Nonprofit Executive Incentive Pay
Steven Balsam
Temple University
Fox School of Business
1801 Liacouras Walk
Philadelphia, PA 19122
[email protected]
and
Erica E. Harris
Rutgers University
School of Business - Camden
227 Penn Street
Camden, NJ 08102
[email protected]
This version: December 2014
Preliminary and Incomplete
We would like to thank the following individuals for their helpful comments: Sudipta Basu,
Larry Brown, Ivo Jansen, Daniel Neeley, Christine Petrovits, and Andrea Roberts, as well as
workshop participants at Drexel University, Rutgers University – Camden, Temple University’s
Conference on the Convergence of Managerial and Financial Accounting Research, the
University of North Carolina at Charlotte, Villanova University, and the 2014 American
Accounting Associations’ Annual and Mid-Atlantic Midyear meetings.
1
Nonprofit Executive Incentive Pay
We utilize information only recently disclosed on Form 990 to examine the use and
consequences of incentive pay, in the form of bonuses, at US nonprofit organizations. While not
used as frequently as in for-profit firms, bonuses are quite common in nonprofits, as we observe
them in approximately 57 percent of our firm year observations. Consistent with for-profit
research, we find that the existence and amount of bonuses are positively associated with firm
size, profitability and competition. However, that bonuses are based on profitability is somewhat
surprising and controversial, as many believe nonprofits should not earn profits. Additionally,
Resource providers may object to the payment of bonuses as being non-charitable and/or a
violation of the non-distribution constraint. Our results in fact suggest that donors and grantors
both look unfavorably at the payment of bonuses, as subsequent donations and government
grants are negatively associated with the amount of bonus paid.
.
2
Nonprofit Executive Incentive Pay
I. Introduction
Prior research (Baber, Daniel, and Roberts 2002, Brickley and Van Horn 2002, Frumkin
and Keating 2010, Hallock 2002, Sedatole, Swaney, Yetman and Yetman 2013) has examined
the relationship between pay and performance in nonprofit organizations. However prior to 2008,
nonprofits were not required to report incentive compensation separately from total pay. As a
result, these prior studies were forced to infer the use of incentive pay. Using detailed
compensation data now available from the redesigned Form 990 Schedule J, we test the
determinants and consequences of actual incentive compensation in the nonprofit sector.
Specifically, our objectives are to understand the setting in which bonuses are paid, as well as the
impact of those bonuses on the organizations’ ability to attract funding.
The nonprofit sector provides a unique setting for the study of incentive compensation.
Given the inability to reduce agency conflicts via equity compensation, nonprofit boards are
primarily limited to cash bonuses to provide managers with the incentives necessary to run
effective operations.1 Moreover, increased scrutiny of overall nonprofit executive pay from both
governmental agencies (IRS 2008, Panel on the Nonprofit Sector 2005) as well as donors (Olson
2007, Buettner 2011, Balsam and Harris 2014) has made the link between incentives and pay
especially important in this sector of our economy.
We find that larger, more profitable organizations with more available cash pay larger
bonuses, while those that rely more heavily on direct donations and government grants award
smaller bonuses. We also observe that bonuses are higher for nonprofits in industries with more
1
Incentives to perform well can also arise from increases in base salary. However, it is unclear whether a raise in
base pay provides the same incentive to the executive, plus it increases the nonprofits fixed costs.
3
competition from nonprofit, and especially, for-profit organizations. Further, we document that
the bonus is related to the compensation setting policies as well as the monitoring environment
of the nonprofit organization. Specifically, organizations using compensation consultants and
with designated compensation committees, pay larger bonuses, while those that require board
approval as well as those with more restricted donations and independent directors, pay smaller
bonuses. Perhaps most interestingly, the existence of a bonus is positively associated with the
amount of salary, indicating that bonuses are, in general, in addition to, rather than a substitute
for, salary.
Our finding that the existence and amount of bonus pay is correlated with profitability
conflicts with the belief of some (Leone and Van Horn 2005, Krishnan, Yetman, and Yetman
2006, Sedatole et al. 2013) that nonprofits do not actively seek profits. Furthermore, the payment
of bonuses may not sit well with certain nonprofit stakeholders, i.e., donors for example, may be
less willing to contribute to nonprofits that have enough money to pay bonuses, and/or because
they are perceived to be operating in a quasi-for-profit manner. Consequently, we also examine
the impact of bonuses on the nonprofits ability to raise funds. Consistent with our conjectures,
we find that donations and government grants are negatively associated with the payment of a
bonus in the prior year. We also find that donations and government grants are negatively
associated with salary and all other compensation, but the associations are significantly more
negative for bonus.
We proceed as follows. Section II includes background and research question
development, section III our model specifications, section IV our sample selection procedures,
and section V our empirical analysis. We present additional analyses in section VI and our
conclusions in section VII.
4
II. Background
2.1 Use of incentive pay
Unlike publicly-traded for-profit organizations, nonprofit organizations are not required
to file financial or proxy statements with the Securities and Exchange Commission. Rather, to
maintain their tax exempt status they are required to file Form 990 with the Internal Revenue
Service (IRS) on an annual basis. Beginning in 2008, Form 990 was revised to provide users
with new information, including information on bonuses paid to top executives. In particular, the
IRS introduced Schedule J, entitled Compensation Information, which includes two parts. Part
one requires the organization to answer eight yes or no questions, with several sub-questions,
related to how compensation is determined at the nonprofit organization. Part two is an open
response section requiring filers to detail the components of their executives’ pay, including
bonus pay.2
Research on executive compensation and its impact on performance is a mainstay of forprofit accounting, economics, finance, and management research. Whereas much of this
literature derives from agency theory (e.g. Jensen and Meckling 1976) and the use of incentive
compensation to resolve the owner-manager conflict, a competing line of research (e.g. Bebchuk
and Fried 2004) argues that observed executive compensation is not an equilibrium outcome, but
rather the result of managerial power or rent extraction. Empirically, researchers have
endeavored to explain the determinants of compensation (Jensen and Murphy 1990b, Murphy
1985, Tosi, Werner, Katz, and Gomez-Mejia 2000), as well as optimal compensation structure.
Jensen and Murphy (1990a) sum it up when they argue “It’s Not How Much You Pay, But
How”.
2
Not all nonprofits are required to provide this additional information. For example a nonprofit with no executive
earning more than $150,000 would generally not be required to file Schedule J.
5
In contrast there is much less research on executive compensation in the nonprofit
literature. Examples include Oster (1998), Baber et al. (2002), Brickley and Van Horn (2002),
Hallock (2002), Frumkin and Keating (2010), and Sedatole et al. (2013). However, because these
papers all use data that predates the 2008 Form 990 revision they are unable to directly examine
the use of bonus compensation in nonprofits. Rather, by using various independent variables to
explain total compensation, they indirectly study the use of pay for performance at nonprofit
organizations. While most of these studies find a link between nonprofit pay and performance,
Hallock (2002) concludes that “evidence linking pay to performance is only robust when
performance is measured as some function of organization size.”
