Board composition and nonprofit conduct: Evidence from hospitals

Journal of Economic Behavior & Organization 76 (2010) 196–208

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Journal of Economic Behavior & Organization journal homepage: www.elsevier.com/locate/jebo

Board composition and nonprofit conduct: Evidence from hospitals James A. Brickley a , R. Lawrence Van Horn b , Gerard J. Wedig a,∗ a b

William E. Simon Graduate School of Business Administration, University of Rochester, Rochester, NY 14627, United States Owen Graduate School of Management, Vanderbilt University, Nashville, TN 37203, United States

a r t i c l e

i n f o

Article history: Received 10 November 2008 Received in revised form 28 June 2010 Accepted 30 June 2010 Available online 7 July 2010 JEL classification: G34, I10, J31, L31 Keywords: Corporate governance Not-for-profit Hospitals Compensation

a b s t r a c t This study uses data from hospitals to test the hypothesis that management representation on nonprofit boards leads to “excessive” CEO pay, defined as compensation that exceeds the level predicted by a market wage model. We document a relatively small, but statistically significant, positive association between CEO pay and “insider” boards that include the CEO and other employees as members. Additional tests confirm that this result is not driven by endogenous board structure and that excess pay is greater in the absence of competition from for-profit hospitals. We then examine whether management board representation is associated with larger underlying agency concerns that lead to reduced donations. Our tests do not support this hypothesis but do, however, reveal a negative correlation between donations and physician representation on the board – suggesting a potential conflict between the interests of donors and non-employee physicians. Our overall evidence provides empirical support for modeling nonprofit organizations as consisting of competing stakeholders. © 2010 Elsevier B.V. All rights reserved.

1. Introduction and overview Private nonprofit organizations are important providers of public goods and services in the United States. Primary decision authority in nonprofit organizations is held by the board of directors. Laws, regulations, competition for donations, and debt markets encourage nonprofit boards to focus on social objectives, thus making their organizations more attractive to potential donors. Nonetheless, regulators frequently voice concerns over whether these mechanisms effectively constrain insiders from expropriating donations and other organizational resources. Recently, for example, the IRS’s director of taxexempt organizations announced intensified oversight of executive compensation after a report “raised some eyebrows” over the level of CEO pay at nonprofit hospitals. She called for making “certain governance practices public.”1 The appropriate role for managers on nonprofit boards is presently an open question. Some academics and professional associations argue that managers of nonprofit organizations should not serve as voting members on their boards.2 They contend that, absent shareholders and takeover markets, independent boards are needed to monitor managers and prevent expropriation of organizational resources.3 On the other hand, CEOs may possess specific knowledge which provides a

∗ Corresponding author at: William E. Simon Graduate School of Business Administration, University of Rochester, Carol Simon Hall, Rochester, NY 14627, United States. Tel.: +1 587 273 1647. E-mail address: [email protected] (G.J. Wedig). 1 Spector (2009). The IRS director’s comments were motivated by an IRS study that found top official pay at nonprofit hospitals averaged $490,000. The top 20 averaged $1.4 million. Other critics have called for additional regulations, such as a Sarbanes Oxley Act for nonprofit organizations (Jones, 2003). 2 For example, see Fama and Jensen (1983a) and Board Source (2002). 3 Historically, IRS rules limited physician representation on nonprofit hospital boards to 20% of the board. This regulation has recently been relaxed. Currently, the IRS requires that community representatives hold at least 50% of board seats in tax-exempt organizations. The IRS also has legal authority 0167-2681/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jebo.2010.06.008

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rationale for their participation on the board. In our data, about half of the hospitals include the CEO as a voting member of the board, while half exclude the CEO from the board. In this paper we provide evidence on whether the composition of hospital boards of directors affects either the ability of their CEOs to expropriate organizational resources or the organization’s ability to raise public donations. We begin by testing whether managerial representation on nonprofit hospital boards increases the likelihood that CEO salaries will exceed the level predicted by a market wage regression (“Management Power Hypothesis”). The null hypothesis is that nonprofit regulations, governance and market mechanisms (e.g., debt markets) constrain CEO power and prevent organizations from paying their CEOs more than the market rate. Prior studies, focusing on for-profit corporations, have tested the Management Power Hypothesis by estimating regressions of CEO pay on board structure (e.g., the percentage of outside directors on the board). We start by estimating similar regressions and find that nonprofit hospital CEOs with voting rights earn an estimated 7–10% higher salary, ceteris paribus. In some of our tests, we also find a significant positive association between CEO pay and the fraction of non-CEO employee board members. In addition, we find that the level of excess pay increases in markets where there is no for-profit competitor in the market to discipline on the behavior of the board. While the documented association between pay and management board rights is consistent with the Management Power Hypothesis, it could also be driven by the joint endogeneity of board composition, CEO ability and pay. For example, organizations that hire more talented CEOs may also pay their CEOs more and grant them more decision rights, such as a seat on the board. Bertrand and Mullainathan (2001) address this problem in samples of for-profit firms by testing whether CEOs are paid in accordance to observable “luck” (by, for example, granting the CEO extra compensation when the firm receives “windfall” cash due to favorable changes in commodity prices or exchange rates). We use a similar approach in this study by focusing on a significant and arguably exogenous source of hospital cash flow – the predicted profits that the hospital enjoys from the Federal Government’s Medicare program (Medicare profits are largely determined by regulatory policies that are established annually in Washington, DC). This additional evidence allows us to reject all but the most restrictive versions of the competitive contracting alternatives to the Management Power Hypothesis. Our findings are broadly consistent with at least two other studies that find diversions of resources by powerful nonprofit CEOs (Fisman and Hubbard, 2005; O’Regan and Oster, 2005). It is important to note, however, that the size of our estimated excess compensation is arguably small relative to the overall size of the organization. For example, a 10% compensation premium relative to the median CEO’s cash compensation of $210,000 translates to $21,000 in excess compensation. This level of excess compensation represents only 3/100 of 1% of a median hospital’s revenue. An important question, therefore, is whether management power also leads to other less visible, but potentially more important forms of expropriation (e.g., choosing organizational investments to satisfy managerial preferences, shirking, and excess payments to other stakeholders). Direct evidence on this issue is hard to provide since these alternative forms of expropriation are difficult to observe and measure. However, indirect evidence can be obtained by examining the relation between managerial voting rights and donations. Fama and Jensen (1983b) and Hansmann (1980) argue that potential donors are less likely to contribute to organizations with governance structures that facilitate the expropriation of donations for private purposes. If management participation on boards creates large agency problems, we should find a negative association between donations and insider boards. Alternatively, CEO power need not result in fewer donations provided that the magnitude of the associated agency problem is small and confined to the modest levels of excess compensation cited above. Consistent with the latter hypothesis, we do not find a negative association between donations and either CEO voting rights or the fraction of non-CEO employee board members. Glaeser (2003) models nonprofit organizations as consisting of stakeholder groups who compete for the organizations’ financial residual. In the case of hospitals, prominent stakeholders include employees, donors, and private physicians who utilize hospitals facilities (e.g., for diagnostic tests and surgery). There are potential conflicts among all three interest groups. For example, physicians may favor investments that increase their private income over investments preferred by donors.4 Interestingly we find that donations decline with physicians on the board. Concerns about physicians could cause donors to view powerful managers as an asset, to the extent that they restrain the influence of powerful groups of physicians. This potentially explains why we find that CEO voting rights have a marginally significant positive effect on donations. Thus, while CEO power on the board might create some agency problems, it might serve to mitigate other agency concerns. Our study has at least three important implications. First, we extend the literature on the timely topic of executive compensation to an important segment of the nonprofit industry (hospitals), finding that management power increases the likelihood of excess CEO compensation. Second, our evidence provides empirical support for modeling nonprofit organizations as consisting of competing stakeholders. Here, it is worth noting that many other nonprofit industries fit the Glaeser model of competing stakeholder groups including, for example, universities. Thus, our results here have a significance that extends beyond hospitals. Third, our evidence suggests that the overall agency problem of having management representation on nonprofit hospital boards is relatively small. It is important to note, however, that hospitals operate in a “mixed

