Board Risk Oversight and Corporate Tax-Planning Practices
Mark S. Beasley
North Carolina State University
Nathan C. Goldman*
North Carolina State University
Christina Lewellen
North Carolina State University
Michelle McAllister
Northern Arizona University
February 2020
Abstract: Risk oversight by the board of directors LVDNH\FRPSRQHQWRIDILUP¶VHQWHUSULVHULVN
PDQDJHPHQWIUDPHZRUNDQGUHFHQWO\ERDUGVKDYHSDLGPRUHDWWHQWLRQWRWKHLUILUP¶VWD[-
planning activities. In this study, we use a hand-collected sample of proxy statement disclosures
DERXWWKHERDUG¶VUROHLQULVNRYHUVLJKWand provide evidence that more robust risk oversight is
associated with lower tax uncertainty in conjunction with lower overall tax burdens. We also find
that the tax activities are concentrated in positions that yield permanent tax benefits, as well as
less risky tax-planning activities. Overall, the evidence is consistent with greater board risk
oversight influencing the firm to engage in more effective tax-planning practices.
____________
*Corresponding Author: 2801 Founders Hall, Raleigh, NC 27695; telephone: 919-515-4510; E-mail: NCGoldma@NCSU.edu.
We appreciate helpful comments from Kris Allee, Alissa Bruhne, Robert Hills (NTA Discussant), Adam Manlove, Michelle
Nessa, Ahn Persson (FARS Discussant), Sonja Rego, Stefan Richter, Casey Schwab, Bridget Stomberg, Brian Williams, Junwei
Xia, and participants of the 2019 Indiana University Spring Tax Camp, the 112
th
National Tax Association (NTA) Annual
Meeting, and the 2020 Financial Accounting and Reporting Section (FARS) Midyear Meeting.
Board Risk Oversight and Corporate Tax-Planning Practices
Abstract: Risk oversight by the board of directors is a key component of a ILUP¶VHQWHUSULVHULVN
PDQDJHPHQWIUDPHZRUNDQGUHFHQWO\ERDUGVKDYHSDLGPRUHDWWHQWLRQWRWKHLUILUP¶VWD[-
planning activities. In this study, we use a hand-collected sample of proxy statement disclosures
DERXWWKHERDUG¶VUROHLQULVNRYHUVLJKWand provide evidence that more robust risk oversight is
associated with lower tax uncertainty in conjunction with lower overall tax burdens. We also find
that the tax activities are concentrated in positions that yield permanent tax benefits, as well as
less risky tax-planning activities. Overall, the evidence is consistent with greater board risk
oversight influencing the firm to engage in more effective tax-planning practices.
Keywords: Board Risk Oversight; Tax-Planning Practices; Enterprise Risk Management; Tax-
Planning Levels; Tax-Planning Volatility
1
INTRODUCTION
We examine the relation between more robust risk oversight by the board of directors
KHUHDIWHUUHIHUUHGWRDV³ULVNRYHUVLJKW´DQGILUPV¶WD[-planning practices. Specifically, we test
whether more robust risk oversight is associated with more effective tax-planning practices as
indicated by lower tax uncertainty in conjunction with lower tax burdens.
1
The effect of risk
oversight on tax planning DQGILUPV¶WD[RXWFRPHVis particularly of interest for several reasons.
Taxes represent one of the largest line item expenses for any firm, inherently incentivizing firms
to curtail the tax liability through effective tax-planning activities. However, firms must balance
any potential tax benefits against associated non-tax costs. Therefore, lowering the tax liability
may not always represent a value-enhancing decision (Scholes, Wolfson, Erickson, Hanlon,
Maydew, and Shevlin 2014). For example, more aggressive and risky tax-planning strategies
may also result in a number of undesirable long-run non-tax costs, including higher future cash
tax outflows, increased reputational costs, higher costs of capital, and decreased financial
statement transparency. Moreover, tax-planning decisions often affect broader operational and
strategic decisions that are the purview of the board of directors. Given the innate complexity
surrounding the implementation of risk-balanced, yet effective tax planning activities, and their
potential effects on operational and strategic firm decisions, we expect that the board of directors
plays an important role in this process.
In recent years, there has been an abundance of financial failures (i.e., the Great
Recession) and FRUSRUDWHPLVVWHSVHJ9RONVZDJHQ¶VJDVHPLVVLRQV:HOOV)DUJR¶VDJJUHVVLYH
sales practices, Apples tax scandal in Ireland) that can be traced back to weak risk oversight.
Key stakeholders now realize that traditional approaches to managing risks may not be effective
1
2XUGHILQLWLRQRI³HIIHFWLYH´WD[-planning strategies, following Scholes et al. (2014), reflects business strategies that maximize
after-tax returns, considering both the tax costs and non-tax costs of the transaction.
2
at identifying and responding to the various types of risks that emerge across an enterprise,
including tax risks. In response, stakeholders are now pressing boards to strengthen the ERDUG¶V
oversight of WKHILUP¶V approach to managing the constellation of risks impacting the enterprise
(Castellano, Lightle, and Baker 2011; Vlasic 2014). For example, the Delaware Supreme Court
recently ruled in support of the view that boards must make an effort to oversee risk (Supreme
Court of Delaware 2019). Concurrent with the rise in stakeholder expectations of greater board
involvement in risk oversight, increased public scrutiny of corporate tax planning practices have
triggered a greater concern among boards and executives to identify and address the risks
generated by firmV¶ tax practices (EY 2016). A number of practitioner publications assert that
boards play an increasingly important role in managing tax risk (Neubig and Sangha 2004; Erle
2008; Wilson 2013; Deloitte 2016; KPMG 2018; EY 2019). Moreover, regulatory authorities in
recent years have placed increasHGSUHVVXUHRQWKHERDUGRIGLUHFWRUVWRHQVXUHWKHILUP¶VWD[ULVN
exposure is consistent with its overall risk appetite (Shulman 2009, 2010; OECD 2009). In sum,
tax planning is an area where we expect board risk oversight to play a vital role in ensuring the
firm is pursuing appropriately risk-balanced tax reduction strategies.
Many organizations are embracing the business paradigm of enterprise risk management
(ERM) in response to the pressures from key stakeholders for greater risk oversight. ERM helps
boards and executives develop holistic, enterprise-wide approaches to identifying, managing, and
monitoring all risks that could potentially affect the achievement of strategic objectives.
Importantly, ERM¶V focus is not on indiscriminate risk minimization, but rather on identifying
and understanding the ILUP¶Vportfolio of risks so that management and the board can make
sound strategic decisions that balance these risks against the pursuit of firm growth. Importantly,
widely-embraced frameworks for ERM plaFHUHVSRQVLELOLW\IRUWKHRYHUVLJKWRIWKHHQWLW\¶VULVN
3
management processes on the board and assert that prudent board oversight is a key component
of the risk management process (COSO 2004, 2009, 2017; ISO 2009, 2018).
Our first hypothesis examines the association between risk oversight and tax uncertainty.
While reducing tax burdens on average can be beneficial to shareholders, there is a limit.
Uncertain tax-planning strategies can lead to future tax payments and penalties, resulting in more
volatile tax outcomes (Klassen, Lisowsky, and Mescall 2016; Ciconte, Donohoe, Lisowsky, and
Mayberry 2016), ultimately creating a greater risk to capital providers (Rego and Wilson 2012;
Hasan, Hoi, Wu, and Zhang 2014; Lewellen, Mauler, and Watson 2019). Moreover, aggressive
tax strategies can subject the firm to public scrutiny (Chen, Schuchard, and Stomberg 2019;
Dhaliwal, Goodman, Hoffman, and Schwab 2019) and reputational damage (Dyreng, Hoopes,
and Wilde 2016; Austin and Wilson 2017). We expect that boards with more robust risk
oversight constrain overly aggressive tax planning, resulting in lower tax uncertainty. Our second
hypothesis tests the association between risk oversight and the overall tax burden. Naturally, a
firm can achieve low tax uncertainty by not engaging in tax planning and paying the statutory tax
rate. However, managing risk does not equate to avoiding all risks, but rather ensuring the firm
takes reasonable risks. Since firms can achieve lower tax burdens without increased uncertainty
(Dyreng, Hanlon, and Maydew 2008; Guenther, Matsunaga, and Williams 2017), we propose
that strong risk oversight promotes tax-planning decisions that reduce WKHILUP¶V tax burden
without exposing it to undue risk.
While we expect that firms with greater board risk oversight are associated with lower tax
uncertainty and lower tax burdens, this expectation is not without tension for two primary
reasons. First, the board is ultimately charged with governing the firm, and, as such, its decisions
may not always prioritize tax benefits (Deloitte 2016; KPMG 2018; EY 2019). Prior literature
4
documents that firms pay high amounts of taxes despite the substantial economic benefits of
avoiding them (e.g., Mills, Erickson, and Maydew 1998) and questions why firms do not avoid
PRUHWD[HV:HLVEDFKZKLFKKDVFRPPRQO\EHHQUHIHUUHGWRDVWKH³XQGHUVKHOWHULQJ
SX]]OH´5HFHQWDQGFRQFXUUHQWVWXGLHVLQGLFDWHWKDWWKLVPD\EHEHFDXVHSD\LQJH[FHVVLYHO\ORZ
tax rates can create substantial non-tax risk for firms (e.g., Neuman 2014; Cook, Moser, and
2PHU%DODNULVKQDQ%ORXLQDQG*XD\7KXVILUPVWKDWDSSHDUµXQGHUVKHOWHUHG¶DQG
choose to forego highly uncertain or tax-minimizing strategies may have maximized the
effectiveness of their tax planning efforts based on thHILUP¶VUHODWLYHVHWRIRSSRUWXQLWLHV)RU
example, the board is not likely to encourage the firm to make a decision that yields a tax benefit
but overall has a negative net present value. In addition, it is possible that boards may even
choose to forego positive-NPV tax strategies to limit public scrutiny or other non-tax
consequences. A second reason we may not observe an association between higher risk oversight
and lower tax burdens is that the technical nature of tax planning may limit the effectiveness of
tax risk monitoring provided by the board (Balakrishnan et al. 2019). In either case, it is possible
that robust board risk oversight has little to no effect on firm tax practices.
Our sample includes non-financial, non-regulated, U.S. corporations belonging to the
Russell 1000 index as of June 2014. We develop and validate a measure of risk oversight using a
hand-collected sample of required proxy statement GLVFORVXUHVRIWKHERDUGV¶LQYROYHPHQWLQULVN
management (SEC 2010). The measure encompasses three factors based on best practices for
ERM (COSO 2004, 2009, 2010; Rittenberg and Martins 2012LQFOXGLQJWKHERDUG¶V
disclosure of a formal responsibility for risk oversight, 2) whether the board consistently engages
in risk monitoring, and 3) whether the board fosters an active risk mindset that incorporates risk
PDQDJHPHQWLQWRWKHILUP¶VVWUDWHJ\DQGRUFRUSRUDWHFXOWXUH. We capture our measure of risk
5
oversight using proxy disclosures in 2014 and examine the association between risk oversight
and our tax outcome measures using firm-years from 2014 through 2017.
We follow prior literature and proxy for tax uncertainty using GAAP effective tax rate
(ETR) volatility and overall tax burden using GAAP ETRs. We use GAAP ETRs to model both
measures of tax planning because GAAP ETRs are commonly used by executives as a salient
PHDVXUHRIDILUP¶VRYHUDOOWD[EXUGHQDQGOHYHORIWD[DYRLGDQFHArmstrong, Blouin,
Jagolinzer, and Larcker 2015; Dyreng, Hanlon, and Maydew 2010). Consistent with our
predictions, we find that more robust risk oversight is associated with lower GAAP ETR
volatility and lower GAAP ETRs. We specifically estimate that firms with the highest level of
risk oversight experience 31.0% lower GAAP ETR volatility and 13.2% lower GAAP ETRs
compared to firms with the lowest level of risk oversight.
2
These results are robust to numerous
alternative proxies for tax uncertainty and levels of tax burden.
We next examine specific tax activities that firms with robust risk oversight might
employ. We first split our sample by tax activities that yield a permanent benefit versus those
that yield a temporary benefit. We find that the negative association between risk oversight and
tax burdens is concentrated among permanent tax positions. This finding suggests that robust risk
oversight is associated with tax planning that will not reverse in future periods, which should
have the largest impact on firm value. We next develop an understanding of the specific tax
planning activities firms choose. We first examine firms that locate income in lower tax
jurisdictions (i.e., income shifting) and document that firms with high-risk oversight engage in
less inbound and outbound income shifting. However, our findings are also significantly
2
Relative to a firm with a low risk oversight score, firms with a high risk oversight score have a 0.019 lower GAAP ETR
Volatility and a 4.14 lower GAAP ETR. Based on their conditional means, these values equate to a 31.0% lower GAAP ETR
Volatility and a 13.0% lower GAAP ETR. See Table 5 for the formal tests that yield these inferences.
6
moderated by firms with a greater foreign presence. Income shifting can be a particularly
uncertain tax planning activity (Towery 2017), but having a multinational tax-efficient supply
chain can significantly moderate any tax uncertainty generated by foreign operations (Drake,
Goldman, and Murphy 2019). As such, we interpret our findings as indicating that firms with a
more robust board risk oversight choose permanent tax planning strategies that both (1)
significantly lower GAAP ETRs and (2) do not significantly increase GAAP ETR volatility.
Additionally, we document that firms with high levels of R&D experience similar levels of
GAAP ETR volatility with higher levels of board oversight, and experience significantly more
tax savings (GAAP ETRs). Finally, we examine whether the board incentivizes managers to
DOLJQWKHILUP¶VWD[practices with WKHILUP¶VULVNpreferences through the design of executive
compensation schemes. We find no evidence that the board incentivizes effective tax practices
using compensation. This result implies that boards likely turn to more direct mechanisms to
influence these activities, such as through conversations and monitoring.