There are significant differences between for-profit and nonprofit entities, differences
that could impact the use and effectiveness of incentive compensation. In particular, the lack of a
residual claimant in a nonprofit organization may accentuate agency problems because equity
compensation and ownership requirements, which are used to reduce this conflict by aligning the
incentives of managers and owners (Core and Larcker 2002), are unavailable. Therefore, out of
necessity, cash incentives play a larger role in nonprofit compensation packages. However,
nonprofits must also avoid both the ‘private inurement’ and ‘excess compensation’ provisions of
Internal Revenue Code sections 501(c)(3), 4941, and 4958. These provisions prohibit nonprofits
from simply distributing net profits to managers and require managerial compensation to be
reasonable.3
Additionally, while agency theory suggests that incentive compensation can mitigate the
owner-manager conflict by aligning interests in for-profit organizations, a multitude of factors
The IRS defines reasonable as: “Reasonable compensation is the value that would ordinarily be paid for like
services by like enterprises under like circumstances. Reasonableness is determined based on all the facts and
circumstances.” (http://www.irs.gov/Charities-%26-Non-Profits/Exempt-Organization-Annual-ReportingRequirements:-Meaning-of-Reasonable-Compensation).
3
6
complicate this analysis in nonprofits. Nonprofits do not have owners. Rather, they have a
variety of stakeholders that include beneficiaries, donors, employees, and volunteers.
Consequently, it is not clear whose interests’ nonprofit managers should be aligned with. Should
they be aligned with the interests of the recipients of their goods or services? Or should they be
aligned with the interests of donors?
The board of directors, which nominally controls the incentive setting process, is likely to
be more aligned with the interest of donors, as most nonprofits require that board members
contribute as part of their service (O’Regan and Oster 2005). Consistent with this Aggarwal,
Evans and Nanda (2012) suggest “directors join the board because they value the right to direct
the organization to pursue the goals of stakeholders that they represent or activities that they
privately value.” As a result, executives’ directives and incentives are extremely opaque in this
complex setting, providing further motivation for our study.
While causing some control problems, the lack of a residual claimant can, however,
provide an advantage to nonprofits in attracting donations given the non-distribution constraint
(Hansmann 1980). However, payment of incentive compensation or bonuses might be seen as
violating that constraint and consequently lead to lower levels of donations. While not looking at
incentive compensation per se, Balsam and Harris (2014) provide evidence that donors withhold
contributions to nonprofits following the disclosure of high compensation. Nonprofits are also
subject to significant regulation at the state and federal levels, including penalties for excess
compensation, which could be triggered by the payment of incentive compensation.4
4
The IRS does not prohibit the payment of bonuses, i.e., prohibitions against the use of incentive pay systems were
removed in 1980 (IRS Counsel Memorandum 38283, Revenue Ruling 8122068). However, payments of large
bonuses could be deemed as excessive pay by the IRS.
7
Additionally, measuring performance is an important issue in providing the proper
incentives. The primary measures of performance in the for-profit literature are market- and
accounting-based, as the goal is to maximize shareholder wealth. Publicly traded firms can
directly measure the increase in shareholder wealth by examining share price performance, or
indirectly by measuring profitability. But how should a nonprofit measure performance? There is
no stock price, and while nonprofits do have accounting-based measures, there is no residual
claimant to lay claim to the profits. Prior nonprofit research has used a variety of performance
measures to explain top executive pay, which include profitability (Brickley and Van Horn
2002), efficiency (Baber et al. 2002), and fundraising (Hallock 2002).
Some argue that profits are not an appropriate performance measure and that the goal of
most nonprofits is long-run breakeven. For example, Leone and Van Horn (2005) argue that
nonprofits do not seek to maximize profits; rather they aim for small profits and to avoid losses.
Krishnan et al. (2006) suggest that “To instill trust in the recipient, the providing firm uses the
nonprofit form of organization to provide a signal that the firm is not interested in profit
maximization, but rather is working to maximize the recipients’ welfare.” Sedatole et al. (2013)
go further, suggesting that nonprofits provide incentives to CEOs to expend residual income to
increase charitable services and avoid violation of the non-distribution constraint, which they
assert requires long-run breakeven. On the other hand, nonprofits need to maintain a financial
cushion, e.g., rainy day fund, as well as resources to acquire future capital assets. Thus, boards
and donors may not be totally averse to nonprofits accumulating profits. Providing empirical
support for this proposition, Chang and Tuckman (1990) find that a large majority of nonprofits
earned surpluses in 1983, and that very few had surpluses close to zero. Ultimately it becomes an
empirical question as to whether nonprofits seek, and nonprofit executives are rewarded for,
8
profits. Providing an answer to the latter question is a focus of our study and leads to our first
formal hypothesis:
H1: Bonuses received by nonprofit executives are positively related to net income.
The possibility exists that nonprofit executives are rewarded based on other performance
measures, e.g., delivery of services (Aggarwal et al. 2012), efficiency (Baber et al. 2012),
fundraising (Hallock 2002); either in addition to, or instead of, profit. This leads to our second
hypothesis:
H2: Bonuses received by nonprofit executives are positively related to performance measures
other than net income.
Murphy and Zabojnik (2002) in discussing trends in CEO pay argue that market forces
“are of first-order importance”. Balsam and Harris (2014) assert nonprofits “compete with other
organizations for employee and executive talent.” While nonprofits may attract individuals who
are less driven by extrinsic rewards, e.g., labor donation (Handy and Katz 1998), in most cases
they need to provide pay packages that are competitive with individuals’ other job opportunities.
An empirical question is whether alternative employers include both nonprofit and for-profit
entities, and whether these labor markets are distinct (Erus and Weisbrod 2003).5 This leads to
our third and fourth hypotheses, which test the response of bonus pay to competition in the labor
market for nonprofit executives:
H3: Bonuses received by nonprofit executives depend on the competition the nonprofit faces
from other nonprofit organizations.
H4: Bonuses received by nonprofit executives depend on the competition the nonprofit faces
from for-profit organizations.
5
While not directly examining this question, using survey data Roomkin and Weisbrod (1999) find that
compensation practices differ between nonprofit and for-profit hospitals, i.e., while for-profit CEOs receive higher
bonuses and total compensation, nonprofit CEOs receive higher salaries.
9
In addition to profits, program expenses, efficiency, revenue sources, and competition,
we identify several economic and monitoring variables posited to play a role in setting incentive
pay in the nonprofit sector. These variables include commonly used variables from the
compensation literature such as firm and board size, as well as variables unique to the nonprofit
sector such as unrestricted cash and the existence of restricted donations. We also study six new
variables included in Schedule J aimed at understanding the compensation setting process used at
nonprofit organizations. Using these variables, our study aims to form a comprehensive
understanding of the use of nonprofit bonus pay.
