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Does operational efficiency spill over onto the tax return?
Allison Koester
McDonough School of Business
Georgetown University
Terry Shevlin
Merage School of Business
University of California - Irvine
Dan Wangerin*
Broad College of Business
Michigan State University
Current Draft: December 9, 2013
First Draft: May 2013
Abstract
We find managers with greater ability to efficiently utilize firm resources engage in greater tax
avoidance. We define managerial ability as a manager’s capacity to “maximize the efficiency of
[firm] resources used for revenue-generating purposes” (Demerjian et al. 2012, p.1) and view
cash outflows to the taxing authorities as an inefficient utilization of resources. Moving from the
lower to upper quartile of managerial ability is associated with a 3.16 (4.39) % reduction in a
firm’s one-year (five-year) cash-based effective tax rate. Cross-sectional tests reveal that higher-
ability managers structure investments in R&D, capital expenditures, and foreign operations
more tax-efficiently.
JEL Classifications: G30; M41
Keywords: tax avoidance; management style; managerial ability
Acknowledgements:
An early draft of this paper benefited from insightful comments by Bok Baik, Bill Baber, Michelle Hanlon, Dan
Lynch, Sarah McVay, Ed Outslay, Kathy Petroni, Isabel Wang, Ryan Wilson, and brownbag participants at
Georgetown University. We thank Peter Demerjian, Baruch Lev, and Sarah McVay for sharing their managerial
ability data and Pete Lisowsky for sharing his tax shelter likelihood data. Koester acknowledges financial support
from Georgetown University’s Center for Financial Markets and Policy.
*Corresponding author: 632 Bogue Street, N231 Business College Complex, East Lansing, MI 48824
Phone: (517) 884-0711, Fax: (517) 432-1101, email: [email protected]
1
1. Introduction
This paper examines and finds evidence that managers with greater ability to efficiently
utilize firm resources engage in greater tax avoidance. Taxes are a significant source of corporate
cash outflows, ranging from 20 to 40 % of pretax income for most public U.S. companies
(Dyreng, Hanlon, and Maydew 2008). As every dollar of taxes paid is a dollar that cannot be
reinvested within the firm, paying taxes could be viewed as an inefficient utilization of
resources.1 Prior research shows that managers have significant influence in shaping firms’ tax
avoidance policies. Dyreng, Hanlon, and Maydew (2010), hereafter DHM, use a manager fixed
effects research design to demonstrate that unobservable manager characteristics have an
economically significant effect on cash-based ETRs. DHM fail to find evidence of a systematic
relation between observable manager characteristics (e.g., education, tenure, age, gender, etc.)
and tax avoidance, leading the authors to conclude that “the executive effects on tax avoidance
appear to be idiosyncratic” (p.1165). Managers’ ability to efficiently utilize firm resources
represents a potentially important factor explaining how managers affect corporate tax
avoidance.
All else equal, we hypothesize a positive relation between managers’ ability to efficiently
utilize firm resources and corporate tax avoidance. Higher-ability managers have a superior
understanding of the environment in which their firm operates (Demerjian et al. 2012),
suggesting they are more likely to identify and/or create tax planning opportunities, better
aligning business and tax strategies. If higher-ability managers themselves do not possess tax-
specific expertise, they can hire individuals or engage outside consultants who are skilled at
1 While firms do benefit from government expenditures (i.e., infrastructure maintenance, well-functioning legal
systems enforcing contracts, public education systems, etc.) that are in part funded by corporate income tax
collections, the “free rider” problem suggests firms could benefit from these government expenditures without
paying income taxes.
2
identifying and implementing tax avoidance strategies (Dyreng et al. 2010). Higher-ability
managers can also create a “tone at the top” throughout the organization that emphasizes
maximizing revenues and minimizing costs. While the same incentives to reduce income taxes
also exist to reduce operating costs, reductions in operating costs are more likely to negatively
impact firm operations. For example, reducing advertising expenditures can reduce future sales,
and reducing product input costs by purchasing inferior materials can result in lower quality
products. Reducing employee compensation can hinder firms’ ability to attract and retain a high-
quality work force. Efficiently utilizing resources by reducing cash outflows to taxing authorities
could be particularly appealing to higher-ability managers especially if such cash reductions do
not directly affect a firm’s operations (e.g., sales, product quality, or human capital).
On the other hand, there are several reasons why we could fail to find evidence consistent
with our hypothesis. The skills necessary to maximize revenues and effectively deploy firm
resources could be distinct from the expertise needed to plan and implement tax avoidance
strategies which busy managers could find too costly to obtain. Even if it is possible to hire
individuals or engage consultants possessing this expertise, the direct costs of tax planning and
implementation and the indirect (or non-tax) costs of tax avoidance (e.g., political costs,
reputation concerns, financial reporting effects, etc.) could exceed the benefits. Therefore, it is
possible we will fail to find a relation between managerial ability and tax avoidance. If higher-
ability managers believe their time is better spent focusing on core operating decisions as
opposed to tax avoidance strategies, or fail to structure their core operating decisions tax-
efficiently, it is possible that higher-ability managers engage in even less tax avoidance than their
lower-ability peers.
3
We operationalize managers’ ability to efficiently utilize firm resources using the
MA_SCORE measure developed in Demerjian, Lev, and McVay (2012). Demerjian et al. (2012)
construct this measure using data envelopment analysis (DEA) which captures how efficiently
managers convert corporate resources into revenues relative to their industry peers, controlling
for firm-specific characteristics affecting firm efficiency. Demerjian et al. (2012) validate the
MA_SCORE by documenting that the measure is strongly associated with manager fixed effects
and has superior ability to explain stock market reactions to CEO turnovers and changes in future
performance relative to other measures used in the literature (e.g., historical firm performance,
CEO media mentions, CEO tenure, etc.). Using the MA_SCORE allows us to examine how a
specific dimension of managerial ability – the efficient utilization of firm resources – is
associated with corporate tax avoidance.
In our main analysis, tax avoidance is operationalized using the cash-based effective tax
rate (ETR), with lower cash ETRs indicating greater tax avoidance.2 After controlling for known
determinants of tax avoidance, year fixed effects, and firm fixed effects, we find a negative and
significant association between managerial ability and cash ETRs. Our results suggest that
managers’ ability to efficiently utilize firm resources has an economically significant impact on
corporate tax avoidance. We find that moving from the lower to upper quartile of managerial
ability is associated with a 3.16 (4.39) % reduction in a firm’s one-year (five-year) cash-based
ETR. Our results hold after considering several alternative explanations including using an
industry- and size-adjusted cash ETR as well as controlling for the effects of incentive
2 We use a cash-based ETR measure (as opposed to a GAAP-based ETR measure) because we are interested in
examining whether higher-ability managers are better able to generate resources to use in revenue-generating
activities. A cash-based ETR measure captures permanent and temporary tax deferral strategies, both of which retain
cash resources within the firm. Because a GAAP ETR measure is accrual-based, it does not reflect cash savings
from temporary tax deferral strategies and is affected by non-cash accruals like changes in the valuation allowance,
tax expense accrued for foreign earnings not considered permanently reinvested, unrecognized tax benefits, etc.
4
compensation and corporate governance on tax avoidance. Our results also hold after controlling
for manager fixed effects, highlighting the ability of managerial teams (e.g., the joint effects of
individual managers working together) to efficiently deploy firm resources as a new and
economically important determinant of corporate tax avoidance.3
To draw stronger inferences, we conduct a difference-in-difference test examining
changes in tax avoidance surrounding CEO turnovers. This test allows us to explore whether
replacing a lower-ability CEO with a higher-ability CEO is associated with an increase in tax
avoidance (and vice versa). Using a turnover event to isolate the CEO’s effect on a change in
MA_SCORE and a change in tax avoidance also helps to rule out the possibility of correlated
omitted variables driving our main findings. Holding all else constant, moving from the lower to
upper quartile of managerial ability is associated with a 3.32 % decline in cash-based ETRs
during the three years following a CEO turnover relative to the three years prior to the turnover.
We conduct a set of tests to shed light on the channels through which higher-ability
managers avoid taxes. While we find no relation between managerial ability and the likelihood
of tax sheltering, we do find that higher-ability managers record larger liabilities for
unrecognized tax benefits (UTBs) related to tax positions unlikely to be sustained upon tax return
audit. These findings suggest that the reduction in cash ETRs associated with managerial ability
is in part attributable to transactions with greater uncertainty in tax treatment.
3 A concurrent working paper by Francis, Sun, and Wu (2013) also studies the relation between managerial ability
and tax avoidance using the Demerjian et al. (2012) ability measure. The authors find a negative relation between
tax avoidance in period t and managerial ability in period t-1, leading the authors to conclude that higher-ability
managers engage in less tax avoidance. We model tax avoidance and managerial ability concurrently, as a dollar of
firm resources saved through tax avoidance can be immediately invested within the firm for revenue-generating
activities. When we include both lagged and concurrent period MA_SCORE as independent variables in our model,
we find a negative coefficient on MA_SCOREt (p < 0.01) and a positive coefficient on MA_SCOREt-1 (p < 0.01).
The sum of the two coefficients is negative and significant (p < 0.01), consistent with higher-ability managers
engaging in greater tax avoidance.