Our findings provide several contributions to the literature. First, we extend the literature
examining the role of the board of directors in shaping corporate taxation (e.g., Minnick and
Noga 2010; Rego and Wilson 2012; Gaertner 2014; Armstrong et al. 2015). Tax planning is an
important mechanism for generating financial and cash flow benefits; however, aggressive and
uncertain tax planning can expose the firm to significant risks (e.g., reputation risk). Given the
important positive and negative effects that tax planning can have on the firm, we expect that the
board has a strong interest in setting a tone at the top that incentivizes effective tax planning.
While prior literature examines board characteristics to infer actions by the board of directors
(i.e., Minnick and Noga 2010; Robinson, Xue, and Zhang 2012; Richardson, Taylor, and Lanis
2013; Brown and Drake 2014; Armstrong et al. 2015), the evidence provided by these studies on
7
whether and to what extent the board influences tax planning is mixed.
3
We extend the literature
by providing direct evidence of the ERDUG¶V actions through their disclosed level of board risk
oversight. Second, our findings provide additional insights into the undersheltering puzzle
(Weisbach 2002). We document that tax risk limits firms incentives to excessively avoid taxes
and that more robust board risk oversight helps the firm better manage tax risk, while also
minimizing the tax burden.
Our study also contributes to the emerging ERM literature (e.g., Baxter, Bedard, Hoitash,
and Yezegel 2013; Cohen, Krishnamoorthy, and Wright 2017; Braumann 2018). ERM
frameworks propose that benefits of ERM include increasing positive outcomes (e.g., lowering
overall tax burdens) while reducing negative surprises and reducing performance variability
(e.g., reducing tax uncertainty) (COSO 2017). Since we expect the board to be a key contributor
to any well-developed ERM system, our finding that greater risk oversight is associated with
lower tax uncertainty and tax burdens demonstrates a tangible benefit to utilizing ERM best-
practices for risk management. Our study speaks to the potential positive outcomes of robust risk
oversight processes in terms of increasing firm value and managing tax risk through effective
practices. Moreover, it provides support for the assertion that implementing these practices can
be worth the resource investment (Cohen et al. 2017; Beasley, Branson, and Hancock 2019).
Our empirical strategy examines an association between robust board risk oversight and
corporate tax practices, which means that we are unable to draw causal inferences. While
endogeneity could be a concern, we highlight two important points. First, it is unlikely that tax
VWUDWHJLHVDUHGULYLQJWKHUREXVWQHVVRIWKHERDUG¶VULVNRYHUVLJKWSUDFWLFHV, given individuals
3
It may be difficult to infer board of director actions from board characteristics because they often provide indirect evidence on
WKHERDUG¶VRYHUVLJKW)RUH[DPSOHLIDEoard is comprised of only people who work for the firm and thus has low independence,
then one can only speculate that the board does not exercise ideal oversight, rather than examine their actual oversight practices.
8
who are considering board membership generally have limited access to information about prior
and ongoing tax planning tactics and strategies. Thus, the relation being a function of reverse
casualty is unlikely to be a concern. Rather, theory from practice suggests that risk oversight
should causally drive tax planning, and our analysis provides evidence to support this theory.
Second, we design our analyses to alleviate concerns related to correlated omitted variables. We
follow prior literature by modeling GAAPETRVol and GAAPETR and include a plethora of
control variables known to be associated with various firm risks and tax planning (Chen, Chen,
Cheng, and Shevlin 2010; Kubick, Lynch, Mayberry, and Omer 2015; Cen, Maydew, Zhang, and
Zuo 2017, among others). We also perform a variety of additional analyses to substantiate our
primary analyses and perform robustness tests, including a falsification test and entropy
balancing.
BACKGROUND AND HYPOTHESES
Enterprise Risk Management (ERM) and Board Oversight
Several corporate governance failures over the past two decades, including the financial
reporting crisis in 2001-2002 and the financial and economic crisis that began in 2008, have
generated significant attention towards the use of ERM practices within firms as well as a
specific interest in the ERDUG¶VUROHLQPRQLWRULQJPDQDJHPHQW¶VULVN-taking activities. The lack
of prudent risk oversight by the board has led to a number of regulatory changes over the past
decade specifically focused on strengthening WKHERDUG¶s role in risk oversight, as well as
increasing transparency around risk oversight efforts (NACD 2009; Dodd-Frank 2010; SEC
2010; S&P 2012; NYSE 2013; NACD 2017, 2018).
Widely-recognized principles-based ERM frameworks, including those developed by the
Committee of Sponsoring Organizations (COSO) and the International Organization for
Standardization ,62SODFHVLJQLILFDQWHPSKDVLVRQWKHERDUG¶VUROHLQRYHUVHHLQJWKHILUP¶V
9
enterprise-wide risk management processes (COSO 2004, 2009, 2017; ISO 2009, 2018). COSO
frameworks include adequate board risk oversight as one of the core principles necessary for
effective systems of internal control and enterprise-wide risk management, and the ISO
emphasizes that oversight bodies, which encompass the board of directors, are accountable for
overseeing risk management.
While these governance expectations emphaVL]HWKHERDUG¶VUROHLQWKHRYHUVLJKWRIall
types of firm risks (e.g., strategic, environmental, financial reporting, compliance, operational),
we focus our study on the association between risk oversight and tax-planning practices, given
these practices can impact multiple aspects of the firm. Corporate taxation has become a matter
of significant public interest, and governments and regulators are more aggressively scrutinizing
corporate tax strategies. Thus, tax risks include significant reputational risks that are of concern
to boards who oversee management¶VULVN-taking actions on behalf of key stakeholders (PwC
2013; EY 2016). As a result, tax oversight is an important strategic priority that may impact the
overall value, reputation, and brand of the firm. Boards are expected to be well-informed about
tax policy developments and trends worldwide (EY 2019), including consideration of how those
GHYHORSPHQWVDQGWUHQGVPD\LPSDFWWKHRUJDQL]DWLRQ¶VRYHUDOOenterprise risk profile.
Tax Practices and Corporate Governance
Two theories dominate the literature examining the strength of corporate governance
PHFKDQLVPVRQILUPV¶WD[EHKDYLRU)LUVW'HVDLDQG'KDUPDSDOD(2006) propose a
complementary association between tax planning and managerial rent extraction7KH³''´
theory proposes that the opacity and complexity of corporate tax planning provide opportunities
for rent extraction in weak governance settings. This theoretical perspective asserts that strong
board governance should reduce corporate tax aggressiveness since it assumes that tax
10
aggressiveness facilitates managerial diversion or suboptimal performance. Complementary to
Desai and Dharmapala (2006), Richardson et al. (2013) use a sample of Australian firms and
provide evidence that a more independent board is associated with higher tax burdens and a
lower likelihood of being challenged by the tax authority. Similarly, Li, Maydew, Willis, and Xu
(2019) and Desai, Dyck, and Zingales (2007) find that country-level reforms to enhance
corporate governance regulations lead to decreases in corporate tax avoidance.
Importantly, the D&D theory proposes that the complimentary association between tax
avoidance and managerial diversion only exists in weak-governance settings. Recent studies
provide evidence that tax aggressiveness is unlikely to facilitate managerial diversion in a
country with an overall strong regulatory environment such as the U.S. (e.g., Blaylock 2016;
Atwood and Lewellen 2019), and thus the D&D theory may not generalize in such settings.
Consistent with this expectation, prior studies of U.S. firms find little on-average association
between board characteristics and tax planning (Minnick and Noga 2010; Armstrong et al. 2015).
A second theory suggests that executing a successful tax-planning strategy generally
ORZHUVWKHILUP¶VWD[EXUGHQWKHUHE\WUDQVIHUULQJZHDOWKIURPWKHJRYHUQPHQWWRVKDUHKROGHUV
Thus, on average, reducing tax burdens is in the best interest of shareholders because it
maximizes shareholder after-tax wealth.
4
However, Armstrong et al. (2015) propose that agency
conflicts may motivate managers to engage in tax planning that is not value-maximizing to
shareholders. *LYHQWKHERDUG¶VFKDUJHWRHQVXUHthat management acts in the best interests of
shareholders (Jensen and Meckling 1976), boards should oversee the underlying tax planning
processes and reporting outcomes to ensure that WKHILUP¶V risk exposure to long-term negative
4
Consistent with this notion, Brown and Drake (2014) document that firms use board interlocks to share information about tax
planning activities which they then use to lower tax burden.
11
impacts does not exceed VWDNHKROGHU¶VDSSHWLWHIRUULVN.
5
Thus, strong risk oversight should help
to optimize its tax planning activities. Consistent with this notion, Armstrong et al. (2015) find
that board independence and financial expertise motivate firms with high (low) levels of tax
planning to decrease (increase) current tax-planning levels.
We argue that the processes the board engages in as part of its governance responsibilities
will affect important firm outcomes. 2YHUVLJKWRIPDQDJHPHQW¶VSURFHVVHVIRUPDQDJLQJULVNVRI
all types is considered a key competency of the board by regulators (SEC 2010) and other
governance leaders (COSO 2009; 2017; NYSE 2013. &262¶V(50IUDPHZRUNSODFHV
board risk oversight as the first among twenty core principles that must be in place for an
organization to have effective enterprise risk management (COSO 2017, p. 27). Thus, risk
monitoring represents an important governance process undertaken by the board. Theoretical and
practitioner publications stress that it is crucial for boards to understand and be involved in tax
risk management (e.g., Neubig and Sangha 2004; PwC 2013; Deloitte 2015, 2016; EY 2016,
KPMG 2018; 2019; Protiviti 2019), particularly given the materiality of tax costs relative to
profitability, concerns regarding a changing regulatory environment, and the potential for
significant reputational and brand harm for overly aggressive tax practices.
Hypothesis Development
Mills et al. (1998) provide evidence that for every $1 invested in tax planning, firms
lower their tax liability by $4. Assuming a strictly linear relation, firms can lower their total tax
liability to $0 simply by investing resources in their tax planning activities. However, firms still
pay significant tax burdens (Weisbach 2002). Prior research indicates that this may be because
5
This is particularly important given that tax planning has important implications for non-tax risks impacting the organization,
VXFKDVWKHWUDQVSDUHQF\RIWKHILUP¶VILQDQFLDOVWDWHPHQWV%DODNULVKQDQHWDODFFHVVWROLTXLGLW\/DZDQG0LOOV15;
Edwards, Schwab, and Shevlin 2016; Campbell, Goldman, and Li 2019), and scrutiny by the media and the general public
$XVWLQDQG:LOVRQ&KHQHWDO'KDOLZDOHWDO0RUHRYHUWKHILUP¶VWD[EXUGHQKDVLPSRUWDQWLPSOLFDWLRQVfor
WKHILUP¶VFXUUHQWDQGIXWXUHSHUIRUPDQFH/HYDQG1LVVLP5RELQVRQ6LNHVDQG:HDYHU
12
paying excessively low tax rates is risky (Gallemore, Maydew, and Thornock 2014; Austin and
Wilson 2017). This indicates that firms may weigh the potential benefits of lowering the tax
liability against the non-tax costs associated with such actions and choose an optimal level of tax
avoidance. Consistent with this optimization strategy perspective, Cook et al. (2017) provide
evidence that on average investors reward expected tax avoidance with a lower cost of equity
capital, but punish firms by increasing the cost of equity capital when firms pay extreme tax
amounts, whether too high or too low. Similarly, Chyz and Gaertner (2017) provide evidence
that executives are relieved of duty when WKHILUP¶VWD[EXUGHQLVLQWKHH[WUHPHtoo high or too
low). Additionally, Drake, Lusch, and Stekelberg (2019) argue that tax avoidance is positively
valued by investors, but only when it is not accompanied by high levels of tax uncertainty. What
remains unclear from this literature are the mechanisms within a firm that ensure it achieves an
optimum tax avoidance strategy.
We expect that governance mechanisms like the board impound these tax-related
considerations into their monitoring actions and, in doing so, help the firm optimize tax
avoidance.
6
Specifically, we predict that more robust risk oversight positively influences
corporate tax-planning practices. Tax experts assert, ³Boards should consider the impact that the
RUJDQL]DWLRQ¶VWD[VWUDWHJLHVKDYHRQLWVFRPSHWLWLYHSRVLWLRQDQGEHFRPIRUWDEOHWKDWWKH
organizatLRQ¶VWD[SROLF\LVVXVWDLQDEOH´ (Deloitte 2015; EY 2019). However, historically, tax
departments have managed the tax function with little involvement from the board, and without
an independent assessment of risk, which violated a core principle of risk management (Neubig
6
We refer to tKHERDUGRIGLUHFWRU¶VPRQLWRULQJDFWLRQVUDWKHUWKDQDVSHFLILFFRPPLWWHH:KLOHFRQFXUUHQWUHVHDUFKVXJJHVWVWKDW
the audit committee often deals with risk management issues (Robinson et al. 2012) and anecdotal evidence suggests that the
audit committee, DVZHOODVWKHULVNFRPPLWWHHLVW\SLFDOO\DVVRFLDWHGZLWKRYHUVHHLQJPDQDJHPHQW¶VULVNPDQDJHPHQWSURFHVV
those sub-committees of the board do so on behalf of the board and it is the full board of directors that has ultimate responsibility
for the oversight and governance of the risk taking of management. As a result, we do not make any conclusions on the actions of
a specific committee or board member.
13
and Sangha 2004). Attention to material weaknesses and issues related to financial reporting of
income taxes (e.g., Drake, Goldman, and Lusch 2016; Gleason, Pincus, and Rego 2017) along
with public scrutiny over corporate taxes (Dyreng et al. 2016; Chen et al. 2018; Dhaliwal et al.
2019) have elevated tax issues to the board in recent years. Thus, we expect that risk oversight is
associated with more effective tax-planning practices. We use the tax outcomes reported in
ILUPV¶ILQDQFLDOVWDWHPHQWVWRLQIHUWKHOHYHODQGFKDUDFWHURIWDx-planning activities, and we
focus on two observable tax outcomes: (1) tax uncertainty, and (2) the level of the tax burden.
