Board Gender Diversity and CEO Inside Debt Compensation

Andrew Prevost*

Grossman School of Business

University of Vermont

55 Colchester Avenue, Burlington, VT 05405

aprevost@bsad.uvm.edu

Arun Upadhyay

Department of Finance

College of Business

Florida International University

11200 S.W. 8th St, RB 247B, Miami, FL 33199

arun.upadhyay@fiu.edu

Abstract

In this study we examine how board gender diversity affects CEO compensation. We argue that

gender diverse boards offer more conservative compensation package that promotes long term

stability. Consistent with this premise, our primary results provide evidence that the presence of

independent female directors on a firm’s board is positively associated with CEO inside debt-like

pension compensation. These results are robust to multiple controls for endogeneity. In further

analysis, we find that a greater presence of independent female board members is positively viewed

by bondholders as evidenced by lower yield spreads. These results are consistent with the view

that gender diverse boards adopt corporate policies that promote the long term viability of a firm.

*Corresponding author.

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1. Introduction

Supported by recent anecdotal evidence demonstrating that greater boardroom diversity is

associated with better corporate performance (e.g. Credit Suisse Research Institute, 2016; Morgan

Stanley Research, 2017), board gender diversity has moved to the forefront of core corporate

governance issues. While women currently hold about 19 percent of board positions in the US,

other countries have adopted voluntary or legislative targets to increase gender diversity. For

example, all publicly listed Norwegian firms are required to reserve 40 percent of board seats for

women (Skroupa, 2016). The European Union is planning to introduce similar legislation that will

apply to all the countries in the EU. 1 In 2009, the Securities Exchange Commission (SEC)

mandated new disclosure rules requiring listed firms to disclose whether they consider diversity

when recruiting new directors.2

Despite this growing focus by regulators, corporations, investors, academics and

policymakers, a nascent body of academic research on the impact of diversity on board decision

quality and corporate performance finds mixed results. Extant research documents that female

directors are more effective monitors (Adams and Ferreira, 2009; Gul et al., 2011; Srinidhi et al.,

2011). In contrast, Adams and Ferreira (2009) report that board gender diversity is associated with

lower firm performance after controlling for endogeneity. Matsa and Miller (2013) and Ahern and

Dittmar (2012) study the 2006 exogenous policy shock in Norway requiring higher female

representation on corporate boards and find a substantial value loss for firms that were forced to

comply. These conflicting findings raise questions about the channel through which gender

diversity affects the firm’s financial claimants. In this study we try to reconcile these findings by

1 See http://online.wsj.com/article/SB10001424052748703712504576244671196828968.html. 2 See http://www.sec.gov/news/press/2009/2009-268.htm

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examining this question from a different perspective that focuses on the risk preferences of

directors that could vary along their gender.

The board of directors is responsible for hiring, evaluating and compensating top

executives. In theory, corporate boards are responsible for offering a compensation contract that

aligns shareholders’ interests with those of the CEO thereby maximizing shareholder value.

However, board decisions are influenced by the beliefs, biases and backgrounds of directors as

well as the negotiations between the directors and the CEOs. Prior research provides evidence that

board characteristics such as director independence (e.g. Core et al., 1999; Chhaochharia and

Grienstein, 2009; Gutherie et al., 2010), board size (Yermack, 1996) and director background

affect the level and composition of CEO compensation. In this paper, we examine how board

gender diversity impacts CEO compensation structure.3 This question gains importance in light of

continuous growth of board gender diversity despite its uncertain relation with firm performance.

This association would provide a better understanding of how board gender diversity affects

organizational outcomes. Designing the optimal CEO compensation structure is an important task

given the impact that compensation incentives have on firm operating and investment strategies,

risk-taking, and ultimately long term growth and shareholder value.

We conjecture that the presence of woman directors could affect the pay-setting process

and CEO pay structure for a number of reasons. First, literature in social psychology, education

and experimental economics documents evidence showing gender differences in risk preferences

and future goal orientation irrespective of the age or socio-economic status. For example, using

data from a survey of high achieving students, Austin and Nichols (1964) report fundamental

3 Anecdotally, greater board gender diversity is associated with higher CEO pay. For example, Mogensen (2016),

citing a 2015 study by Equilar, notes that CEOs of companies with greater gender diversity were paid 15 percent more

than by companies with less diverse boards.

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differences in future career plans and life goals between male and female students: men are more

concerned with money and prestige whereas women appear more concerned about altruism.4

Studies on childhood development and education find gender differences in Future Time

Orientation (FTO) among adolescent and young adults. Early work by Gjesme (1979) and

Sundberg et al. (1983) report female students having greater concern about their future and

independence. In the corporate context, Kanter (1977) argued that increasing diversity at the top

of the corporate hierarchy could bring important changes to organizational functioning. One of the

implications of her arguments is that the incentive pay of top managers and director homogeneity

are substitutes. Westphal and Jazac (1995) argue that when the CEO and directors have similar

demographic backgrounds, the interpersonal trust between the board and the CEO should be higher

and the contingent pay of the CEO lower. Using measures of similarity of functional background,

educational background and insider/outsider status, they find evidence consistent with this

hypothesis. Following this argument, we expect to find a positive association between board

gender diversity and CEO equity incentives which are typically linked to equity performance.

However, equity-linked CEO pay has been found to promote uncertainty and risk-taking

(e.g. Coles, Daniel and Naveen, 2006). Based on social and experimental psychology literature

that suggests gender differences in preferences for risk-taking and means to create value, we

conjecture that equity incentives may not be consistent with the preferences of female directors.

Byrnes, Miller and Schafer (1999) suggest that women are risk averse along various dimensions

and Croson and Gneezy (2009) analyze a number of empirical studies in this line of work and find

statistically significant differences between the risk-taking behavior of men and women in

experimental settings or with investment strategies. Based on these studies, they conclude (p. 448)

4 In a relatively recent study, Wigfield and Eccles (2002) show that these differences persist.

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“women are indeed more risk averse than men”. Prior literature also suggests that female

leadership style is characterized by trust and cooperation (Niederle and Vesterlund, 2007).

Moreover, females are more sensitive to threats and opportunities from the external environment

and are likely to place a greater focus on the long term survival of the firm. Such boards are likely

to institute higher verification standards for any information provided to them (Gul et al., 2011).

Ray (2005) argues that gender diverse boards focus not only on value creation but also on the

sources of value creation. Female directors may care for not only the equity holders but other

stakeholders including creditors. Based on these arguments, we do not expect to find a positive

association between the presence of board gender diversity and CEO equity linked incentives.

While the empirical association between board gender diversity and CEO equity incentives

is ambiguous, we expect to find a positive association between gender diverse boards and the debt-

like component of CEO pay which features greater alignment with bondholder preferences. Jensen

and Meckling (1976) hypothesize that debt-like compensation may alleviate risk-shifting conflicts

between stockholders and bondholders that result from equity incentive compensation. Because

inside- and outside debt feature similar payoffs, debt-like compensation can be used to dissuade

managers from taking excessive risk at the expense of bondholders. In a similar vein, Edman and

Liu (2011) predicts that managers with higher proportions of inside-to-firm debt ratios are more

likely to choose conservative operating policies. Consistent with this premise, recent empirical

research investigates the prevalence and effects of inside debt as a component of managerial

compensation. Sundaram and Yermack (2007) document a negative relation between the level of

managerial pension holdings and the probability of default, suggesting that managers with high

inside debt behave more conservatively. Thus, to promote greater stability and the long-term

viability of a firm, we expect boards with greater gender diversity to use more inside debt

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compensation for the CEOs. The inside debt literature collectively uses deferred- and pension

compensation to measure CEO debt-like compensation. Consistent with the notion that the payoffs

from pension compensation bear a greater resemblance to bond payoffs compared to deferred

compensation (Anantharaman, Fang and Gong, 2014), our results based on S&P 1500 firms

provide strong and consistent evidence that CEOs of firms having boards with greater proportions

of independent women directors have higher pension compensation. In addition, we also find that

more gender diverse boards offer larger future cash compensation and lower equity-based

compensation.

Board structure and CEO compensation are often endogenously determined (Hermalin and

Weisbach, 1998). Extant research reports optimal board structure depends on firm characteristics

(e.g., Raheja, 2005; Coles, Daniel, and Naveen, 2008; Duchin, Matsusaka, and Ozbas, 2010). As

such, the observed association between board gender diversity and CEO compensation could have

alternative explanations due to endogeneity concerns originating from different sources such as

reverse causality, omitted variables, and selection bias. For example, it is possible that the

association is driven by some firms that choose to follow a more conservative operating approach

and have a culture of promoting stability. As a result, these firms may be more likely to hire a

conservative CEO as well as promote a more gender diverse board. To control for the possibility

that unobserved firm-specific factors are associated with CEO compensation components and the

presence of board gender diversity, we employ firm fixed effects regressions and obtain

qualitatively similar results. Similarly, it is also possible that female directors self-select to serve

the boards of more stable firms that pay their CEOs a more conservative pay. Thus, the observed

association between gender diversity on board and pension compensation is not driven by female

directors but is due to reverse causality. To address this potential problem we use a two-stage least

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squares instrumental variable approach. We use a spatial instrument which predicts the hiring of

female directors controlling for firm characteristics including risk and CEO characteristics.

Following Knyazeva, Knyazeva and Masulis (2013), who find that the local directorial labor pool

directly impacts the supply of independent directors, we use county-level ratio of firms with gender

diverse boards to all the firms headquartered in that county (excluding the sample firm) in a given

year as the instrument to predict board gender diversity. Using predicted gender diversity, we

continue to find a positive association between board gender diversity and pension compensation.

To address potential self-selection biases, we use a Heckman two-step selection model.

Additionally, we also employ a propensity score matching methodology. Our results continue to

support our primary findings.

The relation between gender diversity and CEO inside debt compensation suggests a

significant positive association with metrics of bondholder incentive alignment used in the inside

debt literature, including the ratio of CEO inside debt compensation to equity incentives (e.g.

Sundaram and Yermack, 2006) and the CEO-firm relative debt ratio (e.g. Edmans and Liu, 2011

and Cassell et al, 2012). Prior work (e.g. Cassell et al., 2012) suggests debt-like compensation

promotes greater alignment with creditor interests by promoting risk reducing investment policies.

For example, Anantharaman et al. (2014) present empirical evidence that bank loans have higher

prices and fewer covenants when the CEO-firm relative debt-to-equity ratio is higher. If board

gender diversity promotes greater CEO inside debt compensation, then greater diversity should

have a negative effect on the risk premia of the firm’s debt securities.

Our paper contributes to the literature in several ways. First, our paper is closely related to

research that investigates the effect of gender diversity on board governance and oversight (Adams

and Ferreira, 2009; Gul et al., 2011; Srinidhi et al., 2011). These studies report a stronger

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monitoring role associated with the presence of female directors on the board. However, these

studies report mixed evidence on the effectiveness of female directors and how greater diversity

relates to shareholder value. For example, Adams and Ferreira (2009) demonstrate that the

presence of woman directors is negatively associated with the market to book ratio after controlling

for endogeneity. Unlike prior studies that focus on the effectiveness of female directors from the

shareholder perspective, we examine how board gender diversity impacts CEO compensation that

provides greater incentive alignment with creditors. Our finding that the presence of female

directors on the board is associated with greater CEO pension compensation enhances our

understanding of how gender diversity affects board monitoring. These findings help explain why

prior studies find inconsistent results on the value effects of board gender diversity.

Our study also contributes to the CEO inside debt literature. A growing line of research

explores the determinants and consequences of debt-like compensation in executive pay.5 Recent

research shows that executive compensation typically includes a substantial amount of pension

and deferred compensation along with cash and equity incentives (e.g., Bebchuk and Jackson,

2005; Wei and Yermack, 2011). On August 11, 2006, the Securities and Exchange Commission

(SEC) substantially revised the disclosure requirements for executive compensation including new

tables that cover retirement benefits and nonqualified deferred compensation details for each

executive, thereby instigating a new line of research on the causes and effects of debt-like

compensation. Prior research generally focuses on how CEO inside debt compensation affects

corporate investments and the firm’s risk profile (Sundaram and Yermack, 2007; Cassell et al.,

2012; Liu, et al., 2014 and Phan, 2014). Expanding this line of literature, we examine how gender

representation on corporate boards determines CEO inside debt compensation. To the best of our

5 Following the extent literature, we use as ‘inside debt’ and ‘debt-like compensation interchangeably.

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knowledge our study is the first to provide an understanding of the gender-level board dynamics

that affect CEO inside debt compensation.

The remainder of the paper is organized as follows. Section 2 reviews related literature and

develops hypotheses. Section 3 describes the sample and presents preliminary evidence on the

association between gender diversity and CEO compensation components. Sections 4-6 provide

additional evidence on the impact of gender diversity on CEO compensation contracting, and

Section 7 concludes.

2. Literature Review and Hypothesis Development

Corporate boards are responsible for hiring, evaluating and compensating the top

management team. Financial economics research tends to view executive (typically CEO)

compensation from an agency theoretic perspective: shareholders design a contract that aligns their

interests with those of the CEO. As Core, Guay and Larcker (2003) note (p. 27), an efficient (or

optimal) contract is one “that maximizes the net expected economic value to shareholders after

transaction costs (such as contracting costs) and payments to employees.” Prior literature provides

evidence that board characteristics such as director independence (Core et al., 1999; Chhaochharia

and Grienstein, 2009; Gutherie et al., 2010), board size (Yermack, 1996) and director background

affect the level and composition of CEO compensation. Pay mix is determined by the board to

maximize shareholder value but the optimal balance of incentives that maximize shareholder value

could differ from one director to another. For example, Westphal and Jazac (1995) show that CEO

compensation is a function of similarities between the CEO and the directors in terms of their

background and demographic characteristics.