2.2 Impact of incentive pay on revenue generation
In addition to understanding the determinants of nonprofit incentive compensation, we
are also interested in the consequences of using this form of pay. Nonprofits rely on multiple
funding sources including direct donations, government grants and program service revenues. In
the first two of these funding sources the person providing the funding receives nothing of value
in exchange for his or her contribution. According to Emerson (2010) donors “assume that
charity leaders work for free or minimal pay and are shocked to see that they earn six figure
salaries.” As such he/she may object to their contribution being used to reward executives, and
may withhold/reduce future contributions as a result. Balsam and Harris (2014) posit that
“government grantors are interested in executive compensation to avoid making grants to
charities that have the potential to embarrass them in the political sphere.” In contrast they
suggest that patrons, who receive services from nonprofits, are less concerned about executive
compensation. Thus far, Balsam and Harris (2014) is the only study to examine this issue
empirically, and as with the prior literature, they are limited to studying the impact of total
compensation on future donations. While they provide some evidence that donors withhold
10
contributions to nonprofits following the disclosure of high compensation, their findings are
limited to nonprofits with sophisticated donors and nonprofits whose compensation is disclosed
in the media.
This study expands prior research to determine if contributors weigh the forms of
compensation differentially in deciding whether and how much to support a nonprofit. In
particular we expand on Balsam and Harris (2014) by looking at whether the form of
compensation affects future support. Incentive compensation in the form of bonuses confirms
that executives are not donating their labor, providing evidence consistent with the nonprofit
being run like business, and may signal to some donors that the nonprofit is not in need of
contributions if they have excess funds with which to pay a bonus. Anecdotal evidence also
suggests in certain situations bonus payments could be politically unpalatable for nonprofits. As
an example consider the uproar that ensued when a study conducted by Kaiser Health News and
ABC News showed that nonprofit CEOs received bonuses for profit, revenue and other financial
goals, nine of whom received million dollar bonuses (Grassley 2013, Hancock 2013); or the
uproar when it was disclosed AIG would be paying retention bonuses (Andrews and Baker 2009)
when it effectively was a ward of the federal government. Our expectation is that contributors,
but not patrons (to adopt the terminology of Balsam and Harris 2014) react more negatively to
the payment of bonuses than to other forms of compensation. This culminates in our fifth
hypothesis:
H5: Executive bonus payments adversely affect subsequent direct donations and government
grants.
III. Model specification
3.1 Determinants of the use of incentive compensation
11
To examine the factors that explain a nonprofit organizations’ bonus payments we
employ a Tobit model, as the dependent variable, Bonus, is bounded from below at zero. Bonus
is defined as the log level of bonus payments made to the highest paid executive at the nonprofits
in our sample.6 According to the IRS instructions for Schedule J, bonuses include: “payments
based on satisfaction of a performance target (other than mere longevity of service), and
payments at the beginning of a contract before services are rendered (for example, signing
bonus)”. To provide a comprehensive understanding of nonprofit bonus pay, we organize our
independent variables into two categories representing the economic and monitoring
environments in which our sample organizations operate. See Table 1 for detailed variable
definitions.
Looking first at the economic factors, we include variables that proxy for size,
profitability, services delivered, efficiency, and revenue generated. Research in the for-profit
sector has consistently shown (e.g. Balsam 2007 Table 2-4) that the use of incentive
compensation increases with firm size. Accordingly, we expect the use of bonuses in nonprofits
to increase with nonprofit size. We use the log of total assets (Total Assets) to measure firm size
and expect a positive coefficient on this variable.7
Additionally, both the for-profit (e.g., Core, Holthausen and Larcker 1999) and nonprofit
(e.g., Brickley and Van Horn 2002) literatures have shown that compensation increases with the
performance of the organization. However, as discussed above, a number of other papers (Leone
and Van Horn 2005, Krishnan et al. 2006, Sedatole et al. 2013) suggest that nonprofits do not
6
We also require that the executive fit into two of six position codes provided by the Compensation File from the
National Center on Charitable Statistics: officer or key employee. The remaining four position codes, which are not
mutually exclusive, are: individual trustee or director, institutional trustee, highly-compensated employee, former
employee.
7
The results reported below are robust to alternative definitions of nonprofit size such as total revenues as well as
number of employees.
12
pursue accounting profitability, which, if true, implies that they would not base incentive
compensation on profits. To the extent that profits are not an appropriate measure of
performance for a nonprofit, we include program service expenses, revenue generation, and
efficiency as alternative performance measures.8 We measure the profitability of a nonprofit as
Net Income which is calculated as the difference between total revenues and total expenses.9
Consistent with more profit-oriented organizations (proxied for by the existence of profits)
utilizing bonus compensation to incentivize their executives, we expect a positive relationship
between Net Income and Bonus. We use Program Service Expenses as an alternative
performance indicator which measures the magnitude of program services provided by the
organization following Aggarwal et al. (2012). We measure Administrative Efficiency following
Frumkin and Keating (2010) as 1 less administrative expenses/total expenses. They argue that
the more efficient the charity is perceived to be, the greater the reward to the executive, finding
this ratio to be positively associated with compensation, therefore we predict Administrative
Efficiency will have a positive relation with Bonus.
The executive’s ability to generate revenue is part of his/her job and he/she should be
motivated and rewarded for fulfilling this responsibility. However the relationship between
compensation and revenue generation can vary depending on the revenue source and prior
literature finds mixed results. Looking at the relationship between compensation and donations,
Sedatole et al. (2013) find a positive association between compensation and donations, Frumkin
and Keating (2010) fail to find a relationship between changes in donations and the level of
compensation, Oster (1998) finds that compensation decreases as the percentage of revenue from
donations increases and Gaver and Im (2014) find excess CEO compensation is negatively
8
9
Our inferences are unchanged when we include these variables in model (1) individually.
We find similar results if we use return on assets (ROA) as our main performance measure.
13
related to public donations. Finally, Balsam and Harris (2014) show that donors are sensitive to
executive compensation. This last finding suggests that it may not be in the interests of the
nonprofit to raise compensation if it wants to maximize donations. Additionally, Gaver and Im
(2014) also find that excess CEO compensation is negatively related to funding from government
grants. The provision of program services differs from the receipt of donations and government
grants, in that patrons receive a service directly from the nonprofit in return for payment. As a
result, they do not seem to be as concerned with executive compensation (Balsam and Harris
2014). However, Sedatole et al. (2013) find a positive relation between compensation and
program service revenues. Based on this discussion we posit that nonprofits that are heavily
reliant on donations will be reluctant to use bonuses. We also expect government grants will
dampen the use of bonuses. In contrast, we argue that program service revenues proxy for the
business-like operations of the organization and therefore posit a positive relation between Bonus
and Program Service Revenues.
We further argue that competition for executive services from both for-profits and other
nonprofits has the potential to influence the payment of bonuses as well. While the general
expectation is that competition for executive services will drive up the price of those services,
our predictions are tempered by the fact that we cannot directly measure that competition.