5
Prior literature demonstrates that firms’ operating and financing characteristics explain
variation in tax avoidance and these characteristics reflect managers’ operating and financing
decisions. Therefore, in our final set of tests we explore whether higher-ability managers make
more tax-efficient operating and financing decisions given their firms’ operating and financing
characteristics. For example, firms often undertake R&D and establish foreign operations for
non-tax reasons. However, we expect higher ability managers to structure these operating
decisions tax-efficiently (e.g., optimize the U.S. R&D tax credit, structure transfer prices to shift
profits to low tax jurisdictions). We find that R&D activities, capital expenditures, leverage, and
foreign operations are important avenues through which tax avoidance is achieved by higher-
ability managers. Transfer pricing and R&D credits are the two most commonly cited areas of
tax position uncertainty firms report on Schedule UTP (IRS 2013), consistent with these
channels reducing firms’ cash ETRs and increasing their UTBs. Identifying the avenues through
which higher-ability managers are able to achieve tax avoidance is an important contribution to
the literature. In addition, these cross-sectional findings help to further rule out correlated
omitted variable concerns, as these correlated omitted variables would have to explain our main
results as well as all significant interactions in our cross-sectional tests.
We contribute to the tax literature by identifying a new and economically significant
determinant of tax avoidance. With the exception of DHM, prior studies model tax avoidance as
a function of firm-level characteristics and fail to take into account the influence of individual
managers (Gupta and Newberry 1997; Mills 1998; Rego 2003). Our study answers the call by
Hanlon and Heitzman (2010) to further explore the “manager effect” on tax avoidance by
explicitly considering the impact of individual corporate decision-makers on corporate tax
strategies. We compliment and build on the findings reported by DHM in several ways. First,
6
while a manager fixed effects research design captures only unobservable and time-invariant
manager characteristics, we identify an observable and time-varying managerial characteristic
associated with managers’ tax avoidance decisions.4 Second, while directional predictions are
not possible in a fixed effects research design, the Demerjian et al. (2012) MA_SCORE allows
us to make directional predictions regarding the relation between this dimension of managerial
ability and tax avoidance. Third, a manager fixed effects research design requires observing
managers moving across multiple firms over time, limiting studies (and potentially their
inferences) to a relatively small sample. The Demerjian et al. (2012) ability measure allows us to
provide large-sample evidence on the relation between managerial ability and tax avoidance for a
broad set of firms over a long period of time. Finally, Fee, Hadlock, and Pierce (2013) raise
questions about the research design employed in management style studies by showing standard
F-tests for joint significance of manager fixed effects do not provide valid statistical inferences.
Our study is not subject to the econometric concerns raised by Fee et al. (2013).
We also add to the literature linking managerial ability to financial reporting quality, firm
characteristics, and economic outcomes. Specifically, prior research has shown managerial
ability to be associated with greater earnings persistence, accruals quality, and the frequency and
information content of management forecasts (Baik, Farber, and Lee 2011; Demerjian, Lev,
Lewis, and McVay 2013). Other research finds managerial ability to be associated with a lower
likelihood of bankruptcy (Leverty and Grace 2012), greater employment opportunities for
executives (Fee and Hadlock 2003; Rajgopal, Shevlin, and Zamora 2006), and more efficient
4 In a recent study, Law and Mills (2013) find that CEO military experience is associated with a 1-2% reduction in
one-year cash and GAAP ETRs and explains about 4% of the variation in manager fixed effects on corporate tax
avoidance. Using stock option backdating as a proxy for personal tax aggressiveness, Chyz (2013) shows the
presence of executives engaging in this type of behavior is positively associated with the likelihood of tax sheltering.
While these studies examine time-invariant individual characteristics, we study a time-varying characteristic that
captures management teams’ ability to efficiently utilize firm resources.
7
investments in labor (Jung, Lee, and Weber 2013).5 Our findings provide new insights into the
managerial ability literature by identifying a relation between managerial ability and corporate
tax avoidance. Our findings should be of particular interest to board members when considering
the costs and benefits of hiring executives, as we find that managerial ability affects not only
firm operations but also the efficiency with which firms’ internally generated resources are
utilized to obtain tax savings.
The remainder of the paper is organized as follows. Section 2 discusses related literature
and our hypothesis. Section 3 describes our sample and empirical method. Section 4 presents our
empirical results and Section 5 concludes.
2. Related Literature and Hypothesis Development
2.1 Tax Avoidance, Management Style, and Managerial Ability
Following Hanlon and Heitzman (2010), we broadly define tax avoidance as the
reduction of explicit taxes (p.137). Thus, we assume tax avoidance includes the effects of tax
savings from all activities in which the firm engages (e.g., real activities that are tax-advantaged,
identifying and capitalizing upon tax planning opportunities, and targeted tax benefits from
lobbying and political connectedness). Researchers have used a variety of proxies to capture tax
avoidance. Some measures are broad in nature (e.g., cash-based ETRs, GAAP-based ETRs, and
book-tax differences) while others are designed to capture certain types of transactions (e.g.,
permanent discretionary book-tax differences, unrecognized tax benefits, and tax sheltering).
Hanlon and Heitzman (2010) note that most tax research in corporate tax avoidance focuses on
firm characteristics as determinants (Gupta and Newberry 1997; Rego 2003; Wilson 2009;
5 Note that only Demerjian et al. (2013) and Jung et al. (2013) use the managerial ability measure from Demerjian et
al. (2012).
8
Lisowsky 2010), and the authors identify research that considers the impact individual corporate
decision-makers have on a firm’s tax avoidance strategies as a gap in the tax literature.
A separate stream of research has begun to examine the effect of individual managers on
corporate decisions. Bertrand and Schoar's (2003) study of managers’ impact on corporate
financial policy and investment decisions (e.g. dividends, capital expenditures, and mergers and
acquisitions) serves as the foundational paper in what is referred to as the “management style”
literature. The Bertrand and Schoar (2003) management style research design involves tracking
individual managers who move across multiple firms over time and uses manager fixed effects to
capture the influence of individual managers’ unobservable characteristics on corporate
decisions. The management style framework has since been extended to other settings where
researchers have examined the relation between management style and voluntary disclosure
decisions, earnings quality, and the intersection of financial reporting and managerial decision
making (Bamber, Jiang, and Wang 2010; Ge, Matsumoto, and Zhang 2011; Dejong and Ling
2013). DHM extend the management style framework to a tax setting and show a manager-
specific effect on corporate ETRs using a manager fixed effects research design.
A recent paper by Fee, Hadlock, and Pierce (2013) calls into question the causal role of
managerial style. These authors fail to find a relation between managerial style and corporate
policies in CEO turnovers likely to be exogenously determined (e.g., planned retirements due to
CEO age, sudden illness, and death). Moreover, Fee et al. (2013) demonstrate that standard F-
statistics for a test of joint significance of manager fixed effect coefficients used in prior studies
could be econometrically invalid in detecting the presence of significant individual management
style effects. Specifically, the authors highlight that standard asymptotic theory does not apply to
tests where variables are highly serially correlated because the properties of standard F-tests for
9
joint significance are unknown (Wooldridge 2002). Highlighting these concerns, Fee et al.
(2013) find F-tests reveal very significant manager style effects after randomly assigning CEO-
to-CEO movers to a different hiring firm than the one he actually joined. These authors suggest a
more nuanced role for individual manager styles to affect corporate policies that depends on both
(1) time-invariant unobservable idiosyncratic effects and (2) time-varying observable individual
characteristics. By using a time-varying individual management characteristic such as
MA_SCORE, we are able to produce potentially more reliable inferences relative to a manager
fixed effects research design.
Demerjian et al. (2013) modify the management fixed effects research design by
replacing manager fixed effects with the measure of managerial ability from Demerjian et al.
(2012) to assess the impact of individual managers on earnings quality. Specifically, Demerjian
et al. (2013) show that managerial ability is positively (negatively) associated with accruals
quality and earnings persistence (restatements), suggesting that higher-ability managers are
better able to make complex accounting judgments and estimates that reflect the underlying
nature of the firm’s transactions. Replacing manager fixed effects with the Demerjian et al.
(2012) MA_SCORE allows us to (1) examine an observable manager characteristic, (2) develop
and test directional hypotheses, (3) produce valid inferences generalizable to a broader
population with respect to the relation between managers’ ability to efficiently utilize firm
resources and the variable of interest.
2.2 Hypothesis
We predict that, all else equal, managers possessing greater ability to efficiently utilize
firm resources will engage in greater tax avoidance. Following Demerjian et al. (2012), we
10
define managerial ability as how efficiently managers are able to maximize revenues using their
firms’ limited set of resources. By reducing cash outflows to taxing authorities through tax
avoidance, managers retain resources within the firm. If higher-ability managers’ objective is to
enhance firm value, naturally they focus on maximizing profit and cash flow, and a reduction in
taxes increases profit and after-tax cash flow dollar-for-dollar. While tax avoidance strategies
that defer tax payments to future periods do not reduce income tax expense as reported in firms’
financial statements, temporary tax deferral strategies allow firms to allocate cash in different
ways that can have an indirect effect on profit and cash flow. For example, managers with
greater ability to efficiently utilize resources could increase profit indirectly by investing cash
retained through tax deferral strategies towards revenue-generating activities such as increased
advertising or production. In addition, financially constrained firms could use temporary tax
deferral strategies to generate cash as a less costly substitute for debt financing (Edwards,
Schwab, and Shevlin 2013).