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A wide variety of prior research suggests that gender differences in risk preferences exist

across different life stages, socio-economic, and cultural backgrounds as supported by the meta-

analytic findings of Byrnes, Miller and Schafer (1999). Experimental research documents that

women are more likely to perceive the severity of negative outcomes from risky policies at an

elevated levels. Hillier and Morrongiello (1998) examine gender differences in perceptions

involved in physical risk taking in children and found that girls appraised more general risk than

boys. Prior literature on child development and clinical psychology suggests that boys engage in

more risk taking than girls (e.g., Ginsburg & Miller, 1982; Rosen & Peterson, 1990) and have more

frequent and severe injuries than girls (Baker et al., 1984; Canadian Institute of Child Health,

1994). Boys have higher activity levels (Eaton, 1989) and behave more impulsively than girls

(Manheimer and Mellinger, 1967).

Researchers in economics and finance have examined gender differences in investment

allocation decisions and document evidence that women are less likely to participate in stock

markets and conditional upon participation, take less risk (see e.g., Sundén and Surette, 1998;

Barber and Odean, 2001; Dwyer, Gilkeson, and List, 2002; Agnew, Balduzzi, and Sundén, 2003).

Prior work supports the view that gender diversity is an important characteristic that impacts the

decision-making of corporate boards (Adams and Ferreira, 2009; Ahern and Dittmar, 2012; Matsa

and Miller, 2013; Eckbo, Nygaard and Thorburn, 2016). Therefore, female directors may prefer

corporate policies that do not lead to greater risk. For example, Gul et al. (2011) confirm that the

presence of female directors leads to a lower incidence of earnings manipulation and higher

earnings quality. Therefore, we expect boards with gender diversity to favor a compensation

contract that discourages CEO risk taking whereas male directors may have a greater inclination

to offer such a contract only when a firm is already facing uncertainties. Since equity-based and

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other forms of CEO incentives have been found to encourage short-termist behavior including

earnings manipulations and risk taking, female directors are more likely to support a compensation

scheme that would minimize such behaviors.

Based on the rationale that inside- and outside debt feature similar payoffs, Jensen and

Meckling (1976) proposes that debt-like executive compensation provides a linkage between CEO

payoffs and the long term survival of the firm. A growing line of empirical research that finds that

firms paying higher debt-like compensation to their CEOs are associated with more conservative

investment and financing decisions (e.g. Cassell, et al., 2012) and less stock return variability. This

happens due to. While the inside debt literature generally uses pension benefits and deferred

compensation to measure debt-like compensation, there are differences. Retirement pension

contracts (i.e., SERPs) are contractual, established at the onset of the CEOs employment, and

feature post-retirement benefits that resemble bond payoffs (Bebchuk and Fried, 2004). In contrast,

the executive chooses to defer current compensation to future time periods. As Anantharaman et

al. (2014) discuss, deferred income can be invested in the firm’s own stock and deferred

compensation plans typically offer some flexibility in repayment prior to retirement. For these

reasons, the payoffs between deferred compensation and risky debt can diverge. In order to

promote greater stability and less risk-taking, we conjecture that firms with woman directors are

likely to offer their CEOs a compensation package that will have a larger inside debt component.

Firms can increase board gender diversity by appointing female executives as directors and

also by appointing independent female directors. Agency theory predicts the latter should provide

a more effective monitoring role. Further, important monitoring committees such as audit,

compensation or nominating are restricted to independent directors; Adams and Ferreira (2009)

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finds that female directors are highly likely to serve on these important committees. 6 Since

independent directors are likely to more effectively monitor top managers and contribute to

important board tasks, we expect to see a positive association between independent board gender

diversity and CEO inside debt compensation. Further we surmise that the association is driven by

pension compensation, which has payoffs that have greater alignment with bondholder interests:

Hypothesis 1: Board gender diversity is positively associated with CEO pension debt-like

compensation

As noted above, prior work argues that female directors are more effective monitors. Thus,

a related question is if gender diversity improves the efficacy of compensation contracting. The

corporate governance and CEO contracting literatures suggest that corporate boards design

compensation contracts to encourage CEOs to achieve desirable targets. These contracts are

effective only when there is a match between the CEO’s utility, firm resources, and terms and

conditions of the compensation contract. Hermalin and Weisbach (1998) argue that board structure

and CEO compensation evolve as a result of continuous negotiations between the CEO and board.

For example, Cheng (2004) finds that boards increase CEO risk-taking incentives when they are

close to retirement or when there is a greater likelihood of loss if the firm is highly R&D intensive.

In this vein, we examine cross-sectional variation in the use of inside debt in four different

contexts. First, since board gender diversity is associated with greater stability and lower

uncertainty, we expect presence of female directors to be associated with greater CEO inside debt

compensation in firms that face a higher likelihood of future uncertainty. Since the presence of

debt increases bankruptcy risk, we surmise that gender diversity is associated with a larger pension

component in firms that have greater financial leverage:

6 Adams and Ferreira (2009) finds that woman directors are less likely to serve on compensation committees.

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Hypothesis 2a: Board gender diversity is positively associated with CEO pension debt-like

compensation in firms with greater financial risk.

In contrast, the survival and growth of firms in knowledge-intensive industries rely on continuous

innovation and inter-firm arrangements (e.g. Bollingtoft, Ulhoi, Madsen and Neergaard, 2003).

Therefore, we expect to see a smaller pension component in R&D intensive firms with higher

operational risk who need managers to invest in high growth risky assets and therefore are less

likely to be compensated by debt-like compensation which offers fixed payoffs:

Hypothesis 2b: Board gender diversity is negatively associated with CEO pension debt-like

compensation in firms with larger R&D investments.

The CEO compensation mix can also be driven by optimal contracting as compensation

contracts are designed to accommodate CEO’s needs and pressures from the external labor market

and competitive environments. For example firms could add perks if they are desirable to CEOs

and if the marginal cost is lower to the firm (Fama, 1980) or if perks aid managerial productivity

(Rajan and Wulf, 2006). Cheng (2004) finds that when CEOs are closer to retirement they are

likely to promote short term policies if they are compensated in equity linked components. Thus,

we expect gender diverse boards to offer a larger pension compensation to those CEOs that are

closer to retirement:

Hypothesis 2c: Board gender diversity is positively associated with CEO pension debt-like

compensation in firms with older CEOs.

Finally, because managerial talent contributes more to firm performance in highly

competitive industries (Cornaggia, Krishnan and Wang, 2017), higher ability managers may

receive more equity incentive compensation. Accordingly, firms in highly competitive industries

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may need a highly incentivized compensation contract so their CEOs are encouraged to invest in

risky but high growth projects:

Hypothesis 2d: Board gender diversity is negatively associated with CEO pension debt-like

compensation in firms with higher levels of competition.

3. Gender Board Diversity and CEO Compensation Components

3.1 Data and Sample Selection

We use the BoardEx database as the primary source of the board gender diversity measure

and other board characteristics. Following convention in the agency literature, we exclude firms

classified as financial (6000 ≤ SIC ≤ 6999) and utilities (4900 ≤ SIC ≤ 4999). In Table 1A Panel

A provides board characteristics for firms used in our study. For the primary 1998-2015 sample

period, independent women directors comprise approximately 10 percent of the board. Consistent

with a broad literature documenting the prevalence of CEO duality, the CEO holds the position of

board chair in 72.3 percent of all firm-years. Independent (i.e. unaffiliated) directors comprise 72.4

percent of the sample, and the typical board has 9 members. In Panel B, we provide descriptive

statistics for CEO current and debt-like compensation components from the Execucomp database.

For the 14,658 firm-year observations with a complete record of non-missing control variables

used in the cross-sectional analyses, average (median) total compensation (Execucomp item

TDC1) is $5,936,000 ($3,695,000). Equity compensation is total compensation minus cash

compensation and comprises the majority of total compensation with a mean (median) of

$4,681,000 ($2,554,000). Base salary and current bonus have means of $773,000 and $482,000

respectively.

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Following the inside debt literature, total debt-like compensation is the sum of the

aggregate deferred compensation balance and the aggregate actuarial present value of the

accumulated pension benefit. Following changes in the SEC reporting requirements in 2006, firms

are required to report the annual contribution to executive deferred compensation, the deferred

compensation balance, the change in the pension account value, and the pension balance. For the

7,948 firm-years with a complete record of non-missing control variables for the 2006-2015

sample period, the annual deferred compensation contribution is $215,000 and the deferred

balance is $2,283,000. The annual change in pension account value is $503,000, and the mean

value of pension benefits is $3,051,000. Following the methodology described by Sundaram and

Yermack (2007) and Daniel, Li and Naveen (2013), CEO equity compensation (or Inside equity)

is the sum of stock grants, restricted stock, and the present value of stock option holdings. The

Inside debt ratio is the CEO’s personal debt-equity ratio and is based on total debt-like

compensation divided by equity compensation. The Relative debt ratio is the CEO Inside debt ratio

divided by the firm’s debt-equity ratio (e.g., Edmans and Liu, 2011). Based on the 7,617 (6,559)

observations used in cross-sectional analyses, the mean (median) Inside debt ratio (Relative debt

ratio) is 0.257 (0.044) and 3.332 (0.320), respectively.

In Table 1A Panel C, we provide descriptive statistics for the explanatory variables used in

the cross-sectional compensation regressions. We use explanatory variables based on the executive

compensation literature (e.g. Huang, Jiang, Lie and Que, 2017) to explain CEO cash (salary and

bonus) and equity pay. The log of total assets controls for Firm size. We gauge firm performance

with Stock return (Lagged stock return) and ROA (Lagged ROA). We calculate the remaining

financial control variables lagged one year. Lagged leverage and Lagged book-market control for

financial risk and growth opportunities, respectively, while Lagged cash flow volatility controls

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for cash flow risk. Lagged capital expenditure is scaled by total assets, and Lagged tangibility

controls for capital intensity. We define Lagged sales growth as the five-year geometric growth in

sales, and Lagged R&D captures risk associated with innovation. At the CEO level, we include

CEO tenure (in years). Further details about the constructions of these variables are provided in

the Appendix.

In Table 1A Panel D, we present additional CEO- and firm level explanatory variables

commonly used in the debt-like compensation literature (e.g. Sundaram and Yermack, 2007;

Campbell, Galpin and Johnson, 2016). At the firm level, Firm age measures the number of years

since the listing date using the CRSP Header File. At the CEO level, we include CEO age to control

for age-related effects on debt-like compensation. Following Sundaram and Yermack (2007), Tax

loss indicator is a binary variable that controls for the tax benefits associated with deferral of

income to future years. The Herfindahl-Hirschman Index (HHI) controls for the effect of industry

concentration on CEO contracting. Liquidity constraint is a binary variable equal to one if the

operating cash flow is negative and zero otherwise; Sundaram and Yermack (2007) argue that

firms with low liquidity may prefer equity to debt-like compensation. Further details about the

construction of these variables are provided in the Appendix.

3.2. Empirical Results

3.2.1 Univariate Analysis

We compare firm and CEO characteristics between firms that have a gender diverse board

(at least one independent female director) and those that do not. Table 1B provides difference in

means tests for these two groups. Consistent with anecdotal evidence (e.g. Morgensen, 2016),

firms with gender diverse boards are more likely to pay a larger compensation package to their

CEOs. When we compare various components of the pay package, firms with gender diverse board

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pay more compensation to their CEOs across all the components except for the cash bonus.

However, compensation packages are also affected by various other factors such as firm size,

growth opportunities, profitability, and CEO characteristics. Therefore, we also examine whether

firms with gender diverse boards also differ from those that do not.

Consistent with the view that gender diversity promotes greater stability, we find that

gender diverse firms are larger, more profitable, and have lower cash flow volatility. They also

have lower R&D expenditure which has a direct bearing on growth opportunities. Further, firms

with gender diverse boards are more likely to be led by CEOs who hold the position of board chair

and have shorter tenures, and have larger and more independent boards. Finally, firms that are led

by gender diverse boards have higher bond ratings lower yield spreads compared with firms that

have no gender diversity on their boards. These results are consistent with our hypothesis that

bondholders value firms with a gender diverse board. In the next section, we examine the

associations between board gender diversity, CEO compensation and other corporate outcomes in

a multivariate setting.