to impose penalties on nonprofit organizations and individual board members who authorize “unreasonable” compensation to employees (compensation in excess of what “would ordinarily be paid for like services by like enterprises under like circumstances”). 4 The argument that physicians utilize hospital resources to maximize their joint income is a well-established theory in the health economics literature. See, for example, Pauly and Redisch (1973).

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sector,” which contains both for-profit and nonprofit organizations (other mixed sectors include health insurance, nursing homes, education, symphonies, theatres, and museums). As noted above, some of our results also suggest that excess pay is greater in hospitals that do not face the discipline of a for-profit competitor. Thus, our results are potentially a lower bound of agency problems in nonprofit organizations that do not compete with for-profits. Finally, while our findings are drawn from a single segment of the nonprofit sector, nonprofit hospitals comprise a significant segment of the economy (4% of US GDP in 2007) and their effective governance is a matter of ongoing regulatory concern, as well as a “bellwether” for nonprofit regulatory initiatives (see footnote 1). The remainder of the paper is organized as follows. Section 2 describes our sample, while Sections 3 and 4 present our empirical results. Section 5, the conclusion, considers the implications of our study for the optimal structure of nonprofit boards.

2. Data 2.1. Sample design Our sample consists of 308 nonprofit hospitals providing acute care services during the period from 1998 through 2002. A hospital has to meet the following criteria to be in our sample: (1) the hospital responded to the Governance Institute’s bi-annual survey of hospital governance practices in either 2000 or 2002; (2) we can obtain a hard copy of the hospital’s IRS Form 990 for one or more years between 1998 and 2002; (3) the governance survey indicates that the board of directors has “independent” authority on executive compensation5 ; (4) the CEO is an employee of the hospital and is not an employee of a contract management organization; and (5) the hospital’s primary objective is to provide acute care hospital services. These criteria result in a relatively homogenous sample of short-term acute care hospitals for which we have the relevant data to conduct our tests.6 We use data from five different sources. The Governance Institute’s bi-annual survey of hospital governance practices is used to identify the role of the CEO on the hospital board as well as hospital governance practices.7 We obtain values for CEO compensation, total assets, return on assets, private donations and total revenues from an electronic database of IRS 990 forms. To help ensure the accuracy of the compensation information, we manually review each hospital’s IRS Form 990. We obtain information on the CEO’s tenure with the organization, tenure as CEO, age and gender from the American College of Healthcare Executives’ (ACHE) online affiliate directory. The database does not contain the birth date of the executive. We estimate the age based on the year that the executive obtained his/her undergraduate degree. We assume that college graduation occurs at age 22. We use the county to proxy for the hospital’s market area. We obtain data on population density, per capita income, the number of hospitals in the county, percent of the population enrolled in Medicare, percent of the population enrolled in Medicaid and percent of the population in poverty from the Area Resource File. We obtain the hospital case-mix index from the Centers for Medicare and Medicaid Services. The case-mix index is a measure of the service complexity of the hospital. To define a measure of exogenous hospital cash flows, we focus on the Federal Government’s Medicare program. Exogenous cash flows are defined as the hospital’s Medicare revenues minus its predicted Medicare costs, where predicted costs are obtained from an estimated cost function (see Section 3 for more detailed discussions of this measure and why it is likely to be independent of CEO ability). We obtain the necessary data from Medicare Cost Reports and the Federal Register. We collect CEO compensation data for the years 1998–2002, inclusive, yielding a maximum of 1218 non-missing observations for CEO compensation. Governance structure for a given firm varies little across the sample period and both CEO compensation and donations are highly correlated over time. For our initial regressions, which focus on levels of compensation, we restrict the sample to one observation per hospital in order to obtain conservative estimates of the standard errors of our parameter estimates. We select the most recent year that the hospital reports CEO compensation and donations (in cases where the hospital reports values for multiple years). We also conduct analyses of changes in CEO compensation that make use of the entire sample of observations on CEO compensation. Changes in CEO compensation are less correlated over time than levels of CEO compensation. After firstdifferencing, we have 809 valid observations of changes in CEO compensation.

5 Most of the hospitals in our sample (82%) are legally independent in that the board does not report to a “higher authority.” The remaining hospitals are members of systems but their boards have “independent authority on executive compensation.” A system hospital is not included in our sample if its board has to seek approval on executive compensation from a “higher board or authority.” Our primary results are similar when we exclude system hospitals from our analysis. 6 The combined Governance Institute surveys contain information on 651 unique hospitals where the board has independent authority on executive compensation. Our name match of these organizations with the IRS Business Master File and associated IRS Form 990 eliminates all for-profit hospitals (since for-profit hospitals do not file 990’s), reducing the possible observations to 437. A manual review of the IRS Form 990’s to exclude contract-managed hospitals and hospitals that do not focus on short-term acute care eliminates another 129 observations, leaving us with a final sample of 308 hospitals. The number of valid observations varies across tests due to missing values. 7 Not all firms responded to the survey in both years. Where the firm responded in both years, we use the 2002 information. Otherwise, we use the available observation and assume that the governance structure is the same across the 2 years.

