managers of nonprofit organizations are rewarded for performance

NONPROFIT MANAGEMENT & LEADERSHIP, vol. 16, no. 1, Fall 2005 © Wiley Periodicals, Inc. 19

Managers of NonprofitOrganizations Are Rewarded

for PerformanceThomas Carroll, Patricia Hughes,

William LuksetichWe consider the effect of performance on the compensation ofnonprofit executives. Performance is measured as the ratioof revenue from a particular activity (such as fundraising orprogram services) to the expenditures associated with thoseservices, exclusive of managerial compensation. This is con-sistent with previous works, which use measures of size,spending, or budget percentages as measures of performance.We also consider whether the compensation received bythe executives enhances their performance. The empiricalresults support the hypothesis that compensation and perfor-mance are simultaneously determined.

IF YOU WANT TO understand what an economic agent does, firstfind out how he or she is paid. This principle extends back toAdam Smith’s Wealth of Nations and is the major underpinning

of investigations of compensation in labor economics and industrialorganization.

Literature concerned with the determinants of executive com-pensation in the nonprofit sector pales in comparison to the quan-tity and depth of the theoretical and empirical research in thefor-profit sector of the economy. This is partly due to the dearth ofdata on executive compensation in this sector and partly due to theabsence of incentive contracts for managers of nonprofits. Mostresearch on nonprofit executive compensation focuses on the differ-ence between compensation in the for-profit and nonprofit sectors.Compensation in the nonprofit sector is presumed to be less than inthe for-profit sector for numerous reasons: agency problems, man-agerial sorting, shirking, and because clients and donors care more

Note: This article was revised from a paper presented at the 2002 ARNOVA con-ference, Montreal, Canada, November 2002, and the 2003 WSSA Conference,Las Vegas, Nevada, April 2003. We thank Mark Partridge, Patrick Rooney, andthe participants in University of Nevada, Las Vegas Economics Department’sfaculty workshop for valuable suggestions.

20 CA R R O L L, HU G H E S, LU K S E T I C H

about executive compensation in the nonprofit sector, while in thefor-profit sector stockholders care mostly about profits. Researchon the differences in compensation between the two sectors confirmsthe expectation that executives in the for-profit sector receive greatercompensation.

Nonprofits have eschewed profit-sharing contracts because ofpast Internal Revenue Service (IRS) rulings threatening their non-profit status. Steinberg (1990) has noted that although nonprofits(501(c)3’s) cannot distribute their profits to owners (directors), thereis nothing inconsistent with this constraint and the offering of incen-tive contracts or profit sharing to managers of nonprofits. IRS rulingshave become more sympathetic to incentive contracts. Nevertheless,nonprofits remain wary of incentive contracts because they lack clearguidelines about what sort of contracts are permitted. The basicadvantage of having incentive contracts for managers of nonprofits isthe possibility of tying the interests of managers to those of the direc-tors and constituents. Clear guidelines spelling out rewards for per-formance may minimize shirking and enhance performance.

Steinberg argues that there are drawbacks to incentive con-tracts. The output of nonprofits is often difficult to measure mak-ing it difficult to devise an enforceable performance contractespecially since many nonprofits are caregivers or advocacygroups. Moreover, incentive contracts for fundraisers may lead toa perception among donors that their contributions are beingdiverted away from the organization’s major purpose and towardfundraiser compensation. If donors view this as increasing theirprice of giving (they must now give more to have the same effecton the organization’s service activities), fundraisers may find itmore difficult to attract donations. Although measurement of out-put is easier with respect to fundraising efforts, attempts to tierewards to output remain difficult.

Incentive plans based on efficient provision of services focusingon cost minimization may be easier to implement. These contractshave the advantage of freeing resources for program services, donot have any effect on perceived donor prices, and have had IRSapproval. If they are properly constructed, incentives to skimp onquality of service could be minimized.

The compensation of executives is a classic application of theagency problem. At very low levels of compensation, the businessfirm encounters adverse selection of executive talent as those exec-utives attracted by low compensation are incompetent at motivatingemployees and attaining company goals. As compensation increases,profits increase up to the point where productivity gains balancesalary increases and maximum profit is achieved. Beyond such apoint, additional executive pay occurs at the expense of stockholderprofit.

If you want tounderstand what

an economicagent does, firstfind out how heor she is paid.

Thecompensationof executivesis a classic

application of theagency problem.

MA N A G E R S O F NO N P R O F I T OR G A N I Z AT I O N S AR E RE WA R D E D 21

A similar conclusion follows for nonprofit organizations, whichare also “firms” because they allocate scarce resources to achieve theirgoals. Agency problems arise in organizations due to the separationof ownership and management. Contributors to the nonprofit orga-nization can be treated as analogous to the shareholders of the for-profit corporation. The contributor gives money to the managers ofthe organization with an understanding that those funds will be usedfor the common good. The interests of management and the inter-ests of contributors may diverge, leading to agency problems. How-ever, there is an important difference between the agency problemconfronting shareholders and the agency problem confronting con-tributors: while shareholders commit funds permanently to the cor-porations, contributors make periodic payments, which give themmore leverage over managerial behavior. Managers of nonprofits maybe reluctant to place their short-term interests before the long-termgrowth of the organization, resulting in less expense-preferencebehavior. As the organization grows (increases its assets), the willing-ness of managers to pursue their own self-interest will also increase,especially if the returns from expense preference behavior increaserelative to the returns from enhancing performance.

Excessive executive compensation or excessive benefits willdetract from the provision of program services. Instead of easily mea-sured profits, the goal of the nonprofit organization is more nebulous,such as entertainment, education, or health. Because social benefitsare more difficult to measure than are profits, the ability to determineexcessive compensation or inadequate performance is more complexin the nonprofit sector than in the for-profit sector. In the for-profitsector, incentive contracts (such as profit sharing and stock options)reduce the agency problem by rewarding executives for performanceconsistency with stockholder interests. Implied is that executives arerewarded for their performance and that the rewards have effects ontheir performance. If executives in nonprofits are rewarded for per-formance that results in the provision of services at lower costs,agency problems in nonprofit organizations may be reduced. More-over, if nonprofit executives are rewarded for performance, one wouldexpect that it would be enhanced. The simultaneous determinationof salary and performance is rarely addressed in the for-profit litera-ture of executive compensation; a notable exception is an article byCarroll and Ciscel (1980), who adjusted for the endogeneity of profitand sales by a two-stage least-squares process.