3.2.2 Board Gender Diversity and CEO Pay Components

We begin our multivariate analysis by investigating the cross-sectional effect of board

gender diversity, as measured by Independent women directors, on total compensation and its cash

and equity incentive components. Based on the specification of Huang et al. (2017), we specify

the determinants of the components of CEO pay as follows:

𝐶𝐸𝑂 𝑐𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 = 𝛼0 + 𝛼1 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑤𝑜𝑚𝑒𝑛 𝑑𝑖𝑟𝑒𝑐𝑡𝑜𝑟𝑠𝑖,𝑡 + 𝛼2𝐹𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖,𝑡 + 𝛼3𝑆𝑡𝑜𝑐𝑘 𝑟𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 +𝛼4𝐿𝑎𝑔𝑔𝑒𝑑 𝑠𝑡𝑜𝑐𝑘 𝑟𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 + 𝛼5𝑅𝑂𝐴𝑖,𝑡 + 𝛼6𝐿𝑎𝑔𝑔𝑒𝑑 𝑅𝑂𝐴𝑖,𝑡 + 𝛼7𝐿𝑎𝑔𝑔𝑒𝑑 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 + 𝛼8𝐿𝑎𝑔𝑔𝑒𝑑 𝑏𝑜𝑜𝑘 − 𝑡𝑜 −

𝑚𝑎𝑟𝑘𝑒𝑡 𝑖,𝑡 + 𝛼9𝐿𝑎𝑔𝑔𝑒𝑑 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 + 𝛼10𝐿𝑎𝑔𝑔𝑒𝑑 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑖,𝑡 +

𝛼11𝐿𝑎𝑔𝑔𝑒𝑑 𝑡𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 + 𝛼12𝐿𝑎𝑔𝑔𝑒𝑑 𝑠𝑎𝑙𝑒𝑠 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡 + 𝛼13𝐿𝑎𝑔𝑔𝑒𝑑 𝑅&𝐷𝑖,𝑡 + 𝛼14𝐿𝑜𝑔(1 + 𝐶𝐸𝑂 𝑡𝑒𝑛𝑢𝑟𝑒)𝑖,𝑡 +

𝛼15𝐶𝐸𝑂 𝑐ℎ𝑎𝑖𝑟𝑖,𝑡 + 𝐹𝐹𝐼49 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡𝑠𝑗 + 𝑌𝑒𝑎𝑟 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡𝑠𝑡+𝑒𝑖,𝑡

(1)

We present our cross-sectional findings in Table 2, Models 1-4 where each model includes year

and industry (Fama-French 49) fixed effects. In Model 1, we find an insignificant relation between

18

Independent women directors and total compensation. In Model 2, gender diversity negatively

affects equity compensation however the association is statistically insignificant. In Model 3,

consistent with the notion that female board members may have a preference towards non-equity

based compensation, we find that Independent women directors has a significant direction

association with the base salary after controlling for other factors. Finally in Model 4, we find a

statistically insignificant association between board diversity and the cash bonus. While time and

industry fixed effects capture the effects of fluctuations in the economic and industry environments

on CEO compensation, the explanatory variables may omit firm-level characteristics that are

relevant to compensation. Prior work presents evidence that firm and CEO characteristics often

drive board composition, e.g. firm performance or powerful CEOs shaping the board structure

(e.g., Raheja, 2005; Coles, Daniel, and Naveen, 2008; Duchin, Matsusaka, and Ozbas, 2010). A

firm-level fixed effects model produces unbiased estimates assuming that the unobservable firm

characteristics are constant over time and, as such, can be used to address endogeneity problems

(e.g. Wooldridge, 2000). Therefore, in Models 5-8 we re-estimate Equation (1) with firm-level

fixed effects. The Independent women directors coefficient estimates remain qualitatively similar

to the results of Models 1-4.

3.2.3 Board Gender Diversity and CEO Debt-like Compensation Components

In Table 3 we examine the association between Independent women directors and the

components of CEO debt-like compensation over the 2006-2015 period based on the disclosure of

debt-like compensation components beginning in 2006. Following prior research that examines

the determinants of debt-like compensation (e.g. Sundaram and Yermack, 2007), we base our

specification on the measures used in Equation (1) along with additional variables used in the debt-

like compensation literature as explanatory variables as defined by Equation (2):

19

𝐶𝐸𝑂 𝑑𝑒𝑏𝑡 − 𝑙𝑖𝑘𝑒 𝑐𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 = 𝛼0 + 𝛼1𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑤𝑜𝑚𝑒𝑛 𝑑𝑖𝑟𝑒𝑐𝑡𝑜𝑟𝑠𝑖,𝑡 + 𝛼2𝐹𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖,𝑡 +

𝛼3𝑆𝑡𝑜𝑐𝑘 𝑟𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 + 𝛼4𝐿𝑎𝑔𝑔𝑒𝑑 𝑠𝑡𝑜𝑐𝑘 𝑟𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 + 𝛼5𝑅𝑂𝐴𝑖,𝑡 + 𝛼6𝐿𝑎𝑔𝑔𝑒𝑑 𝑅𝑂𝐴𝑖,𝑡 + 𝛼7𝐿𝑎𝑔𝑔𝑒𝑑 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 +

𝛼8𝐿𝑎𝑔𝑔𝑒𝑑 𝑏𝑜𝑜𝑘 − 𝑡𝑜 − 𝑚𝑎𝑟𝑘𝑒𝑡 𝑖,𝑡 + 𝛼9𝐿𝑎𝑔𝑔𝑒𝑑 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 +𝛼10𝐿𝑎𝑔𝑔𝑒𝑑 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑖,𝑡 + 𝛼11𝐿𝑎𝑔𝑔𝑒𝑑 𝑡𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 + 𝛼12𝐿𝑎𝑔𝑔𝑒𝑑 𝑠𝑎𝑙𝑒𝑠 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡 +

𝛼13𝐿𝑎𝑔𝑔𝑒𝑑 𝑅&𝐷𝑖,𝑡 + 𝛼14𝐿𝑜𝑔(1 + 𝐶𝐸𝑂 𝑡𝑒𝑛𝑢𝑟𝑒)𝑖,𝑡 + 𝛼15𝐶𝐸𝑂 𝑐ℎ𝑎𝑖𝑟𝑖,𝑡 + 𝛼16 𝐿𝑜𝑔(1 + 𝐹𝑖𝑟𝑚 𝑎𝑔𝑒)𝑖,𝑡 +

𝛼17𝐿𝑜𝑔(𝐶𝐸𝑂 𝑎𝑔𝑒)𝑖,𝑡 + 𝛼18𝑇𝑎𝑥 𝑙𝑜𝑠𝑠 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑖,𝑡 + 𝛼19𝐻𝐻𝐼𝑖,𝑡 + 𝛼20𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑖,𝑡 +

𝐹𝐹𝐼49 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡𝑠𝑗 + 𝑌𝑒𝑎𝑟 𝑓𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡𝑠𝑡+𝑒𝑖,𝑡

(2)

CEO deferred and pension compensation are reported as yearly totals and as aggregate balances.

Accordingly, the dependent variables for Equation (2) include the log of (1 plus) the yearly

contribution to deferred compensation and the yearly change in pension value, and the log of (1

plus) the aggregate deferred compensation and pension balance. In Table 3 we provide coefficient

estimates using these alternative dependent variables. In Models 1-2, the Independent women

directors coefficient estimate is insignificantly associated with the yearly contribution to deferred

income and is positively related to the logged deferred compensation balance at the 5 percent level.

In contrast, and in support of Hypothesis 1, in Models 3-4 the Independent women directors

coefficient estimate is strongly significantly associated with both the yearly change in the pension

account value and with the pension balance. In Models 5-8 we estimate the models replacing

industry- with firm-level fixed effects. While the Independent women directors coefficient

estimates weaken in Models 5-7, the estimate continues to be significantly associated with the

logged pension balance at the 1 percent statistical level. Overall, Table 3 provides support for the

notion that greater gender board diversity mitigates CEO risk taking through compensation

contracting, in particular through pay that enhances CEO-bondholder interest alignment.

3.2.4 Controlling for Endogeneity

The results in Table 3 demonstrate that firms with board gender diversity is associated with

greater CEO debt-like compensation. However, the presence of independent woman directors and

CEO inside debt compensation could be driven by endogenous factors including omitted variables,

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reverse causality, and measurement error (see, e.g., Hermalin and Weisbach, 1998 & 2001).7 In

an initial step towards addressing this concern, we repeat our primary analyses lagging

Independent women directors one year. These results, reported in Table 4, are qualitatively similar

to the results provided in Table 3.

Next, we examine the association between board gender diversity and debt-like CEO

compensation using two-stage least squares (2SLS) and Heckman selection methodologies. In

Table 5, we employ a 2SLS approach, specifying the Independent women directors as endogenous.

We identify a spatially-based instrument for Independent women directors. The 2SLS approach

requires instruments that are correlated with the presence of female directors but uncorrelated with

CEO compensation. To this end, we construct Board gender diversity county ratio, defined as the

yearly proportion of firms with gender diverse board to all the firms (excluding the sample firm)

in the county of the sample firm’s headquarters. This variable is more likely to capture the supply

of female directors but is unlikely to impact the compensation CEO of a firm directly except

through its effect on board composition. Knyazeva, Knyazeva and Masulis (2013) examine the

local directorial labor pool and find that it directly impacts the supply of independent directors.

Glaeser and Scheinkman (2002), John and Kadyrzhanova (2010), Anderson et al. (2011) and

Balsam et al. (2015) find evidence that companies follow their local and peers when designing

their governance structure. Thus, the county-based instrument appears to be a strong and relevant

instrument.8

7 As documented by Hermalin and Weisbach (1998, 2001), the board of directors is endogenously determined.

Specifically, the authors provide theoretical and empirical evidence that poor performance leads to increases in board

independence. 8 It is possible that industry-level factors that motivate firms in the same geographic area to recruit women directors

may also be correlated with CEO compensation. This should not violate the efficacy of this variable as an instrument

as we control for industry and year effects in both stages of 2SLS estimation.

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The first stage results from the OLS estimation of Independent women directors, reported

in Model 1 of Table 5, show that the instrumental variable (IV) Board gender diversity county

ratio is positive and significantly associated with board gender diversity. Prior studies find that

firms with greater advising needs are more likely to have a large board with a majority of outside

directors (Coles et al., 2008) and particularly a diverse board (Anderson et al., 2011). Also, these

firms are more visible than smaller firm; therefore, female directors with corporate experience

could self-select to more visible and reputable firms. We include variables to capture these effects

and find that independent female directors are more likely to join relatively larger firms but with

CEOs that are also board chairs.

The predicted value of Independent women directors from the first stage is included in the

second stage. The second stage results, reported in Table 5 columns 2-3 using the annual change

in pension value and the logged pension balance, respectively, as dependent variables indicate that

the Independent women directors IV is positively associated with both measures at the 5 percent

level. These results provide evidence that the primary association between board gender diversity

and CEO inside compensation is not driven by reverse causality.

To control for potential self-selection bias in the choice of independent woman directors,

we first calculate the inverse Mills ratio from the first stage probit model using variables from

Table 5 Column 1. The dependent variable is an Independent woman director indicator variable

that takes a value of one if a firm has at least one independent woman director, and zero otherwise.

We include the inverse Mills ratio along with the Independent women directors measure as

explanatory variables in the second stage CEO compensation regressions. Inclusion of the inverse

Mills ratio corrects self-selection bias in the Independent woman directors estimate. As reported

in Table 5 Columns 4-5, the predicted Independent woman directors coefficients continue to be

22

significantly associated with the logged pension balance and change in pension value at the 1

percent levels. These results show that our primary results are not driven by selection bias.

Finally, we employ the propensity score matching (PSM) methodology (Rosenbaum and

Rubin, 1983; 1985) which provides an alternative approach to control for potential self-selection

bias in the choice of independent female directors. The propensity score is the probability of

assignment to the treatment group (firms with board gender diversity), based on observed

covariates. PSM matches treated (firms with gender diverse boards) firms with control (firms with

no board gender diverse diversity) firms on several dimensions, thus enabling the creation of a

control sample of firms that do not appoint an independent female director but are similar to the

sample of firms with board gender diversity.

Using the propensity scores estimated from the probit selection model of Table 5, we

identify a propensity score-matched control sample.9 PSM matching methodologies range from

one-to-one to one-to-many; and as discussed by Tucker (2010), there is no single best matching

approach. Therefore, we employ a series of methodologies: nearest-neighbor, kernel and radius.

The nearest-neighbour approach with replacement picks a single control firm according to the

closest propensity score. Kernel matching uses the entire sample of control firms as matches,

where each unit is weighted in proportion to its closeness to the treated observation. Finally, radius

matching searches for matches with propensity scores within a predefined radius of the treated

firms’ propensity scores. We use small (large) calipers of 0.001 (0.01) to identify sets of matches.

We present comparisons of CEO inside debt compensation using the PSM analyses in

Table 6 Panel A.10 Panels A1-A2 illustrate differences between the treatment and control firms

using the four PSM approaches. Using nearest-neighbour matching, the control sample’s average

9 We conduct the PSM procedure with the PSMATCH2 Stata module (Leuven and Sianesi, 2003). 10 We also estimate regressions using post-matched samples. The results continue to support our primary hypotheses.

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Log(Change in pension value)t+1 is statistically smaller than that of the board gender diversity

sample (1.174 vs. 3.151) at the 1 percent level. Similarly, Log (Pension balance)t+1 is also smaller

and significantly different for the control group compared with the treatment group (3.367 vs

4.198). We find similar results when we use the kernel-matched or radius matched samples using

calipers of 0.01 and, alternatively, 0.001. The differences in both types of compensation

components between the control group and treatment group firms are significant using all the

methods. Overall, the PSM results suggest that the positive association between board gender

diversity and CEO inside debt compensation is not driven by self-selection bias in the choice of

independent female directors.

Finally, to further address the potential self-selection problem, we re-examine our primary

analyses examining board gender diversity and its effects on CEO debt-like compensation on a

post-matched sub-sample of firms. Using nearest-neighbour matching, we estimate our primary

models using the change in pension value and pension balance, respectively, as dependent

variables. The results are presented in Table 6 Panel B and continue to support our primary

hypotheses that board gender diversity is positively associated with CEO inside debt-like

compensation.

3.3 Additional Tests of Robustness

3.3.1 Board Gender Diversity and CEO Gender

It is possible that our results are primarily driven by female CEOs. Since women have a

preference for long term, sustainable performance, corporate boards could compensate female

CEOs with pay packages that align with their preferences including greater debt-like

compensation. On the other hand, if women are generally risk averse as compared to men, a board

might offer its female CEO a compensation package that encourages more risk-taking, i.e. greater

24

equity-based and lower pension compensation. To test these competing hypotheses and to discern

if our primary results are driven by firms with female CEOs, we estimate our primary models as

presented in Table 3 Columns 3-4 with the change in pension value and pension balance,

respectively, as dependent variables. We add an indicator variable for the presence of a female

CEO and its interaction term with the Independent women directors measure. The coefficient

estimates for these models are presented in Table 7 Panel A. The insignificance of the Independent

women directors × Female CEO interaction suggests that our primary results are not driven by the

presence of female CEOs.