However, there are several reasons why we could fail to find evidence consistent with our
prediction. First, the managerial skills necessary to maximize revenues could be very distinct
from the skills required to identify tax planning opportunities. Even if higher-ability managers
can identify and implement tax planning opportunities, executives could lack the specialized
training and expertise required to implement the tax avoidance strategies and the effort to acquire
this type of expertise could be too costly for high-ability executives to obtain. In addition, the
(potentially uncertain) benefits of tax avoidance could not exceed the costs (e.g., direct costs of
tax planning and implementation and non-tax costs associated with avoidance like political costs,
reputation concerns, etc.), resulting in higher-ability managers choosing to engage in less tax
11
avoidance. Thus, the relation between managerial ability and tax avoidance is an empirical
question.
In light of the discussion above, one might wonder how the skills needed to maximize
revenues from a given set of inputs translate to tax avoidance. One possibility is that managers
themselves (e.g., CFOs or tax directors) possess the technical skills to implement successful tax
avoidance strategies. Even if higher-ability managers lack these skills, they possess a superior
understanding of their firm’s business environment, enabling them to better identify tax
avoidance opportunities that can be implemented by outside consultants or internal staff (Dyreng
et al. 2010). Managers with superior ability to efficiently utilize firm resources can also create a
“tone at the top” that emphasizes maximizing revenues and minimizing costs. The same
incentives to reduce income taxes also exist to reduce operating costs, but the consequences of
reducing operating costs could differ. While cutting operating expenditures such as marketing or
product costs could be viewed as efficiently using limited firm resources, cuts to marketing can
reduce sales and cuts to product inputs can yield lower quality products. In contrast, reducing
taxes has no direct effect on a firm’s sales or product quality, suggesting the reduction of taxes
relative to other types of costs could be particularly appealing to higher-ability managers.
3. Research Design
3.1 Main Analysis
In the management style literature, the dependent variable of interest is regressed on a set
of year, firm, and manager fixed effects (Bertrand and Schoar 2003; Bamber et al. 2010; Dyreng
et al. 2010; Ge et al. 2011; DeJong and Ling 2013). Year fixed effects capture the average impact
of unobservable time-variant economy-wide characteristics on the dependent variable, and firm
12
and manager fixed effects capture the average impact of unobservable time-invariant
characteristics of the firm and the manager. To further isolate the manager effect, the model can
also be expanded to include additional variables that capture the impact of observable time-
varying firm characteristics on the dependent variable.
Demerjian et al. (2013) modify this research design to examine the relation between
managerial ability and earnings quality by replacing manager fixed effects with the Demerjian et
al. (2012) managerial ability score (MA_SCORE). We adopt this approach to examine the
relation between managers’ ability to efficiently utilize firm resources and tax avoidance.
Specifically, we construct our model based on DHM and substitute MA_SCORE for manager
fixed effects when estimating the following equation:
[1] TaxAvoidit = α0 + β1MA_SCOREit + Controlsit + Year fixed effects + Firm fixed effects + εit
A finding of 1 < 0 is consistent with managers with greater ability to efficiently utilize firm
resources engaging in greater tax avoidance.6 When estimating Equation 1 and in all other tests,
we estimate standard errors clustering at the firm level.
3.1.1 Dependent Variables
Our primary dependent variable of interest, CASHETR, is firm i’s cash-based ETR in
year t. There are a wide range of proxies used to capture tax avoidance, and researchers are
cautioned to select the proxy most appropriate for their particular research question of interest
(Hanlon and Heitzman 2010). DHM examine the impact of manager fixed effects on both cash-
based and GAAP-based ETRs. Although our research question builds upon DHM, the main
6 We thank Peter Demerjian, Baruch Lev, and Sarah McVay for making their MA_SCORE publicly available at:
https://community.bus.emory.edu/personal/PDEMERJ/Pages/Download-Data.aspx.
13
focus of our study is on the cash-based ETR because this measure best captures tax strategies
that retain resources within the firm that can be invested in revenue-generating activities.
Specifically, the cash ETR reflects both permanent and temporary tax avoidance, as well as the
tax effects of uncertain tax positions unlikely to be sustained upon tax return audit. In contrast,
GAAP-based ETRs do not reflect tax savings from temporary deferral strategies that reduce cash
tax payments and increase firm resources available for deployment in revenue-generating
activities. For example, the GAAP-based ETR also does not reflect the (1) temporary tax savings
from common book/tax differences like accelerated depreciation; (2) tax benefits from taking
uncertain tax positions that could not be sustained upon audit; and (3) future tax benefits a firm
offsets with a valuation allowance.
Following DHM, CASHETR is measured as cash taxes paid as a percentage of pre-tax
book income before special items. Consistent with prior literature, we require observations to
have positive cash taxes paid and positive pre-tax book income before special items, and we
winsorize CASHETR values at zero and one. We specify our cash-based ETR as a 1-year
measure which is appropriate for our tests because MA_SCORE is also constructed at the firm-
year level. In addition, our research design closely follows DHM who also use a 1-year cash-
based ETR measure. However, Dyreng et al. (2008) note that one-year cash-based ETRs are not
strong predictors of long-run cash-based ETRs, suggesting a one-year measure can be a noisy
proxy for long-run corporate tax avoidance. While some of this concern with respect to our study
is alleviated by the Dyreng et al. (2008) finding that low one-year cash-based ETRs are more
persistent than high one-year cash-based ETRs, we also employ 3-year and 5-year cash-based
ETR measures in our empirical analysis. CASHETR3 (CASHETR5) is defined as the sum of
cash taxes paid in years t through t+2 (t+4) divided by the sum of pre-tax income before special
14
items in years t through t+2 (t+4). We also measure MA_SCORE and the other control variables
contemporaneously in our long-run tax avoidance analyses, averaging each variable over the
same corresponding 3-year and 5-year windows.7
Later in the paper we examine if higher-ability managers engage in tax avoidance
strategies at the riskier and uncertain end of the tax avoidance spectrum. To capture the
likelihood of engaging in tax-sheltering (a riskier tax strategy), we re-estimate Equation 1 using
the predicted probability of tax shelter activity (PRED_SHELTER) from Lisowsky (2010) as the
dependent variable. To capture the amount of tax avoidance generated by uncertain tax positions
(an uncertain tax strategy), we re-estimate Equation 1 using actual and predicted unrecognized
tax benefits (UTB and PRED_UTB, respectively) as the dependent variable. For brevity, we
relegate the definitions of these variables to the Appendix and expand more on what these
alternative measures capture in Section 4.5.
3.1.2 Test Variable
We use the variable MA_SCORE, developed in Demerjian et al. (2012), to capture
managers’ ability to efficiently deploy firm resources. The intuition underlying this measure is
based on how efficiently managers can convert resources (e.g., capital, labor, and intangible
assets) into revenues relative to the firm’s industry competitors, with higher-ability managers
being able to generate a higher rate of output from a given set of inputs. Demerjian et al. (2012)
use data envelopment analysis (DEA) to estimate firm efficiency at the industry-year level by
comparing the sales generated by each firm conditional on a vector of inputs (cost of goods sold,
7 We require the sum of cash taxes paid and pretax income before special items to be positive over these 3-year and
5-year windows. We also reset CASHETR3 and CASHETR5 values less than zero to zero and values greater than
one to one.
15
SG&A expenses, net PP&E, net operating leases, net R&D, goodwill, and other intangibles).8
Specifically, the authors conduct DEA at the industry-year level (where industry is defined using
the Fama-French 48 classifications (Fama and French 1997)) by solving the following
optimization problem shown below in equation 2a:
[ ]
The DEA optimization determines a firm-specific vector of optimal weights on the seven
input variables by comparing the inputs of firm i to the inputs of all other firms within the same
industry-year and computes a firm efficiency score θ which takes a value between 0 (for the least
efficient firms) and 1 (for the most efficient firms). To isolate the portion of the efficiency score
attributable to the managerial team, Demerjian et al. (2012) then estimate the Tobit regression
shown below in equation 2b, regressing firm efficiency scores by industry on a set of firm-level
characteristics. After taking into account the effect of firm-level characteristics that explain firm
efficiency, MA_SCORE is constructed using the unexplained portion of θ (e.g., the information
in the residuals) as a measure of managers’ ability to efficiently convert internal firm resources
into revenues.9
[2b] θit = α0 + β1Ln(TotalAssetsit) + β2MarketShareit+ β3PositiveFreeCashFlowit + β4Ln(Ageit)
+ β5BusinessSegmentConcentrationit + β6ForeignCurrencyIndicatorit
+ Year Fixed Effects + εit
Demerjian et al. (2012) validate their measure in multiple ways and also demonstrate the
measure is superior to other proxies for managerial ability used in prior literature (e.g., historical
stock returns, accounting-based performance, and CEO media citations). First, they correlate
MA_SCORE with CEO pay and stock returns. Second, they show a positive stock market
8 Note that inputs and outputs are measured in pre-tax dollars, mitigating the concern that MA_SCORE and tax
avoidance are mechanically related and biasing against finding a significant relation between these variables. 9 See Demerjian et al. (2013) Appendix A for additional information regarding Equation 2b.
