Nonprofit boards: Size, performance and managerial incentives

Journal of Accounting and Economics 53 (2012) 466–487

Contents lists available at SciVerse ScienceDirect

Journal of Accounting and Economics journal homepage: www.elsevier.com/locate/jae

Nonprofit boards: Size, performance and managerial incentives$ Rajesh K. Aggarwal a, Mark E. Evans b, Dhananjay Nanda c,n a

University of Minnesota, Carlson School of Management, 321 19th Avenue South, Room 3-122, Minneapolis, MN 55455, USA Indiana University, Kelley School of Business, 1309 E. 10th Street, Bloomington, IN 47405, USA c University of Miami, School of Business, 5250 University Drive, Coral Gables, FL 33146, USA b

a r t i c l e in f o

abstract

Article history: Received 9 February 2010 Received in revised form 1 July 2011 Accepted 17 August 2011 Available online 3 September 2011

We examine relations between board size, managerial incentives and enterprise performance in nonprofit organizations. We posit that a nonprofit’s demand for directors increases in the number of programs it pursues, resulting in a positive association between program diversity and board size. Consequently, we predict that board size is inversely related to managerial pay-performance incentives and positively with overall organization performance. We find empirical evidence consistent with our hypotheses. The number of programs is positively related to board size. Board size is associated negatively with managerial incentives, positively with program spending and fundraising performance, and negatively with commercial revenue, in levels and changes. & 2011 Elsevier B.V. All rights reserved.

JEL classification: D21 G34 J33 L31 M40 Keywords: Nonprofits Incentives Boards of directors

1. Introduction Governance scholars have long argued that larger boards are potentially more beholden to their firm’s management and are thus controlled by the CEO (Lorsch and MacIver, 1989; Jensen, 1993). The empirical literature on boards of directors in for-profit organizations documents inverse relations between board size and managerial incentives and between board size and firm value (Yermack, 1996). Consequently, a large board is believed to be a manifestation of the agency problem between managers and shareholders as it is more likely to side with management’s interests. This argument is premised on the notion that directors of public corporations have a fiduciary duty to solely protect shareholder interests and that their decisions that accommodate the interests of any other stakeholder group (for example, management) are a violation of this responsibility.1 In contrast to public corporations, nonprofit organizations explicitly acknowledge accommodating multiple stakeholder interests, providing a powerful setting to examine the effect of the

$ This paper was previously titled ‘‘Access, Board Size and Incentives in Nonprofit Organizations.’’ We would like to thank Ross Watts (editor), an anonymous referee, Bill Baber, Raffi Indjejikian, Ram Natarajan, Tatiana Sandino and seminar participants at University of Chicago, Duke University, University of Iowa, MIT, University of Michigan and Seoul National University for their insights. We would also like to thank the National Center for Charitable Statistics (NCCS) for sharing the Form 990 data. n Corresponding author. E-mail address: [email protected] (D. Nanda). 1 Despite the directors’ fiduciary duty, public corporations do implicitly accommodate the interests of parties other than shareholders. For example, a number of publicly traded US companies have included union representatives on their boards of directors as an implicit accommodation of the interests of workers (see Appelbaum and Hunter (2004) who show that such arrangements were particularly prevalent in the airline industry).

0165-4101/$ - see front matter & 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jacceco.2011.08.001

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

467

multiplicity of organization objectives on board size and on performance.2 In this paper, we examine empirically how the scope of nonprofit activities (the number of organizational objectives) affects the size of their board of directors, managerial incentive pay, and enterprise performance. We assume that the function of a nonprofit board is to direct top management activities on behalf of the stakeholders and constituencies that the nonprofit represents.3 To the extent that the board reflects the concerns of multiple stakeholders about their non-profit’s activities, they potentially represent different (possibly conflicting) objectives that influence the nature of contracting between the board and management. For example, a community hospital’s board of directors represents the interests of its stakeholder groups, physicians, patients, staff, and the local community, and directs the hospital’s chief executive’s activities so that their stakeholder’s interests are provided adequate consideration.4 Why are there multiple directors on the board of a nonprofit? The organization benefits from directors because they hold assets that it can employ to further its mission, or help to defray the fixed costs of running a complex organization.5 The directors join the board because they value the right to direct the organization to pursue the goals of stakeholders that they represent or activities that they privately value.6 Further, the non-inurement clause that leads to the absence of residual claimants, who would otherwise direct and focus the organization’s mission, lowers a non-profit organization’s opportunity cost to pursue multiple objectives.7 Our view of the board’s role in nonprofit organizations contrasts with the monitoring view prevalent in the literature on for-profit organizations, which suggests that a firm’s board observes its manager’s actions and intervenes when necessary to resolve agency conflicts between management and owners (see, for example, Hermalin and Weisbach, 2001). A larger board is less likely to intervene because of dysfunctional behavior, such as free-rider problems, thereby exacerbating the agency conflict. We argue that the absence of both residual claimants and a market for corporate control in the nonprofit sector makes nonprofits more prone to develop larger boards where such dysfunctional decision making is potentially more prevalent. We hypothesize that nonprofit firms that pursue more objectives, because of a greater number of stakeholder groups, incur costs associated with running organizations with greater complexity (a diseconomy of scope). Directors reduce this diseconomy by contributing assets to the organization, although they potentially exacerbate the agency conflict between management and the organization’s stakeholders. Specifically, we address three questions. First, is the size of the board of directors associated with the variety of programs pursued by the organization? Second, how does board size affect managerial pay-for-performance? Finally, does board size affect nonprofit organizations’ financial performance? We predict that the number of objectives pursued by a nonprofit is positively associated with the size of its board. A larger board defrays the diseconomy of scope resulting from pursuing multiple objectives. Further, we predict that nonprofit board size is negatively related to managerial pay-performance incentives. This prediction is based on the multitask agency model (see Holmstrom and Milgrom, 1991), which demonstrates that pursuing multiple activities dampens managerial incentives across all tasks. Contrary to the findings in the for-profit literature, we then hypothesize that board size is positively related to a nonprofit organization’s financial performance as reflected in its ability to raise and spend funds. This occurs because each director improves organizational performance by either contributing assets or by helping defray the fixed cost of operations. Consequently, nonprofits’ ability to raise and spend funds increases in board size.8 We test our hypotheses using a sample of 501(c)3 nonprofit organizations that filed Form 990s with the Internal Revenue Service between 1998 and 2003. The sample consists of 159,594 annual observations from 35,945 unique nonprofits. The Form 990 contains information about financial performance, program services offered, listing of officers and directors, and compensation paid to the officers or directors. We find that the number of directors on a nonprofit’s board is positively related to the number of program activities pursued by the organization. In fact, after controlling

2 Organizations that operate exclusively for religious, charitable, scientific, literary, educational purposes, and to prevent cruelty towards children and animals, are exempt from the federal income tax and are commonly referred to as nonprofits (not-for-profits). To maintain its tax-exempt status, a nonprofit organization must satisfy three criteria: one, its activities must satisfy a public policy purpose; two, it must satisfy a non-inurement clause (non distribution constraint) that prohibits the distribution of earnings (revenues less expenses); and three, it must have a governance structure. 3 For example, Ben-Ner and Van Hoomissen (1991) state, ‘‘A nonprofit organization will be formed only if a group of interested stakeholders (individuals or organizations) has the ability to exercise control over the organization. Stakeholder control is a sine qua non for the existence of nonprofit organizations, because it avails the trust required for patronizing the organization, revealing demand to it, and making donations to it.’’ Further, Eldenburg et al. (2004) state, ‘‘choosing [the] objective function is an important responsibility of the governance structure of nonprofit organizations.’’ 4 Drucker (1992) states, ‘‘One of the basic differences between businesses and non-profits is that non-profits always have a multitude of constituencies.’’ 5 For instance, large donors are typically on the board of philanthropic organizations. In 2006, Warren Buffet pledged $30 billion to the Bill and Melinda Gates foundation and was subsequently appointed to its board of trustees. Melinda Gates served on the Duke University board of trustees from 1993–2006, and has made a substantial financial contribution to the university. 6 Stephen Schwarzman, a member of the New York Public Library’s board of trustees, donated $100 million to fund the expansion of the library housed on Fifth Avenue and 42nd street. The expansion plans include the addition of a circulation section within the main library, new rooms and computers for children, a cafe and an information center. The library plans to rename the building after Mr. Schwarzman (The New York Times, March 11, 2008). 7 Past research claims that the non-distribution constraint is both the cause of agency problems that lead nonprofits to be less efficient than forprofits (Alchian and Demsetz, 1972; Fama and Jensen, 1983), and a way to resolve agency problems that would lead for-profit firms to under-supply a good or service (Hansmann, 1996; Weisbrod, 1988; Glaeser and Shleifer, 2001). 8 Similarly, contributors often make restricted donations for a specific activity that they value and are added to the nonprofit’s board. As a result, the number of objectives and board size are increasing in donations, consistent with our findings.

468

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

for nonprofit size, the coefficient on program activities is approximately one, suggesting that each program activity (on average) is associated with one additional director on a board. We construct two measures of pay-performance sensitivity for each CEO or Executive Director. The first, the sensitivity of compensation to financial performance, is derived by regressing the annual change in compensation on the change in revenue and the change in yield (see Baber et al., 2002). The change in revenue measures a nonprofit’s effectiveness in raising funds and the change in yield measures its efficiency in using funds.9 Since managerial performance is unlikely to be solely measured in financial terms, we employ a second measure, the coefficient of variation of chief executive’s compensation, to proxy for incentives based on both financial and non-financial measures. Our results show that both measures of managerial incentives are negatively related to board size. We further find that board size is positively related to nonprofit performance. Program spending and revenue are both positively associated with board size in levels and in changes, with the exception of healthcare nonprofits. To reconcile the results for healthcare nonprofits, we examine the difference in board size-performance relation based on the source of a nonprofit’s revenues. There is a positive association between donation revenue and board size while there is a negative association between commercial revenue and board size. These opposite associations are consistent with the predictions of our theory. Since healthcare organizations largely derive revenue from commercial activities, the net effect of board size on revenue is negative in this sector. The statistical and economic magnitudes of the associations are significant, and are robust to a myriad of specifications and controls for organization characteristics. Our research contributes to the existing accounting and finance literatures on firm governance in several ways. First, we provide the first large sample evidence of the relations between board composition, managerial incentives, and performance in nonprofit organizations. Prior work has focused on the relation between organizational form and board composition (Eldenburg et al., 2004), organizational form and managerial pay-performance incentives (Brickley and Van Horn, 2002), and board composition and nonprofit performance (Brickley et al., 2009), but restricted their analyses to a single nonprofit sector (e.g., health care). Our paper builds on this work by explicitly examining the diversity in objectives and relating it to board size, managerial incentives, and performance in a sample that includes nonprofits representing multiple sectors. Second, we complement prior work that identifies an inverse relation between board size and firm performance in for-profit firms (Yermack, 1996) and show that even though board size is inversely related to managerial incentives, it is positively related to organization performance in nonprofit enterprises. Third, we demonstrate a potential mechanism that explains the Core et al. (2006) association between endowment size and agency problems in nonprofits. Larger endowments are a consequence of greater program diversity and larger boards, which we show are associated with lower managerial pay-performance sensitivity. Finally, we extend Baber et al. (2002) and examine variation in managerial incentives in nonprofits that arise from board size, as well as both financial and nonfinancial performance measures. The remainder of the paper is organized as follows. In Section 2, we develop our hypotheses. In Section 3, we describe our data and measures of board size, programs, nonprofit performance, and managerial incentives. We present the econometric results in Section 4. Section 5 discusses alternative explanations for our results, and Section 6 concludes.