Instead our measures of competition gauge both the competition for executive services as well as
customers.10 In particular we define Nonprofit competition as the number of nonprofits within a
given metropolitan statistical area (MSA)/industry combination, and For-profit competition, as
the number of for-profits within the same metropolitan statistical area (MSA)/industry
10
We were able to identify for-profit organizations in the same industry as the nonprofit under examination by using
the NCCS NTEE/NAICS/SIC Crosswalk compiled by the National Center for Charitable Statistics which we
obtained from http://nccsdataweb.urban.org/kbfiles/786/xwalka.pdf on October 20, 2014.
14
combination. Early research (Hart 1983) in the for-profit realm suggests that product market
competition reduces managerial slack and provides incentives that may lessen the need for
incentive compensation such as bonuses. However later research finds the results are ambiguous
(Scharfstein 1988, Hermalin 1992) or that competition leads to increased incentives (Raith 2003,
Karuna 2007). Looking at competition in the hospital industry Erus and Weisbrod (2003) provide
mixed evidence as to whether product market competition leads nonprofits to “more closely
emulate FPs in terms of use of bonus compensation.” In sum, because we cannot directly
measure the competition for an executive’s services and prior research on the impact of product
market competition on executive compensation is mixed, we include these two variables in our
model, but do not predict their signs.
Turning to the monitoring environment of the organization, Jensen and Meckling (1976)
argue that an inability to monitor managers effectively leads principals to rely on contingent
compensation such as bonuses to align incentives.11 To examine this subject we study measures
of the nonprofit compensation environment newly disclosed on the revised Form 990, e.g., items
such as the establishment of a compensation committee and the use of compensation consultants;
as well as more general variables, i.e., those not specific to compensation, but which also proxy
for the organization’s monitoring environment.
Line 3 of the new Schedule J of Form 990 requires nonprofit organizations to indicate the
method(s) utilized by the organization to set executive pay. Specifically, the IRS requires
nonprofits to disclose which, if any, of the following six pay setting mechanisms (described in
more detail below) they utilize: existence of a compensation committee, existence of a written
employment contract, approval of compensation by the board or compensation committee,
11
A counter-argument, however, is that contingent compensation is only effective if the principal, in this case the
board, can monitor the performance metrics and how they are being measured.
15
employment of an independent compensation consultant, use of a compensation survey or study,
and reference to Form 990s of other organizations. Nonprofits are not required to use any of
these methods, and are not limited to using only one. We operationalize these disclosures in our
analysis coding individual responses as one if the organization reports the use of that mechanism
and zero otherwise.
Compensation Committee refers to the existence of “a committee of the organization's
governing body responsible for determining the top management official's compensation
package” (2012 IRS Form 990 Schedule J instructions). Written Employment Contract refers to
“recent or current written employment agreements” (2012 IRS Form 990 Schedule J
instructions). Compensation Approval refers to “the ultimate decision by the governing body or
compensation committee on behalf of the organization” (2012 IRS Form 990 Schedule J
instructions). We are unaware of any literature examining the impact of the existence of a
compensation committee, compensation contracts, or compensation contract approval on the
level of compensation.12 Each of these three variables is associated with greater monitoring of
executive compensation by the board of directors. Consequently one prediction is that the higher
level of monitoring will reduce the need for incentive compensation. However, to the extent that
these variables are correlated with the sophistication/business-like operation of the organization,
they could be associated with a higher likelihood of using incentive compensation. As a result,
we make no prediction as to their impact on the use of bonuses in our sample.
Compensation consultant refers to the use of “a person outside the organization who
advises the organization regarding the top management official's compensation package” (2012
IRS Form 990 Schedule J instructions). Compensation survey is defined as the organization’s use
12
There is however research on the effect of the independence of the compensation committee on various facets of
CEO pay (Anderson and Bizjak 2003, Daily et al.1996, and Newman and Mozes 1999).
16
of a “study of top management official compensation or functionally comparable positions in
similarly situated organizations” (2012 IRS Form 990 Schedule J instructions). Finally, Other
990 Compensation Comparison refers to the use of compensation information presented on Form
990’s of “similarly situated organizations” (2012 Form 990 Schedule J instructions). Several forprofit papers have examined the impact of using a compensation consultant on executive
compensation, finding mixed results (see Conyon 2011 for a discussion and summary).
However, to the extent that the use of compensation consultant is a proxy for the more businesslike nature of the nonprofit, we expect higher bonus compensation in nonprofits employing
compensation consultants. Further, Murphy (1998) relates that the use of surveys may lead to a
ratcheting up of compensation as few corporations elect to pay below average compensation.
Additionally, Bizjak, Lemmon and Naveen (2008) document the widespread use of
benchmarking to set CEO pay. Assuming the same logic applies in the nonprofit sector we would
expect the use of a compensation consultant, compensation surveys, as well as the use of other
Form 990s as a reference to be associated with higher levels of Bonus.
Our model also includes three variables aimed at capturing the general monitoring
environment of nonprofit organizations. First, we include Restricted Donations which is a proxy
for a more sophisticated and vigilant donor base (Yetman and Yetman 2013). Balsam and Harris
(2014) provide evidence consistent with sophisticated donors, as proxied for by the existence of
restricted donations, being more likely to monitor and react to executive compensation.
Therefore, our expectation is that the level of bonus pay will be inversely related to Restricted
Donations. Next we include two board related variables: board size and the percentage of
independent directors. While prior for-profit literature (Hermalin and Weisbach 1998) suggests
that independent directors are likely to be better monitors, the literature on board size is mixed
17
(Yermack 1996, Coles, Daniel, and Naveen 2008, Boone, Field, Karpoff, and Raheja 2007,
Linck, Netter, and Yang 2008). Therefore, while we expect board independence to be inversely
related to Bonus, we do not predict the association between board size and Bonus.
Finally, we include Unrestricted Cash and Salary as independent variables. The ability of
the nonprofit to pay a bonus will to a large extent depend on cash availability. Consequently we
measure Unrestricted Cash as cash less restricted donations and expect a positive coefficient.
Salary is defined by the IRS as “nondiscretionary payments to a person agreed upon in advance,
contingent only on the payee's performance of agreed-upon services (such as salary or fees)”
(2012 Form 990 Schedule J instructions). We have no a priori expectations for the coefficient on
Salary. While nonprofits need to pay equilibrium compensation, some may be reluctant to use
the term bonus. Those nonprofits will thus pay a higher Salary and a lower or no bonus resulting
in a negative correlation between Salary and Bonus. Alternatively, to the extent that both high
Salary and Bonus are the result of managerial rent extraction (Bebchuk and Fried 2004), we
could observe a positive correlation. Consequently, we include Salary as an independent variable
but do not make a prediction as to its sign. We also include industry and year fixed effects to
control for variations in industry and economic conditions present in our sample. Finally, we use
the natural log of all continuous, non-ratio variables, and winsorize all variables at the 1 and 99
percentiles to mitigate the influence of outliers, and cluster standard errors by nonprofit.
