Public Choice103: 163–183, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

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Agency costs in environmental not-for-profits

JULIE A. HEWITT1 & DANIEL K. BROWN 2

1Department of Agricultural Economics and Economics, Montana State University, 306Linfield Hall, Bozeman, MT 59717-2920, U.S.A.; e-mail: jhewitt@montana.edu, presentaddress: US EPA, Mail Code 2177, 401 M St., SW, Washington, DC 20460, U.S.A.; e-mail:Hewitt.Julie@epa.gov;2School of Law, University of Oregon, Eugene, OR 97403-1284,U.S.A.

Accepted 28 July 1998

Abstract. We examine the behavior of not-for-profits using utility maximization. This leadsto testable hypotheses regarding the costs of agency associated with the activities of not-for-profits. Our tests are similar to those previously employed regarding not-for-profit objectives,but our interpretation is different. Our empirical test uses data on environmental groups. Unlikeprevious studies biased toward a finding of service maximization, we account for endogenousexplanatory variables. A weak statement of our results is that managers of environmentalgroups derive positive marginal utility from expenditures for purposesother than providingservices. There may also be empirical support for a stronger conclusion.

1. Introduction

Many prominent environmental groups have been criticized recently for devi-ating from their stated purposes of providing environmentally related publicgoods. Critics claim that the actions of these environmental organizations aremotivated by the pursuit of donations, membership and power, not simply theprovision of services which markets cannot supply.1 Such accusations are inconflict with the widely-held notion that environmental groups are passionatedefenders of the public’s interest in the environment, natural resources, andwildlife.

In this paper, we investigate the objectives of not-for-profit environmentalgroups using econometric results of a testable hypothesis regarding not-for-profit behavior in general. This hypothesis extends a longstanding literatureon the goals of not-for-profits. Are they attempting to maximize the servicesthey provide donors? Is their goal based purely on self-interest, with theresult that management devotes fewer expenditures to providing services fordonors, spending for their own benefit? Or, is some combination of thesegoals their objective? This motivates the central question of this paper: ifnot-for-profit organizations forgo profit to induce donations, what then is theobjective they aim to fulfill with donations, and can we shed empirical light

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on their objective? A reason to focus on environmental groups stems fromthe fact that they have been largely overlooked in the literature on the maxim-and of not-for-profits. The only other study of environmental not-for-profitsthat we are aware of is Richer (1995) – a study that poses other questions(crowding-out) and takes a different approach to estimation.

Our study departs from the general literature on not-for-profit motivationin two primary ways. First, we think the emphasis on service maximizationversusbudget maximization is a bit of a red herring. The literature has of-ten treated these two maximands as being diametrically opposed,2 offeringneither an explanation for why one objective would be chosen over another,nor the possibility that the true objective is some amalgamation of the twoobjectives. We apply the theory of agency to the not-for-profit sector, andin doing so, arrive at a testable hypothesis of agency on the part of thenot-for-profit firm. The mathematics of our hypothesis differs little from thehypothesis of the earlier literature on serviceversusbudget maximization;our interpretation of results differs significantly. Second, focusing on the not-for-profits’ behavior, we choose to view as empirical the question of whetherfundraising expenditures are an exogenous determinant of contributions orwhether the two are simultaneously determined. We find some evidence thatthey are simultaneously determined, and modify our estimation and testingaccordingly.

The outline of the paper is as follows. In Section 2 we give background onthe rationale for not-for-profit organizations. In Section 3 we discuss the ob-jectives of not-for-profits, including agency costs. A review of the empiricalliterature follows (Section 4). Sections 5–7 describe our model, the data onenvironmental groups, and results; Section 8 concludes.

2. Background: The rationale for not-for-profits’ existence

We start our review of the literature on the rationale for not-for-profits withHansmann (1980), who recognized that what distinguishes not-for-profitsfrom for-profit firms isnot that they do not earn profits, but that they choose acorporate structure wherein they cannot distribute profits to individuals whohave some degree of control in the firm, such as members, officers, direct-ors, or trustees (see also Milgrom and Roberts, 1992: 524–526). We thinkit important to repeat Hansmann’s points (1980: 843, note 32, and p. 881)that certain economic activities have historically been conducted by not-for-profits. Indeed, these activities were conducted by not-for-profits even prior tothe legal definition of not-for-profits, prior to the granting of tax advantages tonot-for-profits, and even before the existence of the for-profit corporate form.Hansmann then focuses on the merits (over and above tax treatment) of the

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not-for-profit formversusthe for-profit form, by describing the several typesof contractual failure that prevent for-profit firms from being the supplier ofcertain services. Emphasizing one of Hansmann’s points, we ask the question:why would not-for-profits have forgone profits before being legally requiredto do so?

