minimum and median (the fifty percent percentile) values.

For our organizational-level covariates, Table 2 shows that on average human services

nonprofits in our sample were approximately 22.5 years of age at the end of fiscal year 2007.

The organizations also had on average of seventy-two percent of revenue concentrated in one

source, and had assets of about $5.66 million. Approximately eleven percent of our sample was

presumptively endowed.10 Finally, Table 2 also shows summary statistics for our four predictors

assessing community vulnerability. These statistics indicate that of the 85 ZIP codes identified in

this study, 48% were low-income communities; 12% were minority communities; 5% were low

mobility communities; and 33% percent were low engagement communities.

<<Table 2>>

In Table 3 we include a correlation matrix with all covariates and dependent variables in

our model. As shown, there are no significant correlations among the covariates.

<<Table 3>>

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Multivariate Findings

Given that the outcomes are expressed in quintiles, we converted their values to

percentage points by multiplying the coefficients in the results tables (Tables 5-8) by 20.

Variance Estimates. We first present estimates of the between ZIP code (τ) and within

ZIP code (σ2) variance from the unconditional models and estimates of the Intraclass Correlation

(ICC) (Table 4). An ICC value of .04 would suggest that approximately 4% of the total variance

is explained by ZIP code level differences. This would indicate that if one theoretically

controlled for all organizational level covariates, there would remain approximately 4% residual

variance due to ZIP code level effects (such as higher demand for services, or shared resources

or management practices).

Given that organizations within one ZIP code may have shared experiences due to ZIP

code level effects, they will be more alike than organizations clustered in a different ZIP code.

Thus, the ICC can also be thought of as the “…correlation among units within the same group”

(Gelman & Hill, 2007, p. 448). While there is no explicit rule to determine when an ICC is large

enough to warrant a multilevel modeling approach, a value less than .05 can be considered small

and the degree of clustering negligible (Raykov, 2013). Even for these small ICC values,

however, a multilevel modeling approach is appropriate if contextual effects are of substantive

interest to the researcher (Raykov, 2013). Thus, we consider any non-zero ICC value of

importance and have modeled all of our outcome variables accordingly.

The results in Table 4 indicate some degree of clustering (or between ZIP code variance)

for the ER, MOS, MU, and ML with respective ICC estimates of 0.04, 0.10, 0.08, and 0.11. The

RA and CML outcome variables have ICC estimates of zero indicating that all of the variance is

explained at the organizational level (i.e., there is no ZIP code clustering effect).11

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<<Table 4>>

Long-Term Financial Capacity: Equity Ratio (ER). Table 5 presents the results of the

additive models for the ER. Controlling for organizational variables did not reduce the

magnitude of the ICC (Model 5b). More importantly, however, adding neighborhood measures

all but eliminated between neighborhood variance (ICC=0.01) (Model 5c). Adding both

organizational and ZIP code covariates reduced the ICC to zero (Model 5d).

Significant predictors of ER (in model 1) included revenue concentration, minority, low-

engagement, and low-mobility communities. Specifically, a one-unit increase in the revenue

concentration index (RCI) corresponded to a -2.62 quintile decrease in ER. Converting this

coefficient to a percentile corresponded to a 52 percentile (-2.62*20=-52.4) decrease for a one-

unit increase in revenue concentration. Interpreting this in terms of the actual values of ER

indicates that for an organization in the 80th percentile, with ER of 0.92 (see Table 2), a one unit

increase in the RCI would correspond to a decrease in ER to the 28th percentile and an ER of

0.27, while holding all other covariates in the model constant.

Organizations located in minority communities are expected to have ER values

approximately one-quintile lower; and nonprofits in low engagement and low-mobility

communities are expected to have ER values 0.52 quintiles and 0.39 quintiles higher,

respectively.

<<Table 5>>

Short-Term Financial Capacity: Months of Spending (MS). For MS (Table 6),

organizational covariates did not reduce the ICC (Model 6b). ZIP code level covariates reduced

the ICC to 0.09 (Model 6c), and adding both organizational and ZIP code covariates reduced the

ICC to 0.08 (Model 6d).