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Table 1 Descriptive statistics. N

Mean

Standard deviation

25th percentile

Median

75th percentile

CEO compensation CEO compensation (in thousands) Change in compensation (in thousands) Percent change in compensation

274 809 809

253.41 16.12 8.7%

159.71 48.72 20.3%

151.74 0.00 0.0%

210.56 10.80 6.1%

305.15 25.51 13.2%

CEO characteristics CEO age CEO tenure with hospital Tenure as CEO of hospital CEO is a woman

219 223 222 298

51.08 12.33 8.64 0.10

6.36 9.06 6.50 0.30

Board characteristics Number of board members Number of employees on board Number of physicians on board Number of outside board members CEO has voting rights on board Management percentage of board Physician percentage of board

298 294 297 294 298 294 297

15 1 3 12 53.4% 5.0% 19.2%

6 1 2 10 0 6.9% 9.9%

Hospital characteristics Total assets (in millions) Hospital case mix Return on assets Donations as percentage of revenue Predicted Medicare profits (in thousands) Predicted Medicare profits/profits positive Predicted Medicare profits/profits negative

300 273 300 298 279 149 130

Market characteristics Population density Per capita income (in thousands) Number of hospitals Medicare percentage in the county Medicaid percentage in the county

294 296 296 111 276

113.02 1.28 3.5% 0.83% 1050.39 3182.90 −1393.78 638 25.91 5 0.17 0.16

168.05 0.20 5.2% 1.52% 5714.00 6915.00 2061.00 1421 6.63 12 0.05 0.13

47 5 3 0

51 10 7 0

55 18 12 0

11 0 2 8 0 0.0% 13.3%

14 1 3 10 1 5.0% 19.0%

17 1 4 13 1 7.1% 25.0%

24.11 1.14 0.8% 0.08% −409.43 258.20 −2000.53 49 21.82 1 0.14 0.08

61.12 1.25 4.0% 0.28% 59.26 1188.50 −485.17

135.95 1.35 6.4% 0.74% 1358.06 3426.14 −155.19

120 24.67 2 0.16 0.13

403 28.28 4 0.19 0.20

This table presents the mean and median values of selected variables employed in our econometric tests of the relation between CEO compensation, donations and hospital governance. Measures of CEO compensation and financial performance are derived from IRS Form 990, Medicare Cost Reports and the Federal Register. Hospital CEO characteristics are obtained from the Academy of Health Care Executives’ (ACHE) affiliate directory. Hospital board characteristics are from the Governance Institute Survey. Market characteristics are from the Area Resource File. The sample is drawn from the period 1998–2002. The most recent observation is used for each hospital. For models of compensation changes we employed data from 1998 to 2002.

2.2. Descriptive statistics Table 1 presents descriptive statistics. The median values for CEO cash compensation, change in compensation and percent change in compensation are $210,560, $10,800, and 6.1%, respectively. The median CEO in our sample is 51 years old, has been CEO for 7 years, and has worked for the hospital for a total of 10 years. Ten percent of the CEOs are women. The median hospital has 14 board members. IRS regulations require that at least 51% of the board members be community representatives. Community outsiders hold 10 seats in the median hospital in our sample (71% of the total), while physicians (who have a non-employee affiliation with the hospital) typically hold three seats. The median hospital has one management director (the CEO); about 30% have one or more non-CEO employee directors (e.g., the CFO). The CEO has full voting rights on the board in 53% of the hospitals. The median hospital in our sample has $61 million in assets, is neither part of a system, nor a religious order, and earns a 4% return on assets. It faces only one competitor in a market with a per capita income of $24,670 and a population density of 120 persons per square mile. Its market has a Medicare percentage of 16% and a Medicaid percentage of 13%. The median hospital receives donations equal to 0.28% of revenues, which is 10% of its net income.8 The mean hospital receives donations equal to 0.83% of revenues. The median value translates into $184,260 in private donations, given median revenues of $65,807,040. Our measure of donations excludes government contributions, such as government grants.9 The median hospital also enjoys $59,260 in “predicted” Medicare profits defined as the difference between Medicare inpatient revenues and predicted Medicare costs. Conditional on earning positive predicted profits, the median amount is approximately $1.2MM. Conditional on predicted profits being negative, the median value is −$485,170. If the Medicare

8

For the purposes of computing donations as a percentage of net income, we exclude observations in our sample where reported net income is negative. Specifically, our measure of donations sums line (1a), “direct public support” and line (1b), “indirect public support” from IRS Form 990. We exclude line (1c), “government contributions (grants)”. 9

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system paid hospital classifications in exact relation to their predicted costs, then our measure would not exhibit any useful variation to conduct our tests. Fortunately, for present purposes, this is not the case and significant variation in predicted Medicare profits exists in both the cross-section and time series. This result is not merely an artifact of our data. Medicare’s own research arm, MedPac, reports that Medicare margins vary both cross-sectionally and over time (MedPac, 2007). 3. Empirical results on CEO compensation 3.1. Insider measures and CEO cash compensation Several researchers have estimated regression models of CEO compensation to provide evidence on the effects of governance features in for-profit organizations.10 In this section, we estimate similar models for our sample of nonprofit hospital CEOs in order to establish a “baseline” of results analogous to those found in the corporate governance literature.11 Table 2 presents six regression models. In each model, the dependent variable is the natural logarithm of cash compensation received by the CEO in the year.12 The primary variables of interest are: (1) a dummy variable equal to one where the CEO is a voting member of the board; (2) the percentage of the board comprised of management employees excluding the CEO (“other management insiders”); and (3) the percentage of the board comprised of physicians.13 The specifications in models 1–3 are based on prior studies of CEO compensation in for-profit firms that control for the natural logarithm of total assets and the return on assets (ROA) in the observation year. Virtually all past studies of CEO pay in the for-profit sector control for organizational size. Many past studies also control for financial performance (stock returns and/or ROA). We augment this basic model by also including a second measure of financial performance which we label “excess donations” to proxy for CEO’s fund raising performance.14 Finally, we also include the hospital’s case-mix index, which proxies for the complexity of the CEO’s job. Models 1–3 include these controls and a single governance variable. The remaining three models enter all of the governance variables simultaneously and sequentially add controls for market and CEO characteristics. These controls are motivated by various past studies of executive pay, as well as wage regressions contained in the labor economics literature. The estimated coefficient for the CEO voting rights variable is positive and significant in all four models in which it is entered (significance levels range from 0.10 to 0.01). The magnitude of the estimated effect is relatively stable across the four models ranging from 0.07 to 0.10. The coefficient from model 6 indicates that CEO voting rights are associated with about a 9% increase in compensation. This translates into $18,950 for the median CEO in our sample with cash compensation of $210,560. The point estimate of the “other management insider” variable is also positive and statistically significant in three of the four models in which it is entered. In model 5, the addition of a single insider to a 14 member board (a 7 percentage point increase in insiders) is predicted to increase CEO compensation by about 3.64% (0.07 × 0.520 = 0.0364). Finally, the estimated effect of percentage physician board members is negative but statistically insignificant in all models. The signs on the control variables are generally as one might expect. The significance levels of the coefficients vary across variables and specifications. Consistent with past studies, the association between compensation and organizational size is positive and highly significant.15 Compensation is also positively associated with the hospital’s financial performance, excess donations, case-mix index, population density, per capita income in the market area, number of hospitals in the market, CEO age, and CEO tenure in the position. All effects, with the exceptions of excess donations, population density and CEO age are statistically significant in model 6. The associations between compensation and tenure with the firm as well as gender (woman) are negative; only the coefficient on tenure with the firm is statistically significant in model 6, however. Our specification in Table 2 is arguably more complete than in most past studies since it includes substantially more controls for CEO and market characteristics, which can influence pay. Nevertheless, our documented association between compensation and board characteristics could be driven by omitted factors other than management power. Of particular concern is a “sorting story” where talented managers receive both higher compensation and decision rights within the organization. As an initial response to this concern, we estimate models where we regress changes in CEO compensation on our governance and control variables (since change regressions are less subject to omitted variable concerns than level

