Accounting and Finance
The effects of board gender diversity on a
firm’s risk strategies
Linda H. Chena
, Jeffrey Gramlichb, Kimberly A. Houserb
a
Department of Accounting, College of Business and Economics, University of Idaho, Moscow,
Idaho, Idaho, USA
b
Department of Accounting, Carson College of Business, Washington State University,
Pullman, WA, USA
Abstract
We study whether board gender diversity (BGD) affects corporate risk
strategies. Specifically, we investigate the association between BGD and firms’
reputation risk and financial risk. Using S&P data from 1997 to 2013, we find
that BGD is negatively associated with tax avoidance, suggesting firms with
gender-diverse boards are more cautious about potential reputation risks
associated with aggressive tax strategies. However, we find that BGD is
positively associated with firms’ financial risk. The combined findings illustrate
that BGD aligns a firm’s risk exposure closer to risk-neutral shareholders’
preferences by reducing reputation risk exposure while enabling necessary
financial risk exposure.
Key words: Board composition; Gender diversity; Reputation management;
Tax avoidance; Financial risk
JEL classification: G12, G30, G32, G34
doi: 10.1111/acfi.12283
We thank Beau Barnes, Jennifer Brown, Marc Cussatt, Katharine Drake, Leslie
Eldenburg, Lillian Mills, Debra Sanders, Andrey Simonov, participants in the 2016
AAA Annual Meeting, the 2016 AAA Western Regional Meeting, and the 2017 Forum
for Economists International’s 9th Global Conference, participants in the University of
Groningen Research Seminar, Washington State University Summer Accounting
Research Camp, and University of Arizona Taxation Reading Group for their
comments.
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1. Introduction
Gender-diverse groups now constitute a majority of the boardrooms of
public companies in the United States and globally.1 When boards include
female members, overall meeting attendance rates are higher, discussions
consider a broader range of alternatives, deliberations are more inquisitive and
apolitical, directors receive more equity-based pay, earnings are higher in
quality and more conservative, and management oversight is stronger.2 Given
these findings, researchers have compared the profitability, stock returns and
firm values of firms overseen by mixed-gender and all-male boards, with mixed
results.3 Focus on current financial performance, however, may be missing the
point: an important role of the board is to encourage management to take
appropriate risks while also to avoid risks that are unlikely to pay off for
shareholders. In this respect, Hutchinson et al. (2015) document Australian
evidence that board gender diversity (BGD) increases the association between
total stock risk and return on assets. Thus, their evidence suggests that BGD is
positively related to awareness of the risk/return relationship involved in many
board decisions.
The relationship between board gender diversity and risk is important to
shareholders because shareholders bear the residual effects on investment value
that result from management’s risky decisions. However, reputation and
financing risks are also important to a broad set of stakeholders outside the
realm of equity investors. Employees stake their livelihoods on the future of a
firm, and board decisions pertaining to reputation and financing risks have
profound effects on a firm’s employees. Unlike shareholders, employees cannot
diversify their investments across a broad spectrum of firms. Like employees,
customers and suppliers depend on a firm’s stability.
We study whether gender differences in risk-taking affect corporate risk
strategies by investigating the association between board gender diversity
(BGD) and firms’ reputation risk and financial risk. If a gender-diverse board
improves board monitoring and accountability (Adams and Ferreira, 2009;
1
Lee et al. (2015) report that in 2015 73.5 percent of 4,218 global firms in the MSCI
Global Director Universe database have at least one woman the board, yet only 20.1
percent of boards have at least female board members.
2
Studies supporting these assertions, and others involving improved board work
effectiveness related to BGD, include Abbott et al. (2012), Adams and Ferreira (2009),
Bart and McQueen (2013), Daily and Dalton (2003), Francis et al. (2015), Gul et al.
(2011), Larcker and Tayan (2013), Simpson et al. (2010), Singh (2008), Srinidhi et al.
(2011).
3
See Carter et al. (2003, 2010), Adams and Ferreira (2009), and Erhardt et al. (2003).
Low et al. (2015) provide further elaboration. SEC Commissioner Mary Jo White cited
some of these findings in a November 2015 speech espousing the benefits of genderdiverse boards (White, 2015).
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Simpson et al., 2010; and Post and Byron, 2015), mitigates the tendency
towards groupthinking (Larcker and Tayan, 2013), incorporates a broader
scope of perspectives into decision-making processes (Watson et al., 1993;
Williams and O’Reilly, 1998; and Daily and Dalton, 2003), and results in higher
levels of complex moral reasoning by incorporating ethical, environmental and
societal responsibilities (Lamsa et al., 2008), then we expect to find a negative
association between BGD and firms’ reputation risk.
A good reputation permits a company ‘to attract resources, enhance its
performance, and build competitive advantage’ while a bad reputation can
alienate a range of stakeholders (Ditlev-Simonsen and Midttun, 2011).4 A firm
loses its good reputation when it fails to meet stakeholders’ reasonable
expectations of performance and behaviour. The possibility of such failure is
referred to as reputational risk (Atkins et al., 2006). Loss of reputation can
destroy brand confidence among customers, invite closer scrutiny from
regulators, impair financial metrics such as revenue and earnings (Deloitte,
2014) and cut a firm’s share price (Atkins et al., 2006).5 Given these costs to a
range of stakeholders, a key role of the board of directors is to monitor and
manage its reputational risk. Accordingly, the composition of a firm’s board is
critical to fulfilling this role.
Survey evidence indicates that managers are concerned about their firms’
reputation risk, defined as ‘the range of possible gains and losses in reputational
capital’ (Fombrun et al., 2000). Among large-firm corporate executives, 87
percent indicate that reputation risk is more important than any other strategic
risk, and 88 percent of them report that their firms explicitly manage reputation
risk (Deloitte 2014). If managing reputation risk is important, then leadership
in the boardroom, where the tone is set, should be imperative.
Our study builds upon previous findings in the following ways. Reputation
risk encompasses a wide range of normative expectations such as corporations’
image, standing, prestige and goodwill that stakeholders hold their firms
accountable for (Shenkar and Yuchtman-Yaar, 1997). Prior studies have
established that BGD improves corporate reputation by documenting a
positive association between BGD and corporate citizenship (Bernardi and
Threadgill, 2010; and Zhang et al., 2013). While taking into consideration
overall corporate citizenship scores as one type of comprehensive corporate
reputation measure, we add to existing studies of reputation risk by
4
According to Deloitte (2014), “. . .a company’s reputation should be managed like a
priceless asset and protected as if it’s a matter of life and death, because from a business
and career perspective, that’s exactly what it is.”
5
More than 25 percent of the collective value of S&P 500 firms is attributed to
reputation (Cole, 2012). Thus, a firm’s reputation is an economically important asset.
Loss of customer trust can result in decreased sale; loss of employee trust can lead to
poor morale and impaired product or service quality; loss of investor trust can result in
reduced share prices and a higher cost of equity (Atkins et al., 2006).
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investigating a special type of reputation consideration, namely tax planning
strategies, to avoid/reduce tax liability. Prior research also demonstrates that
firms do not take advantage of all of the tax planning strategies that would
allow them to reduce tax liability (Desai and Dharmapala, 2006; Blaylock,
2016; and Goh et al., 2016). Shackelford and Shevlin (2001) and Weisbach
(2002) were among the first to question why firms do not take full advantage of
the tax planning strategies available to them, which Graham and Tucker (2006)
refer to as the ‘undersheltering puzzle’. The reason for the ‘undersheltering
puzzle’ is evidenced by Graham et al. (2014), which reveals that 72 percent of
tax executives of publicly traded firms cite reputational concerns as important
constraints to the extent they engage in aggressive tax planning strategies. We
are therefore convinced that a better understanding of the effect of BGD on
firms’ tax planning/avoidance strategies can help us better understand firms’
reputation risk management. In particular, if both corporate citizenship and
tax planning strategies are forms of corporate reputation risk management,
then what is the incremental effect of each?
We measure reputation risk using three measures of tax avoidance: effective tax
rate, effective current tax rate and effective cash tax rate (Dyreng et al., 2008).
Dyreng et al. (2016) note that the term ‘tax avoidance’ encompasses a wide scope
of tax reduction activities, ranging from benign strategies such as taking
advantage of the accelerated depreciation expense deduction, which is allowed by
IRS code, to aggressive tax planning strategies such as using tax shelters, which
under certain circumstances, might not be upheld under the court of law. Using
effective tax rates as reputation measures, we are able to use an inclusive sample
without limiting the sample to certain specific types of aggressive tax strategies.
Next, we study whether gender differences in risk-taking affect firms’
financial risk. Financial risk is defined as a combination of long-term and shortterm liquidity and insolvency risks that are unrelated to current operating
performance.6 These risks reflect board decisions about how to provide the
capital needed to support the firm’s investments and operations. We find that
financial distress scores (Altman, 1968; Ohlson, 1980; and Zmijewski, 1984) are
the measures that most closely resemble the financial risk we intend to capture.
However, financial distress scores encompass weights not only on short-term
and long-term liquidity and solvency measures, but also include profitability
(loss) measures. We therefore construct financial risk measures by putting zero
weights on profitability measures from initial financial distress scores (Altman,
1968; Ohlson, 1980; and Zmijewski, 1984).7
6
Coles et al. (2006) also allude to other firms’ strategic risks such as R&D and longterm investments. We control for these strategic risks exposure in our analyses.
7
If we use original financial distress scores based on Altman (1968), Ohlson (1980), and
Zmijewski (1984), our results remain the same, but modified scores better reflect
financial risk than original scores. While using the modified scores in our analyses, we
also control for profitability.
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We examine S&P firm data for the period of 1997–2013 while controlling for
known determinants of reputation risk8 (i.e. tax avoidance) and financial risk
(i.e. modified bankruptcy risk). We use three alternative measures to capture
BGD, namely a female board presence dummy variable, the proportion of
female board members and the proportion of female key committee members.
We find that BGD is negatively associated with reputation risk and positively
linked with financial risk. We deem this as evidence supporting the view that
firms with gender-diverse boards are more concerned with potential reputation
loss caused by aggressive tax avoidance strategies than all-male boards are. On
the other hand, our evidence shows that BGD is positively linked to financial
risk, consistent with the notion that gender-diverse boards better align the
interests of managers and shareholders. Together, we view the results as
consistent with gender-diverse boards being more aware of the various risks
that a firm faces.
Next, we address possible endogeneity concerns. First, we want to estimate
the impact of BGD on both tax avoidance and financial risk. It is likely,
however, that the two estimations may affect each other. We address this
concern using a two-stage least-squares estimation approach, that is estimating
the two regressions as a combined system. More importantly, as BGD is likely
to be endogenously determined and associated with firm characteristics, we
explicitly treat the BGD variable as an endogenous variable and identify
possible determinants of BGD as instrumental variables. Following prior
literature, we use corporate citizenship (Bernardi and Threadgill, 2010; and
Zhang et al., 2013), size, investment opportunity set (Gaver and Gaver, 1993;
Baber et al., 1996; and McGuire et al., 2014) and market pressure (Myers
et al., 2007; and McGuire et al., 2014) as likely determinants of BGD. We find
that the results from two-stage least-squares estimation while treating BGD as
endogenous are consistent with our baseline results: BGD is negatively
associated with tax avoidance and positively associated with financial risk.
Our findings help resolve concerns with tokenism, as we demonstrate that
BGD has real impact on firms’ risk strategies by reducing firms’ reputation risk
exposure while increasing firms’ financial risk exposure. In addition, we show
that female risk aversion itself does not necessarily translate to overall firms’
risk reduction. On the contrary, our results suggest that as not all risk
exposures are the same, BGD helps align firms’ risk exposure to shareholders’
preferences along different risk dimensions. Better corporate governance is
attained via BGD by improving risk preference alignment between a firm and
its shareholders.
To further address the possible endogeneity concerns caused by a loop of
causality, we investigate how current period change of BGD is associated with
future period change of tax avoidance and financial risk. The results from the
8
While controlling for alternative reputation risk measure, namely corporate citizenship
score.
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two-stage least-squares estimation while treating change of BGD as an
endogenous variable is consistent with prior findings: the increase in female
board presence during the current period is negatively associated with next
period change of tax avoidance but positively associated with next period
change of firm financial risk exposure.
We acknowledge that we are not able to rule out the possibility that our
findings are consistent with the notion that female board members are seasoned
corporate professionals. In addition to their business knowledge, experience
and expertise, female board members may not be as risk averse as the average
female population. However, this plausible explanation for our findings does
not compromise our key conclusion that BGD improves corporate governance,
which is desirable on any front.
The remainder of this paper is organised as follows: Section 2 provides
literature background and hypotheses development; Section 3 explains models,
variable construction and data sources; Section 4 discusses results and
interprets findings; Section 5 summarises our conclusions.
2. Literature review and hypotheses development
2.1. The effect of board gender diversity on corporate governance
Prior research has alluded to the possible impact of BGD on corporate
governance, and, as follows, on decision-making processes by suggesting that
(i) gender-diverse boards serve as better monitors of management, (ii) BGD
improves decision making processes and outcomes, and (iii) women board
members are generally more concerned with ethics than men are.
BGD can improve board monitoring and accountability. Women directors are
more likely than men to be independent-minded. Simpson et al. (2010) argue
that women board members are more likely to be non-employees, are less likely
to have gained their director position because of their background as managers
and are therefore less likely to be beholden to management. Further evidence
suggests that female directors exercise their independence through their service
on audit, governance and nominating committees by promoting better meeting
attendance and holding managers accountable for their performance (Adams
and Ferreira, 2009; and Post and Byron, 2015).
