The Expanding Role of the CFO: Do Outside Directorships Facilitate Strategic Learning?

Sarfraz Khan Department of Accounting University of Louisiana, Lafayette [email protected]

Elaine Mauldin Trulaske College of Business University of Missouri Columbia MO 65211 573-884-0933 [email protected]

February 2018

The manuscript is partially based on the first author’s dissertation at the University of Texas at San Antonio. We thank Sharad Asthana, James Groff, Stewart Miller, Emeka Nwaeze and John Wald for valuable suggestions. We also thank Sung-Jin Park, Juan Manuel Sanchez, Sarah Shonka and workshop participants at University of Arkansas, Florida Atlantic University, and University of Texas at San Antonio for helpful comments on ealier versions of the manuscript.

The Expanding Role of the CFO: Do Outside Directorships Facilitate Strategic Learning?

ABSTRACT We study the association between chief financial officer (CFO) outside board directorships and their home firm strategic financing and investment policies. Using a sample of firms from 2003-2014, we find only about nine percent of CFOs sit on outside boards. We find evidence of strategic learning because CFO outside directorships are associated with greater home firm financial flexibility, as reflected in faster adjustment toward target debt ratios, fewer underinvestment problems, and lower sensitivity between cash holdings and cash flows. We also distinguish CFO ouside director from CEO outside director effects and find that CFOs more likely transfer strategic learning to their home firm. Our findings support the argument that outside directorships provide CFOs an opportunity to network with and learn from other executives and directors, enabling these CFOs to improve strategic financing and investing practices in their home firm. Our findings suggest that CFO outside directorship is not merely an outside time commitment that detracts from the CFO’s primary responsibilities for their home firm.

Keywords: Chief financial officer; Board of directors; Financial policies; Knowledge transfer

Data Availability: Data are available from public sources.

1. Introduction

We examine the association between Chief Financial Officer (CFO) service on an outside board and their home firm’s strategic financing and investing policies. The Chief Executive Officer (CEO) and the board of directors increasingly expect the CFO to excel beyond budgeting and financial reporting by focusing on strategy and optimizing investments (KPMG 2017). Yet, it is far from clear how CFOs develop appropriate skills for these expanded roles. We suggest that outside board service provides one potential channel to alter the skill-set and perspectives of the CFO, benefiting home firm policies tied to strategic finance and investments. However, prior research finds outside directorships increase executives’ financial, status, and other individual perquisites and suggests that executives focus on personal benefits from outside directorships rather than on transferring knowledge to the home firm (Yermack 2004,

Geletkanycz and Boyd 2011, Boivie, Graffin, Oliver, and Withers 2016). Thus, the relationship between

CFO outside directorhips and home firm policies is an empirical issue.

We use CEOs as a baseline and compare CFO to CEO outside directorships. Theory suggests that individual directors carry information across firms, yet we know little about how functional experience affects such knowledge transfer (Shropshire 2010). Prior research finds CFOs respond differently to risk- taking incentives than CEOs suggesting that functional experience could also differentially affect knowledge transfer from outside directorships (Chava and Purnanandam 2010). CFOs may more likely transfer knowledge because, relative to CEOs, knowledge transfer could facilitate future promotion opportunities and their minority representation on an outside board (due to relative scarcity of active CFO board members) increases transfer of knowledge (Shropshire 2010). On the other hand, CEOs may more likely transfer knowledge because, relative to the CFO, their depth of knowledge provides a more nuanced understanding of information that facilitates knowledge transfer (Shropshire 2010). Thus, the relationship between home firm policies and CFO, compared to CEO, outside directorships is also an empirical question.

To test our hypotheses, we use a panel of US firms from 2003-2014 and three different models commonly used in finance related to strategy and optimizing investments, supplemented with our CFO

and CEO outside director variables of interest as well as other home firm board characteristics from prior research. In our main analyses, we find that CFO, but not CEO, outside directorships are associated with strategic capital structure decisions as evidenced by a greater adjustment speed toward home firms’ target market debt ratio (Flannery and Rangan 2006). Second, we find that home firms with CFO outside directorships exhibit fewer underinvestment problems, typically attributed to knowledge transfer while home firms with CEO outside directorships exhibit more overinvestment problems, typically attributed to agency problems (Fazzari et al. 1988). Finally, we find that firms with CFO, but not CEO, outside directorships exhibit less sensitivity of cash holdings to cash flow from earnings, consistent with better management of financial constraints (Almeida et al. 2004). Together, our evidence is consistent with greater CFO outside director knowledge transfer than that from CEO outside directorships. To support our primary findings, we also explore cross-sectional differences in CFO outside directorships. We find more pronounced learning effects when the CFO outside directorship is in the same industry as the home firm or when the CFO has longer tenure on the outside board. These cross-sectional results are consistent with greater learning from related industry boards and from more years of service on an outside board.

Since outside boards likely ask CFOs with better financial policies to join their board due to reputational benefits, our analyses likely suffer from endogeneity. Though our comparison of CFOs to

CEOs somewhat mitigates these concerns because if the results are driven by unobserved industry, business, or selection concerns, there should be no difference between CEO and CFO outside board service. Nevertheless, we perform a number of robustness tests to further address endogeneity. First, we use a pre-post, change, analyses comparing effects when the CFO (or CEO) is or is not an outside director. One advantage of pre-post analyses is that the events of CFO (CEO) directorships are staggered over time, thereby differentiating the directorship events from other economic factors. As discussed in greater detail later in the paper, results for all three dependent variables are generally consistent with the primary analyses.

Next, we use propensity score matching (PSM) developed separately for each dependent variable

(and for CFO versus CEO). Our results for CFO outside directorship are consistent with the primary

2 analyses. However, the PSM results show some evidence of CEO transfer of knowledge to their home firm for adjustment speed to target debt ratio only. Finally, for the CFO outside director analyses of investment efficiency and cash flow sensitivity, we use an instrumental variable (IV) approach.1 For the instrumental variable, we use the annual demand for directors with accounting expertise in the four-digit

SIC code since higher demand for directors with accounting expertise should increase the likelihood of a

CFO obtaining an outside directorship, but external board hiring practices are unlikely to influence the home firm financial policies. We find results consistent with our main analyses for both CFO and CEO outside directorships. Overall, we conclude our results are robust to tests addressing endogeneity.

We provide several contributions to the literature. First, our results consistently find a positive relationship between CFO outside directorships and better strategic financing and investing decisions at the home firm. Thus, our results are consistent with knowledge transfer from CFO outside directorships providing a channel to alter the skill sets of the CFO. The expanding role of CFOs over the last decade expose current-generation CFOs to a more challenging environment, including the need to utilize scarce resources more efficiently (EY 2012; Dobbs et al. 2009). Prior research finds some CFOs are associated with more conservative financial policies, such as higher underinvestment (Hoitash et al. 2016). Our findings suggest that wider exposure to a variety of business practices through outside board experience may offset this conservative tendency. Understanding the sources of knowledge creation around strategic financing and investment policies helps practice identify outside directorships as a possible untapped source of strategic-oriented knowledge for CFOs.

Further, organizations increasingly seek CFOs to serve on the board as an outside director, most commonly on the audit committee (Spencer Stuart 2014, EY 2012). Yet, acting CFOs rarely sit on outside boards, often because of concerns about additional time commitments; almost 60 percent of large firms restrict their CFOs from serving on outside boards (Spencer Stuart 2014, Murphy and Chasan 2013). Prior research finds CFO outside board service does not diminish home firm financial reporting quality even

1 We do not use an additional IV regression for MDR since all MDR regressions are IV. 3 when the likely time commitment is high (Cunningham, Myers, and Short 2017). Combined with prior research, our results suggest organizations should be less hesitant to support CFO outside board service.

Second, we contribute to knowledge transfer theories of board interlocks. We examine one form of non-reciprocal board interlock, executives of one firm sitting on another firm’s board. We do not examine other interlocks to avoid confounding two sets of relationships (Geletkanycz and Boyd 2011). Our findings support theory that individual functional experience does indeed affect knowledge transfer

(Shropshire 2010). We extend outside directorship research beyond CEOs as subjects to explicitly compare CFO and CEO effects. Even though CEOs are the ultimate decision makers in the firm, CFOs provide input to decision making about strategic financing and investing policies that eventually affects the overall value of the firm. Our findings suggest that CFO outside directorships seem to provide more benefits to these home firm policies than do CEO outside directorships.

2. Background and Hypotheses Development

We examine the effects of CFO, versus CEO, outsider board service on home firm strategic financing and investing policies. Though the CEO and the board typically determine major strategic financing and investing decisions, the CFO influences these decisions by providing input, analyses and recommendations to influence major strategic financing and investing decisions (Hoistash et al. 2016,

Morellec et al. 2012, Chava and Purnanandam 2010). Indeed, home firms increasingly expect the CFO to focus on strategy and optimizing investments, in addition to their budgeting and financial reporting roles

(KPMG 2017). In a survey, Tufano and Servaes (2006) find that CFOs consider capital structure, cash management and investment efficiency as part of the CFO’s value to the firm. Prior research supports

CFOs’ influence on financial policies, though not always for the better. For example, CFOs with accounting expertise in high growth industries are associated with greater underinvestment consistent with accountant CFOs being more risk averse and influencing their firm’s investment decisions accordingly (Hoistash et al. 2016).

Outside director service provides a channel for executive learning that may transfer to home firm decisions because directors either initiate and articulate corporate strategy or advise and monitor

4 management’s strategic decisions (Hillman and Dalziel 2003, Pugliese et al. 2009). Based on their board experiences, directors often carry knowledge from one firm to another through director interlocks

(Shropshire 2010). Director interlocks take several forms including non-reciprocal where an executive or director of one firm sits on the board of another firm or reciprocal where two firms share the same director (Shropshire 2010). Prior research examines the different forms in a variety of contexts, revealing both positive and negative effects. For example, reciprocal interlocks increase innovation, but also increase the diffusion of aggressive corporate tax policies and the cessation of quarterly earnings guidance

(Helmers et al. 2017, Brown 2011, Cai et al. 2014). As noted above, we examine one form of non- reciprocal board interlock, CFOs or CEOs of one firm sitting on another firm’s board.

