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

By

Sarfraz Khan Department of Accounting

University of Louisiana, Lafayette

skhan@louisiana.edu

Elaine Mauldin Trulaske College of Business

University of Missouri

Columbia MO 65211

573-884-0933

mauldin@missouri.edu

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

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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

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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.

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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

5

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

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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).

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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

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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

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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

10

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,

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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

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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 + 𝜆′ 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:

𝑀𝐷𝑅𝑖,𝑡+1 = (1 − 𝜆)𝑀𝐷𝑅𝑖,𝑡 + (𝜆𝛽)𝑿𝑖,𝑡 + 𝛿𝑖,𝑡+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.

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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:

𝐼𝑛𝑣𝑒𝑠𝑡𝑖,𝑡 = 𝛼0 + 𝛼1𝑁𝐸𝐺𝑖,𝑡−1 + 𝛼2𝑅𝑒𝑣 𝐺𝑟𝑜𝑤𝑡ℎ𝑖,𝑡−1 + 𝛼3𝑁𝐸𝐺 ∗ 𝑅𝑒𝑣 𝐺𝑟𝑜𝑤𝑡ℎ𝑖,𝑡−1 + 휀𝑖,𝑡 (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:

∆ 𝐶𝑎𝑠ℎ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑡 = 𝛼0 + 𝛼1𝐶𝑎𝑠ℎ 𝐹𝑙𝑜𝑤𝑖,𝑡 + 𝛼2𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄𝑖,𝑡 + 𝛼3𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 + 휀𝑗,𝑡 (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.

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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

15

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).

16

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.

17

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.

18

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

19

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.

20

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).

21

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

22

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.

23

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.

24

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.

25

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.

26

References:

Aggarwal, R., and A. Samwick. 2006. Empire-builders and shirkers: Investment, firm performance, and

managerial incentives. Journal of Corporate Finance 12 (3): 489-515.

Almeida, H., M. Campello, and M. Weisbach. 2004. The cash flow sensitivity of cash. Journal of Finance

59: 1777-1804.

Argote, L., and P. Ingram. 2000. Knowledge transfer: A basis for competitive advantage in firms.

Organizational Behavior and Human Decision Processes 82 (1): 150-169.

Bacon, J., and J. Brown. 1974. Corporate Directorship Practices: Role, Selection and Legal Status of the

Board, A Joint Research Report from the Conference Board and American Society of Corporate

Secretaries, Inc. New York: NY.

Balsam, S., A. J. Irani, and Q. J. Yin. 2012. Impact of job complexity and performance on CFO

compensation. Accounting Horizons 26 (3): 395-416.

Barth, M., W. Beaver, and W. Landsman. 1998. Relative valuation roles of equity book value and net

income as a function of financial health. Journal of Accounting and Economics 25 (1): 1–34.

Bebchuk, L., and J. Fried. 2003. Executive compensation as an agency problem. Journal of Economic

Perspectives 17 (3): 71-92.

Bedard, J., R. Hoitash, and U. Hoitash. 2014. Chief financial officers as inside directors. Contemporary

Accounting Research 31 (3): 787-817.

Bedard, J., S. Chtourou, and L. Courteau. 2004. The effect of audit committee expertise, independence

and activity on aggressive earnings management. Auditing: A Journal of Practice & Theory 23 (2): 13-35.

Biddle, G. C., G. Hilary, and R. S. Verdi. 2009. How does financial reporting quality relate to investment

efficiency? Journal of Accounting and Economics 48: 112-131.

Boivie, S., S. D. Graffin, A. G. Oliver, and M. C. Withers. 2016. Comd aboard! Exploring the effects of

directorships in the executive labor market. Academy of Management Journal 59 (5): 1681–1706.

Boubaker, S., I Derouiche, and D. K. Nguyen. 2015. Does the board of directors affect cash holdings? A

study of French listed firms. Journal of Management & Governance 19 (2): 341-370.

Bradley, D., S. Gokkaya, and X. Liu. 2017. Before an analyst becomes an analyst: Does industry

experience matter? Journal of Finance 72 (2): 751-792

Brickley, J. A., and J. L. Zimmerman. 2010. Corporate governance myths: Comments on Armstrong,

Guay, and Weber. Journal of Accounting & Economics 50 (2/3): 235-245.

Brown, J. L. 2011. The spread of aggressive corporate tax reporting: A detailed examination of the

corporate-owned life insurance shelter. The Accounting Review 86 (1): 23-57.

