Electronic copy available at: http://ssrn.com/abstract=1497205Electronic copy available at: http://ssrn.com/abstract=1497205

Deviations from Expected Stakeholder Management, Firm

Value, and Corporate Governance

Bradley W. Benson* Department of Economics and Finance

Louisiana Tech University Ruston, LA 71270

318-257-2389 bbenson@latech.edu

Wallace N. Davidson III

Finance Department Mailcode 4626 Southern Illinois University

Carbondale, IL 62901 618-453-1429

davidson@cba.siu.edu

Hongxia Wang Institute for Contemporary Financial Studies

College of Business and Economics Ashland University Ashland, OH 44805

419-289-5222 hwang2@ashland.edu

Dan L. Worrell

Dean and Sam Walton Leadership Chair Sam Walton College of Business

University of Arkansas Fayetteville, AR 72701-1201

479-575-5949 dworrell@walton.uark.edu

August 12, 2010 * Corresponding Author

The authors would like to thank seminar participants at the University of Mississippi for many helpful and insightful comments. All errors remain the responsibility of the authors.

Electronic copy available at: http://ssrn.com/abstract=1497205Electronic copy available at: http://ssrn.com/abstract=1497205

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Deviations from Expected Stakeholder Management, Firm

Value, and Corporate Governance

Abstract

In this paper, we examine the relation between deviations from expected investment in stakeholder management and corporate governance. Building good relations with various stakeholders may help create firm value, but investment in stakeholder management beyond what is necessary to create shareholder value may be an agency cost. We propose that high quality corporate governance may mitigate value-destroying investments in stakeholder management. Using an unbalanced panel of 9,051firm-year observations for 1,631 firms, we find that deviations from expected SM are increasing in CEO portfolio delta. We find, however, that deviations from expected stakeholder management are negatively related to proxies for effective board monitoring. More independent boards effectively control deviations from expected investment in stakeholder management. These results are consistent with both the CEO perquisite and the board monitoring hypotheses. We also document that the effect of governance mechanisms varies by industry (consumer or industrial orientation) and SM dimension; their influence is similar in dimensions expected to provide similar benefits across industry orientation, but differs when the benefits are expected to benefit one industry more than the other. Consistent with Jensens Enlightened Value Maximization theory, the results show that corporations with good governance pursue shareholder value maximization while constraining unnecessary investment in stakeholders. JEL classification: G34; J33; L21; M14; M52 Keywords: Corporate Governance; Corporate Social Responsibility; Enlightened Value Maximization; Institutional Ownership; Firm Value; Managerial Ownership Incentives; Stakeholder Theory

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Deviations from Expected Stakeholder Management, Firm Value, and Corporate Governance

1. Introduction

Proponents of stakeholder management argue that a firm should manage the relations with

all of its stakeholders rather than focus on shareholder wealth. Researchers in a wide range of

fields have examined the implications of stakeholder management (e.g. strategic management,

organizational behavior, business ethics, and sustainable development) (Laplume, Sonpar, and

Litz, 2008). The corporate scandals of the early 2000s have brought corporate social performance

and stakeholder management to the forefront (Neubaum and Zahra, 2006)1. Furthermore,

constituency statutes have been enacted in over half of US states in recent years that either require

or permit directors to consider the interests of non-shareholders when conducting their duties

(Keay, 2009).2

The principal argument of stakeholder management (SM) is that organizations should be

operated and managed in the interests of all their constituents who can affect or be affected by the

achievement of the organizations objectives (Freeman, 1984; Donaldson and Preston, 1995).

Stakeholder management captures various firm-stakeholder relationships. Proponents maintain

that stakeholder management is strategically important, and firms can benefit from properly

managing the relationship with these important groups (Fong, 2009; Bhattacharya, Korschun, and

1 Stakeholder management and social performance are similar in their construct such that firms which manage stakeholder relations may be seen as socially responsible. 2 The legal duty of directors to consider the interest of additional stakeholders has also taken hold in the UK. For example, section 172 of the Companies Act 2006 states that directors have a duty to promote the success of the company by considering such factors as: (a) the likely consequences of any long term decision, (b) the interests of the companys employees, (c) the need to foster the business relationships of the company with suppliers, customers and other organizations, (d) the impact of the companys operations on the community and the environment, (e) the desirability of the company maintaining a reputation for high standards of business conduct, and (f) the need to act fairly between members of the company.

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Sen, 2009). Empirical studies, however, have documented that only some stakeholders are critical

to firm value creation (such as Galbreath, 2006; Hillman and Keim, 2001).

One of the downsides to investing in stakeholder management is that it consumes a firms

limited resources. Directing excess resources from shareholders to other stakeholders may hurt

firm value. Some shareholders view stakeholder management as being at odds with profit making

and value maximization. For example, two shareholders at Goldman Sachs protested the

companys donation of land for a nature preserve. The shareholders based their protest on the

idea that while the donation would have benefitted one of the firms stakeholders (the world

environment), it would be costly to shareholders because the land could have been put to

profitable use (Kelly and Davis, 2007).

Stakeholder theory seems to be at odds with value maximization. Jensen (2001; 2002)

addresses this problem and proposes enlightened value maximization. He argues that a firms goal

is to increase firm value, but a firm cannot maximize its value unless it takes care of its

stakeholders. Jensens theory implies that there is an optimal level of stakeholder management

that maximizes shareholder wealth. Similarly, Brickley, Smith, and Zimmerman (2002) argue

that creating shareholder wealth involves allocating resources to all constituencies that affect the

process of shareholder value creation, but only to the point at which the benefits from such

expenditures do not exceed their additional costs (p.113). That is, when the marginal cost of

stakeholder management exceeds the marginal benefit to shareholders, it would not be optimal to

pursue stakeholder management any further. Excess stakeholder management would be

investments in stakeholders beyond this optimal level. Benson and Davidson (2009) find that

while stakeholder management is positively related to firm value, firms do not compensate

managers for stakeholder management; instead, firms compensate their CEOs for achieving the

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firms ultimate goalvalue maximization. Their results indicate that effective managers optimize

relations with stakeholders to accomplish value maximization.

When firms allocate excess resources to stakeholders, this can be considered an agency

cost and shareholder wealth would suffer. As with other agency conflicts, there are various

governance mechanisms that should protect shareholders, and therefore, the quality of corporate

governance may affect a firms stakeholder management policy.

In this paper, we examine how various corporate governance mechanisms affect the

amount of resources diverted toward stakeholders. We propose that firms will invest in

stakeholder management (SM) until the marginal cost of doing so exceeds the marginal benefit to

shareholders. This level of investment in stakeholder management is each firms expected

investment in stakeholder management (ESM). Good corporate governance, proxied by

managerial ownership incentives, board monitoring, and monitoring from institutional investors,

should control deviations from this expected level of stakeholder management.

Using an unbalanced panel of 9,051 firm-year observations for 1,631 firms, we find that

deviations from expected SM are increasing in CEO portfolio delta (scaled wealth-performance

sensitivity), but they are negatively related to proxies for effective board monitoring. More

independent boards effectively control deviations from expected investment in stakeholder

management. These results are consistent with both the CEO perquisite and the board monitoring

hypotheses. We also document that the effect of governance mechanisms varies by industry

(consumer or industrial orientation) and SM dimension; their influence is similar in dimensions

expected to provide similar benefits across industries, but differs when the benefits are expected

to benefit one industry more than the other.

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This study is unique. To the best of our knowledge, we are the first to introduce the

concept of deviation from expected investment in stakeholder management and empirically test

how various governance mechanisms are related to unexpected investment in stakeholder

management. We organize the remainder of this paper as follows. In Sections 2 and 3, we

motivate our research, review the literature, and develop our hypotheses. We discuss our data and

sample in Section 4. In Section 5, we test our hypotheses on the effect of internal governance

mechanisms on excess stakeholder management. We provide concluding remarks in Section 6.

2. Background and motivation

2.1. Stakeholder theory

Under stakeholder theory, the firm is a collection of groups and individuals with a stake in

the firm. The purpose of the firm is to manage the interests of these various stakeholders.