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Endowed organizations were 23 percentiles higher (1.15*20=23)—corresponding to a

MS value of 1.9 (50th percentile) for a non-endowed organization compared to 4.8 (73rd

percentile) for an endowed organization. A one-unit increase in the revenue concentration index

corresponded to a 19 percentile decrease (-0.96*20= -19.2)—moving from 1.9 (50th percentile)

to 0.74 (31st percentile). A one-unit increase in the log of assets corresponded to a three-

percentile increase (0.17*20=3.4), moving from 1.9 (50th percentile) to 2.4 (53rd percentile).

Organizations located in low-mobility communities were predicted to be 15 percentiles higher.

This implies a difference of 1.6 in MS (50th percentile = 1.98 vs 65th percentile=3.6).

<<Table 6>>

Short-Term Financial Sustainability: Mark Up (MU). For MU (Table 7),

organizational covariates reduced the ICC from 0.08 to 0.04 (Model 7b). ZIP code level

covariates reduced the ICC to 0.07 from the conditional model (Model 7c), and adding both

organizational and ZIP code level predictors reduced the ICC to 0.03 (Model 7d).

The endowment, revenue concentration, and log of assets values were all significant.

Endowed organizations were 19 percentiles lower (-0.96*20=-19.2). This implies a difference of

4% for non-endowed (50th percentile=0.04) vs. endowed organizations (31st percentile=0.001).

A one-unit increase in the RCI corresponded to a 25 percentile decrease (1.27*20=25.4)—

moving from 0.04 (at the 50th percentile) to -0.01 (25th percentile). A one-unit increase in the log

of assets corresponded to a five-percentile increase (0.23*20=4.6)—moving from 0.04 (50th

percentile) to 0.06 (55th percentile).

<<Table 7>>

Current-Term Financial Capacity: Months of Liquidity (ML). For ML (Table 8),

organizational covariates did not decrease the ICC (Model 8a). Adding ZIP code level covariates

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reduced the ICC to 0.09 (Model 8b), and adding both organizational and ZIP code level

covariates reduced the ICC to 0.10 from the unconditional model (Model 8c). Finally,

organizations in minority communities were 21 percentiles lower (-1.03*20=-20.6) on ML. This

implies a difference of 1.4 ML less for organizations in minority communities (50th percentile=1

vs. 71st percentile=2.14).

<<Table 8>>

Discussion

First, we discuss findings relating to variation in the financial health of nonprofit HSO.

Next, we discuss findings relating to the financial health of nonprofit HSO and measures of

community vulnerability.

Variation in Financial Health

Our first research hypothesis stated that a portion of the variation in financial health could

be explained by where organizations were located—that is, contextual or ZIP code level factors.

The results for the MOS and MU outcome measures support this hypothesis; the results for the

ER and ML partially support this hypothesis; and results for the RA and CML do not support this

hypothesis.

For the MS, the intercept—interpreted as the predicted MS of an organization at the

average values of organizational level covariates and not located in a community of concern—

was 2.94 or approximately the third quintile, corresponding to a MS of two months (Table 6). A

MS of two months is close to the national median value for nonprofit HSO (Bowman, 2011,

p.92). However, this average intercept value varies by about 0.35 standard deviations (square

root of τ=0.121, Table 6) or nearly a 14-percentile difference across ZIP codes. In other words,

communities are served by nonprofits that have different short-term capacities with a difference

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of about 1.6 months (50th percentile versus 64th percentile). As will be discussed in detail below,

this difference can be partially explained by the location effect of being in a low mobility

community in addition to organizational level differences.

For the ML, the intercept of 2.93 was also at approximately the third quintile

corresponding to a value of 1.5 months (Model 8c, Table 8). This is slightly lower than the

national median of 1.9 for human services nonprofits (Bowman, 2011, p. 92). However, as with

the MS, this intercept value varied by about 0.44 standard deviations (square root of τ=0.192) or

18-percentiles across ZIP codes. In other words, communities are served by human services

organizations that have differing current term capacities with a difference of about 1.2 months

(50th percentile=1 versus 68th percentile=2.16). And this difference can be partially be explained

by the location effect of being in a minority community.