2. Board size, programs, performance and incentives We examine the relation between a nonprofit’s program diversity, stated in its charter, and the size of its board of directors, and then the effect of this endogenously determined board size on the nonprofit’s manager’s incentives and the organization’s financial performance. We begin by presenting a framework in which a nonprofit is initially established by a founder (or a founding group), who defines the scope of its charter. We interpret a nonprofit’s scope as the set of objectives or programs that it pursues. Since a nonprofit’s scope is determined at its inception, we treat a nonprofit’s program diversity as a predetermined organization-level attribute. The nonprofit organization (or its founder) then chooses a board to direct its programmatic activities, the size of which is affected by the organization’s program diversity. The board subsequently structures an incentive contract (as in Holmstrom and Milgrom, 1991) for the nonprofit’s manager in order to guide his activities. In response to his contract, the nonprofit manager chooses his actions that generate output on the various objectives. Note that the board contracts with the manager unconstrained by any fiduciary duty to residual claimants. This characterization reflects the nonprofit orientation of the organization. In the Appendix, we formalize this model of managerial incentives and the endogenous choice of board size in nonprofit organizations. The hypotheses that follow are motivated by the comparative static predictions of our model.

2.1. Board size and objectives We predict that the number of directors on a nonprofit organization’s board is positively related to the number of programs (objectives) it pursues. This follows because the organizational complexity of a nonprofit with greater programs is offset by directors with valuable assets or expertise. 9 Specifically, the change in yield captures the change in the program spending ratio that is not attributable to a change in funds raised by the organization.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

469

A natural question is why individuals with valuable assets wish to join the nonprofit’s board of directors. In exchange for their assets, is it not possible for the nonprofit to guarantee (or contract) that it will deliver output on those tasks about which the individual cares? In our view, a moral hazard problem arises because the nonprofit manager’s activities are unobservable, although the organization’s output is contractible. Board membership confers a director the right to participate in structuring incentive contracts for the manager. However, board membership also imposes costs on the organization, in the form of greater dissonance and disagreement across parties, which limit the size of the board—were there no organizational cost to adding board members, boards would be unboundedly large.10 Nonprofit organizations face legal constraints on their ability to write complete contracts with stakeholders. Glaeser and Shleifer (2001) point out that the use of explicit contracts governing donations can cause them to lose their taxadvantaged status. The fungibility of monetary donations also gives rise to contractual incompleteness, since funds can be shifted across programs to circumvent a contract. In the case of large donations, this problem is more severe as funds are added to an endowment and used over a long period of time. For these reasons, the ability to influence the activities of the nonprofit will depend largely on control rights (board membership). If a donation is large enough and there is sufficient concern about divergence in objectives, then we expect the donor to receive board membership. Our hypothesis that board size is positively related to the number of nonprofit organization objectives is consistent with Eldenburg et al.’s (2004) findings that board size varies with hospitals’ ownership type. They find that church-owned, physician or community-owned and teaching hospitals have larger boards than for-profit hospitals, because they have representatives of their respective constituencies on the board. They conclude that the ownership type of a hospital affects its objective function because their constituencies’ potentially conflicting objectives (e.g., charity care) are added to core hospital goals (e.g., financial viability). 2.2. Nonprofit performance and managerial incentives The potential cost of multiple directors is that they dilute managerial incentives. This occurs because more directors lead to greater disagreement about how much effort the manager should direct to each task.11 Consequently, what limits the size of the board of directors is the contracting friction induced by having multiple directors that reduce the gains from contracting with the manager.12 Interestingly, Brickley et al. (2009) identify a negative association between physician representation on hospital boards and donations, which they suggest is due to conflicts over the use of resources between physicians and donors, who are often also board members. To the extent that greater board size is a proxy for such disagreements, their results complement our hypotheses and results. The contracting friction caused by multiple directors has implications for managerial compensation incentives, or payfor-performance. Our framework implies that managerial incentives on each task decline in the size of the board due to greater disagreement. Since board size increases in the number of tasks, aggregate managerial incentives is inversely related to board size. Consequently, we predict managerial pay-performance sensitivity declines in board size.13 The size of a nonprofit’s board has a direct effect on its performance because it affects both managerial incentives and the size of its asset base or the nonprofit’s ability to defray the fixed costs associated with pursuing multiple activities. We hypothesize that, across organizations, board size is positively related to a nonprofit performance as reflected by organizations’ ability to raise and spend funds.14 Our hypothesis is based on two arguments. First, as described above, nonprofits with a greater set of objectives or programs will have larger boards. Second, if the number of objectives is not too large, nonprofits that pursue more objectives will have greater total output than nonprofits that pursue fewer objectives. This occurs because there is a concave relation between the number of tasks and aggregate nonprofit performance due to the costs of greater organizational complexity with more tasks. Specifically, more tasks lead to more aggregate output, but at a diminishing rate; however, after a certain point activities reduce output. If the number of tasks is not too large, then more tasks are positively associated with performance. These two arguments imply that, crosssectionally, nonprofits with larger boards will have better performance. 10 In some instances, assets that are valuable for production can be bought by the organization or complete contracts can be written governing their use. We assume that these assets will be bought or contracted for and granting board representation to the owners of such assets is unnecessary. We also note that in some instances, nonprofits in effect pay for services from individuals by granting the individual board membership (see Lynn and Smith, 2005). 11 The organizational behavior literature argues that large work groups are associated with productivity losses because of coordination problems (Hackman, 1990). This has led some authors to advocate smaller boards for public for-profit firms (Jensen, 1993). 12 Organizations could also reduce board size in an effort to improve the contracting environment. For example, Joseph and Carol Reich, founders of Beginning With Children Charter School, forced 5 parent and faculty representatives from the 14-member board to resign. The Reich’s claimed that the board reflected too many divergent objectives and required refocusing on the original mission of the school (‘‘Patrons’ Sway Leads to Friction in Charter School,’’ New York Times, June 18, 2007). 13 Eldenburg et al. (2004) find that the likelihood of CEO turnover, conditional on financial firm performance, is greater in more focused hospitals: for-profit, commercial nonprofits, and district. In their sample, these types of nonprofits also have smaller boards than teaching, community, and religious hospitals. Brickley and Van Horn (2002) find no difference in pay-performance sensitivities across for-profit and non-profit hospitals. 14 The objective function of nonprofit firms has been examined by a large literature. Steinberg (2004) characterizes nonprofits as belonging to two types; ‘‘budget maximizers,’’ those that maximize the available funds (e.g., maximizing gross donations), and ‘‘service maximizers,’’ those that maximize expenditures on services (e.g., maximizing net donations). Other authors characterize nonprofits as prestige maximizers, employee income maximizers, income redistributors, or maximizers of the supply of a good or service (Steinberg, 2004; Hansmann, 1980, and 1987.) We assume that revenue and program spending adequately reflect the variety of these objectives.

470

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

Our prediction – that cross-sectionally, in nonprofit organizations, board size is positively related to performance – contrasts with the findings in the for-profit literature, which identifies an inverse relation between board size and incentives (e.g., Yermack, 1996). In for-profit firms, accommodating objectives that are inconsistent with shareholder value maximization leads to a reduction in financial performance. Consequently, an increase in board size reduces firm value. In nonprofits, organization performance increases in board size (as long as the number of programs is not too large) because organization performance is measured as the aggregate performance across multiple programs or objectives, as opposed to performance relative to a single residual claimant’s objective. 3. Data and sample selection We obtain data on Section 501(c)3 nonprofit organizations’ Form 990 filings, from 1998 to 2003, with the Internal Revenue Service (IRS), from the National Center for Charitable Statistics (NCCS). The Form 990s contain information on nonprofit organizations’ financials, compensation paid to officers, directors, and other key employees, as well as their program services. Nonprofits are classified by sector through a system called the National Taxonomy of Exempt Entities (NTEE), which assigns each organization into one of 26 sectors. We require that each sample nonprofit have at least three consecutive years of data. We remove observations for which no officer or key employee is paid, those with missing or negative financial performance variables for any year, or those that experience chief executive turnover during the sample period. We also delete outliers, by year, at the 1% and 99% levels with respect to board size, number of programs, total assets, total compensation, total revenue, and total program spending. If outliers are detected in any year, the organization is excluded from the sample. The final sample consists of 159,594 annual observations and 35,945 unique nonprofits. Table 1 provides descriptive statistics for our sample. Panel A shows the distribution of our sample organizations across NTEE classifications. The sector with the largest number of organizations is ‘‘Human Services’’ (22.71% of the sample), followed by ‘‘Education’’ (13.87%), ‘‘Arts, Culture, and Humanities’’ (9.25%), and ‘‘Healthcare’’ (8.24%). These sectors are also analyzed individually in our empirical tests. Panel B presents other descriptive statistics for the sample. We proxy for the number of nonprofit objectives by the number of programs listed in Part III, ‘‘Statement of Program Service Accomplishments,’’ on the Form 990. In this section of the filing, organizations are required to describe ‘‘their exempt purpose achievements in a clear and concise manner.’’ These achievements are totaled for each nonprofit in order to obtain their number of programs. The mean (median) value for the number of programs is 1.86 (1).15 The number of directors on the board is listed in Part V, ‘‘List of Officers, Directors, Trustees, and Key Employees,’’ on the IRS 990. Board size is defined as the total number of listed directors. The mean (median) number of directors is 12.92 (10). Because this variable is skewed we use its natural logarithm in our specifications. We measure an organization’s size by its beginning of the year book value of total assets. The mean (median) value for assets is $3.85 million ($0.56 million). Because this variable is highly skewed we use the natural logarithm of this variable in our regression specifications to control for nonprofit size. We use total revenues and program spending, and changes in these variables, as measures of financial performance. For our sample, the mean (median) revenue and program spending, from Lines 12 and 13 of Part I on the Form 990, are $2.99 (0.76) million and $2.36 (0.57) million, respectively. We use two measures of CEO pay-performance incentives. Our first measure, financial pay-performance sensitivity, is derived from Baber et al. (2002) as follows. Let PSPENDINGt, REVt, and RATIOt be the amount that a nonprofit spends on program activities, raises in revenue, and the ratio of program expense to total revenue, respectively, in year t. Then PSPENDINGt ¼ REVt  RATIOt

ð1Þ

DPSPENDINGt ¼ ½REVt  RATIOt ½REVt1  RATIOt1 

ð2Þ

DPSPENDINGt ¼ ½DREVt  RATIOt1  þ½REVt  DRATIOt :

ð3Þ

and

or

The first term is the change in program spending that is explained by the change in revenue and the second term is the change in program spending that is explained by the change in the average fraction of each revenue dollar the nonprofit spends on program activities. If we deflate the above by program spending in year t  1, and substitute RATIOt  1 ¼ ((PSPENDINGt  1)/(REVt  1)), we obtain