Hypothesis 1 ± Risk Oversight and Tax Uncertainty
Theoretical research and practitioner publications stress that it is crucial for boards to be
involved in tax risk management. 1HXELJDQG6DQJKDSDJHSURSRVH³7D[ULVN
should be viewed as an integral part of the FRUSRUDWLRQ¶VRYHUDOOHQWHUSULVHULVNPDQDJHPHQWDQG
VKRXOGEHHIIHFWLYHO\PDQDJHGDQGGLUHFWHGE\WKHERDUG´ Deloitte (2016) recommends that the
ERDUG¶VUHVSRQVLELOLWLHVconcerning corporate tax practices include: 1) embedding risk culture
and awareness, 2) defining the tax policy and strategy, 3) setting and monitoring risk appetite,
and 4) and reviewing significant areas of uncertainty and judgment. Furthermore, regulatory
authorities in recent years have placed increased pressure on the board to ensure that WKHILUP¶V
tax uncertainty exposure is consistent with its overall risk appetite (Shulman 2009, 2010; OECD
2009). Monitoring the firm to ensure it is not subject to excessive tax uncertainty is important.
For example, Klassen et al. (2016) suggest that tax practices are an important consideration that
the board needs to evaluate DVSDUWRIWKHILUP¶VRYHUDOO risk strategy and that the board needs to
ensure that the firm is choosing tax positions that bring value to shareholders while also
remaining compliant with the corresponding tax laws. Other studies provide evidence that
overexposure to tax risks and uncertainty can lead to future tax payments and penalties, resulting
14
in more volatile tax outcomes (Ciconte et al. 2016; Hanlon, Maydew, and Saavedra 2017).
Moreover, overly aggressive tax strategies can subject the firm to public scrutiny and
reputational damage (Austin and Wilson 2017; Chen et al. 2018).
Evidence from prior research on the impact of the board and risk management on tax
uncertainty is OLPLWHG5LFKDUGVRQHWDOILQGHYLGHQFHWKDWPDQDJHPHQW¶VFHUWLILFDWLRQWKDW
WKHILUP¶V³V\VWHPRILQWHUQDOFRQWUROVDQGULVNPDQDJHPHQWLVHIIHFWLYH´LVDVVRFLDWHGZLth a
lower likelihood of a tax dispute with the tax authority and higher tax burdens in a sample of
Australian firms. HoweverWKHDXWKRUVGRQRWH[DPLQHWKHERDUG¶VUROHLQWKHULVNPDQDJHment
process. Principles-based ERM frameworks note that effective risk oversight should help reduce
uncertainty by helping organizations ³reduce performance variability´DQG³anticipate risks that
would affect performance and enable them to take action to minimize disruption´&262
p. 7). Therefore, we expect that boards with more robust risk oversight would be more likely to
constrain highly uncertain tax practices. We formally state our first hypothesis:
H1: Risk oversight is negatively associated with tax uncertainty.
Hypothesis 2 ±Risk Oversight and Tax-Planning Levels
One mechanism an organization could employ to reduce tax uncertainty would be to
forego all corporate tax planning and pay taxes strictly in accordance with the statutory tax rate.
However, in the normal course of business, firms can generate significant tax savings by
investing time in tax-efficient business decisions (e.g., locating a new plant in a lower tax state
rather than a high tax state) (Mills et al. 1998). Furthermore, there are many activities that the
firm may not have chosen to implement without a tax benefit, but with a tax benefit became a
positive net present value proposition (e.g., capital expenditures, R&D, and acquisitions).
15
Achieving low tax uncertainty by simply foregoing all available tax avoidance
opportunities represents a wealth transfer from shareholders to the government, which is likely
inconsistent with shareholder preferences. 7KHERDUG¶Vmandate is to ensure that all firm-wide
decisions remain consisteQWZLWKWKHILUP¶Voverall appetite for risk-taking, including its tax
planning choices, not that it avoids or mitigates all risks (COSO 2009).
7
Effective tax planning involves PD[LPL]LQJDILUP¶VDIWHU-tax return by considering the
magnitude of the tax burden along with other non-tax costs that may accompany various tax-
planning strategies, including the risk of avoiding taxes (Scholes et al. 2014). More risk
oversight, such as considering firm-wide risks within WKHFRQWH[WRIWKHHQWLW\¶VEXVLQHVVPRGHO
and strategic initiatives, help the board ensure that the risks facing the organization are within
acceptable levels of risk, consistent with shareholder preferences. Thus, more robust risk
RYHUVLJKWVKRXOGKHOSWKHERDUGHQVXUHWKHILUPLV³WKUHDGLQJWKHQHHGOH´EHWZHHQ preferable tax
planning decisions that lead to overall lower tax burdens, and undesirable tax planning decisions
that require harmful and excessive risk-taking.
8
In sum, we posit that risk oversight is associated
with tax planning that leads to lower overall tax burdens in the financial statements. We formally
state our second hypothesis:
H2: Risk oversight is negatively associated with the level of the tax burden.
While we expect that more risk oversight is associated with corporate tax planning, there
are reasons that the association may not materialize in practice. First, tax planning decisions are
7
Principles-EDVHGIUDPHZRUNVIRUHQWHUSULVHULVNPDQDJHPHQWDOVRQRWHWKDWDEHQHILWRI(50LVWKH³increase of positive
RXWFRPHVDQGDGYDQWDJHZKLOHUHGXFLQJQHJDWLYHVXUSULVHV´DQG³LPSURYHUHVRXUFHGHSOR\PHQW´&262SS-7). These
frameworks also emphasize that effective enterprise-wide risk management is not solely focused on the mitigation of all risks, but
rather is focused on balancing risk-WDNLQJZLWKWKHRUJDQL]DWLRQ¶VRYHUDOOULVNDSSHWLWH
8
We expect that the board should have an interest in tax-planning decisions that that can have important non-tax impacts (e.g.,
reputational effects) and those that interact with important non-tax-related decisions (e.g., tax-efficient business structuring). For
example, if the board is deciding which innovation projects warrant investment, they may choose to focus on projects that qualify
for R&E credits because the net present value of these projects after considering tax credits is higher.
16
typically a component or a result of the firms operational and structural decisions, rather than a
primary driver of them. For example, Williams (2018) and Drake et al. (2019a) document that
U.S. multinational firms commonly locate employees in countries that provide excellent
operating efficiencies (e.g., India, China, and Mexico). While these jurisdictions are known for
having lower statutory tax rates than the U.S., the tax benefits for locating in these jurisdictions
pale in comparison to tax havens, indicating that firms make strategic business decisions that
balance tax benefits against operational efficiencies. To the extent that the board prioritizes
overall profitability over strict tax savings, we may only document a small or no effect of board
risk oversight on tax practices. However, following Drake et al. (2019a), we anticipate that tax
decisions are made simultaneously with operating decisions, but with different weights. We
argue that while tax decisions may not be a first-order effect, their consideration is significant
enough to warrant board oversight.
Furthermore, tax planning often increases a ILUP¶VILQDQFLDOFRPSOH[LW\%DOakrishnan et
al. 2019), indicating that tax planning requires significant technical expertise. Since boards are
not generally predominately comprised of technical tax experts, a lack of technical expertise may
limit the effectiveness of WKHERDUG¶V tax risk monitoring. This possibility would be consistent
with past corporate governance work that finds the level of audit committee industry expertise
DQGILQDQFLDOH[SHUWLVHDIIHFWILUPV¶ILQDQFLDOUHSRUWLQJTXDOLW\&RKHQ+RLWDVK
Krishnamoorthy, and Wright 2014; Krishnan and Lee 2009).
RESEARCH DESIGN
Data and Sample Selection
Table 1 presents our sample selection procedure. We comprise our sample of firms
belonging to the Russell 1000 in 2014. We make this choice because these firms represent a
large portion of the market capitalization in the U.S., and to limit the sample to a reasonable size
17
to facilitate hand collection, reading, and hand-coding of proxy statement data. We begin with
1,021 Russell 1000 firms that can be found in Compustat.
9
We use the most recent proxy
disclosure statement available for each observation as of June 2014 to code the risk oversight
measure for each firm. We exclude 26 firms where the proxy disclosure was not available.
%HFDXVHZHDUHLQWHUHVWHGLQILUPV¶WD[-planning activities, and because firms domiciled in non-
U.S. jurisdictions inherently face different tax-planning incentives, we remove non-U.S.
domiciled firms (n=49). For similar reasons, we also remove financial services firms (n=222)
and utility firms (n=59). Following these steps, our potential sample comprises 665 firms.
From this initial sample, we merge the sample with Compustat, resulting in 656 firms and
2,434 firm-years for the period 2014 through 2017. We retrieve financial variables from
Compustat and board composition data from ISS (formerly RiskMetrics). Following the majority
of tax research (e.g., Brown and Drake 2014; Dyreng et al. 2010, we remove firm-year
observations with losses. We also remove observations without data to calculate our test
variables. Following these cuts, our final sample used for testing is 501 firms comprising 1,595
firm-year observations from 2014 through 2017. We use the measure coded for the 2014 proxy
statement as a proxy for risk oversight over the period 2014-2017 to test our hypotheses.
10
Risk Oversight Measure
The SEC enhanced its proxy disclosure rules in 2010 to require firms to include
LQIRUPDWLRQLQWKHLUDQQXDOSUR[\VWDWHPHQWVUHJDUGLQJWKHERDUG¶VUROHLQULVNRYHUVLJKW7KH
9
The Russell 1000 is a market capitalization-weighted index of the largest 1,000 companies in the United States equity markets,
and our sample is based on members of the index as of June 2014. The index is reconstituted on an annual basis and stocks
deleted between reconstitution dates are not replaced. However, spin-offs and IPOs are added on a quarterly basis. This can cause
the number of companies listed on the index to exceed 1,000.
10
We read and compared a random sample of proxy statement disclosures between 2014 and 2017 and find that most firms do
not substantially change their risk oversight practices (or do not change them at all) over this time period. Thus, it appears that
risk oversight practices are sticky over a finite period. For this reason, we use the coding from 2014 as a proxy for risk oversight
for the period 2014 through 2017. Our inferences are unchanged if we use only the year closest to 2014 to estimate the regression
models.
18
mandate does not specify what risk oversight-related information firms must disclose, nor does it
mandate a specific format for the disclosure or specific risk oversight processes. Thus, firms
have flexibility in how the board structures and discloses its risk oversight information. While it
is possible that tKHSURYLGHGGLVFORVXUHVDERXWWKHERDUG¶VULVNRYHUVLJKWDFWLYLWLHVPD\QRWUHIOHFW
what the board is actually doing, the likelihood of this is low given the regulated nature of this
disclosure and the associated oversight of proxy filings by the SEC. Errors, omissions, and
falsifications of information would be subject to SEC enforcement and prosecution. Furthermore,
any subsequent determination that the board risk oversight information provided is false or
misleading would inform investors as they appoint or remove individuals from service on the
board.
Using hand-collected information, we develop a ILUP¶Vscore of the strength of the risk
oversight processes based on their proxy statement closest to the 2014 year-end. We rely on the
SEC¶V10 Proxy Disclosure Enhancements rule and thought papers and best practices issued
by COSO to identify the three (responsibility, consistency, and risk mindset) best-practice
components (COSO 2009, 2010, 2017; Rittenberg and Martens 2012).
11
The first component (Responsibility) captures whether the proxy statement directly and
verbally articXODWHVWKHERDUG¶VUHVSRQVLELOLW\IRURYHUVHHLQJWKHILUP¶VULVNPDQDJHPHQWV\VWHP
(SEC 2010). Thought leadership papers and empirical research stress the importance of formal
articulation of board risk monitoring responsibilities (COSO 2010; Rittenberg and Martens 2012;
Ittner and Keusch 2014; ISOS2018). Although the board should ultimately be responsible for the
oversight of risk at all firms (COSO 2009), survey evidence indicates that many boards delegate
this responsibility to a subcommittee or do not acknowledge any formal responsibility to oversee
11
We include an online appendix detailing the process of developing this measure and detailed information about each
component.
19
risk (COSO 2010; Ittner and Keusch 2014). Responsibility is equal to 1 if the proxy statement
disclosure directly states that the board is responsible for risk oversight. Companies coded as a 0
for this item either did not directly state where the responsibility for risk oversight resides, stated
that management or a subcommittee is primarily responsible for risk oversight, or used opaque
language when addressing this point.
The second component of risk oversight (Consistency) captures whether the firm
discloses that the board regularly engages in risk monitoring activities. An important component
of the SEC¶V Proxy Disclosure Enhancements requires firms to provide information on
whether and how the board monitors risk (SEC 2010). Thought leadership papers on risk
oversight stress the importance of continuous updating and regular and systematic risk oversight
by the board because risks are constantly evolving (COSO 2009, 2010; Rittenberg and Martens
2012). Survey evidence indicates that boards do not consistently monitor risk in many companies
(COSO 2010; Ittner and Keusch 2014). Consistency is equal to 1 if the proxy statement
disclosure indicates that the board reviews thHILUP¶VULVNPDQDJHPHQWSROLFLHVDQGSURFHGXUHV
or reviews important firm risks at regular time intervals on at least an annual basis (0 otherwise).
The third component (Risk Mindset) addresses whether the firm discloses that the board
engages in monitoring related to ensuring that the firm maintains an appropriate risk mindset or
³WRQHDWWKHWRS´WKDWHPSKDVL]HVWKHLPSRUWDQFHRIULVNPDQDJHPHQW and risk-related corporate
culture, such as the importance of considering WKHFRPSDQ\¶VSRUWIROLRRIULVNVand whether it is
LQDOLJQPHQWZLWKWKHILUP¶VVWUDWHJLFREMHFWLYHVDQGLWVRYHUDOODppetite for risk-taking (COSO
2009). Thought leadership papers stress the importance of the overall culture and tone at the top
that leads to a mindset focused on the integration of WKHILUP¶VDSSHWLWHDQGWROHUDQFHIRUULVNLQWR
the decision-making processes at all levels of the firm (COSO 2009, 2017; Rittenberg and
20
Martens 2012).