In this article, we consider the effect of performance on the com-pensation of nonprofit executives. We measure performance as theratio of revenue from a particular activity (for example, fundraisingor program services) to the expenditures associated with those ser-vices, exclusive of their compensation. This is consistent with previ-ous works, which use measures of size, spending, or budget

Because socialbenefits are more

difficult tomeasure than areprofits, the ability

to determineexcessive

compensationor inadequateperformance ismore complex

in the nonprofitsector than in thefor-profit sector.

percentages as measures of performance (Oster, 1998; Hallock, 2002a,2002b; Twombly et al., 2001). We take the analysis a step further byalso considering whether rewards that executives receive enhancetheir performance. The model used to estimate the simultaneous rela-tionship between performance and compensation is discussed.

LiteratureThe empirical literature concerning executive compensation in non-profits is virtually void of consideration of performance measures asbeing important determinants of such compensation. Oster (1998)notes that part of the reason is that it is difficult to find good outputmeasures to judge executive performance. Hallock (2002a) recog-nizes this and notes that “in the literature on CEO pay and firm per-formance, most papers use relatively few performance measures totest how well the top manager is doing” (p. 6). He attempts to fill thisvoid in the literature with two papers concerned with managerial payin nonprofits. In “Managerial Pay and Governance in American Non-profits” (2002a), Hallock tested the hypothesis that firm size resultsin greater compensation for the top (highest-paid) officer, director,or trustee and top (highest-paid) nonofficer, director, or trustee inthe organization. If managers and directors are rewarded for the orga-nization’s better performance or receive greater compensationbecause of the need to attract highly qualified individuals to managelarge and complex organizations, firm size as measured by assetsize should be associated with higher compensation. If managers oflarge organizations simply have greater budgets whose large variancegives managers more power to pursue their personal goals, then thesize coefficient will be significant, while the performance coefficientwill be insignificant. Our empirical results, reported below, do notsupport this second hypothesis.

Hallock (2002b) also examined whether other measures of man-agerial performance determined executive compensation. Measures ofmanagerial performance include the percentage of expenses for pro-gram services, the total of the funds raised from government and non-government sources, and the difference between total revenues andtotal expenses for a given accounting period. Expanding the model toinclude organizational fixed effects resulted in a decrease in the sizeand significance of the coefficient of firm size. In a second article,Hallock (2002a) compares executive compensation in for-profit andnonprofit organizations. The empirical results for nonprofits presentedin this article are virtually identical to those in the other article.

Estimates of the effects of the other performance measures onexecutive compensation were generally not statistically significantwhen included in the full fixed-effects models. Hallock did find thatthe greater the number of paid directors on the board, the lower thecompensation was paid to managers. There was no relationshipbetween board size as measured by the number of unpaid directors


and executive compensation. Hallock expresses the need for furtherstudies of executive compensation within industries, noting that hisand other studies have assumed that all nonprofits are interested ina relatively common outcome.

In this article, we construct models whereby executive compen-sation in nonprofit organizations is associated with managerial per-formance, taking into account organizational characteristics.Nonprofits have diverse goals and also limited resources available toachieve those goals. There is some reason to assume, therefore, thatthey would make some effort to achieve the desired outcomes in arelatively efficient manner. Moreover, as Steinberg has mentioned,properly constructed incentive packages based on efficient provisionof services have the advantage of being relatively easy to construct.For example, regulations of nursing homes in Minnesota allow non-care operating costs to be recouped in allowable rates if they arewithin industry norms. The regulations also encourage the mini-mization of these costs by allowing homes to earn an efficiencyincentive up to two dollars per resident day as the noncare costsdecline below the industry median. The funds received from theefficiency incentive can be used as desired by the nursing homes(Luksetich, Carroll, and Edwards, 2000).

Our performance measures differ from Hallock’s, who uses rev-enues as determinants of compensation. We assume that managersare rewarded when they increase revenue efficiently. Specifically, wemeasure efficiency as the ratio of revenue to expenses. We examineseparately the compensation received by officers and managers intheir specific areas of responsibility: program services, managementand general, and fundraising. Executive compensation in these areasis dependent on organizational size and the revenues they generate asa proportion of the expenses incurred, exclusive of their own com-pensation in generating those revenues. The models are full fixed-effects models that take into account individual organizationcharacteristics and yearly indicators. It is appropriate to use this modelspecification because it accounts for variation in compensation acrossorganizations that are not captured in the data and allows us to focuson changes in compensation over time due to changes in perfor-mance. The yearly time indicators account for economywide changesthat are common to all organizations. Models absent the fixed-effectsmeasures will bias the compensation and performance estimates ifthese are related to firm characteristics.

Data DescriptionGiven the unique determinants of executive compensation acrossfirms and industries, we concentrate on the effects of increased effi-ciency within firms. The data are from the Urban Institute’s Centerfor Charitable Statistics. It provided IRS Statistics of Income File formdata for all 501(c)(3) organizations with more than $10 million in


Nonprofits havediverse goals

and also limitedresources

available toachieve those

goals.

assets plus a random sample of approximately four thousand smallerorganizations. We have compiled a panel data set covering the period1992 to 1996 of nonprofit organizations filing IRS Form 990. Thesample is dominated by the larger nonprofits in each sector and isnot representative of the nonprofit sector as a whole. While exclud-ing the emerging or declining organizations and undersampling thesmaller firms, we are left with a more homogeneous, albeit larger size,group of organizations.