Stakeholder theory has diverged into at least two approaches. The first is the strategic approach

in which firms manage stakeholder relations in the pursuit of value maximization. Here, value

maximization is the fundamental purpose of the firm with stakeholder management as a means to

an end (Freeman et al., 2004). The second approach is the moral approach where firms manage

stakeholder relations for ethical or moral reasons (Freeman, 1984; Evan and Freeman, 1988).3

One issue in stakeholder theory is the identification of stakeholders. Freeman (1984, p. 46)

is credited with the classic definition in which a stakeholder is "any group or individual who can

affect or is affected by the achievement of the organization's objectives". In a literature review,

Friedman and Miles (2006) find 55 definitions of what constitutes a stakeholder. Stakeholder

3 Donaldson and Preston (1995) define these approaches as instrumental and normative stakeholder theories. Instrumental stakeholder theory is concerned with how managers act if they are to further goals of the organization, such as long-run profit maximization or shareholder value. On the other hand, normative stakeholder theory is used to explain how managers should behave and arrive at the purpose of the organization based on ethical principles. Freeman (1994; 1999) maintains that business and ethical decisions cannot be separated.

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definitions range from those who have a vested interest in the firm (Carroll, 1993) to any entity

that affects or is affected by the firm (Starik, 1993).

Another issue in stakeholder theory/management is the allocation of resources between

stakeholders. Clarkson (1995) argues that all stakeholders have intrinsic value and no stakeholder

is more important than another. However, if managers treat all stakeholders with the same

priority, it would be difficult to manage a firm because various stakeholders have different

interests. For example, suppliers would want to receive high prices, and customers would want

low prices; accomplishing these goals would be unlikely to produce value for another stakeholder

group, shareholders (Sundaram and Inkpen, 2004).

2.2. Value Maximization

Milton Friedman stated that the social responsibility of business is to increase its profits

(Friedman, 1970)4. Brickley et al. (2002) argue that a company must focus its attention on

shareholder wealth to survive in a competitive and technology-oriented business world.

Shareholder wealth maximization has been taught in colleges as a basic concept in finance

courses for decades and is a dominating view of many if not most financial scholars. One of the

major differences between shareholder wealth maximization and stakeholder theories is the role

of managers in the resolution of a firms internal conflicts (Laurent, 2007). Shareholder value

theory emphasizes that the role of managers is to make decisions that enhance shareholder value;

this differs from normative stakeholder theory which maintains that managers should focus on the

welfare of all stakeholders (Laurent, 2007). Proponents of value maximization believe that other

constituencies can become better off if companies pursue shareholders interests. Jensen (2001;

4 Hillman and Keim (2001) argue that social responsibility and stakeholder management are closely related and that social responsibility is a measure of how well a firm manages the relations with its stakeholders. Graves and Waddock (2000) state that social performance can be defined as stakeholder relations. The groups identified in social responsibility discussions are often the same groups identified as stakeholders. As such, corporate social responsibility and stakeholder theory may be directly linked and are at a minimum based on a similar foundation.

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2002) argues that a business cannot maximize wealth simply by stating this as its goal; it requires

working with stakeholders to achieve this objective. So, simply focusing on shareholder value

maximization may be like putting blinders on a horse, and it may not be applicable in todays

business world.5

3. Enlightened value maximization and development of hypotheses

Jensen (2001; 2002) argues that while the goal of the corporation is to maximize

shareholder wealth, this goal cannot be met by treating stakeholders poorly. Companies should

not try to maximize the welfare of all stakeholders, but work with stakeholders to produce

shareholder wealth. More specifically, companies should improve stakeholder welfare until the

marginal cost of doing so exceeds the marginal benefit to shareholders. Thus, Jensens approach

is somewhat consistent with the management of stakeholders as a means to an end where the end

is shareholder value maximization6.

There are at least two questions raised by this logic. The first question is: how would

actions aimed at improving the welfare of stakeholders contribute to shareholder wealth; and the

second is: does stakeholder management actually improve company financial performance and

increase shareholder wealth.

To address the first question, we must realize that the effect of stakeholder management

on shareholder wealth could be bi-directional. There may be rewards for positive actions, as well

as penalties for irresponsible ones. Companies that build better relations with primary

5 In the recent business downturn and stock market drop, the press has increasingly focused on business social responsibility and the relations between business and stakeholders. Value maximization, as the sole firm goal, is viewed very negatively in the popular press and even in the financial press. 6 Instrumental stakeholder theory posits that stakeholder management will lead to better financial performance. Jensens (2001; 2002) enlightened value maximization does differ from instrumental stakeholder theory. Jensen argues that stakeholder theory fails to provide a mechanism for making the tradeoffs between competing stakeholder claims. Enlightened value maximization implies that managers will make tradeoffs between stakeholders using the effect on firm value as the decision criteria.

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stakeholders such as employees, customers, suppliers, and communities may be able to increase

their financial gains (Freeman, 1984). Prior research has documented these rewards (Turban and

Greening, 1996; Greening and Turban, 2000; De Luque, Washburn, Waldman, and House, 2008).

Godfrey (2005) argues that corporate social responsibility and philanthropic activities can

generate positive moral capital among stakeholders which may provide an insurance-like effect

for firm reputation if social problems occur. Companies that mistreat stakeholders may face

penalties. Researchers have documented these penalties (Davidson and Worrell, 1988; Karpoff

and Lott, 1993; Cohen, Fenn, and Naiman, 1995; Davidson, Worrell, and El-Jelly, 1995; Hart and

Ahuja, 1996; Karpoff, Lott, and Rankine, 1998; Karpoff, Lee and Vendrzyk, 1999; Klassen and

Whybark, 1999; Konar and Cohen, 2001; Davidson, Worrell, and El Jelly, 2000).

While stakeholder theory posits that stakeholder management may generate increases in

income or shareholder wealth, companies that spend resources directed at stakeholder welfare, in

excess of the marginal benefits, may find that income and/or shareholder wealth decreases

(Hillman and Keim, 2001). Therefore, whether companies actually increase or decrease

shareholder wealth by managing stakeholder relations is an empirical question.7

Jensens theory of enlightened value maximization suggests that value maximization

cannot be achieved if companies ignore or mistreat their stakeholders. Others have made similar

propositions (Graves and Waddock, 2000; Davis, 2005). Jensens model differs from traditional

instrumental stakeholder theory because his model provides a basis for making tradeoffs between

stakeholder groups. The companys relations with its stakeholders must be rooted in the

7 Numerous researchers have addressed this question with equivocal findings. Many have found a positive relation between stakeholder management and financial performance (Berrone, Surroca, and Trib, 2007; Barbara, Myron, and Bruce, 2006; Hillman and Keim, 2001; Moore, 2001; Ogden and Watson, 1999; Ruf, Muralidhar, Brown, Janney, and Paul, 2001; Waddock and Graves, 1997; Moneva, Rivera-Lirio, and Munoz-Torres, 2007) while others have found a negative or a mixed relation (Meznar, Nigh and Kwok, 1994; Berman et al., 1999; Omran, Atrill, and Pointon, 2002; Brammer, Brooks, and Pavelin , 2006; and Bird, D. Hall, Moment, and Reggiani, 2007).

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companys strategies and must be a means to an endvalue maximization. Accordingly,

managers optimize relations with stakeholders to maximize firm value. Managing stakeholder

welfare may be a partial determinant of firm value. However, deviations from expected

investment in stakeholder management may be detrimental to a firms value.

3.1. Deviations from expected stakeholder management as an agency cost

Once an owner-manager owns less than 100 percent of the corporation, the costs of

providing the manager with pecuniary and non-pecuniary benefits are borne, in part, by

shareholders (Jensen and Meckling, 1976). These costs are called agency costs. If management

spends resources on managing stakeholder relations and the expenditures do not lead to increased

firm value, then these expenditures would be an agency cost. What incentive would management

have to spend excess resources on stakeholders?

Cespa and Cestone (2007) argue that stakeholder management can become a powerful

entrenchment strategy for incumbent CEOs because inefficient managers may commit themselves

to socially responsible behavior to gain stakeholders support against shareholders. Firms have

limited resources, and there are conflicts between shareholders and other constituents over these

resources. Managers must solve these conflicts. Diverting resources from shareholders may either

hurt shareholders or benefit shareholders in the long run. Proponents of the strict shareholder

value maximization philosophy argue that philanthropic activities and maintaining stakeholder

welfare would waste company resources that would be better spent to produce value for

shareholders. However, diverting some resources in the management of stakeholder welfare may

produce long-run value maximization (Jensen, 2001, 2002; Brickley et al., 2002). The question

may not be whether a firm should invest in stakeholder management, but how much should be

invested in the pursuit of shareholder wealth maximization. Finding the optimal level of

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investment likely challenges all firms, but may be easier when there is high quality corporate

governance.