12
Risk Mindset is equal to 1 if the firm discloses that the board is involved in
PRQLWRULQJWKHILUP¶VULVNDSSHWLWHULVN-strategy alignment, or corporate culture about risk (0
otherwise).
13
We review each disclosure and hand-code whether the firm addressed each of the three
best-practice components of risk oversight using a series of dichotomous variables.
14
We
aggregate these three dichotomous variables for each of these three components into a single risk
oversight score that takes a value of 0, 1, 2, or 3 (Risk Oversight). We expect that boards with
high Risk Oversight (i.e., a 3) have a greater adherence to risk management best practices for risk
RYHUVLJKWDQGWKXVKDYHPRUHUREXVWSURFHVVHVIRUPRQLWRULQJWKHILUP¶VULVNPDQDJHPHQW
system, relative to firms with low Risk Oversight (i.e., a 0 or 1).
Validation and Determinants of Risk Oversight
We assert that Risk Oversight captures board monitoring activities and engagement
related to important firm risks. To help validate our construct and provide insights into factors
associated with Risk Oversight, we use multivariate regression to examine factors associated
with variation in Risk Oversight. We estimate the following OLS model:
Risk Oversight
i
ȕ
0
Ȉȕ
k
Risk management variables Ȉȕ
j
Governance variables
Ȉȕ
h
Risk Variables Ȉȕ
n
Firm fundamentals and voluntary disclosure
,QGXVWU\)(İ
it
(1)
We include two variables to capture overall risk management practices. We include ERM,
ZKLFKLVDQLQGLFDWRUYDULDEOHHTXDOWRLIWKHILUPPHQWLRQV³HQWHUSULVHULVNPDQDJHPHQW´LQLWV
12
Board monitoring and support of an appropriate risk mindset helps ensure that all important risks faced by the firm are
identified and understood, and that firm risk-taking is in line with organizational goals (Rittenberg and Martens 2012).
13
Thought leadership papers (e.g., (COSO 2009, 2017; Rittenberg and Martens 2012) indicate that any of these three items
indicate board involvement in activities promoting an adequate risk mindset at the firm.
14
To ensure a high degree of reliability within our coding protocol, two coauthors independently coded each disclosure. Coding
agreement between the co-authors was greater than 90 percent. Once coding was complete, all differences between coders were
reconciled. In addition, a graduate research assistant with no prior experience with the project coded a random sample of 10
percent of the proxy statements disclosures with greater than 90 percent agreement with the reconciled coding. We also used
&RKHQ¶V.DSSDWRFDOFXODWHLQWHU-UDWHUUHOLDELOLW\EHFDXVHVRPHOHYHORIDJUHHPHQWFDQEHUDQGRP&RKHQ¶V.DSSDIRUHDFKLWHP
coded was greater than 0.80, indicating a substantial level of agreement (Landis and Koch 1977; Hallgren 2012).
21
proxy statement risk oversight disclosure (0 otherwise). We also include CRO, which is an
indicator variable equal to 1 if the firm mentions the presence of a Chief Risk Officer in its proxy
statement risk oversight disclosure (0 otherwise). Overall, we expect firms that are employing
ERM will have greater adherence to ERM risk oversight best practices, but it is unclear how the
presence of a CRO would relate to WKHERDUG¶VHQJDJHPHQWLQrisk oversight robustness.
We include several governance variables. Board Inputs is a comprehensive measure of
board quality, defined as the factor score from the number of financial experts on the board, the
size of the audit committee and board, the percentage of independent board members, and the
mean tenure for the board members.
15
We expect that boards with favorable member inputs (i.e.,
more independent, greater expertise) are more likely to view their governance role as one of
objective monitoring, and thus, we expect Board Inputs to be positively associated with Risk
Oversight. We also include two measures of manager entrenchment, using the E-index from
Bebchuk, Cohen, and Ferrell (2009) (E-index) and an indicator variable equal to 1 if the proxy
statement disclosure notes that the CEO is the chairman of the board (CEO is Chair). We do not
predict the signs of the coefficients on the manager entrenchment variables.
We next include measures for various types of risks such as litigation risk (Litrisk), the
risk of financial distress (DistressRisk), and operating volatility (PTROAVol). While we expect
that firms with more inherent risk would demand greater risk oversight, we have no specific
prediction on which risk variables would be most closely related. Lastly, we include variables for
several firm fundamentals, including firm size (Size), operating performance (ROA), and
complexity (RD, Capint, Intang, Foreign, and Geoseg). We expect that larger firms likely have
15
We include a factor score of board variables, rather than board variables individually, because these variables are highly
correlated and therefore may not pick up distinct constructs. Consistent with our expectations, all variables load on one factor
with an Eigenvalue greater than 1.
22
greater resources to invest in ERM practices. Similar to our prediction on inherent risk, we also
conjecture that firms with greater complexity may also demand greater risk oversight. Finally, to
ensure that our measure is not simply capturing variation in voluntary disclosure practices across
firms, we include a measure of overall disclosure propensity (Calls), which is the number of
conference calls with analysts calls held during the year (Brown, Hillegeist, and Lo 2004;
Frankel, Johnson, and Skinner 1999).
16
The coefficient on Calls will be significantly positive if
disclosure propensity drives Risk Oversight (inconsistent with our expectations). We also include
industry fixed effects in the model (Fama French 12 specification).
We present results from this analysis in Table 2. Column (1) presents our risk
management variables. Consistent with our expectation that firms using ERM should have
greater adherence to ERM best practices for risk oversight, we find that ERM is positively
associated with Risk Oversight (Coef. = 0.1993 p < 0.01). We do not find that the presence of a
CRO is associated with Risk Oversight. Column (2) presents our governance variables. Our
evidence that Board Inputs is positively associated with Risk Oversight (Coef. = 0.1273, p <
0.01) suggests more objective and experienced board members will likely engage in more robust
risk oversight. We do not find that manager entrenchment (i.e., E-index and CEO is Chair) is
significantly associated with Risk Oversight.
Column (3) presents our risk variables. Also consistent with expectations, we find
evidence of greater demand for risk oversight in firms with greater inherent risk. Specifically,
Risk Oversight is positively associated with both Litrisk (Coef. = 0.0236, p <0.10) and
DistressRisk (Coef. = 0.0244 p <0.01), but not operating volatility (PTROAVol). Column (4)
presents our firm fundamentals and voluntary disclosure variables. We find Risk Oversight is
16
Conference call data is from Seekingalpha.com. We thank Robbie Moon for sharing this data with us. We set conference call
frequency equal to 0 if there is no data for the firm-year (approximately 1 percent of firm-years).
23
positively associated with Size (Coef. 0.0460, p <0.05) and negatively associated with ROA
(Coef. = -1.6412, p <0.01). We also find that that complexity, in terms of intangible intensity
(Intang, Coef. = 0.2609, p < 0.10) and multinational operations (Geoseg, Coef. = 0.0915, p <
0.10), is positively associated with Risk Oversight. Finally, we do not find that our measure of
voluntary disclosure quantity (Calls) is positively associated with Risk Oversight, consistent with
the view that WKHILUP¶VRYHUDOOGLVFORVXUHSURSHQVLW\GRHVQRWGULYHGLVFORVXUHVDERXWWKHERDUGV
role in risk oversight. Inferences from column (5) with all variables included are similar to those
in columns (1) through (4). In sum, our determinants test helps validate our measure by
providing evidence that it is associated with constructs we believe should be associated with
(ERM, board quality, and risk) and that it is not associated with voluntary disclosure propensity.
Primary Regression Models
To test our hypotheses, which focus on the association between risk oversight and
corporate tax-planning, we estimate the following equation:
GAAPETRVol
i,t
(GAAPETR
i,t
 Į
0
+ ȕ
1
Risk Oversight
i,t
+ ȕ
2
Size
i,t
+ ȕ
3
ROA
i,t
+ ȕ
4
PTROAVol
i,t
+ ȕ
5
RD
i,t
+ ȕ
6
CapInt
i,t
+ ȕ
7
Leverage
i,t
+ ȕ
8
NOL
i,t
+
ȕ
9
ChangeNOL
i,t
+ ȕ
10
Intang
i,t
+ ȕ
11
Inv
i,t
+ ȕ
12
Adv
i,t
+ ȕ
13
Foreign
i,t
+ ȕ
14
Geoseg
i,t
+ ȕ
15
Board Inputs
i
+ ȕ
16
LitRisk
i,t
+ ȕ
17
DistressRisk
i,t ,t
+ Industry F.E. +
<HDU)(İ
i,t
(2)
The dependent variable in equation 2 is either the firm-\HDUREVHUYDWLRQ¶VWKUHH-year
GAAP ETR volatility measured as the standard deviation of GAAP ETR measured across year t-
2, year t-1, and year t (GAAPETRVol),
17
or the firm-\HDUREVHUYDWLRQ¶Vcurrent year GAAP ETR,
calculated as the total tax expense scaled by pre-tax book income (GAAPETR). GAAPETR
FDSWXUHVWKHILUP¶VWRWDOWD[EXUGHQFXUUHQWDQGIXWXUHDFFUXHG in the financial statements. Thus,
17
GAAPETRVol is a three-year measure measured from year t-2 to t, whereas our control variables are each measured in year t.
To mitigate concerns regarding timing differences for these variables, in untabulated analysis, we re-examine our GAAPETRVol
regression with each of our control variables measured as an average over the same three years. Our inferences remain
unchanged.
24
this measure picks up tax-planning strategies that result in permanent tax savings rather than
deferral strategies (Hanlon and Heitzman 2010). GAAPETR, versus other measures of tax
burdens, is appropriate for use in our study because top management and board of directors
consider the GAAP ETR a fundamental and important measure (Graham, Hanlon, Shevlin, and
Shroff 2014). GAAPETRVol captures uncertainty in tax burdens. A negative coefficient on
GAAPETRVol is consistent with risk oversight being associated with lower tax uncertainty, and
thus would be consistent with our H1 hypothesis. A negative coefficient on GAAPETR is
consistent with risk oversight being associated with lower tax burdens, and thus would be
consistent with our H2 hypothesis.
We follow the prior literature and include a plethora of common control variables (Chen,
Chen, Cheng, and Shevlin 2010; Kubick, Lynch, Mayberry, and Omer 2015; Cen, Maydew,
=KDQJDQG=XRDPRQJRWKHUV:HFRQWUROIRUDILUP¶VVLze (Size), profitability (ROA),
R&D investment (RD), capital intensity (CapInt), long-term debt (Leverage), net operating
losses (NOL and ChangeNOL), intangible intensity (Intang), and inventory intensity (Inv). We
control for the extent of foreign operations (Foreign and Geoseg). We also include the volatility
of pretax earnings (PTROAVol) to control for fundamental differences in profitability that may
influence the rate at which firms pay taxes (Guenther et al. 2017). Lastly, we include a measure
of board quality (Board Inputs), litigation risk (LitRisk), and distress risk (DistressRisk), as they
each are shown to be determinants of Risk Oversight in Table 2.
18
We also include industry
(Fama-French 12 industry) and year fixed effects. We winsorize all continuous variables
included in equation 1 at the 1 and 99% levels, and we cluster standard errors by firm. See the
Appendix for a more detailed discussion of how we calculate the variables in equation 2.
18
In untabulated analysis, we also include ERM as a control variable and our inferences remain unchanged. We do not include
ERM in our primary analysis to mitigate multicollinearity because Risk Oversight LVDFRPSRQHQWRIILUPV¶(50
25
RESULTS
Descriptive Statistics
Table 3, Panel A, presents descriptive statistics for our variables used in testing across all
firms. The mean (median) statistic for GAAPETRVol and GAAPETR are 0.061 (0.027) and 0.318
(0.312), respectively. These values are in line with prior literature (Dyreng et al. 2017; Guenther
et al. 2017). The mean (median) value for our independent variable of interest, Risk Oversight, is
1.254 (1.000). This statistic suggests that firms have approximately one of the risk oversight
components. We also document that 36.1 percent of our sample firms have a Risk Oversight
score of 2 or 3 (High Risk Oversight =1). The statistics for our control variables are reasonable
and in line with prior literature. Panel B presents comparative descriptive statistics for firms with
a high (2 or 3) versus low (0 or 1) Risk Oversight score. Consistent with our H1 (H2), we
document that high Risk Oversight firms have a significantly lower mean GAAPETRVol (mean
GAAPETR) than low Risk Oversight firms (p < 0.05).
Table 4 presents our spearman correlation matrix. Consistent with our H1 and H2, we
document a negative and significant correlation with Risk Oversight and GAAPETRVol (-0.034)
and GAAPETR (-0.064). While we caution interpretation of our findings using only univariate
statistics, these correlations provide evidence consistent with our expectations. Furthermore, we
document a positive and significant correlation between our two independent variables of
interest, GAAPETRVol and GAAPETR (0.507, p < 0.01). This finding is consistent with prior
literature that also examines both the levels and volatility of effective tax rates (Dyreng et al.
2008; Guenther et al. 2017; Drake et al. 2019b) and suggests that lower tax burdens are more
persistent. Our remaining correlations are consistent with the prior literature.
26
Primary Multivariate Results
Table 5 presents our primary analysis. Column (1) presents the estimation of equation 2
testing H1. Consistent with our H1, we document a negative and significant coefficient on Risk
Oversight ȕ
1
= -0.0063, t-stat = -2.09). This evidence suggests that for a one-unit increase in
Risk Oversight, firms have a 0.0063 lower volatility of GAAPETR, and thus a firm with a Risk
Oversight score of 3 has 0.0189 lower GAAPETRVol than a firm with a Risk Oversight of 0.
Given a mean of GAAPETRVol of 0.061 (per Table 3), this lower volatility for the highest Risk
Oversight versus the lowest Risk Oversight translates to a 31.0% lower GAAPETRVol.
