An examination of the relationship of governance mechanisms to performance.
Victoria B. McWilliams [b]
Nilanjan Sen [c],[d]
The purpose of this paper is to draw together the many different facets of corporate governance that have been examined in the extensive literature in both strategic management and finance. In particular, we are interested in the relationship between the typical agency theory constructs of monitoring, incentives and ownership structure, with financial performance. First, we catalog this large body of work to see where there are still unanswered questions. We find that previous work has generally focused on examining subsets of governance mechanisms, typically studying one or two governance variables in any one study. Our view is that the most critical issue still to examine, is the ability of firms to choose among a number of different governance mechanisms in order to create the appropriate structure for that firm, given the environment in which it operates. We identify a sample of firms and examine CEO compensation, CEO tenure, board composition, leadership structure and ownership structure and their contr ibution to both market performance, Market Value Added, and risk-adjusted accounting performance, Economic Value Added. In addition, we control for ownership by block-holders, industry performance, and firm size. We examine these measures both individually and as interactions. Our results indicate that while some of the traditional agency variables do impact performance, both individually and as interactions, industry performance is a strong and significant driver of performance for our sample of firms. We conclude that, while there is evidence to support that firms may use governance packages to deal with agency issues, further research could provide important evidence on these issues by focusing on examining a more refined, industry-level context. [C] 2001 Elsevier Science Inc. All rights reserved.
One of the most widely discussed topics in both the academic literature and the business press concerns how to appropriately structure the organization and put into place governance mechanisms that will provide for the most effective decision-making on the part of top managers, particularly CEOs. The move to force firms into more effective governance has been discussed in the boardroom, by institutional investor groups, by business publications, and by government regulators. These discussions by the business community have been paralleled by a great deal of theorizing (e.g., Barkema and Gomez-Mejia, 1998) and empirical study (e.g., Dalton, Daily, Ellstrand and Johnson, 1998) on the part of academics in areas as diverse as accounting, economics, finance, law and management.
Much of the academic work in this area has focused on how to design corporate governance mechanisms that will motivate managers to make choices for the firm that will improve performance. The major emphasis for these streams of research has traditionally been on linking one or two governance mechanisms to one (generally accounting) measure of performance, for example CEO compensation with performance. Much of this literature has provided very little in the way of consistent findings. For example, in their meta-analytic review of the governance literature, Dalton, Daily, Ellstrand and Johnson (1998) find little evidence to support any relationship between governance variables and performance. They conclude that the examination of any of these governance mechanisms in relation to performance is a task that will provide little insight to either the academic community or to practitioners. In our view, their findings are not surprising, given the fact that their analysis examines all governance variables in a uni variate context, without the benefit of control variables. This paper provides additional support for the view that examining governance mechanisms in an isolated context is not a particularly effective way to study these issues.
The view that our paper takes was discussed in a recent paper introducing a group of studies on the topic of CEO compensation and performance. In that paper, Barkema and Gomez-Mejia (1998) suggest that in order to more fully understand organizational issues such as CEO compensation, it is necessary to examine other determinants and organizational characteristics as well. The conclusion their paper draws in studying executive pay is that, "future work should further enhance understanding of the relative importance of firm performance and other criteria, such as the market, peer compensation, and behavior, and how the definition, measurement, and relative importance of these criteria depend on a firm's governance structure and on contingencies such as its strategy, R&D level, market growth, industry concentration and regulation, and national culture" (Barkema and Gomez-Mejia, 1998: p. 140).
Our paper adopts this broad view by examining an array of governance mechanisms, market features and organizational characteristics. The mechanisms we examine, including board structure, leadership structure, the structure of CEO compensation plans, and ownership structure, have never been examined empirically in one study to this point. The ability to compare and contrast the use of this mix of governance mechanisms provides evidence on how organizations might consider using different mechanisms as substitutes or complements, given the possible interaction both within and across different categories of governance mechanisms.
The paper proceeds as follows. First, we present the theoretical framework we use to examine the relationships between corporate governance, market characteristics and firm performance and summarize the substantive empirical literature on this topic. Next, we explain the governance measures we use and the sample we have constructed, along with the rationale for our choice of specific performance measures. We then discuss our empirical analysis after which we offer concluding comments on both this paper and future research in this area.
2. Theory and evidence
Following much of the research on corporate governance, we take an agency theory view to examine the issues in this study. The agency relationship has been described as: "a contract under which one or more persons (the principal(s)) engage another person (the agent) to perform some service on their behalf which involves delegating some decision making authority to the agent" (Jensen and Meckling, 1976: 308). Agency theory focuses on the occurrence and resolution of conflicts of interest among principals and agents. Agency theory is concerned with insuring that a firm's managers act in the interests of its shareholders (owners). Agency theory asserts that firms can employ various mechanisms to align the interests of agents and principals, and to monitor the behavior of agents.
In addition to the agency costs associated with aligning the interests of managers and owners, there are also contractual hazards associated with this relationship. The contracting hazards as suggested by Williamson (1985) include the costs of planning, adapting and monitoring these contractual relationships. The potential for opportunism on the part of a manager makes it critical that organizations design contracts that limit this possibility.
Another perspective that seeks to explain the behavior of managers is stewardship theory. We view this perspective as being complementary in respect to our agency and contracting explanations. As suggested in a recent paper comparing agency theory to stewardship theory, "Research is needed that shows where stewardship theory fits in the theoretic landscape, relative to agency theory, rather than opposed to it" (Davis, Schoorman & Donaldson, 1997: 21). We will examine where the presence of a steward as the manger may mitigate agency problems and enable the organization to reallocate resources that would have previously been expended to provide additional monitoring and/or incentive mechanisms.
In our discussion we adopt the view that there are two important categories of governance and/or contracting mechanisms that the firm can employ in order to align the interests of managers, in this case the CEO, with the interests of owners, in this case shareholders. The two broad categories that we examine are organizational monitoring mechanisms, including board structure and leadership structure; and CEO incentive alignment mechanisms, includeing CEO compensation and ownership structure. We also examine governance mechanisms as substitutes.
2.1. Organizational monitoring mechanisms
Organizations can choose to utilize monitoring mechanisms in order to oversee the activities of top managers, particularly the CEO. For the purposes of our study, we will examine three types of organizational monitoring mechanisms, the leadership structure of the firm, the composition of the board of directors, and the stock ownership of members of the board of directors.
Generally, the monitoring function is the purview of the Board. The Board is charged with the responsibility for making sure that top managers are behaving in a way that will provide the optimal value for shareholders. Within this broad category of monitoring mechanisms, we propose three types of devices that firms may choose to implement to monitor the CEO. The first of these is the firm's leadership structure, or the relationship of the CEO to the Chair of the Board of Directors. In this paper, we will characterize firms that have joined the two titles into one position as having a combined leadership structure, whereas firms that have not joined the titles are referred to as separated structure firms. The combined structure has been criticized as an inappropriate way to design one of the most critical power relationships in the firm (see for example, Jensen, 1993). This view asserts that the CEO who is also Board chair will have a concentrated power base that will allow the CEO to make decisions in their own self-interest and at the expense of shareholders. This view supports the use of a separate leadership structure, or separating the titles into two positions held by two separate individuals. In this way, power is not concentrated in one individual, but spread out in a way that allows the Board to more completely perform its fiduciary duties. A number of empirical studies have provided important insights into the relationship of leadership structure to performance (Baliga, Moyer & Rao, 1996; Berg & Smith, 1978; Lublin, 1992; Rechner & Dalton, 1989; and Rechner & Dalton, 1991). Despite these insights, the evidence is far from conclusive. For example, Rechner and Dalton (1989) examine shareholder returns over a five-year period (1978-1983) and find no significant distinction between the performance of separated and combined structure firms. However, when examining accounting based measures of ROE, ROI and profit margin they find that the separated structure firms outperform the combined structure firms (Rech ner and Dalton, 1991). In contrast, Baliga, Moyer and Rao (1996) find little evidence to support a performance distinction between separated and combined firms when using MVA and EVA as performance measures. So while the empirical evidence is mixed, given the agency issues involved, we expect that the firm's managers will be better monitored when two individuals occupy the Chair and CEO roles (separated structure). Consistent with these arguments, we suggest hypothesis 1 below.
H1: Firms that separate the positions of CEO and Chair of the Board will have better performance than will firms that join the two positions.