This panel creation provides much more information from regres-sion analysis than is available from an unstable, heterogeneous group-ing of organizations representative of the nonprofit sector. These resultsshould be considered representative of compensative behavior for thelarger, stable organizations, and not representative of the industry as awhole. Segal and Weisbrod (1998) construct a similar data set and dis-cuss its biases and strengths in greater detail in an earlier work.

The panel data set contains a considerable number of missingvalues. When nonprofit organizations report their expenditures to theIRS, they are particularly lax in the reporting of fundraising expenses.Accordingly, many organizations report no fundraising expenses; it isunclear whether these expenses are simply not reported, are a viola-tion of law, or are treated as program service expenditures. Trussel(2003) notes that managers of nonprofits have the ability and anincentive to misrepresent their expenses and report fundraisingexpenses as program services spending. The ability to manipulatecosts results from accounting rules governing the allocation ofjoint costs between program and fundraising expenditures. The incen-tive to misrepresent expenses is motivated by the belief that contri-butions from donors are positively related to the ratio of programspending to total spending and negatively related to the fundraisingexpense to donation ratio. Moreover, Trussel also notes that moni-toring groups such as the Better Business Bureau suggest that the pro-gram service ratio is a measure of efficiency, and the United Wayconsiders the ratio in allocation decisions.

Since it cannot be determined whether the zeros reported in thedata set are truly zeros, mistakes in reporting, missing values, orresult from misrepresentation, we drop observations reporting zerocompensation from the sample.

ModelTo determine the influence of executive performance on executivepay, we estimate an empirical model for each of the three functionalareas—fundraising, programs, management, and general—for thecombination of fundraising and program services and for the aggre-gate of the three areas. Aggregating fundraising and program servicescontrols for the possibility that fundraising expenditures aremisreported as program service expenditures, and aggregating allthree areas allows the possibility that either fundraising or program


When nonprofitorganizationsreport their

expenditures tothe IRS, they areparticularly laxin the reportingof fundraising

expenses.

service expenditures are misreported as “other” expenditures, pickedup in management and general expenditures.

For each functional area, compensation in year t for organizationi, Cit, depends on beginning year assets, BAit, the performance vari-able for that function, Pit (discussed below), the quasi-profit, Xit�1,defined as total revenue minus total expenses from the previous year,and a set of indicator variables for the years 1994, 1995, and 1996,Dt. We include an indicator variable for each organization, Di, toidentify and control for their unique (and persistent) characteristics.The objective of fixed-effect pooled regression models of this type is toidentify and control for the unique (and persistent) characteristics ofeach organization. We also included an interactive term relating theperformance variable and the beginning asset variable to test whetherthe effects of performance and organization size are interdependent(for example, whether there are increasing or decreasing returns toperformance as the size of the organization increases).

Performance is measured as the ratio of revenue from each activ-ity (such as fundraising, program services, and total revenue) dividedby expenses associated with that revenue (net of executive compensa-tion for that activity). The performance measure for fundraising is theamount received from private and governmental sources plus revenuesfrom memberships divided by all fundraising expenses, exclusive ofexecutive compensation. The performance of program services admin-istrators is measured by the ratio of revenue from that activity (such asticket sales or tuition) divided by expenditures for producing those ser-vices. A positive association between compensation and performanceimplies revenue-maximizing or cost-minimizing motives, or both; aninverse relation could reflect a desire to subsidize services to the needy,driving the service level beyond the revenue-maximizing level.

Identifying responsibilities of executives in the management andgeneral activity area is not as clear as for those in the program ser-vice and fundraising areas. Their responsibilities cover the myriad ofactivities of their respective organizations, including accounting,legal, office management, investment and board and staff meetingactivities (Pollack and Cordes, 2001). Therefore, we include as mea-sures of efficiency in the general management compensation, the totalrevenue (grants � program revenue � rents, licensing, inventorysales, and others) to management and general expenses, exclusive ofexecutive compensation in this area.

Formally, the model estimated is shown as:

ln(Cit) � �0 � �1 ln(Pit) � �2 ln(BAit) � �3 Xi,t�1

� �T�1996

T�1994

�T DT � �k

i�1

�i Di � �4 ln(BAit) � ln(Pit) � �t

All variables, save the lagged excess and indicator variables, areentered as natural logarithms, which allow us to interpret their


coefficients as elasticities. The excess revenue variable, Xit�1, cannotbe entered logarithmically because it is sometimes negative.

We expect that coefficient �2 will be consistently positive acrossactivities and organization types. Beginning assets proxy the size ofthe organization, indicate investment income, and provide a poolof subsidized funding. Hence, we expect larger organizations to pro-vide greater compensation to their executives. The sign on the laggedexcess is positive if the organization provides a form of delayed profitincentive for executives; the sign on excess could be zero, or evennegative, if showing a loss in the current income statement increasesfundraising revenue the next year.

The sign on �1 is ambiguous; a positive coefficient supports ourcontention that executives will be compensated for achieving the orga-nization’s revenue goal at minimum cost. However, if the price of theorganization’s services varies with activity levels (for example, increasingthe number of students reduces average tuition), and if the organizationpursues a redistribution objective, the sign on �1 could be negative. Theinteractive term between the performance variable and the beginningasset variable will test whether the effects of performance and organi-zation size are interdependent (for example, whether there are increas-ing or decreasing returns to performance as the size of the organizationincreases). The sign on this measure cannot be determined a priori.

Taking the derivative of ln(Cit) with respect to Pit yields:

�

llnn((CPi

i

t

t

))

� � �1 � �4 ln(BAit) � 0 → BA* � e��

��

1

4�

Similarly, we find that �

llnn((BCAit

i

)

t)� � �2 � �4 ln(Pit) � 0 → P*

� e��

��

2

4�.

Empirical results will reflect changes in the responsiveness of com-pensation to performance and assets, respectively, only if those crit-ical values BA* and P* are within the range of observed values.