Earnings better reflect economic performance when boards are genderdiverse. Specifically, BGD is positively related to earnings quality (Srinidhi
et al., 2011) and analyst forecast accuracy (Gul et al., 2013). The relationship
between earnings and stock prices is stronger when gender-diverse boards
oversee them (Gul et al., 2011). Abbott et al. (2012) find that firms with
gender-diverse boards are 38 percent less likely to restate financial statements. Evidence also suggests that female executives apply accounting methods
that are more conservative and produce higher quality accruals (Barua et al.,
2010).
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2.1.1. BGD and the quality of board decisions
Diverse groups tend to make better decisions because they counter the chance
of the groupthink phenomenon that can occur among homogenous groups
(Daily and Dalton, 2003; and Larcker and Tayan, 2013). Groupthink is
particularly problematic in boardrooms because they are often filled with
likeminded board members (Matsa and Miller, 2013), and diversity of opinion
is especially important for board’s monitoring of management on behalf of
shareholders (Aguilera et al., 2008). Corroborative data further support this
view (Jehn et al., 1999). In decision-making, women tend to provide a more
detailed critical analysis of the issues presented (Milliken and Martins, 1996).
More specifically, women generally balance multiple factors in their decisions
and consider a wider variety of stakeholders’ interests (Konrad and Kramer,
2006). This diversity in perspective leads to improved decision-making
processes (Watson et al., 1993; and Daily and Dalton, 2003), and to superior
decisions (Williams and O’Reilly, 1998).
2.1.2. BGD and ethical decisions
BGD likely leads to board decisions that reflect strong consideration of ethical
implications, resulting from significantly higher levels of complex moral
reasoning among women directors (Bart and McQueen, 2013). This result is
consistent with findings that, relative to males, females generally (i) exhibit higher
ethical standards than males do (for examples, see Peterson et al., 2010; Roxas
and Stoneback, 2004; Weeks et al., 1999), (ii) show greater ethical sensitivity
(Simga-Mugan et al., 2005), (iii) place more weight on ethical, environmental and
societal responsibilities (Lamsa et al., 2008), (iv) make more altruistic decisions
(Andreoni and Versterlund, 2001) and (v) avoid engaging in unethical behaviour
when faced with organisational pressures to do so (Wahn, 2003). Franke et al.
(1997) use meta-analysis to document that women generally are more likely than
men to perceive hypothetical business practices as unethical.
In summary, prior literature on corporate governance, business ethics and
management has shed light on why board diversity in general, and BGD, in
particular, can improve corporate governance and board accountability. Next,
we focus on two areas of firms’ strategies, namely tax avoidance and financial
risk, and elaborate on how BGD is likely to affect both types of strategies.
2.2. Gender-diverse boards and tax avoidance
A firm’s positive reputation is a valuable economic asset (Shapiro, 1983).
More specifically, Karpoff (2010) defines business reputation as:
The present value of the improvement in net cash flow and lower cost of capital
that arises when the firm’s counterparties trust that the firm will uphold its explicit
and implicit contracts, and will not act opportunistically to their detriment.
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Like other assets, reputation can be invested in and developed in order to
command higher prices in the product market. Roberts and Dowling (2002)
show that these higher prices are able to produce sustained superior financial
performance. The rewards of higher prices lead firms to develop performance
assurance efforts that, circularly, build even stronger reputations (Klein and
Leffler, 1981). Unlike other assets, however, a positive reputation is strategic in
the sense that competitors cannot easily imitate it (Sim~
oes and Dibb, 2008). In
the financial arena, positive reputations lead to lower costs of equity capital
(Cao et al., 2015) and debt (Anginer et al., 2015).
Firms are concerned about reputation risk (i.e., possible impairments to the
value of their reputation). Among large-firm corporate executives, 87 percent
report that reputation risk is more important than any other strategic risk, and
88 percent report that their firms explicitly manage reputation risk (Deloitte,
2014). If managing reputation risk is important, then leadership in the
boardroom, where the basis for corporate governance is established, should be
imperative.
As BGD is positively linked to greater board focus on ethics, to better
decision-making processes, and to improved monitoring of management, firms
with gender-diverse boards exhibit characteristics consistent with reducing
reputation risk. When a board exerts more effort addressing ethical issues (Bart
and McQueen, 2013), ethical firm behaviour is a likely result. Generally, a
firm’s reputation is enhanced by actions that reflect moral principles such as
honesty, fairness and individual rights. Beyond a greater ethical focus, BGD is
associated with better decision-making processes, which then increases the
likelihood that the board will respond with nuance to ethical issues (Bart and
McQueen, 2013). Finally, because gender-diverse boards serve as better
monitors of management, management under the guidance of a gender-diverse
board is more likely to result in the execution of the board’s decisions (Daily
and Dalton, 2003; Aguilera et al., 2008; and Larcker and Tayan, 2013).
Academia recommends different forms of protection against corporate
reputation risk. For example, Atkins et al. (2006, p. 58) argue that firms should
be committed to social responsibility as protection against reputation risk.
Following this logic, a firm that expends relatively greater resources on CSR
activity should face less reputation impairment loss when key stakeholders are
alienated (Fombrun, 1996). Alternatively, Murray (2004) recommends that
firms appoint non-executive directors with the active and independent role of
guarding the firm’s reputation. These forms of protection reveal management’s
concern about reputation risk but may not reflect its underlying attitude
towards the causes of reputation risk. This view is supported by Davis et al.
(2016) who find the existence of a substitution effect between corporate social
responsibility and corporate tax payments.
To address the question of whether BGD affects reputation risk, we examine
the effects of BGD on tax avoidance. We choose tax avoidance to measure
reputation risk because it provides insight into the trade-off that boards make
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between the benefits of saving cash taxes (and higher earnings per share) versus
the downside of reputation impairment. As mentioned earlier, 72 percent of tax
executives of publicly held companies cite reputation concerns as constraints to
aggressive tax planning strategies (Graham et al., 2014) that would otherwise be
available. Further, firms staffed by reputation-concerned tax executives indicate
higher cash effective tax rates than firms that have reputation-unconcerned tax
executives (Graham et al., 2014). Executive concern about the reputation effects
of low tax payments is well founded. Hanlon and Slemrod (2009) demonstrate
that stock prices decline upon the release of news about a firm’s involvement in
tax shelters; moreover, the stock price decline is greater when the firm’s cash
effective tax rate is perceived as low, which suggests a penalty for firms that are not
paying their ‘fair share’ of tax. Because of concern for costly reputation risk
associated with tax avoidance, and because women are able to consider multiple
ethical issues simultaneously and improve board governance, we expect firms
with gender-diverse boards to have higher tax rates.
Signalling theory provides an alternative view for how positive reputations
can be applied to board selection. It argues that firms with underlying
reputation risks are more likely to appoint a woman to the board (Fombrun
and Shanley, 1990; Basdeo et al., 2006). The notion is that placing a woman on
the board evidences the firm’s concern for risk issues, such as accounting
quality, tax aggressiveness and corporate citizenship, all of which could impair
firm reputation. Under the signalling hypothesis, the woman board member
represents meaningless tokenism that should have no bearing on board
decisions (Kanter, 1977). If women on boards are merely appointed to offset
reputation risks, we would expect their firms to exhibit higher, not lower,
underlying reputation risks. Indeed, to the extent that a firm believes it is
‘protected’ by having a woman on the board, the firm may engage in even
riskier behaviour typical of the type of moral hazard ramification. Therefore,
the direction in which BGD affects firm reputation risk is not clear.
Empirically, Gallemore et al. (2014) find no evidence that firms or executives
incur reputation costs when they are accused of involvement with tax shelters.
Thus, we offer the following null hypothesis:
H10: There is no relationship between the presence of BGD and the extent of tax
avoidance.
2.3. Board gender diversity and financial risk
Managers are likely to be risk-averse in order to protect their firm-specific
human capital (Smith and Stulz, 1985) and maintain their consumption of
perquisites (Williams, 1987). On the other hand, diversified stock investors
facing limited liability are likely to prefer more risky endeavours at the firm
level, especially when investors are protected against wealth-extracting
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managers (Himmelberg and Quadrini, 2002; Himmelberg et al., 2002; Shleifer
and Wolfenzon, 2002; and Castro et al., 2004). As a result, managers may
choose to avoid high-risk positive net present value projects while their
investors would want their firm to take on these projects (Coles et al., 2006). To
address these mismatched risk preferences, finance theorists argue that
managers should be given payoff structures that provide managers with
incentives to accept additional firm risk (Jensen and Meckling, 1976; Leuz
et al., 2003; and John et al., 2008). Indeed, bonus and stock-based compensation schemes now in widespread use provide incentives for managers to
accept greater firm-level risks than they would otherwise choose.
Some evidence suggests that BGD may help align the risk preferences of
shareholders and managers. Adams and Ferreira (2009) show that gender-diverse
boards receive a larger proportion of equity-based compensation than genderhomogenous boards do. Perhaps more important are the boardroom dynamics
and decisions that result when women are on the board. For example, an
examination of the boards of subprime lenders led Muller-Kahle and Lewellyn
(2011) to observe that, because gender diversity increases the range of viewpoints
and modes of decision-making, gender diversity is especially beneficial when risky
strategic choices are being evaluated. Firm-level gender diversity is also positively
associated with the likelihood of undertaking innovation (Østergaard et al.,
2011), and innovation is known to be inherently risky (Treacy, 2004). Finally,
gender-diverse banks appear to take more risks than non-gender-diverse banks
(Adams and Ragunathan, 2013; and Berger et al., 2014). Taken together, these
findings suggest that BGD can help align the preferences of inherently risk-averse
managers with shareholders who seek riskier projects.
On the other hand, there are legitimate reasons why gender-diverse boards
may not exhibit better alignment between the risk preferences of investors and
managers. First, BGD could lead to more dysfunctional boards (Groysberg
and Bell, 2013; and Triana et al., 2014). Second, some academics argue that
women can only be effective on boards when sufficient numbers of women are
present because having only one or two women on a board can result in
tokenism (Torchia et al., 2011). Thus, in cases where there are only one or two
women on board, effective board control could become concentrated within a
smaller, homogenous group of males—a result that is not expected to support
risk alignment. Finally, psychological and sociological research indicate that
women are known to be, on average, slightly more risk averse than men
(Croson and Gneezy, 2009; and Francis et al., 2014). Therefore, based only on
the mean of individual board member risk tolerances, gender-diverse boards
may be more risk averse than male-only boards. If individual risk preferences,
and not the ethics or monitoring effects of gender diversity, determine board
decisions, then it is possible that the dispersion between investor and manager
risk tolerance levels is greater among firms with gender-diverse boards.
Therefore, it is not clear in which direction BGD affects firm financial distress
risk. As follows, we offer the following null hypothesis:
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H20: Firms with gender-diverse boards exhibit no difference in financial risk than
do firms with gender-homogenous boards.
3. Models, variable construction and data
3.1. Models and variable construction
We use the following model to test H1, or whether BGD is associated with
tax avoidance:
Tax Avoidancei;t ¼ a þ b1 BGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
ð1Þ
for each year t and firm i, including year and industry indicator (2-digit SIC
codes) variables. Standard errors are clustered by firm in order to address the
problem of correlated residuals (Petersen, 2009). Three alternative measures are
used to proxy for Tax Avoidance, namely effective tax rate, current effective tax
rate and cash effective tax rate. Effective tax rate (ETR) is the sum of total tax
expense (TXT) divided by the sum of pretax income (PI) over a 5-year rolling
period from t-4 to t, akin to the approach used by Dyreng et al. (2008). Current
effective tax rate (CurETR) is measured by the sum of current tax expense
divided by the sum of pretax income (PI) from t-4 to t, where current tax
expense is total tax expense (TXT) net of deferred income tax (TXDI). Cash
effective tax rate (CETR) is the sum of income tax paid (TXPD) divided by the
sum of pretax income (PI) over the years t-4 to t. For consistent direction
interpretation, we use AvoidETR, AvoidCurETR and AvoidCETR as determinant
variables which are (1)*ETR, (1)*CurETR and (1)*CETR respectively.
To operationalise the measures of BGD, we use three alternative proxies,
namely FemaleDummy, FemaleRatio and FemaleKeyboard, respectively. FemaleDummy
is a dummy variable set to 1 if there are female board members in a given year,
and 0 otherwise. FemaleRatio is the ratio of total female board members over
total board members. FemaleKeyboard is the total count of key board positions
occupied by female board members; key board positions include Corporate
Governance Committee, Compensation Committee, Audit Committee and
Nomination Committee.
Because we are using Tax Avoidance as a measure for reputation risk, we
need to include another variable that can also be used as alternative corporate
reputation measure. Zhang et al. (2013) has established the link between BGD
and corporate citizenship. We include CSR as an alternative measure of
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corporate reputation. CSR is a corporate social responsibility score that reflects
total strengths minus total weaknesses across KLD’s six social rating
categories. The measurement construct is similar to prior studies by Chatterji
et al. (2009), Kim et al. (2012) and Hoi et al. (2013). The six social rating
categories are corporate governance, community, diversity, employee relations,
environment and product.
Variables that are considered to be associated with firm tax avoidance (Hoi
et al., 2013; and Francis et al., 2014) are included in our analysis. Specifically,
NOL is net operating loss carryforward (TLCF) scaled by beginning balance
total assets (AT).9 Change of net operating loss carryforward (DNOL) is the
change of net operating loss carryforward (TLCF) scaled by beginning balance
total assets (AT). FI is foreign income (PIFO) scaled by beginning-of-year total
assets. We use EqInc, PPE, Intangibles, and R&D to capture equity income
(ESUB), total net property, plant and equipment (PPENT), intangible assets
(INTAN) and research and development expense (XRD), respectively, all
scaled by beginning-of-year total assets (AT). In addition, we also include
liquidity, profitability, size, leverage, earnings quality and growth opportunity
measures. Liquidity measure Cash is the sum of cash and marketable securities
(CHE) scaled by beginning total assets (AT). The profitability measure ROA is
income before extraordinary items (IB) scaled by beginning balance total assets
(AT). Size is the natural log of equity market value determined by multiplying
the fiscal year end price of common stock (PRCC_F) by common share
outstanding (SCHO). Leverage is total long-term debt (DLTT) scaled by
beginning balance total assets (AT). To capture earnings quality, we use
DiscAccrual, the estimated residual value of the performance-adjusted accruals
model (Kothari et al., 2005). Growth expectation is measured by M/B, which is
the equity market value defined above scaled by equity book value (CEQ).
iOWN is the percentage of institutional holding of common shares outstanding.