Among large US firms, the incidence of the combined leadership structure is very high, somewhere between 70 and 80% by most accounts (see for example: Brickley, Coles & Jarrell, 1997; Rechner & Dalton, 1991; Berg & Smith, 1978). It appears that a concern with appropriate managerial monitoring has not led to the widespread adoption of a separated leadership structure. This is consistent with the mixed evidence that we find in the academic literature on this subject. One explanation for why a larger number of firms have not separated the positions is that the impact of leadership structure is closely related to the choice of other governance mechanisms, in particular a second type of organizational monitoring mechanism, the composition of the board of directors. A common argument for why the combined structure is not a significant agency problem is that board members from outside the organization serve on compensation and nominating committees and such outside representation keeps management honest. This argument centers around the importance of outside directors who are appointed to monitor a firm's managers and limit managerial opportunism, and on the benefit of having a focus of direction for the firm that the combined structure provides.
The view that the Board of Directors may resolve agency problems through monitoring has been challenged by managerial hegemony theory, which views boards as passive instruments who hold allegiance to the managers who selected them, lack knowledge about the firm, and depend on top executives for information (Kosnik, 1987). Both anecdotal and empirical evidence suggests that outside directors are not always effective monitors of managers (e.g., Baysinger & Hoskisson, 1990; Baysinger, Kosnik & Turk, 1991; Hill & Snell, 1989). However, a number of forces external to the firm have forced directors, particularly outside directors, to seriously consider their responsibilities in this regard. The increasingly active role of large blockholders and the increasing number of shareholder lawsuits are forcing directors to recognize the importance of their fiduciary responsibility to the shareholders.
There is also a fair amount of empirical evidence that supports the position that outside directors have been effective in monitoring managers and protecting the interests of shareholders. Larger numbers of outside directors have been associated with a negative relation between CEO turnover and performance (Weisbach, 1988), a lower probability that the board pays greenmail in a control contest (Kosnik, 1990) and a lower probability that the board adopts a poison pill (Mallette and Fowler, 1992). Schellenger, Wood and Tashakori (1989) find that the presence of outside directors is linked to higher risk-adjusted corporate performance. In addition, Brickley, Coles, and Terry (1994) find that the market reaction to adoption of a poison pill and the probability the pill is used to benefit shareholders in a control contest are both positively related to the proportion of board seats held by outside directors. Coles and Hesterly (1999) find that the presence of these seemingly objective directors enables the board to perform a monitoring function when there is a great deal of power vested in one individual, as in a combined leadership structure. The evidence from Finkelstein and D'Aveni (1994) also supports the view that vigilant boards are positively associated with a combined leadership structure. Based on the issues presented above, we hypothesize:
H2: Firms that select higher proportions of independent outsiders to serve on their Board of Directors will have better performance than will firms with a higher proportion of insiders on the board.
Ownership structure is the final type of monitoring mechanism that we will examine that the firm can implement to increase the incentive for board members to monitor firm managers. With increasing proportions of firm ownership, board members will have a personal wealth incentive to monitor managers, in addition to their fiduciary responsibility as members of the board of directors. For this reason, we will consider board stock ownership to be the third type of monitoring device the firm may choose and hypothesize:
H3: Firms that have higher proportions of stock ownership by their Board of Directors will have better performance than will firms where Board members have lower ownership proportions.
We now turn to a discussion of the other set of governance mechanisms that the firm may choose to implement that are part of the broad category of CEO incentive alignment mechanisms.
2.2. CEO incentive alignment mechanisms
The second broad category of mechanisms the organization may implement in order to address the agency problem between firm owners and firm managers, in this case the CEO of the firm, is to engage in incentive contracting with the CEO. This incentive contracting can include two important devices. The first of these is performance contingent pay. CEO compensation plans that closely link the manager's wealth to the performance of the firm should align CEO interests with interests of outside shareholder. This alignment should induce managers, particularly the CEO, to take actions that create firm value. Based on this reasoning, we hypothesize:
H4: Firms that utilize CEO compensation plans that are positively related to firm value creation will have better performance than will firms where CEO compensation is not closely tied to firm value creation.
The second incentive alignment device the organization may implement to address the agency issue provides for the CEO to have an ownership stake in the firm. An important point here is that, as a CEO's personal wealth is increasingly dependent on firm value, the cost to the CEO of pursuing activities that do not increase shareholder value will increase. These managers may have an incentive to accept these additional costs only if there is a significant personal benefit. As their ownership stake in the firm increases, however, any actions that managers take should be increasingly oriented toward maximizing firm value. That is, there should be a closer alignment between the managers and outside shareholders' interests as the proportion of shares owned by the CEO increases. For these reasons, we hypothesize:
H5: As the level of CEO ownership increases, the firm' s performance will also increase.
In addition to the variables proposed here that relate directly to agency theory explanations of CEO incentive alignment, we also recognize the contribution of stewardship theory to any examination of these issues (Davis, Schoorman, & Donaldson, 1997). One way to measure the stewardship of the CEO would be to determine how long the CEO has been in the current position. The reasoning here is based on the premise that CEO's that are good stewards will retain their positions longer than a CEO that is not a good steward for the corporation. This leads us to the final hypothesis regarding CEO's:
H6: As the tenure of the CEO increases, the firm's performance will also increase.
Another perspective on the role of CEO tenure suggests that long-tenured CEO's may over time become "stale in the saddle" (Miller, 1991, p. 34). The relationship between CEO tenure and firm performance is curvilinear, with the impact on performance increasing to a point and then becoming negative as the CEO becomes rigid and less likely to engage in environmental monitoring and adaptation (Miller, 1991).
2.3. Governance mechanisms as substitutes
While all of the work discussed in this section focuses on the impact of governance on various organizational outcomes, recent papers by Beatty and Zajac (1994), Rediker and Seth (1995), and Sundaramurthy, Mahoney and Mahoney (1997), make the case that governance mechanisms substitute for each other. This perspective suggests that individual governance mechanisms are not independent of each other, as has previously been assumed in much of the empirical governance literature (Rediker & Seth, 1995: 86). We adopt this perspective and expand on this view by proposing a framework for examining how organizations select a package of governance mechanisms, and the impact this selection has on performance.
In order to examine the relationship of governance mechanisms, it is important to more specifically define how organizations make choices on how to design governance packages. Based on the discussions above, we propose two broad categories of governance mechanisms firms will employ. These mechanisms are organizational monitoring and CEO incentive alignment. Within these two broad categories, we have identified multiple devices among which the firm may choose. Organizational monitoring devices include the composition of the board of directors, the leadership structure of the firm, and the ownership structure of the board. CEO incentive alignment devices include CEO compensation, CEO ownership, and CEO tenure.
In our view, firms may substitute governance choices across mechanisms, or may choose to substitute devices within mechanisms. For example, firms in highly technical industries may find it useful to have a board that has a high degree of specialized skills that can only be found in firm insiders. In order to compensate for this lack of external organizational monitoring, the firm may choose to design compensation packages that are very performance sensitive. This perspective is similar to the arguments illustrated in the configurations perspective of organizations (Miller, 1999). The focus of our paper is on the substitution across these mechanisms. Our view is that firms may choose to focus on aligning incentives with one mechanism in order to compensate for agency problems in the other mechanism. In this way, firms select configurations of mechanisms in order to support their strategy and most effectively deal with their specific organizational and environmental contexts. From this reasoning, we hypothesize:
H7a: Firms that utilize governance structures that lack organizational monitoring, yet emphasize CEO incentive mechanisms will have better financial performance than firms that lack organizational monitoring and do not emphasize CEO incentive mechanisms in their governance structures.
H7b: Firms that utilize governance structures that lack CEO incentive mechanisms, yet emphasize organizational monitoring will have better financial performance than firms that lack CEO incentive mechanisms and do not emphasize organizational monitoring in their governance structures.
2.4. Control variables
In addition to the hypothesized relationships, we also include in our study a number of control variables that have previously been important in determining firm performance. As mentioned earlier, large blockholders can also have an important role in firm governance. It is presumed that blockholders, some of which may be activist institutional investors, have the capability to monitor their investments and, by virtue of the magnitude of their investments, can affect managerial behavior. The threat that blockholders will sell large blocks of shares if the firm falls to provide an acceptable return, or is not responsive to governance issues that investors view as critical, is becoming a more significant factor for top managers. There is evidence in the empirical literature that institutional investors and other blockholders do impact managerial behavior (e.g., Brickley, Lease and Smith, 1988 and 1994; and Van Nuys, 1993).