DPSPENDINGt ¼ %DREVt þ DYIELDt , PSPENDINGt1

ð4Þ

where

DYIELDt ¼

½REVt  DRATIOt  : PSPENDINGt1

ð5Þ

15 Since a large fraction of sample nonprofits have only a single program, in subsequent tests we also report results for the subsample of organizations with more than one program.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

471

Table 1 Descriptive statistics. Panel A: Number of organizations by sector NTEE classification code A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

No. of organizations

Percent of total (%)

3324 4985 790 403 2961 1747 856 213 978 933 419 1514 145 1082 1123 8163 378 325 1410 1349 254 134 341 2018 37 63

9.25 13.87 2.20 1.12 8.24 4.86 2.38 0.59 2.72 2.60 1.17 4.21 0.40 3.01 3.12 22.71 1.05 0.90 3.92 3.75 0.71 0.37 0.95 5.61 0.10 0.18

35,945

100.00

Arts, Culture, and Humanities Education Environment Animal-Related Healthcare Mental Health & Crisis Intervention Diseases, Disorders & Medical Disciplines Medical Research Crime & Legal-Related Employment Food, Agriculture & Nutrition Housing & Shelter Public Safety, Disaster Preparedness & Relief Recreation & Sports Youth Development Human Services International, Foreign Affairs & National Security Civil Rights, Social Action & Advocacy Community Improvement & Capacity Building Philanthropy, Voluntarism & Grantmaking Foundations Science & Technology Social Science Public & Societal Benefit Religion-Related Mutual & Membership Benefit Unknown Total no. of organizations

Panel B: Distributional statistics Levels Variable PROG BOARD COMP ASSETS (’000s) PSPENDING (’000s) REV (’000s) Changes Variable %DCOMP %DREV DYIELD %DPSPENDING Coefficient of variation Variable COV (Base) COV (Total)

N

Mean

Std Dev

159,594 159,594 159,594 159,594 159,594 159,594

1.86 12.92 69,737.50 3850.83 2355.74 2990.16

1.21 10.03 50,060.63 11,767.96 5806.79 7087.79

N

Mean

Std Dev

117,280 117,280 117,280 117,280 N 35,945 35,945

7.98 9.72 1.50 11.23 Mean 14.83 14.34

21.79 32.09 29.11 29.56 Std Dev 12.95 13.09

Lower quartile 1.00 5.00 37,381.00 132.93 208.32 287.53

Lower quartile 0.00  5.37  7.70  1.81 Lower quartile 6.18 5.71

Median 1.00 10.00 57,711.00 556.76 565.35 763.95

Median 4.52 5.48 1.00 6.98 Median 10.88 10.18

Upper quartile 3.00 18.00 87,115.00 2316.88 1813.28 2397.30

Upper quartile 11.11 18.59 11.11 18.40 Upper quartile 18.75 18.07

Sample description and variable definitions: a—‘‘Levels Variables’’ include 159,594 organization-year observations for 1998–2003. All data are from organizations’ IRS Form 990. BOARD is the number of directors (Part V, ‘‘List of Officers, Directors, Trustees, and Key Employees’’); PROG is the number of programs (Part III, ‘‘Statement of Program Service Accomplishments’’); COMP is the CEO’s total annual compensation (Part V, ‘‘List of Officers, Directors, Trustees, and Key Employees’’); ASSETS is book value of total assets, in thousands (Part IV, Line 59); PSPENDING is program service expenses (Part I, Line 13); and REV is total revenues (Part I, Line 12). b—‘‘Change Variables’’ include 117,280 organization-year observations for 1999–2003. All data are from organizations’ IRS Form 990. %DCOMP is (COMPt  COMPt 1)/COMPt  1, where COMP is the CEO’s total annual compensation (Part V, ‘‘List of Officers, Directors, Trustees, and Key Employees’’); %DREV is (REVt  REVt 1)/REVt 1 where REV is defined above. DYIELD is [REVt  DRATIO]/PSPENDINGt 1, where REV and PSPENDING are defined above, and RATIO is PSPENDINGt 1/REVt 1. %DPSPENDING is (PSPENDINGt  PSPENDINGt 1)/PSPENDINGt 1, where PSPENDING is defined above. c—‘‘Coefficient of Variation Variables’’ include 35,945 unique organization observations for 1998–2003. At least three consecutive years of data per organization are required. COV (Base) is s(COMP)/mean(COMP) where COMP is defined as base compensation only. COV (Total) is s(COMP)/mean(COMP) where COMP is defined as total compensation.

472

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

Here %DREVt indicates the organizations fundraising and DYIELDt indicates its fund usage performance. Following Baber et al. we posit that compensation partly depends on performance along these two financial dimensions and obtain the regression specification: %DCOMPt ¼ b0 þ b1 %DREVt þ b2 DYIELDt þ et ,

ð6Þ

where %DCOMPt is the percentage change in compensation from year t 1 to year t. In this specification, b1 and b2 represent pay-performance sensitivities to fundraising and fund usage activities. We obtain compensation data for chief executives (CEOs) from Part V of Form 990. If a CEO or Executive Director is not designated, we use compensation of the highest paid officer, director, trustee, or key employee obtained from the same section of the IRS return. We define total compensation as the sum of base compensation, contributions to employee benefit plans and deferred compensation, and expense accounts. We observe similar results if we only use base compensation. %DCOMPt is defined as follows: %DCOMPt ¼

COMPt COMPt1 : COMPt1

ð7Þ

The mean (median) percentage change in total annual CEO compensation is 7.98% (4.52%). We obtain annual revenue from Line 12 of Part I, ‘‘Revenues, Expenses, and Changes in Net Assets or Fund Balances,’’ on the Form 990. %DREVt is defined as follows: %DREVt ¼

REVt REVt1 : REVt1

ð8Þ

The mean (median) percentage change in annual revenue is 9.72% (5.48%). The mean (median) percentage annual change in program spending is 11.23% (6.98%) and the mean (median) value for the annual change in YIELD is 1.50 (1.00). While our descriptive statistics for the change in compensation, revenue and program spending are comparable to Baber et al., our descriptive statistics for YIELD are not. The mean (median) value for change in yield in Baber et al., is  2.18 ( 0.40). One potential reason for this difference is that their sample size is smaller and consists of significantly larger organizations. In contrast, Krishnan et al. (2006) report a median value for change in yield of 0.21, which is comparable to what we report. Since financial performance is unlikely to comprehensively capture a non-profit manager’s performance, we develop an alternative measure for incentives—the coefficient of variation (COV) of total CEO compensation.16 The coefficient of variation is defined as the standard deviation of compensation for the manager divided by the manager’s mean compensation. Higher variation in compensation is indicative of stronger pay-performance incentives conditional on both there being time series variation in performance and the organization’s use of pay-performance incentives. In our reported tests, the coefficient of variation is calculated for total annual compensation, although we obtain qualitatively similar results using base compensation. The mean (median) values for this variable using base and total compensation are 14.83 (10.88) and 14.34 (10.18), respectively. Univariate correlations are presented in Table 2. Panel A shows that board size, number of programs, and organization size are all significantly positively correlated. In particular, the Pearson (Spearman) correlation for board size and number of programs is 0.171 (0.179). Panel B provides preliminary evidence on pay-performance sensitivities. In particular, percentage change in compensation is positively correlated with percentage change in revenue, change in yield, and percentage change in program spending. Pearson (Spearman) correlations among these variables are 0.180 (0.147), 0.045 (0.033), and 0.239 (0.201), respectively. In addition, percentage change in revenue and change in yield are significantly negatively correlated ( 0.537 for Pearson and  0.492 for Spearman). The relations among these variables are further investigated in our multivariate empirical tests. 4. Empirical results Our model predicts relations between the number of non-profit programs (objectives), board size, managerial incentives, and financial performance. In particular, board size increases in the number of programs, managerial incentives decrease in board size, whereas overall performance increases in board size. We begin by presenting univariate evidence on nonprofit organizations that are differentiated by their source of funding. For instance, if the majority funding source for an organization is program service revenues, it often behaves like a for-profit entity.17 In Table 3, Panel A, we provide univariate evidence that program variety, board size, and managerial incentives (as measured by the coefficient of variation in pay) vary systematically with a nonprofit’s orientation as determined by its source of funding. We define two categories of funding: 90% of revenues (defined as the sum of donation revenue, program service revenue, and government grants) from program services (Commercial Nonprofits) and a more balanced mixture of funding from revenues, 16 For instance, Duca (1996) states that, ‘‘Nonprofit social services organizations rarely have a measure of profitability and often have multiple program goals and objectives. This makes it very difficult to identify any one or two performance measures that can be applied across a variety of programs.’’ 17 Steinberg (2004) terms such organizations as ‘‘Commercial Nonprofits’’.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

473

Table 2 Univariate correlations. Panel A: Levels ln(BOARD) ln(BOARD) PROG ln(ASSETS) ln(REV)

PROG 0.17909 o.0001

ln(ASSETS) 0.38617 o .0001 0.16853 o .0001

ln(REV) 0.33342 o.0001 0.20222 o.0001 0.77445 o.0001

0.1707 o .0001 0.32513 o .0001 0.31141 o .0001

0.15026 o.0001 0.21029 o.0001

0.6822 o .0001

%DCOMP

%DREV

DYIELD

%DPSPENDING

0.14658 o.0001

0.03329 o .0001  0.49167 o .0001

0.20065 o.0001 0.50362 o.0001 0.35952 o.0001

Panel B: Changes

%DCOMP %DREV

DYIELD %DPSPENDING

0.17976 o .0001 0.04504 o .0001 0.23947 o .0001

 0.53691 o.0001 0.55692 o.0001

0.40169 o .0001

See Table 1 for sample description and variable definitions. Panel A presents pairwise correlations of levels variables, based on 159,594 observations. Panel B presents pairwise correlations of changes variables, based on 117,280 observations. See Table 1 for sample description and variable definitions. Pearson (Spearman) correlations are presented below (above) the diagonal.

donations, and government grants (Traditional Nonprofits). First, we find that commercial nonprofits are larger (in terms of assets and revenues) and their chief executives have higher annual compensation than traditional nonprofits. Second, and more importantly, we find that commercial nonprofits, which have a largely singular funding source, have fewer programs, smaller boards, and higher managerial incentives than do less focused organizations. As the number of funding sources increases, so does the number of programs and board size, while managerial incentives are dampened. For instance, commercial nonprofits have on average 9.85 directors, 1.60 programs, and a COV of 15.13. In contrast, nonprofits that raise funds through program service revenues, public support, and government grants, have 13.50 directors, 1.92 programs, and a COV of 14.74, on average. The differences across categories are statistically significant. From this preliminary evidence we conclude that focused nonprofits have fewer programs, smaller boards, and higher managerial incentives. This univariate evidence is consistent with our predictions. In addition to breaking out the results for traditional and commercial nonprofits, we also report descriptive statistics for selected sectors. These are Arts & Humanities, Education, Healthcare, and Human Services, and were selected based on their respective sample sizes.18 Panel B reports that only 9.4% of Arts & Humanities organizations are considered commercial nonprofits, while 43.1% of Healthcare organizations are considered commercial nonprofits. In addition, on average, Education and Healthcare organizations are much larger than Arts & Humanities and Human Services organizations. Panel C provides finer partitions of the Education and Healthcare sectors by separating Higher Education and Hospitals & Nursing Homes, respectively. Only 16.7% of universities (Higher Education) are considered commercial nonprofits while 77.4% of Hospitals & Nursing Homes are considered commercial nonprofits. Consistent with Panel A, hospitals and nursing homes have smaller boards, fewer programs, and higher managerial incentives than do universities. To confirm this preliminary evidence we perform several multivariate tests.19 We first examine the impact of a nonprofit’s number of programs on board size, while controlling for organization size. Second, we examine the effect of board size on payperformance sensitivity by augmenting Eq. (6) with interaction terms for board size. Third, we use the coefficient of variation of compensation as an alternative measure of managerial incentives and test for its relation to board size. Finally, we test the association between a nonprofit’s board size and its revenue and program spending performance.