We use iOWN as a raw proxy for corporate governance.
We consider the following model to test H2, whether BGD is associated with
financial risk:
Financial Riski;t ¼ a þ b1 BGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
ð2Þ
for each year t and firm i, including year and industry indicator (2 digit SIC
codes) variables. Again, standard errors are clustered by firm in order to
address the problem of correlated residuals (Petersen, 2009).
9
Compustat variable names are shown in parentheses.
© 2017 AFAANZ
L. H. Chen et al./Accounting and Finance
13
As mentioned before, we refer to financial risk as both long-term and shortterm liquidity and insolvency risk. Therefore, financial risk is different from
other strategic risk exposures such as R&D and other long-term investments
(Coles et al., 2006). Although financial distress scores closely resemble the
financial risk that we want to capture, the financial distress score models
estimated by Altman (1968), Ohlson (1980) and Zmijewski (1984) capture two
types of business risk, the first type is operational risk which is estimated by
profitability and efficiency and the second type is financial risk which is
estimated by both short-term and long-term liquidity and solvency. Therefore,
in order to focus on pure financial risk, we need to eliminate the effect of
operational risk from the original financial distress scores. As a result, Financial
RiskA, Financial RiskO and Financial RiskZ are calculated based on the original
coefficients in models of Altman (1968), Ohlson (1980) and Zmijewski (1984),
respectively, which are measures of both short-term and long-term liquidity
and solvency, but operational risk measures are excluded.10 As a result, we are
able to use proxies for financial risk using the established yet-modified business
risk measures. In order to maintain consistency with respect to results
interpretation, we flip the signs of the original Altman (1968) model, hence, the
higher the Financial RiskA, Financial RiskO and Financial RiskZ, the higher the
financial risk.
3.2. Data
We obtain board member data from RiskMetrics for the sample period
available, 1997 to 2013. Financial reporting data are obtained from Compustat
Annual Fundamental data. Corporate social responsibility data are obtained
from MSCI, formerly known as KLD Research & Analytics and henceforth
referred to as ‘KLD data’. Institutional holding data are obtained from the
Thomson Reuters Institutional Holdings (13F) database. The price level index
used to calculate the original Ohlson Score (Ohlson, 1980) is obtained from the
Federal Reserve Bank of St. Louis website. All continuous variables used in
regression analyses are winsorised at the top and bottom 1 percent levels of
their cross-sectional distributions.
Table 1 Panel A reports board member composition between 1997 and 2013.
Overall, the ratio of female board members to male board members is roughly 1
to 9, but female board member presence has gradually increased during the
sample period from 8.63 percent in 1997 to 14.46 percent in 2013. Thus,
American corporate boardrooms remain dominated by male directors. Table 1
panel B reports board member composition across different firm sizes. Large
firms tend to have higher proportions of female board members than smaller
10
We also use the original bankruptcy scores (Altman, 1968; Ohlson, 1980; and
Zmijewski, 1984) in our analyses. The empirical results are consistent with modified
measures.
© 2017 AFAANZ
L. H. Chen et al./Accounting and Finance
14
Table 1
Summary of board gender composition (1997–2013)
Male
Year
Female
%
N
N
%
722
821
999
1,087
1,196
1,235
1,379
1,496
1,539
1,591
1,590
1,691
1,705
1,761
1,801
1,917
2,054
8.63
8.31
9.20
9.48
9.67
10.27
10.54
10.99
11.33
11.90
11.92
12.29
12.34
12.71
13.13
13.70
14.46
Panel A: Board gender composition by sample year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
7,641
9,060
9,863
10,378
11,173
10,790
11,704
12,114
12,043
11,781
11,748
12,063
12,110
12,094
11,912
12,075
12,150
91.37
91.69
90.80
90.52
90.33
89.73
89.46
89.01
88.67
88.10
88.08
87.71
87.66
87.29
86.87
86.30
85.54
Male
N
Female
%
N
%
11,927
5,106
6,025
1,526
14.54
11.63
8.53
8.15
Panel B: Board gender composition S&P size classifications
S&P Large Cap 500
S&P Mid Cap 400
S&P Small Cap 600
Other
70,085
38,790
64,626
17,203
85.46
88.37
91.47
91.85
firms. For S&P Large Cap 500 firms, S&P Mid Cap 400 firms and S&P Small
Cap 600 firms, female board membership levels are 14.54, 11.63 and 8.53
percent, respectively. However, the reasons behind greater gender diversity in
large firms are undetermined at this point.
Table 2 reports summary statistics beginning with distributional characteristics of tax avoidance measures, financial risk measures, BGD measures and
control variables. Mean values for ETR, CurETR and CETR range from 0.362
to 0.370, although the medians are slightly lower for all three effective tax rate
measures, indicating positive skewness for tax rates that may be due to small
denominators for relatively few firms. The mean (median) Financial RiskA,
Financial RiskO and Financial RiskZ are 3.500 (1.939), 1.439 (1.433) and
1.359 (1.316), respectively, suggesting a skewed distribution of the modified
© 2017 AFAANZ
Tax avoidance measures
ETR
CUR_ETR
CETR
Financial risk measures
Financial RiskA
Financial RiskO
Financial RiskZ
BGD measures
FemaleDummy
FemaleRatio
FemaleKeyBoard
Control variables
CSR
Cash
ROA
Size
Leverage
NOL
FI
EqInc
PPE
Intangible
© 2017 AFAANZ
6.184
1.407
1.280
3.500
1.439
1.359
0.652
0.102
0.907
0.075
0.180
0.052
7.420
0.206
0.064
0.022
0.001
0.291
0.197
19,228
19,586
19,585
23,464
23,464
23,464
17,506
17,083
17,118
17,074
17,052
17,146
17,072
17,072
17,088
17,072
2.794
0.254
0.140
1.545
0.223
0.281
0.040
0.009
0.251
0.237
0.476
0.095
0.938
0.408
0.439
0.527
r
0.362
0.356
0.370
Mean
17,069
17,069
16,828
N
Table 2
Summary statistics (1997–2013)
0.000
0.000
0.000
3.000
0.011
0.037
5.549
0.000
0.000
0.000
0.000
0.056
0.000
3.000
0.011
0.037
5.549
0.000
0.000
0.000
0.000
0.056
0.000
7.531
3.223
3.057
0.108
0.071
0.076
25%
0.000
0.000
0.000
7.531
3.223
3.057
0.108
0.071
0.076
10%
0.000
0.094
0.059
7.288
0.171
0.000
0.003
0.000
0.219
0.124
1.000
0.100
1.000
1.936
1.433
1.316
0.352
0.302
0.291
50%
1.000
0.244
0.102
8.429
0.306
0.032
0.034
0.000
0.386
0.298
1.000
0.167
1.000
1.000
0.615
0.540
0.391
0.365
0.373
75%
(continued)
3.000
0.454
0.152
9.582
0.440
0.144
0.077
0.002
0.637
0.494
1.000
0.222
2.000
0.465
0.102
0.058
0.468
0.526
0.528
90%
L. H. Chen et al./Accounting and Finance
15
© 2017 AFAANZ
Mean
0.041
0.050
3.139
0.728
N
17,072
16,046
17,072
17,506
0.076
0.106
6.246
0.264
r
0.000
0.154
0.983
0.410
10%
0.000
0.154
0.983
0.410
25%
0.008
0.043
2.317
0.782
50%
0.054
0.002
3.709
0.899
75%
0.123
0.046
6.116
0.987
90%
ETR is the sum of total tax expense (TXT) divided by the sum of pretax income (PI) over a 5-year rolling period from t4 to t. CUR_ETR is the
sum of current tax expense divided by the sum of pretax income (PI) over the same 5-year rolling period, where current tax expense is total tax
expense (TXT) net of deferred income tax (TXDI). CETR is the sum of income tax paid (TXPD) divided by the sum of pretax income (PI) over
the 5-year rolling period from t4 to t. Financial RiskA, Financial RiskO and Financial RiskZ are calculated based on the original coefficients in
models of Altman (1968), Ohlson (1980) and Zmijewski (1984), respectively, that are measures of both short-term and long-term liquidity and
solvency, but excluding operational risk measures. FemaleDummy is a dummy variable set to 1 if there are female board members in a given year,
and 0 otherwise. FemaleRatio is the ratio of total female board members over total board members. FemaleKeyboard is the total count of key board
positions occupied by female board members whereas key board positions include Corporate Governance Committee, Compensation
Committee, Audit Committee and Nomination Committee. CSR is the total strengths minus total concerns in KLD’s six social rating categories
which are corporate governance, community, diversity, employee relations, environment and product, respectively. Female is a dummy variable
which is set to 1 if there are female board members and 0 otherwise. ROA, NOL, DNOL, Cash, Leverage, FI and DSales, PPE, Intangible, EqInc
and R&D are income before extraordinary items (IB), net operating loss carryforward (TLCF), change of net operating loss carryforward
(TLCF), cash and marketable securities (CHE), and total long-term debt (DLTT), foreign income (PIFO), change of sales (SALE), total net
property, plant and equipment (PPENT), intangible assets (INTAN), equity income (ESUB), and research and development expense (XRD),
respectively, all of which are scaled by beginning balance total assets (AT). Size is the natural logarithm of equity market value – fiscal year end
price (PRCC_F) multiplied by common share outstanding (SCHO). DiscAccrual is the estimated residual of the performance-adjusted accruals
model (Kothari et al., 2005). M/B is the equity market value scaled by equity book value (CEQ). iOWN is the percentage of institutional holding
of common shares outstanding. All continuous variables are winsorised at the top and bottom 1 percent of their cross-sectional distribution.
R&D
DiscAccrual
M/B
iOWN
Table 2 (continued)
16
L. H. Chen et al./Accounting and Finance
L. H. Chen et al./Accounting and Finance
17
Altman (1968) model and normal distribution for both modified Ohlson (1980)
model and modified Zmijewski (1984) model. Among BGD measures, median of
FemaleDummy is 1.000, indicating that at least fifty percent of the sample firm-years
have at least one female board member. The mean of FemaleRatio is 10.20 percent,
that is on average, with females making up 10.20 percent of boards. The mean of
FemaleKeyBoard is 0.907; that is, on average, females occupy about one key board
position per firm. Most importantly, there are sufficient variations with all three
BGD measures. Mean (median) CSR is 0.075 (0.000), indicating that on average,
a firm’s corporate social responsibility total strengths are more or less offset by
total weaknesses across KLD’s six social rating.
Table 3 reports univariate correlations between the variables. As expected, the
three tax rate measures are positively correlated with each other, with correlation
ranging from 0.581 to 0.800 (Pearson) and 0.502 to 0.697 (Spearman). The three
financial risk measures are positively correlated, with correlation ranging from
0.474 to 0.925 (Pearson) and 0.748 to 0.906 (Spearman). Most importantly, the
three BGD measures are highly correlated, with correlation ranging from 0.707 to
0.868 (Pearson) and 0.844 to 0.805 (Spearman). It is also worthwhile to note that
CSR is positively correlated with all three BGD specifications (both Pearson and
Spearman), which is consistent with the notion that gender diversity is positively
correlated with corporate citizenship (Zhang et al., 2013). The correlations
between control variables and variable of interest are mostly significant at 1
percent significance level, suggesting that it is necessary to include these control
variables in regression analyses to avoid omitted variable concerns. Moreover,
BGD measures are correlated with Cash, ROA, Size, Leverage, FI, Intangible,
R&D and M/B, indicating that firm size, performance measures and firm financial
characteristics are likely determinants of BGD. These variables should therefore
be properly accounted for.
4. Results and discussion
4.1. Baseline results on BGD and tax avoidance
Table 4 reports the base line results of H1, or whether BGD is associated
with tax avoidance. For easier interpretation, we transform ETR, CurETR and
CETR measures by multiplying each variable by (1) and label the
transformed measures as AvoidETR, AvoidCurETR, and AvoidCETR respectively.
Therefore, the higher AvoidETR, AvoidCurETR, and AvoidCETR, the higher tax
avoidance is. The correlation coefficients of FemaleDummy, FemaleRatio and
FemaleKeyBoard are all consistently negative and significant, providing preliminary evidence of the negative association between BGD and tax avoidance.
Thus, H1 is rejected and a relationship between BGD and tax avoidance exists.