In addition to controlling for the effect of blockholders, we also examine firm size and industry performance. Firm size has been shown to have a relationship with other factors we are considering, for example there is a strong and well-documented relationship between firm size and CEO compensation (e.g., Murphy, 1985). Larger firms generally tend to reward executives with larger compensation packages.
The final control variable we include is industry performance. There are a number of important papers that examine the relationship of industry membership and performance, with industry effects typically predicting somewhere between 17 and 20% of financial performance (Schmalansee, 1985; Wernerfelt and Montgomery, 1988; Rumelt, 1991; Powell, 1996). It seems to be the case that organizations are constrained to a certain degree, particularly in the short run, by opportunities available to the industry. Firms have different opportunities to build and define their competitive space (Hamel and Prahalad, 1994). Firms in industries where there are growth opportunities, where there are concentrated competitors, or where markets are stable should have higher profits than industries that are in decline, for example. Firms in dynamic and volatile environments have opportunities to define a new competitive space.
In order to understand better how our study provides a setting that is unique in the long list of examinations of the relationship of firm governance and performance, we provide the following summary of the research that has been published in this area. Table 1 catalogs the literature that has studied the relationship of governance to financial performance. While there have also been a number of studies that have examined relationships of governance to nonfinancial outcomes, we felt that this literature was not directly related to our work and so chose to examine only those studies that linked governance to financial outcomes, as does our paper. This table looks at a number of different issues that could be addressed by any particular study of this kind. The table indicates which studies have looked at each of the different governance variables: leadership structure, board structure, CEO compensation, board and CEO ownership structure, and CEO tenure. In addition, we indicate the control variables that were used, including ownership by blockholders, industry performance, size and level of diversification. The types of performance measures are also listed in three categories: market measures, accounting measures, or other financial performance measures. We also report the size of the sample, and whether the statistical tests were primarily univariate or multivariate. Our study is listed at the top of this table to give some perspective on the gaps that this study fills.
As discussed in our introduction and in the development of our hypotheses, it is critical to examine these governance variables in an integrative framework. The data we have allow us to examine these governance mechanisms and the relationship to performance in this unique fashion. We examine these types of governance variables simultaneously and in the context of both market and accounting measures of performance, while accounting for important control factors, such as industry performance, size and ownership by blockholders. The next sections of the paper describe our sample and variable measures and discuss the methods we use to test the hypotheses.
3. Data and methods
We now turn to a discussion of the data that we use to examine our proposed hypotheses. First we describe our sample and the data collection process. We also explain the measures we use to test the relations proposed in our hypotheses.
3.1. Performance measures
In a number of recent issues of Fortune Magazine, the use of MVA as a performance measure has been discussed. According to Stern Stewart & Co., MVA is "... the definitive measure of corporate success" (Stern Stewart, 1996: 1). This measure has been used to highlight the successes of many firms, including Coca-Cola, AT&T, Quaker Oats, Eli Lilly, GE, Georgia Pacific and Tenneco (Davies, 1995). The CEOs of many of these firms, including former CEO Robert Goizuetta of Coca-Cola and Jack Welch at GE, have been singled out both for their outstanding shareholder performance and corporate reputations.
Another performance measure that has also been discussed at length in the business press, and used extensively in practice, is EVA. According to Stern Stewart & Co., EVA"...is the internal corporate performance measure that best accounts for a firm's MVA" (Stern Stewart, 1996: 2). Stern Stewart goes on to report that standardized EVA is the single best predictor of standardized MVA (with an R-Squared of 0.50), including ROE, cash flow growth, earnings growth, EPS growth, asset growth, dividend growth or sales growth. EVA also can be adapted to measure and evaluate many different internal corporate activities, including divisional performance, project performance, and managerial performance. Firms use projected EVA as a tool to evaluate new projects, and use yearly EVA to evaluate and compensate managers. All of these uses make the EVA framework an important tool, certainly deserving of systematic investigation. Our paper explores the relationship of EVA to a number of different governance structure variables to determine if there are systematic relationships between a firm's governance mechanisms and the firm's EVA. We also examine the relationship of governance mechanisms to MVA, to determine if there are important or systematic differences in the relationships between governance and market performance. We might expect there to be such differences since managers have more opportunity to manipulate accounting measures of performance (such as EVA) than to control market measures of performance (such as MVA).
A number of researchers have examined the relationship of EVA and other performance measures commonly used in the literature, in particular, the relationship of EVA to Net Present Value (NPV) (for example, O'Byrne, 1997; Dillon & Owers, 1997). The findings here have been mixed, and seem to depend quite heavily on the kind of methodology that is used, including how the measurement is defined and the time horizon over which it is measured. These authors find in their preliminary analyses that the associations of EVA with stock market returns are no more compelling than other measures that are more easily and simply calculated, such as net operating profits, free cash flow, and residual incomes. Furthermore, as one set of authors states, "The academic research-based evidence on the effectiveness of EVA-type incentive metrics to influence managerial decisions is at an early stage. These lines of research can be expected to converge and hopefully will clarify what is presently aptly characterized as having differing findings" (Dillon & Owers, 1997, p. 35).
In addition, research has examined what relationships there are between MVA, considered a long-term market performance measure, and other performance measures that are nonmarket-oriented measures or short-term measures. These include EVA, ROE, Cash Flow Growth, EPS Growth, Asset Growth, Dividend Growth, and Sales Growth. According to Stern Stewart (Stern Stewart, 1996), standardized EVA accounts for 50% of the variance in changes in standardized MVA, whereas the next best measure, ROE, accounted for only 35% of the variance. The other measures mentioned above explained between 9% and 22% of the variance.
We collect data from four separate sources. The data sources include both primary and secondary data collection. These data sources provide a data set that enables us to test our hypotheses in respect to the use of these governance mechanisms and the relationship with both accounting (EVA) and market (MVA) measures of performance.
We begin our data construction process by identifying sample firms from our first data source, the Jensen and Murphy executive compensation database. Jensen and Murphy (1990) conduct a survey of executive compensation for 430 large US corporations. The survey covers the time period from 1974 through 1988, and provides us with data on CEO compensation for the entire time period. These data provide one measure for each firm to estimate the performance sensitivity of the CEO's compensation for the time period examined. We then merge these data with our performance measures, EVA and MVA. The data for these measures were obtained from The Stern Stewart Performance 1000 database, which covers the time period from 1984 to 1994. The intersection of these two databases provided us with approximately 150 firms for our sample. The major difference in the two databases is the exclusion of certain industries from the Stern Stewart database. While the Jensen and Murphy database includes firms in the regulated utility and financial services industries, the Stern Stewart database omits these firms. For the purposes of our study, given the regulatory restrictions and industry peculiarities of governance variables for these industries, we felt that this was not a constraining factor.
The next step in our data collection process was to access the data on corporate governance for each firm in our sample. In order to accomplish this, it was necessary to examine the proxy statements for each of the 150 firms in the sample. These data were taken directly from firm proxy statements if they were contained in the Q-File. The Q--File includes annual reports, proxy statements, and 10-K reports for firms that have securities traded in the US and is published by the Q--Data Corp. There were a small number of the original 150 firms for which the proxy statements were not available. This reduced our sample to 144 firms.
The data on the remaining firm variables, such as size, were collected from the Compustat database. We were able to get the additional secondary data on all of the firms described above. Our final sample is comprised of 144 firms for which these comprehensive data were available from these four separate data sources. The individual measures collected from these sources are described in detail in the sections that follow.
3.3.1. Dependent variables. As we have discussed, the dependent variables that we examine are two measures of firm performance, EVA and MVA. EVA focuses on yearly firm profits. A firm's EVA for any particular year is measured as the difference between the firm's after-tax operating profit for that year and the product of the weighted average cost of capital times the capital invested at the end of the previous year (Stern Stewart, 1996). EVA is based on beginning--of--the--year capital since it is assumed that new capital investments take a full year to reach full productivity.
The measure of MVA that is reported in the Stern Stewart database is the difference between the firm's total market value (of debt and equity) at the end of the year, less the cumulative book value of the capital invested in the firm at the end of the same year. Documentation provided by Stern Stewart proposes that MVA reflects the stock market's estimate of the current value of all of the firm's capital investment projects. This includes the value of the projects the firm already has in place and the discounted value of future firm projects expected by investors (Stern Stewart, 1996). This should reflect the current market valuation of the firm over and above (or less than) what has been invested over the life of the firm, both from retaining earnings and additional capital attracted from outside of the firm. Additionally, this number is a cumulative number for all years that the firm has existed. The measures of both MVA and EVA are total values for each firm, so it is necessary to adjust these measures, as described below, to account for differences in the size of the firm.