3.3.2 Alternative Measures of Board Gender Diversity

In our primary analyses, we measure board gender diversity with the proportion of

independent women directors as we expect the effect to be stronger for directors who are less likely

to be influenced by the CEO. To test this assertion, we identify the proportion of female directors

who are current or retired employees of the firm (Employee women directors) as an alternative

dimension of board gender diversity. We also use a broader measure (Women directors) defined

as the ratio of all women directors to board size. Using these alternative measures of board gender

diversity, we re-estimate the models illustrated in Table 3 Columns 3-4 using the change in pension

value and pension balance as dependent variables. The results are provided in Table 7 Panel B and

indicate that employee women directors do not affect CEO compensation. The coefficients on the

Women director measure continues to be significant across all four models, suggesting that the

independent women director category is the primary influence behind CEO compensation.

3.3.2 Exogenous Departure of Independent Directors and Changes in CEO Compensation

Since board composition is a function of firm characteristics and prior firm performance,

the primary association between board gender diversity and CEO compensation could be driven

25

by reverse causality. To further address this concern, we examine the impact of independent

director departures on CEO debt-like compensation. To the extent the presence of independent

female directors is positively related to inside debt, then the departure of female directors should

result in decreased inside debt. However, it is possible that female director departures occur for

reasons that are endogenous to firm performance, CEO influence, and hence CEO compensation

structure. Therefore, we identify exogenous director departures due to death, critical illness, term

limits, or retirement policies of the firm. We obtain this data from Audit Analytics for the period

of 2001-2015. We compare the change in CEO total and pension compensation from pre- to post-

departures of independent female and male directors.

Table 7 Panel C1 presents results for CEO total compensation and Panel C2 presents results

for debt-like pension compensation. Panel A illustrates that CEO total compensation increases

from the pre- to post-departure periods following independent male director departures but the

change is insignificant for female director departures. In contrast and consistent with our primary

hypothesis, Panel B shows that CEO pension compensation significantly decreases following

female independent director departures. The pension contribution does not change significantly

following male director departures. The difference in the changes associated with female vs. male

departures is negative and marginally statistically significant at 10 percent level.

3.4. Gender board diversity and CEO Inside- and Relative Debt Ratios

Our results demonstrating a significant association between gender board diversity and the

pension component of inside debt compensation have a direct bearing on the proportion of inside

debt-to-equity compensation held by the CEO and the CEO-firm relative debt ratio. The inside

debt literature (e.g. Sundaram and Yermack, 2007; Edman and Liu, 2011; Cassell et al., 2012)

shows that the proportionality of CEO inside debt compensation to equity incentives, and the

26

relative proportionality of the CEO debt-equity ratio to that of the firm, negatively affects risk-

taking policy decisions thereby implying there is greater alignment between CEO and creditor

interests when these ratios are higher. Our results suggest that managers receive more pension

debt-like compensation when the proportion of Independent women directors is higher. Because

pension compensation is used by the extent inside debt literature as a component of debt-like

compensation, empirical support for Hypothesis 1-2 implies that gender board diversity increases

the degree of alignment between CEOs and bondholders as gauged by these measures.

We empirically examine this premise in Table 8. First, we regress the logged inside debt

ratio and the logged relative debt ratio on Independent women directors and the additional

explanatory variables specified in Equation (2). In Models 1-3, we examine the explanatory role

of gender board diversity on the inside debt ratio employed by Sundaram and Yermack (2007). To

distinguish if the pension or deferred compensation component is driving the overall result, we

decompose the Inside debt ratio into its components Deferred inside debt ratio and Pension inside

debt ratio. We provide the results in Models 1-3. In Model 1, Independent women directors is

significantly related to the logged Inside debt ratio at the 1 percent level. Models 2-3 demonstrate

that this association is driven by the pension component of inside debt: in Model 2, Independent

women directors is insignificantly associated with Deferred inside debt, however Independent

women directors is positively and strongly significantly related to Pension inside debt in Model 3.

In Models 4-6, we repeat this analysis using the logged relative debt ratio as employed by Cassell

et al. (2012) and related research as the dependent variable. Consistent with the results provided

in Model 1, Independent women directors is strongly associated with the CEO-firm relative debt

ratio. Models 5-6 provide evidence that the association between Independent women directors and

pension debt-like compensation drives this result, based on the coefficient estimate in Model 6 that

27

is significant at the 1 percent level. Overall, Table 8 demonstrates that Independent women

directors is an important covariate in the specification of variables that determine inside- and

relative debt ratios.

To add further insight to our results in Table 8, we use board appointment announcements

as a natural experiment to test if board gender diversity plays a role in CEO incentives. We gauge

incentives using the proportions of debt-like compensation to equity incentives as measured by the

CEO inside debt, CEO deferred inside debt, and CEO pension inside debt measures used above.

Consistent with our prior analyses, we expect to find that boards with greater diversity facilitate

higher pension inside debt compensation relative to equity compensation.

Using data on announcements of director appointments from 2010 to 2015, we employ

alternative matching approaches to insure the robustness of our results. We begin by identifying

changes in the directors from RiskMetric/ISS director level dataset. We then obtain exact dates

when the director appointments were announced using Lexis-Nexis and Mergent online.11 Our

initial sample includes 1,835 director appointment announcements. This sample included 314

announcements of female and 1,521 male director appointments respectively. We then exclude

contaminated announcements, which are announcements accompanied by potentially confounding

events such as mergers, dividend declaration, stock splits, tender offers, new product

announcements, charter amendments, large order announcements or substantial changes in capital

structure. After applying these filters and merging with our bond rating dataset, we are left with

1,004 announcements of which 219 are for female directors.

We identify one (alternatively, up to five) matched control firm(s) based on the year of the

announcement, industry (Fama-French 49) and size (closest total assets within plus / minus 25

11 These databases include the Wall Street Journal, Financial Times and New York Times, as well as other business

news sources.`

28

percent of the sample firm’s total assets) for each sample firm. Following the cross-sectional

analyses, we exclude sample and matched financial and utility firms. Using each treated firm and

its matched firm counterpart, we construct a four-year panel spanning (-2, +1) where year-zero is

the year of the appointment announcement. We create a Treated dummy variable equal to one for

treated firms and zero for matched control firms. To test if the effect of gender diversity on

compensation incentives on treated firms, we create a Post dummy variable equal to one for the

years (0, +1) relative to the appointment year for each treated and control firm. We test the

incremental impact of outside director board appointments on the CEO incentive outcome

variables by interacting Treated with Post using the following regression model:

Outcome variable = α0 + α1Treated + α2Post + α3Treated × Post + Controls + eit (3)

In addition to Treated, Post, and Treated × Post, we include industry- and year fixed

effects. The coefficient estimate α3 measures the net difference in the relative proportion of CEO

inside debt to equity incentives associated with the appointment of outside board members relative

to matched firms. The results of the differences-in differences estimation are presented in Table 9.

In Panel A, we present estimates using female board appointments, and in Panel B we repeat the

analysis using male appointments. In Models 1-3, we provide results using the closest size-

matched control firm, and in Models 4-6 we employ (up to) five matched firms if available based

on the size constraint. In Model (1), we use the logged inside debt ratio as the outcome variable

where the numerator is the sum of deferred and pension compensation. The α3 coefficient estimate

is positive, but not significantly different from zero. Our prior cross-sectional results demonstrate

that greater diversity significantly (insignificantly) tilts the balance of pension (deferred income)

compensation to equity incentives; in the difference-in-differences context, the effect should be

reflected by a significantly positive (insignificant) α3 coefficient estimate. Models 2-3 support our

29

earlier findings: using the logged Pension inside debt ratio, the α3 coefficient estimate is positive

and significant at the 5 percent level. In contrast, the α3 estimate using Deferred inside debt as the

outcome variable is negative and statistically not different from zero. As Models 4-6 illustrate, we

obtain corroborating evidence using (up to) five matched firms. In Panel B, we repeat this process

using male board appointments. The α3 estimates contrast sharply with those obtained from female

appointments. As illustrated in Model 1 (Model 3), the Treated × Post estimates are significantly

negative using 1:1 (1:5) matching algorithms using the aggregate CEO Inside debt ratio as the

outcome variable. This effect persists after decomposing the numerator into pension (Model 2 and

Model 4) and deferred compensation (Model 3 and Model 6) components. Overall, the difference-

in-difference results provided further support for the notion that greater board gender diversity is

associated with a tilt in the balance of pension debt-like compensation relative to equity incentives.

4. Board Gender Diversity and the Efficacy of CEO Inside Debt Contracting

4.1. Cross-Sectional Variation in the Gender Diversity Effect

Hypotheses 2a-2d collectively predict that the association between board diversity and

inside debt compensation varies according to firm, industry, and CEO characteristics. As discussed

above, we expect presence of female directors to be associated with greater debt-like compensation

in those firms that face greater uncertainty associated with financial risk. In Table 10 Panel A,

Models 1-2 include interactions between Independent women directors and Lagged leverage using

the annual lagged change in pension value and the logged pension balance as dependent variables,

respectively. Consistent with the prediction of Hypothesis 2a, the interaction term is positive and

significant indicating that the marginal effect of Independent women directors becomes larger for

higher levels of Lagged leverage. In a similar vein, we also expect to see a smaller debt-like

30

pension compensation component in the compensation mix of CEOs of firms with greater growth

prospects as measured by R&D expenditure. To incentivize managers to high growth risky assets,

optimal CEO contracts are likely to include a higher proportion of equity incentives relative to

debt incentives. Consistent with the prediction of Hypothesis 2b, Panel A Models 3-4 show that

the incremental effect of Independent women directors becomes smaller as lagged R&D becomes

higher.

Table 10 Panel B examines interactions between Independent women directors and market

concentration in Models 1-2 and with CEO characteristics in Models 3-4. The Independent women

directors × HHI interaction tests if the marginal effect of Independent women directors on debt-

like compensation becomes stronger in less competitive (lower HHI) industries. The results

provided in Panel A Models 1-2 support this premise: the interaction is positive and significant at

the 1 percent (5 percent) levels using the logged change in pension value (logged pension balance),

respectively. In Models 3-4 we examine if the impact of Independent women directors on debt-

like compensation varies in CEO age. Consistent with the findings of Cheng (2004), who shows

that CEOs are more likely to promote short term policies if they are closer to retirement and more

of their wealth is tied to the firm’s equity, the Independent women directors × CEO age interaction

is positive and significant at the 10 percent (1 percent) levels using the annual change in pension

value (pension balance) as the dependent variable. Overall, Table 10 supports the predictions of

Hypotheses H2a-H2d by demonstrating the effect of Independent women directors on pension

inside debt compensation varies according to firm, industry, and CEO characteristics.

4.2 Gender Board Diversity and Optimal Inside Debt Compensation

The predictions of Hypotheses 2a-2d conjecture that board gender diversity enhances

efficacy of CEO inside debt compensation contracting. To further enhance our understanding of

31

gender diversity’s impact on CEO contracting, we examine if the presence of women independent

outside directors is associated with the optimal use of inside debt compensation using the

methodological framework of Campbell, Galpin and Johnson (2016). Campbell et al.’s (2016)

empirical approach is to estimate first stage regressions of logged CEO relative debt ratios on

optimal contracting variables and industry indicators. In the second stage, they regress the change

in the relative debt ratio (from t-1 to t) on the lagged t-1 residual from the first-stage regression

model and the additional contracting variables. They interpret the lagged residual as the deviation

from the optimum and demonstrate that the coefficient estimate is negative and significant

implying that firms adjust their CEO relative debt ratios towards the optimum predicted by

contracting variables: ratios that are above optimal (below optimal) in the prior year are associated

with decreases (increases) in the ratio to the following year. We follow this approach using the

specification of Equation (2) to calculate the deviation from the optimal relative debt ratio and

regress the winsorized change in the relative debt ratio on the lagged residual and additional control

variables specified by Equation (2).

We present the results of this analysis in Table 11. In Model 1, consistent with the results

of Campbell et al. (2016) and supporting the intuition that firms adjust the CEOs relative debt ratio

towards the predicted optimum, the lagged Relative debt residual is negative and significant at the

1 percent level. We examine the effect of gender board diversity on this relation. In Models 2-3,

we sort the sample into Independent women director terciles. In the context of our results above,

we expect the adjustment to be stronger when gender board diversity is higher and pension

compensation is more likely to be used to increase CEO-bondholder incentive alignment. Models

2-3 show that the Lag(Relative debt residual) coefficient estimate is larger in magnitude in the top

Independent women directors tercile compare to the lowest tercile, however the estimates are

32

insignificantly different as demonstrated by the Chi-2 statistic in the final row. In Models 5-6, we

provide a similar analysis using the Deferred relative debt ratio. Similar to the results using the

Relative debt ratio, the Lag(Relative debt residual) is larger in the top Independent women

directors tercile segment of the sample however is statistically indistinguishable from the lowest

tercile estimate. In contrast, Models 8-9 show that the adjustment coefficient is statistically

stronger when there is a greater proportion of women independent directors. Consistent with our

earlier results, the coefficient estimate based on the top tercile Independent women directors subset

is significantly larger (at the 1 percent level) than in the bottom tercile. Viewed collectively, these

results support our earlier findings and add insight to the findings of Campbell et al. (2016) by

demonstrating that the adjustment process varies by the form of debt-like compensation and board

member preferences as revealed by the mix of CEO compensation components.