If managerial behavior is decisive in organizational performance,it follows that causation between performance and compensationmay run in both directions. As performance increases, we expectcompensation to increase. The compensation encourages executivesto increase performance. Therefore, we add a second equation andformalize the model as:

ln(Cit) � �0 � �1 ln(Pit) � �2 ln(BAit) � �3 Xi,t�1

� �T�1996

T�1994

�T DT � �k

i�1

�i Di � �4 ln(BAit) � ln(Pit) � �t

ln(Pit) � 0 � 1 ln(Cit) � 2 ln(BAit) � 3 Xit�1

� �T�1996

T�1994

T DT � 4 ln(BAit) � ln(Cit) � eit


We expect largerorganizations toprovide greatercompensation totheir executives.

Empirical ResultsTable 1 presents the descriptive statistics for compensation, perfor-mance, beginning assets, and the lagged income. Note that the num-ber of observations on fundraising will be reduced by nearly60 percent in the estimation procedure because of the plethora ofzeros. While it is conceivable that some organizations employ a vol-untary fundraising staff, it is also likely that organizations hidefundraising expenses (including fundraising compensation) eitherby reporting that amount in other activities (most likely, program ser-vices) or not reporting fundraising activities at all. It is also possiblethat the general manager, or the director of program services, mayalso be the person most responsible for raising donations. Because offewer observations and the very likely effect of measurement error,the results for the fundraising equation may be suspect.

Table 2 presents the pooled regression results for the entire dataset. First, we find a significant positive relation between compensa-tion and performance for total compensation (column 1), generalmanagement (column 2), program services (column 4), and the sumof fundraising and program services (column 5). However, theaccompanying Hausman tests imply rejecting the null hypothesis ofunidirectional causation between performance and compensation.Hence, the coefficients in the original pooled regressions are biasedand inconsistent.

Table 3 contains the estimates of the model that considers thesimultaneity between performance and compensation, whereinthe predicted values of performance (from the first-stage equation)are used in place of the original variables. Predicted values are afunction of the lagged exogenous and endogenous variables and,by definition, cannot be influenced by current factors. The first col-umn of results contains estimates of the determinants of manager-ial compensation (top half of table) and managerial performance(bottom half of table) for the entire sample of organizations. Likeothers, we find that asset size is a major determinant of total exec-utive compensation (column 1); however, we also find that the per-formance measure has a strong positive and significant effect onexecutive compensation, an effect that diminishes as asset sizeincreases. It should be noted that the performance measure, theratio of revenue to expenses (excluding executive compensation),is the reciprocal of the average cost of acquiring an additional dol-lar of revenues. This clearly implies that executives are rewarded forefficiency in their performance of their activities. For the average-sized organization, with assets of $48 million, each 10 percentincrease in performance increases total compensation by 18.2 per-cent. The positive relation contradicts the assumption of expensepreference behavior, whereby executives would pad expense accountsor otherwise reduce efficiency as an alternative means of featheringtheir own nests.


Because of fewerobservations and

the very likelyeffect of

measurementerror, the results

for thefundraising

equation maybe suspect.

Tab

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290


Table 2. Fixed-Effects Pooled Regression Coefficients, Followed by t-Statistics

(1) (2) (3) (4) (5)Log of

Log of Log of Log of Log of Fundraising andTotal Managerial Fundraising Program Services Program Services

Compensation Compensation Compensation Compensation Compensation

Log of relevant predicted 0.4644 0.2216 �0.1693 0.5099 0.7239performance 16.98 8.26 �20.59 71.87 42.45

Log of beginning assets 0.6484 0.6032 0.5092 0.6607 0.676698.67 93.66 58.73 119.68 107.32

Lagged excess revenue 1.58E-11 4.62E-10 4.48E-09 1.16E-09 1.25E-090.04 1.12 8.22 3.43 3.03

Indicator for 1994 �0.0021 �0.0026 0.0049 �0.0126 �0.0227�0.07 �0.09 0.14 �0.50 �0.77

Indicator for 1995 �0.0227 �0.0306 0.0030 �0.0295 �0.0443�0.73 �1.06 0.09 �1.17 �1.50

Indicator for 1996 �0.0528 �0.0817 �0.0275 �0.0455 �0.0613�1.69 �2.82 �0.79 �1.80 �2.08

ORG—C 3.9272 3.1498 3.7467 4.4201 3.5493

Adjusted R2 0.2951 0.2994 0.3270 0.4988 0.3610

Observations 25,900 22,858 10,273 22,009 24,591

F-stat 1,808.34 1,629.08 832.91 3,652.04 2,316.62

Hausman Test

Log of relevant predicted 0.6442 0.6030 0.5113 0.0995 0.6513performance 99.22 93.64 61.30 48.25 433.35

Log of beginning assets 0.9984 0.5408 �0.1595 0.6400 0.216223.61 12.95 �20.17 431.73 51.78

Lagged excess revenue �5.12E-10 �5.13E-11 3.48E-09 4.49E-10 5.35E-10�1.34 �0.12 6.67 4.90 5.43

Indicator for 1994 �0.0091 �0.0135 �0.0071 �0.0117 �0.0137�0.30 �0.47 �0.21 �1.74 �1.96

Indicator for 1995 �0.0488 �0.0468 �0.0053 �0.0269 �0.0308�1.59 �1.62 �0.16 �3.98 �4.40

Indicator for 1996 �0.0855 �0.0982 �0.0121 �0.0598 �0.0542�2.79 �3.41 �0.36 �8.86 �7.75

Residual from first- �0.8784 �0.5458 0.2577 1.0313 1.0300stage regression �15.80 �9.94 31.10 528.21 633.58for Performance

ORG—C 3.3910 2.7893 3.7421 4.4482 4.1509

Adjusted R2 0.3083 0.3051 0.3846 0.9641 0.9638

Observations 25,448 22,530 10,147 21,735 24,238

F-stat 1,621.22 1,414.28 906.74 83,337.36 92,216.37


Table 3. Simultaneous Equation Estimates of Compensation and Performance

All Organizations

Second-Stage Pooled Regressions for Compensation: Log of

(1) (2) (3) (4) (5)Fundraising and

Total Managerial Fundraising Program Services Program ServicesCompensation Compensation Compensation Compensation Compensation