Finally, the MU intercept of 2.96 was estimated to vary by about 0.22 standard deviations

(square root of τ=0.49, Table 7) or by nine percentiles across ZIP codes. This is a difference of

about 0.03 (50th percentile=0.04 versus 59th percentile=0.07). It should be noted, however, that

the adequacy of this value depends on the amount of an organization’s hard assets (e.g., land and

buildings) (Bowman, 2011). More hard assets require greater reserves for maintenance and

replacement. This amount is captured in the “status quo mark up” covariate in model 4 (Table 4).

The “status quo mark up” covariate is not significantly related to MU, indicating that MU values

are not tied to the organization’s hard assets. This is not a surprising finding but may leave the

organization in a precarious financial situation when maintenance or replacement of hard assets

becomes necessary.

For the ER, the small ICC estimate from the unconditional model (ICC=0.043, Table 5,

Model 5a) suggests that the clustering effect may be negligible. Once organizational and ZIP

20

code variables are accounted for, there remains no variance between neighborhoods (Table 5,

Model 5d) and the remaining variance (sigma=1.617) is at the organizational level. Thus the

significance of the ZIP code variables—minority, low mobility, and low engagement

communities—should be interpreted with caution. While there maybe some ZIP code level

effects, such as being located in a minority community, it is likely that the organizational level

factors account for most of this variance.

Financial Capacity, Sustainability, and Community Vulnerability

Our second research hypothesis stated that community variation in the financial health of

human services nonprofits would be negatively correlated with measures of community

vulnerability. Our findings indicated that two of our four communities of concern variables were

significantly associated with our financial health outcome measures. Specifically, human

services nonprofits located in minority communities were, as expected, predicted to be on

average about one-quintile (or 20 percentiles) lower on the ML measure. There was 1.2 fewer

months of liquidity for nonprofit HSO located in minority communities (i.e., 60th percentile=1.54

vs. 40th percentile=0.38, see Table 2 for ML). However, a small reduction in the ICC, from 0.11

(Model 8a) to 0.10 (Model 8c), suggests that there may also be organizational effects driving this

difference. Thus, this finding should also be interpreted with caution.

The low mobility community covariate was significantly associated with the MS, but in

the opposite direction than expected. In particular, nonprofit HSO located in low-mobility

communities were on average about 0.76 quintiles (or, 15 percentiles) higher on the MS,

compared to those not located in low-mobility communities. This suggests nonprofit HSO

located in low-mobility communities are expected to have 1.6 more MS (50th percentile=1.98 vs.

65th percentile=3.6, see Table 2 for MS).

21

Organizational Covariates

Of the organizational covariates, endowed nonprofits were predicted to have higher MS

(23 percentiles=1.15*20), but lower MU values (-19 percentiles=-0.96*20). The higher MS value

is not surprising given that, in general, endowed nonprofits tend to have more assets and

therefore more short-term capacity. However, the negative coefficient for MU is unexpected and

suggests that this particular sample of endowed nonprofits may have higher expenses related to

ownership of their assets and thus not increasing their unrestricted net assets enough to cover

expenses. This conclusion is supported by the non-significant findings between status quo

markup and MU (Table 7).

Organizational assets were also significantly related to MS and MU in the expected

direction, albeit with somewhat small effects—0.17 quintiles or about three percentiles for MS

and 0.23 quintiles or about five percentiles for MU.

Finally, higher revenue concentration was negatively associated with ER, MS, and MU.

For those who argue that less revenue concentration is preferred, our finding may not come as a

surprise. A common notion is that high revenue concentration—relying on just one or two

revenue sources—is to be avoided as it leaves the organization heavily dependent on the

circumstances of a few funders. Similar to investment portfolios, revenue diversification is

preferred and allows the organization to mitigate the risk of revenue fluctuations. However, it is

far from conclusive that universally, lower revenue concentration is better. Rather, there may be

distinctive revenue portfolios for each nonprofit based on its mission and funding environment

(Foster & Fine, 2007). Thus, while we find a negative correlation between three of our outcomes

and the RCI, this may not lead to financial vulnerability.

Implications

22

Can variation in financial health be explained by where a nonprofit HSO is located? For

the short term (MS), and current term (ML) capacity measures, the answer appears to be “yes.”

This has implications for nonprofit HSO as well as for the communities in which they serve.