10 Specific papers on governance and CEO pay in the for-profit sector include Borokhovich et al. (1997), Core et al. (1999), Bertrand and Mullainathan (2001), Benz et al. (2001), Cheng et al. (2001), Cyert et al. (2002), Hartzell and Starks (2002) and Hallock (1997). Related models have also been estimated by labor economists to test for the presence of compensating wage differentials. See, for example, Viscusi (1978), Smith (1979) and Hwang et al. (1992). 11 Ballou and Weisbrod (2003), Eldenburg and Krishnan (2003), Fisman and Hubbard (2005), Frumkin and Keating (2001), Hallock (2000) and Roomkin and Weisbrod (1999) provide evidence on the determinants of managerial pay in nonprofit organizations. None of the existing papers on nonprofit governance examine the effects of board composition on CEO compensation. 12 Similar results are found when we use “total compensation” as the dependent variable (combines cash compensation with other compensation and benefits reported in the IRS Form 990, including deferred compensation). We focus on cash compensation because valuing other compensation is more subjective and not necessarily the same across organizations. 13 We also estimate models that include the total number of board members (not shown here). This variable is statistically insignificant and its inclusion has virtually no effect on the estimated coefficients of the other governance variables. 14 The regression from which these residuals were drawn is available from the authors on request. 15 The size-pay elasticities that we obtain in these models, which cluster around .30, are similar to the size-pay elasticities cited in Milgrom and Roberts (1992), for the for-profit sector (.2 to .3). They are also similar to the size-pay elasticity cited by Oster for the hospital sector (Oster, 1998).

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201

Table 2 OLS regression of log of CEO compensation on governance, hospital, market and CEO characteristics.

Observations Intercept Hospital governance CEO has voting rights

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

228

228

228

225

225

170

***

5.886 (16.43)

***

5.915 (15.90)

Excess donations Case-mix index

5.970 (16.11)

***

6.220 (16.86)

6.175*** (13.53)

−0.041 (0.23)

0.085** (2.10) 0.568** (1.97) −0.124 (0.68)

0.071* (1.81) 0.520* (1.87) −0.190 (1.10)

0.088** (1.96) 0.490 (1.51) −0.079 (0.34)

0.335*** (12.80) 0.463 (1.44) 2.013 (1.29) 0.425*** (3.08)

0.325*** (12.47) 0.532 * (1.67) 1.371 (0.88) 0.388*** (2.81)

0.290*** (10.89) 0.800 ** (2.58) 1.643 (1.10) 0.322*** (2.43)

0.277*** (9.25) 0.823 ** (1.96) 3.045 (1.57) 0.351*** (2.31)

0.036** (1.98) 0.010*** (3.35) 0.00 (0.06)

0.035 (1.64) 0.010*** (2.78) 0.02** (2.14)

0.666** (2.35)

Percentage physicians

Return on assets

5.805 (15.63)

***

0.097*** (2.46)

Percentage other management insiders

Hospital characteristics Log of assets

***

0.330*** (13.01) 0.517 (1.64) 1.673 (1.1) 0.369*** (2.73)

0.329*** (12.56) 0.477 (1.49) 1.926 (1.26) 0.417*** (3.06)

Market characteristics Log of population density Per capita income (in thousands) Number of hospitals CEO characteristics CEO age

Y Y

Y Y

Y Y

Y Y

Y Y

0.003 (0.81) 0.017*** (3.66) −0.010*** (2.82) −0.083 (1.25) Y Y

0.75

0.74

0.73

0.74

0.77

0.78

Tenure as CEO Tenure with firm CEO is a woman Region effects Year effects Adjusted R-squared

This table presents six OLS regressions of the log of CEO cash compensation on governance, hospital, market and CEO characteristics. The sample is restricted to short-term acute care nonprofit hospitals. CEO compensation is taken from column C of IRS Form 990 Part V: “Compensation of Officers and Directors”. The observations are for the period 1999–2002. Hospital governance characteristics are obtained from the bi-annual Governance Institute Survey. Hospital size and ROA are from IRS Form 990, and hospital case-mix index is obtained from the Centers for Medicare and Medicaid Services. Market characteristics are from the Area Resource File. CEO characteristics are from the Academy of Health Care Executives’ (ACHE) online affiliate directory. Regional controls include a dummy variable corresponding to one of the eight census regions of the United States. T-statistics are in parentheses. * Statistically significant at the 0.10 level in two-tailed tests. ** Statistically significant at the 0.05 level in two-tailed tests. *** Statistically significant at the 0.01 level in two-tailed tests.

regressions). The results, which are available from the authors, provide additional support for the Management Power Hypothesis. Our estimates suggest that CEOs with voting rights and more employees on the board receive statistically higher wage increases. The estimated effect of CEO voting rights on the CEO’s annual salary increase ranges from $2898 to $7722 across the various models. In the most complete model, adding an additional management insider to a 14 person board (increasing management representation by about 7%) increases the CEO’s raise by $7015. 3.2. Exogenous cash flows and CEO compensation In this section, we address endogeneity concerns by focusing on the relation between CEO pay and exogenous cash flows. The Management Power Hypothesis makes the following (additional) predictions: (1) the compensation of powerful CEOs (e.g., those with voting rights) increases with exogenous cash flows since they can use their power to expropriate part of the extra cash; (2) the compensation of powerful CEOs does not decline with negative exogenous cash flows since they can use their power to resist pay cuts; and (3) CEOs who do not have sufficient power to obtain excess pay are paid a

