Inside directors, risk aversion, and firm performance

    Inside Directors, Risk Aversion, and Firm Performance Arun D. Upadhyay, Rahul Bhargava, Sheri Faircloth, Hongchao Zeng PII: DOI: Reference:

S1058-3300(16)30014-3 doi:10.1016/j.rfe.2016.12.001 REVFIN 395

To appear in:

Review of Financial Economics

Received date: Revised date: Accepted date:

7 February 2016 17 September 2016 7 December 2016

Please cite this article as: Upadhyay, A.D., Bhargava, R., Faircloth, S. & Zeng, H., Inside Directors, Risk Aversion, and Firm Performance, Review of Financial Economics (2016), doi:10.1016/j.rfe.2016.12.001

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ACCEPTED MANUSCRIPT Inside Directors, Risk Aversion, and Firm Performance

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Arun D. Upadhyay Department of Finance College of Business Florida International University Modesto A. Maidique Campus 11200 S.W. 8th St, RB 247B Miami, FL 33199 E-Mail: [email protected]

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Rahul Bhargava Managerial Science Department College of Business University of Nevada, Reno 1664 N Virginia St, Reno, NV 89557 E-Mail: [email protected]

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Sheri Faircloth Managerial Science Department College of Business University of Nevada, Reno 1664 N Virginia St, Reno, NV 89557 E-Mail: [email protected]

Hongchao Zeng1 Managerial Science Department College of Business University of Nevada, Reno 1664 N Virginia St, Reno, NV 89557 E-Mail: [email protected]

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Corresponding author. We thank Chunlin Liu, Greg Stone, Qun Wu, conference participants at the Academy of Finance 2013 annual meeting, and the University of Nevada Reno for valuable comments and suggestions.

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ACCEPTED MANUSCRIPT Inside Directors, Risk Aversion, and Firm Performance

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Abstract

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Prior literature provides mixed evidence on managerial risk aversion. Using a sample of 1,737 large US firms from 1996 to 2005, we find a negative association between the insider ratio and firm risk. Upon further analysis, we show that firms with a greater insider ratio are also likely to have more conservative CEO compensation and investment policies. Analysis of CEO compensation policies indicates that firms with a greater insider ratio offer lower equity based compensation, lower vega and lower total compensation to their CEOs. Also, firms with a greater insider ratio tend to invest more in tangible assets such as plant and equipment and have lower intangible investments. Consistent with these boards instituting conservative policies, we find that firms with a greater insider ratio perform better when they operate in highly volatile environments. Overall, this study suggests that high-insider boards are more conservative in policy initiation and that such boards are valuable in firms with greater operating uncertainties.

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JEL classification code: G32; G34; K22

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Keywords: Inside directors; Corporate governance; Firm risk; CEO compensation; Investment policy; Firm performance; Operating volatility

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ACCEPTED MANUSCRIPT 1. Introduction The financial meltdown of 2008-2009 refocused the attention of both the media and

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regulators on the failure of corporate governance practices in general, but specifically on

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corporate boards in identifying risky investments of their firms.2 Corporate boards are one of the most important governance mechanisms that shareholders use to protect their interests (Fama and

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Jensen, 1983).3 Boards can influence managerial decisions directly by voting on investment and

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financing policies, or indirectly by altering the incentive structures of top managers. For example, boards often design managerial compensation schemes that encourage managers to

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undertake risky projects that benefit stockholders. Since excessive risk-taking negatively impacts investors, it is important to have a board that provides a balance of monitoring and incentive

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systems to prevent excessive risk taking behaviors. Corporate scandals of the last two decades and subsequent regulatory reforms have

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pushed board structures towards more independence. Enactment of the Sarbanes-Oxley Act (2002) and subsequent adoption of listing requirements by the national stock exchanges have made it mandatory to have a majority of independent directors. A push towards smaller and outsider dominated boards from the proponents of corporate governance reforms has significantly reduced the managerial representation on boards (Linck et al., 2008). These changes may have impacted the quality of managerial evaluation and monitoring effectiveness of boards, as outside board members rely on the inside directors for valuable information about a firm and its investments (Raheja, 2005; Adams and Ferreira, 2007). In the absence of managerial inputs,

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See BusinessWeek 26th September 2008 and New York Times 4th January 2009. In these articles, boards of directors are criticized for their inability to control the financial risks of their firms.

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For a detailed discussion on the role of corporate boards and related research, please refer to Adams et al. (2009).

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ACCEPTED MANUSCRIPT outside directors could find it difficult to evaluate the quality of managerial decision making and may not design a compensation scheme that balances growth with risk taking.

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Like any group, the actions and effectiveness of corporate boards are a function of board

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characteristics (i.e. size and composition (Jensen, 1993)). This study examines the relation between board composition and corporate risk taking. Specifically, we investigate how a greater

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representation of insiders on the board is associated with firm risk. Inside directors share similar

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professional backgrounds, access to firm-specific knowledge, and motivations to reveal information (Raheja, 2005). Therefore, a high-insider board can behave like a homogeneous

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group when performing board functions. Compared with heterogeneous groups, homogeneous groups are less likely to suffer from interaction problems resulting from inability to communicate

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clearly, disagreements on expectations, and lack of cohesion (Steiner, 1972). Watson and Kumar (1992) argue that the nature of group interaction significantly impacts the process of group

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decision making and hence plays an important role in the risk preferences of the group. They find that the facilitating interaction behaviors of homogenous groups lead to less conservative risk-taking decisions. On the other hand, a high-insider board may make more conservative risktaking decisions due to managerial risk aversion. There is a large amount of literature on how inherently undiversified managers with respect to firm-specific risk are more risk averse than outside shareholders (e.g. Meulbroek, 2000; Hall and Murphy, 2002). Further, since a typical CEO has a larger component of her compensation tied up to options or restricted stocks compared with a non-CEO inside director of the firm, she benefits more from the upside potential of a risky project.4 This difference in the level and likelihood of potential payoffs from

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There are multiple studies that have documented a much larger proportion of equity based incentives, delta and vega for CEOs as compared with the other executives (Coles et al., 2006, Kale et al., 2009).

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ACCEPTED MANUSCRIPT risky projects could lead to potential conflicts between the CEO and non-CEO inside directors. When it comes to the evaluation of a project’s riskiness and its pay-off, non-CEO inside

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directors’ career concerns and incentives may be aligned with those of outside directors as both

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groups would lose more from a failed project. Therefore, risky decision-making resulting from the opportunistic behaviors of a CEO could be controlled by its board when non-CEO inside

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directors are incentivized to reveal information to outside directors. Taken together, it is an

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empirical issue whether a high fraction of inside directors on the board is positively or negatively associated with firm risk.

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Using a sample of 1,737 large US firms from 1996 to 2005, we examine the association between the insider ratio and firm risk. Our primary measures of firm risk are return volatility

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(standard deviation of monthly stock returns) and ROA volatility (standard deviation of quarterly ROA). Our analysis indicates that the insider ratio is negatively associated with firm risk. We

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find that a replacement of one outside director by an inside director to the median board with nine directors reduces return volatility (ROA volatility) by 0.78% (8.41%) for the median firm. This evidence is consistent with the managerial risk aversion hypothesis and does not support the hypothesis that insider representation on a board leads to an increase in risk due to group homogeneity.

We further analyze whether the CEO compensation policies and investing decisions of firms with high-insider boards are consistent with the managerial risk aversion hypothesis. A board’s role in the executive pay setting process and the impact of compensation on a CEO’s risk taking behavior is well studied.5 If in their deliberations inside directors provide more precise information to the board, the board would be less likely to have an aggressive compensation 5

Adams et al. (2009) provide a detailed discussion on this issue.

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ACCEPTED MANUSCRIPT package that rewards the CEO for the quality of information provided. Also, if insiders have a more conservative approach in managing the firm, they may be able to shift the board’s overall

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attitude to more conservative compensation policies. In that case, a firm with a larger insider ratio would be more likely to offer its CEO a compensation structure that has a larger fixed

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component and smaller variable components comprised primarily of stocks and options.6 Greater

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holdings of options lead to a greater vega and greater stock holdings lead to a greater delta. Guay

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(1999) finds that it is the vega that contributes most to uncertainty. Our results indicate a negative association between the insider ratio and CEO equity based incentives. Firms with a

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greater insider ratio are also more likely to pay their CEOs a lower total pay. Additionally, the insider ratio is negatively associated with CEO vega. Overall, this analysis indicates that CEOs

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of firms with a greater insider ratio receive a more conservative compensation package. Next, we investigate the association between the insider ratio and investment decisions.