The answer derives from the literature on agency costs (Ross, 1973; Jensenand Meckling, 1976). We take the view that agents (not-for-profits) forgoprofits voluntarily, to serve as abonding costto assure the principals (donors)that it is the principals’ interests that are being served with the donated funds.The bonding costs mitigate the adverse selection problem that would occurif donors could not otherwise distinguish agents providing the demandedservices from those acting in self-interest (for the list of reasons given byHansmann, 1980).3

3. What do not-for-profits do with donations?

With the profit incentive apparently removed, donors are theoretically lessvulnerable to self-interested management by the organization. Still, we mayask the question, does this necessarily assure that the interests of the prin-cipals are being served? Alchian and Demsetz (1972) express this skepticalview of not-for-profits. They ask the question: what does the managementof a firm do? In team production, management’s tasks are nontrivial; theyare “metering input productivity and metering rewards,” and these activit-ies are optimally determined when ownership shares are transferable. In thenot-for-profit firm, ownership is nonexistent (effectively ruled out by thenondistribution constraint), and so any surplus generated by efficient man-agement cannot be capitalized as higher share values. Of course, a surpluscould be distributed in the form of higher salaries.4 Uncompensated directorshave little incentive to ensure that management is appropriately rewarded forefficient behavior, which in turn implies a weaker incentive for managementto act efficiently. Thus, shirking is more likely to occur than in a for-profitfirm.5

In the empirical literature on not-for-profit behavior, two hypotheses re-garding the not-for-profit’s objective function have received attention: servicemaximization and budget maximization. Service maximization embodies thetraditional view of not-for-profits as philanthropic organizations providingservices that markets cannot provide; they carry out the desires of the donors.There is some appeal to service maximization as a goal in not-for-profitgroups. Most not-for-profit managers would profess a strong belief in whattheir organization is trying to accomplish.6 On the other hand, in his muchcited 1971 book, Niskanen argued that the objective of bureaus, including

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not-for-profits, is budget maximization. He arrives at this conclusion by con-tending that items directly entering a bureaucrat’sutility function – itemssuch as “salary, perquisites of the office, public reputation, power, patronage,output of the bureau,” (Niskanen, 1971: 38) – are positively related to the sizeof the bureau’s budget.

Niskanen later (1991) amended his original theory, in part responding totwo subsequent studies of bureaucratic behavior. His amended theory em-phasizes discretionary budget maximization as the usual case and allowsservice maximization to occur as a special case when bureaucrats have de-cided preference for the output – the “zealots” (Niskanen, 1991: 22). Hecites Migué and Bélanger (1974), as the theoretical development behind theamended theory.7

Migué and Bélanger extended the structure of Niskanen (1971) to a theoryof managerial discretion, wherein bureaus maximize theirutility where utilityis defined over output anddiscretionarybudget. Suppose that the marginalcosts and marginal value of the output of the not-for-profit are linear inservice output. The profit function is then quadratic and concave in output.As the firm cannot distribute profits, the residual must be spent internally.At any level of output with positive profit, the options are: a) to spend theresidual on the provision of a greater level of services thus ensuring greatertotal contributions but incurring greater total costs to produce, b) to providethe original level of services at greater than minimum cost, paying more forinputs than their value marginal product, including possibly, hiring inputswith zero marginal productivity, or c) some combination of the two. Miguéand Bélanger refer to the second type of expenditure as discretionary spend-ing. Which of these options a not-for-profit chooses depends upon the utilityfunction of management.

Using the utility maximization framework of Migué and Bélanger (1974),we may reframe the question of the objective of not-for-profits. Although,the empirical literature on not-for-profits has focused on dueling objectivefunctions (service and budget maximization), each is really anoutcomeofthe utility maximization objective, and not a separate behavioralhypothesis.Observable behavior can be used in an empirical analysis to shed some lighton the otherwise fundamentally unknowable tastes and preferences. Further-more, there are not two, but three, interesting behavioral outcomes of theMigué and Bélanger model. Figure 1 shows profits as a function of outputand the preferences leading to these three outcomes.

First note that the vertical axis measures both profit and discretionaryspending, seemingly implying that the entire surplus is devoted to discretion-ary spending. To the contrary: the firm may initially choose output at S1 tomaximize profits (point 1) but they cannot distribute the profits, D1, as profits,

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Figure 1.

but must allocate the residual to other expenditure categories. If a portionof the profits are used to provide a higher level of services (S2), marginalcontributions fall while marginal costs rise, resulting in a lower, but still pos-itive, level of profits (D2) that cannot be distributed as profits (point 2). Thefirm could subsequently pick a still larger level of output (S3) which reducesprofits to zero. All three points can be optimal, depending upon the shape ofthe indifference curves. Point 1 is the optimal allocation if indifference curvesare horizontal, which occurs if the not-for-profit’s marginal utility of servicesis zero. This point corresponds to the budget maximization hypothesis of theliterature.8 Point 3 is the optimal point if indifference curves are vertical,implying a zero marginal utility of discretionary spending. This point corres-ponds to the service maximization hypothesis of the literature. Perhaps moreprobable than these two extremes are tastes supporting the behavior at point 2.This occurs if the not-for-profit’s MRS between services and discretionaryspending is strictly between zero and infinity; these managers will chooseintermediate levels of both discretionary spending and output.9

Determining which of three behavioral outcomes applies to the partic-ular not-for-profit is straightforward if the budget can be decomposed intominimum-cost expenditures in each category and discretionary spending.This task is made difficult by the fact that not-for-profit accounting proced-ures do not reveal this breakdown. Instead, not-for-profit accounting is gearedtoward IRS reporting regulations; the IRS requires not-for-profits to disclose

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their total budget, sources of the funds (including contributions), and uses offunds (including expenses for fundraising, management and service provi-sion). If we think of donations as an intermediate output of the not-for-profit(services would be the final output), and spending on fundraising activities asa primary input, then C= C(F) is a production function relating fundraisingexpenditures (F) to contributions (C).10

We might expect the not-for-profit firm to operate at a point on the result-ing production function where value marginal product of fundraising equalsthe marginal cost of fundraising. If provision of services were the only ar-gument contributing positively to the managers’ utility function, this wouldsurely be so. If, on the other hand, only discretionary spending mattered, thenwe might expect to see the not-for-profit operating well beyond the pointwhere the value marginal product of fundraising equals its marginal cost,especially if managers gain utility from devoting discretionary resources tofundraising. Indeed in this case, fundraising would occur to the point wherethe marginal dollar devoted to fundraising brings in zero contributions (aseemingly inefficient outcome). Finally, if both services and discretionaryspending contribute to utility, then fundraising would occur to the point wherethe last dollar spent on fundraising brings in a positive amount less thanone dollar. We turn next to a discussion of the empirical literature on thecontributions function.