Log of relevant predicted 5.3833 2.9538 �0.3143 �0.5134 �0.0649performance 13.59 7.25 �2.41 �7.43 �4.50



Indicator for 1994 �0.0096 �0.0146 0.0137 �0.0110 �0.0207�0.32 �0.50 0.39 �0.45 �0.48

Indicator for 1995 �0.0454 �0.0451 0.0121 �0.0354 �0.0379�1.49 �1.56 0.34 �1.44 �0.89

Indicator for 1996 �0.0732 �0.0918 �0.0381 �0.0612 �0.1241�2.40 �3.19 �1.10 �2.49 �2.95

Log of relevant �0.2704 �0.1487 0.0043 0.0653 0.0585performance �11.18 �5.98 0.56 15.55 23.14times log of beginningassets

Organizational identifiers �1.8874 �0.1162 4.4229 4.4960 3.9117

Critical value of beginning $443,510,214 $424,108,308 NA $2,586 $3assets

Adjusted R2 0.3112 0.3061 0.3314 0.5182 0.5404Observations 25,466 22,545 9,584 21,576 8,502F-stat 1,644.50 1,421.93 679.51 3,316.60 1,428.66

Second-Stage Pooled Regressions for Performance: Log of:

Fundraising andTotal Managerial Fundraising Program-Services Program Services

Performance Performance Performance Performance Performance

Log of predicted 0.9698 0.1170 �0.5498 0.4980 0.3263compensation 32.52 9.85 �6.15 16.08 20.69

Log of beginning assets 5.3833 0.0777 �0.2791 �0.1208 �0.001113.59 8.59 �4.55 �4.38 �0.08

Lagged excess revenue 4.13E-10 9.51E-10 9.17E-10 �8.03E-10 �3.63E-101.06 8.91 1.01 �2.62 �2.12

Indicator for 1994 �0.0096 �0.0044 0.0126 �0.0019 0.0044�0.32 �0.60 0.30 �0.09 0.38

Indicator for 1995 �0.0454 0.0249 �0.0225 0.0239 0.0082�1.49 3.42 �0.55 1.13 0.72

Indicator for 1996 �0.0732 0.0353 �0.0235 �0.0020 �0.0005�2.40 4.86 �0.57 �0.10 �0.04

Log of relevant performance �0.2704 �0.0063 0.0268 �0.0068 �0.0084times log of beginning �11.18 �9.05 5.11 �3.72 �9.0506assets

Organizational identifiers �1.8874 �0.2260 8.6882 �3.7663 �1.68

Critical value of beginning $36 $105,106,026 $800,715,302 4.40E+31 6.12E+16Assets

Adjusted R2 0.3112 0.0097 0.0106 0.1973 0.0692Observations 25,466 22,560 9,793 21,686 24,363F-stat 1644.50 32.56 15.92 762.28 259.94

(Continued)


Table 3. (Continued)The Arts




Log of relevant predicted 0.9422 3.6701 0.5577 1.5149 0.0045

performance 13.58 3.09 6.01 5.92 0.10

Log of beginning assets �0.1890 0.7990 0.0859 0.5688 0.5581�3.30 9.73 0.15 22.76 19.03

Lagged excess revenue �1.74E-09 2.03E-08 1.68E-08 1.31E-08 1.40E-08�0.76 4.09 3.47 2.65 2.89

Indicator for 1994 0.0174 0.0354 �0.0437 0.0133 0.02950.43 0.39 �0.43 0.14 0.28

Indicator for 1995 0.0159 �0.0385 0.0660 �0.0092 �0.01320.39 �0.42 0.65 �0.10 �0.13

Indicator for 1996 0.0210 �0.1176 0.0687 �0.0959 �0.08930.52 �1.30 0.69 �1.02 �0.85

Log of relevant performance 0.0096 �0.2266 �0.0304 �0.0725 �0.0010times log of beginning assets 2.27 �3.13 �0.91 �4.75 �0.18

Organizational identifiers 1.1957 �0.4260 3.8240 4.8243 4.98772

Critical value of beginning NA $10,842,135 $91,837,872 1.20E+09 NAassets

Critical value of performance $20





Log of predicted 0.1951 0.1861 0.8228 0.6484 0.4935compensation 3.71 3.32 6.79 3.60 5.64

Log of beginning assets 0.1406 0.1446 0.4539 �0.0494 0.18313.31 3.48 5.13 �0.34 2.60

Lagged excess revenue 5.45E-09 5.16E-09 8.69E-09 �6.59E-09 3.06E-093.01 2.86 3.32 �1.15 1.07

Indicator for 1994 0.0094 0.0033 0.0096 �0.0124 0.00100.30 0.10 0.18 �0.12 0.02

Indicator for 1995 0.0242 0.0224 �0.0060 0.0562 �0.01820.77 0.69 �0.11 0.55 �0.35

Indicator for 1996 0.0535 0.0500 �0.0267 0.0208 �0.01101.70 1.55 �0.52 0.21 �0.21

Log of relevant performance �0.0107 �0.0111 �0.0461 �0.0189 �0.0232times log of beginning �3.37 �3.25 �6.23 �1.74 �4.39assets

Organizational identifiers �1.4112 �1.2558 �7.5650 �4.7153 �3.6646

Critical value of beginning $81,484,785 $19,457,219 $56,951,873 7.34E+14 1.66E+09assets

Adjusted R2 0.0150 0.0133 0.0601 0.1106 0.0247Observations 1642 1552 1118 1402 1553F-stat 4.56 4.00 11.19 25.89 6.61


Table 3. (Continued)Education




Log of relevant predicted 4.7729 1.4569 �0.2024 0.4073 �0.0424performance 4.82 1.44 �0.90 2.72 �2.13