Column (2) presents the estimation of equation 2 testing H2. Consistent with our H2, we
document a negative and significant coefficient on Risk Oversight ȕ
1
= -0.0138, t-stat = -2.90).
Results suggest that firms with a Risk Oversight score of 3 have a 4.14 percentage point lower
GAAPETR than firms with a 0 score. Given the mean GAAPETR in our sample of 31.8% (per
Table 3), our findings suggest that a firm with a Risk Oversight score of 3 has 13.2% lower
GAAPETR than firms with a 0 score, which we interpret as evidence that high Risk Oversight is
associated with 13.2% lower levels of tax burdens.
19
Our evidence in columns (1) and (2) is consistent with stronger risk oversight being
associated with lower tax burdens that are also less uncertain. The results suggest that more
robust risk oversight may lead to firm-level decisions that involve structuring tax practices in a
more efficient manner (i.e., greater amounts of tax planning in a less uncertain fashion).
19
A potential correlated omitted variable when examining GAAPETR is GAAPETRVol. To mitigate concerns regarding this issue,
in untabulated analysis, we re-examine our model by including GAAPETRVol in the GAAPETR regression. Our inferences
remain unchanged.
27
Additional Analyses ± Tax Planning
Permanent versus Temporary Tax-Planning Activities
We examine permanent versus temporary book-tax differences to draw conclusions on
WKHQDWXUHRIILUPV¶WD[-planning activities. Permanent positions are inherently different from
temporary positions because the tax benefits associated with the positions do not reverse over
time. The reversal of temporary position creates future tax liabilities, and therefore they, on
average, create less value from a net present value (NPV) perspective.
20
Furthermore, several
studies note that top management cares more about GAAP ETR (affected by permanent positions
only) than cash taxes (affected by both temporary and permanent positions) because the GAAP
ETR directly influences after-tax reported earnings (Robinson et al. 2010; Armstrong, Blouin,
and Larcker 2012; Graham et al. 2014). Moreover, the GAAP ETR is a highly visible measure
that the board can use to monitor and evaluate tax planning efficiency (Armstrong et al. 2012).
For these reasons, we also expect the board to prefer permanent tax positions to those that yield
temporary benefits.
Following Hanlon and Heitzman (2010), we calculate the temporary book-tax differences
(TempBTD) as deferred tax expense grossed up by the statutory tax rate. Following Frank,
Lynch, and Rego (2009), we calculate the permanent book-tax difference (PermBTD) as the
difference between the total book-tax difference (book income less current tax expense grossed
up by the statutory rate) and TempBTD. We re-estimate equation 2 by replacing GAAPETR with
PermBTD and TempBTD and present results in Table 6 Panel A.
21
In column (1), we find a
20
For example, firms receive an R&E tax credit for spending funds on qualified research and development activities and this tax
FUHGLWORZHUVILUPV¶WD[OLDELOLWLHVSHUPDQHQWO\%XWLIWKHILUPZHUHWRVSHQGWKRVHIXQGVRQPRUHFDSLWDOH[SHQGLWXUHVWKe firm
would have more depreciation deductions this year due to accelerated depreciation. However, the total deductions allowed for
depreciation eventually decline and the book deductions become greater than the tax deductions, in which firms would pay more
in tax liability at that time.
21
Because there are clear directional expectations for our findings related to the tests in Table 6, we interpret our evidence using
one-tailed p-values.
28
positive and significant coefficient on Risk Oversight when our dependent variable of interest is
PermBTD ȕ
1
= 0.0019, t-stat = 1.86). However, in column (2), we fail to provide a significant
relation when TempBTD is our dependent variable of interest. These findings suggest that firms
with strong risk oversight appear to be achieving higher levels of tax planning via permanent
rather than temporary tax positions.
Income Shifting Activities
While we are not able to examine the specific permanent tax strategies used by firms, we
use publicly available data to provide insights into the nature of the permanent tax-planning
activities firms are choosing or avoiding. Some tax-permanent planning strategies may create
greater tax and non-tax risks compared to other permanent strategies. Aggressive income shifting
of income abroad is a permanent tax-planning strategy that may increase tax uncertainty because
the IRS may not uphold the position (De Simone, Mills, and Stomberg 2019; Towery 2017) and
also create non-tax risks by drawing public scrutiny (e.g., Dyreng et al. 2016). Thus, we propose
that firms with higher Risk Oversight may prefer to engage in less multinational income shifting.
For this test, we follow the research design of Dyreng and Markle (2016).
22
Table 6,
Panel B presents the results of estimating the Dyreng and Markle (2016) equations. Consistent
with SULRUOLWHUDWXUHZHGRFXPHQWVWDWLVWLFDOO\VLJQLILFDQWFRHIILFLHQWVRQERWKLQERXQGȖ
0
) and
outERXQGș
0
)
income shifting.
23
The interaction between these terms and High Risk Oversight
captures the incremental difference for high-risk oversight on income shifting activities. We
22
Their design uses a system of equations to consider separately inbound and outbound income shifting by regressing changes in
domestic and foreign income on changes in domestic and foreign sales. The joint estimation process enables us to separate
SDUDPHWHUVIRUUHWXUQRQVDOHVIRUHLJQȡIRUGRPHVWLFȡGIURPVKLIWLQJSDUDPHWHUVRXWERXQGșDQGLQERXQGȖ7KHLQWuition
behind the shifting parameters is that a dollar of income shifted out of domestic earnings shifts into foreign earnings. Thus, we
jointly estimate their two equations while also including interaction terms with our High Risk Oversight variable.
23
We also document that the main effects for the return on foreign and domestic sales (0.090 and 0.111, respectively) are
statistically significant (p <0.01), and in line with Dyreng and Markle (2016), which mitigates self-selection concerns. See Table
6, Panel B for details regarding the subsample for this analysis and greater explanation of the Dyreng and Markle (2016)
equations. To help improve interpretation and generalizability of the findings, we interact the independent variables of interest
with High Risk Oversight, rather than the continuous term.
29
GRFXPHQWWKDWERWKLQERXQGȖ
2
) and outbound
ș
2
) income shifting is significantly attenuated for
ILUPVZLWKKLJKULVNRYHUVLJKWȖ
2
= -0.301, t-stat = -1.58ș
2
= -0.183, t-stat = -1.78, for inbound
and outbound income shifting, respectively). These results suggest that firms with higher risk
oversight are associated with significantly less inbound and outbound income shifting, a series of
activities often associated with being high risk and more uncertain.
Usage of a Tax-Efficient Supply Chain
7KURXJKRXWRXUVDPSOHSHULRGWKH86KDGDPRQJWKHZRUOG¶VKLJKHVWVWDWXWRU\WD[UDWH
Prior literature suggests that firms can employ a tax-efficient supply chain as a lower risk
strategy to lower their tax burden (Dyreng, Lindsey, Markle, and Shackelford 2015). While
beneficial, this strategy involves shifting real operations rather than more uncertain income
shifting (Drake et al. 2019a). Boards more engaged in risk oversight may promote tax planning
to structure WKHILUP¶VJOREDORSHUDtions in a tax-efficient manner.
24
Thus, we examine whether
the associations between risk oversight and our tax outcome variables are different for firms with
more extensive foreign operations based on the number of geographic segments (High Geoseg)
and the number of non-tax haven subsidiaries (Nonhaven sub %), which likely have greater
opportunities to increase firm value through tax-efficient planning.
We present the results from this analysis in Table 6, Panel C. We find in columns (1) and
(2) that the negative association between Risk Oversight and GAAPETRVol is not significantly
different for firms with more extensive foreign operations. Moreover, in columns (3) and (4), we
find that the negative association between Risk Oversight and GAAPETR is stronger for firms
with more extensive foreign operations as measured by Geogseg (Coef.
= -0.014, t-stat = -1.78)
24
For example, if a firm is contemplating building a plant outside the U.S. to supply its foreign operations and management
presents the board with a few opportunities, a board with greater engagement in risk oversight may provide input that pushes
management to expand the firm in a manner that generates the greatest long-run value without creating excessive risk (e.g., the
firm builds a plant in a country with more favorable tax laws).
30
and Nonhaven sub % (Coef. = -0.072, t-stat = -1.77). This analysis suggests that risk oversight is
even more strongly associated with lower tax burdens, without increasing tax uncertainty, in
firms with greater opportunities for structuring foreign operations in a tax-efficient manner.
We posit that the results from Panels B and C are due to firms more carefully structuring
their foreign operations via tangible investment and within a tax-efficient supply chain rather
than face the uncertainty surrounding lowering their tax liability via income shifting activities.
Because during our sample period, the U.S. had among the highest corporate statutory tax rate in
the world, a greater tax-efficient supply chain allows firms to source income in their non-U.S.
subsidiaries effectively. We see this outcome reflected in our results showing that firms with
robust risk oversight are associated with lower effective tax rates without higher volatilities.
Research and Development Activities
Firms might also exploit the research and experimentation (R&E) tax credit to generate
permanent book-tax differences (Hanlon et al. 2017). We assume that firms with R&D expenses
likely receive some R&E tax credits while firms with no R&D expenses do not. We then
examine cross-sectionally whether the relation between board risk oversight and tax planning
varies across R&D firms and non-R&D firms (R&DFirm). We present the results in Panel D of
Table 6. Consistent with Panel C, the interaction term is not significant when examining
GAAPETRVol. However, it is negative and significant when examining GAAPETR (column (2),
Coef. = -0.0145, t-stat = -1.67). Because these tax credits can yield tax uncertainty (Towery
2017), we interpret our results as evidence that risk oversight is associated with more efficient
tax planning activities, as indicated by similar levels of tax uncertainty but overall lower tax
burdens.
31
Board Oversight Influence via Compensation Structure
Prior research provides evidence that greater equity risk incentives motivate CEOs to
engage in more risky tax planning (e.g., Rego and Wilson 2012). This suggests that the design of
executive compensation policies may moderate the relation between board risk oversight and tax
risk. We examine whether greater CEO risk-based compensation mitigates the negative
association found in our primary analyses between board risk oversight and tax risk.
To examine the moderating role of equity-based compensation, we focus on thH&(2¶V
Vega, which measures the extent to which the CEO receives compensation from the volatility of
earnings. Following Rego and Wilson (2012), we measure Vega in the prior year to avoid a
simultaneity bias. We re-estimate equation (2), adding CEO Vega
t-1
and the interaction of Risk
Oversight and CEO Vega
t-1
. We present the results of this analysis in Table 7. While our primary
focus in this analysis is tax risk (GAAPETRVOL), for completeness, we also present results with
GAAPETR as the dependent variable.
25
We begin in Column 1 of Table 7 by adding CEO Vega
t-1
to equation 2 without the
interaction term to provide evidence of an overall association between CEO equity risk
incentives and tax risk in our sample period.
26
Next, column 2 presents the interaction of Risk
Oversight and CEO Vega
t-1
. Consistent with our primary analyses, we find that Risk Oversight is
negatively associated with GAAPETRVol (Coef. = -0.007, t-stat = -2.29). However, we do not
find that higher equity risk incentives significantly change the relation between Risk Oversight
25
In untabulated analysis, we replace Vega with CEO Delta and our inferences remain unchanged.
26
In contrast to Rego and Wilson, we do not find a significant association between CEO Vega
t-1
and GAAPETRVOL. This may
be because of significant compensation-related regulations that were implemented in response to the most recent financial crisis
(see SEC Regulation S-K 17 CFR 229 effective 2/28/2010). These regulatory changes implemented by the U.S. Securities and
Exchange Commission in 2010 may deter the use of risk-based compensation to incentivize risky tax planning in a more recent
time period. To ensure that our lack of significant association between Vega and risky tax planning is not the result of differences
in our model or sample composition compared to Rego and Wilson (2012), we replicate the results from Rego and Wilson (2012)
using data from their sample period using two measures of tax planning from our paper (GAAPETR and TotalBTD) and find
similar inferences. However, when we run their model on a broad sample in the 2010-2017 time period, we do not find
significant associations between equity risk incentives and either GAAPETR or TotalBTD.
32
and GAAPETRVol. The analyses in Columns 3 and 4 using GAAPETR as the dependent variable
yield similar inferences. Collectively these analyses suggest that boards are likely influencing tax
PDQDJHUV¶WD[ULVNGHFLVLRQVGLUHFWO\HJWKURXJKFRQYHUVDWLRQVRUJXLGDQFHUDWKHUWKDQ
indirectly through compensation structure.
Components of Board Risk Oversight and Corporate Tax Practices
As described in Section 3 and the Online Appendix, we comprise Risk Oversight as an
aggregate of three components: Responsibility, Consistency, and Mindset. While we believe that
an aggregation of the three components best UHIOHFWVWKHUREXVWQHVVRIWKHERDUG¶VFROOHFWLYHULVN
oversight, it is possible that some of the components may be more important aspects of risk
oversight for different aspects of tax practices than others. To examine this possibility, we re-
estimate equation 2, replacing Board Risk Oversight with its three components included
separately.
27
This analysis provides evidence on which components of risk oversight are most
strongly associated with variation in WD[ULVNDQGWKHOHYHORIWKHILUP¶VWD[EXUGHQ.
We present our results in Table 8 with Column (1) presenting the analysis with
GAAPETRVol as our dependent variable of interest and Column (2) presenting the analysis with
GAAPETR as our dependent variable of interest. We find in Column (1) that GAAPETRVol is
negatively associated with both Consistency (Coef = -0.0110, t-stat = -2.19) and Mindset (Coef =
-0.0120, t-stat = -2.45), suggesting that the ERDUG¶VRYHUDOOrisk mindset along with its regular and
systematic engagement in risk monitoring activities are most useful in helping the board ensure
that the firm is choosing tax positions that bring value to stakeholders with less uncertainty and
greater consistency. In Column (2) we find that GAAPETR is negatively associated with both
Responsibility (Coef = -0.0210, t-stat = -2.31) and Mindset (Coef = -0.0155, t-stat = -1.58),
27
Given our findings in Table 5, we expect Responsibility, Consistency, and Mindset to be negatively associated with GAAPETR
and GAAPETRVol. As a result, we interpret our analysis using one-tailed p-values.