4. Empirical evidence of the effect of fundraising on contributions

Boyle and Jacobs (1978) were probably the first to estimate a not-for-profitcontributions function in order to test the fundraising efficiency hypothesis.They define efficiency in fundraising in marginal terms as spending $1 onfundraising to raise $1 of contributions. In their study, Boyle and Jacobsimplicitly assume that not-for-profit organizations try to maximize servicesprovided, or equivalently, net contributions. They concluded that not-for-profits were inefficient, in that there was substantial room toincrease netcontributions through increased fundraising efforts.

Weisbrod and Dominguez (1986) empirically estimated the fundraisingproductivity of some 300,000 not-for-profit firms using OLS on a panel dataset from IRS form 990 tax returns covering the years 1973 to 1976. They wereinterested in an empirical sense of whether donations to not-for-profits aresensitive to economic variables. They modeled current donations as a functionof current price and lagged fundraising expenditures.11 Though Weisbrod andDominguez did not maintain service maximization to be the goal of the not-for-profit, they also did not find it: rather, they find their results to be notinconsistent with budget maximization.

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Although Weisbrod and Dominguez acknowledged the possibility thatnot-for-profit organizations might be budget maximizers, Steinberg (1986a)was the first person to specifically test for such a possibility. Steinberg embedsboth objectives in a composite objective function by including a parameter,k, such that 0≤ k ≤ 1; when k= 0, budget maximization is the objective,when k = 1, service maximization is the objective, and when k is in theinterior of this range, the firm has mixed motives.12 Steinberg used randomcoefficients estimation on a panel data set covering 2202 not-for-profit firmsover the period 1974 to 1976, and found that firms in some not-for-profitsectors are service maximizers (welfare, education and arts), some are budgetmaximizers (health) and some have mixed motives (research).

In contrast to Rose-Ackerman (1982) and Weisbrod and Dominguez(1986), Steinberg (1986b) suggested that the price of $1 of service provisionwas exactly $1, not $1 divided by the share of the total budget devoted toservice provision. Steinberg argued that the other authors’ price formulationis based on a notion of average price rather than marginal price. Furthermore,although the cost to a donor of $1 of services may be more than $1 (as perthe other authors), the tradeoff the not-for-profit makes – once the donationis received – is whether to devote $1 to services or to discretionary spending;the opportunity cost to the not-for-profit of devoting that $1 to services is itsvalue as discretionary spending. To bring in a donation, the cost to the not-for-profit is the marginal fundraising cost, and will be a parameter we areinterested in estimating, not an explanatory variable.13

Posnett and Sandler (1989) study the 300 largest charities in the U.K. for1985/86. They find support for the hypothesis that charities are net revenue(service) maximizers. They use a model similar to Weisbrod and Dominguez(1986), but also include sources of income for charities other than contri-butions, such as government grants, to test the crowding-out hypothesis.Khanna, Posnett, and Sandler (1995) estimate a model similar to that of Pos-nett and Sandler (1989), expanding from a cross-sectional data set to a panelof the same charities over the period 1983 to 1990. They find results similarto Steinberg (1986a), wherein not-for-profits in certain sectors are revenuemaximizers, and in other sectors are net revenue maximizers; they also findevidence that there are certain sectors in which firms do not even maxim-ize net revenues.14 Clearly, the empirical evidence regarding not-for-profitmotivation is mixed.

The mixed evidence in this literature suggests an unsolved puzzle: the dataused in the various studies cover a broad range of not-for-profits and the mod-els are not so functionally dissimilar as to suggest such dramatically differentresults regarding something so fundamental as a firm’s objective function.The puzzle may well be explained as misplaced emphasis on service and

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budget maximization as opposite objectives, rather than as a tradeoff madein an optimal fashion. The puzzle reduces to a question of managers’ tastes(which in turn reduces to a question of who becomes a manager), as wellas donors’ information and willingness to pay.15 As a guide to the empir-ical analysis to follow, we next describe the not-for-profit’s decision problemformally.

5. A model of not-for-profit behavior

Not-for-profit firms can obtain resources in several ways: membership dues,contributions, other in-kind donations, selling non-public goods and servicesfor a fee, earning interest on capital investment, and making use of volunteerlabor. Not-for-profits can make use of or expend these collected resourcesin several ways: charitable services, fundraising efforts, and administrationor overhead. The nondistribution constraint implies that the sum of sourcesof resources must equal the sum of expenditures of resources, regardlessof the not-for-profit’s objective function. Given these categories of incomeand expenditure, we write the not-for-profit’s nondistribution constraint asfollows:

C+ Z ≡ S+ F+M (1)

where C denotes contributions and dues, Z is all other revenue sources (saleof goods and services, volunteer labor and interest income), S is service ex-penditures, F is fundraising costs, and M is management and overhead costs.Equation (1) must hold as an identity for each not-for-profit.

