board gender diversity and ceo inside debt compensation...
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Board Gender Diversity and CEO Inside Debt Compensation
Andrew Prevost*
Grossman School of Business
University of Vermont
55 Colchester Avenue, Burlington, VT 05405
Arun Upadhyay
Department of Finance
College of Business
Florida International University
11200 S.W. 8th St, RB 247B, Miami, FL 33199
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,
20
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.
21
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.
23
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