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market-determined wage that is independent of the organization’s exogenous cash flow. These predictions do not follow from classical contract theory and therefore can be used to test between the two competing theories. 3.2.1. Creating an exogenous cash flow measure To develop an exogenous measure of hospital resources that can be used to pay excess compensation to a CEO, we focus on the Federal Government’s Medicare program. The Medicare program pays for hospital services received by the elderly and many disabled citizens and accounts for a significant share of the average hospital’s revenues (about 30% on average, MedPac, 2007). Medicare hospital rates are determined by the Federal Government as part of a regulatory process, resulting in a “yardstick” payment that all hospitals of a common classification receive (Shleifer, 1985). For this reason, Medicare payment rates are predetermined with respect to an individual hospital’s actions (including actions that result from the CEO’s ability). Hospital costs must be subtracted from revenues to obtain a meaningful measure of surplus cash flow from Medicare payments. Because costs are arguably endogenous to managerial ability, we employ a two-stage process to compute our exogenous cash flow measure. We first estimate a cost function for Medicare costs per case, using variables that are arguably exogenous to managerial ability and effort, including hospital case mix, wage levels, and other measures. Predicted costs per case from the cost function are then subtracted from Medicare revenues per case, and the result is multiplied by Medicare cases to arrive at what we refer to as “predicted Medicare profits” (or “PMP”).16 This measure reflects “the profits that the hospital would realize from its Medicare program if it managed its Medicare costs as efficiently as the average hospital in our sample.” The intent is to create a measure of Medicare profits that is independent of sample deviations in managerial inputs resulting, for example, from variations in managerial ability.17 A more detailed discussion of the cost function estimation and PMP construction can be found in an empirical appendix available from the authors. 3.2.2. Results using exogenous Medicare cash flow measure Tables 3 and 4 re-estimate the final (most complete) specification in Table 2, now including our exogenous cash flow measure, defined as the mean of the hospital’s PMP across the sample period. Under the Management Power Hypothesis, PMP should have a positive effect on voting CEOs’ compensation when the profit is positive and no effect when the profits are negative. In contrast, the compensation of non-voting CEOs should be independent of the profits in either case. Table 3 partitions the sample into voting and non-voting CEOs. The PMP measure is divided (“splined”) into positive and negative regions to test the hypothesis that any effects on CEO compensation occur only where there is a predicted Medicare surplus. Two specifications are provided in which positive Medicare profits are entered first linearly and then logged. In both cases, the remainder of the specification is identical to specification 6 in Table 2. The results presented in Table 3 provide additional support for the Management Power Hypothesis. Voting CEOs enjoy increased salaries as a function of positive predicted Medicare profits (both linear and logged) and non-voting CEOs do not. Moreover, the significant effect on CEO pay exists only within the positive spline of profits, as should occur if voting CEOs possess some power over their compensation. There is no significant effect on compensation in the region of negative Medicare profits. The magnitude of the effect in Table 3 indicates that a 1MM increase in predicted Medicare profits (say from $0 to $1MM) increases voting CEO pay by 2.4%. The log specification reveals an elasticity between predicted Medicare profits and CEO pay of 0.03, so that a 1% increase in PMP results in a 0.03% increase in CEO pay. For example, a doubling of PMP from $1MM to $2MM increases CEO pay by about 2.1%. In Table 4, we repeat the analysis, but now combine the voting and non-voting samples and interact the PMP measure with a voting dummy. The interaction terms allow a statistical test of the difference in the PMP coefficient across the voting and non-voting samples. The coefficients of the other controls are not interacted with the voting dummy and are instead constrained to be equal across the voting and non-voting samples. An F-test accepts the associated restrictions imposed on this specification, compared to a fully interacted specification. The results in Table 4 are similar to those in Table 3. In model 1, a voting CEO enjoys a 2.8% raise for each $1MM of positive Medicare profit. Likewise, the estimated elasticity in specification 2 is 0.028. Moreover, the differences in the estimated effects of PMP on CEO compensation are statistically significant. Taken together, Tables 4 and 5 provide important confirmatory support for the Management Power Hypothesis and the key results found in Table 2.

16 Because the original regression is log–log, the transformation to a predicted cost value also uses the estimated standard error of the regression to form the prediction. By regulatory design, revenues are positively correlated with expected Medicare costs for each given hospital. Fortunately, for our purposes this correlation is less than one. 17 These steps help to ensure that our measure of costs is independent of managerial inputs. However, this does not eliminate all potential concerns with endogeneity that might cause a correlation between unmeasured managerial ability and predicted Medicare profits. Most obviously, high ability managers may be sought by hospitals with higher predicted Medicare profits. We address this “sorting hypothesis” by estimating models where we focus on the changes in predicted profits and CEO pay. We also point out empirical inconsistencies between our results and the sorting hypothesis below. Second, our measure of predicted profits does not address the potential endogeneity of Medicare volume (number of cases). Here, we appeal to the fact that the number of Medicare cases that a hospital treats is largely predetermined by the age and health status of its market area plus the number of other hospitals in its market area. Nonetheless, we also estimate regressions in which we control for the number Medicare cases in the regression, and find no material effect on our main results, which remain statistically significant (these results are available from the authors).

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Table 3 OLS regressions of log of CEO compensation on exogenous Medicare profits: divided sample results. Voting CEOs

Non-voting CEOs

Model 1

Model 2

Model 1

Model 2

Observations

99

99

84

84

Intercept

5.739 (8.40)***

5.822 (8.710)***

6.741 (9.49)***

6.546 (9.25)***

Exogenous cash flows Predicted Medicare profits/positive (MMs)

0.024 (2.35)***

0.002 (0.15)

0.027 (3.00)*** −0.010 (0.620)

0.014 (0.40)

−0.007 (0.62) 0.040 (0.96)

0.331 (0.44) 0.161 (0.44)

1.127 (1.75)* 0.184 (0.52)

−0.089 (0.25) −0.092 (0.28)

−0.086 (0.24) −0.166 (0.52)

0.316 (6.91)*** 1.160 (1.98)** 6.160 (2.47)*** 0.039 (0.18)

0.300 (6.62)*** 1.192 (2.08)** 6.931 (2.84)*** 0.150 (0.75)

0.250 (5.92)*** 0.571 (0.99) −1.703 (0.50) 0.494 (1.67)*

0.259 (5.99)*** 0.572 (0.98) −1.872 (0.54) 0.663 (2.51)***

0.036 (1.04) 0.000 (2.72)*** 0.013 (1.00)

0.037 (1.09) 0.000 (2.81)*** 0.016 (1.18)

0.003 (0.08) 0.000 (0.66) 0.032 (3.11)***

-0.003 (0.09) 0.000 (0.56) 0.032 (3.13)***

0.002 (0.48) 0.014 (2.31)** −0.006 (1.27) −0.092 (0.97) Y Y

0.002 (0.43) 0.016 (2.80)*** −0.008 (1.80)* −0.067 (0.71) Y Y

0.005 (0.88) 0.020 (2.46)*** −0.015 (2.21)** 0.030 (0.32) Y Y

0.003 (0.59) 0.017 (2.25)** −0.012 (1.95)* 0.021 (0.22) Y Y

0.78

0.79

0.74

0.74

Ln (predicted Medicare profits/positive (MMs)) Predicted Medicare profits/negative (MMs) Hospital governance Percentage other management insiders Percentage physicians Hospital characteristics Log of assets Return on assets Excess donations Case-mix index Market characteristics Log of population density Per capita income (in thousands) Number of hospitals CEO characteristics CEO age Tenure as CEO Tenure with firm Woman Region effects Year effects Adjusted R-squared

0.010 (0.91)

This table presents two regression models of the log of CEO cash compensation on governance, hospital, market and CEO characteristics, with separate estimates provided for voting and non-voting CEO samples. The key right side variable is exogenous cash flow, defined as the hospital’s predicted profits from the Medicare program. Predicted Medicare profits (PMP) are splined into positive and negative regions. In one specification, we use the absolute level of PMP and in the second specification we log positive values of PMP. The sample is restricted to short-term acute care nonprofit hospitals. CEO compensation is taken from column C of IRS Form 990 Part V: “Compensation of Officers and Directors”. The observations are for the period 1999–2002. Hospital governance characteristics are obtained from the bi-annual Governance Institute Survey. Hospital size and ROA are from IRS Form 990, and hospital case-mix index is obtained from the Centers for Medicare and Medicaid Services. Market characteristics are from the Area Resource File. CEO characteristics are from the Academy of Health Care Executives’ (ACHE) online affiliate directory. Regional controls include a dummy variable corresponding to one of the eight census regions of the United States. T-statistics are in parentheses. * Statistically significant at the 0.10 level in two-tailed tests. ** Statistically significant at the 0.05 level in two-tailed tests. *** Statistically significant at the 0.01 level in two-tailed tests.