Lagged excess revenue �9.12E-10 2.67E-10 1.46E-09 3.11E-10 3.00E-09�2.18 0.55 3.34 1.05 5.32

Indicator for 1994 �0.0068 �0.0086 0.0581 0.0041 0.0006�0.13 �0.17 1.42 0.11 0.01

Indicator for 1995 �0.0401 �0.0069 0.0523 �0.0057 �0.0389�0.75 �0.14 1.28 �0.15 �0.72

Indicator for 1996 �0.0946 �0.0542 0.0084 �0.0574 �0.1474�1.77 �1.08 0.21 �1.49 �2.76

Log of relevant performance �0.2038 �0.0307 �0.0010 0.0057 0.0659times log of beginning assets �3.46 �0.51 �0.08 0.64 15.17

Organizational identifiers �3.0327 1.6226 1.0346 2.3077 1.7360

Critical value of $14,874,538,442 NA NA NA $2beginning assets







Lagged excess revenue 8.73E-10 7.81E-10 �1.30E-09 2.99E-10 6.59E-119.52 6.61 �1.09 1.12 0.36

Indicator for 1994 �0.0038 �0.0053 �0.0444 �0.0086 0.0018�0.31 �0.42 �0.78 �0.26 0.09

Indicator for 1995 0.0558 0.0570 �0.0953 0.0321 0.02414.49 4.54 �1.68 0.97 1.13

Indicator for 1996 0.0766 0.0793 �0.0891 0.0145 0.03276.17 6.32 �1.58 0.44 1.53

Log of relevant performance �0.0030 �0.0015 0.0451 �0.0338 �0.0185times log of beginning assets �3.00 �1.01 4.17 �10.90 �9.65

Organizational identifiers 0.3549 0.7594 10.8911 �8.9362 4.39E+12

Critical value of $2,414,361,004,312 NA $660,843,650 1.46E+12 4.39E+12beginning assets


(Continued)


Table 3. (Continued)

Medicine and Health




Log of relevant predicted 10.6859 9.1132 �0.2503 �0.1483 0.0978performance 13.53 9.92 �0.87 �0.88 2.57


Lagged excess revenue 5.32E-09 1.01E-09 1.99E-08 5.31E-09 �6.35E-095.18 1.04 6.97 6.73 �1.53

Indicator for 1994 �0.0349 �0.0425 0.0590 �0.0161 �0.1040�0.82 �0.99 0.67 �0.51 �0.81

Indicator for 1995 �0.0563 �0.0378 0.0328 �0.0532 �0.0653�1.32 �0.88 0.38 �1.69 �0.51

Indicator for 1996 �0.0834 �0.0845 �0.0519 �0.0649 �0.1432�1.95 �1.97 �0.60 �2.06 �1.12

Log of relevant performance �0.6510 �0.5674 0.0093 0.0446 0.0625times log of beginning �13.93 �10.40 0.56 4.36 9.30assets

Organizational identifiers �7.6392 �7.7434 6.4202 5.7751 �0.0370

Critical value of beginning $13,449,781 $9,456,254 NA $28 $0.21assets







Lagged excess revenue 1.73E-09 9.07E-10 �1.47E-08 3.20E-10 �6.90E-106.69 3.97 �2.73 0.68 �1.85

Indicator for 1994 0.0048 0.0046 0.0179 0.0085 0.01350.47 0.46 0.13 0.48 0.90

Indicator for 1995 0.0112 0.0129 0.1000 0.0138 0.01201.09 1.31 0.72 0.78 0.80

Indicator for 1996 0.0087 0.0130 0.1664 �0.0022 0.00750.85 1.33 1.18 �0.12 0.50

Log of relevant performance �0.0144 �0.0093 0.0658 �0.0257 �0.0084times log of beginning assets �11.40 �7.25 2.69 �10.47 �4.40

Organizational identifiers �2.3802 �0.6659 14.1084 �7.3051 �1.3122

Critical value of beginning $6,621,905 $1,651,902 $13,155,894 2.70E+10 6.53E+14assets



Table 3. (Continued)

Religion




Log of relevant predicted �9.5811 15.0086 0.7908 0.4855 �0.1825performance �1.83 2.66 0.17 0.50 �0.83

Log of beginning assets �0.1505 1.2793 0.6018 0.7272 0.6622�0.49 3.94 1.05 4.53 11.64


Indicator for 1994 �0.2173 �0.0739 0.2403 0.3064 0.3092�0.49 �0.17 0.34 0.54 0.72

Indicator for 1995 �0.0097 0.3142 0.4243 �0.0568 0.8551�0.02 0.71 0.57 �0.10 1.93

Indicator for 1996 �0.2308 �0.0517 �0.0221 �0.2144 0.2698�0.53 �0.12 �0.03 �0.38 0.64

Log of relevant performance 0.7164 �0.7184 �0.0790 �0.0201 0.1917times log of beginning assets 2.23 �2.13 �0.28 �0.31 6.59

Organizational identifiers 13.4195 �12.3875 2.9331 1.5696 2.1866

Critical value of beginning $643,278 $1,182,132,700 $22,135 NA $2.59assets





Log of predicted compensation 0.0842 0.0048 0.6330 0.9948 0.21220.71 0.04 1.18 1.50 0.97

Log of beginning assets �0.0728 �0.1804 0.5876 0.3163 �0.1449�0.89 �2.04 1.95 0.67 �0.97

Lagged excess revenue 5.58E-09 4.07E-09 �1.67E-08 �5.70E-08 �1.17E-080.39 0.32 �0.43 �0.59 �0.45

Indicator for 1994 �0.1062 �0.1413 �0.0149 0.1880 0.2811�0.93 �1.26 �0.03 0.36 1.25

Indicator for 1995 �0.0205 �0.0165 0.3865 0.2302 0.1263�0.18 �0.15 0.79 0.44 0.56