In sum we explain the level of Bonus using the following Tobit model. Formally:
Bonus = β0 + β1Total Assets + β2Net Income + β3Program Service Expenses +
β4Administrative Efficiency + β5Direct Donations + β6Government Grants + β7Program
Service Revenues + β8Nonprofit Competition + β9For-Profit Competition +β10 Board
Approval + β11Written Employment Contract + β12Compensation Committee +
β13Compensation Consultant + β14Compensation Survey + β15Other 990 compensation
comparison + β16Restricted Donations + β17Board Size + β18 Percentage of Independent
Directors + β19Unrestricted Cash + β20Salary + Industry fixed effects+ Year fixed effects
+ε
(1)
18
3.2 Impact of incentive compensation on funding sources
To examine the impact of incentive compensation on funding sources, we again employ
a Tobit model, as our dependent variables, Direct Donations and Government Grants, are
bounded from below at zero. Our test variables are related to compensation, in particular the
payment of Bonus compensation, as well as Salary and Other Compensation. Our expectation is
that the coefficient on Bonus will be negative and significant, and significantly less than the
coefficients on Salary and Other Compensation.
As control variables we include a series of variables which have been found by the prior
literature to affect Direct Donations and Government Grants. Tinkelman (1998) and Krishnan
and Schauer (2000) both found a positive relation between nonprofit size and support. As a
result, we include year-end Total Assets and expect it to be positively associated with Direct
Donations and Government Grants. Weisbrod and Dominguez (1986), Posnett and Sandler
(1989), Callen (1994), Tinkelman (1998), Khumawala and Gordon (1997), Parsons (2003),
Tinkelman and Mankaney (2007), Parsons and Trussel (2008), and Tinkelman (2009) all find
that organizational efficiency is associated with the ability to attract donations. We use
Administrative Efficiency, as defined above, to measure organizational efficiency and expect it to
be positively associated with our dependent variables. Weisbrod and Dominguez (1986) and
Tinkelman (1999) find that Fundraising Expenses are positively related to donations, therefore
we expect a positive coefficient on this variable.
Several studies (Weisbrod and Dominguez 1986; Posnett and Sandler 1989; Callen 1994;
Yetman and Yetman 2009) propose that donors refrain from making donations to organizations
that have high levels of Government Grants or Program Service Revenue as they feel that the
nonprofits’ financial needs are being met from other sources. However, other literature (Okten
19
and Weisbrod 2000; Petrovits et al. 2011, Yetman and Yetman 2013) suggests donors may feel
more comfortable contributing to a nonprofit that has other sources of revenue. Consequently,
while we include Program Service Revenues in both models, Government Grants in the model
explaining Direct Donations and Direct Donations in the model explaining Government Grants,
we do not predict the coefficients of these variables. In addition, given that nonprofit revenues
are affected by factors correlated with industry and year, we incorporate fixed effects to control
for industry and year. Finally, to control for the effect of competition for donations and grants
from other nonprofit organizations in the same MSA/industry combinations, we once again
include our Nonprofit Competition control variable.
Following prior literature (e.g., Parsons and Trussel 2008; Petrovits, Shakespeare and
Shih 2011), the control variables are measured with a one year lag. Finally, we use the natural
log of all continuous, non-ratio variables, and winsorize all variables at the 1 and 99 percentiles
to mitigate the influence of outliers, and cluster standard errors by nonprofit.
In sum we examine the impact of the components of the compensation package on the
level of Direct Donations and Government Grants using the following Tobit model. Formally:
Direct Donations = β0 + β1Salary + β2Bonus + β3Other Compensation + β4Total Assets
+ β5Administrative Efficiency + β6Fundraising Expenses + β7Government Grants +
β8Program Service Revenues+ β9Nonprofit Competition + Industry fixed effects + Year
fixed effects + ε
(2)
Government Grants = β0 + β1Salary + β2Bonus + β3Other Compensation + β4Total
Assets + β5Administrative Efficiency + β6Fundraising Expenses + β7 Direct Donations +
β8Program Service Revenues + β9Nonprofit Competition + Industry fixed effects + Year
fixed effects + ε
(3)
IV. Sample selection
To construct our sample, we begin with the Compensation File compiled by the National
Center on Charitable Statistics (NCCS). This dataset is drawn from Schedule J of Form 990 and
20
includes information about compensation components, such as Bonus, which were not separately
disclosed previously. Schedule J must be completed by nonprofit organizations that report
compensation for any officer, director, or employee with total compensation in excess of
$150,000. Because the focus of our study is bonuses, we are restricted to 2008-2011, namely
those years for which Schedule J information is available.13 We also restrict our analysis to
compensation of the highest paid individual, resulting in 66,201 firm-year observations over the
four year period 2008-2011.
We merge our Schedule J compensation data with the Statistics of Income (SOI) dataset.
The SOI dataset includes financial information for all nonprofit organizations with total assets
greater than $50 million plus a stratified random sample of smaller organizations. While the SOI
dataset does not include every nonprofit organization for every year, it does reflect “over 90
percent of all nonprofit revenues” according to Yetman and Yetman (2013, 1049). We lose
17,542 firm-year observations for which all model variables are not available. We delete 836
firm-year observations that appear to be obvious outliers and potential errors (e.g., years with
negative revenues, see Feng, Ling, Neely, and Roberts 2013) in addition to 12,395 firm-year
observations where the highest reported total compensation is less than $150,000.14 As shown in
Table 2, this yields a sample size of 35,428 firm-year observations for 11,314 unique nonprofit
organizations. For our consequences analysis, we use a one-year lag for the independent
variables to allow the dependent variable either donations or government grants time to react,
reducing our sample to 25,081 firm-year observations.
13
The NCCS is in the process of digitizing more recent years at this time.
Our results are robust to including observations with compensation less than $150,000. However, given that firms
are not required to complete Schedule J below this threshold, and that voluntary disclosers may differ from those
required to disclose, we limit our analysis to organizations that are required to disclose this information.
14
21
Table 3 provides the industry and year distributions for our full and bonus samples. As
shown in panel A of Table 3, Hospitals make up the largest category, at 26 percent of our
sample. This is followed by Human services (18 percent), Health (17 percent), Education (12
percent), Universities (10 percent) and Arts, culture, and humanities (five percent). Finally, the
classification “Other industries,” which consists of Environment, International, Mutual benefit,
Public & Societal benefit, and Religion, makes up approximately 12 percent of the sample. We
also observe that the percentage of executives receiving bonuses varies across industries, with
executives in Hospitals receiving bonuses most often, 80 percent of the time, and those in
Education receiving bonuses least often, 39 percent of the time. As shown in panel B, our sample
is fairly evenly distributed between the four sample years, with mean bonus pay trending upward
over the four year period.