Given that interlocks provide both positive and negative information transfers, Shropshire (2010) theorizes that individual characteristics, such as functional expertise and depth of experience, may explain variations in diffusion of information because individual motives for learning and sharing varies. Brown and Drake (2011) begin to examine individual differences when they compare executive directors to non- executive directors. We extend prior research by separately examining CEOs and CFOs. We are interested in whether outside board service increases CFOs’ impact on home firms’ strategic financing and investing policies and whether the functional experience of the CFO results in different knowledge transfer effects from outside board service compared to the CEO.

Historically, sitting CEOs have been the most widely recruited executive for outside directorships

(Fich 2005). Prior research primarily examines the effects of CEO outside directorship on the board firm.

For example, board firm shareholders react positively to appointments of sitting CEOs, especially the first

CEO appointed. (Fich 2005, Fahlenbrach et al. 2010). Research also demonstrates mixed effects of CEO outside board service on board firm performance. Fich (2005) finds long-term board firm performance increases while Fahlenbrach et al. (2010) find little evidence of increases in board firm performance after controlling for endogeneity. Faleye (2011) suggests a potential reason for mixed effects, providing evidence that boards must balance advisory benefits of CEO outside directorships on acquisition returns

5 against distortions in executive compensation where the board firm CEO is paid more and their compensation is less sensitive to firm performance.

Only limited research examines the effects of CEO outside directorships on the home firm, but this research does demonstrate the basic tension between knowledge transfer and agency concerns. Fich

(2005) finds shareholders react negatively to CEO outside board appointments suggesting that home firm investors fear CEOs will spend less time on their primary responsibilities to the home firm, consistent with agency concerns that outside directorships constitute nothing more than managerial opportunism

(Conyon and Read 2006). On the other hand, research in knowledge transfer suggests CEO outside board service is beneficial for the home firm because CEOs can learn the consequences of new strategic alternatives and approaches without exposing their own firm to the direct costs of experimentation

(Geletkanycz and Boyd 2011). Consistent with knowledge transfer, Geletkanycz and Boyd (2011) find evidence of home firm performance increases when home firms face diminishing growth opportunities or in contexts of lower diversification.

Compared to CEOs, outside boards less frequently seek CFOs as outside board members (Spencer

Stuart 2014). However, interest in CFOs as outside directors has increased since the Sarbanes-Oxley Act of 2002 increased concerns for financial expertise on board audit committees (EY 2012, Spencer Stuart

2014). As part of the C-suite, CFOs are primarily responsible for accounting, internal control, risk management, asset preservation, and budgeting (Hoitash et al. 2016). Thus, when seeking financial expertise for the audit committee, outside boards naturally look to sitting CFOs for their accounting expertise and research examining CFO board service primarily focuses on the CFO’s accounting-related role. Prior research finds audit committee accounting expertise, including CFOs, is associated with higher quality internal controls and financial reporting for the outside board firm (e.g., Krishnan and

Visvanathan 2007; Krishnan 2005; Hoitash, Hoitash, and Bedard 2009). Financial reporting quality is also higher when CFOs serve on their home firm board and outside board service does not reduce home firm financial reporting quality (Bedard et al. 2014, Cunningham et al. 2017).

Regardless of the reason for CFO outside directorships, the CFO becomes a full board member, involved in regular board meetings with agendas on operations, risks, and strategies (Klien 2002, Bedard et al. 2004, Brickley and Zimmerman 2010). We suggest CFO outside directorship experience with the full board provides a channel for outside board service to impact home firm strategic finance and investment policies. Consistent with this conjecture, CFOs serving as inside directors on their home firm board increase the speed of adjustment towards the target market debt ratio and reduce the cash flow sensitivity of cash for constrained firms (Mobbs 2017).

2.1 CFO Outside Directorship and Home Firm Strategic Financing and Investing Policies

Competing arguments about whether or not CFO outside directorships improve home firm strategic financing and investing policies suggest that theory remains elusive. On the one hand, knowledge transfer-related theories emphasize learning, social connectedness, and reputation benefits that improve home firm financing and investing policies. Argote and Ingram (2000, 151) define knowledge transfer as

“the process through which one unit (e.g. individual, group, department, division or firm) is affected by the experience of another.” Knowledge transfer implies that each individual or group need not learn just from basic principles but rather can learn from the experience of others.

Knowledge transfer theories suggest home firms could benefit from CFO learning of complementary knowledge relevant to strategic financing and investing policies (Bacon and Brown 1974, Fama and

Jensen 1983). Organizations add new outside directors to the board in order to increase the depth or the diversity of knowledge and experience represented. These executives can benefit from the exposure of alternative points of view, which enhances their abilities to identify and develop high-quality solutions to decisions in their own firm (Burt 2000, Granovetter 1973).

In addition, social connectedness suggests organizations are embedded in a social network cohabited by many firms such that most organizational activities are guided by a network of interpersonal relations

(Granovetter 1985). Prior research finds connected firms draw upon one another to seek tangible and intangible resources; acquiring knowledge from each other in order to become more competitive (Pfeffer and Salancik 1978). For example, CEOs seek advice from outside contacts and this advice-seeking

7 behavior ultimately improves firm performance (McDonald and Westphal 2003, McDonald et al. 2008).

In addition, director ties can serve as a source for critical information, such as strategic shifts (Useem

1984) and decision process (Westphal et al. 2001).

Finally, reputation benefits also suggest improved home firm strategic financing and investing policies. Third parties, such as the home firm board, often lack detailed knowledge of management and rely on visible signals of knowledge and ability (DiMaggio and Powell 1983). An invitation to sit on an outside board provides an acknowledgement of the executive’s expertise that enhances the status and influence of the executive with their home firm (Geletkanycz and Boyd 2011).

In addition, CFOs can foster life-long relationships with individuals on the board, which can be a source of learning through counseling (e.g. McDonald and Westphal 2003). Ellen Richstone, who was a

CFO of a public company, while commenting on the benefits of serving on an audit committee stated that

“I have worked for some amazing audit committee chairs who were there for me when I had questions and had to think through things as a public-company CFO” (McCann 2012). Audit committees frequently schedule meetings with other committees, such as compensation, risk, finance, etc., to handle other issues, primarily issues related to risk (EY 2013b). Thus, the learning benefits of serving on an audit committee should go beyond financial reporting. By sitting on outside boards, CFOs can gain greater skills and expertise from other directors (Fama and Jensen 1983).

In summary, knowledge transfer-related theories suggest that CFOs can gain experience and problem solving knowledge, as well as increased status, from being on the board of a firm that they can then use to improve strategic financing and investing policies in their home firm. For example, CFO experience on an outside board exposes the CFO to new ideas that could reduce the risk aversion of the CFO in their home firm, such as that found by Hoitash et al. (2016).

On the other hand, agency theory suggests the CFO may use outside board experience for their own benefit rather than for their home firm’s benefit or increased influence may not be used to improve strategic financing and investing policies, consistent with firm concerns that CFO outside board memberships cost the home firm without providing benefits. Agency theory perspectives suggest that

8 managers’ personal goals and objectives routinely diverge from those of shareholders (Jensen and

Meckling 1976) and that managers would be more likely to join boards of other firms for personal benefits – such as perks and compensation, increased prestige, and entrenchment at the home firm – than to gain knowledge. Indeed, prior research finds CEO and non-CEO executives, including CFOs, benefit from outside directorships through increased promotions and compensation (Boivie et al. 2016).

The agency argument also suggests that when outside directorships increase the executive’s influence in the home firm, it may actually be to the home firm’s detriment (Bebchuk and Fried 2003). Fich and

White (2003) find evidence that CEOs enjoy higher compensation and decreased turnover when they sit on interlocked boards, while Loderer and Peyer (2002) and Rosenstein and Wyatt (1994) find outside directorship to be wealth-reducing to the home firm. Finally, board memberships are time consuming and result in high opportunity costs for executives (Perry and Peyer 2005, Lipton and Lorsch 1992, Lorsch and Maciver 1989, Neff 1998).

In summary, arguments exist both for and against CFO outside board service improving home firm strategic financing and investing policies. Accordingly, we state our first hypothesis in the null:

HYPOTHESIS 1. Ceteris paribus, there is no difference in home firm strategic financing and investing policies when the firm’s CFO sits on an outside board, compared to when the firm’s CFO does not sit on an outside board.

2.2 CFO versus CEO Outside Directorships

Shropshire (2010) suggests the following individual characteristics increase motivation for learning and sharing: identification with the firm, minority director experience, and depth of home firm experience.

While both the CFO and the CEO likely identify with their home firm, the CFO likely has increased motivation to share to facilitate future promotion opportunities. Next, Shropshire (2010) suggests a director in a minority categorization, whether gender, ethnicity, or functional experience, may increase transfer of knowledge to the home firm because the minority experience on the outside board equips the individual to utilize that experience to influence their home firm. Since CFOs sitting on outside boards is relatively rare, CFOs are likely in the minority of functional experience and, thus, more likely to transfer knowledge than CEOs sitting on outside boards. On the other hand, Shropshire (2010) also suggests that

9 depth of requisite knowledge also increases knowledge transfer. Thus, CEOs may have more depth of experience to relate the knowledge learned from outside board directorships to their home firm.

Prior research comparing the effect of the CFO with that of the CEO provides support for potential difference between CFO and CEO outside directorships. First, research finds differences in CEO and

CFO and financial reporting policies. For example, earnings management (absolute total accruals, discretionary accruals, beating analyst forecasts) and bad news hoarding leading to increased future stock price crash risk was found increasing more in CFO equity incentives than in CEO equity incentives (Jiang et al. 2010, Kim et al. 2011). On the other hand, results examining egregious forms of manipulation resulting in AAERs suggest CFOs are often pressured by CEOs (Feng et al. 2011). Firms also reward

CFOs with higher compensation when they manage expectations or earnings to meet earnings goals

(Balsam et al. 2012).