33
suggesting that the ³WRQHDWWKHWRS´WKURXJKWKHERDUG¶VDFNQRZOHGJPHQWRILWVUHVSRQVLELOLW\IRU
risk oversight DORQJZLWKWKHERDUG¶VRYHUDOOULVNPLQGVHWDUHPRVWKHOSIXOLQhelping the board
H[HUWLQIOXHQFHRQWKHOHYHORIWKHILUP¶VWD[EXUGHQ.
Alternative measures of tax risk and tax planning
Many different measures can proxy for tax-planning activities and tax uncertainty. In
Table 9, we employ other measures to ensure the robustness of our findings.
28
Columns (1) and
(2) re-estimate equation 2 using two alternative tax uncertainty proxies: current year additions to
unrecognized tax benefits (CYUTBINC) and current year penalties and interest disclosed in the
UTB disclosures (CYUTBPEN). A benefit of these alternative tax uncertainty proxies is that they
are measured ex-ante, whereas the volatility of the ETR measures volatility in tax outcomes.
Additionally, we provide evidence on whether the likelihood and outcomes of IRS investigations
differ for firms with stronger board risk oversight. We find that Board risk oversight is
negatively associated with both CYUTBINC and CYUTBPEN, suggesting that stronger board risk
oversight may be associated with a lower likelihood and more preferential outcomes of tax
disputes. Column (3) and (4) re-estimate equation 2 using two alternative measures of tax
burdens: a GAAPETR compiled using the methodology from Henry and Sansing (2018)
(HS_GAAP), and the ETR using current tax expense (CETR). The results in Columns (3) and (4)
are consistent with our primary analyses.
Robustness Tests
Falsification Test
We perform a falsification test to demonstrate that the qualitative information disclosed
regarding risk oversight, rather than the quantity of information, best captures risk oversight. We
28
Given our findings in Table 5, we expect the alternative measures of tax uncertainty and tax burden to be negatively associated
with Risk Oversight. As a result, we interpret our findings using one-tailed p-values.
34
replace Risk Oversight with the log of the number of words in the board risk oversight disclosure
(NWords) and re-estimate our primary analyses in Table 10. We find no evidence that NWords is
significantly associated with either GAAPETRVol or GAAPETR. This analysis provides evidence
that the substance of the content of disclosed board oversight activities, rather than the quantity
of information disclosed, explains variation in tax-planning outcomes.
Entropy Balancing
In addition, we perform an analysis to address functional form misspecification. As
documented in Table 3 Panel B, there are substantial differences between our low and high Risk
Oversight firms. While we mitigate concerns about differences in the two groups of firms by
including control variables, non-linearity between the high and low Risk Oversight firms can
potentially bias our inferences (Hainmuller 2012; Shipman, Swanquist, and Whited 2017). To
mitigate this concern, we entropy balance the first two moments of the control variables of our
high Risk Oversight firms (i.e., those with a 2 or 3 as their score) with the low Risk Oversight
firms (i.e., those with a 0 or 1 as their score).
29
Using this balanced sample, we then re-estimate
equation 2 using High Risk Oversight as the variable of interest. In untabulated tests, our
inferences remain unchanged and thus do not appear to be driven by non-linearity.
CONCLUSION
We examine whether stronger risk oversight by the board of directors is associated with
the effectiveness of corporate tax planning. Specifically, we use hand-collected data from proxy
statement disclosures regarding board risk oversight to examine whether more robust risk
oversight is associated with differences in tax uncertainty and the levels of tax burdens.
Corporations face increasing tax risks as governments deal with fiscal deficits, and the demand
29
In untabulated analysis, we document no differences in the mean and variance across all control variables between the two
groups following this procedure.
35
for tax transparency increases. This reality has triggered greater concern for boards and senior
management to manage tax risk and the related reputational risks proactively. Boards are focused
on monitoring tax risks given the enterprise-wide effect tax planning can have on a number of
operational and strategic decisions for the organization. Our study extends the ERM, corporate
governance, and tax literature by demonstrating the importance of stronger risk oversight in the
context of corporate tax-planning practices. Our findings indicate that boards engaged in more
robust risk oversight, a key pillar of an ERM system, are associated with less volatility in tax
outcomes in conjunction with lower tax burdens. Jointly, these results suggest that risk oversight
is an important corporate governance mechanism that helps to promote more effective tax
planning activities. Moreover, our results further demonstrate that firms avoid excessively low
tax burdens due to risk concerns and highlight a mechanism within firms that helps the firm
optimize their tax avoidance strategy. In sum, we provide initial evidence that boards are playing
an increasingly important role in managing all types of risk across the enterprise, including risks
associated with tax planning and compliance. Additionally, our study is among the first to
provide evidence that connects directly (rather than indirectly) board actions to important firm
outcomes.
We recognize that the study is subject to limitations. First, we assume that there is no
SXUSRVHIXOELDVLQILUPV¶SUR[\VWDWHPHQWGLVFORVXUHVKRZHYHUZHDVVHUWWKDWDQ\LQFRPSOHWHRU
inaccurate disclosure of the information would simply create noise in our measure of risk
oversight, which would bias against finding results. And, proxy filings are subject to SEC
oversight with any errors, omissions, and falsifications of information subject to SEC
enforcement and prosecution. Second, our study is an association study; thus, we are limited in
our ability to demonstrate causal relationships between risk oversight and tax planning.
36
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41
APPENDIX: Variable Definitions
Risk oversight and risk management variables
Risk Oversight A comprehensive measure of risk oversight by the board of directors.
The variable ranges from 0-3. See the online appendix for details
regarding the components of this measure.
High Risk Oversight An indicator variable equal to 1 if Risk Oversight is greater than the
median of 1, 0 otherwise.
NWords The natural log of the number of words in the proxy statement board
risk oversight disclosure
ERM An indicator variable equal to 1 if the firm mentions the phrase
"enterprise risk management" in the board risk oversight disclosure, 0
otherwise.
CRO A measure of ERM sophistication, measured as an indicator variable
equal to 1 if the firm mentions that they have a chief risk officer
(CRO) in the board risk oversight disclosure, 0 otherwise.
Tax variables
GAAPETR The GAAP effective tax rate (TXT/PI), winsorized at 0 and 1. Firms
with pretax book losses (PI <0) are excluded.
GAAPERVol The standard deviation of GAAP ETR from year t-2 to year t.
TempBTD The temporary book-tax difference, calculated as deferred tax expense
(TXDI) divided by the statutory tax rate of 0.35, scaled by total assets
(AT)
PermBTD Total book-tax difference minus Temporary book-tax difference as
calculated above.
TotalBTD The total book-tax difference, calculated as pre-tax book income (PI)
minus the sum of current deferral tax expense and current foreign tax
expense (both scaled by the statutory tax rate).
CETR The current effective tax rate (TXC/PI), winsorized at 0 and 1. Firms
with pretax book losses (PI <0) are excluded.
CYUTBINC Current increases to the UTB (TXTUBPOSINC) scaled by the
beginning of year UTB level (TXUTBBEG).
CYUTBPEN Current year UTB penalties and interest recorded on the income
statement (TXTUBXINTIS) scaled by the beginning of year UTB
level (TXUTBBEG).
HS_GAAP A measure of tax avoidance based on the measure developed by Henry
and Sansing (2016), calculated as total tax expense (TXT) scaled by
total assets (AT).
Control variables and other variables
Size The natural log of the market value of equity (PRCC_F*CSHO)
ROA Earnings before extraordinary items (IB) divided by total assets (AT).
PTROAVol The standard deviation of pretax ROA (PI/AT) from year t-2 to year t.
42
RD Research and development expense (XRD) divided by sales (SALE).
CapInt Gross property, plant, and equipment (PPEGT) divided by total assets
(AT).
Leverage Total long-term debt (DLC+DLTT) divided by total assets (AT).
NOL An indicator variable equal to 1 if the beginning tax loss carryforward
(TLCF) is greater than zero, 0 otherwise.
ChangeNOL The change in the tax loss carryforward (TLCF) from year t-1 to year t
scaled by total assets (AT).
Intang Recorded intangibles (INTAN) divided by total assets (AT).
Inv Inventory (INVT) divided by total assets (AT).
Adv Advertising expense (XAD) divided by total assets (AT).
Foreign and
High Foreign
Foreign is the sum of non-U.S. sales (retrieved from the Compustat
geographic segments file) divided by total sales (SALE). High
Foreign is an indicator variable equal to 1 if Foreign is above the
sample median, 0 otherwise.
CEO Vega The sensitivity of the change in the option value for a 1% change in
stock return volatility, multiplied by the number of options in the
&(2¶VSRUWIROLRPHDVXUHGLQPLOOLRQVRIGROODUV
CEO Delta 7KHVHQVLWLYLW\RIWKHFKDQJHLQD&(2¶VZHDOWKIRUDJLYHQFKDQJH
in stock price (measured in millions of dollars).
GeoSeg and High
GeoSeg
Geoseg is the log of the number of geographic segments (set equal to
1 if missing). High Geoseg is an indicator variable equal to 1 if the
number of geographic segments is above the sample median.
LitRisk Litigation risk, calculated using the coefficients from Rogers and
Stoecken (2005).
DistressRisk The risk of financial distress measured as 1 minus the Altman's Z
score using Begley et al.'s (1996) updated coefficients. Higher values
indicate a higher likelihood of financial distress.
Nonhaven sub % The average ratio of subsidiaries in non-tax haven countries to total
subsidiaries for the five-year period ending in 2014. Subsidiary data
was retrieved from Scott Dyreng's website at
https://sites.google.com/site/scottdyreng/Home/data-and-code/EX21-
Dataset.
Log subs The log of the average number of subsidiaries for the five-year period
ending in 2014. Subsidiary data was retrieved from Scott Dyreng's
website at https://sites.google.com/site/scottdyreng/Home/data-and-
code/EX21-Dataset.
Calls The number of conference calls with analysts held during the year.
R&DFirm Indicator variable equal to 1 if the firm-year observation has a positive
and non-zero RD, and 0 otherwise
43
Governance variables
Board Inputs A comprehensive measure of board quality, defined as the factor score
from a factor analysis of AC FIN, AC SIZE, BD FIN, BD IND, BD
SIZE, AND BD TENURE.
AC FIN The number of financial experts on the audit committee in year t.*
AC SIZE The number of audit committee members in year t.*
BD FIN The number of financial experts on the board in year t. *
BD IND The average percentage of independent board members for year t. *
BD SIZE The number of board members in year t. *
BD TENURE The mean tenure for board members (the mean number of years the
directors have been associated with the firm). *
CEO is Chair An indicator variable equal to 1 if the CEO is the chairman of the
board, 0 otherwise.*
E-index The E-index from Bebchuk, Cohen, and Ferrell (2009). Higher scores
indicate higher managerial entrenchment. Values are set equal to 0 if
missing.
Dyreng and Markle (2016) Variables
ǻ3,)2 Following Dyreng and Markle (2016), (foreign earnings in year t
(PIFO) less foreign earnings in year t-1), scaled by total assets in year
t-1 (AT).
ǻ3,'20 Following Dyreng and Markle (2016), (domestic earnings in year t
(PIDOM) less domestic earnings in year t-1), scaled by total assets in
year t-1 (AT).
ǻ6$/()2 Following Dyreng and Markle (2016), (foreign sales in year t less
foreign sales in year t-1), scaled by total assets in year t-1 (AT). We
compute foreign sales by summing the revenues of non-domestic
segments from the Compustat Segments database.
ǻ6$/('20 Following Dyreng and Markle (2016), (domestic sales in year t less
domestic sales in year t-1), scaled by total assets in year t-1 (AT). We
compute domestic sales by subtracting foreign sales from total global
revenues.
Notes: This table presents variable definitions for the variables used in our study. * indicates data retrieved from
ISS. All continuous variables are winsorized at 1% and 99%.
44
TABLE 1: Sample Selection and Composition
Firms
Firm-years
Firms from the Russell 1000 index as of June 2014
1,021
Less:
Foreign firms (FIC not USA)
(49)
Financial firms (SIC 6000-6999)
(222)
Utilities
(59)
Firms without proxy disclosures available
(26)
Total potential sample firms
665
Less:
Firms without Compustat data 2014-2017
(9) 2,434
Firms with current losses (PI ч 0) (25) (344)
Firms without data to calculate regression variables
(130) (495)
Final sample
501 1,595
45
TABLE 2: Determinants of Risk Oversight
Risk
management Governance Innate Risk
Firm fundamentals
and disclosure
All
variables
(1) (2) (3) (4) (5)
Variables Risk Oversight
Intercept 1.1679*** 1.2469*** 1.3405*** 0.8377*** 0.9022***
[15.66] [12.98] [16.75] [3.63] [3.41]
ERM 0.1993*** 0.000 0.000 0.000 0.1609***
[3.81]
[3.03]
CRO -0.1199
-0.1013
[-1.12]
[-0.95]
Board Inputs
0.1273***
0.0942***
[3.75]
-0.0014
E-index -0.0061
[-0.08]
[-0.36]
0.0942***
CEO is Chair
-0.0084
-0.0156
[-0.18]
[-0.33]
LitRisk
0.0236*
0.0459***
[1.71]
[3.05]
DistressRisk
0.0244***
0.0149**
[4.59]
[2.22]
PTROAVol
0.1558
-0.1499
[0.10]
[-0.16]
Size
0.0236* 0.0460** 0.0389*
[1.71] [2.34] [1.75]
ROA
0.088 -1.6412*** -0.6638
0.0244*** [-3.58] [-1.25]
RD
[4.59] 0.2002 0.4406
0.000 [0.47] [1.01]
CapInt
0.1558 -0.0789 -0.1452*
[0.17] [-0.91] [-1.65]
Intang
0.867 0.2609* 0.2481*
[1.91] [1.73]
Foreign
-0.1132 -0.1166
[-1.29] [-1.34]
Geoseg
0.0915* 0.0950*
[1.71] [1.78]
Calls
-0.0154 -0.0150
[01.37] [-1.33]
Observations 1,595 1,595 1,595 1,595 1,595
Adjusted R-
0.0308 0.0298 0.0352 0.0399 0.0551
Notes: This table presents results from Model (1) estimating the determinants of Risk Oversight. All models include
industry fixed effects (Fama French 12). All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless
noted in the Appendix. *, **, and *** indicate a 10%, 5%, and 1% significance level, respectively, using two-tailed
tests.