The coefficients of CSR are not significant across three alternative tax
avoidance measures, suggesting even if corporate citizenship is associated with
tax avoidance, the results are subsumed by BGD measures in our analyses. In
© 2017 AFAANZ
© 2017 AFAANZ
ETR (1)
CUR_ETR (2)
CETR (3)
Financial RiskA (4)
Financial RiskO (5)
Financial RiskZ (6)
FemaleDummy (7)
FemaleRatio (8)
FemaleKeyBoard (9)
CSR (10)
Cash (11)
ROA (12)
Size (13)
Leverage (14)
NOL (15)
FI (16)
EqInc (17)
PPE (18)
Intangible (19)
R&D (20)
DiscAccrual (21)
M/B (22)
iOWN (23)
Table 3
Correlation matrices
0.624
0.581
0.037
0.068
0.073
.014
0.021
0.020
0.013
0.062
0.059
.027
.013
.059
.056
.015
.025
0.006
0.048
0.032
0.012
0.015
(1)
0.800
0.034
0.037
0.054
0.027
0.033
0.039
0.000
0.038
0.078
.010
.003
.051
.012
.022
.052
0.009
0.013
0.032
0.001
0.031
0.539
(2)
0.078
0.101
0.076
.007
0.008
.003
0.022
0.062
0.129
.138
.007
.029
.072
.007
.066
0.022
0.042
0.014
0.037
0.001
0.502
0.697
(3)
0.474
0.507
0.137
0.106
0.123
0.032
0.449
0.208
.109
.294
.050
.066
.025
.112
0.088
0.242
0.156
0.230
0.051
0.073
0.065
0.067
(4)
0.925
0.107
0.093
0.098
0.059
0.336
0.169
.210
.555
.127
.161
.006
.152
0.032
0.187
0.116
0.035
0.064
0.128
0.019
0.101
0.748
(5)
0.232
0.199
0.235
0.024
0.338
0.124
.095
.612
.081
.039
.023
.135
0.041
0.206
0.121
0.016
0.108
0.090
0.000
0.076
0.786
0.906
(6)
0.783
0.707
0.247
0.155
0.082
.340
.042
.043
.106
.020
.012
0.050
0.130
0.034
0.036
0.051
0.026
0.068
0.062
0.157
0.129
0.253
(7)
0.868
0.322
0.106
0.073
.295
.023
.030
.113
.003
.023
0.053
0.113
0.025
0.039
0.007
0.018
0.069
0.058
0.123
0.109
0.220
0.844
(8)
0.318
0.137
0.066
.369
.058
.040
.133
.015
.003
0.071
0.122
0.024
0.047
0.037
0.006
0.063
0.043
0.160
0.124
0.267
0.805
0.875
(9)
.073
.002
.109
.111
.065
.069
.243
0.078
0.534
0.200
0.136
0.087
0.173
0.008
0.123
0.531
0.511
0.491
0.123
0.063
0.102
0.050
0.048
0.043
0.014
0.090
0.058
0.003
0.282
0.320
0.310
.016
0.065
.277
.024
.017
.159
.002
.080
0.006
0.103
0.096
0.070
0.051
(11)
(10)
.253
.125
.311
.248
.125
.041
0.057
0.329
0.076
0.120
0.085
0.028
0.001
0.098
0.538
0.258
0.234
0.049
0.050
0.043
0.085
0.184
(12)
18
L. H. Chen et al./Accounting and Finance
© 2017 AFAANZ
0.023
0.048
.011
0.302
0.258
0.072
0.080
0.024
0.002
0.046
0.070
0.002
0.616
0.637
0.709
0.100
0.070
0.109
0.040
0.469
0.189
0.094
0.071
0.020
0.123
0.161
0.175
0.141
0.343
0.306
0.367
0.206
0.026
0.331
0.048
0.115
0.368
0.057
0.062
0.128
.011
0.107
0.197
0.084
(14)
(13)
0.044
0.159
0.069
0.029
0.271
0.043
0.028
0.021
0.137
0.123
0.134
0.150
0.055
0.069
0.005
0.015
0.021
.011
0.086
0.189
.014
0.054
(15)
0.032
0.048
0.050
0.078
0.101
0.088
.013
0.195
0.018
0.075
0.132
0.134
0.008
0.129
0.118
0.146
0.132
0.107
0.273
0.360
0.024
0.085
(16)
0.031
0.007
0.071
0.015
0.009
0.013
0.022
0.001
.010
0.122
0.099
0.161
0.101
0.071
0.107
.018
0.132
0.049
0.161
0.114
0.006
0.118
(17)
0.248
0.214
0.129
0.022
0.051
0.039
0.065
0.021
0.166
0.202
0.185
0.061
0.028
0.044
0.027
0.379
0.069
0.062
0.285
0.143
0.074
0.103
(18)
0.037
0.134
.014
0.094
0.018
0.021
0.009
0.113
0.073
0.101
0.103
0.106
0.134
0.000
0.135
0.012
0.159
0.184
0.116
0.165
0.053
0.331
(19)
0.204
0.098
0.014
0.268
0.022
0.171
0.292
0.277
0.249
0.095
0.092
0.092
0.130
0.443
0.024
0.068
0.235
0.144
0.235
0.083
0.306
0.048
(20)
0.085
0.030
0.026
0.042
0.086
0.253
0.174
0.172
0.037
0.022
0.021
0.094
0.260
0.168
0.135
0.173
0.039
0.120
0.064
0.211
0.142
0.237
(21)
0.008
0.062
0.040
0.111
0.474
0.046
0.001
0.088
0.093
0.095
0.152
0.232
0.495
0.492
0.104
0.074
0.197
0.014
0.015
0.007
0.221
0.224
(22)
0.012
0.080
0.073
0.111
0.080
0.143
0.080
0.016
0.076
0.094
0.121
0.034
0.123
.001
0.079
0.022
0.066
0.140
0.109
0.058
0.035
0.075
(23)
Pearson correlations appear below the diagonal and Spearman correlations appear above. Variable descriptions are the same as outlined in the
paper and as appended to Table 2. All continuous variables are winsorised at the top and bottom 1 percent of their cross-sectional distributions.
Correlations in bold are significant at 0.01 level (two tailed).
ETR (1)
CUR_ETR (2)
CETR (3)
Financial RiskA (4)
Financial RiskO (5)
Financial RiskZ (6)
FemaleDummy (7)
FemaleRatio (8)
FemaleKeyBoard (9)
CSR (10)
Cash (11)
ROA (12)
Size (13)
Leverage (14)
NOL (15)
FI (16)
EqInc (17)
PPE (18)
Intangible (19)
R&D (20)
DiscAccrual (21)
M/B (22)
iOWN (23)
Table 3 (continued)
L. H. Chen et al./Accounting and Finance
19
© 2017 AFAANZ
PPE
EqInc
FI
D(NOL)
NOL
Leverage
Size
ROA
Cash
CSR
FemaleKeyBoard
FemaleRatio
FemaleDummy
Intercept
0.00
(0.71)
0.06*
(1.79)
0.55***
(6.33)
0.00
(0.41)
0.00
(0.12)
0.10**
(2.33)
5.43
(0.51)
0.27**
(2.28)
1.33
(1.29)
0.07***
0.00
(1.18)
0.06*
(1.87)
0.55***
(6.33)
0.00
(0.48)
0.00
(0.16)
0.10**
(2.34)
5.44
(0.51)
0.28**
(2.33)
1.36
(1.31)
0.06***
0.21***
(3.40)
0.02***
(3.17)
0.00
(1.12)
0.06*
(1.74)
0.55***
(6.30)
0.00
(0.16)
0.01
(0.25)
0.10**
(2.32)
5.47
(0.51)
0.28**
(2.31)
1.36
(1.30)
0.06***
0.34***
(5.99)
0.00
(0.80)
0.07*
(1.85)
0.73***
(6.07)
0.00
(0.17)
0.01
(0.27)
0.15***
(4.22)
1.95
(0.20)
0.69***
(2.59)
2.11*
(1.92)
0.04
0.35***
(5.39)
0.04***
(3.06)
0.33***
(5.77)
0.33***
(5.89)
0.03***
(2.65)
ETR
AvoidCur
AvoidETR
Table 4
The association between BGD and tax avoidance (H1)
0.00
(1.03)
0.07**
(1.97)
0.72***
(6.07)
0.00
(0.11)
0.02
(0.29)
0.15***
(4.23)
1.94
(0.20)
0.69**
(2.56)
2.15*
(1.96)
0.04
0.24***
(3.22)
0.34***
(5.25)
0.03***
(3.50)
0.00
(1.09)
0.06*
(1.73)
0.72***
(6.06)
0.00
(0.45)
0.02
(0.38)
0.15***
(4.20)
2.05
(0.21)
0.69**
(2.57)
2.14*
(1.96)
0.04
0.36***
(5.53)
0.00
(0.31)
0.13***
(3.56)
0.67***
(7.02)
0.04***
(7.56)
0.08**
(2.48)
0.12***
(3.03)
1.35
(0.10)
0.13
(0.87)
2.48*
(1.91)
0.18***
0.79***
(12.28)
0.05***
(3.58)
AvoidCETR
0.00
(0.27)
0.13***
(3.72)
0.67***
(7.01)
0.04***
(7.62)
0.08**
(2.45)
0.12***
(3.06)
1.38
(0.11)
0.12
(0.82)
2.53*
(1.95)
0.18***
0.31***
(3.91)
0.78***
(12.12)
(continued)
0.04***
(4.24)
0.00
(0.37)
0.12***
(3.44)
0.66***
(6.95)
0.05***
(7.79)
0.07**
(2.24)
0.12***
(3.02)
1.31
(0.10)
0.12
(0.82)
2.53*
(1.95)
0.18***
0.80***
(12.35)
20
L. H. Chen et al./Accounting and Finance
© 2017 AFAANZ
3.03%
3.11%
0.01
(0.32)
3.63%
(0.82)
0.03
(0.80)
0.15
(0.87)
0.15**
(2.84)
0.00
(1.24)
0.03
(0.90)
(2.59)
0.03
(1.27)
0.27*
(1.93)
0.23***
(3.62)
0.00
(2.69)
0.03
(1.33)
0.28**
(2.01)
0.22***
(3.57)
0.00
(0.04)
0.01
(0.29)
(2.64)
0.03
(1.30)
0.27*
(1.92)
0.23***
(3.62)
0.00
(0.01)
0.01
(0.28)
Yes
Yes
11,600
3.12%
AvoidCur
AvoidETR
ETR
(0.80)
0.03
(0.79)
0.17
(0.94)
0.16***
(2.90)
0.00
(1.17)
0.03
(0.91)
Yes
Yes
11,600
3.71%
3.80%
(0.75)
0.02
(0.73)
0.17
(0.98)
0.16***
(2.97)
0.00
(1.14)
0.03
(0.88)
5.61%
(5.87)
0.13***
(4.48)
0.31**
(2.43)
0.09
(1.55)
0.00
(0.75)
0.01
(0.31)
AvoidCETR
(5.82)
0.13***
(4.48)
0.29**
(2.29)
0.09
(1.62)
0.00
(0.67)
0.01
(0.30)
Yes
Yes
11,539
5.71%
5.78%
(5.71)
0.12***
(4.40)
0.29**
(2.25)
0.10
(1.68)
0.00
(0.64)
0.01
(0.36)
Variable descriptions are the same as outlined in Table 2. All continuous variables are winsorised at top and bottom 1 percent of their crosssectional distribution. *, **, *** denote statistical significance at 10, 5 and 1 percent levels, respectively.
Industry Effect
Fixed year effect
N
Adj. R2
iOWN
M/B
DiscAccrual
R&D
Intangible
Table 4 (continued)
L. H. Chen et al./Accounting and Finance
21
22
L. H. Chen et al./Accounting and Finance
addition, tax avoidance is positively associated with firms’ profitability (ROA);
this is intuitive, as profitable firms are faced with the need for tax avoidance. In
addition, NOL is positively associated with tax avoidance, which is consistent
with the fact that NOL will reduce contemporaneous tax liability.
4.2. Baseline results on BGD and financial risk
Table 5 reports the baseline results of H2, or whether BGD is associated with
firms’ financial risk. The correlation coefficients of FemaleDummy, FemaleRatio
and FemaleKeyBoard are all consistently positive and significant across three
financial risk measures, namely Financial RiskA, Financial RiskO and Financial
RiskZ. This is preliminary evidence of the positive association between BGD
and firm financial risk. Accordingly, H2 is rejected and a relationship between
BGD and financial risk exists. The correlation coefficients of Cash are positive
and significant across three alternative financial risk measures, which is
consistent with the notion that firms’ liquidity reduces firms’ financial risk and
firms with high financial risk do not have large portions of cash at hand. The
former likely results from firms’ investment in assets such as working capital,
long-term investments and current business expenses. In addition, financial risk
measures are positively associated with Leverage, NOL and negatively
associated with PPE, Intangible and R&D. The results are generally consistent
with results documented in literature, namely that reduced liquidity, both short
term and long term, will result in the increase of financial risk. All forms of
long-term investment, such as PPE, Intangible and R&D, reduce financial risks.
4.3. Additional analyses
4.3.1. Simultaneous equations while treating BGD as an endogenous variable
Taken together, the baseline models indicate that BGD is negatively associated
with tax avoidance (Table 4) and positively associated with financial risk (Table 5).
Our next step is to address possible endogeneity concerns. First of all, it is possible
that the results reported in Tables 4 and 5 are due to the fact that there are potential
determinants of BGD which are not correctly accounted for. Secondly, as we are
estimating two Equations (1) and (2), which may potentially affect each other when
estimated together. To address the endogeneity concern, the first step we take is to
use two-stage least-squares method to estimate Equations (1) and (2) together
while treating BGD measure as an endogenous variable.
The instrumental variables we use are the following: CSR, Size, Investment
Opportunity Set and Market Pressure. Both CSR and Size are both positively
correlated with BGD measures (Table 3) and are included in Equations (1) and
(2). To assure system identification, we also need variables that are not included
in Equations (1) and (2).