In our study we are interested in the changes in EVA and MVA over a specific time period, in this case from 1984 to 1988. These changes over time provide more information about value creation, and also enable us to compare EVA and MVA. We examine a five-year time period to allow for compensation, ownership, and governance structure to have a material effect on both of our performance measures. We also felt that a five-year time frame would minimize the chance that compensation, ownership structure, and governance structure would change materially for firms as they might during longer intervals.
In addition to examining changes in EVA and MVA values, it was necessary to control for firm size since small or even medium-sized firms with outstanding performance will not rank as highly as larger firms in terms of absolute EVA and MVA. We construct our measure of the change in EVA as follows:
[EVA.sub.1988] + [EVA.sub.1987] + [EVA.sub.1986] + [EVA.sub.1985] + [EVA.sub.1984]/[Capital.sub.1983]
We construct our measure of the change in MVA as follows:
[MVA.sub.1988] - [MVA.sub.1984]/[Capital.sub.1984]
These measures are constructed to deal with the issues of size and comparability over time, and are the dependent variable measures that we use in our analyses. We also constructed the EVA and MVA measures using capital in the terminal year (1987 for EVA, 1988 for MVA) and performed the same sets of analyses. Our results were not substantively different from what is reported here using capital at the beginning of the time period. We also construct variables for industry level EVA and MVA over the same time period using the method described above. We use these variables to control for industry effects on firm-level EVA and MVA.
3.3.2. Independent variables. We collect information on governance structure of the firm from the firms' proxy statements. Given that our dependent variable is the change in either MVA or EVA over the time period 1984-88, and that the Jensen and Murphy data estimates compensation sensitivity over the 1978-88 period, we selected the 1986 proxy year for our proxy data collection. We find leadership structure and board composition generally stable over relatively short periods of time, and it is important to select a year within the time period we are examining. For example, in examining tenure for the CEO, we found that the average tenure for CEO's in our sample was 7 years, and directors are generally elected for a minimum of a two-year term. Therefore, since 1986 was in the middle of the 1984-88 period, we collect our data from these proxy statements. In those instances where the 1986 proxy statement was not available, we collected data from the 1987 proxy statements.
The first independent variable of interest is leadership structure. We hypothesize that the leadership structure of the firm, or the relationship between the CEO and Chair of the Board will have an important impact on performance. As stated previously, firms with two individuals serving as Chair and CEO are classified as separated structure firms. Firms with one individual serving in both capacities are classified as combined structure firms. Each firm is coded with a dummy variable that takes on the value of 1 if there is a combined leadership structure and 0 otherwise.
In order to measure board composition, we again collect data from the proxy statement of the firm. Director classifications are those used in a number of previous studies, including Baysinger and Butler (1985); Brickley, Coles and Terry (1994); MacAvoy, Cantor, Dana and Peck (1983); Hermalin and Weisbach (1988); and Weisbach (1988). Directors that are currently employed by the firm, are retired employees of the firm, or are immediate family members of firm employees are classified as insiders. Gray directors are those individuals with a significant connection to the firm, but are not employees of the firm. These could include suppliers, lawyers, investment bankers, or outside consultants to the firm. Independent outside directors have no substantial business interest in the firm with their only observable connection to the firm being their appointment as a director.
To reflect ownership structure we identify two important variables, the proportion of the firm's shares owned by the CEO and members of the board of directors, and ownership by major blockholders. We obtain the total number of shares for both of these groups from the proxy statement. We then scale this number by the total number of shares outstanding to provide us with measures of the proportion of ownership held by these groups.
Our data for CEO compensation comes from the Jensen and Murphy (1990) database. The Jensen and Murphy database provides sensitivity coefficients for CEOs over the period 1978-1988. Their data come from the Forbes Survey of CEO Compensation. Using a regression analysis over the entire time period, they estimate the sensitivity of the CEO's compensation to changes in shareholder wealth. This coefficient gives the change in CEO salary and bonus associated with a $1.00 change in shareholder wealth. This variable measures CEO pay and reflects how performance--contingent the salary and bonus component of CEO pay are. As discussed in H4, we expect that the sensitivity coefficient will be positively related to both EVA and MVA.
We collect data on CEO tenure from a variety of sources. Our first source, as with our leadership structure measure, comes from the proxy statement of the firm. One difficulty, however, is that there are many CEO's for which these data are not explicitly stated, in particular if the CEO has been in the current position for more than five years. If we were not able to collect the data from this source, we then went to Moody's Manuals of officers and directors in order to ascertain when the CEO took on that position. For many of the missing CEO's we were able to obtain these data within at least a one-year time frame, since the manuals are published only every year. One also suspects that these data are not always the most current, so in general we felt that our measure of CEO tenure, while as comprehensive as we could collect from secondary sources, still suffers from a number of potential measurement issues.
Since the firm's industry should have an impact on the firm's EVA and MVA, we control for industry in our analysis. Firms in the Stem Stewart database are classified into 57 industry groups according to Standard & Poor's industry classifications. We compute the industry average EVA and MVA to use as control variables. These industry averages are the changes in average EVA and MVA for firms in that industry, measured over the sample time period. These industry measures are scaled to account for the size of the average firm in the industry. To control for firm size in our analyses, we use the natural logarithm of the firm's total assets in 1986, which we obtain from the Compustat database.
The final data set that we have constructed is unique in a number of dimensions. First, we combine four separate data sources, three of which have been used to assess a number of issues in the literature on separate occasions, Compustat data, the Jensen and Murphy data, and Stem Stewart data. In addition, we collect comprehensive governance data from the proxy statements of the firms. This process yields a data set with a very complete set of variables to examine the issues proposed. We feel that these data provide one of the first opportunities to examine all of the issues discussed earlier in one study, and to provide evidence on the substitution effects of these governance mechanisms. We now proceed with a reporting of the results of our data analysis and a discussion and interpretation of those results.
Table 2 reports descriptive statistics for sample variables, including means, medians, standard deviations, minimums and maximums. Generally, most of the firms in our sample are combined leadership structure firms, with only 21.5% being in the separated structure category. Sample firms have on average 47.1% of their board seats filled with independent, outside directors (not including gray directors). CEO's own on average a very small proportion of the firm, with a median of 0.1%. Over half of the firms have no investment by institutions, although the maximum of 31.3% seems substantial. These firms are also generally large firms, with the median size at 3.94 billion in assets.
Table 3 presents a correlation matrix for the variables. Among the independent variables we have very few significant correlations, aside from those that have a direct relationship, such as proportion of board ownership, proportion of CEO ownership, and proportion of board ownership without the CEO. CEO tenure is positively correlated with combined leadership structure, proportion of CEO ownership, and proportion of board ownership. It is negatively correlated with firm size. The only additional significant relationships existing for combined leadership structure firms are with the proportion of outside directors and proportion of ownership by blockholders. There is a negative relationship between CEO salary sensitivity and firm size, and between size, firm MVA, board ownership (without the CEO included), CEO tenure, and industry MVA. We also see a positive correlation between industry EVA and industry MVA; however, we do not use these two variables together in any of our analyses.
We use regression analysis to examine our hypothesized relationships. In examining these relationships, there were two issues we had to address in our analyses. First, as was mentioned in the discussion above, that our measure of CEO tenure is not as clean as we would have hoped. For this reason, for all of our subsequent analyses, we report results that include the CEO tenure variable, and results that do not include the CEO tenure variable. We examined CEO tenure both as a linear and a curvilinear relationship. The results were materially unchanged, and so only the linear analysis of tenure is reported here.
Second, the use of ownership variables, both for the CEO and for the board required some additional estimations. The reasoning being that CEO ownership is a subset (and often a proportionately large subset) of board ownership. For this reason, in our analyses, we examine CEO ownership alone, board ownership alone, and board ownership subtracting the CEO's portion from the total board measure.
Tables 4 and 5 report our examination of the main effects of the explanatory variables discussed in hypotheses H1 through H6. Table 4 reports the main effect results with CEO tenure included in the analyses; Table 5 reports the same analyses excluding CEO tenure. All models in both groups of analyses have significant predictive power, with adjusted r-squares ranging from 0.353 to 0.450. All models indicate a strong relationship between industry EVA (or MVA) and performance. When including CEO tenure (Table 4), none of the other explanatory variables provide any significant predictive power. Without the effects of CEO tenure, we see a positive relationship between a combined leadership structure (Chair and CEO are the same person), and EVA. We also see a negative relationship between the proportion of outside directors on the board, and CEO salary sensitivity with MVA. These results are consistent across the models reported in Table 5.