Managerial risk preferences also influence corporate decisions in significant ways over and above firm-specific facts and these risk preferences vary between CEOs and CFOs (Chava and Purnanandam

2010). CFOs with higher risk-taking compensation incentives (vega) are associated with riskier debt maturity structures while CEOs with higher vega hold less cash (Chava and Purnanandam 2010). Given that CEO outside director versus CFO outside director effects are not clear, we state our second hypothesis in the null:

HYPOTHESIS 2. Ceteris paribus, there is no difference in home firm strategic financing and investing policies for CFO outside directorships, compared to CEO outside directorships.

3. Research Design And Sample Selection

3.1 Strategic Financing and Investing Policies

We consider three measures of strategic finance and investment policies commonly used in the finance literature. We begin with one of the most basic strategic finance policy decisions of the firm, capital structure or how to finance the firm (Flannery and Rangan 2006). Theory suggests firms maximize shareholder value by strategically achieving an optimal level of debt versus equity, but that both market imperfections and agency costs inhibit firms’ ability to maintain, or to quickly adjust to changes in,

10 optimal levels of debt (Fischer et al. 1989, Flannery and Rangan 2006, Morellec et al. 2012). Prior research finds that the speed of adjustment to the target market debt ratio (MDR) increases in the presence of board independence, CEO duality, and when the CFO sits on the home firm’s board (Liao et al. 2015,

Mobbs 2017).

Next, we consider a more direct proxy for strategic investment decisions, the efficiency of investments (Inv Eff), defined as the firm investing in all and only projects with positive net present value

(Biddle et al. 2009). Due to moral hazard or adverse selection, firms deviate from the optimal level of investment and either under- or overinvest (Biddle et al. 2009, Jensen 1986, Stiglitz and Weiss 1981).

Since managers have incentive to grow beyond optimal size, both moral hazard and adverse selection suggest self-interest drives managers to overinvest (Biddle et al. 2009; Shleifer and Vishny 1989,

Aggarwal and Samwick 2006, Jensen 1986). Concerns about manager incentives, combined with information asymmetry, in turn lead to capital rationing by suppliers of capital resulting in underinvestment (Biddle et al. 2009). Stronger corporate governance leads to correction of both factors resulting in more efficient investment (Chen and Chen 2012). Therefore, studying over- and underinvestment allows us to better distinguish between knowledge transfer, expected to increase investment efficiency, and agency affects, expected to reduce investment efficiency.

If knowledge transfer-related effects drive the relationship, then we expect CEO or CFO outside directorship reduces underinvestment problems, through easing financial constraints. Prior research finds firms that are embedded in social networks are more likely to raise cheaper capital suggesting that better- connected CEOs or CFOs will be able to relieve financial constraints by raising funds from external sources (Uzzi 1999). In addition, knowledge transfer suggests outside directors will use internally generated cash more efficiently. On the other hand, if agency theory effects drive CEO or CFO outside directorship effects, we would expect increased overinvestment rather than reduced underinvestment because overinvestment problems driven by personal interests may not be curbed.

Finally, we complement our capital structure and investment efficiency measures with the cash flow sensitivity of cash (CFS), a theoretically justified measure of the importance of financial constraints to a

11 firm (Almeida et al. 2004; Denis and Sibilkov 2010). In addition, CFS increases when managers hoard cash and reduces when boards of directors constrain managers (Boubaker et al. 2015, Dittmar and Duchin

2016). Thus, better managed cash holding policies are less sensitive to cash flow from earnings.

3.2 Research Design

To test our hypotheses, we estimate the following general model (time and firm subscripts suppressed):

Financial Policy = β0 + β1 CFO Outside Director + β2 CEO Outside Director + β3 CFO Inside Director + β4 Home Board Fin Exp + β5 Size + 휆′ Other Control Variables + ε (1) where Financial Policy is home firm MDR, Inv Eff, or CFS. CFO (CEO) Outside Director is an indicator variable that equals 1 if the CFO (CEO) sits on an outside board. CFO Inside Director is an indicator variable that equals 1 if the CFO sits on the home firm board, Home Board Fin Exp is the percent of board members with finance expertise.2 We include the later variables to control for other potential home firm board characteristics associated with financing and investing policies (e.g., Bedard et al. 2014;

Mobbs 2017; Geletkanycz and Boyd 2011). In all models, we also include the log of total assets (Size) to control for the size of the home firm. Other Control Variables is a vector of variables specific to each of the three financial policy variables. All variables are defined in Appendix A.

For MDR, model 1 follows Flannery and Rangan (2006) and Mobbs (2017). We estimate the following model and examine how Outside Director influences the speed of the adjustment to the target market debt ratio:

푀퐷푅푖,푡+1 = (1 − 휆)푀퐷푅푖,푡 + (휆훽)푿푖,푡 + 훿푖,푡+1 (2)

Where MDR is a firm’s market debt ratio calculated as the following:

MDRi,t = Di,t/ Di,t+Si,tPi,t (3)

Where Di,t denotes the book value of firm i’s debt at time t, Si,t equals the number of common shares outstanding at time t, and Pi,t denotes the price per share. In model (2) above, 1 – λ denotes the speed of adjustment, and X is the vector of variables that affect the firm’s target debt ratio. Controls include

2 We do not include CEO inside board because almost all CEOs sit on the board (mean .95 and median 1)so there is little variation. 12 earnings, market to book, depreciation, fixed assets, R&D and industry leverage from Flannery and

Rangan (2006, 478). Following Flannery and Rangan (2006), we estimate model 2 with firm and year fixed effects and using an IV approach, where MDR is instrumented by book leverage.3

For Inv Eff, we follow the methodology used in Chen, Hope, Li and Wang (2011). We measure investment efficiency as the deviation from predicted investment that is a function of growth opportunities (Hubbard 1998). Specifically, we use the following model with all variables winsorized at the 1 and 99 percent levels:

퐼푛푣푒푠푡푖,푡 = 훼0 + 훼1푁퐸퐺푖,푡−1 + 훼2푅푒푣 퐺푟표푤푡ℎ푖,푡−1 + 훼3푁퐸퐺 ∗ 푅푒푣 퐺푟표푤푡ℎ푖,푡−1 + 휀푖,푡 (4)

All variables are defined following Chen et al. (2011). We estimate the regression in each two-digit SIC code and year with at least twenty observations. The residual from model 4 is a measure of suboptimal investment. We then classify firms into two groups; negative residuals to represent underinvestment

(UnderInv) and positive residuals to represent overinvestment (OverInv). For models of Inv Eff, we lag the control variables and include financial reporting quality, firm age, tangible assets, slack, Big 4 auditor, and industry fixed effects (Chen et al. 2011). Additionally, we include year dummies to control for any time varying influence on investment efficiency.

For CFS, we follow Almeida et al. (2004) and estimate the following:

∆ 퐶푎푠ℎ 퐻표푙푑𝑖푛𝑔푠푖,푡 = 훼0 + 훼1퐶푎푠ℎ 퐹푙표푤푖,푡 + 훼2푇표푏𝑖푛′푠 푄푖,푡 + 훼3퐴푠푠푒푡푠푖,푡 + 휀푗,푡 (5)

Where cash holdings are defined as cash and marketable securities scaled by assets. Theory suggests only financially constrained firms exhibit a positive and significant coefficient on Cash Flow. Tobin’s Q is included to control for growth opportunities since firms with more opportunity are more likely to hold cash to take advantage of such opportunities. Total assets is included to control for economies of scale in cash management. The model includes firm fixed effects. We first estimate model 5 alone and then within model 1, by adding Outside Director and our additional control variables.

3 We recognize that there is evidence that tax policy affects target debt ratios (see a summary in Shackelford and Shevlin 2001 for a review). We choose to replicate the Flannery and Rangan (2006) model which does include Depreciation as a tax-oriented control and firm fixed effects that may account for the tax policy effects. 13

3.3 Endogeneity

As with other board studies, the relationship between CFO (CEO) Outside Director and home firm financial policies likely suffers from endogeneity concerns introduced by the choice nature of the relationship as well as potential correlated omitted variables (Hermalin and Weistbach 2003). We note that comparison of CFO to CEO Outside Director in all our models should reduce concerns that our results are driven by firm-specific correlated omitted variables because, if so, we would not observe any difference between CFO and CEO outside directorships. Nonetheless, we use several different methods to further address potential endogeneity. For ease of exposition, we describe and report analyses focusing on

CFO outside directorships, but also complete each reported analysis for CEO outside directorships.

Though untabulated, we describe these results alongside the corresponding CFO outside directorship results.

First, we estimate a pre/post model after dropping all observations for firms where either the CFO never has an outside director position (10,493 for MDR, 8,702 for Inv Eff, and 9,412 for CFS) or where the CFO always has an outside director position (181 for MDR, 221 for Inv Eff, and 201 for CFS). Thus, we include firms that change from CFO Outside Director equals one to CFO Outside Director equals zero or vice versa. We further restrict our sample to only those firms that observe a change in the presence or absence of CFO Outside Director with at least two years of data for both presence and absence in the panel. We use a minimum of two years to avoid picking up other economic factors that may affect financing and investing policies and to allow time for CFO Outside Director to impact the home firm’s policies. As a result we drop another 1,167, 774, and 920 observations for MDR, Inv Eff, and CFS, respectively, where the CFO only sat on an outside board for one year. The result is that CFO Outside

Director equals one now represents firm years where the CFO sits on an outside board for two or more years and Outside Director equals zero represents firm years where the same CFOs do not sit on an outside board for two or more years (resulting sample size of 2,129 for MDR, 1,196 for Inv Eff, and 1,861 for CFS). Thus, we examine only firms with changes in CFO Outside Director, approximating a change model while keeping more observations. As in other change models, the event of CFOs accepting an

14 outside board position occurs at different times for different firms, allowing us to distinguish the director event from other economic factors.