5. Gender Diversity and the Cost of Debt Capital

The empirical inside debt literature broadly supports the view that managerial debt-like

compensation encourages more conservative operating policies. For example, Sundaram and

Yermack (2007) show that level of managerial pension holdings is negatively associated with the

probability of default, suggesting that managers with high inside debt behave more conservatively.

Consistently, Cassell et al. (2012) find that when CEO inside debt is high, future stock return

volatility, R&D expenditure and financial leverage are lower, and the extent of diversification and

asset liquidity are higher. Phan (2014) shows that firms with high CEO-firm relative debt-equity

ratios have a greater likelihood of engaging in diversifying acquisitions. Further, Liu, Mauer and

Zhang (2014) find that firms in which CEOs have a higher level of inside debt have significantly

higher cash holdings. With respect to the debt contracting implications of these findings,

33

Anantharaman et al. (2014) present empirical evidence that bank loans have higher prices and

fewer covenants when the CEO-firm relative debt-to-equity ratio is higher. To the extent greater

gender diversity is associated with a higher proportion of CEO debt-like compensation, and based

on the premise that inside debt promotes less managerial risk-taking, then issuers with gender

diverse boards should be associated with a lower cost of debt demanded by investors.

We examine changes in yield spread around the male and female board appointment

announcements employed in the difference-in-differences analysis presented in Table 9. Beginning

with Rosenstein and Wyatt (1990), a line of research (e.g. Lin, Pope and Young, 2003) investigates

the effects of changes in board composition on firm performance and value by analyzing the

market reaction to the announcement of outsider appointments to boards. As Hermalin and

Weisbach (2003) discuss, this approach is a cleaner test of the relation between board composition

and ultimate value compared to cross-sectional analyses. In our context, an additional benefit is

that the prevalence of board appointment announcements affords a relatively large primary sample

size. Following our prior results, we expect a stronger price impact on bonds associated with

announcements of female vs. male board appointees.

Our empirical approach is based on two transaction-level sources of bond price / yield

spread data. First, the Mergent Fixed Income Securities Database (FISD) Transactions file reports

trades made by insurance companies from 1994-2011. We combine this dataset with the Trade

Reporting and Compliance Engine (TRACE), which reports secondary market transactions for

investment grade and high yield debt beginning in 2005 and eliminate duplicate transactions. We

convert individual purchase and sale transactions reported in the FISD and TRACE files to an

aggregate trade-weighted daily yield to maturity using the par amounts of each transaction as

weights. We limit our sample to non-convertible fixed rate debt for which a conventional yield-to-

34

maturity can be calculated. Following the earlier analyses above, we exclude financial- and utility-

classified issuers. We identify all bond transactions occurring within a maximum of 180 days prior

to- and following the announcement date for given firm and select the two closest transactions

prior to- and following the announcement date. The resulting final (bond-level) sample consists of

989 pairs of yield spreads associated with 692 individual bonds issued by 166 unique firms. Table

12 Panel A provides summary statistics for this sample. The typical (median) number of days for

the closest transaction prior to (following) the announcement date is 20 days (19 days). The typical

bond has approximately 6.4 years to maturity at the time of the announcement, and has a Moody’s

rating of Ba3 (numerical rating equivalent of 9).

Table 12 Panel B provides mean (median) yield spreads using the unbalanced sample of

available bonds. For the overall sample, there are a total of 1,004 pairs of yield spreads

corresponding to our sample selection criteria. The mean (median) yield spread corresponding to

the closest date prior to the announcement is 2.4 percent (1.8 percent) and is 2.27 percent (1.63

percent) after. The change in spread is 13 basis points (8 basis points), which is statistically

different from zero at the 1 percent level using the standard t-statistic (Wilcoxon signed rank)

statistic. We segment the full announcement sample into male and female director announcements.

In preliminary support for Hypothesis 3, the typical (median) decrease in spread for the 785 yield

spread pairs associated with male announcements is 5 basis points, compared to 25 basis points

for female director announcements. Following prior literature and motivated by our hypotheses,

we focus on subsets where the incremental effect of greater board diversity should be strongest i.e.

where bondholders have the most to gain from increased interest alignment. First, we examine the

effect of male and female board appointments on high yield issuers (i.e., where the Moody’s rating

is less than Ba1 or lower). As illustrated in Panel B using the unbalanced panel of bonds, male

35

director appointments are associated with statistically insignificant changes in yield spread. In

contrast, female appointments are associated with a mean (median) yield spread decrease of 52

basis points (26 basis points) that is statistically significant at the 1 percent level. Second, we

examine the impact on firms with the greatest ex ante potential for shareholder-bondholder agency

conflicts. We define these firms using the bottom tercile CEO-firm relative debt ratios. Following

our prior results, we use pension debt to gauge CEO bondholder incentive alignment. We find that

while male appointments are associated with small changes in spread, the subset of female

announcements within this subset are associated with mean (median) changes in spread of 40 basis

points (23 basis points). These changes are significant at the 5 percent level.

The unbalanced sample in Panel B consists of varying numbers of issues across issuers.

Thus, the results may be biased by the presence of a firm with a relatively large number of

outstanding issues. In Panel C, we choose one bond issue for each issuer on a given announcement

date based on the issue with the largest par amount outstanding. We repeat the analyses of Panel B

and illustrate the results in Panel C. Viewed collectively, the results reflect those of Panel B: female

board appointments are associated with negative changes in yield spread that are approximately

twice that of male appointments. For high yield issuers, female (male) appointments are associated

with strongly significant (insignificant) changes in spread. Likewise, female (male) appointments

for firms in the lowest CEO-firm relative debt tercile are associated with statistically significant

(insignificant) changes in spread.

7. Conclusions

We examine the association between gender representation on corporate boards and CEO

inside debt compensation. Our results point to a strong and consistent association between board

36

gender diversity and pension debt-like compensation, which features bond-like payoffs. These

results are robust to multiple controls for endogeneity. To the best of our knowledge, our study is

the first to provide a systematic understanding of the board level dynamics relating to gender

diversity that have a bearing on CEO inside debt compensation. Further, we find that gender

diversity plays a role in the use of inside debt according to firm, industry, and CEO characteristics

that are associated with greater financial and operating risks, the firm’s competitive environment,

and the CEO’s horizon. In addition, gender diversity affects the adjustment process towards the

optimal CEO compensation mix. In line with the premise that the association between greater

diversity and inside debt compensation promotes greater alignment with the firm’s fixed income

claimants, we find that bond market participants respond positively to female board member

announcements. Overall, our results show that gender board diversity is a significant, yet

unstudied, factor in the CEO compensation mix that in turn affects the firm’s cost of debt capital.

37

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Table 1A: Summary Statistics Table 1A provides descriptive statistics based on numbers of observations used in the cross-sectional analyses. We provide

additional details about the construction of each variable in the Appendix.

No. Obs. Mean St. Dev. 25th Quartile Median 75th Quartile

Panel A: Board Characteristics

Independent women directors 14,658 0.097 0.092 0 0.100 0.154

CEO chair 14,658 0.728 0.445 0 1 1

Independent directors 14,658 0.724 0.157 0.625 0.750 0.857

Board size 14,658 9.064 2.290 7 9 10

Panel B: CEO Compensation Components ($M), Inside and Relative Debt Ratios

Total compensation (TDC1) 14,658 5,936 9,864 1,768 3,695 7,073

Equity compensation 14,658 4,681 9,404 903 2,554 5,683

Base salary 14,658 773 407 514 720 975

Cash bonus 14,658 482 1,543 0 0 522

Deferred compensation contribution 7,948 215 1,232 0 0 64

Deferred compensation balance 7,948 2,683 10,044 0 130 1,717

Change in pension value 7,948 503 1,368 0 0 208

Pension balance 7,948 3,051 7,912 0 0 1,812

Inside debt ratio 7,617 0.257 0.680 0 0.044 0.266

Relative debt ratio 6,559 3.332 14.185 0 0.320 1.574

Panel C: CEO Current Compensation Control Variables

Firm size ($MM) 14,658 8,017 30,442 658 1,696 5,196

Stock return 14,658 0.151 0.396 -0.059 0.153 0.355

Lagged stock return 14,658 0.171 0.402 -0.047 0.170 0.381

ROA 14,658 0.051 0.099 0.024 0.057 0.095

Lagged ROA 14,658 0.053 0.099 0.026 0.059 0.097

Lagged leverage 14,658 0.205 0.170 0.047 0.195 0.315

Lagged book-to-market 14,658 0.605 0.253 0.416 0.594 0.781

Lagged cash flow volatility 14,658 0.042 0.040 0.017 0.029 0.051

Lagged capital expenditure 14,658 0.055 0.052 0.021 0.038 0.069

Lagged tangibility 14,658 0.271 0.217 0.105 0.204 0.377

Lagged sales growth 14,658 0.069 0.125 0.006 0.053 0.114

Lagged R&D expenditure 14,658 0.047 0.102 0.000 0.004 0.051

CEO tenure (years) 14,658 7.390 7.423 2 5 10

Panel D: Additional CEO Debt-like Compensation Control Variables

Firm age (years) 7,948 27.124 19.542 13.548 20.788 38.071

CEO age (years) 7,948 55.853 6.984 51 56 60

Tax loss indicator 7,948 0.838 0.369 1 1 1

HHI 7,948 0.187 0.177 0.061 0.141 0.231

Liquidity constraint 7,948 0.031 0.172 0 0 0

43

Table 1B: Univariate Analysis This table presents results from difference-in-mean tests for various firm, board and CEO characteristics between

firms with board gender diversity and firms with no board gender diversity. Firms with board gender diversity are

those firms that have at least one independent female director. Firms with no board gender diversity have no

independent female board member. All other variables are defined in the Appendix. ***, **, and * correspond to

significance at the 1, 5, and 10 percent level, respectively.

Panel A: Compensation

Measures

Firms with Board

Diversity

No Board Diversity Difference t-statistic

Log(TDC1) 8.448 7.8667 0.582*** 29.953

Log (Equity) 8.043 7.179 0.864*** 24.881

Log(Salary) 6.683 6.366 0.317*** 18.811

Log (Bonus) 1.000 1.412 -0.412*** 7.899

Log(Change in Pension Value) 2.976 1.198 1.778*** 31.478

Log (Pension Balance) 4.072 1.621 2.451*** 33.031

Panel B: Financial Characteristics

Firm size 8.382 7.057 1.325*** 46.428

ROA 0.051 0.045 0.006** 2.459

Book-to-Market 0.656 0.647 0.009 1.604

Cash flow volatility 0.031 0.048 -0.017*** 18.742

R&D 0.032 0.050 0.018*** 10.565

Panel C: CEO and Board Characteristics

Log(CEO tenure) 1.720 1.983 0.263*** 13.597

CEO Chair 0.746 0.701 0.045*** 4.788

Ln(Board Size) 2.273 2.034 0.239*** 50.326

Independent Directors 0.807 0.731 0.076*** 29.804

44

Table 2: Board Gender Diversity and CEO Current Compensation Components Table 2 provides regression coefficient estimates using the log of (1 plus) each CEO compensation component as the

dependent variables over the 1998-2015 sample period. Models 1-4 use include Fama-French 49 industry effects while Models

5-8 include firm-level fixed effects. P-values based on robust cluster-adjusted standard errors are in parentheses. ***, **, and

* correspond to significance at the 1, 5, and 10 percent level, respectively.

Model (1)

Log (TDC1)

Model (2)

Log (Equity)

Model (3)

Log (Salary)

Model (4)

Log (Bonus)

Model (5)

Log (TDC1)

Model (6)

Log (Equity)

Model (7)

Log (Salary)

Model (8)

Log (Bonus)

Independent women directors 0.0789 -0.0083 0.3889*** -0.1417 -0.1252 -0.0051 0.2952*** 0.4236 (0.613) (0.323) (0.006) (0.733) (0.281) (0.580) (0.002) (0.272)

CEO chair 0.0853*** 0.0026 0.0196 0.0485 0.0443** 0.0013 -0.0234* 0.0818

(0.002) (0.210) (0.413) (0.442) (0.020) (0.488) (0.095) (0.163) Independent directors 0.6230*** -0.0257*** 0.2639*** -0.0417 0.2710*** -0.0152** 0.0959** 0.7514***

(0.000) (0.001) (0.001) (0.868) (0.000) (0.039) (0.044) (0.001)

Log (Board size) 0.1144 -0.0174** 0.2216*** 0.0535 -0.0347 -0.0066 0.0127 0.4044** (0.120) (0.018) (0.001) (0.764) (0.473) (0.183) (0.693) (0.013)

Firm size 0.4254*** 0.0008 0.1527*** 0.2007*** 0.3604*** -0.0002 0.1574*** 0.1205**

(0.000) (0.447) (0.000) (0.000) (0.000) (0.917) (0.000) (0.049)

Stock return 0.2303*** 0.0037 -0.0489* 0.9657*** 0.2406*** 0.0043* 0.0071 0.8317***

(0.000) (0.267) (0.055) (0.000) (0.000) (0.075) (0.621) (0.000)

Lagged stock return 0.1774*** -0.0050* 0.0191 0.4398*** 0.1551*** -0.0047** 0.0151 0.4209*** (0.000) (0.062) (0.442) (0.000) (0.000) (0.021) (0.387) (0.000)

ROA 0.1854 -0.0199 0.2895** 2.5009*** 0.2486** -0.0243** 0.2881*** 2.7119***

(0.319) (0.245) (0.046) (0.000) (0.027) (0.024) (0.000) (0.000) Lagged ROA 0.0082 -0.0307** 0.0243 -0.2103 -0.1075 -0.0081 -0.0147 -0.3083