The remaining analyses focus on our substitution hypotheses, 7a and 7b. In Tables 6 and 7, we report our analyses using board composition interaction terms. In this case, we code the dummy variable 1 if the board is insider controlled (less than 50% of the board is comprised of outsiders). We then interact this dummy variable with each of the main effects hypothesized to examine the sensitivity for each of the main effects when you have a board dominated by insiders. Again, Table 6 reports the analyses including CEO tenure; Table 7 reports the analyses without this variable.
In all of these analyses, the model has significant predictive power, with adjusted r-squares ranging from 0.350 to 0.467. In all of these models, industry EVA (or MVA) has a strong and significant impact on performance for these firms. In Table 6, with CEO tenure included, we see that again, CEO salary sensitivity is significant and negatively related to MVA. However, we also see that the interaction term of board composition and salary sensitivity is positive and significant, suggesting that in cases where the board is composed primarily of insiders, CEO compensation sensitivity has a positive impact on market performance. We also observe a significant and negative relationship when examining the interaction of CEO tenure and board composition and MVA. This would indicate that when insiders dominate the board and the CEO has been in office a long time, market performance declines.
The results in Table 7 reported without the inclusion of CEO tenure indicate similar relationships as reported in Table 5. We still observe the positive impact of a combined leadership structure on EVA and the negative impact of CEO salary sensitivity on MVA. In these estimations, none of the interaction terms are significant.
In Tables 8 and 9, we report our analyses using the leadership structure interaction terms. In this case, we code the dummy variable 1 if there is a combined leadership structure (CEO and Chair are the same individual). We then interact this dummy variable with each of the main effects hypothesized to examine the sensitivity for each of the main effects when you have a board dominated by insiders. Again, Table 8 reports the analyses including CEO tenure; Table 9 reports the analyses without this variable.
In all of the estimations reported in these two tables, all of the models have significant predictive power, with adjusted r-squares ranging from 0.340 to 0.437. All indicate, as before, a strong positive relationship between industry EVA (or MVA) and performance. In Table 8, where estimations are reported with CEO tenure, we find no other significant relationships between the explanatory variables. In Table 9, where estimations exclude the tenure variable, we see again a negative relation between the proportion of outside directors, and market performance in two of the three MVA regression analyses.
The results of our analyses are interesting in a number of dimensions. In interpreting these results, we were interested in examining how firms selected their governance configurations by implementing organizational monitoring devices in comparison to implementing CEO incentive alignment devices. We were looking for systematic relationships between governance configurations and firm performance.
First, we find great consistency across all of our models when examining the impact of industry measures of performance on firm performance. This also accounts for the relatively high proportion of the variance we are able to explain as documented by our adjusted r-square measures of upwards of 35%. This compares very favorably with other governance studies where explaining 5% to 10% of the variance is common.
We also find the evidence regarding the positive relationship of EVA, an accounting measure of performance, to a combined leadership structure an interesting phenomenon. We observed this result in a number of our estimations. This is contrary to what we expected in hypothesis one.
We also find the negative impact of outside directors and CEO salary sensitivity on MVA to be in contrast to what we expected in hypotheses two and four. At first these results presented an interesting puzzle, but a closer look at the MVA measure itself provides some important insights into the relationships we see among these data. These insights may be related to the role of risk in the evaluation of MVA. The EVA measure, which contains a weighted average cost of capital, explicitly controls for the riskiness of the firm. The MVA measure only accounts for the actual accumulated value of the firm, but the risk factor borne by investors to obtain this value is not explicitly reflected in the MVA measure. When you take this into account, the results for CEO salary sensitivity may be explained.
In the case of CEO salary sensitivity, one of the most widely utilized CEO incentive alignment devices, we also observe the impact of risk in the relationship. For example, if firm owners wish to encourage managers to take riskier projects, then making the manager's compensation increasingly dependent on the outcome of that risk taking strategy will not be conducive to undertaking riskier projects. Shareholders in general would like the firm to undertake risky projects, with possibilities of a high return, since shareholders can eliminate their exposure to the total risk of a project through diversification. Managers, due to their firm--specific human capital, however, will be unwilling to bear that risk if their compensation is too performance--sensitive. Therefore, in order to induce managers to undertake riskier projects, the firm's owners must make the managers' compensation less sensitive to performance, and perhaps even utilize some type of fixed salary component to mitigate the impact of risk on the manager. An illustration of this concept is the compensation of Alex Mandl who left AT&T to become CEO of a startup corporation where he received a $20 million "signing bonus" (Kneale, Naik & Ziegler, 1996). Even though Mandl owns a significant proportion of stock and has other types of compensation tied to the value of the firm, the "floor" here is so high, that the manager's compensation risk overall is very low. This provides him with the incentive to bear more risk when making decisions that will potentially create firm value.
We find no evidence in our data to support our hypothesis three regarding ownership of the board, our hypothesis five regarding ownership of the CEO, or our hypothesis six regarding CEO tenure. This lack of these relationships in our sample, particularly when examining the impact of CEO tenure may be related to the high degree of correlation among these variables. For example, CEO tenure is significantly and positively correlated with both the proportion of CEO ownership and the proportion of board ownership. This is to be expected, since often stock and/or stock options are a significant portion of the CEO's compensation package, and one would expect that CEO's that have been in their positions longer would have received more stock-based compensation. We also find a positive relationship between CEO tenure and combined leadership structure, suggesting that there is evidence of the "passing of the baton" phenomenon present in our sample. A CEO that has longer tenure is more likely to also hold the chairman's position. This would seem consistent with stewardship theory explanations. A CEO that has longer tenure in the organization may have proved to the principals (owners) that they require less organizational monitoring, based on prior performance.
The examination of the interaction terms posed in hypotheses seven (a) and seven (b) do provide us with some initial evidence on how these governance mechanisms may be important in terms of the ability of firms to utilize governance configurations, rather than just concentrating on their choices of individual mechanisms. The consistent relationship of the interaction of board composition and salary sensitivity with MVA would suggest that organizations that have boards dominated by insiders, but that provide for CEO incentive alignment, have better market performance. In this situation where the governance structure lacks organizational monitoring, our data provide evidence that CEO compensation packages, at least to some extent, mitigate the firm's agency problems and enhance shareholder value.
The interaction of CEO tenure and board composition is also an interesting result. This result would imply that boards composed of insiders with long tenure CEO's appear to have declining market performance. In the situation where the CEO has been with the firm for a long time, our results suggest that these firms in particular would benefit from establishing independent boards. The independent board will provide organizational monitoring that should lead to increased levels of firm value. The results of tests of hypotheses 7(a) and 7(b) suggest that there is a benefit to the firm to choosing governance configurations that balance organizational monitoring and CEO incentive alignment.
This study provides evidence on the relationship between two widely used performance measures, EVA and MVA, and their relationship to corporate governance. We have examined a number of governance variables that have not been previously examined simultaneously, using a sample that combines information from a number of data sources to examine leadership structure, board composition, board ownership, CEO compensation, CEO tenure and CEO ownership. We do this while controlling for the impact of industry performance, ownership by blockholders, and firm size.
We find significant relationships for the positive impact of industry on both MVA and EVA. The lack of any significant relationship between governance structure and a short--term measure like EVA does not, however, undermine in any way the effectiveness of EVA as a performance measure. In fact, the lack of such a relationship may constitute evidence of the effectiveness of proper governance structures in inducing management to undertake longterm value enhancing projects not necessarily reflected in EVA numbers.
The picture with MVA is somewhat different, however, with the puzzle initially being much more striking. Our initial analyses significantly contradicted our expectations, but as we discussed previously, it appears that risk could play an important role in the case of the MVA measure. The next step in expanding on this issue would be to examine the role of both firm risk and diversification, and their impact on MVA. This may provide some important insights into our initial findings in this area.
The focus of each of the performance measures, EVA and MVA, is quite different. While EVA clearly focuses on yearly operational performance and accounting returns, MVA focuses on a long-range perspective of performance. As discussed previously, MVA represents the measure of performance focused on investors. It captures the extent of wealth created for the shareholders over a given period of time. The alignment of shareholder interests with managerial incentives suggests a positive relationship between MVA and the firm's governance structure designed to reduce the agency costs arising from the separation of ownership and control. It may be desirable to have a similar relationship between EVA and governance structures. However, to the extent that annual EVA represents a short-term performance measure focused on operational efficiencies, long term value maximization need not, and probably will not, induce any significant relationships between EVA and variables representing firm ownership and governance structure.