© 2017 AFAANZ
© 2017 AFAANZ
EqInc
FI
D(NOL)
NOL
Leverage
Size
ROA
Cash
CSR
FemaleKeyBoard
FemaleRatio
FemaleDummy
Intercept
0.01
(0.47)
8.04***
(10.04)
12.57***
(10.60)
0.16***
(2.74)
7.91***
(17.40)
1.32**
(2.20)
23.05
(0.20)
5.41***
(2.70)
32.21***
(3.94)
0.01
(0.41)
8.23***
(10.24)
12.60***
(10.54)
0.13**
(2.13)
7.97***
(17.26)
1.32**
(2.19)
19.57
(0.17)
5.30***
(2.63)
32.56***
(3.93)
2.77***
(3.62)
0.36***
(4.76)
0.00
(0.21)
8.13***
(10.15)
12.52***
(10.50)
0.16***
(2.59)
7.92***
(17.24)
1.33**
(2.20)
21.14
(0.18)
5.33***
(2.64)
32.52***
(3.93)
0.01
(0.72)
2.33***
(19.48)
0.11
(0.35)
0.18***
(10.45)
4.01***
(25.74)
0.47***
(3.29)
14.92
(1.21)
0.99**
(1.97)
3.68
(1.36)
0.96***
(5.42)
0.32***
(8.72)
1.13
(1.61)
1.23*
(1.77)
0.86***
(4.82)
1.28*
(1.82)
Financial RiskO
Financial RiskA
Table 5
The association between BGD and financial risk (H2)
0.01
(1.55)
2.36***
(19.80)
0.11
(0.35)
0.17***
(10.08)
4.01***
(25.87)
0.47***
(3.25)
15.19
(1.23)
1.03**
(2.14)
3.90
(1.44)
1.65***
(7.62)
1.00***
(5.61)
0.19***
(8.43)
0.01*
(1.69)
2.32***
(19.40)
0.15
(0.48)
0.18***
(10.81)
3.98***
(25.65)
0.48***
(3.21)
14.61
(1.19)
1.02**
(2.13)
3.86
(1.44)
0.91***
0.01
(1.22)
1.95***
(19.15)
0.80***
(3.24)
0.13***
(9.36)
4.16***
(32.11)
0.33**
(2.57)
3.94
(0.23)
0.35
(0.88)
2.24
(0.95)
2.72***
(18.03)
0.30***
(9.62)
Financial RiskZ
0.01**
(2.10)
1.99***
(19.51)
0.80***
(3.21)
0.14***
(9.79)
4.17***
(32.20)
0.33**
(2.53)
4.27
(0.25)
0.39
(1.01)
2.45
(1.03)
1.52***
(8.44)
2.77***
(18.05)
(continued)
0.18***
(9.92)
0.01**
(2.35)
1.95***
(19.11)
0.76***
(3.06)
0.12***
(8.66)
4.14***
(32.04)
0.33**
(2.52)
3.64
(0.22)
0.38
(0.99)
2.42
(1.04)
2.68***
(17.61)
L. H. Chen et al./Accounting and Finance
23
© 2017 AFAANZ
1.54***
(4.67)
1.42***
(3.55)
8.72***
(3.65)
3.06***
(4.09)
0.06***
(4.60)
0.40
(1.22)
40.67%
40.48%
51.76%
0.79***
(7.78)
1.22***
(11.14)
0.21
(0.51)
0.39**
(1.98)
0.02***
(3.09)
0.03
(0.34)
Yes
Yes
11,323
51.95%
0.79***
(7.74)
1.22***
(11.06)
0.29
(0.69)
0.37*
(1.91)
0.02***
(3.12)
0.04
(0.38)
1.58***
(4.80)
1.45***
(3.61)
8.80***
(3.69)
3.11***
(4.15)
0.06***
(4.57)
0.38
(1.18)
Yes
Yes
11,312
40.38%
1.54***
(4.70)
1.39***
(3.50)
8.78***
(3.70)
3.07***
(4.13)
0.06***
(4.61)
0.39
(1.19)
52.15%
0.77***
(7.58)
1.22***
(11.08)
0.19
(0.44)
0.41**
(2.07)
0.02***
(3.08)
0.04
(0.43)
59.57%
1.23***
(13.36)
1.55***
(15.92)
0.96**
(2.43)
0.03
(0.18)
0.01**
(2.25)
0.00
(0.05)
1.23***
(13.46)
1.55***
(16.03)
0.90**
(2.29)
0.02
(0.11)
0.01**
(2.21)
0.01
(0.09)
Yes
Yes
11,323
59.69%
Financial RiskZ
59.99%
1.21***
(13.22)
1.55***
(16.02)
0.86**
(2.20)
0.00
(0.01)
0.01**
(2.19)
0.00
(0.00)
Variable descriptions are the same as outlined in Table 2. All continuous variables are winsorised at top and bottom 1 percent of their crosssectional distribution. *, **, *** denote statistical significance at 10, 5 and 1 percent levels, respectively.
Industry Effect
Fixed year effect
N
Adj. R2
iOWN
M/B
DiscAccrual
R&D
Intangible
PPE
Financial RiskO
Financial RiskA
Table 5 (continued)
24
L. H. Chen et al./Accounting and Finance
L. H. Chen et al./Accounting and Finance
25
As noted in the discussion of correlation matrices, performance measures and
firm financial characteristics such as Cash, ROA, Leverage, FI, Intangible, R&D
and M/B are all correlated with BGD measures. Realising that we are not able to
use all these variables as instrumental variables, we use Investment Opportunity
Set and Market Pressure, both of which capture firms’ performance characteristics. Following Gaver and Gaver (1993), Baber et al. (1996), and McGuire
et al. (2014), four different measures, namely investment intensity, growth in
market value of assets, the market-to-book ratio and the ratio of scaled R&D
expenditures, are incorporated into the measure of Investment Opportunity Set.
Investment Opportunity Set is the first principal component score of the four
measures where investment intensity is the sum of capital expenditures (AQC),
research and development expense (XRD) and acquisitions (CAPXV) for the
past 2 years scaled by the sum of depreciation expense (DP) over the same
period. Growth in market value of assets is the geometric growth rate of market
value of assets for the past 2/3 years for which data are available. The market-tobook ratio is the sum of market value of a firm’s assets to the book value of its
assets. The ratio of scaled R&D expenditures is research and development
expense (XRD) divided by total assets (AT).11
Market Pressure is based on the count of positive changes in quarterly
earnings (Myers et al., 2007; and McGuire et al., 2014). Specifically, if splitadjusted quarterly earnings are greater than that of same quarter from previous
year, the count is set to 1 (one positive string) and otherwise to 0. Therefore, the
maximum number of positive string for a given year is 4. Market Pressure is 4
minus to total number of positive string for a given year. Therefore, the
simultaneous equation system that we estimate is the following:
Tax Avoidancei;t ¼ a þ b1 BGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
Financial Riski;t ¼ a þ b1 BGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
ð1Þ
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
ð2Þ
Endogenous variable: BGD, with alternative measures, FemaleDummy,
FemaleRatio and FemaleKeyBoard, respectively.
11
The first principal component accounts for approximately 33 percent of the total
variance.
© 2017 AFAANZ
26
L. H. Chen et al./Accounting and Finance
Instrumental variables: CSR, Size, Investment Opportunity Set and Market
Pressure.
As mentioned before, we use three alternative measures to proxy for Tax
Avoidance, namely AvoidETR, AvoidCurETR and AvoidCETR, respectively. To
streamline the reporting of regression results, instead of using RiskA, Financial
RiskO and Financial RiskZ as measures of Financial Risk alternatively, we use the
first principal component score of RiskA, Financial RiskO and Financial RiskZ as
Financial Risk given the high correlation among these three measures (Table 3).
Table 6 reports simultaneous regression results where BGD is treated as the
endogenous variable. Tax Avoidance specification of AvoidETR, AvoidCurETR
and AvoidCETR are reported in Panels A, B and C, respectively. Throughout the
panels, the simultaneous regression results are consistent with prior results
where BGD is negatively associated with Tax Avoidance and positively
associated with Financial Risk. It is noted that the explanatory powers of the
Financial Risk models are substantially larger than that of Tax Avoidance
models, the former adj. R2s are consistently above 20 percent whereas the latter
are only in the low single percentage points. Yet, the negative association
between BGD and Tax Avoidance is not subsumed. Therefore, we are more
confident that the observed association between BGD and Tax Avoidance and
BGD and Financial Risk is not caused by possible omitted variables.
4.3.2. Simultaneous change equations while treating DBGD as an endogenous
variable
To further address the possible endogeneity concerns caused by loop of
causality, we investigate how the current change of BGD is associated with
future change of Tax Avoidance and Financial Risk by estimating the following
simultaneous equation system:
DTax Avoidancei;tþ1 ¼ a þ b1 DBGDi;t þ b2 CSRi; t þ b3 Cashi;t þ b4 ROAi;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t
þ b9 FIi;t þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t
þ b13 R&Di;t þ b14 DiscAccruali;t þ b15 M=Bi;t
þ b16 iOWNi;it þ ei;t ;
ð3Þ
DFinancial Riski;tþ1 ¼ a þ b1 DBGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 PPEi;t þ b8 Intangiblesi;t
þ b9 R&Di;t þ b10 DiscAccruali;t þ b11 M=Bi;t þ b12 iOWNi;it
þ ei;t ;
ð4Þ
© 2017 AFAANZ
© 2017 AFAANZ
Intercept
FemaleDummy
FemaleRatio
FemaleKeyBoard
CSR
Cash
ROA
Size
Leverage
NOL
D(NOL)
FI
EqInc
PPE
Intangible
R&D
DiscAccrual
M/B
iOWN
Industry Effect
Fixed year effect
Adj. R2
(6.40)
(1.79)
(1.60)
(3.25)
(10.82)
(0.89)
(0.36)
(4.59)
(1.35)
(2.90)
(2.44)
(4.17)
(2.30)
(0.83)
(4.92)
(0.15)
(0.46)
0.31***
0.15*
0.00
0.08***
0.52***
0.01
0.01
0.10***
6.18
0.29***
1.37**
0.08***
0.04**
0.10
0.20***
0.00
0.01
Yes
Yes
2.83%
(7.90)
(27.83)
(7.20)
(8.46)
(45.22)
(7.16)
(0.94)
(0.16)
(2.61)
(14.03)
(16.71)
(3.89)
(1.93)
(2.62)
(1.24)
0.01***
0.24***
0.13***
0.03***
0.38***
0.06***
1.61
0.01
0.55***
0.10***
0.12***
0.17***
0.03*
0.00***
0.01
29.55%
(14.21)
(9.45)
0.26***
0.30***
(2.05)
(3.19)
(10.74)
(0.88)
(0.36)
(4.50)
(1.40)
(2.90)
(2.42)
(4.17)
(2.32)
(0.45)
(4.90)
(0.20)
(0.48)
(2.15)
0.96**
0.01**
0.08***
0.52***
0.01
0.01
0.10***
6.46
0.29***
1.37**
0.08***
0.05**
0.05
0.20***
0.00
0.01
Yes
Yes
2.81%
(5.91)
0.30***
t-stat
Coef.
t-stat
Coef.
Coef.
AvoidETR (H1)
Financial Risk (H2)
AvoidETR (H1)
t-stat
Model 2
Model 1
Panel A Determinant variable AvoidETR and Financial Risk (N = 11,224)
Table 6
Simultaneous regression analysis
26.02%
0.02***
0.24***
0.13***
0.02***
0.38***
0.06***
1.80
0.00
0.55**
0.10***
0.12***
0.20***
0.03*
0.00**
0.01
1.64***
0.28***
Coef.
(8.28)
(25.43)
(6.68)
(7.52)
(41.42)
(6.62)
(0.96)
(0.11)
(2.38)
(12.89)
(15.37)
(4.03)
(1.75)
(2.47)
(1.16)
(9.00)
(13.74)
t-stat
Financial Risk (H2)
0.07*
0.01*
0.08***
0.52***
0.01
0.01
0.10***
6.18
0.29***
1.37**
0.08***
0.04**
0.10
0.20***
0.00
0.01
Yes
Yes
2.86%
0.34***
Coef.
(1.79)
(1.66)
(3.26)
(10.86)
(0.85)
(0.36)
(4.61)
(1.36)
(2.92)
(2.45)
(4.18)
(2.31)
(0.84)
(4.94)
(0.15)
(0.46)
(7.07)
t-stat
AvoidETR (H1)
Model 3
0.14***
0.01***
0.24***
0.13***
0.02***
0.38***
0.06***
1.60
0.01
0.55***
0.10***
0.12***
0.17***
0.03**
0.00***
0.01
Yes
Yes
33.75%
0.19***
Coef.
(10.40)
(9.12)
(30.65)
(7.93)
(9.25)
(49.80)
(7.89)
(1.03)
(0.18)
(2.87)
(15.45)
(18.40)
(4.27)
(2.12)
(2.88)
(1.36)
(11.50)
t-stat
Financial Risk (H2)
L. H. Chen et al./Accounting and Finance
27
© 2017 AFAANZ
Intercept
FemaleDummy
FemaleRatio
FemaleKeyBoard
CSR
Cash
ROA
Size
Leverage
NOL
D(NOL)
FI
EqInc
PPE
Intangible
R&D
DiscAccrual
M/B
iOWN
Industry
Effect
Fixed Year
Effect
Adj. R2
(5.03)
(5.64)
(5.53)
(2.59)
(11.17)
(4.93)
(1.46)
(5.04)
(0.94)
(5.37)
(3.14)
(2.73)
(1.60)
(5.35)
(2.67)
(1.74)
(0.78)
0.29***
0.58***
0.02***
0.07***
0.64***
0.05***
0.04
0.13***
5.12
0.63***
2.11***
0.06***
0.04
0.75***
0.13***
0.00*
0.02
Yes
Yes
29.55%
2.75%
(7.90)
(27.83)
(7.20)
(8.46)
(45.22)
(7.16)
(0.94)
(0.16)
(2.61)
(14.03)
(16.71)
(3.89)
(1.93)
(2.62)
(1.24)
0.01***
0.24***
0.13***
0.03***
0.38***
0.06***
1.61
0.01
0.55***
0.10***
0.12***
0.17***
0.03*
0.00***
0.01
Yes
Yes
(14.21)
(9.45)
0.26***
0.30***
2.57%
Yes
(5.87)
(2.44)
(10.79)
(4.71)
(1.40)
(4.78)
(0.99)
(5.14)
(3.01)
(2.67)
(1.59)
(5.50)
(2.58)
(1.74)
(0.72)
(5.82)
3.20***
0.03***
0.07***
0.64***
0.04***
0.04
0.13***
5.61
0.63***
2.10***
0.06***
0.04
0.83***
0.13***
0.00*
0.02
Yes
(4.01)
t-stat
(H1)
0.25***
Coef.