As an additional check on our results, we also estimate regressions of the changes in CEO pay against changes in PMP. For these purposes, we use lagged changes in PMP to explain current period changes in CEO pay. Although the use of lagged changes reduces the number of degrees of freedom considerably, the fitted effects of PMP display many of the same patterns found in the levels analysis. For example, voting CEOs enjoy dollar raises that are over $11,000 higher for each $1MM increase in positive Medicare profits, while negative Medicare profits have no statistically significant effect

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Table 4 OLS regressions of log of CEO compensation on exogenous Medicare profits: combined sample results. Model 1 Voting CEOs Observations Exogenous cash flows Predicted Medicare profits/positive (MMs)

Model 2 Non-voting CEOs

Difference

0.028 (3.54)***

0.000 (0.06)

Uninteracted controls Intercept Hospital governance Percentage other management insiders Percentage physicians Hospital characteristics Log of assets Return on assets Excess donations Case-mix index Market characteristics Log of population density Per capita income (in thousands) Number of hospitals CEO characteristics CEO age Tenure as CEO Tenure with firm CEO is a woman Region effects Year effects Adjusted R-squared

−0.007 (0.52)

0.048 (1.49)

Non-voting CEOs

Difference

183 0.027 (2.82)***

Ln (predicted Medicare profits/positive (MMs)) Predicted Medicare profits/negative (MMs)

Voting CEOs

183

0.054 (1.66)*

0.028 (3.67)*** −0.015 (1.09)

6.227 (13.87)***

6.354 (14.29)***

0.087 (0.32) −0.015 (0.07)

0.215 (0.80) −0.111 (0.51)

0.281 (9.62)*** 0.719 (1.88)* 3.820 (2.06)** 0.244 (1.56)

0.269 (9.19)*** 0.866 (2.28)** 3.938 (2.13)** 0.329 (2.25)**

0.029 (1.37) 0.000 (2.53)*** 0.024 (2.98)

0.028 (1.34) 0.000 (2.5)*** 0.024 (3.12)***

0.004 (1.25) 0.015 (3.39)*** −0.010 (2.89)*** −0.036 (0.57) Y Y

0.004 (1.17) 0.017 (3.86)*** −0.011 (3.45)*** −0.040 (0.64) Y Y

0.82

0.82

0.000 (0.05) 0.031 (0.93)

0.027 (3.55)*** −0.046 (1.39)

This table presents two regression models of the log of CEO cash compensation on governance, hospital, market and CEO characteristics, combining the voting and non-voting CEO samples, but allowing for separate estimates of the predicted Medicare profits effect. They key right side variable is exogenous cash flow, defined as the hospital’s predicted profits from the Medicare program. Predicted Medicare profits (PMP) are splined into positive and negative regions, and interacted with a voting CEO dummy variable. In one specification, we use the absolute level of PMP and in the second specification we log positive PMP. The sample is restricted to short-term acute care nonprofit hospitals. CEO compensation is taken from column C of IRS Form 990 Part V: “Compensation of Officers and Directors”. The observations are for the period 1999–2002. Hospital governance characteristics are obtained from the bi-annual Governance Institute Survey. Hospital size and ROA are from IRS Form 990, and hospital case-mix index is obtained from the Centers for Medicare and Medicaid Services. Market characteristics are from the Area Resource File. CEO characteristics are from the Academy of Health Care Executives’ (ACHE) online affiliate directory. Regional controls include a dummy variable corresponding to one of the eight census regions of the United States. T-statistics are in parentheses. * Statistically significant at the 0.10 level in two-tailed tests. ** Statistically significant at the 0.05 level in two-tailed tests. *** Statistically significant at the 0.01 level in two-tailed tests.

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Table 5 Tobit regression of hospital donations as a percentage of revenues on governance, hospital, and market characteristics.

Observations Intercept Hospital governance CEO has voting rights

Model 1

Model 2

Model 3

Model 4

Model 5

271

267

270

267

245

**

0.034 (2.17)

*

0.029 (1.85)

Return on assets Case-mix index Board size (in ten’s)

0.031 (1.99)

0.011 (0.57)

−0.018** (2.39)

0.004** (2.10) −0.006 (0.53) −0.020*** (2.62)

0.003* (1.78) 0.007 (0.52) −0.021*** (2.73)

−0.001 (0.48) 0.001 (0.10) −(0.01) (0.90) 0.003* (1.76)

−0.001 (0.70) 0.003 (0.22) −(0.01) (1.05) 0.0002 (1.44)

−0.0003 (0.22) −0.0002 (0.00) −(0.01) (0.88) 0.0002 (0.10)

−0.003 (0.28)

Percentage physicians

−0.001 (0.89) 0.002 (0.10) −(0.01) (1.34) 0.002 (1.51)

−0.001 (0.64) 0.000 (0.00) −(0.01) (1.13) 0.003* (1.75)

***

0.027 (1.76)

0.003* (1.75)

Percentage other management insiders

Hospital characteristics Log of assets

***

Market characteristics Log of population density

Y Y 0.012

Y Y 0.012

Y Y 0.012

Y Y 0.012

−0.001 (1.18) 0.004** (1.94) 0.100 (1.56) 0.057*** (2.92) 0.009 (1.28) −0.001** (2.07) 0.001 (1.30) Y Y 0.012

697.969

682.648

695.784

687.704

644.749

Per capita income (×10,000) Number of hospitals (×1000) County Medicare percentage County Medicaid percentage County percent of persons in poverty State personal income tax rate Region effects Year effects Scale parameter Log likelihood

This table presents five Tobit regressions of hospital donations as a percentage of revenues on governance, hospital, and market characteristics. The sample is restricted to short-term acute care nonprofit hospitals. The donation percentage is defined as the share of total revenue accounted for by nongovernment donations taken from IRS Form 990 Part I. The observations are for the period 1999–2002. Hospital governance characteristics are obtained from the bi-annual Governance Institute Survey. Hospital size and ROA are from IRS Form 990. Market characteristics are from the Area Resource File. Regional controls include a dummy variable corresponding to one of the eight census regions of the United States. Asymptotic T-statistics are in parentheses. * Statistically significant at the 0.10 level in two-tailed tests. ** Statistically significant at the 0.05 level in two-tailed tests. *** Statistically significant at the 0.01 level in two-tailed tests.

on their raise. This suggests that voting CEOs use raises, at least in part, to expropriate exogenous organizational cash flows.18 Finally, it is important to note that hospitals operate in a “mixed sector,” which contains both for-profit and nonprofit organizations (other mixed sectors include health insurance, nursing homes, education, symphonies, theatres, and museums). Agency problems might be more severe in nonprofits (e.g., charities) that do not face this type of competitive discipline. To test this argument, we also estimate the specifications in Tables 3 and 4 with an additional explanatory variable measuring the percent of hospitals it the county that are for-profit.19 Consistent with the “competitive discipline hypothesis,” we find that among voting CEOs, the effect of increasing for-profit penetration in the county market by 10% is to reduce excess

18 Fisman and Hubbard (2005) present evidence that nonprofit managers are more likely to expropriate donations in the form of excess compensation when they are located in states with weak Attorney General oversight. We also estimate the regressions provided in Table 3, now interacting the PMP measure with an index of AG oversight. We find that, within the sample of voting CEOs, AG oversight reduces the effects of PMP on CEO profits, with the negative interaction significant in model 1. In heavily monitored states, the fitted effect of PMP is driven to zero. Results are available from the authors. 19 We are grateful to an anonymous referee for suggesting this regression which is available from the authors upon request.