3.2. Internal governance mechanisms and agency costs

3.2.1. Managerial ownership incentives and agency costs

From a theoretical standpoint, higher pay-performance sensitivity should result in a

convexity payoff that encourages managers to work hard and take on value enhancing risky

projects (Jensen and Meckling, 1996; Myers 1977; Haugen and Senbet, 1981). Accordingly,

increasing the sensitivity of CEO wealth to stock price should better align managerial behavior

with shareholder interests. An unintended effect of high managerial ownership is that it may

expose managers to more risk relative to diversified shareholders. Here, the concavity of the

managers utility function may overcome the convexity of the payoff making managers more risk

adverse (Guay, 1999; Ross, 2004). High managerial ownership may also increase the risk of

managerial perquisite consumption (Baker and Hall, 2004; Edmans, Gabaix, and Landier, 2009).

Despite significant research on this issue, there is substantial debate over the theoretical and

empirical effect of higher pay-performance sensitivity on financial performance and its effect in

deterring various agency-related issues.8,9

8 For example, studies find a positive relation between managerial ownership and firm performance (Mehran, 1995; Core and Larker, 2002), or a positive but decreasing relation between managerial ownership and firm performance (Morck, Schleifer, and Vishney, 1988; McConnell and Servaes 1990, 1995; Hermalin and Weisbach, 1991; Hubbard and Palia, 1995; Holderness, Krosner, and Sheehan, 1999; Anderson and Reeb, 2003; Tian, 2004; Davies, Hillier, and McColgan, 2005; Adams and Santos, 2006; Pukthuanthong, and Roll, Walker, 2007; McConnell, Servaes, and Lins, 2008; Tong, 2008; Benson and Davidson, 2009). Others find no relation between managerial ownership and firm performance (Demsetz, 1983; Demsetz and Lehn, 1985; Agrawal and Knoeber, 1996; Lorderer and Martin, 1997; Cho, 1998; Demsetz and Villalonga, 2001; Himmelberg, Hubbard, and Palia, 1999; Palia, 2001; Coles, Lemmon, and Meschke, 2003; Brick, Palia, and Wang, 2005; and Cheung and Wei, 2006). 9 The remainder of this section focuses on the effects of increased sensitivity of wealth to stock price, delta. However, it is important to note that increasing the sensitivity of CEO wealth to stock return volatility, vega, is seen as one potential mechanism to offset managerial risk aversion. Coles, Daniel, and Naveen (2006) provide evidence that managers with greater convexity in their contracts engage in riskier investment. However, theoretical and empirical studies suggest that increasing convexity does not necessarily increase managerial risk taking (Lambert, Larker, and

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At the heart of the debate is the empirical puzzle of a negative and significant relation

between the CEOs effective ownership percentage (dollar-dollar incentives), b, and firm size

(Demsetz and Lehn, 1985; Jensen and Murphy, 1990; Gibbons and Murphy, 1992; Garen, 1994;

Hadlock and Lumer 1997; Hall and Liebman, 1998; Schaefer 1998; Murphy, 1999; Jin, 2002;

Baker and Hall, 2004; Cichello, 2005; Benson and Davidson, 2009; and Edmans et al. 2009).

Nonetheless, the concept that CEOs of large companies have trivial incentives relative to those of

small companies seems inconsistent with the observation of the very large wealth swings induced

by substantial stock and option holdings of large-company CEOs (Hall and Liebman, 1998; Baker

and Hall, 2004; Core and Guay, 2010). Baker and Hall (2004), however, note that a key

assumption made by researchers examining this puzzle is the marginal product of CEO action on

firm value, . Viewed from two polar extremes, if total incentives (i.e. the marginal return to effort) are equal to b*, this suggests that either = 0 and is independent of firm size or = 1 and scales proportionally with firm size. The former assumption is best modeled using dollar-dollar

incentives, b. Dollar-dollar incentives, consequently, are most effective at deterring agency costs

that have a dollar effect on firm value (i.e. perquisite consumption). The later assumption,

however, is best modeled using dollar-percent incentives. This implies that dollar-percent

incentives are most effective at deterring agency costs that scale with firm size (i.e. corporate

strategy) and their model predicts that for tasks whose marginal products scale with firm size,

optimally low b in large firms should not incur significant agency costs. However, given the

infeasibility of high b in large firms, optimally low b will also induce significant agency costs for

those tasks whose marginal products do not scale with firm size. Therefore, some agency costs are

Verrecchia, 1991; Guay, 1999; Carpenter, 2000; Ju, Leland, and Senbet, 2002; Ross, 2004; Tian, 2005; Parrino, Poteshman, and Weisbach, 2005; Lewellen, 2006; and Hayes, Lemmon, and Qiu, 2010). Furthermore, it is not entirely clear how increasing managerial sensitivity to risk would influence deviations from expected SM.

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best deterred by active board monitoring and bureaucratic control systems rather than managerial

incentives.

Edmans et al. (2009) propose a multiplicative specification for the CEOs utility and

production function. Their model predicts that the dollar increase in firm value has size elasticity

of 1, or is proportional to firm size. They measure CEO incentives using percent-percent

incentives, the dollar change in wealth for a one-percent change in firm value, divided by total

annual pay (i.e. scaled wealth-performance sensitivity).10 Dollar-dollar incentives equal percent-

percent incentives multiplied by CEO wage and divided by firm value. Even a small b can induce

high levels of CEO effort since firm value is substantially greater than the CEOs wage, making

the dollar gains from effort larger than the dollar costs. A key implication of the Edmans et al.

(2009) model, however, is that equity based incentives are only effective at addressing agency

costs that have a multiplicative effect on firm value, or actions that are proportional to firm value,

such as corporate strategy. In contrast, CEO incentives cannot solve additive value-destructive

actions, such as perquisite consumption, as these actions have a minimal effect on stock price and

consequently, CEO wealth. Perks, instead, are best controlled by corporate governance. As the

authors suggest, active monitoring [via corporate governance] and incentives should be used in

tandem; the former to deter additive value-destructive actions, and the latter to encourage

multiplicative value-enhancing efforts (p. 4902).

Our testable hypotheses, therefore, assume that firms will choose CEO equity-based

ownership incentives, delta, to implement value-maximizing decisions. If CEO incentives are in

alignment with shareholder interests, deviations from expected investment in stakeholder

10 Edmans et al. (2009) also demonstrate that this measure has several empirical advantages over previous measures, including size and firm risk insensitivity.

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management should be negatively related to CEO portfolio delta. As a result, the managerial

ownership incentive hypothesis states:

H1: Deviation from expected stakeholder management is negatively related to CEO

portfolio delta. The empirical implication is that higher delta deters agency costs related to activities that

scale firm size, such as corporate strategy. If investment in stakeholder management is necessary

for maximizing firm value from a strategic standpoint, then we would then expect that deviations

from expected stakeholder management are negatively related to CEO portfolio delta.

However, it is also possible that managers may undertake some investment in stakeholder

management for personal interests. For example, non-strategic investments in stakeholder

management may benefit the CEO by increasing job security (Cespa and Cestone, 2007) or

support moral or personal beliefs incongruent with those of the firm. As a result, deviations from

expected investment in stakeholder management may also represent perquisite consumption by

the CEO. As a result, the managerial perquisite hypothesis states:

H2: Deviation from expected stakeholder management is positively related to CEO portfolio delta.

The empirical implication of this hypothesis is that while a higher delta may induce

greater alignment between CEO incentives and investment in stakeholder management related to

corporate strategy, CEOs may invest in superfluous stakeholder management for personal reasons

as a form of perquisite consumption. These investment are likely to be more additive in nature,

and will have a minimal effect on stock price. While they will be costly in dollar terms to the

firm, they will not appreciably affect CEO wealth. We therefore expect that deviations from

expected stakeholder management may also be increasing in CEO portfolio delta.

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3.2.2. Board monitoring and agency costs

Executive perquisites have attracted a lot of attention and criticism from the public and

regulators, especially in the current financial crisis11,12. There are two competing views on

executive perquisites: the outcome of an optimal employment contract, designed to motivate

executives to work harder and enhance productivity (Fama, 1980), and an agency problem leading

to reduced firm value either directly or indirectly (Jensen and Meckling 1976). Empirical studies

have documented mixed evidence. For example, Yermack (2006) find that firms which disclose

the private use of corporate jets witness negative market reactions, and they tend to underperform

their peers with no private jet use disclosures. Similarly, Chen, Chen, and Hui (2009) find that

family ownership reduces managerial perquisite consumption and stock prices drop around the

disclosure of a descendent CEOs perk consumption and Andrews, Linn, and Yi (2009) find that

firms that hid large amounts of CEO perquisites experienced a negative market reaction after their

proxy statements were released. On the other hand, Rajan and Wulf (2006) provide evidence that

executive perks can increase managerial productivity.