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Since insiders play an important role in board deliberations by providing information to the board (Raheja, 2005; Adams and Ferreira, 2007), they would have more influence on the choice of investments, and their risk preferences could affect the nature of investments. We find a negative association between the insider ratio and investments in intangible assets. Alternatively, firms with a greater insider ratio are more likely to invest in capital assets such as property, plant and equipment. These findings are in line with the results on the relationship between the insider ratio and CEO compensation structure. This evidence appears to support the managerial risk aversion hypothesis that firms with a greater representation of insiders on their boards are more likely to make conservative investment decisions.

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Conservative boards could offer a compensation structure that is closer to or farther away from industry norms. Using industryadjusted CEO vega and delta, we find a negative relation between the insider ratio and both CEO vega and CEO delta.

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ACCEPTED MANUSCRIPT Finally, we study the implications of the negative relationship between the insider ratio and firm risk for shareholders of firms with different operating volatilities by examining how

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firm value varies with firm risk. Recent studies on the determinants of board structure suggest

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that firms organize their boards according to their advisory and monitoring needs. Raheja (2005) and Harris and Raviv (2008) argue that firms consider the trade-offs between the costs and

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benefits of a certain board structure. Boone et al. (2007), Linck et al. (2008) and Lehn et al.

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(2009) find that more uncertain environments affect board structures and such firms have smaller boards with more insiders. If insiders reduce firm risk, then we expect to find a positive impact

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of such board structures on risky firms. Using Tobin’s Q and ROA, we find that firms with a greater insider ratio have greater Tobin’s Q and ROA in high-risk firms. The result for this

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sample indicates that a one standard deviation increase in firm risk increases the Tobin’s Q of a firm with an average insider ratio of 0.214 by 3.37%.

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Similar to other studies examining the relation between governance structures and firm characteristics and performance, our study also faces the difficult challenge of addressing potential endogeneity issues, such as the reverse causality problem and simultaneity in the determination of board structure, firm risk, and firm performance. Though we use standard econometric methodologies to address these endogeneity issues, we cannot completely rule out their impact on our results. For example, bond covenants may require firms to maintain an independent board or a certain board size. Listing or regulatory requirements force firms to maintain a board and board committees of a certain size and composition (e.g. audit or compensation committee of at least three members). It is also plausible that chosen board composition and firm characteristics are all endogenous outcomes of an optimal choice based on

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ACCEPTED MANUSCRIPT exogenous factors that are not included in the empirical specification. We use firm fixed effects to control for these unobservable factors.

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The results of this study contribute to the existing literature in two important ways. First,

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it documents the important role of board structure on firm risk and financing policies. In that context, the evidence of a negative association between the insider ratio and firm risk indicates

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the importance of risk effects in board structuring decisions. Recent studies by Linck et al.

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(2008) and Lehn et al. (2009) suggest a trend towards smaller and outsider dominated board structures. Although not analyzed here directly, the results of this study could be helpful in

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understanding why some firms with outsider dominated boards failed to control the risk-taking behavior of their managers. Second, from a board structuring perspective, the results of this

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analysis imply that an altogether different set of concerns and remedies relative to previously documented premiums exists for firms with a high insider ratio.

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The remainder of the paper is organized as follows. Section 2 describes the data and important variables. Section 3 reports the empirical results from the analysis of board composition, firm risk, CEO compensation structure, investment decisions, and firm performance. Section 4 presents robustness checks and sensitivity analyses, while Section 5 provides the conclusion.

2. Data and Variable Definition 2.1. Sample The sample consists of ExecuComp firms from 1996 to 2005. Following previous studies, we exclude utilities and financial services firms.7 Data on board characteristics (i.e., board size and composition, directors’ relationship with the firm, beneficial stock-holdings and voting 7

When repeating the analysis including utilities and financial services firms, we find similar results.

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ACCEPTED MANUSCRIPT rights) come from IRRC and proxy statements, and COMPUSTAT is the source for accounting and financial data. CEO compensation related data is from the ExecuComp database and equity

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returns are from CRSP. For a firm-year observation to be included in the primary sample, data

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must be available on the market value of equity, total sales and the number of business segments. This leads to a total of 7,318 firm-year observations covering 1,737 firms.

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2.2. Variable Definition

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In this study, we use board size and the ratio of inside directors to define board structure. Following prior studies (Yermack, 1996; Anderson et al., 2004; Coles et al., 2008), we use the

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natural log of board size in the regressions8 and we follow the board classification method of Yermack (1996) and Coles et al. (2008). Inside directors are employees, retired employees or

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relatives of employees of the firm. Though “affiliated” or “gray” directors play an important role, they may not have a large stake in the longevity or continuation of the firm. Their incentives are

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quite different from those of employee directors who could have a larger human capital investment in the firm. We employ the standard deviation of monthly stock returns for the prior sixty months as our measure of total risk. This measure captures the overall variability in stock returns and reflects the market’s perceptions about the risks inherent in the firm’s operations. Meanwhile, we use the standard deviation of quarterly ROA for the prior twenty quarters as an alternative proxy of risk. For CEO compensation structure, we use two important variables that have been shown to affect firm risk: equity based incentive compensation and vega. Equity based incentive compensation is the ratio of compensation from stocks and options to the total CEO pay. Following Guay (1999) and Coles et al. (2006), vega is defined as the change in the dollar value 8

Using board size without the log transformation yields similar results.

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ACCEPTED MANUSCRIPT of the executive’s option holdings for a one percent change in the annualized standard deviation of stock returns. For investments, we use two proxies: the ratio of intangible assets to total assets

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and the ratio of capital expenditure to sales. Following Coles et al. (2008), in primary

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specifications we use a simple approximation of Tobin’s Q as a measure of firm valuation. We also check the robustness of performance related results using ROA as an additional measure of

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firm performance.

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Firm specific control measures include firm size, leverage, prior firm profitability, R&D intensity, capital investment intensity, the vega and delta of top executives, insider holdings, and

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firm age. Coles et al. (2008) use R&D investments as a proxy for growth opportunities. Since growth opportunities impact firm risk, we include proxies for growth opportunities. Insider

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ownership is the proportion of equity held by directors and officers in the firm and diversification is measured by the number of business segments in a firm (Yermack, 1996). We

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also control for firm size by including the natural log of a firm’s assets. A firm’s profitability is measured by ROA, which is defined as operating profit scaled by assets. Bargeron et al. (2010) find that the Sarbanes-Oxley Act (SOX) impacts firm risk-taking in general. By adding an indicator variable that takes a value of one for years after 2002 in all the regressions, we control for the impact of SOX. 2.3. Data Description Table 1 provides descriptive statistics for the sample used in this analysis. This table provides 25th percentile, mean, median, 75th percentile, and standard deviation of the variables. To avoid the influence of extreme values, we winsorize all variables at the 1% and 99% levels. The average board size in this sample is slightly over nine and the median board size is nine. The median firm has an insider ratio of 18.2%. These numbers are comparable with Coles 10

ACCEPTED MANUSCRIPT et al. (2008) and Linck et al. (2008). Board size in our sample is similar to Cheng (2008), who uses a sample of firms from the 1996-2004 period, but differs slightly from that of Coles et al.

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(2008) and Linck et al. (2008). The median board size in Linck et al. (2008) is 7 and in Coles et

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al. (2008) is 10. The sample used by Linck et al. (2008) includes many small firms, which have smaller boards. One possible reason why the median board size in the sample used by Coles et

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al. (2008) is larger is because their sample includes banking firms that usually have larger

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boards. However, in terms of board composition, the median ratio of insiders in our sample is very similar to these studies.

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The CEO vega of the median firm is $0.139 million and CEO delta is $0.041 million. The CEO vega in this sample is higher than the CEO vega reported by Coles et al. (2006). This

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difference could be attributed to the difference in the sample period. The median (average) firm in this sample is 17 (16.50) years old and the average firm has a little over $5 billion in book

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value of assets. Operating profitability of the average firm is 14.5 %, while the median firm has 17.5% of its book value of assets financed by long-term debt. The median (average) firm in this sample has a total risk of 38.5% (43.6%) and the ROA volatility in the median (average) firm is 3.3% (5.6%).

2.4. Univariate Analysis

Table 2A presents the correlations between selected variables. Though the insider ratio is positively correlated with return volatility, the correlation is statistically insignificant. Further, the insider ratio is negatively correlated with the ROA volatility. CEO vega is negatively correlated with the insider ratio but positively correlated with the return volatility and ROA volatility. The correlations between CEO vega and the two measures of volatilities are consistent with the results presented by Coles et al. (2006). Equity incentive compensation of CEO is 11

ACCEPTED MANUSCRIPT negatively correlated with the insider ratio and return volatility. The insider ratio is also positively correlated with firm performance measures Tobin’s Q and ROA. These results are

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consistent with the hypothesis that boards with more insiders are more conservative.