One interesting extension to this study would be to examine industry groups. Given the strong and significant relationship in our sample between industry measures of performance and firm performance, the logical next step would be to more closely examine these contexts. The question remains, while industry may explain a great deal of the variance in EVA, do systematic relationships exist between EVA and governance variables within an industry? Looking at groups of industries with similar characteristics, such as high technology environments, stable environments, and so on, might also provide an interesting extension to this literature. The level of diversification could also impact these firms. An important additional piece to this puzzle could be the role that diversification may play in explaining EVA.
While this study has provided an important first step in systematically examining EVA and MVA, there are certainly a number of additional important issues to be studied. The widespread use of these measures as a way to evaluate and compensate managers, rank companies, and make investment decisions would suggest that further inquiry could be a very important contribution to both the study and practice of management.
(a.) Assistant Professor of Management, School of Management, Arizona State University West, 4701 W. Thunderbird Road, P.O. Box 37100, Phoenix, AZ 85069-7100, USA
(b.) Associate Dean, College of Commerce and Finance, Villanova University, 800 Lancaster Ave., Villanova, PA 19085, USA
(c.) Associate Professor of Finance, School of Management, Arizona State University West, 4701 W. Thunderbird Road, P.O. Box 37100, Phoenix, AZ 85069-7100, USA
(d.) Nanyang Business School, Nanyang Technological University, Nanyang Avenue, Singapore 639798
(*.) Corresponding author. Tel.: +1-602-543-6126; fax: +1-602-543-6221.
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Studies on the Relationship of Goverance to Financial Performance Study Citation Sample Leader- Board CEO Board Size ship Structure Compensation Owner- Structure ship This study 144 X X X X Sundamurthy, 261 X X X Mahoney & Mahoney, 1997 McWilliams & 265 X X X Sen, 1997 Baliga, Moyer 58 X X & Rao, 1996 Daily, 1995 70 X X Rosenstein & 170 X X Wyatt, 1995 Brickley, Coles 247 X & Terry, 1994 Daily & 50 X X Dalton, Bank 1994 (a) and 50 match Boyd, 1994 193 X X X Daily & 50 X X X Dalton, Bank 1994 (b) and 50 match Daily & Dalton 186 X X 1993 Daily & 100 X X Dalton, 1992 Study Citation CEO CEO Size Industry Block- Univariate Owner Tenure Performance holders ship Analysis This study X X X X X X Sundamurthy, X Mahoney & Mahoney, 1997 McWilliams & Sen, 1997 Baliga, Moyer X X & Rao, 1996 Daily, 1995 X X X Rosenstein & Wyatt, 1995 Brickley, Coles & Terry, 1994 Daily & X Dalton, 1994 (a) Boyd, 1994 X Daily & X X Dalton, 1994 (b) Daily & Dalton X X 1993 Daily & X Dalton, 1992 Study Citation Multivariate Market Acct Other Financial Ana1ysis Performance This study X X X Sundamurthy, X X Mahoney & Mahoney, 1997 McWilliams & X X Sen, 1997 Baliga, Moyer X X & Rao, 1996 Daily, 1995 X X Rosenstein & X X Wyatt, 1995 Brickley, Coles X X & Terry, 1994 Daily & X X X Dalton, 1994 (a) Boyd, 1994 X X Daily & X X X Dalton, 1994 (b) Daily & Dalton X X 1993 Daily & X X Dalton, 1992 Studies on the Relationship of Goverance to Financial Performance Study Citation Sample Leader- Board CEO Board CEO Size ship Struc- Compen- Owner- Owner- Struc- ture sation ship ship ture Judge & 40 X Zeithaml, 1992 Firms, 114 Directors Rechner & 250 X Dalton, 1991 Baysinger, 176 X Kosnick & Turk, 1991 Pearce & Zahra, 139 X 1991 Yermack, 1990 452 X X X Rosenstein & 1251 X Wyatt, 1990 Schellinger, Wood 526 X & Tashakori, 1989 Molz, 1988 50 X X Morck, Shleifer & 371 X Vishny, 1988 Weisbach, 1988 367 X Kesner, 1987 250 X X Baysinger & 266 X Butter, 1985 Kesner & Dalton, 96 X 1985 Changanti, 42 X X Mahajan & Sharma, 1985 Study Citation CEO Size Industry Block- Uni- Multi- Ten- Perform- holders variate variate ure ance Analysis Analysis Judge & X X X X Zeithaml, 1992 Rechner & X Dalton, 1991 Baysinger, X X X Kosnick & Turk, 1991 Pearce & Zahra, X 1991 Yermack, 1990 X X X Rosenstein & X X X Wyatt, 1990 Schellinger, Wood X X & Tashakori, 1989 Molz, 1988 X X Morck, Shleifer & X X Vishny, 1988 Weisbach, 1988 X X Kesner, 1987 X X Baysinger & X X X Butter, 1985 Kesner & Dalton, X X 1985 Changanti, X Mahajan & Sharma, 1985 Study Citation Market Acct Other Financial Perform- ance Judge & X Zeithaml, 1992 Rechner & X X Dalton, 1991 Baysinger, X Kosnick & Turk, 1991 Pearce & Zahra, X X 1991 Yermack, 1990 X X X Rosenstein & X Wyatt, 1990 Schellinger, Wood X X X & Tashakori, 1989 Molz, 1988 X X Morck, Shleifer & X X Vishny, 1988 Weisbach, 1988 X Kesner, 1987 X X Baysinger & X Butter, 1985 Kesner & Dalton, X 1985 Changanti, X Mahajan & Sharma, 1985 Descriptive Statistics Variable Mean Median SD Minium Maximum Firm EVA -0.031 -0.124 0.46 -0.778 2.624 Firm MVA 0.518 0.266 0.935 -1.821 5.421 Combined Leadership 0.785 1.00 0.412 0.00 1.00 Structure Proportion of Outside 0.471 0.500 0.163 0.00 0.909 Directors Proportion of CEO 0.011 0.001 0.028 0.00 0.165 Ownership Proportion of Board 0.025 0.004 0.049 0.00 0.300 Ownership (--CEO) Proportion of Board 0.036 0.007 0.062 0.000 0.302 Ownership CEO Salary Sensitivity 0.110 0.045 0.251 -0.296 2.008 CEO Tenure 6.983 5.000 6.982 0.000 31.000 Blockholder Proportion 0.036 0.00 0.070 0.00 0.313 Industry EVA 7987.38 3940 11687 559.86 69160 Industry MVA -0.136 -0.174 0.258 -0.904 0.338 Firm Size 0.360 0.258 0.434 -0.417 1.461 (Assets in Millions) Pearson Correlation Coefficients Variable 1 2 3 4 1-Firm EVA 1.0 2-Firm MVA 0.436 [***] 1.0 3-Combined 0.168 [*] 0.055 1.0 Leadership Structure 4-Proportion of -0.088 -0.127 -0.166 [*] 1.0 Outside Directors 5-Proportion of -0.048 0.0067 0.085 -0.054 CEO Ownership 6-Proportion of 0.181 [*] 0.127 -0.028 -0.105 Board Ownership (--CEO) 7-Proportion of 0.121 0.130 0.016 -0.107 Board Ownership 8-CEO Salary -0.051 -0.122 -0.026 -0.040 Sensitivity 9-CEO Tenure 0.017 0.002 0.208 [*] -0.084 10-Blockholder -0.126 -0.024 -0.255 [**] 0.069 Proportion 11-Industry EVA 0.579 [***] 0.339 [***] 0.034 -0.045 12-Industry MVA 0.300 [***] 0.549 [***] 0.097 0.049 13-Firm Size -0.154 -0.283 [***] -0.116 0.068 (In of Assets) Variable 5 