Brown, J. L., and K. D. Drake. 2014. Network ties among low-tax firms. The Accounting Review 89 (2):

483-510.

Burt, R. 2000. The network structure of social capital. in Research in Organizational Behavior, edited by

Robert I. Sutton and Barry M. Staw, 345-432. Greenwich, Conn.: JAI Press.

Cai, Y., D. S. Dhaliwal, Y. Kim, and C. Pan. 2014. Board interlocks and the diffusion of disclosure

policy. Review of Accounting Studies 19: 1086-1119.

Chava, S., and A. Purnanandam. 2010. CEOs vs. CFOs: Incentives and corporate policies. Journal of

Financial Economics 97: 263-278.

27

Chen, W., P. Hribar, and S. Melessa. 2017. Coefficient bais when using residuals as the dependent

variables, Working paper, Unversity of Iowa.

Chen, F., O. Hope, Q. Li, and X. Wang. 2011. Financial reporting quality and investment efficiency of

private firms in emerging markets. The Accounting Review 86 (4): 1255-1288.

Cohen, J. R., U. Hoistash, G. Krishnamoorthy, and A. M. Wright. 2014. The effect of audit committee

industry expertise on monitoring the financial reporting process. The Accounting Review 89 (1): 243-273.

Collins, D., A. Masli, A. Reitenga., and J. Sanchez. 2009. Earnings restatements, the Sarbanes-Oxley Act

and the disciplining of chief financial officers. Journal of Accounting, Auditing and Finance 14 (1): 1-34.

Conyon, M. J., and L. E. Read. 2006. A model of the supply of executives for outside directorships.

Journal of Corporate Finance 12: 645–659.

Cunningham, L. M., L. A. Myers, and J. C. Short. 2017. CFO outside directorships: What happens to

financial reporting quality at the home firm? Working paper, University of Tennessee.

Denis, D. J., and V. Sibilkov. 2010. Financial constraints, investment, and the value of cash holdings. The

Review of Financial Studies 23 (1): 247-269.

DiMaggio, P. J., and W. W. Powell. 1983. The iron cage revisited: Institutional isomorphism and

collective rationality in organizational fields. American Sociological Review 48: 147–160.

Dittmar, A., and R. Duchin. 2016. Looking in the rearview mirror: The effect of managers’ professional

experience on corporate financial policy. The Review of Financial Studies 29 (3): 565-602.

Dobbs, R., M. Giordano, and F. Wenger. 2009. The CFO’s role in navigating the downturn, McKinsey on

Finance, Online Edition: http://www.mckinsey.com/insights/corporate_finance/

the_cfos_role_in_navigating_the_downturn.

Engel, E., F. Gao, and X. Wang. 2015. Chief financial officer succession and corporate financial

practices. Working paper, University of Chicago.

EY. 2012. CFO and Beyond: The Possibilities and Pathways Outside Finance: Online edition. Retrieved

on March 15, 2015, from http://www.ey.com/Publication/vwLUAssets/CFO_and _beyond_-

_The_possibilities_and_pathways_outside_finance_-_2012/$FILE/CFO%20and%20beyond%20-

%20The%20possibilities%20and%20pathways%20outside%20finance%20-%202012.pdf

EY. 2013a. Greater business challenges call for stronger audit committees. Insights 18 (August).

EY (Tapestry Network. Inc.). 2013b. Effective board and committee leadership. Audit committee

leadership network of North America. 43(19): Online edition. Retrieved on May 15, 2015 from

http://www.ey.com/Publication/vwLUAssets/EY-ViewPoints-43-July-2013-Effective-board-and-

committee-leadership-CJ0231/$FILE/EY-ViewPoints-43-July-2013-Effective-board-and-committee-

leadership-CJ0231.pdf

Fahlenbrach, R., A. Low, and R. Stulz. 2010. Why do firms appoint CEOs as outside directors? Journal

of Financial Economics 97: 12–32.

Faleye, O. 2011. CEO directors, executive incentives, and corporate strategic initiatives. The Journal of

Financial Research 34 (2): 241–277.

Fama, E., and M. Jensen. 1983. Separation of ownership and control. Journal of Law and Economics 27:

301-25.

Fazzari, S., R. G. Hubbard, and B. Petersen. 1988. Financing constraints and corporate investment.

Brookings Papers on Economic Activity: 141-195.