Bowman, 2011 describes current term capacity as the amount of liquid working capital required

to pay current bills. We estimate that human services nonprofits located in minority communities

may have 1.2 months lower current term capacity. The reality of nonprofit financial management

is that it often operates on a current term basis, often not having adequate reserves or savings in

case of an emergency or to weather a disruption in funding. This is not unlike vulnerable families

struggling on the fringes of poverty. As the former executive director of the iconic Hull House in

Chicago noted prior to its declaration of bankruptcy in 2012, “…we are very used to social-

service agencies being always on the brink of destruction…Some of the board members didn’t

get the idea of living on the edge. They were coming out of an economy where, if your house is

under water, you walk away from it. The fact is, some of us had learned to breathe under water,

and they didn’t understand that” (Clemenson & Sellers, 2013, p. 256). Thus a 1.2-month

difference in liquidity can mean the difference between an organization closing its doors or

operating through the next month. For low mobility communities, on the other hand, nonprofit

HSO appear to be more financially robust with, on average, 1.6 more MS.

With these considerations in mind, philanthropic and government funders should

encourage minimum reserve requirements (e.g., three months of liquid net assets) acquired

through annual surpluses rather than operating at a deficit or a strictly break-even point.12

Nonprofit organizations are, after all, businesses and in order for these organizations to grow and

expand their services requires them to have enough capital reserves to make investments or

subsidize services to a larger pool of vulnerable clientele. Given both the difficulty and stigma

23

associated with building capital reserves (Calabrese, 2013), though, it is wise for funders to

encourage human services nonprofits to operate with annual surpluses in order to accumulate

reserve funds.

The negative association between ML and minority communities also supports earlier

research by Joassart-Marcelli and Wolch (2003) who found that the extent of poverty in many

low-income areas of southern California often led to severely resource-deprived human service

agencies; and that this, in turn, led to lower program expenditures and to lower quality services.

Limitations

One limitation of this research stems from the use of IRS Form 990 data. While the self-

reported nature of Form 990 can lead to over or under reporting of financial metrics, it is

nevertheless acceptable and comparable to audited financial statements (Froelich, Knoepfle, &

Pollack, 2000). Another limitation is the use of ZIP codes as our definition of community. ZIP

codes range in geographical size. Therefore, the service area of human services nonprofits

located in smaller ZIP codes may have different effects on organizations compared to those

located in larger ZIP codes.

Our focus on just San Diego County also presents a limitation. While it has a diverse

nonprofit sector, San Diego County may not be representative of other counties in the United

States, thus we are limited in the generalizability of our findings. We did find, however, that on

average, the capacity measures (ER, MS, and ML) for human services nonprofits in San Diego

not located in a community of concern, are comparable to national median values.

Due to data availability, we also did not control for other community level attributes such

as levels of social capital, political participation and ideology, or levels of volunteerism—all of

which may affect the amount and types of resources available to human service nonprofits in a

24

given community. Moreover, other types of organizational resources were not taken into

consideration that may affect financial health. For example, human service nonprofits may draw

from a variety of non-monetary resources such as volunteer labor, in kind donations, and the

strength of the governing board’s networks. Another limitation is in the use of the NTEE

classification system. While we use the primary human service category (P), which captures the

broadest group of organizations, this may nevertheless leave out organizations that provide

human service as a secondary rather than a primary activity. We also did not consider the impact

of our financial health measures on organizational survivability and only compare our predicted

values to national medians as reported in Bowman (2011). Thus, we cannot say definitively that

low measures on any of the outcomes leads to organizational closures. Our study also focused on

the three years (2005-2007) just prior to the recession of 2008, which may account for the

relative financial stability of our six outcome measures. Undoubtedly, examining the three years

during and after the recession would paint a much different financial picture.

Directions for Future Research

We follow Marwell and Gullickson’s (2013) reasoning that simply focusing on the

geographical distribution of the number of nonprofits paints only a partial picture of the ability of

nonprofits to serve their communities. In this study, we have attempted to delve deeper, to

investigate more nuanced measures of financial capacity and sustainability as well as links to

measures of community vulnerability. Our findings offer partial support to previous studies

which have shown that nonprofits located in areas with greater vulnerability and thus fewer

resources, tend to reflect the conditions of the community (i.e., battered agencies) (Reed, Lally,

& Quiett, 2003).