Note that the budget constraint contains an obvious functional depend-ence. Resources allocated to fundraising will affect the level of contributionsthrough C(F), while contributions in turn influence the expenditures on theright hand side. We will explore this point further in the empirical portion ofthe paper.

As alluded to above, not-for-profits have been hypothesized to have one oftwo goals or objective functions: service or budget maximization. Althoughwe cannot observe the utility function of the agents that would support aninterior solution, we might nonetheless be able to detect the tradeoff agentsmake. If there were no agents (clearly counterfactual), we might imagine thatdonors’ interests alone are served. Making use of the nondistribution con-straint, Equation (1), donors’ interests are best served when the not-for-profitmaximizes services, or:

maxF,M,S

C(F,M,Spred)+ Z− F−M (2)

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where Spred denotes donors’ex anteprediction of service provision. An or-ganization serving donors would solicit contributions to the point where onedollar devoted to fundraising brings in one dollar of contributions. The sameis true for management costs. Furthermore, this firm would spend moneyon additional service provision up to the point where donors are no longerwilling to donate additionally to the organization.16

If the size of the budget, or C(F,M,Spred) + Z, were the maximand, thefirst-order conditions imply that all three partial derivatives of the contribu-tions function are zero. The distinction between the first-order conditions forthese two maximands is the basis for the hypothesis tests of motivation thathave appeared in the literature. However, agents surely possess some tastefor both services and nonpecuniary benefits, and neither model admits thispossibility.

Suppose instead that: a) agents have a utility function that takes as its ar-gument fundraising, management, and services, b) that contributions are alsoa function of these three variables, and c) that the nondistribution constraintis binding. The agents’ decision problem is then:

maxF,M,S

U[F,M,C(F,M,Spred)+ Z− F−M] (3)

In contrast to the first-order conditions of the two maximization problemsabove, we now cannot say precisely what the derivatives,∂C/∂F and∂C/∂M,equal.17 As the MRS of services for fundraising (management) approacheszero, the firm operates such that∂C/∂F (∂C/∂M) approaches one; as theMRS approaches infinity,∂C/∂F (∂C/∂M) approaches zero. However, ifpreferences are such that the MRS is strictly between zero and infinity, thenthe value of the partial derivatives with respect to F and M will also be strictlybetween zero and one. Of course, because we cannot directly observe theMRS, we cannot draw very specific conclusions from estimating partial de-rivatives of the contributions function, unless the partial derivatives turn outto be insignificantly different from zero or from one. If the partial derivativesare “significantly” between zero and one, the most we can conclude is thatmanagement has positive marginal utility for both discretionary spending andservices. Thus we can maintain as null hypotheses, zero marginal utility offundraising and management, or a marginal rate of substitution of servicesfor fundraising or management of infinity. These two hypotheses have pre-viously been referred to as service maximization and budget maximization,respectively.

The analysis thus far has abstracted from temporal considerations. Theliterature on not-for-profits has reached no consensus on temporal effects.Weisbrod and Dominguez (1986) assumed that contributions are affected bylagged fundraising expenditures, having anticipated a delay between the time

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when fundraising costs are incurred and the time when contributions result.Steinberg (1986b) hypothesized that fundraising and management expendit-ures would have both contemporaneous and one-period lagged effects, whileservice expenditures would have only a lagged effect, being the end result of afundraising campaign. Steinberg (1986a) hypothesized contemporaneous andlagged effects of each category of expenditure on contributions.18 We takethe viewpoint that when annual data are used, the time period is sufficientlylong to treat as contemporaneous what these authors treated as lagged andcontemporaneous effects.19 Thus we maintain that an adequate, if not accur-ate, description of the world is that fundraising and management expenditurescontemporaneously affect contributions, whilepredictedservice expenditureshave a contemporaneous effect.20

Of course, the firms in our dataset will likely have very different contribu-tions functions, and the approach we take to admitting this firm heterogeneityis to estimate a first-order Taylor approximation to the contributions func-tion, rather than the contributions function directly. Our assumption is thatregardless of differences in their contributions functions, all firms operateat points that correspond to a common value for the partial derivatives. Thisassumption is plausible if the market for contributions is characterized by freeentry and exit (Rose-Ackerman, 1996), and donors are sufficiently informedas to send donations to various not-for-profits to maximize the value of thosedonations. Next, we describe our data before turning to the econometricmodel.

6. The dataset

Contributions, fundraising, management, and service expenditures are thevariables of interest in the model outlined in the previous section. These val-ues can be obtained from the IRS tax form 990, a tax return that must be filedby tax exempt organizations. These tax returns are by law public information.To distinguish cross-sectional and time series differences in contributionsfunctions, we collected a panel data set by soliciting the 990 forms from 30groups for the period 1986 to 1992. A third of the groups were unwillingto send us their tax records, or could only supply discontinuous time series.Using the most complete set of returns (uninterrupted time series per firm)resulted in 94 observations from the environmental organizations, as shownin Table 1. The data set was not symmetric in observations per group or in thespecific years reported by each group.