46
TABLE 3: Descriptive Statistics
Panel A - All firms
Percentiles:
Variable
N Mean Median
St Dev
25
th
50
th
75
th
Risk Oversight 1,595 1.254 1.000
0.879
1.000
1.000 2.000
High Risk Oversight 1,595 0.361 0.000
0.480
0.000
0.000 1.000
GAAPETR 1,595 0.318 0.312
0.152
0.238
0.312 0.365
GAAPETRVol 1,595 0.061 0.027
0.091
0.011
0.027 0.069
Size 1,595 9.524 9.326
1.194
8.607
9.326 10.230
ROA 1,595 0.078 0.070
0.050
0.044
0.070 0.106
PTROAVol 1,595 0.024 0.017
0.024
0.009
0.017 0.030
RD 1,595 0.040 0.008
0.070
0.000
0.008 0.050
CapInt 1,595 0.453 0.344
0.334
0.194
0.344 0.659
Leverage 1,595 0.293 0.275
0.184
0.169
0.275 0.389
NOL 1,595 0.898 1.000
0.302
1.000
1.000 1.000
ChangeNOL 1,595 0.001 0.000
0.038
-0.002
0.000 0.002
Intang 1,595 0.298 0.278
0.212
0.119
0.278 0.443
Inv 1,595 0.103 0.079
0.106
0.016
0.079 0.150
Adv 1,595 0.015 0.001
0.029
0.000
0.001 0.019
Foreign 1,595 0.395 0.355
0.339
0.060
0.355 0.620
Geoseg 1,595 1.441 1.386
0.537
1.099
1.386 1.792
Board Inputs 1,595 -0.010 -0.038
0.661
-0.487
-0.038 0.381
LitRisk 1,595 -0.520 -0.949
1.651
-1.563
-0.949 0.078
DistressRisk 1,595 -4.264 -3.259
4.280
-5.299
-3.259 -1.876
Panel B - Univariate Comparisons of High versus Low Risk Oversight firms
High Risk Oversight
Low Risk Oversight
Variable
N Mean St Dev
N Mean
St Dev
Mean Diff t-stat
GAAPETR 576 0.305 0.139
1,019 0.325
0.158
-0.020** -2.52
GAAPETRVol 576 0.054 0.080
1,019 0.065
0.097
-0.010** -2.21
Size 576 9.479 1.094
1,019 9.549
1.247
-0.070 -1.12
ROA 576 0.074 0.043
1,019 0.081
0.054
-0.007*** -2.72
PTROAVol 576 0.024 0.025
1,019 0.025
0.024
-0.000 -0.08
RD 576 0.033 0.057
1,019 0.045
0.076
-0.012*** -3.33
CapInt 576 0.440 0.321
1,019 0.460
0.341
-0.020 -1.15
Leverage 576 0.310 0.187
1,019 0.284
0.181
0.026*** 2.74
NOL 576 0.932 0.251
1,019 0.879
0.326
0.053*** 3.38
ChangeNOL 576 0.002 0.025
1,019 0.001
0.044
0.002 0.82
Intang 576 0.311 0.210
1,019 0.291
0.212
0.020* 1.77
Inv 576 0.109 0.105
1,019 0.100
0.106
0.009* 1.69
Adv 576 0.016 0.028
1,019 0.015
0.030
0.001 0.45
Foreign 576 0.380 0.331
1,019 0.403
0.344
-0.023 -1.32
Geoseg 576 1.461 0.548
1,019 1.429
0.530
0.031 1.11
Board Inputs 576 0.073 0.681
1,019 -0.056
0.645
0.130*** 3.78
LitRisk 576 -0.431 1.679
1,019 -0.571
1.633
0.140 1.63
DistressRisk 576 -3.661 3.052
1,019 -4.605
4.806
0.944*** 4.25
Notes: Panel A presents the descriptive statistics of our sample, and Panel B compares high versus low Risk
Oversight firms. All variables are defined in the appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **, and *** indicate a 10%, 5%, and 1% significance level,
respectively, using two-tailed tests.
47
Table 4: Correlations
Variables 1 2 3 4 5 6 7 8 9 10 11 12 13
14
15 16 17 18 19
1
Risk Oversight 1
2
GAAPETR
-0.064
1
3
GAAPETRVol
-0.034 0.507
1
4
Size 0.001
-0.095
0.015 1
5
ROA
-0.099 -0.296 -0.399 0.175
1
6
PTROAVol -0.009
0.042 0.165
-0.021
0.055
1
7
RD
-0.064 -0.119 0.119 0.256 0.081 0.083
1
8
CapInt -0.017
0.116 -0.068 -0.092
0.024
0.037 -0.306
1
9
Leverage
0.073
0.010 0.028 0.029
-0.125
-0.030
-0.184 0.056
1
10
NOL
0.081
-0.027
0.120
0.022
-0.162 0.046 0.100 -0.166 0.084
1
11
ChangeNOL -0.002
0.061
0.012
-0.060 -0.091
0.017
-0.056
0.005
0.048
-0.020 1
12
Intang
0.050 -0.120 0.055 0.048 -0.227 -0.054
0.028
-0.526 0.184 0.184 0.039
1
13
Inv -0.005
0.073 -0.106 -0.139 0.115 -0.078 -0.227 0.145 -0.116 -0.154
-0.016
-0.349
1
14
Adv
-0.043
0.005
-0.062 0.041 0.188
-0.006
-0.136 0.066
0.026 -0.004 0.022
-0.077 0.162
1
15
Foreign -0.030
-0.087 0.152 0.052 -0.033
0.028
0.295 -0.225 -0.090 0.199
0.008
-0.046 -0.087
-0.020
1
16
Geoseg 0.006
-0.101 0.151 0.044 -0.063 0.041 0.278 -0.201 -0.099 0.213
0.012 0.015
-0.076
-0.188
0.629
1
17
Board Inputs
0.107
-0.020 -0.014
0.252 -0.039
-0.008
-0.133 0.078 0.118 0.036 -0.060
-0.025
0.040
-0.039
-0.018 0.029 1
18
LitRisk
0.045 0.041 0.059 -0.316 -0.117 0.193 -0.050 0.181 -0.052
-0.010 0.031
-0.248 0.142
0.220
-0.062 -0.103 -0.137
1
19
DistressRisk
0.132 0.044 0.095 -0.063 -0.516 -0.107 -0.342 0.145 0.417 0.077 0.042 0.240 -0.052
-0.068
-0.125 -0.086 0.193 -0.047
1
Notes: This table presents our Pearson correlations. Statistics highlighted in bold represent statistical significant at p < 0.10. All continuous variables are
winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix.
48
TABLE 5: Primary Analysis
(1)
(2)
GAAPETRVol
GAAPETR
Variables Coef. t-stat
Coef. t-stat
Intercept 0.0367 1.44
0.4811*** 8.89
Risk Oversight -0.0063** -2.09
-0.0138*** -2.90
Size 0.0042* 1.75
-0.0038 -0.86
ROA -0.8408*** -10.35
-1.3899*** -8.53
PTROAVol 0.6575*** 4.39
0.4911** 2.48
RD 0.0514 1.08
-0.1782** -2.06
CapInt -0.0045 -0.43
0.0032 0.17
Leverage 0.0164 1.07
0.0346 1.28
NOL 0.0034 0.56
-0.0048 -0.38
ChangeNOL -0.0715 -0.82
0.0866 0.50
Intang -0.0239 -1.27
-0.1248*** -3.82
Inv -0.0133 -0.46
0.0157 0.32
Adv 0.1545** 2.01
0.2469 1.38
Foreign 0.0102 1.02
-0.0289* -1.68
Geoseg 0.0092 1.35
-0.0180* -1.79
Board Inputs -0.0022 -0.56
-0.0041 -0.65
LitRisk -0.0007 -0.39
-0.0111*** -3.39
DistressRisk -0.0015** -2.54
-0.0069*** -3.92
Observations 1,595
1,595
Adjusted R-squared 0.3039
0.2157
Notes: This table presents results for our tests examining the association predicted in H1 and H2 between risk
oversight (Risk Oversight) and tax uncertainty (GAAPETRVol) and levels of tax burdens (GAAPETR), respectively.
All variables are defined in the Appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles
unless noted in the Appendix. *, **, and *** indicate a 10%, 5%, and 1% significance level, respectively. T-
statistics are based on robust standard errors clustered by firm. P-values are based on one-tailed t-tests for the
variable of interest. The model includes industry (Fama French 12) and year fixed effects.
49
TABLE 6: Tax Planning Additional Analyses
Panel A: Permanent versus Temporary Tax Planning
(1)
(2)
PermBTD
TempBTD
Variables Coef. t-stat
Coef. t-stat
Intercept -0.0396*** -2.89 0.0379*** 3.78
Risk Oversight 0.0019** 1.86 -0.0008 -0.79
Size 0.0013 1.32 -0.0020** -2.33
ROA 0.4178*** 11.01 -0.0475 -1.22
PTROAVol -0.0655* -1.86 -0.0032 -0.08
RD 0.0263 1.11 -0.0337 -1.63
CapInt -0.0034 -0.80 0.0059 1.48
Leverage -0.0003 -0.05 0.0063 0.86
NOL 0.0013 0.38 0.0015 0.53
ChangeNOL 0.0017 0.05 -0.0012 -0.05
Intang 0.0196*** 2.76 -0.0308*** -4.06
Inv -0.0104 -0.85 -0.0120 -1.01
Adv -0.0955* -1.92 -0.0710** -2.35
Foreign 0.0113*** 2.99 -0.0020 -0.60
Geoseg 0.0065*** 3.08 -0.0039* -1.68
Board Inputs -0.0010 -0.64 0.0006 0.38
LitRisk 0.0034*** 4.69 -0.0009 -1.48
DistressRisk 0.0015*** 2.99 0.0000 0.13
Observations 1,584 1,584
Adj. R-squared 0.3117 0.0860
Notes: This table presents results for our tests examining the association between Risk Oversight and permanent
versus temporary tax planning for firms with data to calculate the variables. All variables are defined in the
Appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **,
and *** indicate a 10%, 5%, and 1% significance level, respectively. T-statistics are based on robust standard errors
clustered by firm. P-values are based on one-tailed t-tests for the variable of interest. The model includes industry
(Fama French 12 specification) and year fixed effects.
50
TABLE 6 (continued)
Panel B: Income Shiftin
g
D.V. = ǻ3,'20 and ǻ3,)2
Coef.
z-stat
,QWHUFHSW)25Į
0.0012
1.060
IntercepW'20ȕ
0.0035***
5.450
2XWERXQG7UDQVIHUVș
1
)
0.3719***
6.080
,QERXQG7UDQVIHUVȖ
1
)
0.4862***
6.810
5R)RUHLJQ6DOHVȡ
fo1
)
0.0881***
6.760
5R'RPHVWLF6DOHVȡ
do1
)
0.1061***
6.980
OutboundTransfers*High Risk Oversightș
2
)
-0.1834**
-1.780
InboundTransfers*High Risk OversightȖ
2
)
-0.3015*
-1.580
RoForeignSales*High Risk Oversight
0.0227
0.720
RoDomesticSales*High Risk Oversight
0.0280
0.920
N
1,090
Adj. R
2 -
ǻ3,'20 Eqn.
0.05
Adj. R
2 -
ǻ3,)2 Eqn.
0.09
Notes: This table presents results examining whether income shifting differs for firms with high risk oversight (High
Risk Oversight=1). We specifically follow '\UHQJDQG0DUNOH¶VHTXDWLRQDDQGE6HH3DJHRIWKHLU
study for additional details as well as page 1626 for their provided code of estimating their system of equations. We
estimate the following system of equations, following Dyreng and Markle (2016):
ǻ3,)2
i,t
Į-ȖȡIǻ6$/()2
i,t
șȡGǻ6$/('20
i,t
İ (3a)
ǻ3,'20
i,t
ȕȖȡIǻ6$/()2
i,t
+ (1-șȡGǻ6$/('20
i,t
ȝ (3b)
We interact each of the terms above with High Risk Oversight.
This test uses a subsample of the primary sample with the following additional data cuts. First, we limit this sample
to only multinational firms (TXFO or PIFO >0), which reduces our original sample by 168 observations. Second,
following Dyreng and Markle (2016), we also drop observations where the sum of sales in the Compustat
geographic segments file is greater than 1% different from total sales in Compustat, dropping 341 obs. Third, we
require firms to have data to estimate Models (3a) and (3b), resulting in a final sample of 1,090 firm-years. All
continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **, and ***
indicate a 10%, 5%, and 1% significance level, respectively. P-values are based on one-tailed t-tests for the variables
of interest (OutboundTransfers*High Risk Oversight and InboundTransfers*High Risk Oversight).