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pay by a half of a percentage point (result significant). Thus, we find that agency problems are expected to be exacerbated in nonprofit organizations that do not compete with for-profits. 3.2.3. Alternative explanations for the results Our results are consistent with the Management Power Hypothesis and inconsistent with many theoretical alternatives based on competitive contracting. We believe that alternative theories fail to answer key questions about the pattern of results seen here. It is worthwhile explicitly considering these alternatives and their limitations relative to the Management Power Hypothesis. Perhaps the major remaining empirical concern is that CEOs in the voting sample might vary in talent and compensation and more talented CEOs might be demanded in hospitals that enjoy greater PMP, in order to provide strategic guidance on how to spend the surplus. We discount this general concern for two reasons. First, sorting explanations imply a positive relation between PMP and CEO pay in both subsamples of CEOs (both voting and non-voting), which we do not find. Second, it is not obvious why we would find an asymmetric PMP effect on compensation based on the sign of PMP (i.e., why we find a null result within the region of negative PMP). 4. Empirical results for private donations 4.1. Background It is important to note that the size of the estimated excess compensation, documented in Section 3, is arguably small relative to the overall size of the organization. For example, a 10% compensation premium (the “upper bound” of our estimate of excess compensation) relative to the median CEO’s cash compensation of $210,000 translates to $21,000 in excess compensation. This level of excess compensation represents only 3/100 of 1% of a median hospital’s revenue. Seen in this light, a relevant question is whether management power (as evidenced by excess compensation) also leads to other less visible, but potentially more important forms of expropriation (e.g., choosing organizational investments to satisfy managerial preferences, shirking, and excess payments to other stakeholders). Direct evidence on this issue is hard to provide since these alternative forms of expropriation are difficult to observe and measure. Nevertheless, indirect evidence can be provided by examining the relation between management power and donations. Fama and Jensen (1983b) and Hansmann (1980) argue that potential donors are less likely to contribute to organizations with governance structures that facilitate the expropriation of donations. Alternatively, CEO power need not result in fewer donations provided that the associated agency problem is confined to modest levels of excess compensation. Moreover CEO power may have collateral benefits to donors that outweigh the problem of excess pay. Recent models of nonprofit organizations stress that there are other parties besides the CEO who compete for the organization’s financial residual. For example, Glaeser argues that talented employees may capture the financial residual of NP organizations (Glaeser, 2003). Given this model, a powerful CEO may be desirable to donors if she can use her power to limit the consumption of perquisites by other stakeholder groups. Thus powerful CEOs may not discourage donations after all. To test the agency cost hypothesis, we estimate regressions of overall organizational donations.20 Donations are a relatively small percentage of hospital revenues (0.28% at the median), but can constitute a sizeable percentage of the hospital’s net surplus (profits) (10% at the median in our sample). There have been several prior studies of the determinants of institution-level donations (Yetman and Yetman, 2003; Khanna and Sandler, 2000; Okten and Weisbrod, 2000; Weisbrod and Dominguez, 1986; Schiff, 1985). To our knowledge, however, previous studies have not examined the effects of governance structure on organizational donations. The multi-stakeholder model makes a second prediction that we are able to test with our specification. Following the Glaeser model, physicians may comprise a group of “talented employees” who might compete for use of the hospital’s financial residual (Pauly and Redisch, 1973). If physicians comprise a competing interest group for the organization’s financial residual, then donors may be reluctant to contribute to hospitals where physicians’ have greater influence on the board. The multi-stakeholder theory predicts that physician influence on the board will discourage donations. 4.2. Results Table 5 provides estimates of the effects of governance structure on private donations. The dependent variable in these regressions is private donations measured as a percentage of hospital revenues. Tobit estimation is used because about 10% of the hospitals in our sample report zero donations. Table 5 provides five different models, using our governance measures, as well as a set of hospital and market-level controls. Both ROA and log assets serve as proxies for the economic stability of the hospital. Donors may be reluctant to contribute to hospitals that are financially unstable. In selecting the set of market controls, we note that markets in which there is a great deal of discretionary income and where the community derives great benefits from charitable services should supply the hospital more private donations,

20 Our measure of donations is taken from the organization’s Form 990. This is a noisy measure of donations to the extent that it does not include contributions made to a hospital foundation dedicated to the hospital, in cases where the hospital enjoys an economic interest in the foundation.

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ceteris paribus. We use per capita income to proxy discretionary income and the county-level percentage of persons in poverty to proxy community benefits from charity. Likewise, we also include the county’s Medicaid percentage, reasoning that Medicaid is a public program that may either “crowd in” or “crowd out” private donations to hospitals. For example, Medicaid coverage may “crowd out” donations if Medicaid coverage results in less community need for hospital charity care. On the other hand, Medicaid coverage may “crowd in” private donations if Medicaid coverage induces the hospital to offer services that traditionally lose money, which in turn stimulates private donations. We also include a control for the percent of the population receiving Medicare coverage using the same rationale. We include the number of other hospitals in the county to control for the number of other institutions competing for donations. The predicted effect of other hospitals in the market on the hospital’s own donations is negative. We also include a count of the number of outsider board members because a larger number of outsiders might reflect a larger potential pool of donors.21 Finally, we include the state income tax rate as a measure of the donor’s net cost of giving. Our major finding in Table 5 is that private donations are not negatively related to CEO voting rights or the percentage of other management insiders on the board. If anything there is a positive relation between donations and CEO membership on the board. The marginally significant point estimate in model 5, indicates that the inclusion of the CEO on the board increases donations as a percentage of revenues by three tenths of a percent (almost 10% of the median organization’s level of donations). It is worth noting that the resulting increase in donations exceeds our estimates of excess pay in the prior section. These results contradict the predictions of Fama and Jensen and instead support the null hypothesis. We also find that percent of physicians on the board discourages donations. The results indicate that a 10 percentage point increase in physicians on the board (e.g., an addition of a single physician to a ten-member board), reduces donations as a percentage of revenues by two-tenths of 1%. This is what we would expect to find if donors viewed physicians as a competing interest group. The result is therefore consistent with the multi-stakeholder model of Glaeser.22 The multi-stakeholder model may also provide a rationale for why donors may view the existence of powerful CEOs favorably. Among the set of control variables, we find that per capita income is positively related to donations, as expected, while Medicare coverage appears to “crowd in” donations. Percent of the population in poverty reduces donations. Other controls are statistically insignificant, although number of outsiders on the board is positive and significant in models 2 and 3. Overall, our tests fail to reject the null hypothesis, which states that insider boards do not lead to a reduction in donations and may, in fact, have the opposite result. Our results also provide some support for the model of competing stakeholder groups described by Glaeser (2003), in that we find that physician representation leads to fewer donations.