18 In Table 4–9, multivariate regressions are estimated within each of these sectors in order to help mitigate endogeneity concerns for the pooled sample. Specifically, it is possible that the (unobserved) nature of the organization jointly determines organization size and type, as well as board size and number of programs. By performing analyses within-sector, we hold organizational nature constant (assuming sector membership is an effective proxy for organizational nature), and thus mitigate concerns about joint determination and endogeneity. 19 In all of our tests, in addition to the initial treatment of outliers discussed in Section 3, we delete outliers with respect to the three variables in Eq. (6), and COV, at the 1% and 99% levels.

474

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

Table 3 Commercial nonprofits and sector descriptive statistics. Panel A: Traditional vs. commercial nonprofits Traditional nonprofits Variable ASSETS REV COMP BOARD PROG COV

Commercial nonprofits

N

Mean

Median

Variable

28,257 28,257 28,257 28,257 28,257 28,257

3,589,383 2,319,728 65,455 13.5016 1.9172 14.7433

575,932 673,281 54,931 11.3333 1.1667 10.8813

ASSETS REV COMP BOARD PROG COV

N

Mean

Median

7688 7688 7688 7688 7688 7688

5,692,339 5,026,551 80,5901 9.8498 1.5955 15.1344

732,349 1,162,759 65.444 8 1 10.8900

Mean diff.

Median diff.

nnn nnn nnn

þþþ þþþ þþþ þþþ þþþ

nnn nnn nn

Panel B: Selected sectors Arts & Humanities Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

Education N

Mean

3324 3324 3324 3324 3324 3324 3324

0.094 3,244,395 1,471,517 58,374 14.6062 1.7921 15.5517

Median 0 420,378 469,824 45,032 12 1 11.2477

Healthcare Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

N

Mean

Median

4985 4985 4985 4985 4985 4985 4985

0.3168 6,928,133 3,754,608 77,437 12.6132 1.6505 14.6401

0 752,895 903,769 61,481 9.6667 1 10.7816

N

Mean

Median

8163 8163 8163 8163 8163 8163 8163

0.2482 3,162,136 2,966,907 64,108 12.3976 2.0065 13.7048

0 585,896 916,082 55,522 10.8333 1.1667 10.1557

Human services N

Mean

Median

2961 2961 2961 2961 2961 2961 2961

0.4306 8,796,344 7,587,890 94,584 13.1402 1.7029 14.5004

0 1,834,087 2,131,389 76,453 11.75 1 11.2471

Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

Panel C: Education and Healthcare Higher education Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

Other education N

Mean

Median

335 335 335 335 335 335 335

0.1671 42,355,455 20,690,350 142,715 24.4695 2.0252 13.0929

0 29,041,488 17,184,225 141,420 23.75 1.2 10.7943

Hospitals & Nursing Homes Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

Variable COMM_NFP90 ASSETS REV COMP BOARD PROG COV

N

Mean

Median

4650 4650 4650 4650 4650 4650 4650

0.3275 4,375,842 2,534,506 72,735 11.7590 1.6236 14.7516

N

Mean

Median

1966 1966 1966 1966 1966 1966 1966

0.2569 3,570,120 3,542,878 81,316 12.8948 1.7486 14.8514

0 937,452 1,165,803 66,553 11.4 1 11.3954454

0 626,992 799,636 58,967 9.25 1 10.7805

Other healthcare

N

Mean

995 995 995 995 995 995 995

0.7739 19,122,733 15,580,348 120,798 13.6250 1.6127 13.8068

Median

Variable

1 7,145,439 7,531,550 102,784 12.3333 1 10.9596

COMM_NFP90 ASSETS REV COMP BOARD PROG COV

See Table 1 for sample description and variable definitions. Panel A compares traditional non-profit organizations with commercial non-profit organizations. Organizations with a proportion of program service revenue (IRS Form 990, Line 2) to total revenue (Lines 1, 2, and 3) of greater than 90% are considered commercial nonprofits (COMM_NFP90). Categories are determined based on average values (over at least 3 years) of revenues for the period 1998–2003. Panels B and C compare descriptive statistics for organizations in different sectors. ***, ***, and * represent significance at the 1%, 5%, and 10% levels, respectively, where significance is based on a t-test of mean differences. þþþ , þþ, and þ represent significance at the 1%, 5%, and 10% levels, respectively, where significance is based on a Wilcoxon rank-sum test.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

475

Table 4 Board size and programs. Dependent variable: ln(BOARD) Panel A: Pooled analysis (all sectors) Full sample

Intercept PROG ln(ASSETS) F-stat R2 N

PROG41

Traditional NFPs

Coef.

t

Coef.

t

 0.1209363 0.0863337 0.1524231

 1.38 26.85 76.52

 0.1001109 0.0760316 0.1492344

 0.72 14.88 53.2

369.09 0.21 35,945

Coef. 0.032 0.0666952 0.1479137

170.54 0.21 17,278

Commercial NFPs t

Coef.

0.35 18.73 63.52

 0.6355 0.1007699 0.1708531

273.53 0.20 28,257

t  3.00 13.95 45.34 127.84 0.29 7688

Panel B: Sector analysis Arts & Humanities Coef. Intercept PROG ln(ASSETS) F-stat R2 N

0.0508797 0.0677129 0.1714251

Education

Healthcare

t

Coef.

t

0.61 5.70 26.98

 0.3315529 0.1034277 0.1766316

 4.78 10.53 34.38

419.23 0.19 3324

765.78 0.25 4985

Coef. 0.5158596 0.1212118 0.1160852

Human services t

Coef.

5.99 11.48 20.26

 0.181678 0.0933087 0.1721601

281.74 0.16 2961

t  3.48 15.78 42.97 1352.49 0.25 8163

See Table 1 for sample description and variable definitions. All variables are averages for each organization over 1998–2003. The dependent variable is ln(BOARD). There are 35,945 unique organization observations in the full sample. Panel A presents results for the full sample, the sample of organizations with more than one program, and traditional and commercial nonprofits, defined in Table 3. Sector dummies are included in all regressions. t-stats are based on robust standard errors.

4.1. Number of programs and board size To examine whether a nonprofit’s board size is related to its number of programs, we estimate the following crosssectional specification: lnðBOARDi Þ ¼ b0 þ b1 PROGi þ b2 lnðASSETSi Þ þ ti þ ei :

ð9Þ

ln(BOARDi) is the natural log of average board size over the sample period, PROGi is the average number of programs over the sample period, and ln(ASSETSi) is the natural log of average beginning of the year book value of assets over the sample period. Sector effects are represented by ti. Because we use averages, this specification is estimated over the cross-section of 35,945 organizations. The results are presented in Table 4. Panel A reports results from four different samples. Results based on the full sample show that the number of programs is positively related to board size. The magnitude of the coefficient implies that each program is (on average) associated with one board member.20 As a robustness check, we also restrict our sample to organizations with more than one program. We do this because the median number of programs in our sample is one, and we want to avoid fitting a large mass of observations clustered at one program. The results are similar although the coefficients are smaller in magnitude than in the full sample. Consistent with our predictions, this evidence suggests that board size is positively related to the number of programs pursued by a nonprofit.21 We also perform analyses separately for traditional and commercial nonprofits. The marginal effect of programs on board size is larger for commercial nonprofits than for organizations that rely on a mixture of funding sources (0.101 vs. 0.067). This suggests a concave relation between board size and the number of programs. In other words, for more focused nonprofits, the number of programs and the size of the board is small. Adding programs is more likely to increase the size of the board than it is for less focused nonprofits, which have larger boards. This is consistent with the idea that for focused nonprofits the cost of an additional program is large. Consequently, the associated benefits – additional board members and the assets they control – are correspondingly larger. We also report results by estimating the board size regression for 20

Since we take the logarithm of board size, the marginal effect on board size is e0.086  1. In untabulated robustness tests, we also estimate regressions using the raw (unlogged) value for board size. Since this dependent variable can only take integer values, we perform year-by-year Poisson regressions and find that the coefficient on number of programs is positive and significant. We have also examined the relation between board size and number of programs stratifying the sample by nonprofit organization size as measured by revenues, and our results are unaffected. 21

476

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

four different sectors (Panel B). The results for each sector are consistent with the main results—board size is positively related to the number of programs pursued by a nonprofit. 4.2. Board size and pay-performance incentives We next examine the effect of board size on pay-performance sensitivity. Our first set of tests is based on the sensitivity of CEO compensation to financial performance. We estimate the following version of Eq. (6): %DCOMPit ¼ b0 þ b1 %DREVit þ b2 DYIELDit þ b3 lnðBOARDit Þ þ b4 ð%DREVit  lnðBOARDit ÞÞ þ b5 ðDYIELDit  lnðBOARDit ÞÞ þ b6 lnðASSETSit1 Þ þ b7 ð%DREVit  lnðASSETSit1 ÞÞ þ b8 ðDYIELDit  lnðASSETSit1 ÞÞ þ mt þ ti þ eit

ð10Þ

This specification extends Eq. (6) by including the interaction effects of board size, along with the main effects. Thus, the coefficients on the interaction terms represent the incremental effect of an increase in board size on the pay-performance sensitivity with respect to the percentage change in total revenue and yield. Our model predicts that the coefficients on the board size interactions will be negative, indicating that managerial incentives are decreasing in board size. Year effects, mt, and sector effects, ti, are included in all regressions. We also augment the regression with ln(ASSETS) and its interaction with change in revenue and change in yield to control for any non-linear size effect between pay-performance sensitivity and nonprofit size. The results for the full sample are presented in Table 5, Panel A. As predicted, the interactions for board size are negative and significant. We further analyze the pay-performance relation with board size by separately examining traditional nonprofits and commercial nonprofits. Interactions for board size are negative in all cases and significant with Table 5 Board size and pay performance sensitivity. Dependent variable: %DCOMP Panel A: Pooled analysis (all sectors) Full sample

Intercept %DREV DYIELD ln(BOARD) %DREVnln(BOARD) DYIELDnln(BOARD) ln(ASSETS) %DREVnln(ASSETS) DYIELDnln(ASSETS)

Traditional NFPs

Commercial NFPs

Predicted ln(Board)

Coef.

t

Coef.

t

Coef.

t

Coef.