(0.949) (0.019) (0.809) (0.513) (0.356) (0.404) (0.861) (0.311)

Lagged leverage 0.1714* -0.0162** 0.1677** -0.0336 -0.3802*** 0.0106* -0.0059 -0.5763*** (0.054) (0.023) (0.034) (0.880) (0.000) (0.062) (0.903) (0.008)

Lagged book-to-market -0.4624*** -0.0157** 0.1643*** 0.0340 -0.5442*** -0.0148*** 0.0181 0.4839***

(0.000) (0.036) (0.007) (0.854) (0.000) (0.005) (0.657) (0.009) Lagged cash flow volatility 0.1837 0.0299 -0.6957 -0.5990 -0.0163 0.0083 0.2307 -1.6131*

(0.696) (0.332) (0.145) (0.501) (0.961) (0.762) (0.254) (0.057)

Lagged capital expenditure 0.1632 -0.0137 -0.5832** -0.5346 -0.0654 -0.0265 -0.1098 -1.4153* (0.633) (0.574) (0.047) (0.599) (0.783) (0.369) (0.620) (0.078)

Lagged tangibility -0.4776*** -0.0041 0.0450 0.0854 -0.3008** -0.0039 0.1629* 0.2275

(0.000) (0.665) (0.695) (0.760) (0.014) (0.767) (0.088) (0.586) Lagged sales growth 0.0450 0.0076 -0.3239*** 0.0576 0.2148** -0.0002 0.0494 -0.0408

(0.734) (0.446) (0.003) (0.821) (0.013) (0.980) (0.445) (0.851)

Lagged R&D 0.4930** 0.0028 -0.0848 -0.9958** -0.0997 -0.0010 -0.0760 -1.7204*** (0.018) (0.882) (0.607) (0.033) (0.724) (0.960) (0.625) (0.003)

Log (1+CEO tenure) -0.0292* 0.0034** 0.0632*** -0.0663* -0.0164* 0.0038*** 0.0869*** -0.1365***

(0.084) (0.015) (0.000) (0.091) (0.080) (0.000) (0.000) (0.000)

FFI49 fixed effects Yes Yes Yes Yes

Firm fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

No. Obs. 14,658 14,658 14,658 14,658 14,658 14,658 14,658 14,658

R-squared 0.476 0.058 0.256 0.481 0.726 0.451 0.694 0.639 F-statistic 111.5 2.050 37.60 24.56 68.06 2.040 46.49 321.5

45

Table 3: Board Gender Diversity and CEO Inside Debt Compensation Components Table 3 provides regression coefficient estimates using the log of (1 plus) CEO debt-like compensation components as the

dependent variables over the 2006-2015 sample period. Models 1-4 use include Fama-French 49 industry effects while Models

5-8 include firm-level fixed effects. P-values based on robust cluster-adjusted standard errors are in parentheses. ***, **, and

* correspond to significance at the 1, 5, and 10 percent level, respectively.

Model (1)

Log (Deferred

Contribution)

Model (2)

Log (Deferred

Balance)

Model (3)

Log (Change in

Pension Value)

Model (4)

Log (Pension

Balance)

Model (5)

Log (Deferred

Contribution)

Model (6)

Log (Deferred

Balance)

Model (7)

Log (Change in

Pension Value)

Model (8)

Log (Pension

Balance)

Independent women directors 0.6950 2.2497** 2.7881*** 4.2859*** 0.2638 0.0391 0.5345 0.8820*** (0.273) (0.013) (0.000) (0.000) (0.575) (0.933) (0.236) (0.009)

CEO chair -0.0064 -0.1627 0.3886*** 0.4672*** 0.0118 0.0287 0.0735 0.0403

(0.951) (0.265) (0.000) (0.001) (0.850) (0.659) (0.162) (0.395) Independent directors 0.9755** 2.2273*** 1.7501*** 2.6028*** 0.7851** 0.9400** 0.4601 0.0949

(0.039) (0.001) (0.000) (0.000) (0.021) (0.020) (0.103) (0.681)

Log (Board size) 0.8708*** 2.0022*** 0.7161** 1.2680*** -0.2642 0.4432** 0.1413 0.2071 (0.002) (0.000) (0.017) (0.002) (0.193) (0.033) (0.480) (0.229)

Firm size 0.3411*** 0.7150*** 0.5231*** 0.5983*** 0.0174 0.4355*** 0.3470*** 0.4467***

(0.000) (0.000) (0.000) (0.000) (0.827) (0.000) (0.000) (0.000) Stock return -0.1400 0.0202 -0.0078 -0.1124 -0.1541** 0.0130 -0.0236 -0.0741*

(0.105) (0.868) (0.932) (0.344) (0.013) (0.842) (0.652) (0.090)

Lagged stock return -0.0105 -0.1505 0.0099 -0.0149 0.0360 -0.1068* 0.0727 -0.0344 (0.905) (0.238) (0.917) (0.898) (0.553) (0.094) (0.203) (0.441)

ROA 0.9759** 1.2363* 0.7944 1.2373* 1.2452*** 0.3377 0.6959** 0.6088***

(0.038) (0.071) (0.105) (0.052) (0.000) (0.253) (0.040) (0.010) Lagged ROA -0.2053 0.5332 1.0408** 1.0867* 0.3795 0.1578 0.4724* 0.2105

(0.628) (0.357) (0.015) (0.059) (0.168) (0.575) (0.063) (0.392)

Lagged leverage -0.3556 -0.3550 0.7006* 0.9661* -0.1973 -0.2055 -0.1478 0.0936 (0.344) (0.508) (0.085) (0.079) (0.422) (0.445) (0.531) (0.640)

Lagged book-to-market -0.3988 -0.7406* 0.1036 0.3913 -0.1184 -0.4746** 0.4200** 0.3142**

(0.176) (0.089) (0.741) (0.367) (0.533) (0.014) (0.020) (0.034) Lagged cash flow volatility -4.1427*** -5.3037** -0.8462 -0.6843 0.0246 -0.8791 1.1768* 1.6090***

(0.002) (0.024) (0.583) (0.747) (0.975) (0.296) (0.093) (0.005)

Lagged capital expenditure 0.6790 -1.9552 -2.8891 -5.5026** 0.3145 -0.1625 -0.3951 -0.0969 (0.672) (0.391) (0.106) (0.023) (0.683) (0.840) (0.549) (0.849)

Lagged tangibility -0.1106 0.0970 0.6421 1.5104* -1.3839*** -0.8331 0.4685 0.6849*

(0.840) (0.900) (0.265) (0.063) (0.002) (0.117) (0.283) (0.059) Lagged sales growth -0.5541 -1.8335*** -1.9138*** -2.5630*** 0.1055 0.0043 -0.0055 -0.0964

(0.216) (0.002) (0.000) (0.000) (0.679) (0.985) (0.980) (0.540)

Lagged R&D -2.0123** -3.0006** -5.0613*** -7.5280*** 1.1090 -0.3482 -0.6672 -1.2868** (0.030) (0.044) (0.000) (0.000) (0.385) (0.759) (0.256) (0.023)

Log (1+CEO tenure) -0.0225 0.3190*** 0.0161 0.1369 0.1885*** 0.6765*** 0.2408*** 0.4986***

(0.722) (0.001) (0.810) (0.142) (0.000) (0.000) (0.000) (0.000) Log (1+Firm age) 0.1068 0.4312*** 0.6890*** 0.9235*** 0.1217 0.2235 0.1566 0.1242

(0.259) (0.001) (0.000) (0.000) (0.602) (0.336) (0.451) (0.545)

Log (CEO age) -0.3650 0.8183 0.9716* 2.0030*** -0.8943** -1.0379** 0.4059 1.7528*** (0.432) (0.240) (0.051) (0.004) (0.023) (0.050) (0.284) (0.000)

Tax loss indicator 0.1843 0.3376 -0.0380 -0.0772 0.0864 0.2121** 0.0578 -0.0999 (0.237) (0.160) (0.808) (0.724) (0.366) (0.021) (0.518) (0.190)

HHI 0.0141 -0.3892 0.0362 -0.0988 2.0003*** 1.5867*** -0.9295 0.0788

(0.976) (0.498) (0.935) (0.881) (0.000) (0.003) (0.106) (0.836) Liquidity constraint 0.3027 0.3684 0.3847 0.3524 0.2375* 0.0380 0.2604** 0.2016**

(0.152) (0.264) (0.114) (0.255) (0.055) (0.785) (0.022) (0.022)

FFI49 fixed effects Yes Yes Yes Yes

Firm fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

No. Obs. 7,948 7,948 7,948 7,948 7,948 7,948 7,948 7,948

R-squared 0.186 0.341 0.398 0.414 0.749 0.882 0.841 0.942

F-statistic 8.489 24.35 29.29 29.30 4.696 12.26 11.44 12.76

46

Table 4: Lagged Board Gender Diversity and CEO Inside Debt Compensation Components Table 4 provides regression coefficient estimates using the log of (1 plus) CEO debt-like compensation components for the

following fiscal year as the dependent variables over the 2006-2015 sample period. ***, **, and * correspond to significance

at the 1, 5, and 10 percent level, respectively.

Model (1)

Log (Deferred Contribution)t+1

Model (2)

Log (Deferred Balance) t+1

Model (3)

Log (Change in Pension Value) t+1

Model (4)

Log (Pension Balance) t+1

Independent women directors 0.7633 2.1617** 2.8935*** 4.4078***

(0.241) (0.021) (0.000) (0.000)

CEO chair 0.0180 -0.1786 0.3760*** 0.4869*** (0.865) (0.225) (0.000) (0.001)

Independent directors 0.9570** 2.4316*** 1.5629*** 2.5106***

(0.047) (0.001) (0.002) (0.000) Log (Board size) 0.9275*** 1.9532*** 0.6997** 1.2719***

(0.001) (0.000) (0.019) (0.003)

Firm size 0.3322*** 0.7042*** 0.5006*** 0.6037*** (0.000) (0.000) (0.000) (0.000)

Stock return 0.2104** 0.1610 0.0110 -0.0688

(0.019) (0.211) (0.906) (0.560) Lagged stock return -0.0247 -0.1103 -0.0140 -0.0337

(0.786) (0.394) (0.878) (0.781)

ROA 0.5758 1.8820*** 0.9424** 1.3589** (0.237) (0.008) (0.040) (0.035)

Lagged ROA -0.2059 0.4359 0.7568* 0.9981*

(0.628) (0.468) (0.082) (0.081) Lagged leverage -0.3175 -0.3459 0.7147* 0.8874

(0.418) (0.527) (0.080) (0.111)

Lagged book-to-market -0.4788 -0.7476* 0.1188 0.3410 (0.121) (0.096) (0.703) (0.437)

Lagged cash flow volatility -4.3101*** -5.7304** -0.9406 -0.9963

(0.001) (0.015) (0.539) (0.644) Lagged capital expenditure 1.1940 -1.7349 -2.4473 -5.4153**

(0.459) (0.448) (0.180) (0.027)

Lagged tangibility -0.2322 0.0720 0.4335 1.4422* (0.674) (0.926) (0.451) (0.079)

Lagged sales growth -0.7191 -1.7984*** -1.8474*** -2.6783***

(0.107) (0.003) (0.000) (0.000)

Lagged R&D -1.9988** -3.2828** -5.1863*** -7.6074***

(0.036) (0.030) (0.000) (0.000)

Log (1+CEO tenure) -0.1021 0.1688* -0.0319 0.0949 (0.114) (0.086) (0.634) (0.315)

Log (1+Firm age) 0.1104 0.4228*** 0.6774*** 0.9191*** (0.255) (0.001) (0.000) (0.000)

Log (CEO age) -0.3732 0.7288 0.8637* 1.6785**

(0.437) (0.301) (0.081) (0.016) Tax loss indicator 0.1317 0.3642 -0.0570 -0.0610

(0.415) (0.135) (0.714) (0.785)

HHI 0.1591 -0.1914 0.1675 -0.1274 (0.749) (0.743) (0.709) (0.847)

Liquidity constraint 0.3081 0.4460 0.2849 0.3911

(0.138) (0.177) (0.223) (0.211) FFI49 fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

No. Obs. 7,810 7,810 7,810 7,810

R-squared 0.181 0.335 0.395 0.410 F-statistic 8.351 24.24 28.97 28.89

47

Table 5: Board Gender Diversity and CEO Inside Debt Compensation:

Two Stage Least Squares and Heckman Selection Estimations Table 5 provides two-stage least squares and Heckman model regression coefficient estimates using the logs of (1 plus) CEO

change in pension value and the aggregate pension balance as the second-stage dependent variables over the 2006-2015 sample

period. P-values based on robust cluster-adjusted standard errors are in parentheses. ***, **, and * correspond to significance

at the 1, 5, and 10 percent level, respectively.