28

Feng, M., W. Ge, S. Lou, and T. Shevlin. 2011. Why do CFOs become involved in material accounting

manipulation? Journal of Accounting and Economics 51: 21-36.

Feldmann, D. A., W. J. Read, and M. J. Abdolmohammadi. 2009. Financial restatements, audit fees, and

the moderating effect of CFO turnover. Auditing: A Journal of Practice & Theory 28 (1): 205-223.

Fich, E., and L. White. 2003. CEO compensation and turnover: The effects of mutually interlocked

boards. Wake Forest Law Review 38: 935.

Fisher. E., R. Heinkel, and J. Zechner. 1989. Dynamic capital structure choice: Theory and tests. Journal

of Finance 44: 19-40.

Flannery, M., and K. Rangan. 2006. Partial adjustment toward target capital structures. Journal of

Financial Economics 79: 469-506.

Geletkanycz, M., and B. Boyd. 2011. CEO outside directorships and firm performance: A reconciliation

of agency and embeddedness views. Academy of Management Journal 54 (2): 335-352.

Ge, W., D. Matsumoto, and J. Zhang. 2011. Do CFOs have styles of their own? An empirical

investigation of the effect of individual CFOs on accounting practices. Contemporary Accounting

Research 28 (4): 1141-1179.

Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate financial

reporting. Journal of Accounting and Economics 40: 3-73.

Granovetter, M. 1973. The strength of weak ties. American Journal of Sociology 78: 85-98.

Granovetter, M. 1985. Economic action and social structure: The problem of embeddedness. American

Journal of Sociology 91(3): 481-510.

Helmers, C., M. Patnam, and P. R. Rau. 2017. Do board interlocks increase innovation? Evidence from a

corporate governance reform in India. Journal of Banking and Finance 80: 51-70.

Hermalin, B. E., and M. S. Weisbach. 2003. Boards of directors as an endogenously determined

institution: A survey of the economic literature. Economic Policy Review - Federal Reserve Bank of New

York 9 (1): 7-26.

Hillman, A. J., and T. Dalziel. 2003. Boards of directors and firm performance: Integrating agency and

resource dependence perspectives. Academy of Management Review 28 (3): 383-396.

Hoitash, U., R. Hoitash, and J. Bedard. 2009. Corporate governance and internal control over financial

reporting: A comparison of regulatory regimes. The Accounting Review 84 (3): 839-867.

Hoitash, R., U. Hoistash, and A. C. Kurt. 2016. Do accountants make better chief financial officers?

Journal of Accounting and Economics 61: 414-432.

Horner, S. V. 2016. CEO Directors: Going it alone or clustering on boards? Academy of Strategic

Management Journal 15 (1): 32-40.

Hubbard, R., 1998. Capital market imperfections and investment. Journal of Economic Literature 36:193-

225.

Imbens, G., and D. Rubin. 1997. Bayesian inference for causal effects in randomized experiments with

noncompliance. Annals of Statistics 25: 305–327.

Imbens, G., and J. Wooldridge. 2009. Recent developments in the econometrics of program evaluation.

Journal of Economic Literature 47: 5-86.

Jayaraman, S., and T. Milbourn. 2015. CEO equity incentives and financial misreporting: The role of

auditor expertise. The Accounting Review 90 (1): 321-350.

29

Jensen. M. 1986. Agency costs of free cash flow, corporate finance, and takeovers. American Economic

Review 76 (2): 323-329.

Jensen, M., and W. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and

ownership structure. Journal of Financial Economics 3: 305-360.

Jiang, J., K. Petroni, and I. Wang. 2010. CFOs and CEOs: Who has the most influence on earnings

management? Journal of Financial Economics 96 (3): 513-526.

Kayhan, A., and S. Titman. 2007. Firm’s histories and their capital structures. Journal of Financial

Economics 11: 5-50.

Kim, J., Y. Li, and L. Zhang. 2011. CFOs versus CEOs: Equity incentives and crashes. Journal of

Financial Economics 101: 713-730.

Kim, K., E. Mauldin, and S. Patro. 2014. Outside directors and board advising and monitoring

performance. Journal of Accounting and Economics 57: 110-131.

Klien, A. 2002. Audit committee, board of director characteristics, and earnings management. Journal of

Accounting and Economics 33 (3): 375-400.

Kothari, S. P., A. J. Leone, and C. E. Wasley. 2005. Performance matched discretionary

accrual measures. Journal of Accounting and Economics 39: 163-197.