9.86 0.55 0.39  0.10  0.03  0.02 0.09  0.02  0.02

3.91 18.85 13.17  1.24  5.23  3.54 2.44  10.27  7.26

10.68 0.53 0.40  0.03  0.02  0.01 0.10  0.02  0.02

3.61 16.83 12.65  0.35  3.16  2.52 2.48  9.85  7.56

6.48 0.69 0.27  0.43  0.05 -0.02 0.07  0.03  0.01

1.46 8.84 2.80  2.14  2.65  1.32 0.79  4.06  0.78

10.84 0.70 0.54  0.71  0.08  0.08 0.08  0.03  0.02

F-stat R2 N

18.53 0.09 117,280

64.68 0.07 91,804

18.53 0.09 25,476

t 4.23 14.05 10.91  1.81  4.13  3.82 2.08  11.39  7.76 80.86 0.07 117,280

Panel B: Sector analysis Arts & Humanities Coef. Intercept %DREV DYIELD ln(BOARD) %DREVnln(BOARD) DYIELDnln(BOARD) ln(ASSETS) %DREVnln(ASSETS) DYIELDnln(ASSETS) F-stat R2 N

11.07 0.46 0.35  0.29  0.03  0.02  0.06  0.02  0.02

Education t

6.51 5.25 4.23  1.12  1.86  1.18  0.51  2.98  2.69 28.52 0.05 10,448

Healthcare

Human services

Coef.

t

Coef.

t

Coef.

8.04 0.62 0.36 0.09  0.05  0.04 0.04  0.03  0.01

5.63 7.07 3.89 0.38  2.97  3.10 0.38  3.82  1.52

4.44 0.64 0.47 0.34  0.05  0.02 0.17  0.02  0.02

2.53 5.81 3.91 1.23  2.98  1.22 1.43  2.74  2.46

5.78 0.50 0.24  0.11  0.05  0.02 0.08  0.01 0.00

28.93 0.06 16,034

18.53 0.08 9609

t 5.33 6.40 3.01  0.64  3.19  1.69 0.96  2.01  0.23 59.83 0.10 27,756

See Table 1 for sample description and variable definitions. The dependent variable is the percent change in total CEO compensation for each organization for 1999–2003. Sector and year dummies are included in all regressions. t-stats are based on standard errors clustered by organization. Panel A presents results for the full sample, traditional and commercial nonprofits, as well as ‘‘Predicted ln(Board)’’. ‘‘Predicted ln(Board)’’ is the fitted value from a regression of ln(BOARD) on PROG and sector and year indicator variables. Panel B presents results by sector.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

477

the exception of the interaction of board size with yield for commercial nonprofits. The coefficients are larger in absolute value for the commercial nonprofit sample, consistent with the idea that the marginal costs of board expansion increase with nonprofit focus. In order to mitigate endogeneity concerns, we also estimate the regression using ‘‘predicted’’ board size. Predicted board size is the fitted value from a regression of board size on number of programs, sector effects, and year effects. The results are consistent with those from the full sample. In Panel B, we present results by sector. The change-in-revenue interactions are negative and significant for each sector except for Arts & Humanities. The change-in-yield interactions are negative and significant for Education, and negative and insignificant for the other sectors. Overall, these results are consistent with those for the full sample—increases in board size are associated with decreases in pay-performance sensitivity, particularly when performance is measured using the change-in-revenue. In an unreported robustness test, we replace the change in revenue and yield with the change in program spending, %DPSPENDINGit. We use this specification in order to investigate an alternative performance measure based on the percentage change in program spending alone. We choose this performance measure because, based on the derivation in Section 3, the percentage change in program spending is the sum of the percentage change in revenue and change in yield. Thus, this specification combines the performance measures used in the regressions so far and avoids potential (negative) collinearity problems when using percentage change in revenue and change in yield separately. Results are qualitatively similar to those reported.22 In nonprofit organizations, managerial performance is likely to be measured in non-financial as well as financial terms, thereby limiting the prior measure’s ability to sufficiently capture pay-for-performance. Our second measure of managers’ pay-performance incentives, the coefficient of variation of annual compensation, captures incentives tied to both financial and non-financial performance. To examine the relation between compensation variability and board size, we estimate the following regression specification: COVi ¼ b0 þ b1 lnðBOARDi Þ þ b2 COVðREVENUEi Þ þ b3 lnðASSETSi Þ þ ti þ ei

ð11Þ

COVi measures each organization’s manager’s coefficient of variation of total compensation. Sector effects, ti, are included in all specifications. Since larger organizations are likely to have lower performance volatility and consequently a lower compensation coefficient of variation, we add the coefficient of variation of total revenue, COV(REVENUEi), as an additional regressor. We control for organization size by using the natural logarithm of organization assets, ln(ASSETSi), as an independent variable. Table 6 presents the results. In Panel A, we first present OLS results, followed by two-stage least squares (2SLS) results, and results split along traditional and commercial nonprofits. OLS results show that board size is negatively associated with managerial incentives. We also estimate this relation via 2SLS using the number of programs pursued by a nonprofit as an instrument. We choose number of programs as an instrument because of its exogenous nature (both institutionally and in our theory) and positive association with board size. Using the instrumented board size yields similar results as the OLS specification. Finally, the results are consistent for traditional nonprofits as well as for commercial nonprofits, although the magnitude of the coefficient is larger for commercial nonprofits, consistent with results in Table 5. Finally, the results presented by sector in Panel B are generally consistent with our pooled results—board size is negatively associated with managerial incentives. 4.3. Board size and performance Our final set of tests examines the effect of a nonprofit’s board size on its program spending and fund raising ability. We estimate the following equation, in both levels and changes: PERFi ¼ b0 þ b1 lnðBOARDi Þ þ b2 lnðASSETSi Þ þ ti þ ei

ð12Þ

PERFi is the performance measure used in the regression, either the nonprofit’s average program spending or average revenue in levels tests (Table 7), or program spending growth rate and revenue growth rate in changes tests (Table 8). Our model predicts that the coefficient b1 will be positive, indicating that organizational performance is increasing in board size. Sector effects, ti, are included in all regressions, and year effects are included in change regressions. Table 7 reports results based on averages of program spending and revenues. In Panels A1 and B1, we use pooled program spending and pooled revenues, respectively, as the performance dependent variable. The pooled results are consistent with our hypothesis—board size is positively associated with organization performance. In Panels A2 and B2, we report separate sector program spending and revenues, respectively. The sector results are mixed. The results for Arts & Humanities and Education are generally consistent with the pooled results, while the results for Human Services are insignificant. Notably, the results for the healthcare sector indicate that board size is negatively related to nonprofit performance. While inconsistent with our theory, these results are consistent with Yermack’s (1996) results for for-profit firms. We return to this issue below.23 Table 8 reports results based on changes. In Panels A1 and B1, we pool all sectors and use the change in log program spending and the change in log revenues, respectively, as the dependent variable. The results for the full sample are 22 In unreported results, we also examine the relation between board size and pay-performance sensitivity stratifying the sample by nonprofit organization size as measured by revenues, and our results are unaffected. 23 There are two points worth noting here. First, within every sector, there are a substantial number of both commercial and traditional nonprofits, with different funding sources for each. We examine the implications of these differences in Table 9. Second, our sector classifications are not exhaustive–our full sample results include nonprofits that are not included in the individual sector analysis.

478

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

Table 6 Board size and compensation coefficient of variation. Dependent variable: COV(Comp) Panel A: Pooled analysis (all sectors) OLS

Intercept ln(BOARD) PROG COV (Rev) ln(ASSETS)

First stage Coef.

t

Coef.

t

28.63  1.35

13.26  13.49

 0.10

 1.08

0.16  0.72

36.43  18.47

0.09 0.00 0.15

25.77  6.56 78.12

F-stat R2 N

86.69 0.09 35,945 Traditional NFPs

Intercept ln(BOARD) COV (Rev) ln(ASSETS)

Second stage t

28.48  3.38

17.55  4.85

0.15  0.39

43.59  3.29

116.57 0.08 35,945

115.64 0.08 35,945

Commercial NFPs

Coef.

t

Coef.

t

28.55  1.00 0.14  0.81

11.97  9.00 32.03  18.17

27.88  1.72 0.27  0.55

5.46  7.24 19.35  6.42

F-stat R2 N

Coef.

67.30 0.08 28,257

25.07 0.13 7688

Panel B: Sector analysis Arts & Humanities Coef. Intercept ln(BOARD) COV (Rev) ln(ASSETS)

31.46  1.58 0.11 –1.16

F-stat R2 N

Education t 19.78  4.67 8.71 –8.56 66.81 0.07 3324

Healthcare

Coef.

t

21.51  1.07 0.16 –0.60

18.34  4.29 14.61 –6.41 106.05 0.08 4985

Coef. 19.87  1.27 0.13 –0.37

t 11.09 –3.37 9.85 –3.04 42.02 0.06 2961

Human services Coef. Intercept ln(BOARD) COV (Rev) ln(ASSETS) F-stat R2 N

20.24 –1.80 0.22 –0.51

t 20.63 –8.66 19.29 –6.48 187.67 0.12 8163

See Table 1 for sample description and variable definitions. All variables (except coefficient of variation) are averages for each organization over 1998– 2003. The dependent variable is coefficient of variation of total CEO compensation in all specifications. There are 35,945 unique organization observations in the full sample. Panel A presents results for the full sample, based on two-stage least squares, as well as traditional and commercial nonprofits. Sector dummies are included in all regressions. Panel B presents results of analyses by sector. t-stats are based on robust standard errors.

consistent with those for averages of program spending and revenue—changes in board size are positively associated with changes in performance. These results maintain when we split the sample and examine commercial and traditional nonprofits separately. For commercial nonprofits, changes in board size are positively associated with changes in revenues, while the association between changes in board size and changes in program spending is insignificant. The sector-specific results are generally insignificant, with the exception of the Human Services sector.24

24 In unreported results, we also examine the relation between board size and nonprofit performance stratifying the sample by nonprofit organization size as measured by revenues, and our results are unaffected.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

479

Table 7 Effect of board size on program spending and revenues (averages). Dependent variable: ln(avg PS expense) Panel A1: Pooled analysis (all sectors) Full sample

Traditional NFPs

Coef. Intercept ln(BOARD) ln(ASSETS)

5.75 0.05 0.56

F-stat R2 N

t 53.01 6.23 165.60

Commercial NFPs

Coef.

t

Coef.