Next, though we include a set of control variables from prior research in each of our models, multiple regression requires proper specification of the functional form of the model. Since our treatment group

(CFO Outside Director=1) is much smaller than our control group, we also use PSM to alleviate concerns about functional form misspecification (Shipman et al. 2017). We perform matching with replacement and we restrict our matches within the caliper of 0.01.4 Additionally, we use the “nearest-neighbor” matching technique to construct our control sample. The determinants model is detailed in Appendix B,

Panel A. Depending on the outcome variable, we estimate different models that include the board variables and control variables that are specifically related to each outcome variable (MDR, UnderInv,

OverInv, and CFS). Our results show that CFO Inside Board, CEO Outside Director and Size are positively associated with CFOs obtaining outside board seats in all models (p<.01).5

Appendix B, Panel B presents results of post-matching covariate balance tests. Covariate balance reported t-statistics reveal no significant difference in variables between the two subsamples. In addition, we report normalized differences for covariates that are all less than the cutoff point of 0.25, which suggests covariate balance (Jayaraman and Milbourn 2015; Imbens and Wooldridge 2009). We calculate normalized differences as the difference in means of two samples divided by the average of the group standard deviations (Imbens and Rubin 1997; Jayaraman and Milbourn 2015). Overall, these results suggest our matching procedure generates a sample that is similar to our treatment group on key observable characterstics. However, we include each of the variables in our multiple regression analysis to adjust for any remaining differences in covariates between groups (Shipman et al. 2017).

4 In unreported analyses, we also perform our PSM analyses without replacement and find similar results. 5 We note that in general home firm financial characteristics are associated with CFO outside directorship consistent with prior research findings that board firms more likely appoint outsiders from high performing firms (Fich 2005). 15

3.4 Sample Selection

Table 1 presents a summary of our sample derivation. We begin with 40,423 firm years (2003-2014) with

CEO and CFO data from Audit Analytics Morningstar database. We begin the sample in 2003 so that we are post-SOX and because this is when the Morningstar database becomes more widely populated. The

Morningstar database provides information on individual company directors and officers, including each individual’s executive and board roles, along with biographical data from the firm’s proxy statement. We parse the biographical data to create individual types of expertise which we then aggregate to the firm level (e.g., see Home Board Fin Exp as defined in Appendix A). Morningstar provides raw data on approximately 4,000 publicly traded companies each year, though the number changes on a year to year basis. However, Morningstar only provides data on firms that are publicly traded at the time of data extraction, resulting in survivorship bias. As a result, we build a database that backfills firms that were present in earlier years that are not present in each additional year. Our yearly data begins in 2009. From the resulting combined database, we extract firms with both CEO and CFO data and create our individual

CEO and CFO variables. Using the unique person identifier for each CEO (CFO) firm year, we search for other public companies where that person is on the board of directors in a given year. We create variables for the board industry and board tenure for each additional board and add it to the home firm observation to use in subsequent cross-sectional analyses.

We then combine our CFO / CEO sample with firm financial data from Compustat North American annual files and we exclude firm years missing compustat data, regulated utilities (SIC codes 4949 to

4999), and financial firms (SIC codes 6000 to 6999), resulting in 30,161 available firm years. We also drop observations missing variables for home firm board resulting in 27,160 full sample firm years.

Finally, we exclude observations with missing variables to calculate models for the three dependent variables, resulting in a final sample of 13,776 for MDR, 10,893 for Inv Eff, and 12,397 for CFS.

4. Results

4.1 Descriptive Statistics

Table 2, Panel A provides summary statistics of board characteristics and size for the full sample of firm years. About 9 (10) percent of firm-years have CFOs with outside (inside) directorships and about 24 percent of firm-years have CEOs with outside directorships. Home firm boards have about 9 percent directors with finance expertise. Table 2, Panel A also provides summary statistics for each financial policy model.

Table 2 Panel B and Panel C provides summary statistics by the presence or absence of CFO Outside

Director and CEO Outside Director respectively, including t-tests for differences in continuous variables and chi-squared tests for differences in discrete variables. As expected, we report significant differences in home firm board characteristics. Table 2, Panel D provides the time trend in CFO Outside Director and

CEO Outside Director. The results show an increasing trend in CFO outside directorships in years 2004-

2007 that plateaus over the next several years. The significant jump in the years of 2005 and 2006 is consistent with an increased demand for financial expert directors around the time period leading up to subprime mortgage meltdown (Krantz 2008). Additionally, there is a consistent increase in the CEO outside directorship over the sample period albeit not as substantial as increase in CFO Outside

Directorship. Finally, Table 3 provides Pearson correlations among variables of interest. CFO Outside

Director is positively correlated with MDR and Cash Flow, but not with overall Inv Eff.

4.2 MDR

Table 4 results for the estimation of MDR. As noted above, we use an IV approach where current market leverage is instrumented with book leverage and includes year and firm fixed effects.6 However, in the last two columns, using the change and PSM samples, we substitute industry fixed effects for firm fixed effects (untabulated results remain similar when including firm fixed effects with fewer observations). All

6 Flannery and Rangan (2006) use the IV approach to address the correlation between a panel’s lagged dependent variable and error term. In addition, they use firm fixed effects to capture the impact of intertemporally constant, but unmeasured, effects on each firm’s target leverage because they find that these unobserved effects explain a large proportion of the cross-sectional variation in target debt ratios. 17 results use robust standard errors clustered by firm. Column 1 reports results using the full sample with

MDR related controls. The coefficient on MDR finds an adjustment speed of 0.429 (1 – 0.571), suggesting that firms adjust approximately 43 percent towards their target market debt ratio in a year. In column 2, we introduce our variables of interest, CFO (CEO) Outside Director, and interactions between

MDR and both CFO and CEO outside director variables. These interaction reflects the change in adjustment speed related to the incidence of CFO and CEO outside directorship. We find a negative and significant (insignificant) coefficient on CFO (CEO) outside director, suggesting that CFO outside directorship, but not CEO outside directorship, significantly increases adjustment speed (adjustment speed equals 1 - MDR).

In Columns 3 and 4, we consider a levels model for the subsample of firms CFO Outside Director equals zero or one, respectively. For the sub-sample where CFO Outside Director equals one, the coefficient on market leverage is 0.388, which results in an adjustment speed of 0.612. Compared to the adjustment speed in the sub-sample where CFO Outside Director equals zero, firms where CFOs sit on outside boards adjust to their target market debt ratio around 18 percent (0.569 – 0.388) faster than firms where CFOs do not sit on outside boards. Similarly, in Columns 5 and 6, we consider a levels model for

CEO Outside Director equals zero or one, respectively. The coefficients on MDR are 0.533 and 0.541 in the two columns, reflecting a similar difference in the adjustment speed regardless of CEO outside directorship. Thus, results in columns 3 to 6 further substantiate results reported in Column 2.

For our further tests of endogeneity, we report additional results for CFO Outside Director (similar

CEO Outside Director results are not tabulated). In Column 7 we consider corresponding results from the pre/post, change, design. Even in a much smaller sample, the speed of adjustment continues to be higher for years in the presence of Outside Director by around 12 percent. Finally, in Column 8, we use the PSM sample. The results are consistent with that reported in the preceding columns, with the speed of adjustment about 7 percent higher in firms with CFO Outside Director present. Overall, the results across all specifications suggest that firms adjust to their target market debt ratio more quickly when their CFO sits on outside boards, consistent with knowledge transfer theories. The untabulated results for CEO

18 outside directorships are consistent with the primary results. We note that in both CFO and CEO PSM models, the coefficient on CEO Outside Director is similar to that of CFO Outside Director suggesting some knowledge transfer from CEO outside directorships.

4.3 Inv Eff

Table 5 presents results for Inv Eff, by UnderInv and OverInv. In Columns 1 and 2, we present results based on OLS. Consistent with knowledge transfer predictions, the coefficient on CFO Outside Director is negative and significant (p <0.05) for UnderInv and not significant for OverInv.7 Related to CEO

Outside Director, we find a significant association with OverInv (p <0.01) and this association is positive, but no association with UnderInv (p >0.10).

Table 5, Columns 3 and 4, present corresponding results for the pre/post, change, subsample. The coefficient on CFO Outside Director is negative and significant for UnderInv (p<.05) and not significant for OverInv. Untabulated CEO pre/post results find CEO Outside Director is not significantly related to either under- or overinvestment. Finally, in the last two columns, results from PSM continue to find that

CFO Outside Director is negative and significant for UnderInv (p<0.10), and not significant for OverInv.

Untabulated CEO PSM results find a small negative coefficient on CEO Outside Director for underinvestment, again providing some evidence of knowledge transfer. Interestingly, in this model, CFO

Outside Director remains significantly negative and larger in magnitude than CEO Outside Director.

Overall, results in Table 5 consistently show a significantly negative association between CFO Outside

Director and UnderInv across all specifications, but find little evidence of a relationship between OverInv and CFO Outside Director.8 Additionally, results do not show consistent association between CEO

7 In untabulated results, we also use multinomial logistic regression. In this specification, we separate our full sample into quartiles and treat our dependant variable as categorical. The middle two quartiles serve as a base group and the two extreme quartiles serve as UnderInv and OverInv. The results again find CFO Outside Director negatively associated with UnderInv (p<0.01), but not significantly associated with OverInv. 8 Chen, Hribar and Melessa (2017) suggest that using a residual from an OLS regression as a dependent variable in a second-step regression may bais estimated coefficients. To address this concern, we re-estimate (untabulated) our regressions after including the first-step regressors in addition to all the second-stage regressors, as suggested by Chen et al. (2017). Using these alternative specifications do not materially change our results. 19

Outside Directorship, and OverInv and UnderInv. Combined, our findings suggests greater knowledge transfer from CFO, than CEO, outside directorships.