Coef.
t-stat
Coef.
t-stat
Avoid
Financial Risk (H2)
AvoidCurETR (H1)
CurETR
Model 2
Model 1
Panel B Determinant variable AvoidCurETR and Financial Risk (N = 11,224)
Table 6 (continued)
26.02%
Yes
0.02***
0.24***
0.13***
0.02***
0.38***
0.06***
1.80
0.00
0.55**
0.10***
0.12***
0.20***
0.03*
0.00**
0.01
Yes
1.64***
0.28***
Coef.
(8.28)
(25.43)
(6.68)
(7.52)
(41.42)
(6.62)
(0.96)
(0.11)
(2.38)
(12.89)
(15.37)
(4.03)
(1.75)
(2.47)
(1.16)
(9.00)
(13.74)
t-stat
Financial Risk (H2)
2.99%
Yes
0.26***
0.02***
0.07***
0.64***
0.04***
0.04
0.13***
5.11
0.63***
2.11***
0.06***
0.04*
0.75***
0.13***
0.00*
0.02
Yes
0.42***
Coef.
(5.85)
(5.85)
(2.69)
(11.60)
(5.07)
(1.52)
(5.24)
(0.98)
(5.58)
(3.26)
(2.84)
(1.66)
(5.55)
(2.77)
(1.81)
(0.81)
(7.44)
t-stat
AvoidCurETR (H1)
Model 3
33.75%
Yes
0.14***
0.01***
0.24***
0.13***
0.02***
0.38***
0.06***
1.60
0.01
0.55***
0.10***
0.12***
0.17***
0.03**
0.00***
0.01
Yes
0.19***
Coef.
(10.40)
(9.12)
(30.65)
(7.93)
(9.25)
(49.80)
(7.89)
(1.03)
(0.18)
(2.87)
(15.45)
(18.40)
(4.27)
(2.12)
(2.88)
(1.36)
(11.50)
t-stat
Financial Risk (H2)
28
L. H. Chen et al./Accounting and Finance
© 2017 AFAANZ
Intercept
FemaleDummy
FemaleRatio
FemaleKeyBoard
CSR
Cash
ROA
Size
Leverage
NOL
D(NOL)
FI
EqInc
PPE
Intangible
R&D
t-stat
Coef.
t-stat
Financial Risk (H2)
Coef.
t-stat
AvoidCETR (H1)
Model 3
Coef.
t-stat
Financial Risk (H2)
(continued)
0.74*** (12.05) 0.27*** (13.23) 0.70*** (10.99) 0.29*** (12.93) 0.83*** (13.76) 0.19*** (10.47)
0.44*** (3.98)
0.35***
(9.55)
2.45*** (4.24)
1.83***
(9.11)
0.20*** (4.07)
0.15***
(10.68)
0.01***
(2.93) 0.01*** (8.03)
0.02***
(3.64) 0.02*** (8.39)
0.01***
(3.21) 0.01*** (9.39)
0.15***
(5.02) 0.24*** (24.90)
0.15***
(4.88) 0.24*** (22.85)
0.15***
(5.13) 0.24*** (27.80)
0.57***
(9.12) 0.11*** (5.45)
0.57***
(8.96) 0.11*** (5.10)
0.57***
(9.31) 0.11*** (6.09)
0.08***
(7.65) 0.03***
8.52)
0.07***
(8.77) 0.02*** (7.57)
0.08***
(7.97) 0.03*** (9.45)
0.09*** (2.99)
0.38***
(40.20) 0.09*** (2.93)
0.38***
(37.00) 0.09*** (3.05)
0.38***
(44.89)
0.10***
(3.66)
0.06***
(6.81)
0.10***
(3.52)
0.06***
(6.33)
0.10***
(3.73)
0.06***
(7.61)
1.26
(0.22)
1.74
(0.92) 1.68
(0.28)
1.95
(0.94) 1.27
(0.22)
1.75
(1.03)
0.13
(1.02)
0.00
(0.10) 0.12
(0.98) 0.01
(0.13) 0.13
(1.04)
0.00
(0.12)
2.52*** (3.55)
0.56**
(2.39) 2.52*** (3.47)
0.55
(2.19) 2.52*** (3.62)
0.56***
(2.67)
0.19***
(7.86) 0.10*** (12.81)
0.19***
(7.74) 0.10*** (11.82)
0.19***
(8.03) 0.10*** (14.30)
0.13***
(5.45) 0.12*** (14.99)
0.14***
(5.38) 0.12*** (13.86)
0.13***
(5.56) 0.12*** (16.74)
0.20
(1.29)
0.20***
(4.06) 0.26
(1.64)
0.23***
(4.18) 0.20
(1.34)
0.20***
(4.56)
Coef.
Coef.
Coef.
t-stat
AvoidCETR (H1)
Financial Risk (H2)
AvoidCETR (H1)
t-stat
Model 2
Model 1
Panel C Determinant variable AvoidCETR and Financial Risk (N = 11,166)
Table 6 (continued)
L. H. Chen et al./Accounting and Finance
29
© 2017 AFAANZ
0.02
0.00***
0.01
Yes
Yes
25.53%
(1.23)
(1.24)
(0.44)
0.06
0.00
0.01
Yes
Yes
4.80%
(1.39)
0.06
(2.68)
0.00
(1.37) 0.01
Yes
Yes
4.64%
(1.22)
(1.27)
(0.45)
0.02
0.00**
0.01
Yes
Yes
22.50%
Coef.
Coef.
(1.26)
(1.26)
(0.45)
t-stat
AvoidCETR (H1)
(1.25)
0.06
(2.54)
0.00
(1.29) 0.01
Yes
Yes
5.00%
t-stat
Financial Risk (H2)
Model 3
0.02
0.00***
0.01
Yes
Yes
29.98%
Coef.
ð2Þ
ð1Þ
(1.55)
(3.00)
(1.53)
t-stat
Financial Risk (H2)
Endogenous variable: BGD with FemaleDummy, FemaleRatio and FemaleKeyBoard alternatively.
Instrumental variables: CSR, Size, Investment Opportunity Set and Market Pressure.
Variable descriptions are the same as outlined in Table 2. All continuous variables are winsorised at top and bottom 1 percent of their crosssectional distribution. *, **, *** denote statistical significance at 10, 5 and 1 percent levels, respectively.
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
Financial Riski;t ¼ a þ b1 BGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
Tax Avoidancei;t ¼ a þ b1 BGDi;t þ b2 CSRi;t þ b3 Cashi;t þ b4 ROAi;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
DiscAccrual
M/B
iOWN
Industry Effect
Fixed Year Effect
Adj. R2
t-stat
Coef.
t-stat
Coef.
Coef.
AvoidCETR (H1)
Financial Risk (H2)
AvoidCETR (H1)
t-stat
Model 2
Model 1
Panel C Determinant variable AvoidCETR and Financial Risk (N = 11,166)
Table 6 (continued)
30
L. H. Chen et al./Accounting and Finance
L. H. Chen et al./Accounting and Finance
31
Endogenous variable: DBGD, with alternative measures, DFemalecount,
DFemaleRatio and DFemaleKeyBoard, respectively.12
Instrumental variables: CSR, Size, Investment Opportunity Set and Market
Pressure.
DFinancial Riski is the change of the first principal component score of RiskA,
Financial RiskO and Financial RiskZ between period t and t1.
Table 7 reports simultaneous regression results where DBGD is treated as the
endogenous variable. DTax Avoidance specification of DAvoidETR, DAvoidCurETR
and DAvoidCETR is reported in Panels A, B and C, respectively. First of all, the
results on the association between DBGDt and DFinancial Riski,t+1 are very
consistent with all DBGD specifications, namely DFemalecount, DFemaleRatio and
DFemaleKeyBoard. However, the results on the association between DBGDt and
DTax Avoidancei,t+1 depend on particular specification of DTax Avoidance. The
association becomes insignificant when DAvoidETR is used, and weakly negatively
significant when DAvoidCurETR is used, but remains negatively significant when
DAvoidCETR is used. We interpret that the overall results remain consistent with
the notion that BGD is negatively associated with Tax Avoidance and positively
associated with Financial Risk.
4.4. Results summary
We find evidence that BGD is negatively associated with reputation risk,
measured by tax aggressiveness. The result is consistent with the notion that
BGD helps preserve valuable intangible assets. We find evidence that BGD is
positively associated with financial risk, consistent with the view that BGD
likely helps reduce agency costs and motivate managers to take additional
business risks. Our results support the notion that BGD, as opposed to gender
homogeneity, leads to better alignment between management’s interests and
incentives with long-term shareholder interests. Thus, it appears BGD has the
effect of encouraging firms to engage in additional risk in valuable investment
projects while at the same time lowering unnecessary reputation risk exposure.
5. Conclusion
Prior archival research has focused on the effects of board gender diversity on
accounting and stock returns. However, boards are responsible for overseeing
the risk/return relationship that drives firm value and prior research has
notably failed/neglected to address the risk aspect of this important trade-off.
12
DFemalecount is set to 1 if the female board member count for period t is greater than
the count for period t1, and 0 otherwise. DFemaleRatio is set to 1 if FemaleRatio for
period t is greater than FemaleRatio for period t1, and 0 otherwise. DFemaleKeyBoard is
set to 1 if FemaleKeyBoard for period t is greater than FemaleKeyBoard for period t1, and 0
otherwise.
© 2017 AFAANZ
© 2017 AFAANZ
Intercept
DFemaleDummy
DFemaleRatio
DFemaleKeyBoard
Reputation
Cash
ROA
Size
Leverage
NOL
D(NOL)
FI
EqInc
PPE
Intangible
R&D
DiscAccrual
M/B
iOWN
Industry Effect
Fixed Year Effect
Adj. R2
(1.38)
(1.30)
(2.72)
(2.92)
(2.89)
(1.28)
(1.34)
(2.09)
(0.32)
(0.62)
(0.40)
0.00
0.02
0.08***
0.01***
0.04***
0.01
0.02
0.10**
0.00
0.01
0.01
Yes
Yes
0.57%
0.00
0.10***
0.69***
0.01
0.04
0.01
1.05
0.84***
0.69
0.05*
0.09***
0.36***
0.00
0.00
0.59***
Yes
Yes
4.75%
(0.77)
(2.68)
(9.28)
(0.76)
(1.14)
(0.22)
(0.11)
(6.20)
(0.81)
(1.96)
(3.08)
(3.14)
(1.45)
(0.19)
(12.93)
(0.40)
(3.07)
0.59*** (12.93) 0.01
0.20
(0.40)
0.61***
t-stat
Coef.
Coef.
t-stat
DFinancial Risk (H2)
DAvoidETR (H1)
Model 1
0.00
0.09*
0.72***
0.00
0.04
0.01
1.44
0.84***
0.67
0.05*
0.09***
0.28**
0.00
0.00
0.60***
Yes
Yes
4.71%
(0.12)
(2.45)
(9.47)
(0.36)
(1.21)
(0.31)
(0.14)
(6.14)
(0.78)
(1.88)
(3.22)
(2.13)
(1.39)
(0.09)
(12.90)
0.01***
0.01***
0.09***
0.00
0.01**
0.01
Yes
Yes
5.91%
0.00
0.02***
0.08***
0.01***
0.04***
0.11***
(0.98)
0.15
Coef.
(12.90) 0.01
t-stat
(3.40)
(3.60)
(5.14)
(1.01)
(2.08)
(0.83)
(0.91)
(3.42)
(8.25)
(5.13)
(8.33)
(5.22)
(0.83)
t-stat
DFinancial Risk (H2)
0.60***
Coef.
DAvoidETR (H1)
Model 2
Panel A Determinant Variable DAvoidETR and DFinancial Risk (N = 8,875)
Table 7
Simultaneous regression analysis for changes
0.60
0.00
0.11***
0.68***
0.01*
0.04
0.01
1.28
0.84***
0.69
0.06*
0.09***
0.39***
0.00
0.01
0.58***
Yes
Yes
4.18%
0.58***
Coef.
0.00
Coef.
(1.30)
(1.26)
(1.84)
(0.33)
(0.59)
(0.11)
(3.73)
(1.85)
(1.26)
(2.90)
(3.47)
(2.94)
(0.11)
t-stat
DFinancial Risk (H2)
(1.47)
0.58***
(1.39)
0.00*
(2.61)
0.02
(8.71) 0.08***
(1.65) 0.01***
(1.06)
0.04***
(0.20)
(0.12)
(5.82)
(0.75)
(1.89)
0.01
(2.84)
0.01
(3.31)
0.08*
(1.39)
0.00
(0.25) 0.01
(11.68)
0.00
Yes
Yes
0.61%
(11.68)
t-stat
DAvoidETR (H1)
Model 3
32
L. H. Chen et al./Accounting and Finance
© 2017 AFAANZ
Intercept
DFemaleDummy
DFemaleRatio
DFemaleKeyBoard
CSR
Cash
ROA
Size
Leverage
NOL
D(NOL)
FI
EqInc
PPE
Intangible
R&D
DiscAccrual
M/B
iOWN
Industry Effect
Fixed Year Effect
Adj. R2
ETR
Model 2
(10.71)
(1.71)
(2.20)
(1.37)
(6.48)
(0.98)
(1.84)
(1.51)
(0.50)
(4.16)
(0.23)
(2.83)
(1.10)
(1.83)
(0.48)
(0.71)
(10.71)
0.59***
1.01*
0.01**
0.06
0.58***
0.01
0.07*
0.05
6.01
0.68***
0.24
0.10***
0.04
0.25*
0.00
0.02
0.59***
Yes
Yes
3.29%
(1.38)
(1.30)
(2.72)
(2.92)
(2.89)
(1.28)
(1.34)
(2.09)
(0.32)
(0.62)
(0.40)
0.01
0.02
0.10**
0.00
0.01
0.01
Yes
Yes
0.57%
(0.40)
(3.07)
0.01
0.61***
0.00
0.02
0.08***
0.01***
0.04***
t-stat
Coef.