16
reaction to CEO turnover announcements when a higher-ability CEO replaces a lower-ability
CEO (and a negative stock market reaction when the new CEO has lower ability). Finally, the
authors find that hiring a CEO with greater (lesser) ability than the firm’s former CEO is
associated with improvements (declines) in future firm performance.
3.1.3 Control Variables
We include time-varying firm-level characteristics known to be associated with tax
avoidance as control variables in Equation 1 to reduce the possibility that MA_SCORE is simply
capturing the effect of these characteristics on tax avoidance. Following DHM, we include the
following tax avoidance control variables in Equation 1: research and development expense
(R&D), advertising expense (AD), capital expenditures (CAPX), leverage (LEV), foreign
operations (FOREIGN), firm size (SIZE), and intangible assets (INTANG). Prior research
generally finds that cash-based ETRs are decreasing in R&D, CAPX, LEV, FOREIGN, and
INTANG, and increasing in AD (Chen et al. 2010; Dyreng et al. 2010; Rego and Wilson 2012).
We also include a variable that captures net operating loss utilization (NOL_DECREASE) and
predict a negative relation between this variable and cash-based ETRs.10
All variables are defined
in the Appendix and all continuous variables are winsorized at the 1st and 99
th percentiles. In
addition, we also include year fixed effects and firm fixed effects in Equation 1, eliminating the
possibility that MA_SCORE picks up the effects of either macro-economic characteristics that
affect all firms in a particular year or firm characteristics that do not vary over time. Thus we
10
Rego and Wilson (2012) use pre-tax return on assets as a proxy for tax planning when modeling tax avoidance. In
addition, accounting-based measures similar to pre-tax return on assets have been used as proxies for managerial
ability (e.g., Baik et al. 2011). We argue that higher-ability managers engage in tax planning strategies that enable
the more efficient deployment of firm resources. Thus, we do not include pre-tax return on assets in Equation 1 to
avoid controlling for the effects of tax planning captured by MA_SCORE. Untabulated tests reveal that pre-tax
return on assets and MA_SCORE are positively correlated (Spearman coefficient=0.33). When we include the
variable as an additional control in Equation 1, the coefficients on pre-tax return on assets and MA_SCORE are both
negative and significant (p<0.01).
17
identify the relation between corporate tax avoidance and managerial ability using firm-specific
variation across time in the two variables.
3.2 Isolating the Effect of a Single Manager versus the Managerial Team
One potential limitation of the MA_SCORE measure is that it captures both managers’
ability to efficiently utilize firm resources and all firm characteristics not explicitly included in
Equation 2b. Demerjian et al. (2012) address this limitation by demonstrating that MA_SCORE
is associated with manager fixed effects, reducing the concern that the residual simply captures
firm characteristics omitted from Equation 2b. To provide stronger evidence of MA_SCORE
capturing the effects of individual managers, we employ a difference-in-difference research
design that exploits CEO turnovers to further rule out the concern that MA_SCORE is capturing
firm and not manager characteristics. If MA_SCORE does in fact capture a manager effect and
not a firm effect, we should observe a change in tax avoidance after a new CEO with differing
ability joins a firm. In contrast, we should fail to observe a significant difference in the relation
between changes in tax avoidance and managerial ability following CEO turnovers if
MA_SCORE simply captures firm characteristics.11
We modify Equation 1 and estimate the following regression to examine the association
between changes in tax avoidance and changes in managerial ability following CEO turnovers:
[3a] ΔCASHETR3it = α0 + β1ΔMA_SCORE3it + β2TURNOVERit
+ β3ΔMA_SCORE3it*TURNOVERit + ΔControlsit + Year fixed effects + εit
11
We focus on CEOs and not CFOs in this analysis for two reasons. First, CEOs generally have the most influence
setting the “tone at the top,” the effects of which cascade down to other managers within the organization (Feng, Ge,
Luo, and Shevlin 2011). Second, we rely on Execucomp data to identify managerial turnover events. Execucomp
collects data relating to the five most highly compensated employees, which includes the CEO in almost every firm.
In contrast, other types of executives (e.g., CFOs) are not uniformly among the five most highly compensated
employees across Execucomp firms over time, resulting in the potential misclassification of a treatment firm (e.g., a
firm with a CFO departure) as a control firm (e.g, a firm without a CFO departure).
18
The dependent variable in Equation 3a, ΔCASHETR3, is defined as the difference between the
3-year cash ETR in t+1 through t+3 and the 3-year cash ETR in t-3 though t-1 for firm i.
ΔMA_SCORE3 is the difference between firm i’s managerial ability score summed over t+1
through t+3 (which reflects the new CEO’s ability) and firm i’s managerial ability summed over
t-3 though t-1 (which reflects the prior CEO’s ability). We add the indicator variable
TURNOVER, which is set equal to one if a CEO departs firm i in year t and zero otherwise. We
omit the turnover year from our analysis to control for the fact that CEOs depart on various dates
throughout year t. We also restrict the sample to exclude any observations with more than one
CEO turnover occurring from t-3 through t+3.
We are interested in the coefficient on the interaction of ΔMA_SCORE3 and
TURNOVER, and β3 < 0 is consistent with a higher-ability CEO engaging in greater tax
avoidance relative to his lower-ability predecessor (or a lower-ability CEO engaging in less tax
avoidance relative to his higher-ability predecessor). The control variables are the same as
presented in Equation 1 but measured as the difference between their values summed from t+1
through t+3 and their values summed from t-3 though t-1. We denote these controls adding the
prefix ‘Δ’ and the suffix ‘3’ to each variable (i.e., ΔR&D3, ΔAD3, ΔCAPX3, ΔLEV3,
ΔFOREIGN3, ΔSIZE3, ΔINTANG3, and ΔNOL_DECREASE3).
We also control for the effects of potentially correlated omitted variables related to CEO
turnover and tax avoidance by using propensity score matching (PSM). Following prior literature
(e.g., Desai et al. 2006; Skaife et al. 2013), we estimate a logistic regression modeling the
probability of a CEO departure in year t as a function of firm characteristics including size
(SIZE), leverage (LEV), growth (GROWTH and BTM), performance (INDROA), and the
monitoring environment (ANALYST and INST_OWN) using Equation 3b shown below. See the
19
Appendix for detailed variable definitions. We include MA_SCORE in Equation 3b to ensure the
ability of the managerial team in year t is not a correlated omitted variable.
[3b] TURNOVERit = α0 + β1SIZEit + β2INDROAit + β3GROWTHit + β4LEVit + β5BTMit
+ β6ANALYSTit + β7INST_OWNit + β8MA_SCOREit + Year fixed effects + εit
After estimating the probability of CEO turnover, we match without replacement each
treatment firm (TURNOVER=1) with a control firm (TURNOVER=0) using a caliper of three
%. By using PSM, we impose no assumptions about the functional form of the relation between
the selection variables with the outcome variable. However, the results from PSM can be
sensitive to the variables included in the regression used to estimate propensity scores.
Therefore, we also use OLS to estimate Equation 3a on the sample of Execucomp firms with
data available to estimate Equation 3b. The difference-in-difference design we use in both the
PSM and OLS estimations controls for the effects of any time-invariant correlated omitted
variables that are not included in the models. In addition, using PSM mitigates the Fee et al.
(2013) critique that managerial style is observable only after endogenous turnover decisions, as
PSM controls for factors found to be associated with forced CEO dismissals.
4. Findings
4.1 Sample and Summary Statistics
Table 1 shows the distribution of firm-years in our sample. We begin our analysis in 1994
to allow for the adoption of SFAS 109 (i.e., a consistent financial reporting regime) and end our
analysis in 2010 because this is the latest year in which the MA_SCORE data is available for the
full set of Compustat firms at the time of our study. We require firm-year observations to have
non-missing values for the variables required to estimate Equation 1, yielding a sample of 44,616
firm-year observations that are approximately evenly distributed across our 17-year time period.
20
Table 2 reports descriptive statistics for our regression variables. The distribution of
CASHETR and the control variables are comparable with descriptive statistics reported in prior
studies (Dyreng et al. 2010; Edwards et al. 2013). The mean CASHETR value is 27.5 %, with an
interquartile range of 8.8 to 36.9 %. Consistent with Dyreng et al. (2008), values for our long-run
ETR measures (CASHETR3 and CASHETR5) are higher than the one-year cash-based ETR
measure. The firm-years in our sample are smaller (SIZE) than those in DHM’s sample because
we use firms from the Compustat universe while DHM limit their analysis to Execucomp
firms.12
Table 3 presents the Pearson correlations between our variables of interest. We find a
negative and significant correlation between MA_SCORE and CASHETR, CASHETR3, and
CASHETR5 (p<0.01), consistent with our prediction.13
We also find that MA_SCORE is
positively and significantly correlated with proxies capturing the riskier and uncertain ends of the
tax avoidance spectrum (e.g., PRED_SHELTER, UTB, and PRED_UTB).14
Overall, the
correlations between MA_SCORE and our tax avoidance proxies provide initial evidence
consistent with our predictions. The majority of the control variables exhibit significant
correlations with our tax avoidance proxies, highlighting the importance of controlling for these
factors in our multivariate tests.