25

Our study is just a first step, however, analyzing the three years just prior to the Great

Recession of 2008. A follow up study can be conducted on years during and following the Great

Recession to examine how nonprofit HSO coped with and adapted to such dire environmental

distress (see footnote 12). Future studies should also include other indicators of the ability for

nonprofits to serve their communities such as measures of effectiveness or quality of service.

Future studies should also examine a greater variety of measures relating to community

vulnerability, direct indicators of resource, or alternative geographical units of analysis (e.g.,

census tracts). Finally, there is also a possibility that organizations may self-select into specific

neighborhoods rather than being affected by neighborhood conditions. Given that we model our

data cross-sectionally, however, we were not able to differentiate between these two processes.

Future studies should examine longitudinal changes in the financial health and neighborhood

effects over a longer period of time in order to tease out this process.

Overall, this study supports the idea that minority communities are not just vulnerable in

terms of its residents but also in terms of its organizational landscape and efforts should be made

to build the financial infrastructure of nonprofits to ensure that human service nonprofits have

the financial resources required to better meet individual and community needs.

26

ENDNOTES

1 Previous research has shown that ZIP code-level data can be an acceptable geographic

unit of analysis for community-level studies (i.e., Small & McDermott, 2006; Fox & Rodriguez,

2014).

2 Non-active are shell organizations where “...total expenses are equal to or less than the

sum of depreciation and interest paid,” have no cash, and zero or negative assets (Bowman,

Tuckman, & Young, 2012, p. 13). Thus we exclude organizations where total assets equal to or

less than zero.

3 Bowman (2011) recommends using at least three year’s worth of data to measure valid

capacity and sustainability measures.

4 Examination of these records revealed that values on the original Form 990 were filled

out incorrectly. For example, one organization’s total assets were incorrect and its net assets

were also incorrect.

5 We conclude that for organizations without satellite offices, the address reported on

their Form 990 operates as both an administrative and service office. This is reasonable given

that a nonprofit operating at only one address will likely have both service and administrative

functions occurring at this one locale.

6 While 40% is a threshold often used for impoverished conditions (Wilson, 1996;

Jargowsky, 1997), Swaroop and Morenoff (2006) found that participation in civic organizations

decreased when rates of disadvantage in a community reach approximately thirty-three percent.

7 For further details regarding SANDAG’s methodology for calculating communities of

concern, see: (http://www.sandag.org/uploads/2050RTP/F2050rtp4.pdf).

27

8 We thank Andrew Gelman and his statistics consulting group at the Columbia

University Statistics Department for suggesting the simpler quintile approach.

9 This is preferable to group mean centering if the focus of the study is on contextual

(level 2) factors, and level 1 covariates are “viewed as nuisance variables that need to be

controlled for” (Enders & Tofighi, 2007, p. 128).

10 A nonprofit is presumptively endowed if it has securities and other investments

exceeding its total expenses (Bowman, 2011).

11 Given that the questions of interest in this study relates to the degree of variance

explained by ZIP code level factors, and that the unconditional models for the RA and CML do

not show clustering effects, only the results of the additive models for the ER, MS, MU, and ML

outcomes will be presented.

12 We thank the comments of an anonymous reviewer for this suggestion.

28

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Table 1. Bowman (2011) financial capacity and sustainability definitions and formulas

Long Term Objective: Maintain Services

Measure Question Addressed Formula (non-endowed) Formula (endowed)

Equity Ratio (Capacity) How much of the organization's assets are financed by debt to meet its long term objective of maintaining services?

(net assets)/(total assets)(a) (total net assets – endowment assets)/(total assets – endowment assets)(b)

Return on Assets (Sustainability)

Is the organization earning enough to maintain positive equity ratio and have the long term sustainability to maintain services?

100%*(change in net assets)/(total assets)(c )

100% * (operating surplus)/(total assets-endowment assets)(d)

Short Term Objective: Resilience

Months of Spending (Capacity)

How much does the organization have in reserves to maintain spending on services in case of financial shocks or loss in revenue?

12*(unrestricted financial assets-equity in PP&E)/(spending on operations)(e)

12* (operating reserves or cash plus savings + 10% of marketable securities)/(spending on operations)(f)

Mark up (Sustainability) What is the amount of surplus relative to the size of the budget?