Both theories, however, are consistent on one point; weak corporate governance and

executive personal tastes affect perquisite awards and consumption. Yermack (2006) documents

that variables related to CEO characteristics have significant explanatory power for executives

personal use of corporate jets. Andrews, Linn, and Yi (2009) find that firms with weak corporate

governance are more likely to award perquisites to executives. These results imply that

11 For example, there are a lot of negative reports criticizing the CEOs of the big three auto makers who flew to Washington in their luxury corporate jets to request 25-billion-dollar taxpayer bailout money on November 19, 2008, and the 1.22 million dollar office refurbishing expenses of the Merrill Lynch CEO in early 2008 while the securities firm was cutting thousands of jobs, and etc. 12 To improve the transparency of disclosure of executive perquisite consumption to investors, the SEC tightened up its disclosure requirements. The new rule not only lowers the minimum threshold amount for reporting from $50,000 to $10,000, but also provides interpretive guidelines on the classification of perquisite. There is loophole in this rule because it does not specify clearly between an expense and a perquisite. We cannot completely rule out the possibility that executives report some perks, for instance, investment in stakeholder management because of their own personal interests, as normal business expenses.

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executives personal tastes may play a significant role in stakeholder management; executives

may tend to invest in stakeholder management unrelated to firm value; and ineffective governance

may not be able to deter but encourage this kind of stakeholder management behavior. As

indicated earlier, this kind of behavior may become more additive. The rationale is simple. If

CEOs can spend corporate resources for their personal use (e.g. luxury office, traveling), we can

reasonably argue that they may invest in stakeholder management based on their personal

interests. This investment would simply be another form of perquisite. This type of investment

may be more harmful to a firm because of the hidden nature of this type of perquisites.

A boards attitude towards various stakeholders is likely to become an important

determinant of its stakeholder policy. In addition, many state statutes have challenged the

traditional view of the shareholder-only fiduciary duty of the board and specified that boards have

the right to take the interests of all stakeholders into account (McDonnell, 2004). Under this

pressure, boards may be more likely to apply enlightened stakeholder management in dealing

with stakeholders while maximizing shareholder wealth. So, effective monitoring by boards may

mitigate agency problems related to the amount of resources diverted to other stakeholders.

Board composition may affect the boards management policy. Regulators and academics

believe that outside directors are generally more effective monitors than inside directors. The

Sarbanes-Oxley Act and the exchange rules in 2002 require that the majority of the board be

independent. Numerous studies link the proportion of outside directors to financial performance

and shareholder wealth (e.g. Rosenstein and Wyatt, 1990; Byrd and Hickman, 1992; Brickley,

Coles, and Terry, 1994; Cotter, Shivdasani, and Zenner, 1997). Outside dominated boards may

be less likely to provide other stakeholders with excess resources, and they would be more likely

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to optimize the level of stakeholder management. In other words, outside dominated boards

should be in a better position to control unwarranted investment in stakeholder management.13

Board size is another major characteristic of corporate boards (Lipton and Lorsch, 1992;

Jensen, 1993; Yermack, 1996). Even though there is debate regarding the effect of board size on

its decisions, the commonly accepted idea is that small boards are better than large ones because

large boards place a greater emphasis on politeness and courtesy, making it easier for CEOs to

control. Empirical studies support this idea (e.g. Yermack, 1996; Eisenberg, Sundgren, and Wells,

1998). If small boards are more effective in monitoring management, small boards may be more

likely to control superfluous resources diverted to stakeholder management.

Thus, more effective monitoring by the board is expected to reduce agency costs between

managers and shareholders by preventing managers from making unnecessary investment in

stakeholder management as a potential means of perquisite consumption. Therefore, the board

monitoring hypothesis is as follows:

H3: Deviation from expected stakeholder management is negatively related to proxies for effective board monitoring (i.e. smaller board size and more independent directors).

The empirical implication of this hypothesis is that higher delta may increase agency costs

between managers and shareholders related to investment in stakeholder management as a form of

perquisite consumption. This type of additive value-destructive investment is best deterred by

active monitoring by the board of directors. We therefore expect that deviations from expected

stakeholder management may be decreasing in proxies for effective board monitoring.

13 Other research has related board composition to stakeholder management. Johnson and Greening (1999) find that outside director dominated boards tend to pursue greater stakeholder management. Similarly, Kassinis and Vafeas (2002) find that the likelihood of becoming an environmental lawsuit defendant decreases with the number of outside directors.

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3.2.3. The Equivocal Effect of Duality

When the CEO also holds the title of board chair, CEO duality, power may concentrate in

the CEOs position. Duality may allow the CEO to control information available to other directors

impeding effective monitoring (Jensen, 1993). However, empirical work has not supported the

concept that duality can lead to agency costs. For example, Brickley, Coles, and Jarrell (1997)

show that CEOs are awarded the chair position as a normal part of the succession process;

successful CEOs later become CEO/Chair. While duality may have an impact on deviations from

expected stakeholder management, we do not have strong priors with respect to empirical

outcomes. It is possible that duality proxies for the CEOs power over the board of directors, thus

impeding effective monitoring by the board. It is also possible that duality is endogenously

determined, such that CEOs that are less likely to engage in unnecessary investment in

stakeholder management are awarded the chair position. We therefore treat the expected impact of

duality as an open empirical issue.

3.2.4. Institutional and blockholder ownership and agency costs

Given their large financial stake, institutional investors and blockholders have incentives,

resources, and the ability to mitigate agency problems (Edwards and Hubbard, 2000) and monitor

a firms stakeholder policy. Empirical research supports this idea (Brickley, Lease, and Smith,

1988; Hartzell and Starks, 2003; Holderness, 2003; Jiambalvo, Rajgopal, and Venkatachalam,

2002; McConnell and Servaes, 1990). Empirical studies have also explored the role of

blockholders in the conflict between stakeholder management and financial performance. For

example, institutional ownership plays an important role in monitoring and influencing CEOs

attention to corporate social performance (Graves and Waddock, 1994; Wright et al., 2002;

18

Wright, Ferris, Sarin, and Awasthi, 1996; Johnson and Greening, 1999; Neubaum and Zahra,

2006).

Our focus, however, is on the role of institutional investors in preventing needless

investment in stakeholder management. A higher concentration of institutional investor ownership

should also lead to lower levels of unnecessary investment in stakeholder management.

Consequently, the institutional ownership hypothesis is as follows:

H4: Deviation from expected stakeholder management is negatively related to the percentage of shares held by institutional investors.

3.2.5. Operationalizing expected stakeholder management

Our theoretical arguments allude to a value maximizing, or optimal, level of stakeholder

management. Our measurement of optimal stakeholder management is limited, however, by a

lack of empirical literature on this concept. Consequently, as a starting point we assume that

management has an incentive to invest in stakeholder management to increase firm value either

voluntarily or due to pressure from regulators, investors, the board of directors, the media, or the

product market. And like other financial decisions, management has a target level of investment

in stakeholder management which is believed to maximize shareholder wealth. Given the

difficulty in accurately assessing the optimal level of stakeholder management, empirical

evidence and corporate practice suggest that industry average provides a useful starting point for

arriving at this value. Masulis (1983), for example, finds that when firms issuing debt move

toward the industry average from below, the market reacts more positively than when the firm

moves away from the industry average, indicating that industry average provides a proper

benchmark for optimal capital structure. Similarly, Frank and Goyal (2009) find that the median

industry leverage is one of most reliable factors in the capital structure decisions of publicly

19

traded American firms. Extending this logic from the capital structure literature, an optimal level

of stakeholder management exists, but not every firm achieves this value-maximizing level.

Industry average provides an approximate estimate for the optimal level of stakeholder

management 14

However, given the difficulty in measuring actual investment in stakeholder management

and the unique characteristics of each firm, a simple industry average is likely to be a noisy proxy.

Specifically, in addition to industry, the optimal level of stakeholder management may be

explained by firm value and other observable firm specific characteristics, such as firm size,

financial constraints, and growth opportunities. Such an interpretation is also more consistent with

Jensens (2001, 2002) argument that value is the criterion to stakeholder management. A more

detailed discussion of the operationalization of our measures used for deviation from expected

stakeholder management is provided in section 4.2.1.