4.4 CFS

Table 6 reports the results of CFS. We calculate standard errors robust to heteroskedasticity and cluster by firm, including only manufacturing firms, consistent with Almeida et al. (2004).9 The primary explanatory variable is Cash Flow. The coefficient on Cash Flow reflects the cash flow sensitivity of cash holdings. A significantly positive coefficient on Cash Flow is consistent with firms hoarding cash. We replicate Almeida et al. (2004) results in the first column. Column 1 presents the results from the baseline regression model for the full sample. The coefficient on cash flow is positive and significant for the full sample, reflecting positive sensitivities of cash to cash flows.

In Column 2 (full sample), we include CFO Outside Director and CEO Outside Director and their interactions with Cash Flow along with other board variables. In these regressions, the overall effect of

CFO Outside Director on CFS is reflected in the joint coefficients on βCash Flow + βCash Flow * CFO

Outside Director, which is reported at the bottom of the table. If outside directorships provide opportunities to acquire skills and knowledge that CFOs can use to better manage cash holdings, then we expect the sum of βCash Flow + βCash Flow * Outside Director to be insignificant. The F-test for the joint coefficient shows that the p-value is greater than 0.10 (insignificant), suggesting better cash management for firms whose CFOs sit on outside boards, consistent with knowledge transfer theories.

Similarly, the overall effect of CEO Outside Director is reflected in the joint coefficient on on βCash

Flow + βCash Flow * CEO Outside Director reported at the bottom of the table. The F-test shows the joint coefficient to be positive and significant (p <0.05), indicating increase in hoarding cash. Overall, results suggest that while CFO Outside Director results in better cash management, CEO Outside

Director has no such effect.

9 Almost 45 percent of firms are manufacturing firms. When CFS analyses are performed on the full sample, we find results similar to those of manufacturing firms. Thus, the results of CFS appear to generalize to other industries (excluding financial and utilities). 20

Columns 3 and 4 report results for CFO Outside Director equals zero and CFO Outside Director equals one, respectively. The coefficient on Cash Flow in Column 3 is positive and significant (p<0.01) indicating cash hoarding for CFOs without outside directorships. The coefficient on Cash Flow in

Column 4 is insignificant, indicating results consistent with knowledge transfer theories. Columns 5 and 6 report results for CEO Outside Director equals zero and CEO Outside Director equals one, respectively.

We find that coefficient on Cash Flow is positive and significant in both columns (p <0.01), indicating

CEOs tend to hoard cash regardless of outside directorship. Finally, Columns 7 (pre/post, change) and 8

(PSM) further confirm results reported in other columns related to CFO Outside Director. In untabulated

CEO results, we continue to find significant positive coefficients on Cash Flow, consistent with cash hoarding.

4.5 CFO Outside Board Service and Board Firm Characteristics

We perform two sets of cross-sectional analyses to support our primary findings that CFOs transfer knowledge from outside directorships to their home firm. Since we generally do not find similar effects for CEO outside directorships, we do not perform a similar analysis for CEOs. We first consider whether the outside directorship is in the same industry as the home firm. Prior research finds outside director industry expertise improves board monitoring functions (Wang et al. 2015, Cohen et al. 2014). We also examine whether learning is more evident when the CFO has longer tenure at the board firm because learning occurs with experience, consistent with positive effects of tenure on outside directors’ advisory performance (Kim et al. 2014).

Table 7 reports results of cross-sectional analyses related to variations in learning across different board firms. Panel A reports descriptive statistics across CFOs’ home and board firms. We find that the mean (median) board firm size is bigger (smaller) than home firms, suggesting that in general board firms are smaller but board firms in the top quartile are substantially bigger than the home firm in the top quartile. The CFOs mean tenure in the home firm is 6.30 years compared to 2.87 in the board firm, consistent with more experienced CFOs sitting on outside boards. Panel B reports industry composition of

CFO outside directorships using 15 industry categories identified in Barth et al. (1998).10 We show home firms in rows and board firms in columns. The numbers in diagonals (highlighted in bold) represent outside directorships within the same industry. About 39 percent of outside directorships are within the same industry.

In our cross-sectional analyses, we form sub-samples based on Same (Diff) Industry and then we form sub-samples based on Long (Short) Tenure (defined as greater [less] than the median two years board service). Panels C, D and E report results of our cross-sectional regressions for MDR, InvEff, and CFS, respectively, by industry and tenure. All regressions include dependent variable-specific controls including home firm board and financial policy variables. Panel C reports results of MDR and provides some evidence to suggest that adjustment speed to target market debt ratio is faster when CFOs hold outside directorships in firms that are within the same industry, or when tenure is longer. Panel D, reports results of Inv Eff, separately for UnderInv and OverInv. Interestingly, we find that board membership in the same industry significantly reduces underinvestment, but also increases overinvestment. Results also indicate that long tenure at the outside board is negatively associated with underinvestment and not significantly related to overinvestment. Panel E reports results of CFS. Results indicate that cash holding is less sensitive to cash flow changes when directorships are within industry and when the board tenure is longer.11 Overall, results from Table 7 provide evidence consistent with industry knowledge and outside board tenure increasing learning.

10 There are 1677 number of home firms with 1956 board firms because a few CFOs sit on more than one board. For ease of exposition, we use Barth et al. (1998) 15 industry categories. In the multivariate analyses we use Fama and French 48 industry categories. 11 In unreported regressions, we also analyze whether CFOs learn more from sitting on high performance boards. For this, we create a dummy variable for each of the three financial policies taking the value of one if the board firm is in the top quartile of the financial policy. We find little evidence that board performance matters to CFO learning beyond industry and tenure effects tabulated in Table 7. These results are consistent with findings of Bradley, Gokkaya and Liu (2017) who show industry-related experience to be an important determinant of analyst performance. However, we note that these analyses are performed on an even smaller sample size, and therefore, it is difficult to draw many conclusions from them. 22

4.6 IV Regressions

For Inv Eff and CFS, we conduct, but do not tabulate, IV regressions using an endogeneous treatment effect specification (Maddala 1983).12 We use the annual demand for directors with accounting expertise in the 4 digit SIC code (Acct. Director Demand) as an instrument. Theoretically, higher demand for directors with accounting expertise should increase the likelihood of Outside Director, but external board hiring practices are unlikely to influence the home firm financial policies. Mean (Median) of Acct

Director Demand is 9 (3) with a standard deviation of 12.82. Acct Director Demand is positively associated with Outside Director (p<0.01) in both models. Furthermore, the F statistics, 768.07 (p<0.01) for Inv Eff and 686.23(p<0.01) for CFS, in the first stage are significantly greater than the critical value of

10 (Staiger and Stock 1997). Although, our instrument appears to be valid, we note that these results must be interpreted with caution.13

For InvEff, the second stage IV results are performed using the full sample, including an interaction term between CFO (CEO) Outside Director and a dummy for underinvestment firms (Outside Director *

UnderInv Firm). In the second stage results, the interaction term is negative and significant (p<0.01) for

CFO Outside Director and positive and significant (P<0.10) for CEO Outside Director, consistent with

CFO outside directorships reducing underinvestment and CEO outside directors increasing underinvestment. For CFS, the second stage results are consistent with our reported results. That is, firms with CFOs on outside board manage their cash more efficiently and with more positive effect than CEO outsider directorships.

12 All untabulated results are available upon request from the authors. 13 We also estimate, but do not report, IV regressions using CFO prior audit partner or manager experience as an instrument. Theoretically, former audit partners or managers are in high demand because they are skeptical and well equipped to manage the external auditor relationship (EY 2013a). Yet, because their prior experience is primarily in accounting and auditing, it is less likely that their experience as an audit partner or manager, as opposed to their experience as a CFO, is associated with home firm financial policies. We find that this alternative instrument is positively associated with CFO Outside Director (p<0.01) in the first stage and our results in second stage remain similar to those with when Acct. Director Demand is used as an intsrument. 23

4.7 Additional Cross-sectional Analyses

We suggest that if CFOs transfer knowledge, we should find positive learning effects for home firms with more financial policy constraints because these firms have more room to benefit from CFO learning. For

MDR, we consider home firm size. In particular, since smaller firms experience higher adjustment costs due to more volatile cash flow and tighter debt covenants, we expect the effects of CFO outside directorship will be greater in smaller home firms. We divide the sample based on above or below median total assets. Consistent with knowledge transfer, we find the effect of CFO Outside Director is concentrated in smaller home firms; on average, CFO Outside Directorship is associated with around 21 percent increase in adjustment speed in smaller firms, while only four percent in larger firms. For CFS, we divide the full sample into unconstrained and constrained firms, respectively, where firms above

(below) the sample median total assets represent unconstrained (constrained) firms. Similar to Almeida et al. (2004), the coefficient on Cash Flow for unconstrained firms is -0.033 (p > 0.10) and for constrained firms is 0.031 (p < 0.01), consistent with constrained firms hoarding cash and sacrificing current net present value projects.

5. Conclusion

We examine the effects of CFO outside board directorships on home firm financial policies. Overall, our findings are more consistent with knowledge transfer theory than agency theory predictions. Specifically, we find that firms with CFOs on outside boards reach their target market debt ratio more quickly, are less likely to suffer from underinvestment while not increasing overinvestment, and their cash holdings are less sensitive to cash flow. In addition, we find that home firms with more constraints benefit more from these directorships. We also find that the positive effects are generally more evident when the CFO sits on a board in the same industry or for a longer time. Overall, the results are consistent with outside directorships enabling CFOs to become more connected to other executives and directors who can be a source of counsel and insights. These CFOs utilize their acquired knowledge to positively influence the home firm’s financial policies. Thus, home firms can benefit from CFOs sitting on outside boards. We find less evidence of learning from CEO outside directorships.