100%*(change in unrestricted net assets + depreciation)/(spending on operations)(g)

100% * (operating surplus + depreciation)/(spending on operations)(h)

Status Quo Mark up How much should be set aside to maintain capital preservation?

3.4%*(total assets/spending on operations)(i)

3.4% * (total assets – endowment assets)/(spending on operations)(j)

Current Term Objective: Paying Bills

Months of liquidity (Capacity)

How much does the organization have in working capital to pay bills?

12 * (nonprofit current assets – current liabilities – temporarily restricted net assets)/(spending on operations)(k)

12 * (nonprofit current assets – current liabilities – temporarily restricted net assets)/(spending on operations)(k)

Change in liquidity (Sustainability)

Is the organization maintaining adequate amount of liquidity for the current term?

(months of liquidity, year t) – (months of liquidity, year t-1)

(months of liquidity, year t) – (months of liquidity, year t-1)

(a) pre-2008 Form 990, lines: (73B/59A); post-2008 Form 990, lines: Part X, (33B/16B)

33

(b) pre-2008 Form 990, lines: (73B-54aB-54bB-56B)/(59A-54aB-54bB-56B); post-2008 Form 990, lines: Part X, (33B-11B-12B)/(16B-11B-12B)

(c) pre-2008 Form 990:100* (73B-73A)/(59A); post-2008 Form 990: Part X, 100*(33B-33A)/(16B)

(d) pre-2008 Form 990: 100*[(1e+2+3+6c+9c+10c+11)+.05*(54aA+54bA+56A)-17]/(59B-54aB-54bB-55cB); post-2008 Form 990: [(Part VIII,1hA+2gA+5A+6dA+8cA+9cA+10cA+11eA)+.05*(Part X,11A+12A)-(Part IX,25A)]/(Part X,16B-11B-12B)

(e) pre-2008 Form 990: 12*[(67B)-(55cB+57cB-64aB-64bB)]/(44A-42A); post-2008 Form 990: 12*[Part X:27B-(10cB-20B-23B)-21B]/(Part IX:25A-22A)

(f) pre-2008 Form 990: 12*[(45B+46B)+.10*(54aB+54bB)]/(17-42A); post-2008 Form 990: 12*[Part X:(1B+2B)+.10*(11B-12B)]/(Part IX:25A-22A)

(g) pre-2008 Form 990: 100*(67B-67A+42A)/(44A-42A); post-2008 Form 990: 100*[(Part X:27A-27B)+(Part IX:22A)]/(Part IX: 25A-22A)

(h) pre-2008 Form 990: 100*[(1e+2+3+6c+9c+10c+11)+.05*(54aA+54bA+56A)-(68B-68A)-(69B-69A)-44A+42A]/(44A-42A); post-2008 Form 990: [(Part VIII,1hA+2gA+5A+6dA+8cA+9cA+10cA+11eA)+.05*(Part X,11A+12A)-(Part X,28B-28A)-(Part X,29B-29A)-(Part IX,25A+22A)]/(Part IX,25A-22A)

(i) pre-2008 Form 990: 3.4*(59A)/(44A-42A); post-2008 Form 990: 3.4*(Part X:16B)/(Part IX: 25A-22A)

(j) pre-2008 Form 990: 3.4*(73B-54aB-54bB-56B)/(44A-42A); post-2008 Form 990: 3.4*(Part X:33B-11B-12B)/(Part IX: 25A-22A)

(k) pre-2008 Form 990: 12*[(45B+46B+47cB+48cB+49B+52B+53B)-(60B+61B+68B)]/(44A-42A); post-2008 Form 990: 12*[Part X:(1B+2B+3B+4B+8B+9B)-(17B+18B+19B+21B+28B)]/(Part IX:25A-22A)