5.88 0.11 0.53

50.49 12.83 134.63

5.72 0.07 0.60

1727.40 0.62 35,945

1171.16 0.59 28,257

t 23.60 4.48 97.78 828.91 0.75 7688

Panel A2: Sector analysis Arts & Humanities Coef. Intercept ln(BOARD) ln(ASSETS)

t

6.83 0.07 0.45

F-stat R2 N

Education

69.10 3.45 51.89

Healthcare

Coef.

t

5.97 0.02 0.56

1708.39 0.51 3324

52.80 0.99 53.32

Human services

Coef.

t

4.23  0.09 0.72

2991.39 0.64 4985

32.85  2.90 77.03

Coef. 5.63  0.02 0.61

3239.74 0.67 2961

t 72.58  1.15 86.95 5480.04 0.62 8163

Dependent variable: ln(avg revenues) Panel B1: Pooled analysis (all sectors) Full sample

Traditional NFPs

Coef. Intercept ln(BOARD) ln(ASSETS)

5.74 0.05 0.58

F-stat R2 N

t 55.77 7.69 185.14

Commercial NFPs

Coef.

t

Coef.

t

5.80 0.10 0.56

52.78 13.13 152.80

5.87 0.07 0.60

25.05 4.96 102.62

2136.55 0.69 35,945

1497.66 0.66 28,257

912.25 0.78 7688

Panel B2: Sector analysis Arts & Humanities Coef. Intercept ln(BOARD) ln(ASSETS) F-stat R2 N

6.55 0.08 0.50

Education t 74.90 4.63 66.45

2804.95 0.63 3324

Coef. 5.92 0.04 0.58

Healthcare t 53.15 2.12 56.50 3872.55 0.72 4985

Coef. 4.41  0.06 0.72

Human services t 38.49  2.03 87.00 4167.59 0.73 2961

Coef. 5.79  0.01 0.61

t 81.14  0.87 95.58 6719.17 0.68 8163

See Table 1 for sample description and variable definitions. All variables are averages over 1998–2003, resulting in 35,945 unique organization observations. Panel A presents results for the logarithm of average program spending as the dependent variable. Panel A1 presents results for the full sample, traditional nonprofits, and commercial nonprofits, and Panel A2 presents sector subsample results. Panel B presents results for the logarithm of total revenues as the dependent variable. Panel B1 presents results for the full sample, traditional nonprofits, and commercial nonprofits, and Panel B2 presents sector subsample results. Sector dummies are included in the full sample regressions (Panels A1 and B1). t-stats are based on robust standard errors.

While we generally find a positive association between board size and nonprofit performance, we also find a negative association between board size and healthcare nonprofit performance. Healthcare nonprofits are particularly important because, as Table 3, Panel B, shows, healthcare nonprofits are on average the largest nonprofits in our sample. Table 3, Panel B, also provides some clues as to why we may find the negative relation between board size and healthcare nonprofit

480

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

Table 8 Effect of board size on program spending and revenues (changes). Dependent variable: change in ln(PS expense) Panel A1: Pooled analysis (all sectors) Full sample

Traditional NFPs

Coef. Intercept Dln(BOARD) Dln(ASSETS) lagDln(PSPENDING)

0.094 0.004 0.029  0.041

F-stat R2 N

t 3.67 3.02 13.40  7.08

Coef. 0.100 0.005 0.028  0.061

36.28 0.02 80,319

Commercial NFPs t

Coef.

3.42 2.99 10.62  9.64

0.082 0.001 0.030 0.068

33.30 0.02 62,692

t 1.62 0.49 8.33 4.76 8.43 0.03 17,627

Panel A2: Sector analysis Arts & Humanities Coef. Intercept Dln(BOARD) Dln(ASSETS) lagDln(PSPENDING)

0.102 0.006 0.020  0.147

Education t

14.66 1.33 3.97  7.82

F-stat R2 N

Healthcare

Coef.

t

0.084 0.000 0.036 0.006

26.26 0.04 7006

19.03 0.03 6.73 0.40

Human services

Coef.

t

Coef.

0.067  0.004 0.037 0.035

10.60  0.62 5.12 1.57

0.083 0.005 0.027 0.042

18.33 0.02 10,927

8.45 0.02 6580

t 26.51 2.25 3.30 3.30 52.37 0.03 19,416

Dependent variable: change in ln(revenues) Panel B1: Pooled analysis (all sectors) Full sample

Intercept Dln(BOARD) Dln(ASSETS) lagDln(REV)

Traditional NFPs

Coef.

t

0.081 0.008 0.082  0.170

2.87 4.73 18.06  34.09

F-stat R2 N

Coef. 0.084 0.008 0.095  0.196

71.81 0.09 80,319

Commercial NFPs t

Coef.

2.91 4.40 15.69  37.26

0.046 0.007 0.048 0.026

73.34 0.11 62,692

t 0.74 2.33 9.28 1.88 8.30 0.06 17,627

Panel B2: Sector analysis Arts & Humanities

Intercept Dln(BOARD) Dln(ASSETS) lagDln(REV) F-stat R2 N

Education

Coef.

t

0.097 0.005 0.088  0.242

12.84 0.95 6.07  16.83 65.41 0.12 7006

Coef. 0.089  0.004 0.092  0.151

Healthcare

Human services

t

Coef.

t

17.31  0.86 7.81  11.01

0.080 0.006 0.072  0.067

12.37 1.10 4.98  3.53

39.47 0.09 10,927

7.93 0.05 6580

Coef. 0.093 0.010 0.062  0.130

t 26.19 3.61 8.88  11.47 65.89 0.07 19,416

See Table 1 for sample description and variable definitions. There are 80,319 organization-year observations in the full sample, which requires each organization have at three consecutive years of data. Panel A presents results for the change in the logarithm of program spending as the dependent variable. Panel A1 presents results for the full sample, traditional nonprofits, and commercial nonprofits, and Panel A2 presents sector subsample results. Panel B presents results for the change in the logarithm of total revenues as the dependent variable. Panel B1 presents results for the full sample, traditional nonprofits, and commercial nonprofits, and Panel B2 presents sector subsample results. Independent variables include the contemporaneous change in the logarithm of board size, contemporaneous change in the logarithm of total assets, and the lagged change in either ln(PSPENDING) or ln(REV). Year indicators are included in Panels A2 and B2. t-stats are based on standard errors clustered by organization.

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

481

performance—healthcare nonprofits derive more of their revenues from commercial sources than do other nonprofits, as previously established in Brickley et al. (2009). From the perspective of the theory, one can view nonprofit revenues or performance as coming from two main sources—commercial revenues and donations from board members.25 Donations from board members defray the fixed costs of running the nonprofit. The theoretical prediction is that commercial revenues decrease in board size whereas donation revenues increase in board size. As long as the number of programs is not too large, aggregate revenues will increase in board size, which is what we identified in results reported in Tables 7 and 8. This suggests an additional test in which we separately examine the relation between commercial revenue and board size and donation revenue and board size. In Table 9, Panel A, we examine the association between the revenue levels and board size, and in Panel B, we examine the association between revenue changes and changes in board size. In Panel A, for the full sample, the association between commercial revenues and board size is negative, but insignificant, while the association between donation revenues and board size is positive and significant. The signs of the coefficients on board size are as predicted by the theory. We then split our sample into our four main sectors. The results for the healthcare sector show that there is a negative and significant association between commercial revenues and board size, and a positive and significant association between donation revenues and board size. This result is consistent with our theory and suggests that the revenue source matters for the overall relation between revenues and board size. In particular, as healthcare depends more on commercial revenues, the association between aggregate revenues and board size is negative. For the other sectors, we find a similar negative and significant association between commercial revenues and board size, and a positive and significant association between donation revenues and board size for education and human services. However, for arts and humanities, we find a positive and significant association between commercial revenues and board size. However, as Table 3, Panel B, shows, arts and humanities nonprofits tend to derive less of their revenues from commercial sources. In untabulated results, if we restrict the subsample of arts and humanities nonprofits to those with positive commercial revenues, we find a negative and significant association between commercial revenues and board size. Overall, these results are consistent with our theory. In Table 9, Panel B, we examine changes in revenues split by the two types of revenue. In general, the association between changes in commercial revenues and changes in board size is insignificant. The association between changes in donation revenues and changes in board size is positive and significant, again highlighting the importance of revenue source on the relation between board size and financial performance.

4.4. Robustness tests Our empirical tests rely on a number of assumptions about the measures constructed from Form 990 data. To examine whether our inferences are affected by these assumptions we conduct a number of sensitivity tests that relax these assumptions or make alternative assumptions about the data. We describe these below. 1. Our first robustness test deals with the measure of board size. In the prior analyses, board size is defined as the total number of directors, officers, or key employees from Part V of IRS Form 990, and compensation is defined as the sum of base compensation, benefits, and expense accounts. We re-run all tests by measuring board size as the total number of unpaid directors and measuring compensation as annual base compensation. Our results and inferences are unaffected.26 2. Our next test deals with the definition of the chief executive or the highest ranked officer in a nonprofit organization. For purposes of measuring incentives, we identify the top officer as the CEO (or other comparable title). If such a title is not found, we use the highest paid officer or director. These criteria are consistent with those used in other studies (e.g., Baber et al., 2002). As a robustness check, we identify the top officer as the highest paid officer regardless of whether he or she is identified as the CEO. All results are similar to those reported in the prior sections. 3. The results described in the previous sections include ‘‘government related’’ nonprofits. Since ‘‘government-related’’ nonprofits can be fundamentally different from other nonprofits we performed all tests using a sample that excluded organizations with more than 40% of revenues (Form 990 Lines 1 through 3) from government sources. This criterion reduces the sample size by approximately 25%. All results are qualitatively similar to those reported previously. 4. Our measure of the number of programs pursued by a nonprofit may be subject to bias if nonprofits report the number of programs for publicity reasons and not to reflect the true goals of the organization. To address this concern we use the program-related expenses in the Form 990 to construct an index that captures program diversity. This index is similar in spirit to a Herfindahl index used to measure the degree of competition in a given sector. Our variant of this 25

We thank the anonymous referee for suggesting these tests to us. We also considered a third measure for board size which excludes any individual who works at the non-profit on a full-time basis. This measure is highly correlated with our reported measure (r ¼0.97) and number of unpaid directors (r ¼ 0.97). Therefore, we consider each measure an adequate proxy for board size. 26

482

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

Table 9 Effect of board size on commercial revenues and donations (averages and changes). Panel A: Averages FULL SAMPLE Comm. Rev.

Donations

Coef. Intercept ln(BOARD) ln(ASSETS)

2.08  0.04 0.87

F-stat R2 N

t 3.00 1.03 55.07

Coef.

t

1.84 1.45 0.40

3.33 45.56 34.23

318.43 0.19 35,945

311.26 0.20 35,945

ARTS & HUMANITIES

EDUCATION

Comm. Rev.

Intercept ln(BOARD) ln(ASSETS)

Donations

Comm. Rev.

Coef.

t

Coef.

t

4.38 0.69 0.31

9.10 6.33 7.52

3.21 0.75 0.50

10.73 9.74 18.81

F-stat R2 N

93.21 0.05 3324

Donations

Coef.

F-stat R2 N

t 3.83 2.90 18.94

Coef.  1.40 1.62 0.59

226.14 0.08 4985

t  4.06 18.99 19.17 939.28 0.26 4985

HUMAN SERVICES

Comm. Rev.

5.63 0.95 1.37

1.66  0.30 0.73

464.58 0.22 3324

HEALTHCARE

Intercept ln(BOARD) ln(ASSETS)

Coef.

Donations

t  7.86  5.88 27.10

Comm. Rev.

Coef.

t

3.16 2.11 0.12

5.73 15.56 3.04

370.74 0.20 2961

Coef.  3.93  0.31 1.12

169.22 0.12 2961

Donations t 10.12  3.45 33.54

Coef.  0.12 1.78 0.48

718.96 0.14 8163

t  0.41 25.35 19.94 1006.63 0.24 8163

Panel B: Changes FULL SAMPLE Comm. Rev.