A complication in the data is that the not-for-profits in Table 1 havedifferent fiscal years. To ameliorate this problem, the quarter in which anorganization’s fiscal year began was noted (although fiscal years did not al-

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Table 1. Data set

Organization Average total

Largest to smallest Observations revenue Years

(average total revenue) ($ 000)

Nature Conservancy 5 238,780 1988–1992

Ducks Unlimited Inc. 3 59,386 1990–1992

Sierra Club 4 34,389 1987–1990

The Humane Society of the U.S. 6 17,472 1987–1992

The Cousteau Society 5 15,174 1988–1992

Natural Resources Defense Council 5 15,125 1987–1991

Clean Water Action 5 10,295 1988–1992

National Parks & Conservation Assn. 3 8,535 1989–1991

People for the Ethical Treatment of

Animals 5 8,024 1987–1991

Sierra Club Legal Defense Fund 5 8,020 1987–1991

Green Peace International 6 7,336 1987–1992

Center for Marine Conservation 5 6,592 1988–1992

Defenders of Wildlife 6 4,854 1987–1992

Environmental Law Institute 3 3,615 1990–1992

Union of Concerned Scientists 5 3,512 1987–1991

American Rivers 6 1,477 1987–1992

Clean Water Fund 4 1,393 1989–1992

Rainforest Action Network 4 1,279 1989–1992

Rainforest Alliance 4 483 1988–1991

Environmental Action 5 431 1987–1991

ways begin with the beginning of a quarter for macroeconomic statistics).Variables for which quarterly measurements were available were then adjus-ted to coincide with the organization’s accounting period. All dollar valuesin the data set were deflated by the Consumer Price Index (CPI). The errorin approximation is in the stochastic term. Age of a firm is included becausewe presume that,ceteris paribus, the older an organization is the higher thelevel of contributions it will obtain, due to learning and reputation effects. Thesquare of age was also included to allow for diminishing returns to age.21

The unemployment rate was expected to have a negative impact oncontributions. As many donors to environmental groups make relativelysmall donations, the ability to contribute is probably better measured byemployment or unemployment than income. Conversely however, a high un-

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employment rate may proxy a phenomenon whereby recent college graduateshave fewer labor market employment opportunities, and are willing to volun-teer for or take temporary, lower paying jobs as fundraisers for environmentalgroups. The year was included to account for any exogenous trends not other-wise explicitly included in the model that affect all organizations across time,such as changes in not-for-profit laws or accounting practices. The year wasnot expected to have a big impact given the short time series, and its signcannot be predicted.

7. The econometric model of contributions

The econometric model for the first-order Taylor Series approximation to thecontributions function is as follows:

Cit = β0+ β1Fit + β2Mit + β3Spredit + β4Ageit

+β5Age2it + β6 Unemploymentt + β7Yeart + εit (4)

where i indexes individual organizations, t indexes year, Cit is contributions,Fit and Mit are fundraising and management expenditures, Spred

it is donors’estimate of services (all in dollars), Age is the age of an organization, Age2

is the age of an organization squared, Unemployment is the unemploymentrate, Year is a time trend, andεit is the error term. The linear functional formin Equation (4) implies straightforward calculation of the comparative staticderivatives of interest:∂C/∂F= β1, ∂C/∂M = β2, and∂C/∂Spred= β3.

The appropriate technique for estimation of Equation (4) turns on thedistribution of εit . That our data set is a panel implies the possibility ofcross-sectional heteroskedasticity and serial correlation. To address thesepossibilities for the random error structure, we employed the pooled dataprocedure of Kmenta (1986: 618–622). Kmenta’s procedure makes use of theconsistency of OLS in the presence of heteroskedasticity and serial correla-tion to obtain consistent estimates of the correlation and variance parameters.These parameters are used to transform the model – and data – into a modelwith i.i.d. error terms, so that application of OLS results in estimates that areunbiased as well.

To maximize the usable observations from the data we collected, wemanually programmed the steps outlined in Kmenta (1986), expressly al-lowing unequal numbers of observations per group.22 We chose to estimatethe autocorrelation coefficient as the sample correlation between one period’sresidual and the next period’s, to ensure that the coefficient would not exceedone in absolute value, given the (varied but still) few time series observa-tions per cross-sectional unit. Finally, in the cross-sectional heteroskedasticity

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stage of estimation, we adjusted our estimates of each cross-sectional unit’svariance by the number of observations, T, rather than the number ofobservations adjusted for degrees of freedom T− K.23

7.1. Endogeneity of fundraising and management

Although the contributions function has as arguments the expenditures onfundraising, management and (predicted level of) services, suggesting caus-ality in one direction, the not-for-profit firms also face the nondistributionconstraint in Equation (1). In the annual data reported by not-for-profits tothe IRS, revenues (including contributions) must be allocated to expendituresand some portion would be spent on fundraising and management activities.24

Endogeneity has been raised as a possibility before (Steinberg, 1986a), butnever statistically tested. We performed a Hausman (1978) specification testto determine whether this hypothesized joint dependency is indeed present.The Hausman test statistic’s value is 5.46, which rejects the null hypothesisthat fundraising and management are exogenous.25 A simultaneous equationsapproach appears to be warranted.26 Thus we have two econometric issuesto deal with: the panel nature of the dataset (and the implications for errorstructure), and the endogeneity of the explanatory variables of interest (andthe implications for estimation technique).