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Table 2B presents results from the difference-in-means tests. We decompose sample firms into quartiles based on the inside ratio and then compare the means of selected variables

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between the top and bottom quartiles. On average, firms with more insiders are smaller and less

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leveraged. These firms have lower R&D investments, lower intangibles investment, greater capital investments and lower CEO equity based incentives when compared with those firms

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with fewer insiders. There is no significant difference in CEO vega between the two groups of firms. Firms with fewer insiders tend to be older. Firms with a smaller insider ratio perform

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poorly when compared with firms that have a greater insider ratio. When it comes to risk measures, firms with more insiders on the board exhibit a larger return volatility but these two

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groups of firms do not differ in ROA volatility. However, it’s difficult to make any inference from these simple univariate analyses that do not control for many firm characteristics such as size, leverage, and investments that are also related to volatilities and firm risk. Thus a multiple regression model will greatly assist in isolating any relationship between the insider ratio and firm risk while controlling for the variation in other, well-established firm and corporate governance variables. In the next section, we analyze the association between the insider and firm risk in a multivariate setting. 3. Multivariate Results 3.1. Insider Ratio and Firm Risk We begin the multivariate analysis by analyzing the association between the insider ratio and firm risk. We use two primary measures of firm risk: return volatility (the standard deviation 12

ACCEPTED MANUSCRIPT of monthly stock returns) and ROA volatility (the standard deviation of quarterly ROA). Since the relationship between board characteristics, firm risk, and other firm characteristics is

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endogenous and may be affected by unobservable factors (Hermalin and Weisbach, 1998), we

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first use 2SLS (IV) estimation and then continue with firm fixed-effects and OLS with industry fixed-effects estimations. There are two endogeneity concerns that need to be addressed when

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examining the relation between board composition and firm risk. The first concern is that the

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findings may simply be driven by unobservable variables affecting both board composition and firm risk. Second, board composition itself may be the result of firm risk. For example,

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theoretical works such as Raheja (2005) and Harris and Raviv (2008) show that firms with greater monitoring costs are better off by having fewer outsiders on their boards. Since firms

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with greater volatility of returns are more opaque, it is possible that due to high monitoring costs in such firms, the shareholders choose fewer outsiders on such boards, which mechanically

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increases the ratio of insiders for a given board size. In 2SLS (IV) estimation, we first model board composition and then examine the association between predicted board composition and firm risk using the following specification: Stage 1: Insider Ratio = η0 + η1 (Industry Median Insider ratio) + η2 (Control Variables) +η3 (Industry Dummies) + ε (1) Stage 2: Firm Risk = γ0 + γ 1 (Predicted Value of Insider Ratio) + γ2 (Control Variables) + γ3 (Industry Dummies) + ε (2)

Equation (1) models insider ratio using the median insider ratio of the 2-digit SIC group to which the sample firm belongs in a given year as an instrument variable. Prior literature finds some evidence that firms structure their governance systems following their peers (Glaeser and Scheinkman, 2002; John and Kadyrzhanova, 2010). However, there is no reason to believe that

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ACCEPTED MANUSCRIPT the common governance structure would have a direct impact on an individual firm’s performance.

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For control variables, we closely follow studies on board structure such as Boone et al.

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(2007), Coles et al. (2008) and Linck et al. (2008). Control variables include board size, firm size, diversification, ROA, leverage, R&D intensity, insider ownership, and firm age. In addition,

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we also include a SOX indicator variable. SOX and subsequent listing requirements of national

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stock exchanges in the US asked companies to have a majority of outside directors on their boards. While the 2SLS (IV) regression might correct for the endogeneity issues, the method

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could introduce other measurement issues. For example, Larcker and Rusticus (2010) state that use of instrumental variables often leads to inaccurate results because of over-identification

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problems. Therefore, we evaluate the strength of the 2SLS (IV) procedure by first examining whether the procedure is appropriate for the study, then we test the instrument for both sufficient

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predictive power and over identification. The endogeneity test (Hausman, 1978) yields a χ2statistics of 34.60, so we reject the null hypothesis that the insider ratio is exogenous. To test the predictive power of the instrument, we conduct a partial F-test and it yields a value of 28.47. Staiger and Stock (1997) recommend that a value of 10 or more on the F-test is adequate to classify an instrument as a good predictor. To check the robustness, results from 2SLS (IV) estimation are then compared with results from fixed-effect and OLS estimations. Results from these estimations are presented in Table 3. In Table 3, the first stage of 2SLS (IV) estimation indicates that even after controlling for firm characteristics such as board size, firm size, leverage, profitability, and the SOX Act (2002), the median insider ratio of the industry impacts a firm’s insider ratio positively. The coefficient on Industry Median Insider Ratio is positive and significant at the 1% level in the first stage 14

ACCEPTED MANUSCRIPT estimation. The second stage estimation indicates that the insider ratio impacts return volatility negatively. The coefficient on the predicted insider ratio is negative and significant at the 1%

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level in two out of three regressions. The coefficient on the insider ratio is -0.027 from OLS

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estimation and -0.039 from the firm fixed effects estimation. The negative coefficients on the insider ratio are consistent with the hypothesis that boards with a greater insider ratio have a

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more conservative governance approach and have a risk reducing effect on the firm. In economic

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terms, the results indicate replacing one outsider by an insider on a median board would reduce return volatility by approximately 0.78%.9 Using an alternative measure of firm risk, ROA

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volatility yields similar results. Across all the three models, 2SLS (IV), OLS and fixed effects, using ROA volatility as dependent variable yields negative and significant coefficients on the

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insider ratio. Results from the OLS estimation indicate that replacing one outsider by an insider

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on a median board would reduce ROA volatility by approximately 8.41%.10 These results imply

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that a greater representation of insiders on the board has a risk reducing impact on the firm, a supposition which we test next by exploring some of the sources that these boards could possibly use to impact firm risk.

3.2. Insider Ratio and Conservative CEO Compensation For corporate boards, one way to implement either more risky or less risky policies is to design a compensation contract for the CEO that encourages or discourages risk taking (Guay, 1999). CEO’s can be given a greater number of stock options to encourage adoption of risky policies (Coles et al., 2006). There is a stream of literature that has examined the association 9

Replacing one outsider by an insider on a median board of nine directors would change the insider ratio by 1/9 or 0.111. This would change the total risk of the median firm by: insider ratio coefficient*.111/ (median return volatility). From OLS estimation, this change would be = 0.111*-0.027/0.385 or -0.78%. 10

Coefficient on insider ratio from OLS estimation is -0.025 significant at the 1% level. In economic terms that would be = 0.111*-0.025/0.033 or -8.41%.

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ACCEPTED MANUSCRIPT between board structure and CEO compensation from an agency perspective. For example, Yermack (1996) finds a stronger pay-performance sensitivity of CEO compensation in firms

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with smaller boards. Core et al. (1999) find that larger boards pay a greater level of total

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compensation, salary and cash to the CEO. They also find that boards with a greater representation of insiders pay a lower level of total compensation and lower level of cash

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compensation to the CEO. These results cannot be explained using an agency framework. This is

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because, like larger boards, insiders are not considered to be effective monitors of the CEO (Hermalin and Weisbach, 2003). Core et al. (1999) conclude that outside directors are not better

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monitors than managerial directors.

In this section, we re-examine the relation between board composition and CEO

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compensation schemes from the perspective of managerial risk aversion. It could be argued that inside directors do not play any direct role in the CEO pay setting process as they do not serve on

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the compensation committee of a board. However, the information provided by inside directors directly or indirectly as they serve with independent directors on other committees could certainly be used to evaluate and compensate the CEO. We analyze different aspects of CEO compensation such as equity based incentive compensation, total compensation, and finally vega of the CEO. If boards with a greater ratio of insiders are more conservative, we expect to find a negative association between the insider ratio and CEO equity based incentives which supposedly encourage CEOs to invest in risky projects. Since inside directors have hardly any role in the pay-setting process, it is also possible to see an insignificant coefficient on the insider ratio. For robustness, we also analyze the total compensation of CEOs as the coefficient on equity incentives may be mechanically driven if the board changes total compensation without changing the equity-based compensation. Further, since the compensation structure and board 16

ACCEPTED MANUSCRIPT composition are endogenously determined and they both may be influenced by unobservable factors, to estimate a relation between board composition and CEO compensation, we rely on

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2SLS (IV), firm fixed-effects, and OLS regressions. Estimated coefficients from the three

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regressions are presented in Table 4. In the 2SLS (IV) estimations, we present results from the second stage of estimation that uses the predicted insider ratio as an independent variable.