V. Results
5.1 Descriptive Statistics
Table 4 provides descriptive statistics for the dependent and independent variables in our
model. We include the percentage of the sample that reports non-zero values for each variable as
a means of understanding the use of these sources in our sample. We find that over half (57
percent) of our nonprofit year observations report paying bonuses. Further, the mean Bonus is
$91,863, with a maximum of almost $8 million (untabulated). We also find that Bonus is a small
fraction of total revenue, which has a sample mean (median) of $125 million ($30 million), or
total assets with a sample mean (median) of $239 million ($69 million). Finally we note the very
large negative mean figure for Unrestricted Cash of approximately -$26 million; indicating that
on average sample firms have little unrestricted cash on hand.
22
Turning to our bonus and non-bonus sample descriptive statistics, we note that Salary is
significantly (p-value < 0.000) greater in nonprofits that pay bonuses. In particular, the mean
(median) Salary in nonprofits that pay bonuses is about $447,000 ($362,030) versus $ 291,627
($232,149) in nonprofits which do not. We also document significant differences between our
treatment and control firms with respect to the majority of our remaining test and control
variables.
We examine, but do not tabulate, the correlations among the independent variables.
Consistent with prior literature, size (Total Assets) is significantly correlated with the majority of
the other control variables. For example, the highest correlation is the Spearman correlation of
0.54 between Total Assets and Program Service Revenues. While there are some significant
correlations, those correlations are of relatively low magnitude and the corresponding Variance
Inflation Factors are less than two, which suggest that multicollinearity is not a problem.
5.2 Determinants of bonus
In Table 5 we analyze the determinants of nonprofit bonuses. As expected we find that
larger organizations pay higher bonuses. In support of hypothesis one we also find that more
profitable nonprofits pay higher bonuses as the coefficient on Net Income is positive and
significantly different from zero. In terms of our alternative performance measures, we find the
coefficients on Program Service Expenses, a measure of services delivered, as well as
Administrative Efficiency, a measure of the efficiency with which those services are delivered,
are both insignificantly different from zero. Similarly we do not find that “fundraising success”
leads to higher bonuses, as we find an insignificant coefficient on Program Service Revenues,
and negative and significant coefficients on Direct Donations and Government Grants. Thus we
do not find support for hypothesis two.
23
Turning from performance to competition we find the coefficients on both Nonprofit and
For-profit Competition to be positive and significant, consistent with executive bonuses
increasing with the competition for their services. Additionally we find that the coefficient on
For-profit Competition to be statistically greater than that on Nonprofit Competition (p-value
<0.000). Thus we find support for hypotheses three and four.
Related to the six compensation setting practices we find that the existence of Board
Approval reduces the amount of the bonus, while the existence of a Compensation Committee,
the hiring of a Compensation Consultant, and the use of a Compensation Survey all are
associated with an increase in the bonus amount, i.e., are positive and significant. With respect to
the monitoring environment, we find that organizations with more sophisticated donors
(Restricted Donations), and more independent boards (Percentage of Independent Directors) pay
lower bonuses, although find no effect for Board Size. We do find, as expected, that the
availability of cash leads to higher bonuses. Finally, the coefficient on Salary is positive and
significant, indicating that the Bonus is in addition to, not in place of, Salary.
In addition to our Bonus findings, column II presents very similar results when we define
our response variable as a ratio of bonus to total pay. That is, our results are identical with the
addition of a significant coefficient on our additional performance measure, Program Service
Expenses, and the loss of significance on Compensation Survey. Columns III and IV of Table 5
present determinants models for Salary and Other Compensation. Here we note that these
components are not significantly related to Net Income our main performance measure,
indicating that salary and other compensation mechanisms are not related to executive
performance. We do find that salary is positively related to Program Service Expenses
24
suggesting that salary is more closely tied to the scope of the services provided by the
organization.
5.3 Impact of incentive compensation on funding sources
In addition to understanding the types of nonprofit organizations utilizing incentive pay
Table 6 presents results which suggest the consequences of this pay type. In particular, we define
our bonus test variable as the percentage of bonus pay to total compensation as a means of
quantifying the use of incentive pay and show the impact of this ratio on various performance
measures. We focus on the six performance measures defined previously: net income, program
service expenses, administrative efficiency, program service revenues, direct donations, and
government grants. In the first three columns of Table 6 we observe that future net income,
program service expenses, and program service revenues are positively associated with the
percentage of total pay at risk. That is, we find that organizations that pay a larger portion of
their executives’ compensation in the form of bonus have higher future Net Income, Program
Service Revenues, and produce more mission related activities (Program Service Expenses). We
do not find that bonus has any impact on one-year-ahead administrative efficiency. Turning to
our revenue source variables, we find that the ratio of bonus to total pay has a significant
negative impact on future Direct Donations.15 We fail to find a significant impact on one-yearahead Government Grants, but do note a negative coefficient on this test variable.
VI. Additional Analyses
15
We note that this finding is stronger than that of Balsam and Harris (2014) who fail to find an association between
total compensation and subsequent donations for their overall sample, i.e., they only find their result for nonprofits
whose compensation is disclosed in the media and nonprofits with sophisticated donors. While we cannot
definitively reconcile these two differing results we note that they use change model whereas we use a log levels
model, and more importantly the sample selection criteria in the two studies differ. That is, this project focuses on
organizations reporting total compensation in excess of $150k required to file schedule J) while Balsam and Harris
(2014) incorporate firms of all size. Finally our samples are also drawn from different time periods. Balsam and
Harris (2014) look at nonprofits from 2002 through 2008, we study 2008 through 2011.
25
6.1 Impact of for-profit competition on selection of performance measures in bonus contract
As noted above, there is debate over whether nonprofit managers should be incentivized
to report higher net income. At the same time nonprofit compensation packages have to be
competitive with those in the for-profit sector, especially for those executives with opportunities
to work in for-profits. Table 5 in fact shows that the existence of for-profit competition
significantly increases the bonus amount. In this section we examine the impact of for-profit
competition on the performance measures used in the bonus contract. We create a new indicator
variable, no for profit competition that takes the value of one if the nonprofit does not face forprofit competition and zero otherwise. We then interact that variable with Net Income to see
whether it affects the weight applied to Net Income. In untabulated analysis we find a negative
and significant coefficient on this interaction, as well as finding the sum of Net Income plus Net
Income*no for profit competition is insignificantly different from zero. Thus nonprofits without
for profit competition do not appear to use profitability as a performance measure.
6.2 Concentration of executive power
One of the regularities of the for-profit literature is that executives who are also board
chair receive higher levels of compensation (Mallette, Middlemist and Hopkins 1995, Sridharan
1996, Core et al. 1999, and Conyon and Murphy 2000), which could be considered payment for
additional services rendered or rent extraction. While we do not have the identity of the board
chair, our data does allow us to determine if the highest paid executive also sits on the board, as
well as the number of positions that individual holds, e.g., CEO and President is two positions. In
untabulated analyses we rerun models (1) and (2) incorporating an indicator variable taking the
value of 1 if the highest paid executive is also listed as a director, and then, separately,
incorporating a discrete variable for the number of positions (within the organization) held by
26
that individual. In our determinants model we observe that the coefficients on the newly added
variables are positive and significant, i.e., they increase the bonus amount. More importantly, we
find that the signs and significance levels on the remaining coefficients are almost identical to
those reported earlier. Thus, while the concentration of power in the highest paid executive does
appear to increase the amount of that bonus, it does not appear to affect the relationship between
bonus and the primary variables we study.