Dealing with the simultaneity and the panel nature of the data simultan-eously will require instruments for the endogenous variables, fundraising andmanagement. As is frequently the case, our instruments are not ideal, due todata limitations. Thus, it is appropriate to ask what is the direction of the biasin the OLS coefficients. The bias in the estimated coefficient on fundraisingis positive if the true parameter,β1, is less than or equal to one; a similarconclusion applies toβ2. In our discussion of what not-for-profits do withdonations, we noted three probable conclusions regardingβ1: that it equalsone, equals zero, or equals some value between zero and one. No hypothesisregarding not-for-profit behavior supports a value ofβ1 greater than one. Ifwe use OLS to estimate Equation (4), the coefficients obtained forβ1 andβ2

are biased upwards. Such estimation may, however, be useful in corroboratingour hypothesis of not-for-profit behavior.

To deal with both the simultaneity and the panel nature of the data, wedeveloped the following estimation technique. As always, the objective infinding instruments for F and M is to seek exogenous variables which areuncorrelated with the error terms in the equation of interest and are correlatedwith the endogenous explanatory variables. Estimating instruments for F andM would likely benefit as much from not assuming that errors are i.i.d. aswould estimation of C. Thus, we estimate the instruments, Finst and Minst,using Kmenta’s pooled data procedure.27 The explanatory variables which

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are included in the instrument estimation are Age, Age2, Unemployment, andYear.28

The predicted service variable (Spred) was similarly created, though fordifferent reasons. As explained above, we assume that donors do not knowex antethe value of this year’s service provision.29 Therefore, they can onlymake an educated guess by using the information they have about an organ-ization to predict the value of the services that the organization will provide.The managers of an organization would also want to predict as closely as pos-sible donors’ expectations, Spred, when making the actual service expendituredecision, S. The value for Spred was derived by using a pooled procedure asfor F and M but with S (current year’s services) as the dependent variable.

We present the results of estimation of Equation (4) in Table 2, using boththe pooled procedure (ignoring simultaneity) and the pooled procedure withinstruments for the endogenous right hand side variables. The R2 values forthe instrument equations are presented at the bottom of Table 2. Both mod-els explain most of the variation in the dependent variable, and also have anumber of significant coefficients. We now turn to the interpretation of ourresults.

The coefficient on fundraising can be interpreted as the impact on contri-butions of the last dollar an organization spends on fundraising. Thus a dollarspent on fundraising brings in $1.40 in contributions in the pooled model, andbrings in $0.03 in the instrumented model. According to the pooled model,not-for-profits could increase contributions by spending more on fundraising;in the instrumented model, not-for-profits appear to have nearly exhaustedtheir ability to bring in any additional contributions by increasing fundraisingexpenditures. The coefficient for the pooled model is significantly differentfrom zero, but not significantly different from one (at the 95% level), whilethe coefficient in the instrumented model is insignificantly distinguished fromzero. Thus, in the parlance of the previous literature on not-for-profit be-havior, we would conclude that not-for-profits are underachieving servicemaximizers according to the pooled model results, while they are budgetmaximizers according to the instrumented results. Our interpretation of theseresults is that the tastes of managers of not-for-profits can be said to have zeromarginal utility of fundraising in the pooled model, and zero marginal utilityof services in the instrumented model.

Although these results appear contradictory (at opposite ends of the be-havioral spectrum), we have yet to exhaust the interpretation of them. TheHausman specification test leaves us more than agnostic regarding the modelin which to place more confidence. Given that fundraising is endogenous andthe trueβ1 is less than one (supported by hypothesis tests of both models),then the estimatedβ1 of the pooled model is biased upwards, casting signific-

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Table 2. Results

Parameters estimated Pooled procedure Pooled with instr.

Dependent var. = Contributions, Cit (t-statistic) (t-statistic)

β1: Fundraising or its instrument 1.399 0.0273

(5.767) (0.124)

β2: Management or its instrument 2.500 –0.384

(7.079) (–3.331)

β3: Predicted services (Spred) 0.572 –0.311

(10.40) (–1.034)

β4: Age 0.0680 0.555

(3.548) (8.032)

β5: Age2 –0.00106 –0.00338

(–2.894) (–5.440)

β6: Unemployment –0.145 –0.751

(–2.788) (–4.204)

β7: Year 0.0843 0.573

(1.718) (3.558)

β0: Constant 0.0582 –0.0707

(0.342) (–0.647)

Adjusted R2 0.9504 0.9518

Instrument estimation R2

Fundraising 0.618

Management 0.714

Predicted services 0.490

ant doubt on the service maximizing and zero-marginal-utility-of-fundraisingconclusions. On the basis of the instrumented results, we may conclude thatenvironmental groups do derive utility from discretionary spending, contraryto the widespread view that these groups “wear white hats.” In this respect,environmental groups are no less self-centered than health, research, socialwelfare and religious charities have been demonstrated to be (Steinberg,1986a; Khanna, Posnett, and Sandler, 1995).30 There is statistical supportfor a stronger conclusion that environmental groups are operating at a pointwhere their marginal utility of services is zero. This conclusion depends onthe quality of our instruments, which we freely admit is the weakest part ofthis analysis.

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Our hypotheses regarding the utility function of not-for-profit manage-ment are similar regarding interpretation of the coefficient on management.In the pooled model, the additional dollar of management expenditures bringsin $2.50 of contributions, while in the instrumented model contributions fallby $0.38 when an additional dollar is devoted to management. The pooledestimate ofβ2 is significantly different from both zero and one, as is theinstrumented estimate ofβ2. Again, although the pooled estimate ofβ2 lendsmore support to the service maximization (or zero marginal utility of manage-ment expenditures) hypothesis, it is biased upwards, while the instrumentedestimate is (in principle) unbiased and suggestive of the budget maximization(or zero marginal utility of services expenditures relative to management).