34

Table 2 - Summary Statistics of Outcome Variables and Covariates

1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile

Outcome Variables(a)(b) min 20th 40th 50th 60th 80th max

equity ratio -6.75 0.13 0.53 0.66 0.78 0.92 1.00

return on assets -12.30 -0.05 0.00 0.01 0.05 0.14 20.01

months of spending -345.88 -0.15 1.35 1.98 3.28 7.88 287.45

mark up -6.66 -0.03 0.02 0.04 0.08 0.20 19.38

status quo mark up(d) -0.05 0.01 0.03 0.03 0.05 0.13 5.26

months of liquidity -242.20 -1.05 0.38 1.03 1.54 4.12 161.17

change in months of liquidity -8.47 -0.64 -0.10 0.05 0.27 0.89 357.73

Organization Covariates(a)(b) mean standard dev. Min Max

revenue concentration index(d) 0.72 0.20 0.22 1

age (in years) 22.53 15.57 1.5 79.81

assets (in millions) 5.66 13.90 0.021 133

Endowed (dichotomous) 0.11 0.31 0 1

Zip Code Covariates(e)(f) mean standard dev. Min Max

low income(g) 0.48 0.50 0 1

minority (h) 0.12 0.32 0 1

low mobility (i) 0.05 0.21 0 1

low engage (j) 0.33 0.47 0 1

(a) N=222

(b) Average values for FY 2005-2007

(c) not an outcome variable, used as covariate for mark up model

(d) RCI = Σ(Revenue(i)/Total Revenue)2

(e) N=85

(f) Average values for 2005-2007

35

(g) dichotomous where 1= 33% or more of households have an annual income < $30,000

(h) dichotomous where 1=65% or more of the population is non-White

(i) dichotomous where 1=25% or more of households have no automobile; and/or 25% or more of the population is disabled; and/or 20% or more of the population is >=75

(j) dichotomous where 1= 20% or more of households do not speak English as a primary language; and/or 20% or more of the population aged twenty-five and older has less than a high school education

36

Table 3. Correlation Table of Model Covariates and Outcome Variables (N=222)

1 2 3 4 5 6 7 8 9 10 11 12 13 14

(1) Equity Ratio(a) - (2) Return on Assets(a) -0.30 1.00

(3) Months of Spending (a) -0.02 0.01 1.00 (4) Mark up(a) 0.09 0.03 -0.34 1.00

(5) Status Quo Mark Up (a) 0.07 0.00 -0.41 0.74 1.00 (6) Months of Liquidity (a) -0.04 0.00 0.84 -0.19 -0.36 1.00

(7) Change in Months of Liquidity(a) 0.07 0.00 -0.32 0.92 0.86 -0.19 1.00 (8) Endowed -0.13 0.19 0.21 0.05 0.03 0.12 0.06 1.00

(9) Revenue Concentration Index -0.18 -0.01 -0.05 -0.04 -0.07 -0.02 -0.05 -0.18 1.00 (10) Age 0.12 -0.12 0.12 -0.09 -0.13 0.11 -0.10 0.06 -0.25 1.00

(11) Log of Assets 0.09 0.10 0.13 0.06 0.10 0.02 0.02 0.26 -0.16 0.38 1.00 (12) Low Income Community(b) 0.04 0.08 -0.08 0.03 0.10 -0.08 0.01 -0.08 -0.14 0.02 0.06 1.00

(13) Minority Community(b) -0.06 -0.02 0.05 -0.09 -0.03 0.04 -0.05 -0.10 -0.07 0.02 0.00 0.29 1.00 (14) Low Mobility Community(b) 0.09 0.00 0.06 -0.01 -0.05 0.07 -0.03 -0.10 -0.11 0.10 0.00 0.24 -0.01 1.00

(15) Low Engagement Community(b) -0.01 0.12 0.01 -0.10 -0.04 -0.04 -0.07 -0.07 0.02 0.09 0.00 0.24 0.55 -0.09

(a) Outcome Variable (b) ZIP code level covariate

37

Table 4. Unconditional random intercepts model of financial capacity and sustainability measures for human service nonprofits FY2005-2007 (San Diego County, CA) (n=222)(a)

Long Term Objectives

Short Term Objectives

Current Term Objectives

Equity

Ratio (ER)

Return on Assets (RA)

Months of Spending

(MS)

Mark up (MU)

Months of

Liquidity (ML)

Change in Months of Liquidity (CML)

Constant(b) 2.99** (0.10)

2.99** (0.09)

2.99** (0.11)

2.97** (0.11)

3.02** (0.11)

2.99** (0.08)

Random Effects Parameters

0.09 0.00 0.2 0.15 0.21 0.00

2 1.91 1.41 1.78 1.85 1.78 1.41

Intraclass Correlation (c) 0.04 0.00 0.10 0.08 0.11 0.00

(a) robust standard errors

(b) standard error in parenthesis

(c) ICC= tau/(tau+sigma^2)