Finally, given our operationalization of optimal stakeholder management, our conclusions

depend on a key assumption. Namely, that the sample mean, conditional upon industry and other

observable firm specific factors, is a reasonable proxy for optimal stakeholder management. To

the extent that this assumption is violated, our measures of deviation from expected stakeholder

management may be biased.

4. Sample selection and data

4.1. Sample selection

We obtain our initial sample from the KLD Statistical Tool for the Analysis of Trends in

Social and Environmental Performance (KLD) database, an annual statistical database of over 90

14 The use of industry average as a proxy for the optimal level is also validated by common practice in financial statement analysis, in which greater deviations from the industry average are a potential indicator of financial problems within the firm.

20

social and environmental indicators provided by KLD Research and Analytics, Inc. The database

reports social, environmental, and governance performance indicators for S&P 500, Domini 400

Social, and Russell 1000 (starting in 2001) and Russell 3000 (starting in 2003) companies

between the years of 1991 to 2008. We use the KLD database because it is a measure of social

performance (relations with stakeholders) that has been developed independently of this studys

researchers, and therefore, does not suffer from potential researcher bias that might occur if we

use our own definition of stakeholder management and corporate social performance.

Furthermore, the KLD database is a well-established measure of both stakeholder

management and corporate social performance. Researchers have certified its quality and it has

been widely used in empirical studies.15 16

Our initial KLD sample has 26,575 firm years from 1991 to 2008. We match the KLD

data with COMPUSTAT accounting data, resulting in a sample composed of an unbalanced panel

of 24,813 firm year observations for 4,552 firms.17 We match this set of firms to Standard and

Poors EXECUCOMP database, which includes annual compensation data from proxy statements

for the five highest paid executives for firms in the S&P 500, the S&P MidCap 400, and the S&P

SmallCap 600. We lose one year of data because the EXECUCOMP data begins in 1992. This

results in an unbalanced panel of 15,761 firm year observations for 2,297 firms from 1992 to

2008. To account for the influence of internal governance mechanisms, we obtain board of

15 For example, Chatterji, Levine, and Toffel (2009) conclude that while the KLD database does not reflect all available information on stakeholder management and corporate social performance, it is a good predictor of more narrow measures such as compliance with environmental regulations. 16 See Agle, Mitchell, and Sonnenfeld (1999), Berman, Wicks, Kotha, and Jones (1999), Graves and Waddock (1994), Hillman and Keim (2001), Johnson and Greening (1999), Turban and Greening (1996), Waddock and Graves (1997), and Coombs and Gilley (2005). 17 A primary problem with the KLD data is that the data lacks a numeric unique identifier (i.e. CUSIP) for years prior to 1995 in which to merge with COMPUSTAT. We try several methods to obtain a COMPUSTAT unique identifier (GVKEY) for the initial sample. First, we backfill early years of data (prior to 1995) with CUSIPs from the same firm if it appears in the database in a later year. Next, we attempt to merge companies with COMPUSTAT using CUSIP, ticker, and the first 15 letters in the company name. Lastly, we hand verify our merge results from the steps above and hand match firms with COMPUSTAT for any remaining firms not captured in previous steps.

21

director characteristics from the RiskMetrics IRRC (IRRC) database and institutional ownership

data from the Thomson 13F (13F) database. Due to data limitations in IRRC, board characteristics

are only available from fiscal year 1995 onward.18 We obtain stock return volatility, volume, and

shares outstanding data from the Center for Research in Securities Prices (CRSP) database. We

omit regulated utilities (SIC codes 4910-4949), depository institutions (SIC codes 6000-6099) and

holding or investment companies (SIC codes 6700-6799) from our sample. The final matched

sample, after combining KLD, COMPUSTAT, EXECUCOMP, IRRC, 13F, and CRSP data,

contains an unbalanced panel of 9,051 firm year observations for 1,631 firms from 1995 to 2008.

4.2. Variable descriptions

We rely on the KLD database for stakeholder management and social issue performance

data, COMPUSTAT for accounting data, EXECUCOMP for managerial ownership,

compensation data, and CEO data, IRRC for board and CEO data, 13F for institutional ownership

data, and CRSP for volume traded and shares outstanding data. We winsorize all variables at the

upper and lower 1% to reduce any possible impact from outliers.

4.2.1. Deviation from expected stakeholder management

Enlightened value maximization posits that firms will invest in stakeholder management

(SM) until the marginal cost of doing so exceeds the marginal benefit to shareholders, and we call

this the expected investment in stakeholder management (ESM). While we can observe the effect

of a firms actual investment in stakeholder management, SM, ESM is unobservable. Therefore,

we use two proxies for ESM to measure the deviation of each firm from expected SM.

18 The Risk Metrics IRRC proxy database reports the year using the meeting date. We assume that the proxy date is approximately 3 months after fiscal year end and the meeting date follows by 1 month. So, firms with fiscal years ending in December 1995 will be matched with IRRC observations with meeting dates in April 1996 or earlier.

22

To begin with, we create a measure of the deviation of each firm from expected

stakeholder management (DESM), which is the residual from a cross-sectional regression of SM

on size, financing constraints, performance, and growth opportunities estimated at time t for all

firms in the same 4-digit Global Industry Classification System (GICS) industry19:

1

where SM is the firms stakeholder management score, size is LNSALES, financing constraints

are measured by CASH/TA and TD/TA, performance is measured by ROA and TRS, growth

opportunities is measured by Q and DESM = . The definitions of variables in regression equation (1) are described below. The residuals are a proxy of deviations from expected

investment in SM given industry, year, and observable firm specific factors. To construct

measures for SM, we follow the procedure used in prior research in the area (Waddock and

Graves, 1997; Hillman and Keim, 2001; Coombs and Gilley, 2005), and construct a measure of

the firms stakeholder management (SM) performance using the KLD categories of employee

relations (EMP), diversity issues (DIV), product issues (PRO), community relations (COM), and

environmental issues (ENV). These five categories parallel the primary stakeholder groups with

regard to employees (including diversity initiatives), customers (product safety/quality), the

19 Following Benson and Davidson (2009), we use 4-digit GICS industry group rather than the more common 2-digit SIC code or Fama-French (FF) industry classification for several reasons. First, GICS categorization is based on both a firms operational characteristics and information on investors perceptions as to what constitutes the firms main line of business. Second, Chan, Lakonishok, and Swaminathan (2007) demonstrate that a 4-digit GICS code classification using 24 categories achieves the same level of discrimination as the FF procedure using 48 categories. Finally, the ability to effectively discriminate between industries using a smaller number of categories results in more industry peers each year than the FF procedure, resulting in potentially more accurate industry averages. A breakdown of 4-digit GICS and FF industry classifications by year for the KLD database is provided in Appendix C (available from the authors upon request).

23

natural environment, the community, and suppliers (to the extent that certain diversity initiatives

are directed toward suppliers). We detail the calculation of the SM variables in Appendix A.

As a second proxy for the deviation from expected SM, we assume that ESM is close to

the industry average for each firm in each year. Therefore, we use industry adjusted SM (IASM)

as an additional measure of the deviation of each firm from expected stakeholder management:

SM 2 where industry adjusted SM (IASM) is equal to the difference between each firms actual

investment in SM at time t and the industry average SM (IAVGSM), the average value of peer

firms within each companys 4-digit GICS industry group at time t.

4.2.2. Managerial ownership incentives

We use delta as a proxy for CEO equity-based compensation and ownership incentives.

As in Edmans et al. (2009), delta is the dollar change in CEO portfolio wealth for a 1% change in

firm value scaled by total annual compensation. We estimate each CEOs portfolio of stock and

options using data from the EXECUCOMP database as in Core and Guay (2002). We detail the

calculation of the CEO portfolio delta in Appendix B. We use log of delta (LNDELTA) in our

analysis to account for the high skewness and kurtosis in the variable.

4.2.3. Board monitoring

We proxy for effective monitoring by the board of directors using four measures: board

size, the percentage of independent directors on the board, board independence, and CEO duality.