Krantz, M. 2008. Getting on boards: Worth the effort? CFO.com (online edition).

http://ww2.cfo.com/human-capital-careers/2008/06/getting-on-boards-worth-the-effort/.

Krishnan, J. 2005. Audit committee quality and internal control: An empirical analysis. The Accounting

Review 80 (2): 649-675.

Krishnan, G., and G. Visvanathan. 2007. Reporting internal control deficiencies in the post-Sarbanes-

Oxley era: The role of auditors and corporate governance. International Journal of Auditing 11: 73-90.

Li, C., L. Sun, and M. Ettredge. 2010. Financial executive qualifications, financial executive turnover,

and adverse SOX 404 opinions. Journal of Accounting and Economics 50: 93-110.

Lipton, M., and J. Lorsch. 1992. A modest proposal for improved corporate governance. Business Lawyer

48: 59-77.

Loderer, C., and U. Peyer. 2002. Board overlap, seat accumulation and share prices. European Financial

Management 8:165-192.

Lorsch, J., and E. Maciver. 1989. Pawns or Potentates: The Reality of America's Corporate Boards.

Cambridge, MA: Harvard Business School Press.

Maddala, G. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge, MA:

Cambridge University Press.

McCann, D. 2012. Should you be on a board? CFO.com (online edition).

http://ww2.cfo.com/leadership/2012/06/should-you-be-on-a-board/.

McDonald, M., and J. Westphal. 2003. Getting by with the advice of their friends: CEOs’ advice

networks and firms’ strategic response to poor performance. Administrative Science Quarterly 48: 1-32.

McDonald, M., P. Khanna, and J. Westphal. 2008. Getting them to think outside the circle: Corporate

governance, CEOs’ external advice networks, and firm performance. Academy of Management Journal

51: 453-475.

Menon, K., and D. D. Williams. 2008. Management turnover following auditor resignations.

Contemporary Accounting Research 25 (2): 567-604.

30

Mobbs, S. 2017. Firm CFO Board Membership and Departures, Working paper, University of Alabama.

Morellec, E., B. Nikolov, and N. Schurhoff. 2012. Corporate governance and capital structure dynamics.

Journal of Finance 67 (3): 803-848.

Murphy, M., and E. Chasan. 2013. A boardroom with a view. CFO Journal May 7.

Myers, M. 1984. The capital structure puzzle. Journal of Finance 39 (3): 574-592.

Neff, T. 1998. So many boards … so little time. Chief Executive March: 36-40.

Perry, T., and U. Peyer. 2005. Board seat accumulation by executives: A shareholder's perspective.

Journal of Finance 60 (4): 2083-2123.

Pfeffer, J., and G. Salancik. 1978. The External Control of Organizations: A Resource Dependence

Perspective. New York, NY: Harper & Row.

Pugliese, A., P. Bezemer, A. Zattoni, M. Huse, F. A. J. Van den Bosch, and H. W. Volberda. 2009.

Boards of directors’ contribution to strategy: A literature review and research agenda. Corporate

Governance: An International Review 17 (3): 292–306.

Rosenstein, S., and J. Wyatt. 1994. Shareholder wealth effects when an officer of one corporation joins

the board of directors of another. Managerial and Decision Economics 15: 317-327.

Shackleford, D. A., and T. Shevlin. 2001. Empirical tax research in accounting. Journal of Accounting

and Economics 31: 321-387.

Shipman, J. E., Q. T. Swanquist, and R. L. Whited. 2017. Propensity score matching in accounting

research. The Accounting Review 92 (1): 213-244.

Shleifer, A., and R. Vishny. 1989. Management entrenchment: The case of manager-specific investments.

Journal of Financial Economics 25:123-139.

Spencer Stuart. 2014. Recruiting the Next Generation of Financial Experts to Boards. July. Chicago, IL:

Spencer Stuart.

Staiger, D., and J. H. Stock. 1997. Instrumental variables regression with weak instruments.

Econometrica 65: 557-586.

Stiglitz, J., and A. Weiss. 1981. Credit rationing in markets with imperfect information. American

Economic Review 71: 393-410.

Tufano, P., and Servaes, H. 2006. CFO views on the importance and execution of the finance function.

Deutsche Bank.

Useem, M. 1984. The Inner Circle: Large Corporations and Business Politics in the U.S. and U.K. New

York, NY: Oxford University Press.