Two Stage Least Squares Estimates Heckman Selection Model Estimates

Model (1) Model (2) Model (3) Model (4) Model (5)

Independent

women directors

Log (Pension

Balance) t+1

Log (Change in

Pension Value) t+1

Log (Pension

Balance) t+1

Log (Change in

Pension Value) t+1

First-stage estimates

Second-stage estimates

Second-stage estimates

Second-stage estimates

Second-stage estimates

Board gender diversity county ratio 0.027**

(0.041) Independent women directors IV 51.512** 36.131** 3.670*** 1.726***

(0.011) (0.014) (0.000) (0.001)

CEO chair 0.012*** 0.041 0.091 0.457*** 0.390*** (0.000) (0.855) (0.574) (0.000) (0.000)

Independent directors 0.183*** -4.244 -3.060 3.772*** 2.293***

(0.000) (0.162) (0.169) (0.000) (0.000) Log (Board size) 0.042*** -0.335 -0.416 1.582*** 0.955***

(0.000) (0.661) (0.460) (0.000) (0.000)

Firm size 0.012*** -0.000 0.093 0.597*** 0.501*** (0.000) (0.999) (0.620) (0.000) (0.000)

Stock return -0.005 -0.052 0.076 -0.186 0.083

(0.112) (0.636) (0.375) (0.211) (0.494) Lagged stock return -0.007** 0.333* 0.285** 0.005 0.068

(0.018) (0.066) (0.034) (0.971) (0.566)

ROA 0.002 0.977* 0.811* 0.431 0.521 (0.888) (0.089) (0.068) (0.589) (0.425)

Lagged ROA -0.010 0.186 0.477 1.381* 1.493**

(0.536) (0.749) (0.279) (0.061) (0.013) Lagged leverage -0.001 0.800* 0.594* 1.326*** 0.974***

(0.940) (0.090) (0.087) (0.000) (0.000)

Lagged book-to-market -0.017 1.046** 0.620* 0.454 0.027

(0.114) (0.046) (0.098) (0.114) (0.909)

Lagged cash flow volatility -0.016 -0.879 -0.622 -2.739* -2.228*

(0.761) (0.638) (0.647) (0.067) (0.068) Lagged capital expenditure -0.094** -0.784 -0.344 -3.953** -2.978**

(0.045) (0.745) (0.847) (0.011) (0.019)

Lagged tangibility 0.023 1.294 0.836 2.089*** 1.486*** (0.178) (0.114) (0.146) (0.000) (0.000)

Lagged sales growth -0.072*** 0.183 0.161 -2.393*** -1.349***

(0.000) (0.871) (0.847) (0.000) (0.001) Lagged R&D -0.005 -6.492*** -4.617*** -7.035*** -5.004***

(0.852) (0.000) (0.000) (0.000) (0.000)

Log (1+CEO tenure) -0.006*** 0.432*** 0.168 0.274*** 0.004 (0.002) (0.005) (0.130) (0.000) (0.936)

Log (1+Firm age) 0.005 0.759*** 0.577*** 0.968*** 0.760***

(0.137) (0.000) (0.000) (0.000) (0.000) Log (CEO age) -0.023 3.392*** 2.039*** 3.195*** 1.768***

(0.162) (0.000) (0.000) (0.000) (0.000)

Tax loss indicator 0.002 0.266 0.175 -0.208 -0.113 (0.658) (0.226) (0.273) (0.132) (0.318)

HHI 0.023 -0.546 -0.198 0.453 0.770***

(0.182) (0.520) (0.740) (0.166) (0.004) Liquidity constraint -0.009 0.552** 0.643*** 0.549 0.692**

(0.255) (0.040) (0.003) (0.129) (0.019) Inverse Mills Ratio -2.217** -1.571**

(0.029) (0.033)

FFI49 fixed effects Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes

No. Obs. 7,810 7,810 7.810 7.810 7.810

R-squared/ Wald-Chi Sqd. 0.292 0.439 0.405 4800.37 4093.22

F-statistic 25.86 24.98 30.26

48

Table 6: Board Gender Diversity and CEO Pension Compensation:

Propensity Score Method Table 6 presents results using alternative PSM matching methods. We use a sub-sample of firms matched on their

propensity to appoint independent woman director on the basis of variables used in the 1st column of Table 5. All the

variables are defined in Appendix 1. ***, **, and * correspond to significance at the 1, 5, and 10 percent level,

respectively.

Panel A: Average Treatment Effects

Panel A1: Outcome Variable = Log (Change in Pension Value) t+1

Method Treated Controls Difference T-statistic

Unmatched 3.151 1.174 1.977*** 27.252

Nearest-Neighbor 3.151 2.539 0.612** 2.484

Radius (Caliper = 0.001) 2.904 2.274 0.160*** 3.950

Radius (Caliper = 0.010) 3.147 2.531 0.616** 2.521

Kernel 3.151 2.386 0.765*** 6.004

Panel A2: Treatment Variable = Log (Pension Balance) t+1

Method Treated Controls Difference T-statistic

Unmatched 4.198 1.536 2.662*** 28.763

Nearest-Neighbor 4.198 3.367 0.831** 2.521

Radius (Caliper = 0.001) 3.886 3.113 0.773*** 3.674

Radius (Caliper = 0.010) 4.194 3.358 0.836*** 2.572

Kernel 4.198 3.223 0.975*** 5.832

Panel B: Regression Estimates Using Nearest-Neighbor Matched Control Firms

Log (Pension Balance) t+1 Log (Change in Pension Value) t+1

Independent women directors 3.986*** 2.236**

(0.002) (0.019)

CEO chair 0.483*** 0.436***

(0.003) (0.000)

Independent directors 3.672*** 1.899***

(0.000) (0.002)

Log (Board size) 1.344*** 0.991***

(0.006) (0.005)

Additional control variables Yes Yes

FFI49 fixed effects Yes Yes

Year fixed effects Yes Yes

No. Obs. 5,520 5,520

R-squared 0.432 0.389

F-statistic 56.332 53.121

49

Table 7: Additional Tests of Robustness Table 7 Panel A tests if the Independent women directors effect varies accord to CEO gender. Panel B provides coefficient of

alternative definitions of gender board diversity. Panel C illustrates changes in CEO pension inside debt surrounding exogenous

departures of independent directors. P-values based on robust cluster-adjusted standard errors are in parentheses. ***, **, and *

correspond to significance at the 1, 5, and 10 percent level, respectively.

Panel A: Board Gender Diversity and CEO Gender

Log (Pension Balance) t+1 Log (Change in Pension Value) t+1

Independent women directors 3.441*** 1.807***

(0.000) (0.003)

Independent women directors*Female CEO -1.221 -0.040

(0.738) (0.989)

Female CEO 0.462 0.107

(0.439) (0.826)

CEO chair 0.370*** 0.372***

(0.000) (0.000)

Independent directors 1.731*** 0.869**

(0.005) (0.048)

Log (Board size) 1.373*** 1.018***

(0.000) (0.000)

Additional control variables Yes Yes

FFI49 Industry fixed effects Yes Yes

Year fixed effects Yes Yes

No. Obs. 7,810 7,810

R-squared 0.414 0.354

F-statistic 31.16 30.17

Panel B: Alternative Measures of Board Gender Diversity

Log (Pension Balance) t+1 Log (Change in Pension Value) t+1

Women directors 3.954*** 3.650*** 2.400*** 2.177***

(0.000) (0.000) (0.000) (0.000)

Employee Women directors 2.667 1.946

(0.384) (0.364)

CEO chair 0.440*** 0.499*** 0.391*** 0.426***

(0.001) (0.000) (0.000) (0.000)

Independent directors 2.879*** 3.588*** 1.615*** 2.038***

(0.000) (0.000) (0.001) (0.000)

Log (Board size) 1.372*** 1.662*** 0.968*** 1.150***

(0.000) (0.000) (0.000) (0.000)

Additional control variables Yes Yes Yes Yes

FFI49 Industry fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

No. Obs. 7,810 7,810 7,810 7,810

R-squared 0.428 0.424 0.390 0.388

F-statistic 25.64 25.19 31.53 30.79

Panel C: Exogenous Departures of Independent Directors

Departure Type No. Obs. Pre-departure Post-departure Change T-statistic

Panel C1: Mean Logged CEO Total Compensation

Independent Female Director 193 8.387 8.494 0.107 1.134

Independent Male Director 1567 8.245 8.336 0.091** 2.459

Difference in Changes 0.142 0.158 0.016 0.285

Panel C2: Mean CEO Change in Logged Pension Value

Independent Female Director 176 3.563 3.004 -0.559* 1.676

Independent Male Director 1277 2.675 2.635 -0.040 0.348

Difference in Changes 0.888 0.369 -0.519* 1.701

50

Table 8: Impact of Board Gender Diversity on CEO Inside Debt and Relative Debt Ratios Table 8 provides regression coefficient estimates using the log of (1 plus) the CEO inside debt ratio and the log of (1 plus) the

CEO-firm relative debt ratio, and components based on deferred and pension compensation over the 2006-2015 sample period.

***, **, and * correspond to significance at the 1, 5, and 10 percent level, respectively.

Model (1)

Log (Inside Debt Ratio)

Model (2)

Log (Deferred Inside Debt Ratio)

Model (3)

Log (Pension Inside Debt Ratio)

Model (4)

Log (Relative Debt Ratio

Model (5)

Log (Deferred Relative Debt Ratio)

Model (6)

Log (Pension Relative Debt Ratio)

Independent women directors 0.1803*** 0.0114 0.1854*** 1.1363*** 0.4300** 0.5605***

(0.008) (0.778) (0.001) (0.004) (0.036) (0.006) CEO chair 0.0096 -0.0107 0.0229*** 0.0383 -0.0463 0.0753***

(0.368) (0.132) (0.007) (0.561) (0.200) (0.005)

Independent director per 0.1435** 0.0648** 0.0952** 0.0191 0.3309* 0.1711 (0.010) (0.039) (0.050) (0.955) (0.069) (0.304)

Log (Board size) 0.0976*** 0.0550*** 0.0506** 0.3757* 0.1114 0.0465

(0.001) (0.006) (0.040) (0.066) (0.312) (0.640) Firm size 0.0161*** 0.0063 0.0116*** 0.0040 0.0087 0.0242

(0.005) (0.134) (0.008) (0.910) (0.668) (0.173)

Stock return -0.1113*** -0.0638*** -0.0560*** -0.3261*** -0.0220 0.0018 (0.000) (0.000) (0.000) (0.000) (0.489) (0.949)

Lagged stock return -0.0584*** -0.0380*** -0.0259*** -0.3305*** -0.1076** -0.0616*

(0.000) (0.000) (0.003) (0.000) (0.013) (0.064) ROA -0.1576 -0.0581 -0.1391 1.5227*** 0.8420*** 0.3666*

(0.115) (0.220) (0.149) (0.000) (0.000) (0.093)

Lagged ROA 0.0322 0.0329 0.0106 0.2766 0.0519 0.0453 (0.514) (0.236) (0.803) (0.397) (0.798) (0.804)

Lagged leverage 0.0214 -0.0083 0.0257 -2.3234*** -1.4032*** -0.7127***

(0.598) (0.756) (0.428) (0.000) (0.000) (0.000) Lagged book-to-market 0.0517 0.0052 0.0493 -0.5061** -0.3436*** -0.1279

(0.152) (0.809) (0.114) (0.028) (0.008) (0.240)

Lagged cash flow volatility 0.2080 -0.0192 0.2446* 0.0372 0.1232 -0.0521 (0.176) (0.856) (0.053) (0.978) (0.894) (0.916)

Lagged capital expenditure -0.4600*** -0.1552 -0.3187** -2.0239** -0.3306 -0.9232**

(0.004) (0.166) (0.011) (0.035) (0.406) (0.039) Lagged tangibility 0.0875 -0.0091 0.1038** 0.5596 0.0314 0.2830**

(0.155) (0.831) (0.028) (0.106) (0.813) (0.034)

Lagged sales growth -0.1707*** -0.1039*** -0.0863*** -1.1866*** -0.2457 -0.4110***

(0.000) (0.001) (0.007) (0.000) (0.135) (0.001)

Lagged R&D -0.3405*** -0.0597 -0.3095*** -1.7514** 0.3769 -1.1636***

(0.000) (0.293) (0.000) (0.014) (0.425) (0.001) Log (1+CEO tenure) -0.0087 0.0014 -0.0129** 0.0501 -0.0134 -0.0484***

(0.197) (0.748) (0.019) (0.218) (0.575) (0.009) Log (1+Firm age) 0.0470*** 0.0111 0.0405*** 0.2499*** 0.0311 0.1324***

(0.000) (0.140) (0.000) (0.000) (0.415) (0.000)

Log (CEO age) 0.2274*** 0.0718** 0.1880*** 1.8278*** 0.4987*** 0.8077*** (0.000) (0.026) (0.000) (0.000) (0.005) (0.000)

Tax loss indicator -0.0041 -0.0032 -0.0013 0.1513 0.0757 0.0253

(0.812) (0.776) (0.926) (0.165) (0.240) (0.604) HHI -0.0489 -0.0403 -0.0180 -0.3541 -0.0930 -0.1537

(0.232) (0.167) (0.606) (0.141) (0.501) (0.251)

Liquidity constraint -0.0222 -0.0066 -0.0247 0.3616 0.0615 0.0745 (0.482) (0.779) (0.337) (0.103) (0.629) (0.456)

FFI49 fixed effects Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

No. Obs. 7,617 7,617 7,617 6,554 6,554 6,554 R-squared 0.252 0.126 0.242 0.219 0.156 0.188

F-statistic 15.80 6.476 11.74 15.95 7.076 8.150

51

Table 9: Difference in Differences Estimates Using Director Appointments Table 9 provides difference-in-differences estimates corresponding to male and female board of director appointments.

Treated firms are matched to control firms by Fama-French 49 industry, year, and size (total assets). We provide

estimates using the closest matched firm and, alternatively, up to five matched firms. The outcome variable is the log

of CEO inside debt (pension and deferred compensation, respectively) scaled by CEO equity. We provide variable

descriptions in the Appendix. P-values based on robust cluster-adjusted standard errors are in parentheses. ***, **,

and * correspond to significance at the 1, 5, and 10 percent level, respectively.