First, we measure board size (BRDSIZE) as the number of directors serving on the board during

the year. Next, we classify directors as insiders (employed by the firm), affiliated (e.g. former

employees, family members of employees, or those with business relations with the firm) and

24

independent (Baysinger and Butler, 1985). We then create a variable for the percentage of outside

directors serving on the board (PIND) calculated as the percentage of outside directors relative to

total directors on the board. We also create a composite measure of board independence

(BRDIND), calculated as the ratio of independent directors to insider and affiliated directors

multiplied by the inverse of board size. Higher values of this measure indicate more effective

board structure (i.e. higher ratio of independent directors and/or smaller board size). Finally, we

determine whether the CEO holds the title of CEO and COB (DUALITY) and create a dummy

variable equal to one in such cases, and zero otherwise.

4.2.4. Institutional and blockholder ownership

We develop a measure of institutional investor ownership percentage (INSTOWN), which

is the percentage of shares held by institutions during the year.

4.2.5. Controls

We include several additional control variables to proxy for various firm specific factors

which have been shown to be determinants of stakeholder management, internal governance

structure, and firm value.

Cash. High cash balances may exacerbate agency problems (Jensen, 1986) leading to

inefficient investment. We control for cash using the ratio of cash to total assets (CASH).

Capital structure (Leverage). Theoretical and empirical research has found that leverage is

positively related to firm value. However, Titman (1984) and Maksimovic and Titman (1991)

argue that some firms also consider the costs imposed on non-financial stakeholders in

undertaking relationship-specific investments when making capital structure decisions. For

example, research has shown that firms selling unique products (Titman and Wessels, 1988), or

25

firms entering into bilateral relationships (Banerjee et al., 2008) or strategic alliances (Kale and

Shahrur, 2007) maintain or lower leverage. We proxy for the firms capital structure using the

debt ratio (TD/TA); we calculate it as book value of total debt to book value of total assets.

Firm size. Research has shown that firm size is positively related to board size and CEO

compensation, but negatively related to firm value. We control for firm size using the log of sales

(LNSALES).

Firm Performance. We measure accounting performance using return on assets (ROA),

calculated as earnings before interest and taxes divided by total assets. As an additional measure

of financial performance, we use the 1-year total return to shareholders (TRS), including the

monthly reinvestment of dividends. We obtain this measure from the COMPUSTAT database.

4.2.6. Instruments

We use several instruments to proxy for various firm specific factors which have been

shown to be determinants of internal governance structure.

Growth opportunities and information asymmetry. Jensen (1993) argues that monitoring

high growth firms is costly, and Fama and Jensen (1983) suggest that firms with higher stock

return volatility have higher levels of information asymmetry. Prior research has suggested that

the monitoring costs associated with higher levels of information asymmetry are inversely related

to board size and independence (Linck et al., 2008; Adams and Ferreira, 2007; Raheja, 2005;

Maug, 1997). Similarly, Demsetz and Lehn (1985) note that higher levels of information

asymmetry (as proxied by higher volatility) are associated with more managerial discretion,

necessitating higher levels of variable compensation. Following Linck, et al. (2008) we proxy for

growth opportunities and information asymmetry using Tobins Q, the level of R&D

expenditures, and total firm risk. As in Smith and Watts (1992), we calculate Tobins Q (Q) as the

26

market value of equity minus the book value of equity plus the book value of assets all divided by

the book value of assets.20 R&D expenditures (R&D/TA) are the dollar value of R&D

expenditures scaled by total assets. Many firms in the COMPUSTAT database have missing

values for R&D. We set R&D expenditures equal to zero when the value is missing. Total equity

risk is measured using volatility (VOLATILITY), which is the annualized standard deviation of

monthly stock returns calculated over 36 months.

Capital intensity. Previous research has found that managerial ownership and

compensation are related to the capital intensity of the firm. We include a variable for capital

intensity (CAP), measured as the net property, plant, and equipment to total assets.

CEO characteristics. Hermalin and Weisbach (1998) suggest that firms will add insiders

to the board of directors as the CEO approaches retirement as part of the succession process. We

use CEO age (AGE) as a proxy for the length of time to retirement of the CEO.

Risk aversion. Following Coles, Naveen, and Naveen (2006), we include CEO tenure

(TENURE), measured as the time the CEO has been in the current position, and CEO cash

compensation (TCC), measured as salary plus bonus, as proxies for the CEOs level of risk

aversion. CEOs with longer tenures may be entrenched, making them more likely to avoid risk

(Berger et al., 1997). However, Guay (1999) argues that higher levels of cash compensation

reduces manager risk aversion because cash compensation is unrelated to performance and

permits their personal diversification.

Turnover. As in Hartzell and Starks (2003), we use turnover (TURNOVER), the log of

one plus the ratio of monthly volume to number of shares outstanding in the month prior to the

institutional ownership observation, as an instrument for institutional investor ownership.

20 We use Q rather than MKBK as in Linck, et al. (2008) to account for the problem of many firms having negative or small values of book value of equity in the denominator. This leads to negative values of MKBK or abnormally large values of MKBK that are driven by small book values of equity rather than large market values of equity.

27

4.3. Descriptive statistics

Table 1, Panel A, presents descriptive statistics. The mean (median) deviation from

expected stakeholder management score (DESM) is 0.02 (0.02). The measure ranges from a

minimum score of 0.88 to a maximum score of 0.93. The mean (median) industry adjusted stakeholder management score (IASM) is 0.04 (0.05). The measure ranges from a minimum score

of 1.01 to a maximum score of 1.14. The mean (median) stakeholder management score (SM) is 0.05 (0.00). The measure ranges from a minimum score of 1.12 to a maximum score of 1.16.

-----Insert Table 1 About Here-----

The mean (median) value for DELTA is 446.49 (85.41). Our sample boards average 9.66

directors (BRDSIZE). Approximately 29.82% of directors are independent (PIND), and the CEO

is also Chair (DUALITY) approximately 66% of the time. Institutional ownership (INSTOWN)

averages 75.41% of shares outstanding. Finally, our sample consists of large industrial firms. The

mean (median) value of total sales is $6.669 ($2.142) billion. However, the sample includes a

broad range of firms with total sales ranging from $94.06 million to $81.303 billion.

Table 1, Panel B, presents correlations of key variables for our sample. The correlations

between the two measures of deviation from expected stakeholder management (DESM and

IASM) and SM are significant. However, the correlations between DESM and IASM or SM are

less than 90%, suggesting that they may capture different dimensions of overall deviation from

expected stakeholder management. Lastly, we find that less financially constrained firms (higher

CASH/TA and lower TD/TA) and firms with higher ROA are associated with higher deviations

from expected SM (IASM and SM).21 This finding may also reflect that firms with slack financial

resources are associated with higher levels of SM (Waddock and Graves, 1997).

21 Note that we control for CASH/TA and TD/TA in equation 1 when calculating DESM. So, it is not surprising that DESM does not exhibit a similar significant correlation with these measures.

28

The correlations between LNDELTA and two of three measures of deviation from

expected and actual SM are positive and significant. Interestingly, we also find that dollar/dollar,

or effective percentage owned, incentives, (Jensen and Murphy, 1990) have a negative and

significant correlation with both measures of deviation from expected SM and actual SM (un-

tabulated). This finding is consistent with the predictions of Hall and Baker (2004) who note that

dollar/dollar incentives are more effective at deterring tasks that do not scale with firm size.

Deviations from expected SM, therefore, may be additive value destructive and reflect a form of

perquisite consumption by the CEO. Combined, these findings provide initial support for the CEO

perquisite consumption hypothesis (H2). Conversely, more effective corporate governance is

associated with lower deviations from expected SM; it has positive and significant correlations

with BRDSIZE, negative and significant correlations (DESM and IASM) with PIND, and

negative and significant correlation (SM) with INSTOWN. Furthermore, higher LNDELTA is

associated with more effective board governance (smaller BRDSIZE, higher PIND, separation of

CEO and COB title) and lower levels of institutional ownership (INSTOWN). Overall, this initial

evidence supports the board monitoring hypothesis (H3). As noted by Edmans et al. (2009), more

effective corporate governance is associated with higher managerial incentives, the latter being an

effective means of addressing agency costs that are proportionate with firm value. However,

incentive pay is ineffective at deterring perquisite consumption, which is largely independent of

firm value; additive value destructive actions are best controlled by direct monitoring, rather than

incentive pay.22 Our initial observations suggest that firms recognize this tradeoff and adjust

accordingly.

22 As a final note, we find that percent/percent incentives (LNDELTA) are independent of firm size (LNSALES), whereas the dollar/dollar (Jensen and Murphy, 1990) and dollar/percent (Baker and Hall, 2004) measures have negative and significant correlations with firm size (un-tabulated). This makes the percent/percent measure an empirically desirable measure of managerial incentives (Edmans et al., 2009).