We add to the literature on CFOs and complement the literature on CEOs holding outside board positions (e.g., Rosenstein and Wyatt 1994; Geletkanycz and Boyd 2011). Compared to CEO outside directorships, our results suggest that outside directorships can enrich CFO learning about strategic financing and investing policies which should increase the CFO’s value as part of the top management team of the home firm.

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Appendix A: Variable Definitions Variable Definition CFO Outside Director Equals 1 if the CFO sits on an outside board, else zero. CFO Inside Director Equals 1 if the CFO sits on the home firm’s board, else zero. CEO Outside Director Equals 1 if the CEO sits on an outside board, else zero. Percent of home firm board financial experts, defined as experience in Home Board Fin Exp investment banking, the SEC, loan/credit rating, or financial analyst. Size Natural log of total assets. Sub-sample based on whether the board firm is in the same (different) industry Same (Diff) Ind as the home firm, using Fama and French 48 industry classifications. Sub-sample based on whether CFO’s tenure on outside board is above (below) Long (Short) Tenure the median of all CFOs’ outside board tenure. Adjustment Speed Variables λ which equals 1-coefficient on MDR from the Flannery and Rangan (2006) Adjustment Costs model, where next period MDR is regressed on contemporaneous MDR, which is instrumented using book-leverage. Market debt ratio which is book-value of short-and long-term debt divided by MDR market value of assets. Earnings Net income before extraordinary items divided by total assets. MTB Market to book ratio of assets. Depreciation Depreciation divided by total assets. Fixed Assets Property, plant and equipment divided by total assets. R&D Research and Development expense divided by total assets. Median industry MDR, calculated yearly based on Fama and French 48 industry Industry Leverage classification. Book leverage Book debt ratio. Investment Efficiency Variables The absolute value of the residual from the Chen et al. (2011) model (firm subscripts suppressed): Investt = α0 + α1NEGt-1 + α2%RevGrowtht-1 + α NEG*%RevGrowth + ε where NEG equals 1 for negative revenue, else Inv Eff 0 t-1 t zero. The model is estimated cross-sectionally by two digit SIC code and year with at least 20 observations. UnderInv (OverInv) is the absolute value of negative (positive) residuals from the model. Absolute value of the residual from the Kothari et al. (2005) performance- FRQ matched discretionary accrual model. Tangibility Property, plant and equipment divided by total assets. Slack Cash divided by total assets. Big Auditor Equals 1 if the financial statements are audited by Big 4 auditor, else zero. UnderInv Firm Equals 1 if the residual from investment efficiency model is negative, else zero. Cash Flow Sensitivity Variables Δ Cash Holdings t minus t-1 cash and marketable securities divided by total assets. Earnings before extraordinary items plus depreciation minus dividends divided Cash Flow by total assets. Tobin’s Q Market to book value of assets.

Appendix B Panel A:Logit Regression of CFO Outside Directorship (used in PSM) MDR Under Over Investment CFS Investment

CFO Inside Board 0.306*** 0.470*** 0.661*** 0.232** (2.82) (3.09) (3.11) (2.02) CEO Outside Director 0.714*** 0.684*** 0.848*** 0.706*** (10.95) (7.20) (6.16) (10.61) Home Board Fin Expertise 0.094 0.890** 1.242*** 1.287*** (0.32) (2.28) (2.78) (4.54) Size 0.412*** 0.401*** 0.283*** 0.387*** (23.26) (13.58) (6.74) (21.75) MDR -0.609*** (-2.87) Earnings 0.169 (1.22) MTB 0.032** (2.10) Depreciation -3.389* (-1.93) Fixed Assets -0.374*** (-2.70) R&D 1.959*** (6.42) Industry Leverage -0.069 (-0.17) FRQ 0.721** 0.511 (2.08) (1.45) Firm Age 0.053 0.012 (0.79) (0.12) Tangibility -1.432*** -1.861*** (-4.23) (-4.16) Slack 0.008*** 0.005* (4.66) (1.67) Big Auditor 0.418** 0.237 (2.56) (1.18) CashFlow -0.090 (-1.00) Tobin Q 0.083*** (6.26) Observations 13776 7,087 3,806 12398 Likelihood Ratio χ2 1099.46*** 573.42*** 177.38*** 931.06*** Pseudo R-Square 0.129 0.134 0.084 0.1158 Note: This table reports coefficients and related t-statistics in parenthesis from probit regressions in which dependent variable is CFO Outside Director. All variables are defined in Appendix A. ***, ** and * reflect statistical significance at the 1%, 5% and 10% level, respectively.

Appendix B Panel B: PSM Covariate Balance Sheet For CFO Outside Director Variables MDR UnderInvestment OverInvestme CashFlow Sensitivity nt Dir No t-Stat Diff Dir No t- Diff Dir No t-Stat Diff Dir No t- Diff Dir Dir Stat Dir Dir Stat

CFO Inside Director 0.10 0.10 0.44 0.02 0.10 0.09 0.96 0.05 0.10 0.11 -0.13 -0.01 0.09 0.09 -0.50 -0.02 CEO Outside Director 0.49 0.48 0.71 0.03 0.48 0.51 -0.79 -0.04 0.39 0.38 0.17 0.01 Home Board Fin Exp 0.08 0.09 -0.63 -0.02 0.09 0.09 0.22 0.01 0.13 0.14 -0.33 -0.03 0.08 0.08 0.10 0.00 Size 7.58 7.54 0.50 0.02 7.39 7.41 -0.24 -0.01 6.11 6.04 0.40 0.03 7.44 7.47 -0.27 -0.01 MDR 0.16 0.16 0.31 0.01 Earnings 0.00 -0.03 1.47 0.06 MTB 2.23 2.29 -0.52 -0.02 Depreciation 0.04 0.03 1.62 0.06 Fixed Assets 0.39 0.38 0.83 0.03 R&D 0.09 0.10 -1.43 -0.05 Industry Leverage 0.10 0.10 1.53 0.06 FRQ 0.09 0.09 0.63 0.04 0.13 0.14 -0.74 -0.06 Firm Age 2.95 2.93 0.45 0.03 2.67 2.69 -0.43 -0.03 Tangibility 0.15 0.16 -0.36 -0.02 0.16 0.15 0.66 0.05 Slack 9.94 8.79 0.73 0.04 9.77 9.56 0.12 0.01 Big Auditor 0.91 0.91 0.10 0.01 0.86 0.85 0.23 0.02 Cash Flow -0.05 -0.05 -0.12 0.00 Tobin's Q 2.60 2.54 0.50 0.02 Observations 1445 1445 634 634 333 333 1234 1234 Note: This table presents means and related t-statistics of PSM variables for treatment (CFO Outside Director=1) and match (CFO Outside Director=0 ) firm-year observations. NormDif (normalized difference) is the difference in means of the two groups divided the the average standard deviations. A NormDif of 0.25 or less suggests an acceptable balance (Imbens and Wooldridge 2009).

TABLE 1 Sample selection # of firm years in MorningStar with information on CFO (sample period: 2003-2014) 40423 Observations dropped after merging with Compustat 644 Observations dropped for missing Acct Director Demand 467 Observations dropped in financial and utilities industry 9151 Observations Available for Analyses 30161 Observations dropped for missing values of additional home firm board and finance policy variables 3761 Observtaions available for PSM 27160 Observations with CFO Outside Director =1 2476

Adjustment Speed Analyses Observations with Morningstar and Compustat Data 30161 Observations dropped with missing values 15769 Observations dropped with only one year of CFO Outside Director = 1 or CFO Outside Director=0 in panel 626 Observations available for analyses 13776

Investment Efficiency Analyses Observations with Morningstar and Compustat Data 30161 Observations dropped with missing values 19268 Observations available for analyses 10893

Cash Flow Analyses Observations with Morningstar and Compustat Data 30161 Observations dropped outside manufacturing industry (SIC 2000- 4000) 15607 Observations dropped with missing values 2157 Observations available for analyses 12397

TABLE 2 Panel A: Descriptive statistics Variable Mean Median SD 25% 75% Full Sample (n=27,160) CFO Outside Director 0.09 0.00 0.29 0.00 0.00 CFO Inside Director 0.10 0.00 0.30 0.00 0.00 CEO Outside Director 0.24 0.00 0.43 0.00 0.00 Home Board Fin Exp 0.09 0.00 0.12 0.00 0.14 Size 6.09 6.16 2.34 4.58 7.67 Adjustment Speed (n=13776) Lead MDR 0.16 0.09 0.19 0.00 0.24 MDR 0.15 0.09 0.19 0.00 0.23 Earnings -0.10 0.06 0.61 -0.06 0.12 MTB 2.53 1.49 3.71 0.98 2.54 Depreciation 0.04 0.03 0.03 0.02 0.05 Fixed Assets 0.43 0.33 0.34 0.16 0.61 R&D 0.10 0.03 0.20 0.01 0.11 Industry Leverage 0.10 0.06 0.09 0.02 0.16 Book Leverage 0.21 0.14 0.29 0.00 0.29 Investment Efficiency (n=10893) InvEff 0.14 0.09 0.19 0.04 0.16 FRQ 0.12 0.06 0.18 0.03 0.13 Tangibility 0.17 0.12 0.17 0.05 0.24 Slack 8.67 1.64 23.78 0.41 6.32 Big Auditor 0.70 1.00 0.46 0.00 1.00 UnderInv Firm 0.65 1.00 0.48 0.00 1.00 Cash Flow Sensitivity (n=12397) Δ Cash holding 0.00 0.00 0.12 -0.04 0.04 Cash Flow -0.16 0.03 0.68 -0.11 0.08 Tobin's Q 2.82 1.70 3.93 1.23 2.71