Indicator for 1996 0.0115 0.0245 0.6611 0.0654 0.10270.10 0.22 1.47 0.13 0.47

Log of relevant performance 0.0012 0.0083 �0.0477 �0.0472 �0.0025times log of beginning assets 0.16 0.98 �1.52 �1.14 �0.18

Organizational identifiers 0.7719 2.1501 �5.5341 �9.2129 0.3750

Critical value of beginning NA NA $578,123 NA NAassets

Adjusted R2 0.1251 0.2390 0.0991 0.0057 0.0944Observations 154 115 40 79 121Critical Value of BA 4.12 6.11 1.61 1.06 2.79

The predicted performance variable in the compensation equa-tion is based in part on the lagged compensation variable. This solvesproblems of estimation associated with simultaneity; moreover, it alsoaddresses the question of the timing of rewards for performance.While our original model assumes compensation is based on currentperformance, the instrumental variable includes factors from the pre-vious year. Consequently, past performance as a determinant of cur-rent compensation is also captured in the estimates.

If tenure is a prime determinant of compensation in nonprofits,one would expect that over time, better managers replace the lessable. We cannot account for executive turnover in our data source,IRS Form 990, which reports the compensation of all key employees,without identifying individuals.

The coefficient on the interaction term between beginning assetsand (predicted) performance is negative and statistically significantfor the total compensation equation. This implies that as total assetsincrease, the sensitivity of compensation to performance decreases,reaching zero when assets equal $443,510,214. For 1.3 percent of allorganizations (those whose assets exceed this critical value), com-pensation increases only if performance decreases. This could alsoimply that the boards of directors of very large organizations havemore difficulty monitoring managerial behavior, resulting in less effi-cient or expense preference behavior. We also find that performanceappears to increase with executive compensation; however, the sig-nificant negative coefficient on the interaction term implies aninverse relation between total performance and executive pay, whichstrengthens as asset size increases. This may reflect the fact that team-work is more prevalent among the smallest nonprofits, whereasrivalry among fundraising, general management, and programservices increases as organizations grow larger.

Columns 2, 3, 4, and 5 in Table 3 contain the estimates of theexecutive compensation and performance equations for the func-tional areas of general management, fundraising, program services,and the combination of fundraising and program services, respec-tively. The equation for general management (column 2) parallels theresults for total compensation. As performance increases, so doesmanagerial compensation. The interaction term shows that thereward for performance decreases as asset size increases, disappear-ing when assets reach $424 million (1.3 percent of the organizations).Once again we find that the larger the compensation and the largerthe organization, the better is the organization’s performance,although the cross-product term does lead to the conclusion thatreturns to compensation and organization size increase at a declin-ing rate. Nevertheless, there is strong evidence of economies of scalewith respect to the size of the organization.

With the exception of managerial compensation (column 3), per-formance has a consistently positive impact on compensation whenthe effect of the interaction term including asset size is incorporated.


For 1.3 percentof all

organizations . . . ,compensationincreases onlyif performance

decreases.

The interaction term is statistically insignificant in the fundraisingequation, implying that the apparent negative elasticity of fundrais-ing compensation with respect to fundraising performance is invari-ant with respect to the size of the organization. When we add thepositive interaction term in the program services equation, we find pos-itive elasticities of compensation with respect to performance for allbut the smallest organizations. This result carries over when the pro-gram services and fundraising performance measures are included inthe combined program service–fundraising compensation equation.

While the general performance equation may show evidence ofmanagerial conflict, the performance equations by functional area aremore consistent. General managerial performance increases with bothassets and managerial compensation, until assets reach $105 million(8.1 percent of organizations). General management efficiency is alsopositively related to the previous year’s excess of revenue over cost.Fundraising efficiency is inversely related to both asset size and exec-utive compensation; small organizations, most in need of outsidefunds to survive, may overspend on fundraising. We find that as man-agerial compensation increases, performance increases in each areasave fundraising. As asset size increases, the negative elasticity offundraising performance with respect to fundraising compensationapproaches zero, becoming positive at $800 million (0.6 percent oforganizations). The inverse relation between size and performancealso occurs in program services. It is as if large organizations can taketheir fundraising sources for granted, pursuing managerial interestsat the expense of efficiency. Ironically, small organizations may under-compensate managers to the point of failing to achieve optimal per-formance. This is particularly likely for program services, where thepositive elasticity of performance with respect to compensationimplies a suboptimal compensation. In the equation combining pro-gram services and fundraising, we find a weaker relation between per-formance and compensation, but the tendency of the elasticity ofperformance with respect to compensation to decline as asset sizeincreases implies an optimal compensation level only when assetsize reaches into the trillions of dollars.

ArtsThe arts organizations show a positive elasticity of compensationwith respect to managerial performance for all areas of responsibil-ity except for the combination of fundraising and program services.This relation tends to weaken as organizations grow for managerialcompensation and program services. We find that total compensationis inversely related to assets for most organizations; the interac-tion term implies that for organizations with a performance ratioabove 20, compensation is positively related to assets. There is a pos-itive relation between compensation and assets for each of the func-tional areas, although that relation for fundraising is not statisticallysignificant.


Ironically, smallorganizations

mayundercompensatemanagers to thepoint of failing toachieve optimalperformance.

In all cases, performance is positively related to compensation,although this relation weakens as organizations grow. Overall, thereis fairly consistent evidence that performance has a positive influenceon compensation and that compensation has a positive impact onperformance within the arts.

EducationAmong educational establishments, performance has a positiveimpact on compensation for total compensation, managerial com-pensation, and program services. For fundraising, performance is sta-tistically insignificant. When fundraising is combined with programservices, the interaction term implies a positive and strengtheningrelation. We also find that the elasticity of compensation with respectto assets is consistently positive and significant.

We find that as compensation increases, performance increases inall areas except fundraising. Since university presidents, chancellors,or provosts are often the chief fundraisers, the positive relationbetween general managerial performance and managerial compensa-tion may be the most relevant result. Generally the relation betweenperformance and asset size is statistically significant. Well-endoweduniversities tend to share their largess with their key administrators.