4.2 Main Analysis
12
While MA_SCORE values in our sample are slightly higher than those reported in Demerjian et al. (2012), this
difference appears to be primarily attributable to eliminating observations from our sample with negative pre-tax
income before special items to calculate the cash ETR. Our sample firms have greater profitability relative to the
Demerjian et al. (2012) sample, which corresponds to higher managerial ability. The standard deviation of
MA_SCORE is comparable to Demerjian et al. (2012), suggesting MA_SCORE values are distributed similarly
within our sample. 13
Consistent with our multivariate tests of long-run ETRs, we sum MA_SCORE over the 3-year (5-year) period
corresponding to CASHETR3 (CASHETR5) and report these pairwise correlations in Table 3. 14
We discuss in more detail what underlying constructs these alternative proxies for tax avoidance capture and their
relation to managerial ability later in the paper.
21
Table 4 presents the results from estimating Equation 1. In Column 1 of Panel A we
present the results from a baseline model which regresses CASHETR on the control variables
from DHM and year fixed effects. Most of the control variable coefficients are significant in the
predicted direction. For example, we find that CASHETR is decreasing in research and
development expenses (R&D), capital expenditures (CAPX), leverage (LEV), and NOL
utilization (NOL_DECREASE). In Column 2 we add firm fixed effects to the model. While most
of the time-varying firm characteristics become insignificant in Column 2, the adjusted R2
increases from 1.26 to 24.49 % relative to Column 1, consistent with firm fixed effects
subsuming much of the explanatory power of these firm-level characteristics.
When we include MA_SCORE in Column 3 its coefficient is negative and highly
significant (p < 0.01), consistent with our prediction that higher-ability managers engage in tax
avoidance that increases firm resources (in this case, cash) available for revenue-generating
activities. The results also suggest that this dimension of managerial ability has an economically
significant effect on tax avoidance. Using the coefficient estimates presented in Column 3 and
holding all control variables constant, moving from the lower to upper quartile of managerial
ability is associated with a 3.16 % reduction in a firm’s 1-year cash-based ETR.
In Panel B we re-estimate Equation 1 using cash-based ETRs measured over longer time
periods. Column 1 presents our analyses using CASHETR3 as the dependent variable. Because
CASHETR3 is measured over the period t through t+2, we also average the values of
MA_SCORE and each control variable over the same time period in our regression analysis so
our dependent and independent variables are measured concurrently. Column 2 presents the
results from repeating this analysis using a 5-year cash-based ETR calculated over the period
from t through t+4 (CASHETR5) and measuring MA_SCORE and all control variables over the
22
same 5-year window. In both columns, we continue to find a negative and significant coefficient
on MA_SCORE (p<0.01), consistent with higher-ability managers engaging in tax avoidance
strategies which reduce cash tax payments over the long-run.15
We also continue to find that
managerial ability has an economically significant effect on tax avoidance. Using the coefficient
estimates presented in Column 1 (2) and holding all control variables constant, moving from the
lower to upper quartile of managerial ability is associated with a 3.70 (4.39) % reduction in a
firm’s 3-year (5-year) cash-based ETR.
4.3 Robustness Tests
Having provided evidence that managerial ability is associated with greater tax
avoidance, we next perform several robustness tests to help rule out potential alternative
explanations for our findings. The results of these tests are shown in Table 5 where we report the
coefficient and t-statistics on MA_SCORE but suppress the coefficients and t-statistics on all
control variables for brevity.
One potential alternative explanation for our results is that the negative association
between MA_SCORE and cash-based ETRs is driven by firms with greater resources available
for tax planning that also belong to industries with lower effective tax rates. Balakrishnan,
Blouin, and Guay (2012) highlight the importance of industry membership and resources
available for tax planning in assessing the extent of a firm’s tax avoidance. To ensure our
primary results are not driven by firms belonging to industries with lower ETRs and with greater
resources available for tax planning, we re-estimate Equation 1 using the Balakrishnan et al.
(2012) industry-size adjusted cash ETR measure. Specifically, we adjust CASHETR for firm i in
15
Untabulated results confirm our findings are robust to using a GAAP-based ETR measure as the dependent
variable in Table 4. Using a one-year GAAP ETR yields a MA_SCORE coefficient of -0.30 (p<0.05), while three-
and five-year GAAP ETRs yield MA_SCORE coefficients of -0.12 (p<0.01) and -0.16 (p<0.01), respectively.
23
year t by subtracting the mean CASHETR corresponding to the same size quintile and industry.16
Column 1 of Table 5 shows that we continue to find a negative and significant coefficient on
MA_SCORE (p<0.01).
Another possible alternative explanation for our main findings is that MA_SCORE is
simply capturing better corporate governance. If better governance structures allow firms to
identify, hire, and retain managers with higher ability to efficiently utilize resources, it could be
that the corporate governance structure of a firm is driving our results. We conduct several
robustness tests controlling for corporate governance to help rule out this possible alternative
explanation. Because it is difficult to capture the construct of corporate governance with a single
variable, we employ multiple proxies for corporate governance commonly used in the tax
avoidance literature. Following Desai and Dharmapala (2006, 2009), we control for corporate
governance using the %age of shares held by institutional owners (INST_OWN). In addition, we
also control for corporate governance using the G-Index from Gompers et al. (2003) following
Hanlon and Slemrod (2009) and Wilson (2009). Our finding that managers with greater ability to
utilize firm resources engage in more tax avoidance is robust to the inclusion of these corporate
governance controls. In Column 2 of Table 5 we find that INST_OWN is positively related to
CASHETR (p<0.05) and the MA_SCORE coefficient remains negative and significant (p<0.01).
In Column 3 we use G_INDEX as our governance proxy. Note that the inclusion of this variable
reduces our sample by 75%, as G_INDEX is only available for S&P 1500 firms through 2007.
The MA_SCORE coefficient again remains negative and significant (p<0.01) after controlling
for corporate governance using these proxies, and we fail to find a relation between CASHETR
and the G_INDEX (p>0.10). Replacing G_INDEX with the E-Index variable from Bebchuk et al.
16
Size quintiles are formed based on total assets and industry membership is defined using the Fama-French 48
industry classifications.
24
(2009), which is also only available for S&P 1500 firms through 2007, yields similar results
(untabulated). These findings help to rule out corporate governance as a correlated omitted
variable in our study.
We also consider the possibility that incentive compensation is a correlated omitted
variable. Several studies have shown a relation between incentive compensation and cash-based
ETRs (Rego and Wilson 2013; Gaertner 2013). If high-ability managers receive greater incentive
compensation, it is possible that the MA_SCORE coefficient is simply capturing the relation
between incentive compensation and cash effective tax rates. In addition, Hanlon and Shevlin
(2002) note that stock options can generate large tax return deductions, which lowers cash taxes
paid (the CASHETR numerator). However, because stock options were not recognized as an
income statement expense for most of our sample period, pre-tax book income (the CASHETR
denominator) does not reflect stock options compensation. This asymmetric treatment of stock
options compensation mechanically lowers our CASHETR measure.
To alleviate the concern that incentive compensation is a correlated omitted variable,
following DHM we include an estimate of the tax return deduction generated from stock option
exercises as an additional control variable. EST_OPTION is defined as the average annual value
realized from option exercises by the top executives, grossed up by the fraction of options owned
by the covered executives, and scaled by average total assets. Including this variable
significantly reduces our sample to 10,366 firm-year observations, as EST_OPTION requires
Execucomp data available only through 2006. Column 4 of Table 5 shows that the
EST_OPTION coefficient is negative and significant (p < 0.01), consistent with stock option
deductions lowering firms’ cash ETRs. More importantly, the MA_SCORE coefficient remains
negative and highly significant (p < 0.01) in the presence of EST_OPTION, indicating this type
25
of incentive compensation is not driving our main finding that higher-ability managers engage in
greater tax avoidance.
Lastly, it is possible that the relation between tax avoidance and managerial ability is
driven by time-invariant unobservable manager characteristics. While we have discussed the
advantages of our research design relative to a manager fixed effects research design, including
manager fixed effects in our analysis (albeit in a smaller subsample) helps to confirm whether
managerial ability has incremental explanatory power for tax avoidance. Column 5 presents the
results from re-estimating Equation 1 after including both MA_SCORE and manager fixed
effects using a sample 17,929 firm-year observations representing 2,279 unique firms. Following
DHM, we restrict this analysis to a sample of executives moving across firms over time with
each executive holding a position at each firm for a minimum of three years. After controlling
for the firm characteristics identified in Equation 1 and year, firm, and executive fixed effects,
we continue to find a negative and significant coefficient on MA_SCORE (p<0.01). This result
indicates that the MA_SCORE measure captures non-stationary manager effects (e.g., variation
in managerial ability over time, managerial learning, and synergies created by the joint effects of
managerial teams that work together to efficiently deploy firm resources).