**p<0.05

38

Table 5. Comparative random intercepts model of long term capacity (equity ratio -ER) for human service nonprofits FY2005-2007 (San Diego County, CA) (n=222)(a)

Model 5a Model 5b Model 5c Model 5d


Endowed (dichotomous)

-

0.31 (0.30)

- 0.18

(0.31)

Revenue Concentration (proportion) -

2.37** (0.48)

- -2.62** (0.45)

Age (in years)

-

0.01 (0.01)

- 0.002

(0.002)

Log of Assets

-

-0.07 (0.06)

- -0.06 (0.06)

Zip Code Covariates (dichotomous)

low income community -

- -0.14 (0.22)

-0.24 (0.19)

minority community

- -

-0.96** (0.29)

-1.09** (0.24)

low mobility community

- -

0.54** (0.21)

0.39** (0.39)

low engage community

- -

0.43** (0.20)

0.52** (0.19)

Constant 2.99** (0.10)

2.99** (0.10)

3.02** (0.16)

3.07** (0.15)


0.09 0.11 0.01 0.00

2 1.91 1.64 1.89 1.62

Intraclass Correlation 0.04 0.06 0.01 0.00

(a) robust standard errors; standard error estimates in parenthesis

**p<0.05

*p<0.10

39

Table 6. Comparative random intercepts model of short term capacity (months of spending - MS) for human service nonprofits FY2005-2007 (San Diego County, CA) (n=222)(a)




1.12** (0.24)

- 1.15** (0.23)

Revenue Concentration (proportion)

-0.97* (0.48)

- -0.96** (0.47)

Age (in years)

-0.004 (0.01)

- -0.01 (0.01)

Log of Assets

0.16** (0.05)

- 0.17** (0.05)


low income community

- -0.04 (0.26)

-0.10 (0.23)

minority community

- -0.45 (0.34)

-0.43 (0.30)


- 0.67** (0.30)

0.76** (0.76)


- 0.25

(0.28) 0.34

(0.23)

Constant

2.99** (0.11)

2.99** (0.10)

2.94** (0.15)

2.94** (0.13)


0.2 0.16 0.18 0.12

2 1.78 1.5 1.77 1.49



**p<0.05

*p<0.10

40

Table 7. Comparative random intercepts model of short term sustainability (mark up -MU) for human service nonprofits FY2005-2007 (San Diego County, CA) (n=222)(a)




-0.94** (0.38)

- -0.96** (0.39)

Status Quo Mark up

0.20 (0.20)

- 0.21

(0.19)


-1.22** (0.52)

- -1.27** (0.51)

Age (in years)

-0.01 (0.01)

- -0.01* (0.01)

Log of Assets

0.23** (0.06)

- 0.23** (0.06)



- 0.25

(0.26) 0.07

(0.22)

minority community

- -0.35 (0.30)

-0.48 (0.30)


- 0.32

(0.36) 0.21

(0.35)


- 0.05

(0.26) 0.12

(0.24)

Constant

2.97** (0.11)

0.23** (0.10)

2.85** (0.17)

2.96** (0.15)


0.15 0.08 0.13 0.05

2 1.85 1.67 1.84 1.68



**p<0.05

*p<0.10

41

Table 8. Comparative random intercepts model of current term capacity (months of liquidity - ML) for human service nonprofits FY2005-2007 (San Diego County, CA) (n=222)(a)

Model

8a Model 8a Model 8b Model 8c



-0.19 (0.36)

- -0.18 (0.35)


-0.312 (0.48)

- -0.36 (0.47)

Age (in years)

-0.01 (0.01)

- -0.01 (0.01)

Log of Assets

-0.02 (0.06)

- -0.01 (0.06)



- 0.05

(0.27) 0.04

(0.27)

minority community

- -0.99** (0.33)

-1.03** (0.33)


- 0.58* (0.32)

0.58* (0.32)


- 0.50

(0.27) 0.53* (0.27)

Constant

3.02** (0.11)

3.03** (0.11)

2.92** (0.14)

2.93** (0.14)


0.21 0.25 0.17 0.19

2 1.78 1.75 1.74 1.71



**p<0.05

*p<0.10

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