Donations

Coef. Intercept Dln(BOARD) Dln(ASSETS) lagDln(REV)

 0.05 0.01 0.05  0.17

F-stat R2 N

t  0.02 0.97 5.78 21.21

Coef. 0.34 0.04 0.14  0.29

17.06 0.03 80,319

t 1.97 3.28 9.08 42.98 62.94 0.09 80,319

ARTS & HUMANITIES

EDUCATION

Comm. Rev. Coef. Intercept Dln(BOARD) Dln(ASSETS) lagDln(REV) F-stat R2 N

0.03 0.03 0.03  0.11

Donations t 0.78 0.78 1.94 4.73 5.55 0.02 7006

Coef. 0.08 0.03 0.18  0.24

Comm. Rev. t 1.80 1.62 4.21 7.72 14.46 0.07 7006

Coef. 0.15 0.03 0.11 0.22

Donations t 4.77  0.60 2.85  8.03 13.28 0.05 10,927

Coef. 0.12 0.07 0.13  0.33

t 2.44 1.27 3.40 19.50 65.16 0.11 10,927

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

483

Table 9 (continued ) Panel B: Changes HEALTHCARE

HUMAN SERVICES

Comm. Rev. Coef. Intercept

t

0.18 0.004 0.05  0.16

Dln(BOARD) Dln(ASSETS) lagDln(REV)

Donations

4.29 0.09 2.74  4.33

F-stat R2 N

5.06 0.03 6580

Coef. 0.12 0.12 0.15  0.30

Comm. Rev. t 1.65 1.99 2.80 15.29 42.67 0.09 6580

Coef. 0.17  0.01 0.05  0.17

Donations t 5.87 0.33 4.06 10.25 21.04 0.03 19,416

Coef. 0.14 0.02 0.12  0.28

t 4.41 0.93 3.76  19.47 66.90 0.08 19,416

See Table 1 for sample description and variable definitions. This table presents results for the full sample, and by sector, for performance regressions, analyzing commercial revenues and donation revenues, separately. Panel A presents results for averages. Dependent variables include the log of average commercial revenues and the log of average donations. Sector indicators are included for the full sample. Panel B presents results for changes. Dependent variables include the change in log of commercial revenues and the change in log of donations. Sector and year indicators are included for the full sample; year indicators are included for sector samples. t-stats are based on standard errors clustered by organization.

index is Pi ¼

PN

j¼1



1

2 PSPENDINGij TOTAL PSPENDINGi

ð13Þ

PSPENDINGij is the program spending on program j and organization i. TOTAL PSPENDINGi is measured as the sum of all the program-related expenses from Part III for organization i. This measure captures the number of programs that are weighted based on their relative program spending. For instance, for organizations with one program, Pi ¼12 ¼1. For organizations with 2 programs which have equal amounts of program expenses, Pi ¼(1/(2  0.52))¼2. In contrast, if one program has expenses twice as large as the other, Pi ¼(1/(0.662 þ0.332))¼1.84. When using this measure of program diversity, all results are similar to those previously reported.27 5. Finally, we conduct two additional robustness tests that address the concerns raised by Krishnan et al. (2006) implying that some nonprofit organizations misreport fundraising expenses. If program expenses are misreported, our independent variables are measured with error and the relations identified could potentially be spurious. We first eliminate organizations that report zero fundraising expenses, since their data are most likely to be suspect. Using this restricted sample, we re-estimate all analyses and obtain similar results. Next we conduct all analyses separately for organizations that employ an outside accountant to audit their statements and those that do not. Krishnan et al. report that organizations with audited statements are less likely to misreport expenses. Our results remain qualitatively similar in both samples, albeit somewhat stronger in statistical terms for organizations that employ an outside accountant and somewhat weaker in the sample of organizations that do not have audited statements. 5. Alternative explanations While our results are supportive of our theory, there are other potential explanations for some of our results. However, no single alternative theory seems to explain all of our results. In this section, we describe the alternative explanations and reasons why they do not comprehensively explain our findings. The first alternative story is based on the managerial power hypothesis. Under this hypothesis, powerful CEOs choose larger boards to entrench themselves and insulate themselves from risk, resulting in lower incentives. Empirically, we find that larger boards are associated with better nonprofit performance, which seems inconsistent with CEO entrenchment. Further, we do not find that CEOs with larger boards earn excess compensation—the coefficient on the direct effect of board size on CEO compensation is negative but insignificant in our full sample (see Table 5). A second alternative is that more talented CEOs self-select into nonprofits that pursue a larger set of programs. Since the number of programs positively affects board size, the positive association between board size and nonprofit performance is likely due to CEO talent. So, board size is simply a proxy for CEO talent. However, CEOs at nonprofits with larger boards should then have greater pay-performance incentives. Empirically, we find the opposite—incentives are lower at nonprofits with larger boards. Further, in a well-functioning labor market, more talented CEOs should be paid higher than their less talented counterparts. However, our results in Table 5 do not show a relation between board size and CEO pay. 27 We also considered a third measure for number of objectives, which counts only those programs with associated program spending. This measure is highly correlated with number of programs (r ¼ 0.96) and our Herfindahl-based measure (r ¼ 0.86). Therefore, we consider each measure an adequate proxy for number of objectives.

484

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

A third alternative is that, in nonprofits, larger boards are more effective monitors (contra Yermack, 1996), which results in better nonprofit performance. Because of more effective monitoring, the use of pay-performance sensitivity as an incentive device is potentially reduced. There are two concerns with this story. First, the monitoring hypothesis is silent on the association between the number of tasks and board size, since under the monitoring hypothesis directors exist to monitor and do not offset the fixed costs of greater organizational complexity associated with more tasks. Empirically, the positive association between tasks and board size is not explained by monitoring. Second, the monitoring hypothesis predicts that CEOs with large boards should earn less total compensation, since they are exposed to less compensation risk from incentives. Empirically, we do not find that CEOs with larger boards earn less compensation—the coefficient on the direct effect of board size on CEO compensation is negative although insignificant in our full sample in Table 5. While undoubtedly there could be other alternatives, our model provides a parsimonious description of the relations between the number of programs, board size, managerial incentives, and organizational performance in nonprofits that is supported by the data.

6. Conclusion In this paper, we study the effect of a nonprofit organization’s board of directors on its performance and on its manager’s incentives. The primitive or exogenous source of variation in our work is the number of objectives or programs that a nonprofit pursues. Individual directors bring assets that are valuable to the nonprofit, but at the cost of greater disagreement and dissonance. These assets offset the greater organizational complexity associated with having more tasks to perform. Consequently, we predict that board size is positively related to the number of programs a nonprofit pursues. Further, nonprofit managerial incentives are inversely related to board size due to disagreement and dissonance. Crosssectionally, nonprofit performance in fund raising and program spending is positively related to board size as long as the number of programs is not too large. We test these predictions using data on nonprofits from their Form 990 filings with the IRS. Our empirical results are consistent with our predictions. Specifically, we find that board size is positively associated with the number of programs pursued by a nonprofit. In addition, we find that a nonprofit manager’s pay-performance sensitivities are negatively related to board size. We also show that program spending and revenue are positively related to board size in levels and changes, with the exception of healthcare nonprofits. When we further examine the difference in nonprofit performance based on the source of revenues, we reconcile the results for healthcare nonprofits as well. There is a positive association between donation revenue and board size while there is a negative association between commercial revenue and board size. These opposite associations are consistent with the predictions of our theory. As healthcare nonprofits derive more of their revenues from commercial activities than do other nonprofits, this explains the negative association between overall healthcare nonprofit revenue and board size. Our results are robust to various specifications and the inclusion of numerous controls. Prior work on the governance of nonprofit organizations has largely ignored the role of organizational scope in affecting enterprise performance. Our main contribution to this literature is to demonstrate that, in addition to ownership structure (see Eldenburg and Krishnan, 2003), program diversity affects governance, managerial incentives and ultimately organization performance. Further, recent regulatory concern regarding excess managerial pay in nonprofit organizations is based on a benchmark against comparable for-profit organizations, where the benchmark organization is of the same size and in the same sector.28 However, these studies ignore the diversity of stakeholders, programs and board structure, thereby limiting their ability to provide meaningful inferences. Our theory of multiple objectives represented by directors on a board also has important implications for research on governance, managerial incentives and firm performance in for-profit enterprises. We show that including other objectives, in addition to shareholder value maximization, potentially dampens managerial incentives to maximize shareholder value and thereby adversely impacts firm performance. However, accommodating these other objectives may enhance firm value by enlarging a firm’s asset base.29 While our empirical results are consistent with our predictions, we have only begun to explore board heterogeneity and its effect on enterprise performance. Our proxies for board heterogeneity are rather crude. While it is certainly plausible that greater board size reflects greater disagreement about organization objectives, it would be preferable to have direct measures of differences in board members’ objectives. For example, European firms often have employee union representation on the board, bank or creditor representation on the board, and in some cases, charitable foundation representation on the board. In the US, defense contractors often have individuals affiliated with the Department of Defense on their board, and health insurance firms include former political appointees. To the extent that we have been able to find results consistent with the theory in US nonprofit data, this suggests that the presence of multiple objectives may in fact explain board behavior more generally. 28

See, the IRS’s 2009 Nonprofit Hospital project at www.IRS.gov/newsroom. For instance, the ownership of Chrysler (55 percent) and General Motors (17.5 percent) by the United Auto Workers (UAW) union potentially creates a conflict of interest with shareholders of the two corporations. However, the UAW, in exchange, promised not to engage in strikes or work stoppages, which benefits Chrysler and GM shareholders. Consequently, the auto firms face a tradeoff between diluted managerial incentives to increase share price (because of accommodating worker interests) versus reducing the costs associated with strikes. 29

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

485

Appendix A. Model of board size and incentives in nonprofits In this appendix, we present a theory of the size of the board of directors based on the multitasking model of Holmstrom and Milgrom (1991). There exist a set of N activities that the organization can perform. These activities are established in the organization’s charter and are taken as pre-determined. The board of directors is of size m and is chosen to maximize the value of the nonprofit. The board oversees the organization. The board also helps to defray the fixed costs of running the nonprofit, or, equivalently, contribute assets to the nonprofit. For example, our model applies to the case of a museum with a network of potential donors. The museum benefits from the donors, who receive board representation if they make a sufficiently large financial contribution to the organization. Note that donors may choose to become directors for a variety of reasons, including nonpecuniary private benefits. We assume that directors value the activities that the nonprofit performs, and therefore care about nonprofit output. While directors are valuable because they contribute assets or defray fixed costs, they are also costly. More directors potentially lead to greater dissonance and disagreement about objectives. Further, if the board provides monitoring, more board members may lead to free-riding and reduced monitoring (see Yermack, 1996). Thus, there is a tradeoff associated with more board members, and the nonprofit must choose the size of the board. After the board is chosen, the board subsequently contracts with the nonprofit’s manager over the amount of work performed on each of the nonprofit’s activities. The choice of board size occurs at time 0. We assume that every nonprofit incurs a cost k(m, N), where k depends upon both the size of the board and the number of activities. The payoff (net of payment to the manager and cost k) to the nonprofit as a whole is

pðm,NÞ ¼ zðm,NÞkðm,NÞ,

ð14Þ

where z(m, N) represents the net payoff to the board from contracting with the manager. We seek the equilibrium board size mn that maximizes the payoff p. At time 1, the organization employs a manager to produce output on the N tasks. For task i, iAN, the gross output from the manager taking action xi is vi ¼ xi þ ei