(1.37)
(1.30)
(8.07)
(0.92)
(2.05)
(1.89)
(0.56)
(4.85)
(0.25)
(3.20)
(1.05)
(1.04)
(0.48)
(0.66)
(12.62)
(0.33)
0.05
t-stat
(12.62)
0.00
0.05
0.62***
0.01
0.07**
0.06*
5.70
0.67***
0.22
0.09***
0.03
0.14
0.00
0.02
0.60***
Yes
Yes
4.71%
Model 3
0.01***
0.01***
0.09***
0.00
0.01**
0.01
Yes
Yes
5.91%
0.00
0.02***
0.08***
0.01***
0.04***
0.11***
0.01
Coef.
(3.40)
(3.60)
(5.14)
(1.01)
(2.08)
(0.83)
(0.91)
(3.42)
(8.25)
(5.13)
(8.33)
(5.22)
(0.83)
t-stat
1.42***
0.01***
0.07
0.57***
0.02*
0.07
0.05
5.36
0.68***
0.22
0.10**
0.04
0.26*
0.00
0.02
0.56***
Yes
Yes
4.18%
0.56***
Coef.
(H1) DFinancial Risk (H2) DAvoidCur
0.60***
Coef.
ETR
and DFinancial Risk (N = 8,875)
(H1) DFinancial Risk (H2) DAvoidCur
t-stat
ETR
Coef.
DAvoidCur
Model 1
Panel B Determinant Variable DAvoidCur
Table 7 (continued)
(2.64)
(2.63)
(1.25)
(5.61)
(1.83)
(1.56)
(1.33)
(0.38)
(3.58)
(0.19)
(2.49)
(0.97)
(1.70)
(0.43)
(0.68)
(8.66)
(8.66)
0.01
0.01
0.08*
0.00
0.01
0.00
Yes
Yes
0.61%
0.58***
0.00*
0.02
0.08***
0.01***
0.04***
0.00
Coef.
(1.30)
(1.26)
(1.84)
(0.33)
(0.59)
(0.11)
(3.73)
(1.85)
(1.26)
(2.90)
(3.47)
(2.94)
(0.11)
t-stat
(H1) DFinancial Risk (H2)
t-stat
ETR
L. H. Chen et al./Accounting and Finance
33
© 2017 AFAANZ
Intercept
DFemaleDummy
DFemaleRatio
DFemaleKeyBoard
CSR
Cash
ROA
Size
Leverage
NOL
D(NOL)
FI
EqInc
PPE
Intangible
R&D
DiscAccrual
(1.28)
(1.34)
(2.09)
(0.32)
(1.38)
(1.30)
(2.72)
(2.92)
(2.89)
0.01**
0.08
0.18
0.04**
0.10
0.06
18.82
1.08***
1.50
0.02
0.02
0.24
0.00
(2.21)
0.00
(1.00)
0.02
(1.12) 0.08***
(1.98) 0.01***
(1.33)
0.04***
(0.95)
(0.86)
(3.62)
(0.80)
(0.30)
0.01
(0.24)
0.02
(0.96)
0.10**
(0.55)
0.00
(0.40)
(3.07)
0.54***
(5.35) 0.01
3.04*** (2.81)
0.61***
t-stat
Coef.
Coef.
t-stat
DFinancial Risk (H2)
DAvoidCETR (H1)
Model 1
0.01**
0.07*
0.21**
0.01
0.10***
0.06*
19.47*
1.07***
1.51
0.01
0.01
0.21
0.00
Coef.
(2.56)
0.00
(1.74)
0.02***
(2.55) 0.08***
(1.60) 0.01***
(2.58)
0.04***
(1.86)
(1.75)
(7.07)
(1.59)
(0.42)
0.01***
(0.30)
0.01***
(1.44)
0.09***
(1.14)
0.00
0.11***
(3.40)
(3.60)
(5.14)
(1.01)
(0.91)
(3.42)
(8.25)
(5.13)
(8.33)
(5.22)
(0.83)
t-stat
DFinancial Risk (H2)
(10.18) 0.01
t-stat
0.53*** (3.09)
0.53***
Coef.
DAvoidCETR (H1)
Model 2
Panel C Determinant Variable DAgg_CETR and DFinancial Risk (N = 8,875)
Table 7 (continued)
2.82***
0.01***
0.08
0.21
0.04**
0.09
0.07
17.34
1.07***
1.44
0.02
0.01
0.16
0.00
0.49***
Coef.
0.00
Coef.
(continued)
(1.30)
(1.26)
(1.84)
(0.33)
(3.73)
(1.85)
(1.26)
(2.90)
(3.47)
(2.94)
(0.11)
t-stat
DFinancial Risk (H2)
(3.39)
0.58***
(2.61)
0.00*
(0.99)
0.02
(1.32) 0.08***
(2.44) 0.01***
(1.28)
0.04***
(1.03)
(0.80)
(3.65)
(0.78)
(0.33)
0.01
(0.17)
0.01
(0.67)
0.08
(0.57)
0.00
(4.87)
t-stat
DAvoidCETR (H1)
Model 3
34
L. H. Chen et al./Accounting and Finance
© 2017 AFAANZ
(0.62)
(0.40)
t-stat
Coef.
t-stat
Coef.
ð4Þ
ð3Þ
(0.59)
(0.11)
t-stat
DFinancial Risk (H2)
0.04
(0.66) 0.01
0.49***
(4.87)
0.00
Yes
Yes
Yes
Yes
1.21%
0.61%
Coef.
DAvoidCETR (H1)
Model 3
Endogenous variable: DBGD with DFemalecount, DFemaleRatio, and DFemaleKeyBoard alternatively.
Instrumental variables: CSR, Size, Investment Opportunity Set and Market Pressure.
Variable descriptions are the same as outlined in Table 2. All continuous variables are winsorised at top and bottom 1 percent of their crosssectional distribution. *, **, *** denote statistical significance at 10, 5 and 1 percent levels, respectively.
þ b10 DiscAccruali;t þ b11 M=Bi;t þ b12 iOWNi;it þ ei;t ;
þ b5 Sizei;t þ b6 Leveragei;t þ b7 PPEi;t þ b8 Intangiblesi;t þ b9 R&Di;t
DFinancial Riski;tþ1 ¼ a þ b1 DBGDi;t þ b2 CSRi;t þ b3 Cashi; t þ b4 ROAi;t
þ b14 DiscAccruali;t þ b15 M=Bi;t þ b16 iOWNi;it þ ei;t ;
þ b10 EqInci;t þ b11 PPEi;t þ b12 Intangiblesi;t þ b13 R&Di;t
þ b5 Sizei;t þ b6 Leveragei;t þ b7 NOLi;t þ b8 DNOLi;t þ b9 FIi;t
(2.08)
(0.83)
t-stat
DFinancial Risk (H2)
0.03
(1.06) 0.01**
0.53*** (10.18) 0.01
Yes
Yes
Yes
Yes
4.93%
5.91%
Coef.
DAvoidCETR (H1)
Model 2
DTax Avoidancei;tþ1 ¼ a þ b1 DBGDi;t þ b2 CSRi; t þ b3 Cashi;t þ b4 ROAi;t
M/B
0.04
(0.64) 0.01
iOWN
0.54***
(5.35) 0.01
Industry Effect
Yes
Yes
Fixed Year Effect Yes
Yes
1.15%
0.57%
Adj. R2
t-stat
Coef.
Coef.
t-stat
DFinancial Risk (H2)
DAvoidCETR (H1)
Model 1
Panel C Determinant Variable DAgg_CETR and DFinancial Risk (N = 8,875)
Table 7 (continued)
L. H. Chen et al./Accounting and Finance
35
36
L. H. Chen et al./Accounting and Finance
To fill this gap, this paper focuses on the risk component of firm value. The
evidence suggests that gender-diverse boards appear to be effective at
encouraging managers to take financing risks that could increase value, while
simultaneously discouraging those managers from accepting reputation risks
that could impair value.
Firm value is important to equity and debt investors. However, stability and
continuity are likely to be relatively more important to a diverse group of
stakeholders that includes employees, customers, suppliers and the community
in which a firm operates. These stakeholders are, for example, usually less able
than shareholders to diversify in order to reduce their risks. Many of them trust
corporate boards’ decisions hoping they make wise decisions when assessing
risks that could impair firm reputation or result in bankruptcy. Boards
frequently trade-off risks and rewards for their stakeholders, and it is likely that
not all corporate boards address these issues with equal capability. The
evidence reported in this paper suggests that gender-diverse corporate boards
are more risk-aware than firms with all-male boards.
Our results supplement prior findings on the positive association between
BGD and corporate citizenship (Bernardi and Threadgill, 2010; and Zhang
et al., 2013) where corporate citizenship can be viewed as an alternative
measure of firm reputation. Our results are also consistent with the findings by
Francis et al. (2014) in which the authors document that female CFOs are less
tax aggressive than their male counterparts. However, Francis et al. (2014)
attribute their results to the risk aversion of female CFOs. In other words,
female executives will impact firms’ risk strategies by reducing firm risk
exposure simply because female CFOs are more risk averse then male CFOs. If
that were the case with female executives, then female board presence also
increases the overall corporate boards’ risk aversion and increases the gap
between risk-neutral shareholders’ risk preference and firms’ risk strategies. To
verify these results, we investigated the association between BGD and a firm’s
financial risk while controlling for financial risk determinants and other firm
characteristics and concluded that a gender-diverse board aligns a firm’s risk
exposure closer to risk-neutral shareholders’ preferences by reducing reputation risk exposure while enabling necessary financial risk exposure. We
maintain the assumption that from portfolio-diversified shareholders’ perspectives, a healthy exposure to financial risk is necessary in order to obtain a
return on equity that would be higher than a risk-free rate of return. In
addition to their business knowledge, experience and expertise, female board
members may not be as risk averse as the average female population. We
acknowledge that we are not able to rule out the possibility that our findings
are consistent with the notion that female board members are seasoned
corporate professionals. However, this plausible explanation for our findings
does not compromise our key conclusion that BGD improves corporate
governance, which is desirable on any front.
© 2017 AFAANZ
L. H. Chen et al./Accounting and Finance
37
References
Abbott, L., S. Parker, and T. Presley, 2012, Female board presence and the likelihood of
financial restatement, Accounting Horizons 26, 607–629.
Adams, R., and D. Ferreira, 2009, Women in the boardroom and their impact on
governance, Journal of Financial Economics 94, 291–309.
Adams, R., and V. Ragunathan, 2013, Lehman sisters, Working paper (University of
New South Wales and University of Queensland).
Aguilera, R., I. Filatotchev, H. Gospel, and G. Jackson, 2008, An organizational
approach to comparative corporate governance: costs, contingencies, and complementarities, Organization Science 19, 475–492.
Altman, E., 1968, Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy, Journal of Finance 23, 589–609.
Andreoni, J., and L. Versterlund, 2001, Which is the fair sex? Gender differences in
altruism, Quarterly Journal of Economics 116, 293–312.
Anginer, D., S. Mansi, A. Warburton, and C. Yildizhan, 2015, Firm reputation and cost
of debt capital, Working paper (Virginia Tech University).
Atkins, D., I. Bates, and L. Drennan, 2006, Reputational Risk: A Question of Trust
(Les50ns Professional Publishing, London).
Baber, W. R., S. N. Janakiraman, and S.-H. Kang, 1996, Investment opportunities and
the structure of executive compensation, Journal of Accounting and Economics 21,
297–318.
Bart, C., and G. McQueen, 2013, Why women make better directors, International
Journal of Business Governance and Ethics 8, 93–99.
Barua, A., L. Davidson, D. Rama, and S. Thiruvadi, 2010, CFO gender and accruals
quality, Accounting Horizons 24, 25–39.
Basdeo, D., K. Smith, C. Grimm, V. Rindova, and P. Derfus, 2006, The impact of
market actions on firm reputation, Strategic Management Journal 27, 1205–1219.
Berger, A., T. Kick, and K. Schaeck, 2014, Executive board composition and bank risk
taking, Journal of Corporate Finance 28, 48–65.
Bernardi, R., and V. Threadgill, 2010, Women directors and corporate social
responsibility, Electronic Journal of Business Ethics and Organization Studies 15, 15–
21.
Blaylock, B. S., 2016, Is tax avoidance associated with economically significant rent
extraction among U.S. firms?, Contemporary Accounting Research 33, 1013–1043.
Cao, Y., J. Myers, L. Myers, and T. Omer, 2015, Company reputation and the cost of
equity capital, Review of Accounting Studies 20, 42–81.
Carter, D., F. D’Souza, B. Simkins, and W. Simpson, 2010, The gender and ethnic
diversity of US boards and board committees and firm financial performance,
Corporate Governance: An International Review 18, 396–414.