Regarding services, our null hypothesis in all cases is that the coefficienton services is zero. This hypothesis is rejected in the pooled model, althoughwe fail to reject it in the instrumented model. Again, the endogeneity of vari-ables on the right-hand side of the pooled model leads to biased coefficients,and so we find the failure to reject the null in the instrumented model com-forting, although it sheds no light on the shape of not-for-profit managements’utility functions. Other explanatory variables in Table 2 have coefficients con-sistent with our expectations. Age is positive as expected and age squared isnegative indicating diminishing returns to age of an organization. If the un-employment rate rises by one percentage point, it costs environmental groupsin terms of contributions. Also, the time trend (not associated with the firms’age) is positive. This trend is picking up any number of effects which are notexplicitly specified in the model, such as changes in public attitudes towardenvironmental causes or changes in not-for-profit accounting practices. Theconstant term is negative but insignificant.

8. Conclusion

Our intent in this paper has been to analyze the behavior of many of theprominent environmental groups in the U.S. We do so by first examiningthe behavior of not-for-profits in general, and then applying our model todata on environmental groups. Not-for-profits are agents who forgo profitsas a bonding cost to lend assurance to principals (donors) that the agentsare acting on the principals’ behalf. This bonding cost is only one form ofagency cost identified by Jensen and Meckling (1976). Two other forms ofagency cost exist: costs of monitoring activities undertaken by principals,and residual losses which occur even when optimal levels of bonding andmonitoring are undertaken. The residual losses occur because agents derivepositive utility from nonpecuniary benefits or discretionary spending overwhich the agents have control. The particular form of residual loss demon-

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strated in this paper arises from agents’ taste for discretionary spending onfundraising and management. Jensen and Meckling’s context is the for-profitfirm, where transferability of shares acts as a check on agent behavior thatis too self-interested, and so we should not be surprised when agency costsoccur in not-for-profits which lack ownership in a strict sense. The not-for-profit context was first addressed in the work of Migué and Bélanger (1974),in their model of bureaucratic objectives.

Three hypotheses regarding the tastes and preferences of not-for-profitmanagement result: that managers have zero marginal utility of services, zeromarginal utility of discretionary spending, or a marginal rate of substitutionbetween services and discretionary spending that is strictly between zero andinfinity. The first two hypotheses lead to behavior referred to in the literatureheretofore as budget maximization and service maximization, respectively.As in earlier literature on the subject, an empirical investigation of not-for-profit behavior with respect to contributions and expenditures sheds light onthe preferences of the firms’ managers. However, rather than pitting two du-elling objective functions against one another, our hypothesis tests are part ofa unified framework of not-for-profit behavior.

Another contribution to the literature on not-for-profit behavior that we of-fer here is an argument for and strong statistical evidence that commonly usedexplanatory variables in estimating a contributions function are endogenousand correlated with the error terms. The endogeneity of fundraising andmanagement is due to the not-for-profit organizational form, specifically thenondistribution constraint that implies that all contributions must be allocatedto some expenditure category, if not to savings. Endogeneity leads to biasedcoefficient estimates. We demonstrate the coefficient bias to be positive, im-plying that previous studies are prejudiced toward finding that not-for-profitsare service maximizers with zero marginal utility of discretionary spending.

Our empirical application is based upon data on environmental not-for-profits. We chose to focus on environmental groups because they have beenlargely ignored by the not-for-profit literature. Thus, we loosely ask the ques-tion, “are environmental not-for-profits different than other not-for-profits?”The results of our instrumental variable model are not strictly comparable toother studies which do not account for endogeneity. However, we concludethat environmental groups are not unlike not-for-profits in the health, reli-gious, social welfare and research sectors in deriving positive marginal utilityfrom discretionary spending.

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Notes

1. See, for example, Gifford (1990). Among the first to raise doubts about the honor ofenvironmental organizations is Chase (1986).

2. The exceptions in the literature are Migué and Bélanger (1974) and Niskanen (1991).3. The distinction between the literatures on principal–agent relationships and agency costs,

the problems of asymmetric information, and the forms and limits of organization is, atbest, an artificial one (Jensen and Meckling, 1976: 309). One could view the not-for-profitstructure as reducing donors’ monitoring costs, and hence the likelihood of moral hazardon the part of the not-for-profit firm.

4. Reasonable compensation of management is permitted by law, but prohibitions againstexcessive compensation are either vague or not well enforced (Hansmann, 1980: 873).

5. This shirking is presumably on the part of both the hired inputs and management. Manage-ment shirking would include shirking in the metering tasks (and preventing the shirkingof the hired inputs) and/or shirking in ensuring that donors’ preferences and interests arebeing met.

6. Professing a strong belief would occur because those who manage not-for-profit self-select such employmentbecausethey possess such tastes, act as if they have such tastes,or both. In addition, competition between various not-for-profits for donations, as wellas relatively free entry into the environmental not-for-profit market, may limit how far agroup can stray from its stated purpose.

7. For empirical evidence casting doubt on his earlier theory, Niskanen (1991) cites Johnsonand Libecap (1989). They found that agency growth has little impact on bureaucrats’salaries, casting significant empirical doubt on one of the critical links between utilitymaximization and the budget maximization objective necessary for Niskanen’s (1971)theory to have explanatory value.