6 7 1-Firm EVA 2-Firm MVA 3-Combined Leadership Structure 4-Proportion of Outside Directors 5-Proportion of 1.0 CEO Ownership 6-Proportion of 0.262 [**] 1.0 Board Ownership (--CEO) 7-Proportion of 0.656 [***] 0.900 [***] 1.0 Board Ownership 8-CEO Salary -0.019 -0.013 -0.009 Sensitivity 9-CEO Tenure 0.588 [***] 0.178 0.411 [***] 10-Blockholder -0.082 0.081 0.026 Proportion 11-Industry EVA -0.040 0.199 [*] 0.137 12-Industry MVA -0.083 -0.008 -0.044 13-Firm Size -0.161 -0.207 [*] -0.235 (In of Assets) Variable 8 9 10 11 1-Firm EVA 2-Firm MVA 3-Combined Leadership Structure 4-Proportion of Outside Directors 5-Proportion of CEO Ownership 6-Proportion of Board Ownership (--CEO) 7-Proportion of Board Ownership 8-CEO Salary 1.0 Sensitivity 9-CEO Tenure -0.086 1.0 10-Blockholder 0.099 -0.065 1.0 Proportion 11-Industry EVA -0.012 -0.001 -0.099 1.0 12-Industry MVA 0.002 0.005 -0.005 0.405 [***] 13-Firm Size -0.217 [**] -0.201 [*] -0.153 -0.052 (In of Assets) Variable 12 13 1-Firm EVA 2-Firm MVA 3-Combined Leadership Structure 4-Proportion of Outside Directors 5-Proportion of CEO Ownership 6-Proportion of Board Ownership (--CEO) 7-Proportion of Board Ownership 8-CEO Salary Sensitivity 9-CEO Tenure 10-Blockholder Proportion 11-Industry EVA 12-Industry MVA 1.0 13-Firm Size -208 [*] 1.0 (In of Assets) (*.)[less than].05; (**.)[less than].01; (***.)[less than].001. MVA/EVA with CEO Tenure Main Effects Variable EVA MVA EVA Combined 0.178 -0.227 0.176 Leadership (0.111) (0.183) (0.110) Proportion of -0.124 -0.550 -0.119 Outside Directors (0.233) (0.387) (0.231) Proportion of -1.46 CEO Ownership (1.548) Proportion of Board Ownership (--CEO) Proportion of -0.072 0.534 Board Ownership (0.637) (1.054) CEO Salary -0.060 -0.453 -0.066 Sensitivity (0.201) (0.334) (0.201) CEO -0.001 -0.004 0.002 Tenure (0.006) (0.010) (0.007) Blockholder -0.154 -0.206 -0.195 Proportion (0.530) (0.867) (0.529) Industry 1.162 [***] 1.325 [***] 1.141 [***] EVA(MVA) (0.144) (0.144) (0.144) Size -0.021 -0.099 -0.022 (0.042) (0.070) (0.042) F-Value 8.897 [***] 12.76 [***] 9.080 [***] Adjusted .357 .450 .362 R-Squared Variable MVA EVA MVA Combined -0.235 0.181 -0.222 Leadership (0.183) (0.111) (0.184) Proportion of -0.559 -0.108 -0.537 Outside Directors (0.387) (0.233) (0.389) Proportion of -0.525 -1.613 -0.819 CEO Ownership (2.566) (1.582) (2.603) Proportion of 0.395 0.922 Board Ownership (0.774) (1.258) (--CEO) Proportion of Board Ownership CEO Salary -0.458 -0.066 -0.460 Sensitivity (0.334) (0.201) (0.335) CEO -0.001 0.002 -0.001 Tenure (0.011) (0.007) (0.011) Blockholder -0.218 -0.214 -0.240 Proportion (0.869) (0.533) (0.872) Industry 1.315 [***] 1.125 [***] 1.319 [***] EVA(MVA) (0.144) (0.148) (0.145) Size -0.103 -0.020 -0.099 (0.070) (0.042) (0.070) F-Value 12.708 [***] 8.044 [***] 11.307 [***] Adjusted .449 .357 .447 R-Squared (*.)p [less than].10; (**.)p [less than].05; (***.)p [less than].001 MVA/EVA with CEO Tenure Main Effects Variable EVA MVA EVA Combined 0.191 [*] -0.230 0.186 [*] Leadership (0.98) (0.200) (0.98) Proportion of -0.143 -0.870 [**] -0.161 Outside Directors (0.207) (0.424) (0.206) Proportion of -1.156 1.959 -0.846 CEO Ownership (1.216) (2.448) (1.177) Proportion of 0.727 1.308 Board Ownership (0.717) (1.415) (--CEO) Proportion of Board Ownership CEO Salary -0.147 -0.752 [**] -0.158 Sensitivity (0.184) (0.371) (0.184) -0.001 -0.004 0.002 Blockholder -0.272 -0.323 -0.218 Proportion (0.499) (1.004) (0.496) Industry 1.045 [***] l.180 [***] 1.045 [***] EVA(MVA) (0.134) (0.155) (0.130) Size -0.042 -0.147 -0.047 (0.036) (0.073) (0.036) F-Value 10.40 [***] 10.36 [***] 11.74 [***] Adjusted 0.356 0.355 .356 R-Squared Variable MVA EVA MVA Combined -0.243 0.184 [*] -0.226 Leadership (0.199) (0.098) (0.198) Proportion of -0.908 [**] -0.155 -0.864 [**] Outside Directors (0.422) (0.207) (0.422) Proportion of 2.477 CEO Ownership (2.381) Proportion of Board Ownership (--CEO) Proportion of 0.159 1.498 Board Ownership (0.542) (1.085) CEO Salary -0.772 [**] -0.143 -0.754 [**] Sensitivity (0.370) (0.184) (0.370) -0.001 0.002 -0.001 Blockholder -0.263 -0.190 -0.344 Proportion (1.00) (0.496) (0.995) Industry 1.177 [***] 1.078 [***] 1.177 [***] EVA(MVA) (0.155) (0.131) (0.154) Size -0.158 -0.040 -0.148 [**] (0.072) (0.036) (0.072) F-Value 11.74 [***] 1l.64 [***] 11.93 [***] Adjusted .356 .353 .360 R-Squared (*.)p [less than].10; (**.)p [less than].05; (***.)p [less than].001 MVA/EVA with CEO Tenure Board Composition Interaction Terms (Board Composition = 1 if the board is insider controlled) Variable EVA MVA EVA Combined 0.182 -0.242 0.177 Leadership (0.111) (0.182) (0.111) Proportion of -0.192 -0.579 -0.220 Outside Directors (0.248) (0.407) (0.247) Proportion of -1.841 -0.882 CEO Ownership (1.774) (2.870) Proportion of 0.436 0.818 Board Ownership (0.783) (1.248) (-CEO) Proportion of -0.444 Board Ownership (0.647) CEO Salary 0.202 -1.142 [**] 0.193 Sensitivity (0.280) (0.456) (0.280) CEO 0.002 0.002 -0.001 Tenure (0.007) (0.011) (0.006) Board Composition x -0.474 1.422 [**] -0.470 CEO Salary (0.374) (0.611) (0.374) Sensitivity Board Composition x -0.007 -0.050 [*] -0.006 CEO Tenure (0.018) (0.029) (0.018) Board Composition x 1.769 8.385 0.225 CEO Ownership (4.458 (7.286) (4.229) Blockholder -0.196 -0.236 -0.140 Proportion (0.534) (0.858) (0.532) Industry 1.130 [***] 1.364 [***] 1.167 [***] EVA(MVA) (0.150 (0.145) (0.146) Size -0.018 -0.106 -0.022 (0.043) (0.070) (0.043) F-Value 6.203 [***] 9.273 6.647 [***] Adjusted .354 .463 .353 R-Squared Variable MVA EVA NVA Combined -0.248 0.177 -0.254 Leadership (0.181) (0.111) (0.180) Proportion of -0.600 -0.207 -0.603 Outside Directors (0.404) (0.246) (0.405) Proportion of -1.620 -0.525 CEO Ownership (1.723) (2.810) Proportion of Board Ownership (-CEO) Proportion of 0.477 Board Ownership (1.049) CEO Salary -1.146 [**] 0.197 -1.149 [**] Sensitivity (0.455) (0.279) (0.455) CEO -0.000 0.002 -0.002 Tenure (0.010) (0.007) (0.011) Board Composition x 1.423 [**] -0.465 1.434 CEO Salary (0.609) (0.372) (0.609) Sensitivity Board Composition x -0.048 [*] -0.007 -0.050 [*] CEO Tenure (0.029) (0.018) (0.029) Board Composition x 7.151 1.619 8.021 CEO Ownership (6.846) (4.435) (7.244) Blockholder -0.210 -0.174 -0.217 Proportion (0.854) (0.531) (0.856) Industry 1.370 [***] 1.148 1.362 [***] EVA(MVA) (0.144) (0.146) (0.144) Size -0.108 -0.020 -0.111 (0.070) (0.043) (0.069) F-Value 10.165 [***] 6.784 10.133 [***] Adjusted .467 .358 .466 R-Squared (*.)p [less than].10; (**.)p [less than].05; (***.)p [less than].001 MVA/EVA without CEO Tenure Board Composition Interaction Terms (Board Composition = 1 if the board is insider controlled) Variable EVA MVA EVA Combined 0.196 [**] -0.220 0.189 [*] Leadership (0.099) (0.198) (0.098) Proportion of -0.154 -0.599 -0.178 Outside Directors (0.216) (0.439) (0.215) Proportion of -1.833 -0.269 -1.388 CEO Ownership (1.462) (2.926) (1.412) Proportion of 0.832 1.353 Board Ownership (0.722) (1.408) (--CEO) Proportion of Board Ownership CEO Salary 0.075 -1.223 [**] 0.047 Sensitivity (0.254) (0.507) (0.253) Board Composition x -0.403 0.980 -0.379 CEO Salary (0.334) (0.666) (0.334) Sensitivity Board Composition x 1.843 7.027 1.572 CEO Ownership (2.401) (4.817) (2.392) Blockholder -0.270 -0.306 -0.210 Proportion (0.499) 0.994) (0.497) Industry 1.032 [***] 1.148 [***] 1.066 [***] EVA(MVA) (0.134) (0.155) (0.131) Size -0.037 -0.142 [*] -0.044 (0.037) (0.072) (0.036) F-Value 8.516 [***] 8.894 9.291 [***] Adjusted .356 .367 .354 R-Squared Variable MVA EVA MVA Combined -0.234 0.185 [*] -0.229 Leadership (0.198) (0.099) (0.197) Proportion of -0.642 -0.198 -0.631 Outside Directors (0.437) (0.215) (0.433) Proportion of 0.363 CEO Ownership (1.412) (2.844) Proportion of Board Ownership (--CEO) Proportion of 0.169 0.963 Board Ownership (0.570) (1.131) CEO Salary -1.265 [**] 0.050 -1.236 [**] Sensitivity (0.505) (0.255) (0.505) Board Composition x 1.016 -0.369 0.999 CEO Salary (0.665) (0.335) (0.663) Sensitivity Board Composition x 6.665 0.093 5.915 CEO Ownership (4.801) (2.102) (4.175) Blockholder -0.246 -0.183 -0.269 Proportion (0.992) (0.498) (0.988) Industry 1.148 [***] 1.076 [***] 1.159 [***] EVA(MVA) (0.155) (0.132) (0.153) Size -0.154 [**] -0.139 -0.143 [*] (0.071) (0.037) (0.072) F-Value 9.785 [***] 9.131 [***] 9.919 [***] Adjusted .368 .350 .371 R-Squared (*.)p [less than].10; (**.)p [less than].05; (***.)p [less than].001 MVA/EVA with CEO Tenure Leadership Structure Interaction Terms (Leadership Structure = 1 if the Chair and CEO are same individual) Variable EVA MVA EVA Combined 0.133 -0.304 0.133 Leadership (0.155) (0.255) (0.153) Proportion of -0.092 -0.528 -0.094 Outside Directors (0.248) (0.414) (0.239) Proportion of -0.703 22.745 CEO Ownership (66.293) (109.57) Proportion of 0.442 1.152 Board Ownership (0.831) (1.359) (-CEO) Proportion of 0.445 Board Ownership (0.811) CEO Salary -0.540 -0.711 0.542 Sensitivity (0.959) (1.591) (0.948) CEO -0.003 -0.029 -0.003 Tenure (0.041) (0.068) (0.021) Leadership 0.496 0.257 0.498 Structure x CEO (0.977) (1.620) (0.968) Salary Sensitivity Leadership -0.007 0.030 0.006 Structure x CEO (0.018) (0.069) (0.022) Tenure Leadership 0.005 -23.853 -2.124 Structure x CEO (0.041) (109.68) (1.992) Ownership Blockholder -0.250 -0.244 -0.248 Proportion (0.551) (0.897) (0.541) Industry 1.117 [***] 1.320 [***] 1.117 [***] EVA(MVA) (0.152) (0.147) (0.150) Size -0.022 -0.098 -0.022 (0.043) (0.072) (0.043) F-Value 5.900 [***] 8.287 [***] 6.500 [***] Adjusted .340 .432 .347 R-Squared Variable MVA EVA MVA Combined -0.297 0.142 -0.284 Leadership (0.252) (0.153) (0.254) Proportion of -0.506 -0.101 -0.543 Outside Directors (0.398) (0.247) (0.413) Proportion of -8.101 0.348 CEO Ownership (64.627) (106.192) Proportion of Board Ownership (-CEO) Proportion of 1.090 Board Ownership (1.317) CEO Salary -0.672 -0.512 -0.651 Sensitivity (1.571) (0.954) (1.587) CEO -0.018 0.004 -0.010 Tenure (0.034) (0.039) (0.064) Leadership 0.224 -0.469 0.204 Structure x CEO (1.604) (0.973) (1.617) Salary Sensitivity Leadership 0.018 -0.002 0.010 Structure x CEO (0.036) (0.039) (0.065) Tenure Leadership -2.184 6.540 -0.988 Structure x CEO (3.223) (64.584) (106.167) Ownership Blockholder -0.268 -0.235 -0.236 Proportion (0.884) (0.548) (0.896) Industry 1.318 [**] 1.132 [***] 1.314 [***] EVA(MVA) (0.146) (0.149) (0.147) Size -0.098 -0.024 -0.104 (0.072) (0.043) (0.071) F-Value 9.121 [***] 6.456 [***] 8.999 [***] Adjusted .437 .345 .434 R-Squared (*.)p [less than].10; (**.)p [less than].05; (***.)p [less than].001 MVA/EVA with CEO Tenure Leadership Structure Interaction Terms (Leadership Structure = 1 if the Variable EVA MVA EVA Combined 0.125 -0.182 0.119 Leadership (0.188) (0.239) (0.118) Proportion of -0.100 -0.915 [**] -0.118 Outside Directors (0.210) (0.434) (0.210) Proportion of -6.300 8.377 -5.991 CEO Ownership (6.245) (12.737) (6.238) Proportion of 0.730 1.292 Board Ownership (0.719) (1.425) (-CEO) Proportion of Board Ownership CEO Salary -0.736 -0.491 -0.743 Sensitivity (0.908) (1.834) (0.908) Leadership 0.623 0.279 0.617 Structure x CEO (0.928) (1.872) (0.928) Salary Sensitivity Leadership 5.358 -6.633 5.361 Structure x CEO (6.352) (12.885) (6.352) Ownership -0.290 -0.322 -0.235 Blockholder Proportion (0.504) (1.016) (0.501) Industry 1.053 [***] 1.191 [***] 1.082 [***] EVA(MVA) (0.135) (0.157) (0.132) Size -0.049 -0.140 -0.055 (0.037) (0.075) (0.037) F-Value 8.421 [***] 8.214 [***] 9.240 [***] Adjusted .353 .347 0.353 R-Squared Variable MVA EVA MVA Combined -0.195 0.143 -0.198 Leadership (0.238) (0.117) (0.236) Proportion of -0.954 -0.134 -0.876 [**] Outside Directors (0.432) (0.208) (0.428) Proportion of 9.159 CEO Ownership (12.699) Proportion of 1.292 Board Ownership (1.425) (-CEO) Proportion of 0.600 1.430 Board Ownership (0.710) (1.398) CEO Salary -0.524 -0.914 -0.320 Sensitivity (1.832) (0.895) (1.802) Leadership -0.267 0.800 -0.452 Structure x CEO (1.871) (0.915) (1.841) Salary Sensitivity Leadership -6.911 -1.487 0.288 Structure x CEO (12.872) (1.573) (3.102) Ownership -0.264 -0.302 -0.312 Blockholder Proportion (1.013) (0.504) (1.013) Industry 1.189 [***] 1.045 [***] 1.178 [***] EVA(MVA) (0.157) (0.135) (0.155) Size -0.150 [**] -0.045 -0.145 [*] (0.074) (0.037) (0.074) F-Value 9.049 [***] 9.202 [***] 9.144 [***] Adjusted 0.348 0.352 0.350 R-Squared (*.)P [less than].10; (**.)p [less than].05; (***.)p [less than].001
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|Author:||Coles, Jerilyn W.; McWilliams, Victoria B.; Sen, Nilanjan|
|Publication:||Journal of Management|
|Date:||Jan 1, 2001|
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