Panel B: Descriptive statistics by CFO Outside Director CFO Outside Director=1 CFO Outside Director=0 Diff Variables N Mean Median Std Dev Mean Median Std Dev CFO Inside Director 27160 0.11 0.00 0.31 0.10 0.00 0.30 0.01** Home Board Fin Exp 27160 0.08 0.00 0.12 0.09 0.00 0.12 -0.01*** CEO Outside Director 27160 0.48 0.00 0.49 0.21 0.00 0.41 0.27*** Size 27160 7.68 7.83 2.18 5.93 6.00 2.29 1.76***

Panel C: Descriptive statistics by CEO Outside Director CEO Outside Director=1 CEO Outside Director=0 Diff Variables N Mean Median Std Dev Mean Median Std Dev CFO Inside Director 27160 0.06 0.00 0.24 0.11 0.00 0.31 -0.50*** Home Board Fin Exp 27160 0.08 0.00 0.00 0.10 0.00 0.13 -0.02*** CFO Outside Director 27160 0.19 0.00 0.39 0.06 0.00 0.24 0.13*** Size 27160 7.44 7.56 1.96 5.66 5.72 2.28 1.77***

Panel D: Trend in CFO and CEO Outside Directorships CFO Outside Director CEO Outside Director Year N Mean Std. Dev Mean Std. Dev 2003 1247 0.05 0.23 0.19 0.39 2004 1711 0.08 0.27 0.22 0.41 2005 1907 0.09 0.29 0.22 0.42 2006 2323 0.09 0.29 0.23 0.42 2007 2517 0.09 0.29 0.24 0.43 2008 2591 0.08 0.28 0.24 0.42 2009 2580 0.09 0.29 0.23 0.42 2010 2591 0.10 0.30 0.24 0.43 2011 2480 0.10 0.30 0.25 0.44 2012 2310 0.09 0.29 0.25 0.43 2013 2437 0.10 0.30 0.26 0.44 2014 2466 0.11 0.31 0.27 0.44 Total 27160 0.09 0.29 0.24 0.43 Note: This table provides summary statistics for the full sample in panel A. Panel B (C) provides summary statistics separately for firms whose CFOs (CEOs) sit on outside boards and firms whose CFOs (CEOs) do not sit on outside boards. The column Diff represent differences between variables for those two groups. *, **, *** denotes a difference in the mean under a t-test (Chi- Square test) with a two-tailed p-value of less than 0.10, 0.05, and 0.01, respectively for continuous (indicator) variables. Panel D provides yearly mean and standard deviation of both CFO and CEO outside directorships.

TABLE 3 Correlations 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 CFO Outside Director 1.00 2 Size 0.22 1.00 3 CFO Inside Director 0.01 -0.11 1.00 4 Home Board Fin Exp -0.03 -0.18 -0.03 1.00 5 CEO Outside Director 0.19 0.37 -0.08 -0.08 1.00 6 Lead MDR 0.02 0.24 0.03 -0.10 0.08 1.00 7 MDR 0.02 0.25 0.03 -0.10 0.08 0.84 1.00 8 Earnings 0.05 0.50 -0.11 -0.09 0.12 0.03 0.03 1.00 9 MTB -0.02 -0.40 0.10 0.08 -0.10 -0.19 -0.22 -0.64 1.00 10 Depreciation -0.05 -0.09 0.00 0.00 -0.05 0.10 0.12 -0.16 0.03 1.00 11 Fixed Assets -0.03 0.08 0.03 -0.13 0.03 0.24 0.26 0.01 -0.08 0.57 1.00 12 R&D -0.02 -0.45 0.04 0.14 -0.10 -0.17 -0.17 -0.69 0.53 0.07 -0.10 1.00 13 Industry Leverage 0.01 0.25 0.02 -0.18 0.12 0.32 0.36 0.15 -0.20 0.11 0.38 -0.31 1.00 14 Book Leverage 0.02 0.02 0.06 -0.05 0.03 0.58 0.65 -0.31 0.25 0.16 0.20 0.14 0.16 1.00 15 InvEff -0.02 -0.22 0.08 0.09 -0.06 0.00 -0.01 -0.34 0.33 -0.04 -0.11 0.41 -0.20 0.16 16 FRQt-1 -0.03 -0.34 0.08 0.09 -0.08 -0.04 -0.06 -0.42 0.38 0.02 -0.04 0.32 -0.15 0.16 17 Sizet-1 0.22 0.99 -0.10 -0.17 0.35 0.21 0.22 0.43 -0.36 -0.06 0.05 -0.38 0.20 0.00 18 Firm Aget-1 0.09 0.29 0.01 -0.22 0.22 0.07 0.10 0.17 -0.18 -0.04 0.19 -0.18 0.13 0.02 19 Tangibilityt-1 -0.04 0.18 0.06 -0.12 0.01 0.27 0.28 0.11 -0.12 0.42 0.77 -0.23 0.41 0.15 20 Slackt-1 0.01 -0.21 0.00 0.13 -0.04 -0.15 -0.16 -0.17 0.15 -0.24 -0.30 0.26 -0.21 -0.08 21 Big4t-1 0.12 0.58 -0.15 -0.01 0.20 -0.01 0.01 0.25 -0.15 -0.02 -0.02 -0.12 0.02 -0.05 22 UnderInv Firm 0.02 0.10 0.01 -0.09 0.06 0.06 0.07 0.15 -0.18 -0.09 -0.06 -0.31 -0.05 -0.04 23 Δ Cash Holding 0.00 0.00 -0.01 0.02 0.00 -0.06 -0.04 0.04 0.02 -0.01 -0.03 -0.02 0.02 -0.05 24 Cash Flow 0.03 0.45 -0.12 -0.08 0.09 0.00 0.01 0.92 -0.61 -0.13 0.00 -0.59 0.11 -0.31 25 Tobin's Q -0.02 -0.37 0.11 0.12 -0.08 -0.19 -0.21 -0.61 0.97 0.11 -0.03 0.50 -0.21 0.23

15 16 17 18 19 20 21 22 23 24 15 InvEff 1.00 16 FRQt-1 0.26 1.00 17 Sizet-1 -0.27 -0.36 1.00 18 Firm Aget-1 -0.16 -0.16 0.32 1.00 19 Tangibilityt-1 -0.11 -0.11 0.18 0.07 1.00 20 Slackt-1 0.12 0.17 -0.21 -0.13 -0.32 1.00 21 Big4t-1 -0.12 -0.23 0.57 0.03 0.06 -0.06 1.00

22 UnderInv Firm -0.23 -0.09 0.14 0.15 -0.06 -0.01 0.00 1.00 23 Δ Cash Holding -0.09 -0.02 -0.03 -0.02 0.01 -0.06 0.02 0.10 1.00 24 Cash Flow -0.24 -0.44 0.41 0.11 0.11 -0.12 0.27 0.06 0.04 1.00 25 Tobin's Q 0.25 0.40 -0.34 -0.19 -0.10 0.08 -0.11 -0.14 0.01 -0.66 Note: Correlations significant at 5 percent level are presented in bold.

Table 4 Partial Adjustment Model (1) (2) (3) (4) (5) (6) (7) (8) Full Sample Full Sample CFO CFO CEO CEO Change CFO PSM Outside Outside Outside Outside CFO Director=0 Director=1 Director=0 Director=1 MDR 0.571*** 0.577*** 0.569*** 0.388*** 0.533*** 0.541*** 0.916*** 0.950*** (23.54) (36.14) (22.08) (4.38) (18.34) (14.32) (32.6) (31.77) Earnings -0.012** -0.012*** -0.013** -0.008 -0.013** -0.003 0.020 0.002 (-2.33) (-3.47) (-2.38) (-0.29) (-2.44) (-0.15) (0.48) (0.34) MTB -0.000 -0.000 -0.000 -0.004* -0.001 -0.001 0.000 -0.000 (-0.55) (-0.32) (-0.18) (-1.69) (-0.81) (-0.62) (0.15) (-0.30) Depreciation -0.039 -0.045 -0.034 -0.123 0.044 0.087 -0.261 0.273*** (-0.52) (-0.77) (-0.43) (-0.43) (0.56) (0.34) (-1.38) (2.72) Size 0.023*** 0.023*** 0.024*** 0.016 0.024*** 0.021*** 0.000 0.000 (5.77) (9.26) (5.65) (1.62) (5.13) (2.77) (0.25) (0.40) Fixed Assets 0.012 0.013* 0.013 0.033 0.011 0.001 0.015 -0.017* (1.00) (1.67) (1.10) (0.69) (0.88) (0.04) (1.20) (-1.93) R&D 0.003 0.000 0.000 0.052 -0.003 -0.033 0.022 0.016 (0.19) (0.04) (0.02) (1.18) (-0.22) (-0.68) (0.69) (0.98) Industry Leverage -0.001 -0.010 -0.018 0.152 0.010 -0.016 -0.207*** -0.120** (-0.03) (-0.42) (-0.60) (1.45) (0.28) (-0.30) (-3.35) (-2.27) Inside Director 0.007 0.007 0.008 0.007 0.010* -0.011 0.001 -0.009 (1.25) (1.53) (1.30) (0.40) (1.73) (-0.64) (0.20) (-1.59) Board Fin Exp -0.022 -0.020 -0.018 -0.160*** -0.022 -0.035 -0.022 -0.018 (-1.44) (-1.42) (-1.12) (-2.78) (-1.25) (-0.94) (-1.07) (-1.19) CEO Outside Director -0.001 0.001 0.000 -0.003 0.006 0.009* (-0.23) (0.43) (0.06) (-0.43) (1.05) (1.79) CFO Outside Director 0.001 -0.003 0.001 -0.010 0.009* (0.03) (-0.52) (0.15) (-0.30) (1.77) CFO Outside Director * MDR -0.067** -0.112** -0.068*** (-2.35) (-2.30) (-2.80) CEO Outside Director * MDR -0.014 -0.016 -0.063** (-0.94) (-0.54) (-2.57) Adjustment Speed λ = (1-βMarket 0.424 0.431 0.612 0.467 0.459 Leverage) Observations 13776 13,776 12497 1279 10,267 3,509 2,129 2558 Fixed Effects Firm & Yr Firm & Yr Firm & Yr Firm & Yr Firm & Yr Firm & Yr Ind & Yr Ind & Yr Adj R-square 0.304 0.305 0.294 0.361 0.269 0.336 0.769 0.780 Notes: The table reports results of MDR partial adjustment using two-stage least square IV following Flannery and Rangan (2006). The dependent variable is Lead MDR (t+1), which is instrumented using book leverage. All variables are defined in Appendix A. Intercept is included, but not reported. Standard errors are robust to heteroskedasticity and clustered by firm. The notation ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.