8. Technically speaking, the budget maximizing point is at a higher level of services if budgetsize is determined by total revenue rather than total revenue net of minimum costs ofservice provision.

9. This type of behavior has also been posited in the for-profit sector, in an important paperby Jensen and Meckling (1976).

10. Below, we will allow management and services to also enter the contribution function.11. Current price is the price of a dollar of service provision in terms of households’ after-tax

income, divided by the percent of the average contribution devoted to service provision. Ina theoretical paper, Rose-Ackerman (1982) also advocates including such a price variablethat adjusts for contributions devoted to service expenditures, on the presumption thatservices enter donors’ utility.

12. Steinberg’s formulation is not necessarily inconsistent with our utility maximizationmodel, though his objective function would amount to a linear (constant MRS) utilityfunction.

13. Were we modeling donor behavior, the donor’s marginal price would surely depend ontheir marginal income tax rate and income tax deduction status. See, e.g., Kingma andMcClelland (1995).

14. Richer (1995) is an empirical paper in the same vein as Weisbrod and Dominguez (1986),Posnett and Sandler (1989) and Khanna, Posnett, and Sandler (1995). Richer focuseson environmental organizations, as we do, and finds that environmental groups do notdevote enough of their budgets to fundraising, and are neither maximizing net revenuenor revenue. He also is concerned with testing the crowding-out hypothesis, whereas weare not. Our papers differ significantly in the econometric approach, as well.

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15. If special skills are required to be a manager of a not-for-profit in, say, the arts sector,if those skills are not easily acquired in a market, and if those who have such skills andare managers also have a strong taste for the output of their not-for-profit, then we wouldexpect not-for-profits in the arts to appear to be service maximizers. If the skills requiredof managers in health-related not-for-profits are straightforwardly acquired in a market,individuals without an extraordinary preference for providing health services might beexpected to acquire such skills and enter the field to capture the rents of discretionaryspending, causing health not-for-profits to appear to be more selfishly motivated. Thisstory is a more compelling explanation of results such as Steinberg’s (1986a) than anexplanation that arts and health not-for-profits are simply inherently different.

16. Unless∂Spred/∂S is exactly zero, this third condition implies that∂C/∂Spred must bezero. The value of∂Spred/∂S would be approximately one if donors have rational expect-ations, and thus over time, accurately predict actual services expenditures. However, suchan assumption is not necessary. We maintain simply that∂Spred/∂S is nonzero.

17. The partial derivative,∂C/∂S, however, will still equal zero, so long as the marginal utilityof services is positive – a likely happenstance.

18. Steinberg (1986a) also includes estimates of alternative models that treat contributionsand service expenditures as endogenous, and contributions and fundraising expendituresas endogenous. He argues that the single equation model is most appropriate, although nostatistical evidence is provided.

19. Alternately, we could view the contemporaneous nature as being a result of the likeli-hood that people either respond to fundraising literature when it is received or they donot donate. Furthermore, although fundraising literature may raise awareness that leadsto later donations, we suggest it is still likely that fundraising spurs contemporaneousdonation as well. This would possibly overstate the importance of contemporaneous ex-penditures, but this would be a more serious problem if we were to use disaggregate donordata, rather than donor data aggregated by firm.

20. Donors would likely use more information than is contained in last year’s service ex-penditures when anticipating this year’s services. Therefore, it is inappropriate to simplyuse previous services to anticipate donors’ expectations concerning current services.

21. Age was obtained from either Arnold and Gottlieb (1993) or Gifford (1990).22. Relying on a preprogrammed estimation package (such as the POOL command in

SHAZAM; see White, 1993), generally requires an equal number of time series obser-vations per cross-sectional unit. Had we not programmed the estimation, our dataset of 94observations would have been much smaller (68 observations) and required the arbitrarychoice of preserving a high number of time periods or of groups.

23. This was done for the rather practical consideration that K equals 8 and T ranges from3 to 6. Adjusting by T yields the maximum likelihood estimate rather than the unbiasedestimate.

24. Service costs are probably also jointly dependent with contributions. However, since it ispredicted services (Spred), not services, that enter (3) and as this variable was calculatedusing exogenous information, the problem of joint dependency between S and C has beencorrected by using Spred.

25. We also constructed two separate tests of the endogeneity of fundraising alone and ofmanagement alone. These tests were consistent with the joint test above.

26. The only other simultaneous equations approach in the empirical branch of this literatureis Steinberg (1986a). However, despite differences between them, he dismisses the 3SLSresult in favor of the OLS results on the basis of simplicity, and not on the basis ofspecification test results.

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27. Of course, a one- or two-way, fixed effect or random effects model would perhaps beappropriate in estimating the cross-sectional and/or time series variations. While we maynot have identified the best panel data approach, we would argue that our approach issuperior mainly because it deals with the apparent endogenous explanatory variable issueas well as the pooled nature of the data.

28. We would have preferred to have better instruments such as the number of donors, indic-ators of the size distribution of donations and number of full-time equivalent employees.Acquiring these data would have been much more costly than acquiring the data usedhere. Note the instrument equation R2 values in Table 2; clearly there is some informationcontent in the instruments.

29. Indeed, they may never completely know what services an environmental group has per-formed (due to the problems of asymmetric information), or the value of those services(being nonmarket goods).

30. Environmental groups might compare with many other types of not-for-profits, had thesestudies considered endogeneity.

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