VII. Conclusions
Using data available from the redesigned Form 990 Schedule J, we test the determinants
and consequences of bonuses in the nonprofit sector. We generally find that larger, more
profitable organizations are more likely to award higher levels of bonus compensation. We also
find that competition from both nonprofits and for-profit organizations increase bonus
compensation, consistent with competition for the executives’ services driving up equilibrium
compensation. Given that our findings, that the existence and amount of bonus pay is correlated
with profitability, conflicts with the belief of some (Leone and Van Horn 2005, Krishnan et al.
2006, Sedatole et al. 2013) that nonprofits do not actively seek profits, we further investigate this
issue. We find that nonprofits that have for-profit competition do indeed reward their top
executives based on profitability, but that non-profits without for-profit competition do not.
In looking at the consequences of nonprofit compensation we find that bonuses tend to be
negatively associated with lower future donations and government grants. We believe these are
important findings both for academic researchers and nonprofit practitioners interested in
understanding and developing effective CEO pay packages in this vital, yet relatively under
studied sector of our economy. This study intends to lay the ground work for future work in this
27
area as more detailed and plentiful compensation data becomes available for US nonprofit
organizations.
28
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33
Table 1 – Variable Definitions
Dependent Variables
Log of highest paid executive bonus
Log of highest paid executive base compensation
Log of highest paid executive total compensation less
Other Compensation
bonus less salary
Bonus to Total Compensation
Bonus / Total Compensation
Economic Independent Variables
Total Assets
Log of year-end Total Assets
Net Income
Log of (Total Revenues – Total Expenses)
Program Service Expenses
Log of Program Service Expenses
Administrative Efficiency
1- (Administrative Expenses / Total Expenses)
Direct Donations
Log of Direct Donations
Government Grants
Log of Government Grants
Program Service Revenue
Log of Program Service Revenues
Log of frequency of organizations in same Metropolitan
Nonprofit competition
Statistical Area (MSA)/industry combination as sample
firm, where industry is measured using NTEE
Log of frequency of organizations in same Metropolitan
For-profit competition
Statistical Area (MSA)/industry combination as sample
firm, where industry is measured using NAICS
Monitoring Independent Variables
= 1 for organizations reporting that the board or
Board approval
compensation committee is responsible for approving
executive compensation
= 1 for organizations reporting the use of a written
Written employment contract
employment contract
= 1 for organizations reporting the use of compensation
Compensation committee
committee
= 1 for organizations reporting the use of other Form
Other 990 comparison
990s to set executive compensation
= 1 for organizations reporting the use of a compensation
Compensation consultant
consultant to set executive compensation
= 1 for organizations reporting the use of a compensation
Compensation survey
survey to set executive compensation
Log of total permanent and temporarily restricted
Restricted Donations
donations
Board Size
Log of board size
Percentage of Independent Directors
= Independent board members / Board size
Unrestricted Cash
Log of total cash less restricted donations
Bonus
Salary
34
Table 2 – Sample Selection
By Firm-year observation
Schedule J observations available for analysis from NCCS
compensation database 2008-2011
Less: Observations for which all model variables are not
available
Less: Obvious outliers (total revenue, total expenses, total
assets less than zero)
Less: Observations with Total Compensation less than
$150,000 (not required to file Schedule J)
66,201
-17,542
-836
-12,395
Firm-year observations
35,428
Firm-year observations with bonus schemea
20,358
Percentage firm-year observations with bonus schemea
57.46%
By Unique Firm
Unique nonprofit firms
Unique nonprofit firms with bonus schemea
Percentage unique nonprofit firms with bonus schemea
11,314
6,292
55.61%
Firms paying a bonus in 1 year
1,824
Firms paying a bonus in 2 years
1,348
Firms paying a bonus in 3 years
1,389
Firms paying a bonus in 4 years
1,731
We define bonus scheme as organizations that either report the use of contingent compensation or report
non-zero bonus pay to their top executive in any of the four years in our sample.
a
35
Table 3 – Sample Distribution
Panel A: Industry distribution
Industry group
Arts, culture, and humanities
Education
Universities
Hospitals
Health
Human services
Other industries (environment,
international, mutual benefit, public
& societal benefit, religion)
Total
Frequency
%
Firm-years
with bonus
1,638
4,320
3,596
9,423
5,908
6,233
5%
12%
10%
26%
17%
18%
665
1,664
1,426
7,511
3,910
3,024
% Firmyears with
bonus
41%
39%
40%
80%
66%
49%
4,310
12%
2,158
50%
35,428
100%
20,358
57%
Panel B: Year distribution
Year
Frequency
%
2008
2009
2010
2011
Total
8,081
9,314
8,796
9,237
35,428
23%
26%
25%
26%
100%
Firm-years
with bonus
4,520
5,420
5,262
5,156
20,358
% Firm-years
with bonus
56%
58%
60%
56%
57%
Mean bonus
amount
$82,313
$82,525
$97,742
$104,034
$91,863
36
Table 4 – Descriptive Statisticsb
% firmFull Sample
Bonus Sample
years
(N=35,428)
(N=20,358)
reporting
non-zero
Mean
Median
Mean
Median
values
Salary
99%
380,721
290,000
446,673
362,030
Bonus
44%
91,863
0.0
159,864
50,000
Other Compensation
36%
209,418
49,245
305,662
78,308
Bonus to Total Compensation
44%
0.07
0.00
0.13
0.10
Total Revenue
100%
125,827,509
30,110,337 175,185,440
44,429,618
Total Assets
100%
239,618,436
69,117,667 306,578,444
84,825,639
Net Income
99%
5,161,989
748,750
7,521,084
1,240,896
Program Service Expenses
99%
105,025,060
23,573,577 146,125,591
35,286,533
Administrative Efficiency
0.85
0.85
0.88
0.88
99%
Direct Donations
86%
10,494,994
1,039,496
11,055,469
744,815
Government Grants
47%
6,903,943
0.0
7,223,135
0.0
Program Service Revenue
87%
102,477,856
17,451,110 149,202,561
28,448,946
Nonprofit Competition
58%
90.1
33.0
82.7
32.0
For-Profit Competition
8%
0.1
0.0
0.0
0.0
Board Approval
79%
0.8
1.0
0.8
1.0
Written Employment Contract
34%
0.3
0.0
0.3
0.0
Compensation Committee
57%
0.6
1.0
0.6
1.0
Compensation Consultant
38%
0.4
0.0
0.5
0.0
Compensation Survey
67%
0.7
1.0
0.7
1.0
Other 990 Compensation Comparison
26%
0.3
0.0
0.3
0.0
Restricted Donations
72%
43,823,480
1,524,806
45,664,320
927,876
Board Size
100%
20.1
16.0
19.1
15.0
Percentage of Independent Directors
97%
85%
94%
82%
90%
Unrestricted Cash
59%
-26,055,383
510,726
-22,497,124
1,384,076
See Table 1 for variable definitions. bValues in the table represent raw values, i.e., unlogged and unwinsorized.