5. Conclusions This study documents a statistically significant association between hospital CEO compensation and CEO power on the board. Additional tests suggest that this correlation is not driven by the joint endogeneity of CEO pay, ability and decision rights. Nonetheless, the point estimates in our regressions suggest that the excess pay obtained by powerful CEOs is relatively small. Our evidence suggests that excess pay is moderated, to some extent, by competition with other hospitals, including for-profit hospitals. For this reason, our compensation results may form a lower bound of what may be expected in some other segments of the nonprofit sector. We also examine whether CEO power is associated with larger, hard-to-observe agency problems by examining the relation between CEO power and donations. We do not find that donors are more reluctant to give to organizations with powerful CEOs. We conclude that the agency problems associated with CEO power in our sample are largely confined to modest amounts of excess compensation. Finally, we also document a negative correlation between donations and physician representation on the board – suggesting a potential conflict between the interests of donors and non-employee physicians over the allocation of organizational resources. This evidence, along with our findings on management power, provides empirical support for modeling nonprofit organizations as consisting of competing stakeholders. CEOs with voting rights are relatively common in nonprofit organizations. Our study points to a possible cost of this organizational structure. Presumably, there may be offsetting benefits that we have not considered. Similarly, while physicians on the board might be associated with reduced donations, there are potential offsetting benefits that might explain why they commonly serve on hospital boards. We conclude that optimal board structure in a nonprofit organization is a complex and unresolved issue that requires further study.

21 Controlling for board size and/or the number of outside directors allows us to rule out the interpretation that our physician and management variables simply pick up an absence of generous donors, who would otherwise be represented on the board. The presence or absence of generous donors in the community should be reflected in the board size coefficient while the effects of physician and management influence are reflected in our study variable coefficients. 22 We also estimate instrumental variable (IV) regressions in which we address the possible endogeneity of physician membership on the board by using an instrumented value for physician membership on the board. For these purposes, we use the percentage of physicians between ages 45 and 64 as our main instrument. The instrumental variable results continue to show a significant negative impact of physician membership on the board on organizational donations.

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Acknowledgments We would like to thank Jason Barro, Howard Berman, David Klein, Jerry Zimmerman, participants at the University of Rochester’s conference on The Organizational Economics of Health Care and seminar participants at the Wharton School for useful comments on earlier drafts of this paper. References Ballou, J., Weisbrod, B., 2003. Managerial rewards and the behavior of for-profit, governmental, and nonprofit organizations: evidence from the hospital industry. Journal of Public Economics 87, 1895–1920. Benz, M., Kucher, M., Stutzer, A., 2001. Stock options: the managers’ blessing: institutional restrictions and executive compensation. Working Paper 61, University of Zurich Institute for Empirical Research in Economics. Bertrand, M., Mullainathan, S., 2001. Are CEO’s rewarded for luck? The ones without principals are. Quarterly Journal of Economics 116, 901–932. Board Source, 2002. Should the CEO be a Voting Board Member?, http://www.boardsource.org. Borokhovich, K., Brunarski, K., Parrino, R., 1997. CEO contracting and anti-takeover amendments. Journal of Finance 52, 1495–1517. Cheng, S., Nagar, V., Rajan, M., 2001. Control versus risk in stock-based incentives: evidence from antitakeover regulation. Working Paper, University of Michigan Business School. Core, J., Holthausen, R., Larcker, D., 1999. Corporate governance, chief executive compensation and firm performance. Journal of Financial Economics 51, 371–406. Cyert, R, Kang, S., Kumar, P., 2002. Corporate governance, takeovers and top-management compensation: theory and evidence. Management Science 48, 453–469. 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., Jensen, M., 1983a. Separation of ownership and control. Journal of Law and Economics 26, 301–325. Fama, E., Jensen, M., 1983b. Agency problems and residual claims. Journal of Law and Economics 26, 327–349. Fisman, R., Hubbard, G., 2005. Precautionary savings and the governance of nonprofit organizations. Journal of Public Economics 89, 2231–2243. Frumkin, P., Keating, E., 2001. The price of doing good: executive compensation in nonprofit organizations. Working Paper, Harvard University. Glaeser, E., 2003. The Governance of Not-for-Profit Organizations. University of Chicago Press, Chicago. Hallock, K., 1997. Reciprocally interlocking boards of directors and executive compensation. Journal of Financial and Quantitative Analysis 32, 331–343. Hallock, K., 2000. Governance and managerial pay in American nonprofits. Working Paper, University of Illinois. Hansmann, H., 1980. The role of nonprofit enterprise. Yale Law Journal 89, 835–901. Hartzell, J., Starks, L., 2002. Institutional investors and executive compensation. Working Paper, New York University Stern School of Business. Hwang, H., Reed, R., Hubbard, C., 1992. Compensating wage differentials and unobserved productivity. Journal of Political Economy 100, 835–858. Jones, J., 2003. NY’s attorney general seeks to apply Sarbanes-Oxley Act. The Nonprofit Times, March 1. Khanna, J., Sandler, T., 2000. Partners in giving: the crowding-in effects of UK government grants. European Economic Review 44, 1543–1546. Medicare Payment Advisory Commission, 2007. Healthcare Spending and the Medicare Program. MEDPAC, Washington DC. Milgrom, P., Roberts, J., 1992. Economics, Organization and Management. Prentice Hall, Englewood Cliffs. Okten, C., Weisbrod, B., 2000. Determinants of donations in private nonprofit markets. Journal of Public Economics 75, 255–272. O’Regan, K., Oster, S., 2005. Does the structure and composition of the board matter? The case of nonprofit organizations. Journal of Law, Economics and Organizations 21, 205–227. Oster, S., 1998. Executive compensation in the nonprofit sector. Nonprofit Management and Leadership 8, 207–221. Pauly, M., Redisch, M., 1973. The not-for-profit hospital as a physicians’ cooperative. American Economic Review 63, 87–99. Roomkin, M., Weisbrod, B., 1999. Managerial compensation and incentives in for-profit and nonprofit hospitals. Journal of Law, Economics and Organizations 15, 750–781. Schiff, J., 1985. Does government spending crowd out charitable contributions? National Tax Journal 38, 535–547. Shleifer, A., 1985. A theory of yardstick competition. The RAND Journal of Economics 16, 319–327. Smith, R., 1979. Compensating wage differentials and public policy: a review. Industrial and Labor Relations Review 32, 339–352. Spector, M., 2009. IRS probes nonprofit pay practices. The Wall Street Journal (April). Viscusi, K., 1978. Wealth effect and earnings premiums for job hazards. The Review of Economics and Statistics 60, 408–416. Weisbrod, B., Dominguez, N., 1986. Demand for collective goods in private nonprofit markets: can fundraising expenditures help overcome free-rider behavior? Journal of Public Economics 30, 83–95. Yetman, M., Yetman, R., 2003. The effect of nonprofits’ taxable activities on the supply of private donations. National Tax Journal 56, 243–258.