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Table 4 presents estimated coefficients for CEO equity incentive and CEO total pay. The

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coefficients on the predicted insider ratio are negative and significant at the 1% level across all the models except one. A negative coefficient on the insider ratio is consistent with the

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hypothesis that boards with a greater ratio of insiders offer a lower equity based compensation component as well as total compensation. Thus, these boards are more conservative in that they

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offer the CEO a compensation package that discourages risk taking. In terms of the economic significance of these results, the OLS estimation of equity based incentive pay suggests that

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replacing one outside director with an inside director in the median firm reduces the ratio of equity based incentives to total CEO pay by approximately 5.60%, and such a replacement would reduce total CEO pay in the median firm by approximately 1.24%.11 Thus, these results indicate that boards with a greater insider ratio pay their CEOs more conservatively. Another common measure of risk-encouraging CEO compensation is vega. Coles et al. (2006) find that a high vega compensation to CEOs encourages them to pursue riskier financial, investment and business policies. If the boards with a greater insider representation want to encourage their CEOs to pursue more conservative policies, such boards can design CEO

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The coefficients on insider ratio from OLS estimations of CEO equity incentive and CEO total pay are -0.283 and -0.874 significant at the 1% level. Thus, replacing one outsider by an insider on a median board of nine directors would change the CEO equity incentive by 0.111*-0.283/0.561= 5.60%. Similarly, from such a replacement the CEO total pay would change by = 0.111*-0.874/7.824 or -1.24%.

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ACCEPTED MANUSCRIPT compensation structures to yield a lower vega. Therefore, we analyze the relation between the insider ratio and CEO vega. Since CEO delta and the compensation structure of other top CEOs

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capture a broader executive compensation policy of a firm, we also control for these variables in

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vega analysis. The results from 2SLS (IV), OLS and firm fixed-effects analysis of CEO vega are presented in Table 5. As expected, the results indicate a negative relation between the insider

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ratio and CEO vega across all the three regressions. These results indicate that boards with a

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greater insider ratio offer their CEOs a compensation packages that promote more conservatism. 3.3. Insider Ratio and Conservative Investments: Intangibles and Capital Investments

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An important function of the board is to monitor the investing activities of the management. Insiders play an important role by providing valuable insights and internal

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information related to a project. If the managers are risk-averse, they might advise against investing in risky projects. In that case, we expect to see firms with a greater ratio of insiders on

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their boards investing less in risky projects. Generally, investments in intangible assets are considered more risky whereas capital investments are considered less risky as it is easier for outside investors to observe and value such investments (Kothari et al., 2002; Bhagat and Welch, 1995). Table 6 presents results from an analysis of investments in intangible assets and in capital assets. Although in this analysis, we hypothesize board composition impacting investment decisions, it is equally plausible that the firms structure their boards according to their business and operating environment. Since the results from this analysis can also be potentially impacted by endogeneity, we use 2SLS (IV), OLS, and firm fixed-effects models to examine the relation between the insider ratio and these investments. The results from the analysis of investments in intangible assets yield negative coefficients on the insider ratio variable. Out of the three regressions, two are significant at the 18

ACCEPTED MANUSCRIPT 1% level, but the coefficient from OLS analysis is insignificant. These results indicate that generally boards with a greater insider ratio have smaller investments in intangible assets. The

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analysis of capital investments, on the other hand, yields positive and significant coefficients on

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the insider ratio in all three regressions. A positive and significant coefficient on the insider ratio is consistent with the hypothesis and indicates that boards with a greater insider ratio invest more

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in capital intensive projects. Overall, analysis from this section indicates that managerial

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directors prefer more conservative investments that reduce volatility. 3.4. Insider Ratio, Firm Risk and Firm Performance: Tobin’s Q and ROA

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The results thus far indicate that firms with a greater insider ratio of board members are managed more conservatively than those whose boards have a greater ratio of outsiders. A

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conservative compensation and investment policy could lead to a greater entrenchment of the management resulting in a loss for shareholders. On the other hand, if conservative policies

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balance risk taking with value creation, a greater representation of insiders on boards could be more valuable. Recent theories on board structure suggest that firms choose board size and composition to optimize the associated board costs and benefits (Raheja, 2005; Adams and Ferreira, 2007; Harris and Raviv, 2008). Since firm risk increases the cost of capital, firms would benefit by reduced risk. If board structures are chosen to maximize investors’ value, then firms with high risk should benefit from boards with a greater insider ratio. On the other hand, in lowrisk firms the costs of conservative boards would outweigh the benefits associated with such board structures. Since inside directors are considered as less effective monitors of the CEO, the entrenchment costs associated with a greater insider ratio could also be greater, especially in low-risk firms. We estimate the following regression to examine whether the valuation impact of a board with a greater ratio of insider directors varies with firm risk: 19

ACCEPTED MANUSCRIPT Performancei,t= α+β1 (Insider Ratioi,t) + β2 ((Insider Ratio i,t ) * (Risk i,t)) + β3 (Risk i,t) + δ(Controls i,t) + ε i,t

(3)

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Where Performance = Tobin’s Q or ROA.

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Table 7 presents the regression results from the above model, using Tobin’s Q and ROA as a firm’s performance measures, respectively. Since the board composition and firm

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performance relation is likely to be impacted by endogeneity, we use three different estimation

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techniques to check the robustness of the results: 2SLS (IV), OLS and firm fixed-effects. Thus, we present a total of six regression estimates in Table 7.

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In Table 7, the interaction term between insider ratio and firm risk is positive and significant across all the six columns, and the level of significance is at either 1% level or 5%

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level. The positive values of interaction terms indicate that as firm risk increases, the insider ratio

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becomes more valuable to the firm. In economic terms, this coefficient implies a 3.37% increase

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in Tobin’s Q if the return volatility of the average firm with nine board members changes by a one standard deviation (0.192).12 Similarly, from ROA analysis too, it is clear that a firm with a greater insider ratio benefits when the firm’s risk increases. The OLS estimation with ROA as the dependent variable indicates that an average firm with 21.4% inside directors experiences an increase of 2.15% in its ROA when its return volatility increases by one standard deviation.13 Ftests indicate that the joint effect of Predicted Insider Ratio/Insider Ratio, its interaction with

12

The economic impact is calculated using results from OLS estimations of Table 7 for an average firm in the sample. The average firm has 0.214 insider ratio and Tobin’s Q of 2.10. For one standard deviation change in return volatility (0.192), ∆(Tobin’s Q) = [{Coefficient of (Insider Ratio*Return Volatility)*(Average Insider Ratio)* ∆(Return Volatility)}/Average Tobin’s Q] = 1.724*0.214*0.192/2.105 = 0.0337 or 3.37%. 13

∆(ROA) = [{Coefficient of (Insider Ratio*Return Volatility)*(Average Insider Ratio)* ∆(Return Volatility)}/ Average ROA] = 0.076*0.214*0.192/0.145 = 0.0215 or 2.15%.

20

ACCEPTED MANUSCRIPT Return Volatility, and Return Volatility is positive and significant in most model specifications, suggesting that the net marginal effect of higher risk and more insiders is not zero at the mean.

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3.5. Insider Ratio, Firm Risk, and Firm Performance: Alternative Measures of Firm Risk

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To test the robustness of firm performance results, we repeat the firm fixed-effects analysis similar to the one presented in Column 3 of Table 7 using operating volatilities to

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measure firm risk.14 The estimation results are presented in Table 8. The dependent variable in

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all these regressions is Tobin’s Q. Alternative operating volatility measures are standard deviation of monthly cash flow from operations scaled by assets in column 1, standard deviation

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of monthly ROA in column 2, standard deviation of monthly sales scaled by assets in column 3, and standard deviation of daily market returns in column 4, respectively. As in Table 7, to gauge

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the impact of insiders on firms with greater operating uncertainties, we include the interaction of the volatility measures with the insider ratio in each of these four regressions. Thus the

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coefficient estimate on the interaction terms captures the impact of the insider ratio on firm performance in highly volatile firms. Consistent with the results presented in Table 7, we find that the coefficient estimates on the interaction term are positive and significant across all the four regressions. The statistical significance varies from a 10% level in Column 3, to a 5% level in Columns 2 and 4, and to a 1% level in Column 1. Similar to the results in Table 7, F-tests in Table 8 show that the joint effects of Insider Ratio, its interaction with alternative volatility measures, and alternative volatility measures are positive and significant, evidence that the net marginal effect of higher risk and more insiders is not zero at the mean. These results are consistent with the primary hypothesis that firms with greater operating uncertainties benefit by having a greater representation of inside directors on their boards. 14

The results are similar using OLS and 2-SLS (IV) estimation procedures.

21

ACCEPTED MANUSCRIPT 4. Sensitivity and Robustness Check To test the robustness of results, we perform several sensitivity and diagnostic analyses.

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We find that the results are robust to various alternative specifications.