4.4 Difference-in-Difference Tests Surrounding CEO Turnovers
Table 6 presents the results from our CEO turnover analysis. Difference-in-difference
tests help to rule out the possibility of correlated omitted variables driving our main findings. We
first model the determinants of CEO turnover and compute propensity scores to identify control
firms (i.e., TURNOVER = 0) with the closest propensity as treatment firm-years (i.e.,
26
TURNOVER = 1) to replace the CEO. Column 1 of Panel A presents the results from estimating
the CEO turnover model specified in Equation 3b. We use the propensity scores generated from
the regression results presented in Column 1 of Panel A to identify a matched sample of 557
treatment and 557 control observations. Treatment and control firms are matched without
replacement using a caliper of three %. Column 2 of Panel A shows that treatment firm mean
values are statistically indistinguishable from control firm mean values for seven of the eight
variables included in Equation 3b, consistent with the equation yielding a matched sample
capable of providing us with reliable statistical inferences.17
Column 1 of Panel B presents the results from estimating the difference-in-difference
regression specified in Equation 3a using the PSM sample of 1,114 matched observations. The
negative and significant coefficient on the interaction of ΔMA_SCORE3 and TURNOVER
indicates that a new CEO with higher ability hired by firm i is able to avoid more taxes than his
lower-ability CEO predecessor at firm i. Holding all else constant, moving from the lower to
upper quartile of the change in managerial ability following a CEO turnover results in a 3.32 %
decline in a firm’s CASHETR during the 3 years following the turnover relative to the 3-year
period prior to the predecessor CEO departure. We acknowledge that PSM samples can be
sensitive to the variables included in the first-stage regression, so as a sensitivity test we re-
estimate Equation 3a using of the full sample of Execucomp firms with available data yielding a
sample of 3,928 observations. These results are presented in Column 2 and yield similar
inferences consistent with higher-ability CEOs engaging in greater tax avoidance relative to their
lower-ability predecessors. The findings in Table 6 corroborate our main finding that higher-
ability managers are able to avoid more taxes.
17
While the 1.5% difference in average INST_OWN between the treatment group and control group is marginally
statistically significant (p=0.09), the difference is not economically significant.
27
4.5 Do Higher-Ability Managers Engage in More Risky and Uncertain Tax Avoidance
Strategies?
Thus far, our empirical tests suggest that managers with superior ability to efficiently
deploy firm resources engage in greater tax avoidance that reduces cash payments to tax
authorities. Reducing cash tax payments can be accomplished in a variety of ways, and we are
particularly interested in whether higher-ability managers use strategies generally perceived to be
at the riskier and uncertain end of the tax avoidance spectrum. Therefore, we examine the
relation between managerial ability and both tax sheltering and uncertain tax positions by re-
estimating Equation 1 substituting proxies for these constructs as the dependent variable.
Hanlon and Heitzman (2010) note that the “problem with tax shelters is that it is almost
always ambiguous whether the transaction is [legally] permissible or not” (p.137, footnote 39),
making transactions with the potential to be deemed by the IRS or U.S. Tax Court to be tax
shelters one of the riskiest types of tax avoidance strategies. We operationalize tax sheltering
(PRED_SHELTER) using the predicted value from the tax shelter model in Lisowsky (2010).18
See the Appendix for additional details regarding how this variable is constructed. Column 1 of
Table 7 shows the results from estimating Equation 1 after replacing CASHETR with
PRED_SHELTER from Lisowsky (2010). The model’s adjusted R2 of 0.92 suggests our
independent variables provide significant explanatory power. The MA_SCORE coefficient is not
18
Research that examines tax sheltering often relies on limited samples. For example, Graham and Tucker (2006)
identify 43 unique tax shelter firms (152 tax shelter firm-years) during 1975-2000 and Wilson (2009) identifies 59
unique tax-shelter firms (215 tax-shelter firm-years) during 1975-2002. Lisowsky (2010) identifies 211 unique firms
and 267 firm-years using listed and reportable transactions data required to be disclosed to the IRS via Form 8886
during 2000-2004. Lisowsky (2010) builds upon Wilson’s (2009) tax shelter model by including additional control
variables that capture the tax shelter characteristics discussed in the Treasury (1999) conceptual framework, and
demonstrates that his model provides significant incremental explanatory power relative to Wilson’s (2009) model
in predicting the likelihood of tax sheltering. We thank Pete Lisowsky for sharing his tax shelter probability data.
28
significantly different from zero (p>0.10), consistent with higher-ability mangers being no more
or less likely to engage in tax sheltering activities relative to lower-ability managers.
We also examine the relation between managerial ability and unrecognized tax benefits
(UTBs). Managers face inherent uncertainty in applying tax-related legislative statutes,
administrative practices, and judicial case law to determine if, when, where, and at what rate an
item is taxable or deductible for tax purposes. UTBs reflect the contingent liabilities for the tax
benefits not currently recognized in the financial statements related to uncertain tax positions.
Thus, UTBs represent tax avoidance strategies where a manager is uncertain as to the ultimate
outcome of the tax position. In addition, Lisowsky et al. (2013) find that UTBs are positively
associated with confidential tax shelter disclosures, suggesting UTBs are an alternate means of
identifying tax strategies at the riskier end of the tax avoidance spectrum.19
UTB data are available beginning in 2007, so we estimate Equation 1 using the subset of
firm-years from 2007 through 2010 with non-missing UTB values. In Column 2 of Table 7 we
estimate Equation 1 with UTB values scaled by total assets as the dependent variable. Again, the
model’s adjusted R2 of 0.83 highlight’s the model’s significant explanatory power. We find a
positive relation between managerial ability and UTBs, consistent with higher-ability managers
engaging in uncertain tax positions that could be disallowed by the taxing authorities upon tax
return audit.
As noted above, one limitation of using actual UTBs as a proxy for tax avoidance
activities at the uncertain end of the tax avoidance spectrum is that UTB data are not required to
19
We acknowledge that whether these two variables capture risk is an open question in the tax literature.
Specifically, Guenther et al. (2012) find that future stock return volatility is unrelated to actual or predicted UTBs,
and Hutchens and Rego (2012) find that implied cost of equity capital measures are unrelated to predicted tax
sheltering and unrelated to UTBs after controlling for weak corporate governance. Consistent with Hanlon and
Heitzman (2010), we view these two variables as capturing tax strategies at the riskier/uncertain end of the
avoidance spectrum, regardless of whether they are associated with future stock return volatility or implied cost of
equity capital measures.
29
be disclosed until 2007, resulting in a substantial reduction in sample size in Column 2 relative to
Column 1. To overcome this data limitation we rely upon the UTB prediction model from Cazier
et al. (2009) as used in Rego and Wilson (2012) to estimate predicted UTB values (PRED_UTB)
for our entire sample period (1994-2010). We regress UTB values from 2007 through 2012 on
UTB determinants (pre-tax return on assets, firm size, foreign operations, R&D, leverage,
market-to-book ratio, SG&A, and sales growth) and apply these coefficient estimates to data
from our sample of firm-years. See the Appendix for additional details regarding the UTB
prediction model.
We present the results from re-estimating Equation 1 with PRED_UTB as the dependent
variable in Column 3.20
The adjusted R2 of 0.921 suggests our model provides significant
explanatory power, and we again find evidence consistent with higher-ability managers engaging
in uncertain tax positions that could be disallowed upon tax return audit.21
Results are robust to
using the natural log of UTB and PRED_UTB (Lisowsky et al. 2013). In sum, the results
presented in Table 7 suggest that higher-ability managers do not use strategies perceived to be at
the riskiest end of the tax avoidance spectrum (e.g., tax sheltering) but do use strategies where
the ultimate outcome of the tax position is uncertain (e.g., uncertain tax positions).22
20
Table 3 shows that PRED_SHELTER, UTB, and PRED_UTB exhibit statistically significant correlations
(p<0.01) and much weaker correlations with CASHETR (0.0339, -0.0605, and -0.0418, respectively). The low
correlations indicate these proxies pick up characteristics of tax avoidance distinct from those captured by the
CASHETR specifications in our main analyses. 21
The adjusted R2 and t-statistics for some control variables in Columns 1 and 3 are very large due to inclusion of
the same or similar highly correlated variables in the underlying prediction model used to estimate tax shelter
prediction probabilities and expected UTBs. We include these control variables in the data tabulated for Table 7 to
use the same set of controls across regression specifications reported throughout the paper. Untabulated tests
confirm that the sign and significance of the MA_SCORE coefficient remains unchanged when variables used in the
underlying prediction model (i.e., SIZE for Column 1 and R&D and FOREIGN for Column 3) are omitted from the
regression specifications. 22
Including firm fixed effects adds significant explanatory power to the models reported in Table 7. Specifically, the
incremental adjusted R2 attributable to firm fixed effects is 0.2425 for the predicted tax shelter model, 0.6956 for the
UTB model, and 0.1596 for the predicted UTB model.
30
4.6 Cross-sectional Tests
Firms’ operating and financing characteristics reflect managerial decisions and are
important variables that explain variation in tax avoidance. To better understand the ways
through which higher-ability managers avoid taxes, we examine whether higher-ability managers
make more tax-efficient operating and financing decisions. For example, managers invest in
R&D, make capital expenditures, and structure foreign operations to enhance their firm’s
operations. We expect higher-ability managers to be better at exploiting the tax benefits and
claiming more aggressive positions on their tax returns related to their operating and financing
decisions (e.g., deem questionable research-related expenditures as qualifying for the R&D tax
credit, engage in favorable transfer pricing that might be deemed not at arms’ length, etc.), which
lowers their firm’s cash ETR and increases their firm’s UTB. Therefore, we expect to observe
cross-sectional variation in the relation between operating and financing characteristics and tax
avoidance in firms with higher-ability managers.
We conduct cross-sectional tests examining this conjecture by re-estimating Equation 2
and allowing the parameter estimates on each time-varying firm characteristic to vary with
managerial ability. Specifically, we create the binary indicator variable MA_HIGH, which is set
equal to one when MA_SCORE is above the sample median, and set equal to zero otherwise.