Uzzi, B. 1999. Embeddedness in the making of financial capital: How social relations and networks

benefit firms seeking financing. American Sociological Review 64: 481-505.

Wang, C., F. Xie, and M. Zhu. 2015. Industry expertise of independent directors and board monitoring.

Journal of Financial and Quantitative Analysis 50 (5): 929–962.

Wang, X. 2010. Increased disclosure requirements and corporate governance decisions: Evidence from

Chief Financial Officers in the pre- and post–Sarbanes-Oxley periods. Journal of Accounting Research 48

(4): 885-920.

Westphal, J., M. Seidel, and K. Stewart. 2001. Second-order imitation: Uncovering latent effects of board

network ties. Administrative Science Quarterly 46 (4): 717-747.

31

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.

Home Board Fin Exp Percent of home firm board financial experts, defined as experience in

investment banking, the SEC, loan/credit rating, or financial analyst.

Size Natural log of total assets.

Same (Diff) Ind Sub-sample based on whether the board firm is in the same (different) industry

as the home firm, using Fama and French 48 industry classifications.

Long (Short) Tenure Sub-sample based on whether CFO’s tenure on outside board is above (below)

the median of all CFOs’ outside board tenure.

Adjustment Speed Variables

Adjustment Costs

λ which equals 1-coefficient on MDR from the Flannery and Rangan (2006)

model, where next period MDR is regressed on contemporaneous MDR, which

is instrumented using book-leverage.

MDR Market debt ratio which is book-value of short-and long-term debt divided by

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.

Industry Leverage Median industry MDR, calculated yearly based on Fama and French 48 industry

classification.

Book leverage Book debt ratio.

Investment Efficiency Variables

Inv Eff

The absolute value of the residual from the Chen et al. (2011) model (firm

subscripts suppressed): Investt = α0 + α1NEGt-1 + α2%RevGrowtht-1 +

α0NEG*%RevGrowtht-1 + εt where NEG equals 1 for negative revenue, else

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.

FRQ Absolute value of the residual from the Kothari et al. (2005) performance-

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.

Cash Flow Earnings before extraordinary items plus depreciation minus dividends divided

by total assets.

Tobin’s Q Market to book value of assets.

32

Appendix B Panel A:Logit Regression of CFO Outside Directorship (used in PSM)

MDR Under

Investment

Over Investment CFS

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.

33

Appendix B Panel B: PSM Covariate Balance Sheet For CFO Outside Director

Variables MDR UnderInvestment OverInvestme

nt

CashFlow Sensitivity

Dir No

Dir

t-Stat Diff Dir No

Dir

t-

Stat

Diff Dir No

Dir

t-Stat Diff Dir No

Dir

t-

Stat

Diff

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).

34

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

35

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

36

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.

37

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

38

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.

39

Table 4

Partial Adjustment Model

(1) (2) (3) (4) (5) (6) (7) (8)

Full Sample Full Sample CFO

Outside

Director=0

CFO

Outside

Director=1

CEO

Outside

Director=0

CEO

Outside

Director=1

Change

CFO

CFO PSM

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

Leverage)

0.424 0.431 0.612 0.467 0.459

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.

40

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.

41

Table 6:

CFS

(1) (2) (3) (4) (5) (6) (7) (9)

Full

Sample

Full

Sample

CFO Outside

Directorship=0

CFO Outside

Directorship=1

CEO Outside

Directorship=0

CEO Outside

Directorship=1

CFO

Change

PSM

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

Director

0.005

(1.56)

0.006*

(1.72)

-0.020*

(-1.87)

-0.002

(-0.59)

-0.001

(-0.31)

CFO Outside

Director

-0.003

(-0.69)

-0.003

(-0.38)

-0.002

(-0.31)

-0.000

(-0.09)

0.003

(0.79)

Cash Flow *CFO

Outside Director

-0.014

(-0.46)

-0.031

(-0.97)

-0.010

(-1.22)

Cash Flow * CEO

Outside Director

-0.008

(-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 &

Yr

Firm &

Yr

Firm & Yr Firm & Yr Firm & Yr Firm & Yr Ind &

Yr

Ind & Yr

F-Test: Cash Flow + Cash Flow *

CFO Outside Director

0.025

(0.418)

-0.001

(0.985)

0.012

(0.130)

F-Test: Cash Flow + Cash Flow *

CEO Outside Director

0.031**

(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.

42

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

CF

O F

irms

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

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

43

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.