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4.1. 3-SLS Estimation of Insider Ratio, Firm Risk and Tobin’s Q

The results from the firm fixed effects regressions presented in the previous sections

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indicate there is strong evidence that boards with a greater ratio of insiders reduce firm risk. Yet,

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since boards are endogenously determined (Hermalin and Weisbach, 1998 & 2003) there are other plausible explanations of these results. For example, it could be argued that relatively safer

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firms choose to have a greater representation of insiders (Lehn et al., 2009; Boone et al., 2007; Linck et al., 2008). Similarly, firm risk and firm performance could also arise endogenously

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(Guay, 1999; Coles et al., 2006). To address these issues, we use a simultaneous estimation method (3-SLS) as used by some other studies on corporate boards (Bhagat and Black, 2001;

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Coles et al., 2008). Specifically, using a 3-SLS approach we estimate regressions in firm risk, insider ratio and firm performance simultaneously. There are a number of empirical studies that have presented evidence on the determinants of board structure (Coles et al., 2008; Boone et al., 2007; Linck et al., 2008), and we closely follow their specifications for simultaneous estimation of board composition. Table 9 reports results from the 3-SLS estimations and they are consistent with the previous results presented in Table 3. From the insider ratio estimation, we find that the coefficient on return volatility is negative and significant at the 1% level but the coefficient on Tobin’s Q is positive and significant at the 1% level. In the return volatility estimation, the coefficient on insider ratio is negative and significant at the 1% level. Finally, in the Tobin’s Q regression the coefficient on return volatility is positive but insignificant. However, the coefficient on the insider ratio is 22

ACCEPTED MANUSCRIPT positive and significant at the 1% level. The coefficient on the interaction term of insider ratio and return volatility is also positive and significant at the 1% level. Overall, the results from

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simultaneous estimation confirm our primary hypothesis that inside directors are conservative in

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their action and highly risky firms are valued highly when they have a greater representation of inside directors.

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4.2. Alternative Measures of Key Variables

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Regressions using alternate measures of the key variables of firm value, board size, board composition, firm size, and leverage, and growth opportunities are consistent with the primary

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regression results, suggesting that a greater insider ratio is associated with lower firm risk. Using alternative measures for control variables does not change the results qualitatively. For example,

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for firm size we use the natural log of sales and market capitalization. For leverage, we use quasi-market leverage defined as book value of long-term debt scaled by book value of long-

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term debt and market value of equity, and the results are similar. Using the count of board members instead of taking the logarithmic value of board size does not change the results. Thus, the primary results are robust to alternative measures of key variables. 4.3. Regression Diagnostics

To test the sensitivity of this analysis to serial correlation and to the impacts of outliers and influential observations, we use several alternative techniques. First, we estimate primary regressions using median regressions, which are less sensitive to extreme values (Gompers et al., 2003; Coles et al., 2008). The results are similar to the firm fixed effects regressions. We also repeat the tests using a random effects model and by using the year-by-year results in the FamaMacBeth (1973) procedure. Both approaches lead to similar inferences. 5. Conclusion 23

ACCEPTED MANUSCRIPT Prior literature generally analyzes the role of insiders using an approach in which they are assumed to be proxies of CEO influence or the providers of information to outside directors. We

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explore the impact of the insider ratio using a risk-based hypothesis. There is a strand of

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literature documenting evidence that inside directors exhibit risk aversion driven by their career concerns. Also, compared with an average stockholder, inside directors, being undiversified are

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considered as more risk averse. We study the effects of boards with a greater representation of

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inside directors on firm risk and the type of incentive that such boards offer to CEOs to affect the firm risk.

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Consistent with the risk-reduction hypothesis, we find a negative association between the insider ratio and firm risk. Boards with a greater insider ratio pay a lower vega to CEO’s,

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which suggests an incentive scheme that discourages risk taking. The evidence indicates that firms with a greater insider ratio offer their CEOs a compensation structure that is less sensitive

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to changes in their stock prices. Upon further analysis, we find that boards with a greater insider ratio are likely to adopt more conservative investing policies: while the insider ratio is negatively associated with intangible assets, it is positively associated with capital investment intensity. The positive relationship between the insider ratio and capital investment intensity indicates that boards with a greater insider ratio are more likely to invest in more tangible assets. In further analysis, we show that these boards are more valuable when firms face uncertain operating environments. Using various estimation techniques, we find a positive association between the insider ratio and Tobin’s Q and ROA in risky firms. The positive association between the insider ratio and Tobin’s Q remains when we use alternative proxies of operating uncertainties. Overall, the results of this study suggest that boards with a greater insider ratio manage their firms more conservatively. Investors appear to value these conservative management styles 24

ACCEPTED MANUSCRIPT in firms that face operational uncertainties. Prior literature presents some evidence indicating a trend towards outsider-dominated boards (Linck et al., 2008; Lehn et al., 2009). Results of this

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taking by some firms and thus lead to unintended consequences.

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study may be useful in the analysis of whether such a trend may have encouraged more risk

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ACCEPTED MANUSCRIPT References

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Adams, R. B. and D. Ferreira, 2007. A Theory of Friendly Boards. The Journal of Finance 62, 217-250.

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Adams, R. B, B.E. Hermalin and M.S. Weisbach, 2009. The role of boards of directors in corporate governance: A conceptual framework & survey. Journal of Economic Literature, forthcoming.

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Anderson, R.C., S.A. Mansi and D.M. Reeb, 2004. Board characteristics, accounting report integrity, and the cost of debt. Journal of Accounting and Economics 37, 315-342.

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Bargeron, L.L., K.M. Lehn and C.J. Zutter, 2010. Sarbanes-Oxley and corporate risk-taking. Journal of Accounting and Economics 49, 34-52.

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Bhagat, S. and B. Black, 2001. The non-correlation between board independence and long term firm performance. Journal of Corporation Law 27, 231-274.

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Bhagat, S. and I. Welch, 1995. Corporate research and development investments: International comparisons. Journal of Accounting and Economics 19, 443-470.

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Boone, A. L., L. C. Field, J. M. Karpoff, and C. G. Raheja, 2007. The determinants of corporate board size and composition: An empirical analysis. Journal of Financial Economics 85, 66-101.

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Cheng, S., 2008. Board size and the variability of corporate performance. Journal of Financial Economics 87, 157-176. Core, J., R. Holthausen and D. Larcker, 1999. Corporate governance, chief executive officer compensation and firm performance. Journal of Financial Economics 51, 371-406. Coles, J. L., N. Daniel and L. Naveen, 2006. Managerial incentives and risk-taking. Journal of Financial Economics 79, 431-468. Coles, J.L., N. Daniel and L. Naveen, 2008. Boards: Does one Size fit all? Journal of Financial Economics 87, 329-356. Fama E. F. and J. MacBeth, 1973. Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607-636. Fama, E. F. and M. C. Jensen, 1983. Separation of ownership and control. Journal of Law and Economics 26, 301-325. Glaeser, E. L. and J. A. Scheinkman, 2002. Non-Market Interactions. Working paper, Harvard University. 26

ACCEPTED MANUSCRIPT Gompers, P. A., J. L. Ishii, and A. Metrick, 2003. Corporate governance and equity prices. Quarterly Journal of Economics 118, 107-155

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Guay, W., 1999. The sensitivity of CEO wealth to equity risk: An analysis of the magnitude and determinants. Journal of Financial Economics 53, 43-71.

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Hall, B. J. and K. J. Murphy. 2002. Stock Options for Undiversified Executives. Journal of Accounting and Economics 33, 3-42.

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Harris, M. and A. Raviv, 2008. A theory of board control and size. Review of Financial Studies 21, 1797-1832.

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Hausman, J.A., 1978. Specification tests in econometrics. Econometrica 46, 1251-1271.

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Hermalin, B. E. and M. S. Weisbach, 1998. Endogenously chosen boards of directors and their monitoring of the CEO. American Economic Review 88, 96-118. Hermalin, B. E. and M. S. Weisbach, 2003. Board of directors as an endogenously determined institution. Economic Policy Review 9, 1-20.

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John, K. and D. Kadyrzhanova, 2010. Spillover effects in the market for corporate control. Working paper, University of Maryland.

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Jensen, M., 1993. The modern industrial revolution, exit and the failure of internal control systems. Journal of Finance 48, 831-880. Kale, J.R., E. Reis and A. Venkateswaran, 2009. Rank-order tournaments and incentive alignment: The effect on firm performance. Journal of Finance 64, 1479-1512. Kothari, S., T. Laguerre and A. Leone, 2002. Capitalization versus Expensing: Evidence on the Uncertainty of Future Earnings from Capital Expenditures versus R&D Outlays. Review of Accounting Studies 7, 355-382. Larcker, D.F. and T.O. Rusticus, 2010. On the use of instrumental variables in accounting research. Journal of Accounting and Economics 49,186-205. Lehn, K., S. Patro and M. Zhao, 2009. Determinants of the size and structure of corporate boards: 1935-2000. Financial Management 38, 747-780. Linck, J., J. Netter and T. Yang, 2008. The determinants of board structure. Journal of Financial Economics 87, 308-328. Meulbroek, L.K, 2002. A senior manager’s guide to integrated risk management. Journal of Applied Corporate Finance 14, 56-70. 27

ACCEPTED MANUSCRIPT Raheja, C., 2005. Determinants of board size and composition: a theory of corporate boards. Journal of Financial and Quantitative Analysis 40, 283-306.