1:1 Matching 1:5 Matching

Panel A: Female Appointments

Model (1)

Log (Inside Debt Ratio)

Model (2)

Log (Pension Inside Debt Ratio)

Model (3)

Log (Deferred Inside Debt Ratio)

Model (4)

Log (Inside Debt Ratio)

Model (5)

Log (Pension Inside Debt Ratio)

Model (6)

Log (Deferred Inside Debt Ratio)

Treated -0.0071 -0.0341 0.0263 -0.0279 -0.0322 -0.0005

(0.817) (0.192) (0.141) (0.284) (0.148) (0.973) Post 0.0139 -0.0176 0.0358* 0.0128 -0.0029 0.0140

(0.677) (0.457) (0.088) (0.460) (0.791) (0.254)

Treated × Post 0.0242 0.0568** -0.0336 0.0186 0.0307* -0.0036

(0.487) (0.027) (0.123) (0.371) (0.052) (0.790)

FFI49 Fixed effects Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

No. Obs. 585 585 585 1,331 1,331 1,331

R-squared 0.355 0.363 0.261 0.168 0.147 0.107 F-statistic 143.0 6.273 6.455 26.44 2.339 9.006

Panel B: Male Appointments

Treated 0.0334 0.0186 0.0129 0.0334 0.0218 0.0264*

(0.218) (0.357) (0.490) (0.218) (0.172) (0.058)

Post 0.0195 0.0165 0.0023 0.0195 0.0055 0.0083

(0.198) (0.153) (0.818) (0.198) (0.326) (0.149)

Treated × Post -0.0457** -0.0263* -0.0203* -0.0457** -0.0200* -0.0231**

(0.014) (0.059) (0.083) (0.014) (0.062) (0.012)

FFI49 Fixed effects Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

No. Obs. 2,238 2,238 2,238 2,238 5,028 5,028

R-squared 0.121 0.137 0.061 0.121 0.108 0.063

F-statistic 33.00 5.091 54.91 33.00 5.901 3.824

52

Table 10: Cross-Sectional Variation in the Board Gender Diversity Effect Table 10 provides regression coefficient estimates using the log of (1 plus) CEO debt-like compensation components as

the dependent variables over the 2006-2015 sample period. Panel A provides estimates for interactions of Independent

women directors with lagged leverage and lagged R&D expenditure, and Panel B includes interactions with HHI and

logged CEO age. P-values based on robust cluster-adjusted standard errors are in parentheses. ***, **, and * correspond

to significance at the 1, 5, and 10 percent level, respectively.

Panel A: Financial Risk and Operating Risk

Model (1)

Log (Change in

Pension Value)

Model (2)

Log (Pension

Balance)

Model (3)

Log (Change in

Pension Value)

Model (4)

Log (Pension

Balance)

Independent women directors 1.6395* 2.2949* 3.4387*** 5.1290***

(0.067) (0.059) (0.000) (0.000)

Independent women directors × Lagged leverage 6.0852* 10.5483**

(0.071) (0.021)

Independent women directors × Lagged R&D -17.4339*** -22.5905**

(0.006) (0.014)

Additional control variables Yes Yes Yes Yes

FFI49 fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

No. Obs. 7,948 7,948 7,948 7,948

R-squared 0.399 0.416 0.399 0.416

F-statistic 28.22 28.16 28.14 27.98

Panel B: Competitive Environment and CEO Age

Model (1)

Log (Change in

Pension Value)

Model (2)

Log (Pension

Balance)

Model (3)

Log (Change in

Pension Value)

Model (4)

Log (Pension

Balance)

Independent women directors 0.6552 2.3227* -29.2752* -64.8038***

(0.471) (0.074) (0.076) (0.005)

Independent women directors × HHI 11.5313*** 10.6142**

(0.001) (0.038)

Independent women directors × Log(CEO age) 7.9792* 17.1936***

(0.053) (0.003)

Additional control variables Yes Yes Yes Yes

FFI49 fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

No. Obs. 7,948 7,948 7,948 7,948

R-squared 0.401 0.416 0.399 0.417

F-statistic 29.32 28.49 28.06 28.33

53

Table 11: Board Gender Diversity and Optimal Compensation Policy Table 11 presents least squares coefficient estimates for models using the winsorized change in relative debt regressed on the lagged relative debt residual and additional control variables.

We provide variable descriptions in the Appendix. P-values are given in parentheses and are based on robust cluster-adjusted standard errors. ***, **, and * correspond to significance

at the 1, 5, and 10 percent level, respectively.

Relative Debt Ratio Deferred Relative Debt Ratio Pension Relative Debt Ratio

Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Model (9)

Full

Sample

Bottom Tercile

Gender Diversity

Top Tercile

Gender Diversity

Full Sample Bottom Tercile

Gender Diversity

Top Tercile

Gender Diversity

Full Sample Bottom Tercile

Gender Diversity

Top Tercile

Gender Diversity

Lag (Relative debt residual) -0.2108*** -0.2010*** -0.2087***

(0.000) (0.000) (0.000)

Lag (Relative pension debt residual)

-0.9041*** -0.9068*** -1.0043*** (0.000) (0.000) (0.000)

Lag (Relative deferred debt residual) -0.9860*** -0.7708*** -1.1173***

(0.000) (0.000) (0.000) Additional control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes

FFI49 Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

No. Obs. 5,050 1,827 1,646 4,887 1,720 1,627 4,893 1,740 1,604 R-squared 0.251 0.410 0.376 0.622 0.661 0.705 0.679 0.675 0.765

F-statistic 6.764 2.668 3.411 41.23 15.37 20.13 44.06 10.32 28.40

Difference in Bottom vs. Top Tercile Residual coefficients χ2 0.05 0.81 13.95 (p-value) (0.817) (0.369) (0.000)

54

Table 12: Bond Yield Spread Changes Around Director Appointment Announcements Table 12 provides changes in yield spread surrounding director appointment announcements. The 989 bond-level yield spread pairs

reflect 692 individual bonds issued by 166 unique firms. Yield spreads, and changes in yield spread, are winsorized at the 5 percent tails.

*, ** and *** indicate significance at 10 percent, 5 percent and 1 percent levels, respectively.

Panel A: Descriptive statistics

Mean Std. Dev. Q25 Median Q75

Days before announcement date 34.1205 38.89954 5 20 48

Days after announcement date 34.5637 39.1797 6 19 49.5

Time to maturity 9.2010 6.4068 3.5068 6.4068 9.6986

Moody’s rating 9.0273 3.4363 6 9 11

Panel B: Change in yield spread using the full unbalanced sample

No. Obs. Pre-Ann. Post-Ann. Mean (median)

change in spread

t-statistic

(Signed rank statistic)

All appointments 1,004 0.0240 0.0227 -0.0013*** 0.000

(0.0183) (0.0163) (-0.0008)*** (0.000)

Male director appointment 785 0.0250 0.0240 -0.0010*** 0.003

(0.0190) (0.0172) (-0.0005)*** (0.000)

Female director appointment 219 0.0204 0.0179 -0.0025*** 0.000

(0.0154) (0.0128) (-0.0012)*** (0.000)

High yield bonds

Male director appointment 233 0.0442 0.0438 -0.0004 0.478

(0.0426) (0.0410) (-0.0000) (0.169)

Female director appointment 44 0.0440 0.0389 -0.0052*** 0.004

(0.0432) (0.0337) (-0.0026)*** (0.000)

Tercile 1 relative debt

Male director appointment 231 0.0276 0.0271 -0.0005 0.418

(0.0255) (0.0224) (-0.0010)*** (0.005)

Female director appointment 43 0.0265 0.0225 -0.0040** 0.029

(0.0261) (0.0172) (-0.0023)** (0.013)

Panel C: Change in representative bond yield spread

No. Obs. Pre-Ann. Post-Ann. Mean (median)

change in spread

t-statistic

(Signed rank statistic)

All appointments 230 0.0319 0.0302 -0.0017*** 0.002

(0.0277) (0.0250) (-0.0018)*** (0.000)

Male director appointment 181 0.0328 0.0316 -0.0013** 0.043

(0.0283) (0.0276) (-0.0015)*** (0.002)

Female director appointment 49 0.0286 0.0253 -0.0033*** 0.004

(0.0249) (0.0200) (-0.0025)*** (0.005)

High yield rating

Male director appointment 79 0.0501 0.0469 -0.0004 0.583

(0.0463) (0.0476) (-0.0015) (0.137)

Female director appointment 17 0.0477 0.0438 -0.0039*** 0.006

(0.0484) (0.0453) (-0.0042)*** (0.010)

Tercile 1 relative debt

Male director appointment 93 0.0313 0.0307 -0.0006 0.528

(0.0291) (0.0280) (-0.0017) (0.104)

Female director appointment 28 0.0264 0.0237 -0.0027* 0.070

(0.0213) (0.0172) (-0.0021)* (0.079)

55

Appendix: Variable Definitions Variable Name Description and Source

Panel A: Board Variables

Independent women directors Ratio of the number of independent female directors to board size Source: Boardex/RiskMetrics Independent directors Ratio of the number of independent directors to board size Source: Boardex/RiskMetrics Board Size Number of directors. Source: Boardex/RiskMetrics CEO chair Indicator variable equal to one CEO of a firm is also board chair, zero otherwise. Source: Boardex/RiskMetrics

Board gender diversity county ratio

gender diversity

Ratio of firms with at least one woman director to all firms, excluding the sample firm, in the sample firm’s county in

a given year Source: Boardex/RiskMetrics Panel B: Compensation Dependent Variables

TDC1 Total CEO compensation (sum of salary, bonus, grant-date fair values of option and stock awards, non-equity incentive

plan compensation, deferred compensation earnings reported as compensation, and other equity compensation.)

Source: Execucomp Equity Difference between CEO total compensation and CEO cash compensation (TDC1–TOTAL_CURR). Source:

Execucomp Base salary Annual dollar value of the CEO’s base salary (SALARY). Source: Execucomp

Bonus Annual dollar value of the CEO’s bonus (BONUS). Source: Execucomp

Deferred contribution Aggregate CEO contributions to non-tax-qualified deferred compensation plans during the year

(DEFER_CONTRIB_EXEC_TOT). Source: Execucomp

Deferred balance Aggregate CEO deferred compensation balance in non-tax-qualified compensation plans (DEFER_BALANCE_TOT).

Source: Execucomp

Change in pension value Increase in the actual value of the CEO’s defined benefit and actual pension plans during the year plus above-market

or preferential earnings from deferred compensation plans (PENSION_CHG). Source: Execucomp

Pension balance Actuarial present value of the CEO'’s accumulated pension balance (PENSION_VALUE_TOT). Source: Execucomp

Inside debt CEO debt-like compensation (sum of PENSION_VALUE_TOT, DEFER_BALANCE_TOT) divided by the aggregate

value of the CEO’s stock and option portfolio (in $000s) using the methodology of Daniel, Li and Naveen (2013).

Source of inputs: Execucomp. Relative debt CEO inside debt ratio divided by the firm’s debt ratio (Sum of short-term (DLC) and long-term (DLTT) debt divided

by the market capitalization of equity (CSHO×PRCC). Source of inputs: Execucomp, Compustat

Deferred inside debt CEO deferred compensation balance (DEFER_BALANCE_TOT) divided by the aggregate value of the CEO’s stock

and option portfolio. Source of inputs: Execucomp

Pension inside debt CEO pension compensation balance (DEFER_BALANCE_TOT) divided by the aggregate value of the CEO’s stock

and option portfolio. Source of inputs: Execucomp

Deferred relative debt Deferred inside debt ratio divided by the firm’s debt ratio. Sources of inputs: Execucomp, Compustat

Pension relative debt Pension inside debt ratio divided by the firm’s debt ratio. Sources of inputs: Execucomp Compustat

Panel C: Current and Debt-like Compensation Control Variables

Firm size Log of total assets (AT). Source: Compustat

Stock return Cumulated 12-month monthly stock return ending one month prior to the fiscal year end date. Source: CRSP

Lagged stock return Cumulated 12-month monthly stock return ending 13 months prior to the fiscal year end date. Source: CRSP ROA Income before extraordinary items (IB) divided by current total assets (AT). Source: Compustat

Lagged ROA Income before extraordinary items (IB) divided by current total assets (AT) for the prior fiscal year. Source: Compustat

Lagged leverage Sum of interest-bearing debt (DLC+DLTT) divided by total assets (AT) for the prior fiscal year. Source: Compustat

Lagged book-to-market Total assets (AT) divided by ((AT-CEQ+(PRCC_F*CHSO) for the prior fiscal year. Source: Compustat

Lagged cash flow volatility Standard deviation of yearly earnings before interest and taxes plus depreciation and amortization divided by total

assets (EBITDA/AT), for the five years ending the prior fiscal year. Source: Compustat

Lagged capital expenditure Capital expenditure scaled (CAPX) by total assets (AT) for the prior fiscal year. Source: Compustat

Lagged tangibility Net property, plant and equipment (PPENT) divided by total assets (AT) for the prior fiscal year. Source: Compustat

Lagged sales growth Three-year geometric growth in sales (SALE) ending the prior fiscal year. Source: Compustat

Lagged R&D Research and development expense (XRD) divided by total assets (AT) for the prior fiscal year. Source: Compustat

CEO tenure The number of years the individual has held by position of CEO. Source: Execucomp

Firm age The number of years from the firm’s IPO date. Source: CRSP Header File

CEO age The age of the CEO. Source: Execucomp

Tax loss indicator Binary variable equal to one if the firm has tax loss carry-forwards (TLCF) reported for that year. Source: Compustat

HHI Herfindahl Hirschman Index for the firm’s 3-digit SIC code, calculated as ∑ 𝑠𝑖2𝑁

𝑖=1 , where si is the proportion of

sales of firm I in the issuer’s 3-digit SIC industry and N is the number of firms in the industry. Source: Compustat

Liquidity constraint Binary variable if operating cash flow (OCF) is negative and zero otherwise. Source: Compustat