29

5. Analysis of the effect of internal governance mechanisms on the deviation from expected stakeholder management

We begin by examining whether internal governance mechanisms are related to deviations

from expected stakeholder management. Previous research has explored the relation between SM

and firm performance using pooled ordinary least squares regressions, which assumes that a

firms level of SM is exogenous to the firm (Hillman and Keim, 2001). Modeling the relation in

this manner fails to control for unobserved firm heterogeneity, or omitted variable bias. As a

result, it is possible that these results are biased due to multiple occurrences of the omitted

variable across time periods. We, therefore, use firm fixed effects (FE) regression as the

estimation method to control for the presence of unobserved firm effects.

Specifically, we test whether changes in internal governance mechanisms affect deviations

from expected stakeholder management or are endogenously determined by the firm specific

factors. The model we test is as follows:

DESM

/

3

where deviation from expected stakeholder management (DESM) is a function of managerial

ownership incentives (LNDELTA), board monitoring (BRDSIZE, PIND, DUALITY),

institutional ownership (INSTOWN), controls (CASH/TA, TD/TA, LNSALES, ROA, TRS), and

dummy variables for year. The definition of variables in regression equation (3) is as described in

section 4.2. The sign beneath each variable indicates the expected relation between the dependent

variable and relevant independent variables.

30

Table 2, column 1 reports results of fixed-effects (FE) estimates of regression equation

(3).23 Here and throughout the table we compute the t (z)-statistics using robust standard errors

clustered at the firm level (see Rogers, 1993; Wooldridge, 2002). The estimated coefficient for

LNDELTA is positive and significant at the 5% level (t = 2.18). Higher managerial incentives are

associated with larger deviations from expected stakeholder management. This result supports the

CEO perquisite hypothesis (H2). While the signs on BRDSIZE and PIND are as predicted, only

the estimated coefficient for BRDSIZE is significant at the 1% level (t = 3.01). Larger boards are

associated with larger deviations from expected stakeholder management. This provides partial

support for the board monitoring hypothesis (H3). The estimated coefficients for DUALITY and

INSTOWN are insignificant. Column 2 includes our composite measure for BRDIND rather than

BRDSIZE and PIND. While the sign on BRDIND is as predicted, the estimated coefficient for

this variable is not significant. The significance and direction of the estimated coefficients for

LNDELTA, DUALITY, and INSTOWN are similar to those in column 1.

-----Insert Table 2 About Here-----

While our initial finding provides some evidence that higher levels of managerial

incentives are associated with greater deviations from expected SM and that more effective

internal governance mechanisms work to lessen the deviations from expected SM, these

deviations may also reflect either an under- or over-investment from expected SM. While a

negative value of DESM (a negative residual) suggests an under-investment in SM, a positive

value of DESM (a positive residual) suggests an over-investment. Internal governance

mechanisms may influence under-investment and positive deviations from expected SM

23 Pooling tests for firm fixed effects and year fixed effects are significant at the 1% level, supporting the inclusion of firm and time effects in the model. The conclusion of subject specific parameters in the model is also supported by a modified Wald statistic for group-wise heteroskedasticity in the residuals of a fixed effect regression model, following Greene (2003, p. 598). This statistic is consistent at the 1% level. Finally, a Hausman test for random effects is significant at the 1% level, supporting the use of the fixed effects model over the random effects model.

31

differently. To test this directly, we first sort DESM into quintiles. Firm observations in the

bottom two quintiles, those with the most negative values, are classified as under-investing in SM.

Firm observations in the top two quintiles, those with the most positive values, are classified as

over-investing in SM. We classify firms in the middle quintile as near expected SM and treat

them as a benchmark. We then estimate a multinomial logit model that predicts the likelihood that

a firm will be in one of the two extreme quintile groups as opposed to the middle quintile.24

Columns 3 and 4 contain results for a multinomial logit regression of equation (3).

Column 3 contains estimated results for the under-investment in SM firms. The estimated

coefficient for BRDSIZE is negative and significant at the 1% level (z = 3.77). Larger boards are associated with a lower relative risk of under-investment in SM. Column 4 contains estimated

results for the over-investment in SM firms relative to the normal investment in SM firms. The

estimated coefficient for BRDSIZE is now positive and significant at the 5% level (z = 2.25),

while the estimated coefficient for PIND is negative and significant at the 5% level (z = 2.40). More effective board monitoring (i.e. smaller boards and more independent directors) are

associated with a lower relative risk of over-investment in SM. Columns 5 and 6 report results

using BRDIND rather than BRDSIZE and PIND. Column 5 contains estimated results for the

under-investment in SM firms relative to the normal investment in SM firms. The estimated

coefficient for BRDIND is positive and significant at the 10% level (z = 1.90). Column 6 contains

estimated results for the over-investment in SM firms relative to the normal investment in SM

firms. The estimated coefficient on BRDIND is now negative and significant at the 1% level (z =

2.68). More independent boards are associated with a higher relative risk of under-investment in 24 We use a multinomial logit model rather than an ordered logit model because we are uncertain of the ordinality of the ESM measure. As noted by Long (1997), using a nominal model when the dependent variable is ordinal results in a loss of efficiency since information is ignored. However, applying an ordinal model to a nominal dependent variable may result in biased estimates. In cases where there is a question about the ordinality of the dependent variable, the loss of efficiency of using a nominal model is outweighed by the potential bias of ordinal model.

32

SM and lower relative risk of over-investment in SM. Overall, these results suggest that more

effective board monitoring (i.e. smaller boards and more independent directors) may be effective

at constraining over-investment, or positive deviations from expected SM. Our results continue to

provide support for the board monitoring hypothesis (H3).

Columns 7 and 8 contain estimated results for regression equation (3) replacing DESM

with IASM as a proxy for the deviation from expected investment in SM. The significance and

direction of the estimated coefficients for LNDELTA, BRDSIZE, PIND, BRDIND, and

INSTOWN are similar to those in columns 1 and 2. The estimated coefficients for DUALITY,

however, are now significant. This finding is consistent with prior research; successful CEOs,

such as those better at maximizing shareholder wealth, become COB (Brickley et al., 1997).

Lastly, the deviation of observed SM from the expected level of SM may be conditional

upon the financing constraints faced by each firm. Under this assumption, SM is more likely to

deviate from expected SM when managers are less financially constrained and agency problems

are more likely to exist. We use cash and leverage as proxies for financial constraints. Large cash

balances may exacerbate agency problems (Jensen, 1986). As such, managers of firms with large

cash balances may have a tendency to overinvest in SM. Conversely, firms with low leverage may

be less financially constrained, which may cause managers to over-invest in SM. We follow the

procedure used in Biddle et al. (2009) to proxy for the financial constraints faced by each firm.

We rank firms into deciles based on cash and leverage (we first multiply leverage by minus one

so that it is decreasing in the level of financial constraint). We then create a composite measure

for the propensity to over-invest in stakeholder management (OVER), which is the average of the

ranked values of cash and negative leverage re-scaled so that the values range between zero and

one. Higher values for OVER reflect less financially constrained firms, which may increase the

33

likelihood that observed SM deviates from expected SM; managers of these firms may have a

greater propensity to over-invest in SM. We test this by creating interaction terms between each

of our internal governance mechanism variables (LNDELTA, BRDSIZE, PIND, DUALITY, and

INSTOWN) and OVER.

Specifically, we test whether changes in internal governance mechanisms, conditional on

whether the firm is financially constrained and more likely to over-invest in SM, affect observed

SM or are endogenously determined by the firm specific factors. The model we test is as follows:

SM

4

where stakeholder management (SM) is a function of managerial ownership incentives

(LNDELTA), board monitoring (BRDSIZE, PIND, DUALITY), institutional ownership

(INSTOWN), interactions between internal governance mechanisms and OVER, controls

(LNSALES, ROA, TRS), and dummy variables for year. The definition of variables in regression

equation (4) is as mentioned in section 4.2. We exclude CASH and TD/TA from our list of

controls because they are used to calculate OVER. The estimated coefficients (1, 2, 3, 4, and 5) measure the relation between internal governance mechanisms and SM for the most financially

constrained firms. The sum of the coefficients (1 + 6, 2 + 7, 3 + 8, 4 + 9, 5 + 10) measure the relation between internal governance mechanisms and SM for the least financially

constrained firms. The sign beneath each variable indicates the expected relation between the

dependent variable and relevant independent variables.