ð15Þ

where ei  N½0, s2i  is a normally distributed shock to the performance measure vi for task i. For simplicity, we assume that the variances of the N shocks are identical (s2i ¼ s2 for all i) and that the N shocks are uncorrelated. We assume that the manager dislikes working on tasks. The disutility from working on task i is given by 1=2ci x2i . For notational parsimony, we assume that ci ¼m 40 for all i. In other words, the cost of working on tasks is increasing in the number of directors. This captures the idea that there is dissonance and disagreement if there are more directors. The intuition here is that if the board is large, then there is likely to be disagreement about which tasks the manager should perform, and the cost of performing tasks is then raised. We assume that the manager is risk averse with coefficient of absolute risk aversion r. We assume that the N directors are risk neutral. Further, we restrict our analysis to linear contracts. Holmstrom and Milgrom (1987) show that linear contracts are equilibrium contracts in this setting, although there may also be nonlinear equilibrium contracts. The board offers the manager a contract of the form N X

w ¼ dþ

a i vi :

ð16Þ

i¼1

So, the manager receives a fixed payment of d and performance related payments of aivi for all iAN tasks on which the manager may work. The expected net payoff to the board from contracting with the manager is z, where z¼

N X

Eðvi Þw ¼

i¼1

N X i¼1

xi w ¼

N X

ð1aÞxi d,

ð17Þ

i¼1

which depends on the number of tasks N. This payoff to the board is net of payments to the manager and independent of the other fixed costs k, which have already been committed before contracting with the manager. We can now define the manager’s aggregate certainty equivalent utility from contracting with the board as u ¼ dþ

N X i¼1

ai xi 

N N mX rX x2i  a2 s2 : 2 i¼1 2i¼1 i

ð18Þ

At time 2, the manager maximizes this certainty equivalent over the N tasks and chooses an equilibrium activity level, xi, for each task i. At time 3, outputs are realized and all payoffs are made. At time 2, we find the manager’s optimal actions by taking the N first order conditions and solving. We get the optimal action taken on each task i is xni ¼

ai m

:

ð19Þ

486

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

The board’s net payoff from contracting with the agent at time 1 can be rewritten as z¼

N X

xi 

i¼1

N N mX rX x2i  a2 s2 u0 , 2 i¼1 2i¼1 i

ð20Þ

where we assume that the agent will be held to his reservation utility, u0. Substituting the agent’s action xi on every task i into the board’s objective function and maximizing over the contract weights ai yields the equilibrium contracts:

ani ¼

1 : mr s2 þ 1

ð21Þ

It follows that the manager’s incentives for any task i are decreasing in the manager’s risk aversion, r, and the variance of the performance measures, s2. These are standard results for the linear principal-agent model. More importantly, the manager’s incentive on any task i is decreasing in board size m. Substituting, in equilibrium, the amount of managerial action on task i as a function of board size is xni ¼

1 : m2 r s2 þ m

ð22Þ

Given the incentives and actions on the N tasks, we can now examine how board size influences outcomes. Nonprofit value p at time 0 is comprised of both the board’s net payoff z to contracting with the agent and the fixed cost of running the nonprofit. We assume that the cost k(m, N) is given by

kðm,NÞ ¼

N2 : 6m3

ð23Þ

The cost of running a nonprofit is convex in the number of tasks. More tasks increase the complexity of the nonprofit, thus reducing overall nonprofit value. At the same time, the cost of engaging in more tasks can be offset by more board members. This happens because with more tasks, larger boards can monitor or oversee those tasks. More generally, one can think of this as more board members help to defray the fixed costs of running the nonprofit or, alternatively, contribute assets to the nonprofit, which offset the nonprofit’s fixed costs. These fixed costs are increasing in the number of tasks. The equilibrium board size is obtained by maximizing p with respect to m. With this structure in place, the following proposition establishes the existence of an equilibrium board size. Proposition 1. For Nr2s4 large enough, there exists an optimal board size mn 40. Optimal board size mn is increasing in the number of tasks N. Proof. Maximizing p with respect to m, we obtain the following necessary and sufficient first order and second order conditions: NðNm2 r 2 s4 2m3 r s2 þ 2Nmr s2 m2 þ NÞ 2m4 ðmr s2 þ1Þ2

¼0

ð24Þ

and Nðm2 r 2 s4 þ3mrs2 þ 1Þ m3 ðmrs2 þ 1Þ3

o0,

ð25Þ

where we have used the first order condition to simplify the second order condition. The second order condition holds for m40. Solving the first order condition yields:   1 1 Nr 2 s4 1 mn ¼ 10N þ N2 r 2 s4 þ 2 4 þ y þ , ð26Þ 36y r s 6r s2 where vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u   sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi u 3 1 39N 1 1 N 11N 2 t 2 2 3 3 6 3 N  4 8þ 2 4 : y¼ þ 15N r s þ N r s  3 6 þ 216 r s2 432 r s r s r s

ð27Þ

Nr2s4 40.09017 (hence, large enough) is necessary for y to be real valued, and is sufficient for y to be positive and directly implies that mn 40. Using the implicit function theorem on the first order condition yields @mn ðmn r s2 þ 1Þ3 ¼ 4 0, n n @N 2m ððm Þ2 r 2 s4 þ 3mn r s2 þ 1Þ which establishes that board size is increasing in the number of tasks.

ð28Þ &

Because the nonprofit optimizes board size, there is no equilibrium relation between board size and nonprofit value except as a function of changes in underlying parameters. The key underlying parameter is the number of tasks. Since

R.K. Aggarwal et al. / Journal of Accounting and Economics 53 (2012) 466–487

487

board size is increasing in the number of tasks, we now show that for certain parameter values, nonprofit value is also increasing in the number of tasks. Differentiating nonprofit value in equilibrium, pn, with respect to N yields @pn 3ðmn Þ2 2Nmn r s2 2N : ¼ @N 6ðmn Þ3 ðmn r s2 þ1Þ This is positive if   pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 1 10N þ N2 r 2 s4 þ 2 4 þ6y 4 2 N 2 r 2 s4 þ6N þNr s2 þ 2 : 6y r s rs

ð29Þ

ð30Þ

While the above condition is not transparent, it is intuitively simple. Aggregate nonprofit output is concave in the number of tasks, as more tasks lead to greater organizational complexity. If the number of tasks is not too large, nonprofit output will increase in the number of tasks. The above equation establishes the condition under which this holds. If the above condition is met, nonprofit output will increase in the number of tasks. Since in equilibrium, board size is increasing in the number of tasks, we have that, cross-sectionally, there will be a positive association between board size and nonprofit output. Overall, our model has several testable implications. First, the number of directors on the board is increasing in the number of tasks performed by an organization. Second, incentives are decreasing in the number of directors on the board. Third, cross-sectional variation in the number of tasks implies that organizations with more tasks will have both more directors and greater nonprofit value. These implications form the basis of the hypotheses we test in the paper. References Alchian, A., Demsetz, H., 1972. Production, information costs, and economic organization. American Economic Review 62, 777–795. Appelbaum, E., Hunter, L., 2004. Union participation in strategic decisions of corporations. In: Freeman, R.B., Hersch, J., Mishel, L. (Eds.), Emerging Labor Market Institutions for the Twenty-First CenturyUniversity of Chicago Press, Chicago, IL, pp. 265–291. Baber, W.R., Daniel, P.L., Roberts, A.A., 2002. Compensation to managers of charitable organizations: an empirical study of the role of accounting measures of program activities. The Accounting Review 77, 679–693. Ben-Ner, A., Van Hoomissen, T., 1991. Nonprofit organizations in a mixed economy: a demand and supply analysis. Annals of Public and Cooperative Economics 62, 519–550. Brickley, J.A., Van Horn, R.L., 2002. Managerial incentives in nonprofit organizations: evidence from hospitals. Journal of Law and Economics 45, 227–249. Brickley, J.A., Van Horn, R.L., Wedig, G.J., 2009. Board Composition and Nonprofit Conduct: Evidence from Hospitals. University of Rochester and Vanderbilt University, Working Paper. Core, J., Guay, W., Verdi, R., 2006. Agency problems of excess endowments in not-for-profit firms. Journal of Accounting and Economics 41, 307–333. Drucker, P.F., 1992. Managing the Non-profit Organization: Principles and Practices. HarperCollins Publishers, New York, NY. Duca, D.J., 1996. Nonprofit Boards: Roles, Responsibilities, and Performance. John Wiley & Sons, New York, NY. Eldenburg, L., Hermalin, B.E., Weisbach, M.S., Wosinska, M., 2004. Governance, performance objectives and organizational form: evidence from hospitals. Journal of Corporate Finance 10, 527–548. Eldenburg, L., Krishnan, R., 2003. Public versus private governance: a study of incentives and operational performance. Journal of Accounting and Economics 35, 377–404. Fama, E.F., Jensen, M.C., 1983. Agency problems and residual claims. Journal of Law and Economics 26, 327–366. Glaeser, E., Shleifer, A., 2001. Not-for-profit entrepreneurs. Journal of Public Economics 81, 99–115. Hackman, R.J., 1990. Groups That Work (and Those That Don’t). Jossey-Bass, San Francisco, CA. Hansmann, H.B., 1980. The role of nonprofit enterprise. Yale Law Review 89, 835–898. Hansmann, H.B., 1996. The Ownership of Enterprise, Cambridge, MAHarvard University Press. Hermalin, B.E., Weisbach, M.S., 2001. Boards of Directors as an Endogenously Determined Institution: A Survey of the Economic Literature. UC Berkeley and University of Illinois, Working Paper. Holmstrom, B., Milgrom, P., 1987. Aggregation and linearity in the provision of intertemporal incentives. Econometrica 55, 303–328. Holmstrom, B., Milgrom, P., 1991. Multitask principal agent analyses—incentive contracts, asset ownership, and job design. Journal of Law, Economics, and Organization 7, 24–52 (Special Issue). Jensen, M.C., 1993. The modern industrial revolution, exit and the failure of internal control systems. Journal of Finance 48, 831–880. Krishnan, R., Yetman, M.H., Yetman, R.J., 2006. Expense misreporting in nonprofit organizations: an agency based analysis. The Accounting Review 81, 399–420. Lorsch, J., MacIver, E., 1989. Pawns or Potentates: The Reality of Americas Corporate Boards, Boston, MAHarvard Business School Press. Lynn, L., Smith, S., 2005. The Performance Challenge in Nonprofit Organizations. University of Washington, Working Paper. Steinberg, R., 2004. The Economics of Nonprofit Enterprises. Elgar, Northampton, MA. Weisbrod, B., 1988. The Nonprofit Economy, Cambridge, MAHarvard University Press. Yermack, D., 1996. Higher market valuation of companies with a small board of directors. Journal of Financial Economics 40, 185–212.