Carter, D., B. Simkins, and W. Simpson, 2003, Corporate governance, board diversity
and firm value, Financial Review 38, 33–53.
Castro, R., G. Clementi, and G. MacDonald, 2004, Investor protection, optimal
incentives, and economic growth, Quarterly Journal of Economics 119, 1131–1175.
Chatterji, A., D. Levine, and M. Toffel, 2009, How well do social ratings actually
measure corporate social responsibility?, Journal of Economics & Management
Strategy 18, 125–169.
Cole, S., 2012, The impact of reputation on market value, World Economics 13(3), 47–
68.
Coles, J., N. Daniel, and L. Naveen, 2006, Managerial incentives and risk-taking,
Journal of Financial Economics 79, 431–468.
© 2017 AFAANZ
38
L. H. Chen et al./Accounting and Finance
Croson, R., and U. Gneezy, 2009, Gender differences in preferences, Journal of
Economic Literature 47, 448–474.
Daily, C., and D. Dalton, 2003, Women in the boardroom: a business imperative,
Journal of Business Strategy 24, 8–9.
Davis, A., D. Guenther, L. Krull, and B. Williams, 2016, Do socially responsible firms
pay more taxes?, The Accounting Review 91, 47–68.
Deloitte, 2014, 2014 Global Survey on Reputation Risk: Reputation@Risk, October.
Available at: http://www2.deloitte.com/global/en/pages/governance-risk-and-compliance/
articles/reputation-at-risk.html
Desai, M., and D. Dharmapala, 2006, Corporate tax avoidance and high-powered
incentives, Journal of Financial Economics 79, 145–179.
Ditlev-Simonsen, C., and A. Midttun, 2011, What motivates managers to pursue
Corporate Social Responsibility (CSR)?, Corporate Social Responsibility and Environmental Management 18, 25–38.
Dyreng, S., M. Hanlon, and E. Maydew, 2008, Long-run corporate tax avoidance, The
Accounting Review 85, 1163–1189.
Dyreng, S., M. Hanlon, and E. Maydew, 2016, When does tax avoidance result in tax
uncertainty? SSRN working paper, Available at: http://ssrn.com/abstract=2374945
or https://doi.org/10.2139/ssrn.2374945
Erhardt, N., J. Werbel, and C. Shrader, 2003, Board of director diversity and firm
financial performance, Corporate Governance: An International Review 11, 102–111.
Fombrun, C. J., 1996, Reputation: Realizing Value from the Corporate Image (Harvard
Business School, Boston, MA).
Fombrun, C., and M. Shanley, 1990, What’s in a name? Reputation building and
corporate strategy, Academy of Management Journal 33, 233–258.
Fombrun, C., N. Gardberg, and M. Barnett, 2000, Opportunity platforms and safety
nets: corporate citizenship and reputation risk, Business and Society Review 105, 85–
106.
Francis, B., I. Hasan, Q. Wu, and M. Yan, 2014, Are female CFOs less tax aggressive?
Evidence from tax aggressiveness, Journal of American Taxation Association 36(2),
171–202.
Francis, B., I. Hasan, J. Park, and Q. Wu, 2015, Gender differences in financial
reporting decision making: evidence from accounting conservatism, Contemporary
Accounting Research 32, 1285–1318.
Franke, G., D. Crown, and D. Spake, 1997, Gender differences in ethical perceptions of
business practices: a social role theory perspective, Journal of Applied Psychology 82,
920–934.
Gallemore, J., E. Maydew, and J. Thornock, 2014, The reputation costs of tax
avoidance, Contemporary Accounting Research 31, 1103–1133.
Gaver, J. J., and K. M. Gaver, 1993, Additional evidence on the association between the
investment opportunity set and corporate financing, dividend, and compensation
policies, Journal of Accounting and Economics 16, 125–160.
Goh, B. W., J. Lee, C. Y. Lim, and T. Shevlin, 2016, The effect of corporate tax
avoidance on the cost of equity, The Accounting Review 91, 1647–1670.
Graham, J. R., and A. L. Tucker, 2006, Tax shelters and corporate debt policy, Journal
of Financial Economics 81, 563–594.
Graham, J., M. Hanlon, T. Shevlin, and N. Shroff, 2014, Incentives for tax planning and
avoidance: evidence from the field, The Accounting Review 89, 991–1023.
Groysberg, B., and D. Bell, 2013, Dysfunction in the boardroom, Harvard Business
Review 91, 88–97.
Gul, F., M. Hutchinson, and K. Lai, 2013, Gender-diverse boards and properties of
analyst earnings forecasts, Accounting Horizons 27, 511–538.
© 2017 AFAANZ
L. H. Chen et al./Accounting and Finance
39
Gul, F., B. Srinidhi, and A. Ng, 2011, Does board gender diversity improve the
informativeness of stock prices?, Journal of Accounting and Economics 51, 314–338.
Hanlon, M., and J. Slemrod, 2009, What does tax aggressiveness signal? Evidence from
stock price reactions to news about tax shelter involvement, Journal of Public
Economics 93, 126–141.
Himmelberg, C., and V. Quadrini, 2002, Optimal financial contracts and the dynamics
of insider ownership, Working paper (New York University).
Himmelberg, C., R. Hubbard, and I. Love, 2002, Investor protection, ownership and the
cost of capital, Working Paper No. 2834 (World Bank Policy Research).
Hoi, C., Q. Wu, and H. Zhang, 2013, Is corporate social responsibility (CSR) associated
with tax avoidance? Evidence from irresponsible CSR activities, The Accounting
Review 88, 2025–2059.
Hutchinson, M., J. Mack, and K. Plastow, 2015, Who selects the ‘right’ directors? An
examination of the association between board selection, gender diversity and
outcomes, Accounting & Finance 55, 1071–1103.
Jehn, K., G. Northcraft, and M. Neale, 1999, Why differences make a difference: a field
study of diversity, conflict, and performance in workgroups, Administrative Science
Quarterly 44, 741–763.
Jensen, M., and W. Meckling, 1976, Theory of the firm: managerial behavior, agency
costs and ownership structure, Journal of Financial Economics 3, 305–360.
John, K., L. Litov, and B. Yeung, 2008, Corporate governance and risk-taking, Journal
of Finance 63, 1679–1728.
Kanter, R., 1977, Men and Women of the Corporation. (Basic Books, New York, NY).
Karpoff, J., 2010, Reputation and the invisible hand: A review of empirical research.
Working paper for the Oxford Academic Symposium on Reputation. Available at:
http://www.sbs.ox.ac.uk/sites/default/files/CCR/Docs/2010Symposia-WP-Karpoff.
pdf
Kim, Y., M. Park, and B. Wier, 2012, Is earnings quality associated with corporate
social responsibility?, Accounting Review 87, 761–796.
Klein, B., and K. Leffler, 1981, The role of market forces in assuring contractual
performance, The Journal of Political Economy 89, 615–641.
Konrad, A., and V. Kramer, 2006, How many women do boards need?, Harvard
Business Review 84, 22.
Kothari, S., A. Leone, and C. Wasley, 2005, Performance matched discretionary accrual
measures, Journal of Accounting and Economics 39, 163–197.
Lamsa, A., M. Vehkapera, T. Puttonen, and H. Pesonen, 2008, Effect of business
education on women and men students’ attitudes on corporate responsibility in
society, Journal of Business Ethics 82, 45–58.
Larcker, D., and B. Tayan, 2013, Pioneering women on boards: Pathways of the first
female directors, Working paper (Stanford University).
Lee, L., R. Marshall, D. Rallis, and M. Moscardi, 2015. Women on Boards: Global
Trends in Gender Diversity on Corporate Boards (MCSI Inc.). Available at: https://
www.msci.com/documents/10199/04b6f646-d638-4878-9c61-4eb91748a82b
Leuz, C., D. Nanda, and P. Wysocki, 2003, Earnings management and investor
protection: an international comparison, Journal of Financial Economics 69, 506–527.
Low, D., H. Roberts, and R. Whiting, 2015, Board gender diversity and firm
performance: empirical evidence from Hong Kong, South Korea, Malaysia and
Singapore, Pacific-Basin Finance Journal 35, 381–401.
Matsa, D., and A. Miller, 2013, A female style in corporate leadership? Evidence from
quotas, American Economic Journal: Applied Economics 5, 136–169.
© 2017 AFAANZ
40
L. H. Chen et al./Accounting and Finance
McGuire, S. T., T. C. Omer, and J. H. Wilde, 2014, Investment opportunity sets,
operating uncertainty, and capital market pressure: determinants of investments in tax
shelter activities?, The Journal of the American Taxation Association 36, 1–26.
Milliken, F., and L. Martins, 1996, Searching for common threads: understanding the
multiple effects of diversity in organizational groups, Academy of Management Review
21, 402–433.
Muller-Kahle, M., and K. Lewellyn, 2011, Did board configuration matter? The case of
US subprime lenders, Corporate Governance: An International Review 19, 405–417.
Murray, K., 2004, Reputation? Managing the single greatest risk facing business today,
Journal of Communication Management 8, 142–149.
Myers, J. N., L. A. Myers, and D. Skinner, 2007, Earnings momentum and earnings
management, Journal of Accounting, Auditing and Finance 22, 249–284.
Ohlson, J., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal
of Accounting Research 18, 109–131.
Østergaard, C., B. Timmermans, and K. Kristinsson, 2011, Does a different view create
something new? The effect of employee diversity on innovation, Research Policy 40,
500–509.
Petersen, M., 2009, Estimating standard errors in finance panel data sets: comparing
approaches, Review of Financial Studies 22, 435–480.
Peterson, R., G. Albaum, D. Merunka, J. Munuera, and S. Smith, 2010, Effect of
nationality, gender and religiosity on business-related ethicality, Journal of Business
Ethics 96, 573–587.
Post, C., and K. Byron, 2015, Women on boards and firm financial performance: a
meta-analysis, Academy of Management Journal 58, 1546–1571.
Roberts, P., and G. Dowling, 2002, Corporate reputation and sustained superior
financial performance, Strategic Management Journal 23, 1077–1093.
Roxas, M., and J. Stoneback, 2004, The importance of gender across cultures in ethical
decision-making, Journal of Business Ethics 50, 149–165.
Shackelford, D. A., and T. Shevlin, 2001, Empirical tax research in accounting, Journal
of Accounting and Economics 31, 321–387.
Shapiro, C., 1983, Premiums for high quality products as returns to reputations,
Quarterly Journal of Economics 98, 659–679.
Shenkar, O., and E. Yuchtman-Yaar, 1997, Reputation, image, prestige, and goodwill:
an interdisciplinary approach to organizational standing, Human Relations 50, 1361–
1381.
Shleifer, A., and D. Wolfenzon, 2002, Investor protection and equity markets, Journal of
Financial Economics 66, 3–27.
Simga-Mugan, C., B. A. Daly, D. Onkal, and L. Kavut, 2005, The influence of
nationality and gender on ethical sensitivity: an application of the issue-contingent
model, Journal of Business Ethics 57, 139–152.
Sim~
oes, C., and S. Dibb, 2008, Illustrations of the internal management of corporate
identity, in: T. Melewar, ed., Facets of Corporate Identity, Communication and
Reputation. (Routledge, London and New York).
Simpson, W., D. Carter, and F. D’Souza, 2010, What do we know about women on
boards?, Journal of Applied Finance 2, 27–39.
Singh, V., 2008, Transforming Boardroom Cultures. Report for the UK Resource Centre
for Women in Science, Engineering and Technology, Bradford, UK.
Smith, C., and R. Stulz, 1985, The determinants of firms’ hedging policies, Journal of
Financial and Quantitative Analysis 20, 391–405.
Srinidhi, B., F. Gul, and J. Tsui, 2011, Female directors and earnings quality,
Contemporary Accounting Research 28, 1610–1644.
© 2017 AFAANZ
L. H. Chen et al./Accounting and Finance
41
Torchia, M., A. Calabr
o, and M. Huse, 2011, Women directors on corporate boards:
from tokenism to critical mass, Journal of Business Ethics 102, 299–317.
Treacy, M., 2004, Innovation as a last resort, Harvard Business Review 82, 29–30.
Triana, M., T. Miller, and T. Trzebiatowski, 2014, The double-edged nature of board
gender diversity: diversity, firm performance, and the power of women directors as
predictors of strategic change, Organization Science 25, 609–632.
Wahn, J., 2003, Sex differences in competitive and compliant unethical work behavior,
Journal of Business and Psychology 18, 121–128.
Watson, W., K. Kumar, and L. Michaelsen, 1993, Cultural diversity’s impact on
interaction process and performance: comparing homogeneous and diverse task
groups, Academy of Management Journal 36, 590–602.
Weeks, W., C. Moore, J. McKinney, and J. Longenecker, 1999, The effects of gender
and career stage on ethical judgment, Journal of Business Ethics 20, 301–313.
Weisbach, D. A., 2002, Ten truths about tax shelters, Tax Law Review 55, 215–253.
White, M., 2015, The pursuit of gender parity in the American boardroom. Speech
presented to the Women’s Forum of New York, November 19. Text Available at:
http://www.sec.gov/news/speech/gender-parity-in-the-american-boardroom.html
Williams, J., 1987, Perquisites, risk, and capital structure, Journal of Finance 42, 29–48.
Williams, K., and C. O’Reilly, 1998, Demography and diversity in organizations: a
review of 40 years of research, Research in Organizational Behavior 20, 77–140.
Zhang, J. Q., H. Zhu, and H. Ding, 2013, Board composition and corporate social
responsibility: an empirical investigation in the Post Sarbanes-Oxley era, Journal of
Business Ethics 114, 381–392.
Zmijewski, M., 1984, Methodological issues related to the estimation of financial distress
prediction models, Journal of Accounting Research 22, 59–82.
© 2017 AFAANZ