Table 5 Investment Efficiency (1) (2) (3) (4) (5) (6) Basic Change CFO Director PSM CFO Director UnderInv OverInv UnderInv OverInv UnderInv OverInv CFO Outside Director -0.009** -0.004 -0.014** 0.039 -0.007* 0.018 (-2.17) (-0.23) (-2.32) (1.49) (-1.75) (1.03) CEO Outside Director -0.004 0.030*** 0.008 0.028 -0.004 0.013 (-1.37) (2.86) (1.35) (1.37) (-0.97) (0.66) FRQ 0.009 0.224*** 0.009 0.122 0.017 0.185*** (0.95) (6.20) (0.44) (1.63) (1.02) (3.75) Size 0.001 -0.044*** -0.001 -0.043*** 0.001 -0.033*** (1.04) (-8.69) (-0.25) (-4.61) (0.82) (-5.28) Firm Age 0.003* -0.003 0.009 0.020 0.008** 0.013 (1.69) (-0.37) (1.38) (0.83) (2.54) (0.83) Tangibility -0.010 0.064 -0.100*** -0.263*** -0.062*** -0.080 (-0.97) (1.62) (-4.54) (-3.18) (-3.35) (-1.00) Slack 0.000** -0.001** 0.000 -0.002*** 0.000*** -0.002*** (2.19) (-2.21) (1.12) (-3.69) (2.63) (-3.80) Big Auditor -0.015*** 0.040*** -0.006 -0.039 -0.010 0.020 (-4.72) (2.82) (-0.40) (-0.70) (-1.32) (0.64) Inside Director 0.013*** 0.057** 0.028** 0.033 0.021*** 0.018 (3.12) (2.18) (1.99) (0.42) (3.09) (0.60) Board Fin Exp -0.013 -0.041 -0.108*** -0.071 -0.090*** -0.079 (-1.33) (-1.01) (-3.12) (-0.57) (-4.98) (-1.23) Observations 7087 3806 793 403 1268 606 Fixed Effects Ind & Year Ind & Year Ind & Year Ind & Year Ind & Year Ind & Year Adj R-sq 0.326 0.220 0.419 0.172 0.368 0.207 Note: The table reports results of investment efficiency. The dependent variable is UnderInv in odd columns and OverInv in even columns. All variables are defined in Appendix A. T-statistics are presented in parentheses and standard errors are robust to heteroskedasticity and clustered by firm. The notation ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.

Table 6: CFS (1) (2) (3) (4) (5) (6) (7) (9) Full Full CFO Outside CFO Outside CEO Outside CEO Outside CFO PSM Sample Sample Directorship=0 Directorship=1 Directorship=0 Directorship=1 Change CFO Cash Flow 0.038*** 0.039*** 0.038*** 0.062 0.037*** 0.095*** 0.030* 0.022*** (4.43) (4.94) (4.75) (1.58) (4.45) (4.54) (1.75) (2.81) Tobin's Q 0.002 0.002 0.001 0.008* 0.002* 0.001 -0.001 0.001 (1.32) (1.45) (1.03) (1.80) (1.72) (0.33) (-0.44) (1.22) Size -0.001 -0.001 0.002 -0.043** 0.004 -0.020** -0.002 0.001 (-0.12) (-0.21) (0.35) (-2.43) (0.91) (-2.37) (-0.97) (0.78) Inside Director -0.002 -0.002 0.006 -0.008 0.019 0.004 -0.003 (-0.32) (-0.27) (0.27) (-0.85) (1.51) (0.62) (-0.41) Board Fin Exp -0.018 -0.022 -0.058 -0.036 0.044 -0.016 0.026 (-0.75) (-0.86) (-0.53) (-1.31) (0.73) (-0.58) (1.41) CEO Outside 0.005 0.006* -0.020* -0.002 -0.001 Director (1.56) (1.72) (-1.87) (-0.59) (-0.31) CFO Outside -0.003 -0.003 -0.002 -0.000 0.003 Director (-0.69) (-0.38) (-0.31) (-0.09) (0.79) Cash Flow *CFO -0.014 -0.031 -0.010 Outside Director (-0.46) (-0.97) (-1.22) Cash Flow * CEO -0.008 Outside Director (-0.59)

Observations 12397 12397 11162 1235 9210 3187 1861 2468 Adj R-sq/Prob>χ2 0.026 0.026 0.027 0.043 0.027 0.049 0.007 0.004 Fixed Effects Firm & Firm & Firm & Yr Firm & Yr Firm & Yr Firm & Yr Ind & Ind & Yr Yr Yr Yr F-Test: Cash Flow + Cash Flow * 0.025 -0.001 0.012 CFO Outside Director (0.418) (0.985) (0.130) F-Test: Cash Flow + Cash Flow * 0.031** CEO Outside Director (0.016) Note: The table reports the results from OLS regressions of change in cash holdings on cash flow and the interaction Cash Flow * CFO Outside Director for the sample manufacturing firms (SICs 2000 to 3999). All variables are defined in Appendix A. The standard errors are adjusted for heteroskedasticity and clustered by firm. T-statistics are presented in parentheses below coefficients. Intercept is included, but not reported. At the bottom of the table, p-value of the joint coefficients on Cash Flow + Cash Flow * CFO Outside Director and Cash Flow + Cash Flow * CEO Outside Director are reported in parenthesis. All tests are two-sided and the notation ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.

TABLE 7 Cross-sectional analyses of board firm characteristics Panel A: Descriptives Home Firm Board Firm N Mean Median SD 25p 75p N Mean Median SD 25p 75p Size (Total Assets) 1677 11721.33 2352.60 27862.61 441.02 9511.86 1956 17400.60 826.16 158591.70 148.25 3234.51 Tenure 1677 6.30 5.00 5.05 2.00 9.00 1980 2.87 2.00 2.89 1.00 4.00 Δ Cash Holding 1235 0.00 0.00 0.10 -0.03 0.03 697 0.01 0.00 0.10 -0.02 0.03 Market Leverage 1279 0.17 0.13 0.16 0.04 0.24 1094 0.16 0.10 0.19 0.00 0.25 Inv Eff 940 0.12 0.08 0.16 0.04 0.15 510 0.12 0.08 0.16 0.04 0.15 UnderInv 940 0.68 1.00 0.47 0.00 1.00 510 0.62 1.00 0.48 0.00 1.00 Panel B: Industry composition Board Firms 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Total 1 Chemicals 24 7 2 6 4 4 24 9 17 12 5 6 2 1 0 123 2 Computers 8 236 2 21 0 4 71 2 14 12 18 1 13 2 2 406 3 Extractive 1 0 2 0 7 0 22 0 0 8 6 0 14 1 0 61 4 Financials 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 Food 5 5 0 4 24 8 30 3 4 10 3 15 3 0 0 114

CFO Firms 6 Insurance & Real Estate 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 Manufacturing 32 72 10 24 17 6 220 13 23 33 17 29 14 18 3 531 8 Mining/Construction 2 0 7 0 0 0 0 4 0 6 0 0 0 0 0 19 9 Pharmaceuticals 11 18 1 17 4 14 38 1 175 16 8 10 0 1 0 314

10 Retail 12 7 0 12 0 5 30 3 2 39 13 11 2 2 0 138 11 Services 0 10 0 5 0 3 16 0 11 8 29 11 0 4 0 97 12 Textile/Print/Publish 10 17 0 6 2 1 50 9 0 20 4 4 5 8 0 136 13 Transportation 0 0 0 3 0 0 1 0 0 0 0 0 2 0 0 6 14 Utilities 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 Other 0 3 0 0 0 0 3 0 0 0 5 0 0 0 0 11 Total 105 375 24 98 58 45 505 44 246 164 108 87 55 37 5 1956

Panel C: Cross-sectional regressions of MDR (1) (2) (3) (4) Sim Ind Diff Ind Long Tenure Short Tenure

MDR 0.241** 0.532*** 0.205** 0.278*** (3.13) (6.83) (2.14) (3.88)

Controls Yes Yes Yes Yes

Adjustment Speed λ = (1-βMarket Leverage) 0.759 0.468 0.795 0.722 N 490 746 340 929 Adj. R-Square 0.394 0.364 0.4439 0.2665

Panel D: Cross-sectional regressions of Inv Eff (1) (2) (3) (4) UnderInvest OverInvest UnderInvest OverInvest

Sim Ind -0.015** 0.051* (-2.27) (1.89) Long Tenure -0.017*** -0.034 (-2.67) (-1.04)

Controls Yes Yes Yes Yes

Observations 635 305 635 305 Adj. R-Square 0.177 0.098 0.401 0.09

Panel E: Cross-sectional regressions of CFS (1) (2) (3) (4) Sim Ind Diff Ind Long Tenure Short Tenure

Cash Flow 0.019 0.040*** 0.035 0.026*** (0.94) (3.79) (0.94) (2.71)

Controls Yes Yes Yes Yes

Observations 501 734 306 929 Adj. R-Square 0.132 0.031 0.039 0.023 Note: The table provides results of cross-sectional analyses. Variables are defined in Appendix A. In panel A, we report descriptives of home and board firms. Panel B provides corss-sectional grid of industry composition. Panels C, D, and E regression results for MDR, Inv Eff, and CFS models, respectively. All tests are two-sided and the notation ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.