Non-Bonus Sample
(N=15,070)
Mean
291,627
0.0
79,402
0.00
59,150,086
149,162,440
1,975,097
49,502,524
0.86
9,737,851
6,472,747
39,357,649
99.9
0.1
0.8
0.4
0.5
0.2
0.6
0.3
41,336,698
21.4
90%
-30,862,221
T-test
p-value
Median
232,149
0.0
30,905
0.00
20,328,226
55,255,466
347,182
16,104,573
0.88
1,530,590
0.0
9,544,609
34.0
0.0
1.0
0.0
0.0
0.0
1.0
0.0
2,851,418
17.0
100%
5,386
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0008
0.0300
0.2633
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0472
0.3031
0.0000
0.0000
0.0391
37
Table 5 – Determinants of Nonprofit Compensation
Dependent Variable: defined in
column heading
Predicted
sign
Bonus
Salary
Other
Compensation
Bonus to Total
Compensation
Coefficient
p-value
Coefficient
p-value
Coefficient
p-value
Coefficient
p-value
3.406***
0.000
0.396***
0.000
0.000
0.987
-0.030*
0.071
0.720***
0.003
0.003
0.607
-0.006*
0.067
0.009
0.103
0.072***
0.000
0.242**
0.033
-0.771***
0.000
-0.065
0.164
0.435***
0.000
0.812***
0.000
0.510***
0.000
0.142***
0.001
-0.012***
0.006
0.305***
0.000
-0.922***
0.000
-0.015***
0.000
-0.495***
0.000
0.021***
0.000
0.002***
0.000
0.002*
0.057
-0.017
0.325
-0.001***
0.003
-0.001***
0.000
0.000
0.903
0.002**
0.014
0.022***
0.003
-0.069***
0.000
-0.005
0.230
0.030***
0.000
0.075***
0.000
0.005
0.341
-0.002
0.625
-0.001***
0.002
-0.005
0.251
-0.064***
0.000
0.001***
0.003
0.019***
0.000
YES
YES
35,428
0.3209
0.000***
-28.297***
11.214***
0.000
0.000
0.885***
0.094***
+
Total Assets
0.000
0.000
0.078***
0.000
+
Net Income
0.000
0.296
0.057
0.017***
Program Service Expenses
+
0.321
0.000
-0.636
-0.128***
+
Administrative Efficiency
0.434
0.000
-0.051***
-0.005***
˗
Direct Donations
0.009
0.000
-0.079***
0.000
Government Grants
0.000
0.784
0.014
0.002***
+
Program Service Revenues
0.503
0.001
0.110**
0.015***
?
Nonprofit Competition
0.011
0.000
1.212***
0.103***
?
For-Profit Competition
0.002
0.000
-3.607***
-0.197***
?
Board Approval
0.000
0.000
-0.261
-0.013**
?
Written Employment Contract
0.245
0.046
1.715***
0.078***
?
Compensation Committee
0.000
0.000
3.948***
0.192***
+
Compensation Consultant
0.000
0.000
0.472*
0.027***
+
Compensation Survey
0.092
0.001
-0.057
0.016**
+
Other 990 Compensation Comparison
0.820
0.024
-0.030*
-0.004***
˗
Restricted Donations
0.074
0.000
-0.075
0.088***
?
Board Size
0.701
0.000
-2.970***
-0.552***
Percentage of Independent Directors
0.000
0.000
0.056***
-0.001
+
Unrestricted Cash
0.000
0.109
1.266***
Salary
?
0.000
Industry fixed effects
YES
YES
Year fixed effects
YES
YES
N
35,428
35,428
Adjusted R2
0.0525
0.0310
Model p-value
0.000***
0.000***
*significant at 10% level, **significant at 5% level, ***significant at 1% level (two-tailed).
See Table 1 for variable definitions.
Constant
?
YES
YES
35,428
0.0337
0.000***
38
Table 6 – Impact of incentive compensation on future performance
Dependent Variable: defined in
column heading
Predicted
sign
Constant
?
Lagged Bonus/Total Compensation
?
Lagged Salary/Total Compensation
?
Lagged Total Compensation
?
Lagged Total Assets
+
Lagged Administrative Efficiency
+
Lagged Government Grants
?
Lagged Direct Donations
?
Lagged Program Service Revenues
?
Lagged Fundraising Expenses
+
Lagged Net Income
?
Nonprofit Competition
+
Net
Income
Program
Service Exps
Program
Service Revs
Administrative
Efficiency
Direct
Donations
Government
Grants
Coefficient
p-value
Coefficient
p-value
Coefficient
p-value
Coefficient
p-value
Coefficient
p-value
Coefficient
p-value
-11.697***
0.000
2.540***
0.001
-0.785
0.115
-0.114
0.496
1.076***
0.000
-0.645
0.359
0.025**
0.040
0.075***
0.000
0.113***
0.000
0.003
0.840
-3.712***
0.000
0.338***
0.002
0.325***
0.000
0.176***
0.000
0.533***
0.000
6.271***
0.000
0.034***
0.000
0.051***
0.000
0.143***
0.000
-0.006***
0.005
0.002*
0.097
0.097***
0.000
-7.934***
0.000
1.066**
0.046
0.869**
0.014
0.290**
0.029
0.761***
0.000
2.197***
0.002
0.164***
0.000
-0.091***
0.000
2.394
0.103
-1.187**
0.011
-0.194
0.489
-0.643***
0.000
0.737***
0.000
1.579***
0.003
0.096***
0.000
-28.480***
0.000
-1.308
0.275
0.976
0.206
-0.598**
0.031
-0.086
0.505
5.136***
0.000
-0.047***
0.000
0.049***
0.000
-0.273***
0.000
0.144***
0.000
0.005
0.263
0.000
0.907
-0.001
0.504
0.001**
0.012
0.822***
0.000
0.000
0.563
0.000
0.106
0.000
0.488
0.000
0.212
0.000**
0.018
0.000***
0.007
-0.050***
0.000
0.429***
0.000
0.025***
0.000
-0.055***
0.008
0.529***
0.000
0.564***
0.000
0.286***
0.000
0.044***
0.004
-0.449***
0.000
YES
YES
24,190
-0.8825
0.000***
YES
YES
24,190
0.0410
0.000***
YES
YES
24,190
0.0359
0.000***
YES
YES
24,190
0.0732
0.000***
-0.097**
0.012
Industry fixed effects
YES
YES
Year fixed effects
YES
YES
N
24,190
24,190
Adjusted R2
0.0732
0.0126
Model p-value
0.000***
0.000***
*significant at 10% level, **significant at 5% level, ***significant at 1% level (two-tailed).
See Table 1 for variable definitions.
39