HealthThe results for health care organizations, predominantly nonprofithospitals, are consistent with the results for the arts, education, andorganizations in general. Improved performance increases compen-sation for aggregate compensation, the general manager, and programservices. This relation weakens with the increase in asset size, becom-ing negative for general management at $9.5 million (72 percent oforganizations) and for total compensation at $13.4 million (62 per-cent of organizations). Although there is a negative coefficient on thelog of predicted performance for program services, this coefficient issmall and insignificant. By contrast, the positive coefficient on theinteraction term implies a positive and strengthening relationbetween performance and compensation of the director of programservices (for example, the medical chief of staff) for all but the small-est 5 percent of medical organization. However, the compensation offundraisers is invariant with respect to performance, even whenfundraising activities are compared with program services. When pro-gram services and fundraising are aggregated, we find that the posi-tive relation between performance and compensation increases asasset size increases.

We find that performance is positively related to managerialcompensation for small organizations, becoming negative for total com-pensation and general managers when medical organizations reachmoderate size. Above $1.6 million in assets for general management(90 percent of organizations) and $6.6 million in assets for aggregatecompensation (79 percent of organizations), performance decreases


The results forhealth care

organizations,predominantly

nonprofithospitals, are

consistent withthe results for

the arts,education, andorganizations

in general.

with compensation, implying that managers are overcompensated. Bycontrast, there is a negative relation between compensation and perfor-mance for fundraisers in small organizations, becoming positive whenassets exceed $13 million (63 percent of organizations). Apparentlysmall organizations, which rely most heavily on donations, tend to over-compensate fundraisers; as assets grow, alternative funding sources alsogrow, reducing the desperation to attract donations at any cost. Finally,there is a consistently positive relation between performance and com-pensation for program services and for the combination of program ser-vices and fundraising; although there is tendency for this relation toweaken, compensation of program service directors increases perfor-mance over the range of assets observed for this group.

ReligionThe types of nonprofits thus far considered are much more likely torely on funds from their program services than are nonprofit religiousorganizations. Consequently, it is much more likely that measuringperformance using monetary measures such as the ratio of revenuesearned to expenses incurred is a better proxy measure for perfor-mance in these organizations than for religious organizations. Reli-gious organizations provide a myriad of services, both sectarian andsecular. Because our performance measure is the best we can do, wemake estimates of the compensation and performance for religiousorganizations in an effort to determine whether the model presentedhere can be generalized to all types of nonprofits.

The Urban Institute’s National Center for Charitable Statisticspublication, National Taxonomy of Exempt Entities Core Codes,includes in its classification of religious organizations advocacy orga-nizations, fundraising organizations, church organizations of everydenomination, media and communication organizations, and a myr-iad of other organizations undertaking religiously related activities.

While the coefficient on performance is negative and insignifi-cant in the total compensation equation, the positive and signifi-cant coefficient on the interaction term implies that efficiencyis valued by religious organizations once assets exceed $643,000(80 percent of religious organizations). The compensation of thegeneral manager is positively related to performance for all religiousorganizations, although this relation does weaken as asset sizeincreases. There is no relation between performance and compen-sation for either fundraising or program services; this lack of sig-nificance persists when program services and fundraising arecombined. There is a strong positive relation between managerialcompensation and asset size for all but the combined compensationequation and, as usual, fundraising. Performance is largely unre-lated to managerial compensation; perhaps our performance mea-sure is a poor proxy for efficiency in religious organizations becausethe principals of religious organizations are largely motivated bynonpecuniary pursuits.


Perhaps ourperformance

measure is a poorproxy for

efficiency inreligious

organizationsbecause theprincipalsof religious

organizations arelargely motivatedby nonpecuniary

pursuits.

Some Tentative ConclusionsEconomists and other social scientists have long been perplexedabout the relation between managerial compensation and organi-zational performance in the nonprofit sector. We have found evidencethat executives of relatively large organizations in the nonprofit sec-tor are more similar to their for-profit counterparts than was thoughtheretofore. First, not only do executives tend to be rewarded for per-formance (revenue per dollar of noncompensation expenditure) butthat compensation generally has a positive impact on performance.We are confident that directors of program services and general man-agers are positively rewarded for achieving their lofty goals with rea-sonable efficiency. We find little or no evidence of expense preferencebehavior—either compensation increasing at the expense of effi-ciency or efficiency decreasing with respect to managerial compen-sation. Unfortunately, the reluctance of organizations to reportfundraising activities means that we can be considerably lessconfident of how money-people are rewarded. Do poor performance-based revenues encourage nonprofits to redouble fundraising efforts,or are reported fundraising activities full of sound and fury, signify-ing nothing? That is an agenda for further research.

THOMAS CARROLL is professor of economics at the University of Nevada,Las Vegas, where he teaches courses in the economics of discriminationand statistics at various levels. He is also president and chief economistat Thomas Carroll and Associates, Ltd., which specializes in litigationeconomics.

PATRICIA HUGHES is a professor of economics at St. Cloud State Univer-sity (SCSU). She is currently the director of the Graduate Program ofPublic and Nonprofit Institutions at SCSU. Her research interests focuson efficiency, funding, and management of nonprofit and governmentorganizations.

WILLIAM LUKSETICH is a professor of economics at St. Cloud State Uni-versity. He has done extensive research in the economics of nonprofitsincluding the economics of symphony orchestras, the efficiency offundraising activities, the demand for museum services, and the deter-minants of charitable contributions.

References

Carroll, T., and Ciscel, D. H. “The Determinants of ExecutiveCompensation: An Econometric Survey.” Review of Economics andStatistics, Feb. 1980, 62 (1), 7–13.


We have foundevidence thatexecutives of

relatively largeorganizations in

the nonprofitsector are moresimilar to their

for-profitcounterparts than

was thoughtheretofore.

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