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Staiger, D. and J.H. Stock., 1997. Instrumental variables regression with weak instruments. Econometrica 65, 557-86.

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Steiner, I. D., 1972. Group process and productivity. New York: Academic Press.

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Watson, W.E. and Kumar, K., 1992. Differences in decision making regarding risk taking: A comparison of culturally diverse and culturally homogeneous task groups. International Journal of Intercultural Relations 16, 53-65.

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Yermack, D., 1996. Higher market valuation of companies with a small board of directors. Journal of Financial Economics 40, 185-212.

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ACCEPTED MANUSCRIPT Table 1: Summary Statistics

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The sample consists of ExecuComp firms from 1996-2005. Board size is the number of directors. The Insider Ratio is the ratio of inside directors to the board size. Industry Median Insider Ratio is the median ratio of insiders in all the firms belonging to the same 2-digit SIC group that the sample firm belongs to. Insider Ownership is the ratio of stocks held by the directors and officers of a firm. CEO Dual takes a value of one if a firm’s CEO also holds board chair position, and zero otherwise. CEO Tenure is the tenure of current CEO. CEO Total Pay is the natural logarithm of the sum of CEO’s salary, bonus, restricted stock grants, value of options granted and all other compensations. CEO Equity Incentive is the ratio of equity linked component of CEO’s compensation to total pay. CEO Vega is the dollar change in the CEO’s total pay for a 1% change in a firm’s annualized risk. CEO Delta is the dollar change in the CEO’s total compensation for a 1% change in the stock price. Non-CEO Vega and Non-CEO Delta variables are the mean Vega and mean Delta of the top four non-CEO executives. Firm Age is the natural log of the number of years a firm has been on CRSP. Firm Size is the book value of a firm’s assets in millions. Diversification is number of operating segments of a firm. Tobin’s Q is the ratio of (market value of equity + book value of debt) to book value of assets. ROA is operating income scaled by book value of assets. Leverage is the ratio of long-term debt to assets. R&D Intensity is the ratio of R&D investment to sales. Capital Investment is the ratio of capital expenditures to sales. Intangibles Investment is the ratio of intangible assets to assets. Return Volatility is the standard deviation of monthly stock returns for the last five years. Daily Return Volatility is the standard deviation of daily market return for the prior one year. ROA Volatility is the standard deviation of the quarterly ROA for the last twenty quarters. Cash Flow Volatility is the standard deviation of the quarterly cash flows from operations scaled by book value of assets for the last twenty quarters. Sales Volatility is the standard deviation of the quarterly sales scaled by book value of assets for the last twenty quarters. All the variables are winsorized at 1% and 99%. Variables Q1 Mean Median Q3 Std. Deviation Board Size 7.000 9.096 9.000 11.000 2.492 Insider Ratio 0.125 0.214 0.182 0.286 0.115 Industry Median Insider Ratio 0.143 0.188 0.182 0.214 0.051 Insider Ownership 0.008 0.090 0.035 0.105 0.145 CEO Dual 1.000 1.000 1.000 0.000 0.466 CEO Tenure 2.000 7.587 5.000 10.000 7.366 CEO Total Pay 7.096 7.853 7.824 8.561 1.202 CEO Equity Incentive 0.316 0.512 0.561 0.736 0.281 CEO Vega ($ 000) 32.499 1112.560 139.060 608.522 6061.210 CEO Delta ($ 000) 8.790 522.018 41.783 167.712 4812.790 Non-CEO Vega ($ 000) 122.907 1703.64 367.294 1130.350 6867.270 Non-CEO Delta ($ 000) 98.025 4163.660 336.586 1189.23 51027.800 Firm Age 2.197 2.808 2.833 3.466 0.888 Firm Size ($ mill.) 501.930 5104.080 1203.949 3539.089 22014.300 Diversification 1.000 2.442 2.000 3.000 1.710 Tobin’s Q 1.282 2.105 1.683 2.437 1.923 ROA 0.093 0.145 0.141 0.195 0.081 Leverage 0.039 0.196 0.175 0.292 0.168 R&D Intensity 0.000 0.046 0.005 0.044 0.059 Capital Investment 0.026 0.060 0.045 0.077 0.055 Intangibles Investment 0.597 0.707 0.765 0.863 0.210 Return Volatility 0.296 0.436 0.385 0.535 0.192 Daily Return Volatility 0.019 0.027 0.025 0.033 0.012 ROA Volatility 0.017 0.056 0.033 0.065 0.069 Cash Flow Volatility 0.025 0.049 0.040 0.062 0.035 Sales Volatility 0.067 0.151 0.112 0.191 0.126

29

ACCEPTED MANUSCRIPT

Table 2: Univariate Analysis of Board Structure and Firm Risk Panel A: Correlation Matrix CEO Vega

Equity Incentive

Tobin’s Q

ROA

1.000 0.554 0.048 -0.021 0.043 -0.371 -0.346 -0.150 0.446 0.051 0.027 0.219

1.000 0.011 0.012 0.094 -0.429 -0.230 -0.026 0.404 -0.032 -0.082 0.089

1.000 0.178 0.170 0.081 0.179 -0.035 0.039 -0.038 0.043 0.047

1.000 0.170 0.130 0.342 -0.012 0.062 -0.193 -0.119 0.133

1.000 0.306 -0.045 -0.251 0.282 0.008 0.045 -0.047

1.000 0.110 -0.240 -0.351 0.014 0.052 -0.026

Panel B: Difference-Of-Means Test

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Firm Size

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ROA Volatility

Leverage

R&D

Insider Ownership

CEO Tenure

DSOX

1.000 -0.155 0.003 -0.072 -0.097

1.000 -0.079 -0.011 0.059

1.000 0.211 -0.021

1.000 -0.002

1.000

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Return Volatility

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Insider Ratio Return Volatility ROA Volatility CEO Vega Equity Incentive Tobin’s Q ROA Firm Size Leverage R&D Intensity Insider Ownership CEO Tenure DSOX

Insider Ratio 1.000 0.064 -0.034 -0.012 -0.213 0.056 0.029 -0.226 -0.089 -0.016 0.268 0.251 -0.182

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This table presents pair wise correlation coefficients between selected variables.

1.000 0.253 -0.168 -0.164 -0.089 0.075

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This table presents results from a difference-of-means test on various selected firm characteristics between sub-samples of firms that are in the 1st and 4th quartile of the insider ratio. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

Return Volatility ROA Volatility Leverage Firm Size Tobin’s Q ROA R&D Intensity Intangibles Investments Capital Investments Firm Age CEO Total Pay CEO Vega CEO Equity Incentive

Fewer Insiders

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More Insiders 0.457 0.053 0.174 6.764 2.209 0.113 0.021 0.695 0.067 2.591 7.528 1125.581 0.302

0.407 0.053 0.213 7.615 1.921 0.102 0.056 0.713 0.055 3.031 8.121 1095.092 0.405

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Diff. 0.050*** -0.000 -0.039*** -0.851*** 0. 287*** 0.011*** -0.035*** -0.018*** 0.012*** -0.560*** -0.593*** 30.489 -0.103***

t-Stats 9.657 0.601 6.72 25.042 8.512 3.825 6.176 3.062 10.021 14.057 21.199 0.205 12.328

ACCEPTED MANUSCRIPT Table 3: Board Structure and Firm Risk

Return Volatility

0.571*** (8.560)

ROA Leverage

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Insider Ownership Firm Age

Capital Investments CEO-Dual DSOX

(-5.310) -0.104*** (-11.186) -0.024*** (-14.639) -0.586*** (-35.606) -0.035*** (-2.920) 0.772*** (18.832) 0.001*** (4.410) -0.045*** (-21.660) 0.056 (1.395) -0.011*** (-3.046) 0.023*** (4.093) -0.548*** (-5.310) Yes Industry 7318 0.5407

(-1.137) -0.075*** (-10.053) -0.020*** (-13.881) -0.617*** (-40.129) -0.005 (-0.474) 0.843*** (21.800) 0.000 (0.291) -0.043*** (-20.999) 0.018 (0.452) -0.003 (-1.046) 0.048*** (14.634) -0.027* (-1.137) Yes Industry 7318 0.5391

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R&D Intensity

Intercept Fixed Effects No. of Observations Adj. R-Sqd.