Each firm characteristic control variable in Equation 2 is interacted with MA_HIGH. The
interaction terms capture differences in how high-ability managers achieve tax avoidance
through the operating and financing decisions these firm characteristics represent.
Regression results are reported in Table 8. In addition to CASHETR, we also use
PRED_UTB as an alternative dependent variable to capture more risky and uncertain types of tax
31
avoidance activities.23
CASHETR is the dependent variable in Column 1, and we find that the
MA_HIGH coefficient is negative and significantly associated with CASHETR (p < 0.05).
PRED_UTB is the dependent variable in Column 2 and again we find that the MA_HIGH
coefficient is positive and significantly associated with PRED_UTB (p < 0.01). These results are
consistent with higher-ability managers engaging in greater tax avoidance. The interaction
coefficients in both columns show significant cross-sectional differences in the relation between
firm characteristics and tax avoidance in firms with higher-ability managers, consistent with the
conjecture that higher-ability managers make more tax-efficient operating and financing
decisions.
The coefficient on the R&D*MA_HIGH interaction term is negative and significant in
Column 1 (p < 0.05) and positive and significant in Column 2 (p < 0.01). These results indicate
that higher-ability managers reduce cash ETRs and increase UTBs through investments in R&D.
The coefficient on CAPX*MA_HIGH is negative and significant in Column 1 (p < 0.05),
consistent with capital expenditures reducing cash ETRs in firms where managerial ability is
high. Higher-ability managers in firms with greater leverage also have significantly lower cash
ETRs and higher UTBs (p < 0.05).24
Finally, the coefficient on FOREIGN*MA_HIGH is
negative and significant in Column 1 (p < 0.05), suggesting that higher-ability managers
structure their firm’s foreign operations to significantly reduce cash tax payments.
23
We do not consider SHELTER as a dependent variable in our cross-sectional tests because we find no relation
with this variable and MA_SCORE in Table 7. We focus on PRED_UTB as opposed to UTB because both measures
generate similar inferences in Table 7, but UTB results in a sample size loss of nearly 75 percent relative to
PRED_UTB. 24
Note that while debt is considered a tax shield because interest payments are deductible while dividend payments
are not, interest reduces both cash taxes paid (the CASHETR numerator) and pre-tax book income (the CASHETR
denominator), making its effect on CASHETR ambiguous. However, leverage is positively correlated with other
capital structure decisions such as off-balance sheet financing activities (Callahan et al. 2012) which reduce firms’
cash ETRs and increase UTBs (Mills and Newberry 2005).
32
These tests highlight R&D activities, capital expenditures, leverage, and foreign
operations as important avenues through which tax avoidance is achieved by higher-ability
managers. Taken together, the results of the cross-sectional tests reported in Table 8 provide
evidence that higher-ability managers structure their operating and financing decisions in a
manner which lowers their tax payments. In addition, these findings help to further rule out
concerns that our results are driven by correlated omitted variables, as these correlated omitted
variables would have to explain our main results as well as all significant interactions in our
cross-sectional tests.
5. Conclusion
We examine whether managers with superior ability to efficiently utilize firm resources
engage in greater corporate tax avoidance. High-ability managers “maximize the efficiency of
[firm] resources used for revenue-generating purposes” (Demerjian et al. 2012, p.1), and
reducing cash outflows through tax avoidance is consistent with managers making decisions that
more efficiently deploy firms’ internal resources. We find evidence consistent with higher-ability
managers engaging in more tax avoidance that reduces their firms’ cash tax payments.
Specifically, moving from the lower to upper quartile of managerial ability is associated with a
3.16 % reduction in a firm’s one-year cash-based ETR and a 3.70 (4.39) % reduction in a firm’s
three-year (five-year) cash-based ETR. Our difference-in-difference tests using changes in tax
avoidance surrounding CEO turnovers provide further evidence that higher-ability managers
engage in greater tax avoidance. Finally, we find that managers with greater ability to efficiently
utilize firm resources are no more likely to engage in tax sheltering but do recognize greater
UTBs. This suggests that higher-ability managers utilize some tax strategies at the uncertain end
33
of the tax avoidance spectrum. Our cross-sectional tests reveal that higher-ability managers
structure their operating (i.e., R&D, capital expenditures, and foreign operations) and capital
structure decisions more tax-efficiently relative to lower-ability managers.
We contribute to the tax avoidance literature by identifying managerial ability as a new
and economically significant determinant of corporate tax avoidance. Our research answers the
call by Hanlon and Heitzman (2010) to further explore the effect of individual managers on
corporate tax avoidance. We compliment and build upon the findings in Dyreng, Hanlon, and
Maydew (2010), who demonstrate a manager-specific effect on corporate ETRs using a
“manager fixed effects” research design. While a general finding that “managers matter” is an
important contribution to the literature, this research design does not (1) capture observable
managerial characteristics, (2) allow for directional predictions regarding the effect of managers
on tax avoidance, or (3) provide large-sample evidence regarding the relation between manager
characteristics and tax avoidance. By using the managerial ability score developed in Demerjian
et al. (2012), we overcome these limitations and are not subject to the econometric challenges of
a manager fixed effects research design raised in Fee et al. (2013). Our cross-sectional findings
identify avenues through which higher-ability managers make more tax-efficient operating and
financing decisions, contributing to our understanding of the specific decisions managers make
that achieve tax avoidance. Taken together, our results confirm the existence of a manager effect
on tax avoidance documented in DHM, and provide new evidence on the types of manager and
firm characteristics that explain corporate tax avoidance activities.
We also contribute to the managerial ability literature. DHM fail to find evidence of a
systematic relation between observable manager characteristics and tax avoidance, leading the
authors to conclude that “the executive effects on tax avoidance appear to be idiosyncratic”
34
(p.1165). Our finding that managers’ ability to efficiently deploy firm resources for revenue-
generating activities is an economically important manager characteristic that affects corporate
tax avoidance. Many of the documented determinants of tax avoidance (e.g., industry, capital
structure, location of operations, etc.) are the result of years of strategic decision-making, and
quickly changing these firm-level characteristics to achieve additional tax avoidance can be
difficult. In contrast, our findings suggest that higher-ability managers are better able to identify
and implement activities that lead to lower cash-based ETRs relative to other industry
competitors operating in similar environments. Our findings suggest higher-ability managers
better align their tax and business strategies, which should be of particular interest to board
members when considering the costs and benefits of hiring executives.
35
Appendix
Dependent Variables
CASHETRit
Cash taxes paid (txpd) divided by pre-tax book income before special items
(pi-spi). Observations with (pi-spi) < 0 are set to missing. Values less than zero
are reset to zero and values greater than one are reset to one.
CASHETR3it
Average of cash taxes paid (txpd) in periods t through t-2 divided by pre-tax
book income before special items (pi-spi) in periods t through t-2.
Observations with a three-year average of pre-tax book income before special
items < 0 are set to missing. Values less than zero are reset to zero and values
greater than one are reset to one.
CASHETR5it
Average of cash taxes paid (txpd) in periods t through t-4 divided by pre-tax
book income before special items (pi-spi) in periods t through t-4.
Observations with a five-year average of pre-tax book income before special
items < 0 are set to missing. Values less than zero are reset to zero and values
greater than one are reset to one.
Industry-size
adjusted
CASHETR it
Difference between the cash ETR for firm i and the mean cash ETR for the
corresponding set of firms belonging to the same Fama-French 48 industry and
asset-size quintile in year t (Balakrishnan et al. 2012)
ΔCASHETR3it Difference between the 3-year cash ETR in t+1 through t+3 and the 3-year
cash ETR in t-3 though t-1
PRED_SHELTERit
Predicted value from the following logit regression presented in Column 5 of
Table 4 in Lisowksy (2010):
TaxShelterIndicatorjt = 8.502 + 0.136*BTDjt + 0.424*DAPjt − 0.671*Leveragejt
+ 1.311*Sizejt + 2.425*ROAjt + 8.673*ForeignIncomejt − 1.026*R&Djt
+ 1.039*TaxHavenjt + 0.017*GAAPETRjt-1+ 0.969*EqEarnjt – 0.731*MezzFinjt
+ 3.008*Big5jt + 0.952*Litigationjt + 0.100*NOLjt
Prediction values are bound between zero and one by construction. See Table
2 of Lisowsky (2010) for additional details and variable definitions.
UTBit Unrecognized tax benefits (txtubend) deflated by total assets (at)
PRED_UTBit
Calculated by applying the coefficients from Equation 1 from Rego and
Wilson (2012) estimated using 10,023 observations from 2007 through 2012,
adjusted R2 = 0.1215) to observations from 1994 through 2010:
UTBjt = 0.00197 + 0.00670*PTROAit + 0.00033477*SIZEit + 0.00679*FOREIGNit
+ 0.05091*RDit + 0.00158*LEVit − 0.00005150*MTBit + 0.00630*SGAit
_______ − 0.00142*SALES_GROWTHi
PTROA is pre-tax return on assets (pit÷att-1), SIZE is the natural log of total
assets (log(att)), FOREIGN is an indicator variable for the presence of foreign
operations (pifot ne . and ne 0), RD is research and development expenses
(xrdt÷att-1), LEV is leverage calculated as debt to assets ((dlct+dlttt)÷ att-1),
MTB is the market-to-book ratio ((prcc_ft*cshot)÷ceqt), SGA is selling,
general, and administrative expenses (xsgat÷att-1), and SALES_GROWTH is
the annual %age changes in sales ((salet-salet-1)÷salet-1). Following Rego and
Wilson (2012), dlc, xrd, and xsga are reset to zero when missing.