34

Column 9 contains results of fixed-effects (FE) estimates for regression equation (4). With

the exception of the estimated coefficient for DUALITY, which is positive and significant at the

10% level (t = 1.66), none of the estimated coefficients on the main effects (i.e. the most

financially constrained firms) for LNDELTA, BRDSIZE, PIND, and INSTOWN are significant.

However, as predicted, the estimated coefficients for the interaction between OVER and

BRDSIZE is positive and significant at the 1% level, while the estimated coefficients for the

interaction between OVER and DUALITY is negative and significant at the 1% level. This

suggests that the relation between internal governance and investment in SM is conditional upon

whether a firm is more likely to over-invest, and suggests that these mechanisms may be more

effective at constraining excess SM. Furthermore, the overall relation between internal

governance and SM for the least financially constrained firms (measured by the sum of the

coefficients for the internal governance mechanism variables and the interaction terms between

internal governance mechanisms and OVER) for LNDELTA and BRDSIZE are positive and

significant at the 5% and 1% levels, respectively, while the sum of coefficients for DUALITY is

negative and significant at the 5% level. While we find that the relation between INSTOWN and

SM is significantly higher for less financially constrained firms, the overall relation is not

significant. Our conclusion using BRDIND in column 10 is similar to that in column 9.

5.1.1. Robustness tests

In the presence of unobserved firm effects, firm fixed effects (FE) regression is commonly

suggested. However, FE estimation may be unsuitable in our unbalanced panel for several

reasons. First, several of our primary variables of interest, such as BRDSIZE and PIND, are

relatively time invariant and cannot be estimated with FE regression as they would be absorbed in

the within transformation of the variable. Second, FE estimation requires significant within panel

35

variation for important right-hand side variables to produce consistent and efficient estimates

(Wooldridge, 2002, p. 286). For example, the within standard deviations for BRDSIZE (1.04) and

PIND (9.13) are substantially smaller than the between standard deviations (2.21) and (12.55),

respectively. Furthermore, while our sample spans 13 years, on average firms remain in our

sample for approximately half of this period. Consequently, for robustness we also run random-

effects (RE) GLS regression on the unbalanced panel, and we use FE estimation on a balanced

panel of firms (153 firms and 1,989 firm-year observations) that remain in our sample for all 13

years of our sample (reported in Appendix C and available from the authors upon request).25 Our

results are similar to those using the FE model on the unbalanced panel, except that the estimated

coefficients for PIND and BRDIND remain negative, but are generally significant.

5.2. Exploring causality

The estimated coefficients in Table 2 may be biased as the various internal governance

mechanisms are endogenously formed (e.g. Hermalin and Weisbach, 2003). We address

endogeneity concerns in two ways. First, to ensure that causality runs from internal governance to

deviation from expected stakeholder management, we re-estimate regression equations (3) and (4)

replacing contemporaneous internal governance variables with their lagged values and also using

contemporaneous internal governance variables while including a lagged dependent variable in

25 In cases where researchers are interested in the inferring significance of key variables in the model, Wooldridge (2002, pg. 290) suggests a t-statistic Hausman test computed as (FE/ RE)/{[se(FE)]2-[se(RE)]2}1/2 to determine whether a random effects estimator is appropriate. While the overall Hausman (1978) test is significant at the 1% level, the t-statistic Hausman tests are insignificant on all of the variables of interest, supporting the use of the random-effects model in our analysis. Consequently, we use random-effects (RE) GLS regression as the estimation method for our unbalanced panel. However, as noted by Mundlak (1978), one assumes that the researcher is making the assumption that the omitted variable, ci, is uncorrelated with xit when using a random-effects model. If this assumption is violated, the random-effects estimators are inconsistent. To minimize this issue, we include dummy variables for industry and year in our models to control for part of the ci correlated with xit. Still, a limitation of using the random effects model in our context is that we can only infer the significance of the relations for key variables (i.e. governance), rather than all variables in our model.

36

our regression equation. Our results are similar to those reported in Table 2 (un-tabulated and

available from the authors upon request).

While lagging the measurement of the dependent and/or independent variables partially

controls for simultaneity, it does not provide a complete remedy. Our analysis to this point has

treated the internal governance variables as exogenous, but if any one of these variables is

determined simultaneously with deviation from expected stakeholder management, it violates the

least squares regression assumption that the regressors are uncorrelated with the error term. To

eliminate the endogeneity problem from simultaneity bias, we endogenize LNDELTA, BRDIND,

and INSTOWN given the existing literature on managerial ownership incentives (Coles et al,

2006), board structure determinants (Linck et al., 2008), and institutional ownership (Hartzell and

Starks, 2003) by developing the following four regression equations:26

5

/ 6

/ 7

26 While DUALITY may also be endogenously formed, we treat it as exogenous because it is a binary variable. We also use the composite measure of effective board monitoring (BRDIND), rather than BRDSIZE and PIND separately to simplify interpretation of our results and due to difficulties identifying a set of instruments that is sufficiently correlated with PIND. The set of instruments is strongly correlated with BRDIND.

37

8 where the definition of variables is as mentioned in section 4.2 and the list of controls remains the

same as in regression equation (3). The four equations, regression equations (5) (8) are solved

using system of simultaneous equations using first-difference three-stage least squares (FD3SLS)

to control for unobserved firm heterogeneity, or omitted variable bias.27

Table 3 reports results of FD3SLS estimates of the system of four equations.28 Column 1

contains FD3SLS results for regression equation (5). The estimated coefficient for BRDIND

remains negative and significant at the 1% level (z = 6.26). More effective monitoring by the board of directors (smaller boards with more independent directors) leads to lower deviations

from expected SM. This continues to support the board monitoring hypothesis (H3). The

estimated coefficient for DUALITY also remains negative and significant at the 5% level (z =

2.73). However, the estimated coefficient for LNDELTA, while positive, is no longer significant. Furthermore, the estimated coefficient for INSTOWN is now negative but not

significant.

-----Insert Table 3 About Here-----

The results in columns 2 and 3 for regression equations (6) and (7), respectively, also

provide some additional useful information. For example, the negative and significant estimated

27 In the absence of specification problems, the use of a systems procedure, such as 3SLS, is asymptotically more efficient than a single-equation procedure such as 2SLS. But single-equation methods are more robust to misspecification (Wooldridge, 2002 p. 222). Our conclusions are unchanged using 2SLS (un-tabulated and available from the authors upon request). 28 A DavidsonMacKinnon (1993) test for endogeneity is significant at the 1% level supporting the use of the 3SLS model in Table 4. However, when conducting 2SLS/3SLS the set of instruments, z, must also be highly correlated with the endogenous regressors, xk, but uncorrelated with the disturbance process, u. The first condition, that the set of instruments is sufficiently correlated with the endogenous regressors, is supported by estimated coefficients and F statistics for the set of instruments that are significant at the 1% level, high Shea (1997) partial R2 values, and a Kleibergen-Papp (2006) LM statistic that is significant at the 1% level. The latter condition, that the set of instruments is uncorrelated with (orthogonal to) the disturbance process is supported by a non-significant Hansen-Sargan J-statistic.

38

coefficients for DESM in columns 2 and 3 suggest that there is an inverse relation between

deviations from expected SM and the level of CEO incentives and board independence. Firms

with lower deviations from expected SM have CEOs with higher incentives. Higher levels of

managerial incentives potentially expose the firm to perquisite consumption in the form of

unnecessary investment in SM. However, board independence also increases in tandem with the

level of CEO incentives. The negative and significant estimated coefficient for BRDIND and

positive but not significant estimated coefficient for LNDELTA in the main equation suggest that

greater monitoring by the board effectively constrains this perquisite consumption by the CEO in

the form of unwarranted investment in SM.

5.2.1. Industry level analysis of the effect of internal governance mechanisms on deviations from expected investment in stakeholder management and components

Although we find some support for a relation between various internal governance

mechanisms and deviations from expected SM, industry likely plays an important role in

determining the nature of the firm-level stakeholder environment, and as a result, influences the

relative importance and allocation of resources to various competing stakeholder groups. For

example, investment in diversity, community, and environment related stakeholder management

may play a role in differentiating ones product for consumer oriented firms. As a result, some

consumers may be willing to pay a premium for such products, leading to better financial

performance. Higher investment in these dimensions of SM for industrial oriented firms,

however, may provide limited financial benefits. Conversely, strong relations with employee,

supplier, and product related stakeholder groups are likely to benefit both consumer and industrial