2SLS 2nd Stage

OLS

Fixed Effects

ROA Volatility

-0.039***

-0.091**

-0.025***

-0.015**

(-2.648) -0.046*** (-5.946) -0.011*** (-3.507) -0.427*** (-36.050) -0.009 (-0.761) -0.283*** (-4.763) 0.001*** (4.982) 0.027*** (6.107) -0.294*** (-8.298) -0.003 (-1.199) 0.001 (0.245) -0.039*** (-2.648) Yes Firm 7318 0.1378

(-2.046) -0.002 (-0.620) -0.004*** (-5.464) -0.151*** (-22.737) 0.017*** (3.680) 0.405*** (24.515) -0.000*** (-3.831) -0.009*** (-10.833) 0.016 (0.995) -0.002 (-1.630) 0.017*** (7.579) -0.091** (-2.046) Yes Industry 7318 0.2973

(-4.083) -0.008*** (-2.740) -0.004*** (-7.368) -0.150*** (-23.486) 0.009** (1.982) 0.386*** (24.033) -0.000*** (-3.087) -0.008*** (-9.253) 0.010 (0.621) -0.004*** (-2.868) 0.012*** (8.536) -0.025*** (-4.083) Yes Industry 7318 0.3110

(-2.403) 0.002 (0.698) -0.014*** (-10.049) -0.077*** (-15.106) 0.021*** (4.278) 0.206*** (7.935) -0.000 (-1.367) -0.000 (-0.030) -0.065*** (-4.337) -0.005*** (-4.343) 0.008*** (7.277) -0.015** (-2.403) Yes Firm 7318 0.2084

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Diversification

-0.027*

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Firm Size

-0.058*** (-10.978) -0.008*** (-7.233) 0.034*** (3.027) -0.058*** (-8.017) -0.145*** (-5.160) 0.002*** (21.564) -0.005*** (-3.229) 0.067** (2.418) -0.014*** (-5.933) -0.020*** (-6.494) -0.058*** (-10.978) Yes Industry 7318 0.2290

-0.548***

D

Predicted Insider Ratio/ Insider Ratio Board Size

Fixed Effects

OLS

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Industry Median Insider Ratio

2SLS 2nd Stage

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Dependent Variable:

2SLS 1st Stage Insider Ratio

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This table presents results from 2SLS, OLS and fixed-effects regressions. In OLS, t-statistics are computed based on Huber-White-sandwich estimator of variance clustering on firm-level indicators. All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

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ACCEPTED MANUSCRIPT Table 4: Board Structure and Conservative CEO Compensation: Equity Incentive and Total Pay

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This table presents results from fixed effects regressions. The dependent variables are CEO Equity Incentive and Total Pay. In OLS, t-statistics are computed based on Huber-White-sandwich estimator of variance clustering on firm-level indicators. All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. Dependent Variable CEO Equity Incentive

Diversification ROA Equity Return Leverage

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Insider Ownership CEO Tenure

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DSOX Intercept Fixed Effects No. of Observations Adj. R-Sqd.

32

CEO Total Pay

2SLS

OLS

Fixed Effects

-2.262*** (-3.921) -0.065 (-0.885) 0.419*** (19.698) -0.032*** (-3.413) 0.862*** (6.297) 0.000*** (3.537) -0.242** (-2.423) 1.141*** (6.293) 0.003** (2.165) -0.004* (-1.787) -0.023 (-0.722) Yes Industry 7318 0.4452

-0.874*** (-5.864) -0.163** (-2.248) 0.490*** (28.281) -0.014 (-1.568) 0.466*** (3.534) 0.000*** (3.842) -0.368*** (-3.689) 1.186*** (6.557) -0.004*** (-3.248) -0.002 (-0.747) 0.074*** (3.688) Yes Industry 7318 0.4441

-0.280*** (-2.665) -0.306*** (-4.653) 0.510*** (19.922) 0.024*** (2.910) 0.924*** (7.307) 0.000*** (2.634) -0.811*** (-8.165) -0.828*** (-2.791) 0.004*** (3.261) -0.004** (-2.538) 0.028 (1.623) Yes Firm 7318 0.3609

RI

-0.070* (-1.863) -0.047*** (-2.580) 0.098*** (12.218) -0.000 (-0.083) 0.315*** (9.659) 0.000*** (7.193) -0.262*** (-8.068) -0.226 (-1.350) -0.001*** (-2.774) -0.003*** (-5.148) -0.026*** (-4.181) Yes Firm 7318 0.0507

D

R&D Intensity

-0.283*** (-6.968) -0.027 (-1.470) 0.067*** (16.598) -0.003 (-1.299) 0.235*** (6.640) 0.001*** (9.467) -0.111*** (-3.804) 0.374*** (4.026) -0.002*** (-5.101) -0.002*** (-3.312) -0.016** (-2.539) Yes Industry 7318 0.0395

SC

Firm Size

-0.569*** (-2.818) -0.095*** (-4.565) 0.054*** (11.017) -0.009*** (-3.071) 0.362*** (8.639) 0.000*** (6.907) -0.121*** (-3.615) 0.429*** (4.199) -0.000 (-0.275) -0.003*** (-4.565) -0.037*** (-3.561) Yes Industry 7318 0.0395

NU

Board Size

OLS

MA

Predicted Insider Ratio/ Insider Ratio

2SLS

Fixed Effects

ACCEPTED MANUSCRIPT Table 5: Board Structure and Conservative CEO Compensation: Vega

PT

This table presents results from fixed effects regressions. The dependent variables are CEO Vega and Delta. In OLS, t-statistics are computed based on Huber-White-sandwich estimator of variance clustering on firm-level indicators. All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. Dependent Variable: CEO Vega 2SLS OLS Fixed Effects -3.203*** -0.521** -2.112* (-2.720) (-2.404) (-1.665) -0.600*** -0.460*** -0.435** (-4.407) (-4.152) (-2.059) 0.011 0.036 0.092 (0.349) (1.482) (1.101) -0.046** -0.027 -0.023 (-2.084) (-1.469) (-0.855) 0.991*** 0.858*** 0.972** (2.899) (2.999) (2.355) 0.002*** 0.001*** 0.001*** (2.696) (2.603) (3.079) -0.041 0.044 0.193 (-0.209) (0.281) (0.602) 0.470 0.592* 0.149 (1.137) (1.769) (0.157) 0.003 -0.000 0.007* (1.082) (-0.028) (1.900) 0.011** 0.011*** 0.020*** (2.483) (2.942) (3.503) 0.805*** 0.802*** 0.694*** (9.960) (10.757) (22.336) 0.349*** 0.341*** 0.347*** (15.660) (16.535) (41.672) -0.001 -0.001 -0.007 (-0.127) (-0.143) (-1.595) -0.041 0.096** -0.067 (-1.171) (2.144) (1.152) Yes Yes Yes Industry Industry Firm 7318 7318 7318 0.3652 0.3544 0.3540

RI

Predicted Insider Ratio/ Insider Ratio

SC

Board Size Firm Size

NU

Diversification ROA

MA

Equity Return Leverage R&D Intensity

TE

CEO Tenure

Non-CEO Delta DSOX

AC CE P

CEO Delta Non-CEO Vega

D

Insider Ownership

Intercept Fixed Effects No. of Observations Adj. R-Sqd.

33

ACCEPTED MANUSCRIPT Table 6: Board Structure and Investments: Intangibles and Capital Investments

PT

This table presents results from 2SLS, OLS and fixed effects regressions. The dependent variables are Intangibles and Capital Investment. In OLS, t-statistics are computed based on Huber-White-sandwich estimator of variance clustering on firm-level indicators. All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. Dependent Variable Intangibles Investment

ROA Leverage R&D Intensity Insider Ownership

DSOX

Intercept Fixed Effects No. of Observations Adj. R-Sqd.

OLS

Fixed Effects

0.115*** (3.902) 0.019** (2.549) 0.007*** (4.362) -0.003*** (-2.683) -0.001 (-0.051) 0.075*** (5.260) 0.229*** (7.654) -0.000*** (-3.989) -0.003 (-1.273) -0.008*** (-5.331) Yes Industry 7318 0.4808

0.031** (2.433) -0.000 (-0.023) 0.004*** (2.697) -0.003*** (-3.263) 0.012 (0.909) 0.060*** (4.675) 0.201*** (7.098) 0.000 (0.813) -0.005** (-2.201) -0.013*** (-15.432) Yes Industry 7318 0.4602

0.032*** (3.956) 0.004 (1.003) -0.001 (-0.797) -0.000 (-0.751) -0.006 (-0.673) -0.011 (-1.627) 0.080*** (3.781) 0.000 (0.388) -0.026*** (-11.802) -0.011*** (-13.721) Yes Firm 7318 0.0341

RI 2SLS

SC

-0.024*** (-2.895) -0.024*** (-5.435) 0.037*** (22.747) 0.002*** (2.954) -0.079*** (-6.314) -0.029*** (-4.240) 0.003 (0.133) -0.000** (-2.040) 0.005** (2.353) 0.024*** (15.672) Yes Firm 7318 0.1618