51
TABLE 6 (continued)
Panel C: Foreign Operations
(1) (2)
(3) (4)
GAAPETRVOL
GAAPETR
Variables Coef. t-stat Coef.
t-stat
Coef. t-stat Coef.
t-stat
Intercept 0.027 1.01 0.031
1.18
0.448*** 7.86 0.458***
8.17
Risk Oversight -0.007** -2.21 -0.006**
-1.81
-0.014*** -2.72 -0.014***
-2.43
Geoseg 0.000 0.04
-0.002 -0.14
Risk Oversight*Geogseg 0.003 0.59
-0.014** -1.78
Nonhaven sub % 0.063**
1.66
0.167***
2.77
Risk Oversight*Nonhaven sub % -0.018
-0.69
-0.072**
-1.77
Log subs 0.008*** 3.22 0.007***
2.62
0.005* 1.49 0.003
0.71
Size 0.006*** 2.51 0.007***
2.52
-0.000 -0.10 0.000
0.09
ROA -0.916*** -9.80 -0.945***
-9.73
-1.490*** -8.43 -1.525***
-8.48
PTROAVol 0.658*** 4.21 0.656***
4.15
0.472** 2.28 0.483***
2.34
RD 0.057 1.18 0.054
1.03
-0.177** -1.95 -0.242***
-2.67
CapInt 0.003 0.27 -0.001
-0.10
0.007 0.33 -0.001
-0.06
Leverage 0.023* 1.44 0.025*
1.49
0.039* 1.40 0.050**
1.70
NOL -0.003 -0.49 -0.001
-0.11
-0.020* -1.36 -0.022*
-1.40
ChangeNOL -0.021 -0.19 -0.022
-0.20
0.136 0.76 0.134
0.77
Intang -0.028* -1.38 -0.036**
-1.68
-0.131*** -3.74 -0.140***
-3.84
Inv -0.000 0.00 -0.008
-0.23
0.015 0.27 -0.004
-0.08
Adv 0.158** 2.02 0.172***
2.11
0.284* 1.49 0.370**
1.95
Foreign 0.005 0.43 0.009
0.91
-0.034** -1.84 -0.042***
-2.49
Board Inputs -0.002 -0.44 -0.001
-0.29
-0.005 -0.79 -0.005
-0.70
LitRisk -0.000 -0.11 0.000
0.08
-0.010*** -2.94 -0.009***
-2.65
DistressRisk -0.002*** -3.34 -0.002***
-3.37
-0.008*** -3.72 -0.008***
-3.79
Observations 1,472 1,447
1,472 1,447
Adjusted R-squared 0.244 0.248
0.185 0.188
52
Notes: This table presents results for our additional analyses examining whether the association between Risk Oversight and efficient tax planning is stronger in
settings with greater opportunities for tax-efficient supply chain management. All variables are defined in the Appendix. All continuous variables are winsorized
at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **, and *** indicate a 10%, 5%, and 1% significance level, respectively. T-statistics are based on
robust standard errors clustered by firm. P-values are based on one-tailed t-tests for the variable of interest. The model includes industry (Fama French 12
specification) and year fixed effects.
53
TABLE 6 (Continued)
Panel D: Research and Development
(1)
(2)
GAAPETRVOL
GAAPETR
Variables Coef. t-stat
Coef. t-stat
Intercept 0.0350 1.41 0.4842*** 9.21
Risk Oversight -0.0085** -2.02 -0.0063 -1.14
Risk Oversight*R&Dfirm 0.0040 0.69 -0.0145* -1.67
R&Dfirm -0.0075 -0.70 -0.0014 -0.09
Size 0.0050** 2.14 -0.0053 -1.21
ROA -0.8468*** -10.47 -1.3642*** -8.36
PTROAVol 0.6613*** 4.45 0.4935** 2.49
CapInt -0.0074 -0.73 0.0105 0.55
Leverage 0.0163 1.04 0.0342 1.29
NOL 0.0037 0.61 -0.0047 -0.37
ChangeNOL -0.0730 -0.86 0.0892 0.50
Intang -0.0297* -1.68 -0.1085*** -3.40
Inv -0.0170 -0.57 0.0323 0.67
Adv 0.1560** 2.04 0.2454 1.33
Geoseg 0.0136** 2.44 -0.0260*** -3.01
Board Input -0.0027 -0.71 -0.0025 -0.40
LitRisk -0.0006 -0.30 -0.0121*** -3.65
DistressRisk -0.0016*** -2.66 -0.0064*** -3.79
Observations 1,595 1,595
Adj. R-squared 0.3027 0.2131
Notes: This table presents results for our additional analyses examining whether the association between Risk
Oversight and efficient tax planning is stronger in settings with R&D activities. All variables are defined in the
Appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **,
and *** indicate a 10%, 5%, and 1% significance level, respectively. T-statistics are based on robust standard errors
clustered by firm. P-values are based on one-tailed t-tests for the variable of interest. The model includes industry
(Fama French 12 specification) and year fixed effects.
54
TABLE 7: Executive Compensation Analysis
(1) (2)
(3)
(4)
GAAPETRVOL
GAAPETR
Variables Coef. t-stat Coef. t-stat
Coef.
t-stat Coef. t-stat
Intercept 0.031 1.09 0.033 1.14
0.439***
8.12 0.439*** 8.06
Risk Oversight -0.007** -2.27 -0.007** -2.29
-0.015***
-3.08 -0.015*** -3.07
CEO Vega
t-1
0.005 0.66 -0.007 -0.62
0.009
0.61 0.009 0.38
Risk Oversight*CEO Vega
t-1
0.008 1.41
0.000 0.01
Size 0.006** 2.10 0.006** 2.02
0.002
0.38 0.002 0.37
ROA -0.893*** -9.03 -0.895*** -9.02
-1.526***
-8.31 -1.526*** -8.30
PTROAVol 0.711*** 4.49 0.710*** 4.48
0.546**
2.58 0.546** 2.58
RD 0.038 0.76 0.044 0.88
-0.201**
-2.19 -0.201** -2.17
CapInt -0.007 -0.59 -0.007 -0.64
0.007
0.33 0.007 0.33
Leverage 0.034** 1.99 0.034** 2.04
0.070**
2.43 0.070*** 2.43
NOL 0.004 0.56 0.003 0.54
-0.010
-0.76 -0.010 -0.76
ChangeNOL -0.056 -0.55 -0.056 -0.54
0.097
0.54 0.097 0.54
Intang -0.025 -1.21 -0.025 -1.22
-0.116***
-3.42 -0.116*** -3.42
Inv 0.003 0.09 0.004 0.11
0.029
0.54 0.029 0.54
Adv 0.159** 2.01 0.166** 2.07
0.171
0.94 0.171 0.93
Foreign 0.014 1.33 0.013 1.29
-0.026
-1.42 -0.026 -1.42
Geoseg 0.008 1.10 0.008 1.14
-0.022**
-1.97 -0.022** -1.96
Board Input -0.003 -0.82 -0.003 -0.79
-0.006
-1.01 -0.006 -1.00
LitRisk -0.002 -0.89 -0.002 -0.92
-0.012***
-3.74 -0.012*** -3.75
DistressRisk -0.002** -2.34 -0.002** -2.31
-0.011***
-5.25 -0.011*** -5.24
Observations 1,489 1,489
1,489
1,489
Adjusted R-squared 0.238 0.239
0.189
0.188
Notes: This table presents results for our additional analyses examining whether the association between Risk Oversight and efficient tax varies with
compensation incentives. All variables are defined in the Appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the
Appendix. *, **, and *** indicate a 10%, 5%, and 1% significance level, respectively. T-statistics are based on robust standard errors clustered by firm. P-values
are based on two-tailed t-tests for the variable of interest. The model includes industry (Fama French 12 specification) and year fixed effects.
55
TABLE 8: Component Analysis of Board Risk Oversight
(1)
(2)
GAAPETRVOL
GAAPETR
Variables Coef. t-stat
Coef. t-stat
Intercept 0.0319
1.23
0.4841***
8.88
Responsibility 0.0035
0.69
-0.0210**
-2.31
Consistency -0.0110**
-2.19
-0.0042
-0.48
Mindset -0.0120***
-2.45
-0.0155*
-1.58
Size 0.0040*
1.71
-0.0037
-0.83
ROA -0.8367***
-10.35
-1.3969***
-8.60
PTROAVol 0.6644***
4.45
0.4867**
2.46
RD 0.0420
0.88
-0.1685*
-1.94
CapInt -0.0050
-0.48
0.0031
0.16
Leverage 0.0187
1.21
0.0323
1.20
NOL 0.0044
0.73
-0.0054
-0.42
ChangeNOL -0.0751
-0.89
0.0923
0.53
Intang -0.0221
-1.19
-0.1291***
-3.95
Inv -0.0090
-0.31
0.0113
0.23
Adv 0.1478*
1.95
0.2521
1.40
Foreign 0.0084
0.85
-0.0274
-1.62
Geoseg 0.0098
1.46
-0.0182*
-1.80
Board Input -0.0014
-0.36
-0.0050
-0.80
LitRisk -0.0007
-0.37
-0.0113***
-3.42
DistressRisk -0.0015**
-2.54
-0.0070***
-3.96
Observations 1,595 1,595
Adj. R-squared 0.3069 0.2162
Notes: This table presents results for our additional analyses examining the association between Responsibility,
Consistency, and Mindset, and efficient tax varies with compensation incentives. All variables are defined in the
Appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **,
and *** indicate a 10%, 5%, and 1% significance level, respectively. T-statistics are based on robust standard errors
clustered by firm. P-values are based on one-tailed t-tests for the variable of interest. The model includes industry
(Fama French 12 specification) and year fixed effects.
56
TABLE 9: Robustness Tests: Alternative Tax Variable Proxies
Tax Uncertainty
Tax Burdens
(1) (2)
(3)
(4)
CYUTBINC CYUTBPEN
HS_GAAP
CETR
Variables Coef. t-stat Coef. t-stat
Coef.
t-stat Coef. t-stat
Intercept 0.0025 0.03 -0.0016 -0.09
0.0252***
2.93 0.3798*** 6.04
Risk Oversight -0.0129** -1.69 -0.0029* -1.41
-0.0014**
-2.01 -0.0131** -2.22
Size 0.0160** 2.11 0.0012 0.86
-0.0007
-1.16 0.0015 0.30
ROA 0.3783* 1.84 -0.0223 -0.65
0.2583***
7.68 -1.3370*** -7.85
PTROAVol 0.2511 0.91 0.0509 0.88
0.0344
1.51 1.0115*** 4.24
RD 0.6066** 2.34 -0.0106 -0.53
-0.0152
-1.03 -0.1059 -0.80
CapInt -0.0123 -0.46 -0.0063 -0.94
0.0016
0.55 -0.0348 -1.63
Leverage 0.0507 1.18 0.0119 1.18
0.0039
0.88 0.0207 0.63
NOL -0.0252 -0.80 0.0016 0.28
-0.0011
-0.56 -0.0249* -1.89
ChangeNOL 0.2823 0.95 0.0609** 2.19
0.0315
0.91 0.1928 1.00
Intang 0.0034 0.08 -0.0206** -2.02
-0.0143***
-2.93 -0.0110 -0.26
Inv 0.0920 1.10 0.0045 0.19
0.0078
1.05 0.0933 1.46
Adv 0.1959 0.77 0.1180 1.33
0.0634**
2.14 0.5395*** 2.85
Foreign -0.0360 -1.29 0.0068 1.07
-0.0054**
-2.26 0.0043 0.20
Geoseg 0.0012 0.07 0.0021 0.54
-0.0037***
-2.89 -0.0156 -1.21
Board Input -0.0254** -2.40 0.0046* 1.68
0.0006
0.66 -0.0117 -1.38
LitRisk 0.0048 0.90 -0.0009 -0.68
-0.0020***
-4.04 -0.0110** -2.41
DistressRisk -0.0012 -0.47 0.0001 0.37
-0.0010***
-3.13 -0.0073*** -3.89
Observations
1,498
1,595
1,595
1,485
Adjusted R-squared
0.0521
0.0170
0.5033
0.1607
Notes: This table presents results for our tests examining the association between Risk Oversight and alternative proxies for tax uncertainty (columns 1 and 2)
and tax burdens (columns 3 and 4). All variables are defined in the Appendix. All continuous variables are winsorized at the 1
st
and 99
th
percentiles unless noted
in the Appendix. *, **, and *** indicate a 10%, 5%, and 1% significance level, respectively. T-statistics are based on robust standard errors clustered by firm. P-
values are based on one-tailed t-tests for the variable of interest. The model includes industry (Fama French 12 specification) and year fixed effects
57
TABLE 10: Falsification Test:
Alternative Risk Oversight Proxies
(1) (2)
GAAPETRVol GAAPETR
Variables Coef. t-stat Coef. t-stat
Intercept 0.009 0.25 0.479*** 7.56
Nwords 0.003 0.50 -0.004 -0.53
Size 0.006** 2.54 -0.001 -0.27
ROA -0.881*** -10.07 -1.438*** -8.40
PTROAVol 0.665*** 4.34 0.500** 2.47
RD 0.046 0.96 -0.193** -2.12
CapInt -0.003 -0.31 0.006 0.29
Leverage 0.026* 1.65 0.044 1.53
NOL 0.001 0.16 -0.008 -0.61
ChangeNOL -0.057 -0.60 0.105 0.61
Intang -0.022 -1.18 -0.124*** -3.67
Inv 0.006 0.21 0.042 0.85
Adv 0.176** 2.21 0.283* 1.55
Foreign 0.012 1.16 -0.027 -1.55
Geoseg 0.009 1.25 -0.019* -1.80
Board Input -0.004 -1.14 -0.007 -1.04
LitRisk -0.001 -0.45 -0.012*** -3.46
DistressRisk -0.002*** -3.26 -0.008*** -3.84
Intercept 0.009 0.25 0.479*** 7.56
Observations 1,595 1,595
Adjusted R-squared 0.231 0.175
Notes: This table presents results for our falsification tests examining the association between Risk Oversight and
tax uncertainty (GAAPETRVol) and tax burdens (GAAPETR) after replacing Risk Oversight with the log of the
number of words in the proxy statement (NWords). All variables are defined in the Appendix. All continuous
variables are winsorized at the 1
st
and 99
th
percentiles unless noted in the Appendix. *, **, and *** indicate a 10%,
5%, and 1% significance level, respectively. T-statistics are based on robust standard errors clustered by firm. P-
values are based on two-tailed t-tests for the variable of interest. The model includes industry (Fama French 12
specification) and year fixed effects.