AC CE P

Firm Age

-0.022 (-0.744) -0.032*** (-2.717) 0.004 (1.348) -0.001 (-0.536) -0.235*** (-6.770) -0.075*** (-4.039) 0.368*** (10.635) -0.000 (-0.174) -0.004 (-1.152) 0.035*** (9.576) Yes Industry 7318 0.6835

NU

Diversification

-0.236* (-1.663) -0.061*** (-4.896) -0.002 (-0.723) 0.000 (0.229) -0.221*** (-5.628) -0.072*** (-3.666) 0.431*** (11.950) 0.001*** (3.552) -0.007* (-1.816) 0.029*** (5.954) Yes Industry 7318 0.6840

MA

Firm Size

OLS

D

Board Size

2SLS

TE

Predicted Insider Ratio/ Insider Ratio

Capital Investment

Fixed Effects

34

ACCEPTED MANUSCRIPT Table 7: Board Structure, Volatility and Firm Performance: Tobin’s Q and ROA

PT

This table presents results from 2SLS, OLS and fixed effects regressions. The dependent variables are Tobin’s Q and ROA. In OLS, t-statistics are computed based on Huber-White-sandwich estimator of variance clustering on firm-level indicators. All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

Dependent Variable Tobin’s Q

Board Size

Diversification

R&D Intensity

AC CE P

Capital Investment Insider Ownership

Return Volatility (β3)

Intercept Fixed Effects F-Test (β1+β2+ β3) (p-value) No. of Observations Adj. R-Sqd.

OLS

Fixed Effects

-0.471 (-1.458)

0.571*** (5.602)

0.091*** (2.823)

-0.017 (-0.951)

1.268** (1.973) -0.287*** (-3.398) -0.469*** (-14.196) 0.040*** (3.648) 1.610*** (11.110) -1.266*** (-9.880) 0.080 (0.122) 0.550 (1.394) 0.006*** (5.178) -0.121*** (-2.709) -1.426*** (-7.521) -0.025 (-0.933) Yes Firm -0.629 (0.107) 7318 0.1131

0.113*** (2.855) 0.001 (0.187) 0.007*** (3.978) -0.002*** (-2.730)

0.076** (2.353) 0.000 (0.007) -0.001 (-0.682) -0.004*** (-5.011)

0.034** (1.974) 0.005 (1.030) -0.018*** (-10.474) -0.001** (-2.063)

-0.158*** (-10.772) -0.396*** (-6.110) -0.051** (-2.579) -0.001*** (-4.547) -0.001 (-0.585) -0.103*** (-4.680) 0.010** (2.233) Yes Industry 0.581*** (0.000) 7318 0.2745

-0.093*** (-6.668) -0.111** (-2.215) -0.005 (-0.348) -0.000 (-0.988) -0.007*** (-3.565) -0.092*** (-12.215) 0.001 (0.754) Yes Industry 0.159** (0.022) 7318 0.2517

-0.116*** (-16.870) -0.155*** (-4.340) -0.054*** (-7.344) -0.000 (-0.153) -0.014*** (-5.860) -0.053*** (-11.790) 0.001 (0.692) Yes Firm -0.036 (-0.106) 7318 0.0746

RI

1.724*** (3.042) -0.321** (-2.492) 0.144*** (5.294) -0.064*** (-4.322) 4.493*** (12.476) -1.280*** (-4.584) 8.493*** (8.498) 0.671 (1.006) 0.006*** (3.374) -0.079** (-2.187) -0.313 (-1.275) -0.259*** (-9.960) Yes Industry 0.898** (0.036) 7318 0.3372

TE

Leverage

DSOX

3.100*** (3.896) -0.244* (-1.784) 0.143*** (4.760) -0.055*** (-3.881) 4.756*** (12.346) -1.390*** (-5.290) 4.177*** (8.176) 1.035 (1.601) 0.003 (1.491) -0.060 (-1.613) -0.533* (-1.806) -0.217*** (-5.001) Yes Industry 2.512*** (0.000) 7318 0.3434

D

ROA

Firm Age

-0.513** (-2.198)

MA

Firm Size

-0.055 (-0.649)

35

ROA

2SLS

SC

Predicted Insider Ratio/ Insider Ratio*Return Volatility(β2)

OLS

NU

Predicted Insider Ratio/ Insider Ratio (β1)

2SLS

Fixed Effects

ACCEPTED MANUSCRIPT Table 8: Board Structure, Operating Uncertainty and Firm Performance This table presents results from fixed effects regressions. The dependent variable is Tobin’s Q. All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

Insider Ratio*Cash Flow Volatility(β1)

Dependent Variable: Tobin’s Q -0.207 -0.081 (-1.382) (-0.265)

PT

-0.489** (-2.595) 9.512*** (3.895)

4.073** (2.406)

SC

Insider Ratio*ROA Volatility(β2) Insider Ratio*Sales Volatility(β3)

NU

Insider Ratio*Daily return Volatility(β4) Board Size

Diversification ROA

AC CE P

Capital Investment

TE

D

Leverage

Insider Ownership Firm Age

-0.380*** (-2.973) -0.593*** (-12.120) 0.020 (1.225) 4.227*** (16.153) -0.542*** (-2.701) -1.494** (-2.399) 0.911 (1.581) 0.004* (1.897) -0.307*** (-4.373) -3.039** (-2.097)

MA

Firm Size

R&D Intensity

Cash Flow Volatility(γ1) ROA Volatility(γ2)

-0.445*** (-3.174) -0.655*** (-12.028) 0.035* (1.925) 4.184*** (14.241) -0.563** (-2.517) -1.723** (-2.572) 0.737 (1.160) 0.004* (1.780) -0.292*** (-3.762)

1.742* (1.795)

-0.521*** (-4.063) -0.544*** (-11.202) 0.035** (2.165) 3.531*** (17.899) -0.752*** (-3.790) 0.010 (0.160) 0.091 (0.173) 0.619*** (3.377) -0.266*** (-3.766)

0.105 (0.438)

Daily Return Volatility(γ4)

Intercept Fixed Effects F-Test (α+βi+γi) (p-value) No. of Observations Adj. R-Sqd.

0.878** (2.069) -0.508*** (-3.605) -0.544*** (-10.066) 0.045** (2.422) 3.755*** (15.700) -0.958*** (-4.377) -2.453*** (-3.795) -0.338 (-0.524) 0.006*** (2.761) -0.215*** (-2.849)

-2.172** (-2.482)

Sales Volatility(γ3)

DSOX

3.206** (2.058)

RI

Insider Ratio (α)

-0.159*** (-7.241) Yes Firm 5.984*** (0.000) 7318 0.1037

36

-0.147*** (6.672) Yes Firm 1.794* (0.081) 7318 0.1025

-0.123*** (5.383) Yes Firm 1.766** (0.038) 7318 0.1024

8.019** (2.348) -0.102*** (4.262) Yes Firm 12.103*** (0.000) 7318 0.1084

ACCEPTED MANUSCRIPT Table 9: Board Structure, Operating Uncertainty and Firm Performance: 3SLS Estimation This table presents results from 3SLS estimation. The dependent variables are Insider Ratio, Return Volatility and Tobin’s Q All the regressions include intercept. t-statistics are reported in parentheses below each coefficient estimate. *, **, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

PT

Dependent Variable Insider Ratio

Return Volatility

0.072*** (21.223)

(20.903)

-0.382*** (-91.579)

NU

Insider Ratio

SC

Return Volatility

0.651 (1.633)

-2.516***

13.070***

(-97.669)

(6.314) 7.083*** (3.845)

Board Size

-0.152***

-0.392***

0.805***

(-19.975)

(-19.831)

(8.372)

Firm Size

MA

Insider Ratio * Return Volatility

Tobin’s Q

0.187***

RI

Tobin’s Q

-0.024***

-0.061***

0.197***

(-15.546)

(-15.445)

(13.319)

-0.002**

-0.006**

-0.028***

(-2.343)

(-2.254)

(-2.903)

-0.858***

-2.240***

8.026***

(-33.783)

(-35.052)

(41.008)

0.019

0.051

-0.817***

(1.554)

(1.637)

(-7.418)

-0.226***

-0.554***

7.189***

(-4.719)

(-4.411)

(20.381)

TE

D

Diversification

Leverage R&D Intensity

AC CE P

ROA

Capital Investment

-0.557* (-1.832)

Insider Ownership

0.001*** (11.163)

(10.500)

(-3.679)

Firm Age

-0.043***

-0.113***

0.163***

(-20.073)

(-20.287)

(7.162)

-0.020***

0.057***

-0.193***

(-5.927)

(6.372)

(-4.944)

Yes

Yes

Yes

Industry

Industry

Industry

7318

7318

7318

2873.77

1287.89

2019.46

DSOX Intercept Fixed Effects No. of Observations Chi-Sqd.

37

0.003***

-0.006***