36
Appendix (cont.)
Independent Variables
MA_SCOREit
Managerial ability score from Demerjian et al. (2012), computed using data
envelopment analysis (DEA) where total sales is optimized using the vector of
inputs including net PP&E, operating leases, R&D, purchased goodwill &
intangibles, cost of goods sold, and SG&A. The DEA is optimized at the
industry and year level and a firm efficiency score is computed. The firm
efficiency score is then regressed on firm characteristics (size, market share,
positive free cash flow, age, business segment concentration, a foreign
currency indicator, and year indicators), and the residual from this regression
is the managerial ability score. See Demerjian et al. (2012) for additional
details.
ΔMA_SCORE3it Difference between the sum of MA_SCOREit in t+1 through t+3 and the sum
of MA_SCOREit in t-3 through t-1
R&Dit Research and development expense (xrd) deflated by sales (sale)
ADit Advertising expense (xad) deflated by sales (sale)
CAPXit Ratio of capital expenditures (capx) to gross property, plant, and equipment
(ppegt)
LEVit Ratio of total debt (dltt+dlc) to total assets (at)
FOREIGNit Indicator coded equal to one if pre-tax foreign income from operations (pifo)
is non-zero, and coded equal to zero otherwise
SIZEit Natural log of one plus total assets (at)
INTANGit Ratio of intangible assets (intan) to total assets (at)
NOL_DECREASEit Indicator coded equal to one if the value of the NOL carryforward (tlcf)
decreased in year t
TURNOVERit Indicator coded equal to one when the CEO leaves the firm in year t, and
coded zero otherwise (source: Execucomp)
INDROAit Ratio of income before extraordinary items (ib) to average total assets (at)
minus the industry-year median at the 2-digit SIC code level
GROWTHit Percentage sales growth in current year sales calculated as sales in year t
divided by sales in year t-1 (sale), minus one
BTMit Ratio of the book value of common equity (ceq) to market value of common
equity (csho*prcc_f)
ANALYSTit Natural log of one plus the number of analysts issuing annual earnings
forecasts for firm i in year t (source: IBES)
INST_OWNit Percentage of shares held by institutional shareholders (source: Thomson 13F
Institutional Holdings)
G_INDEXit Corporate governance index based on 24 input variables (see Gompers et al.
2003 for additional details)
Notes: This appendix describes in detail our dependent and independent variables used in our empirical tests. All
variable source names in parentheses refer to Compustat unless otherwise stated.
37
References
Baik, B., D. B. Farber, and S. Lee. 2011. CEO ability and management earnings forecasts.
Contemporary Accounting Research 28 (5): 1645–1668.
Balakrishnan, K., J. L. Blouin, and W. R. Guay. 2012. Does tax aggressiveness reduce corporate
transparency? SSRN Scholarly Paper. Rochester, NY: Social Science Research Network.
Bamber, L. S., J. Jiang, and I. Y. Wang. 2010. What’s my style? The influence of top managers
on voluntary corporate financial disclosure. The Accounting Review 85 (4): 1131–1162.
Bebchuk, L., A. Cohen, and A. Ferrell. 2009. What matters in corporate governance? Review of
Financial Studies 22 (2): 783–827.
Bertrand, M., and A. Schoar. 2003. Managing with style: The effect of managers on firm
policies. The Quarterly Journal of Economics 118 (4): 1169–1208.
Callahan, C. M., R. E. Smith, and A. W. Spencer. 2012. An examination of the cost of capital
implications of FIN 46. The Accounting Review 87 (4): 1105–1134.
Cazier, R. A., S. O. Rego, X. Tian, and R. J. Wilson. 2009. Early evidence on the determinants
of unrecognized tax benefits. SSRN Scholarly Paper. Rochester, NY: Social Science
Research Network.
Chen, S., X. Chen, Q. Cheng, and T. Shevlin. 2010. Are family firms more tax aggressive than
non-family firms? Journal of Financial Economics 95 (1): 41–61.
Chyz, J. A. 2013. Personally tax aggressive executives and corporate tax sheltering. Journal of
Accounting and Economics 56 (2–3): 311–328.
Dejong, D., and Z. Ling. 2013. Managers: Their effects on accruals and firm policies. Journal of
Business Finance & Accounting 40 (1-2): 82–114.
Demerjian, P., B. Lev, and S. McVay. 2012. Quantifying managerial ability: A new measure and
validity tests. Management Science 58 (7): 1229–1248.
Demerjian, P. R., B. Lev, M. F. Lewis, and S. E. McVay. 2013. Managerial ability and earnings
quality. The Accounting Review 88 (2): 463–498.
Desai, H., C. E. Hogan, and M. S. Wilkins. 2006. The reputational penalty for aggressive
accounting: earnings restatements and management turnover. Accounting Review 81 (1):
83–112.
Desai, M. A., and D. Dharmapala. 2006. Corporate tax avoidance and high-powered incentives.
Journal of Financial Economics 79 (1): 145–179.
Desai, M. A., and D. Dharmapala. 2009. Corporate tax avoidance and firm value. Review of
Economics and Statistics 91 (3): 537–546.
Dyreng, S. D., M. Hanlon, and E. L. Maydew. 2008. Long‐run corporate tax avoidance. The
Accounting Review 83 (1): 61–82.
———. 2010. The effects of executives on corporate tax avoidance. Accounting Review 85 (4):
1163–1189.
Edwards, A., C. Schwab, and T. J. Shevlin. 2013. Financial constraints and the incentive for tax
planning. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network.
Fama, E. F., and K. R. French. 1997. Industry costs of equity. Journal of Financial Economics
43 (2): 153–193.
Fee, C. E., and C. J. Hadlock. 2003. Raids, rewards, and reputations in the market for managerial
talent. Review of Financial Studies 16 (4): 1315–1357.
Fee, C. E., C. J. Hadlock, and J. R. Pierce. 2013. Managers with and without style: Evidence
using exogenous variation. Review of Financial Studies 26 (3): 567–601.
38
Feng, M., W. Ge, S. Luo, and T. Shevlin. 2011. Why do CFOs become involved in material
accounting manipulations? Journal of Accounting and Economics 51 (1–2): 21–36.
Francis, B., X. Sun, and Q. Wu. 2013. Managerial ability and tax avoidance. SSRN Scholarly
Paper. Rochester, NY: Social Science Research Network.
Gaertner, F. B. 2013. CEO after-tax compensation incentives and corporate tax avoidance.
SSRN Scholarly Paper. Rochester, NY: Social Science Research Network.
Ge, W., D. Matsumoto, and J. L. Zhang. 2011. Do CFOs have style? An empirical investigation
of the effect of individual CFOs on accounting practices. Contemporary Accounting
Research 28 (4): 1141–1179.
Gompers, P., J. Ishii, and A. Metrick. 2003. Corporate governance and equity prices. The
Quarterly Journal of Economics 118 (1): 107–156.
Graham, J. R., and A. L. Tucker. 2006. Tax shelters and corporate debt policy. Journal of
Financial Economics 81 (3): 563–594.
Guenther, D. A., S. R. Matsunaga, and B. M. Williams. 2012. Corporate tax aggressiveness and
firm risk. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network.
Gupta, S., and K. Newberry. 1997. Determinants of the variability in corporate effective tax
rates: Evidence from longitudinal data. Journal of Accounting and Public Policy 16 (1):
1–34.
Hanlon, M., and S. Heitzman. 2010. A review of tax research. Journal of Accounting and
Economics 50 (2–3): 127–178.
Hanlon, M., and T. Shevlin. 2002. Accounting for tax benefits of employee stock options and
implications for research. Accounting Horizons 16 (1): 1–16.
Hanlon, M., and J. Slemrod. 2009. What does tax aggressiveness signal? Evidence from stock
price reactions to news about tax shelter involvement. Journal of Public Economics 93
(1–2): 126–141.
Hutchens, M., and S. O. Rego. 2012. Tax risk and the cost of equity capital. SSRN Scholarly
Paper. Rochester, NY: Social Science Research Network.
Internal Revenue Service, 2013. Schedule UTP TY2011 Filing Statistics (as of August 2013).
http://www.irs.gov/Businesses/Corporations/UTPFilingStatistics (last accessed
November 18, 2013).
Jung, B., W.J. Lee, and D. P. Weber. 2013. Financial reporting quality and labor investment
efficiency. Contemporary Accounting Research, forthcoming.
Law, K. and L. F. Mills. 2013. Following the rules? Corporate tax reporting by CEOs with
military experience. Unpublished working paper, The University of Texas at Austin.
Leverty, J. T., and M. F. Grace. 2012. Dupes or incompetents? An examination of management’s
impact on firm distress. Journal of Risk and Insurance 79 (3): 751–783.
Li, C., L. Sun, and M. Ettredge. 2010. Financial executive qualifications, financial executive
turnover, and adverse SOX 404 opinions. Journal of Accounting and Economics 50 (1):
93–110.
Lisowsky, P. 2010. Seeking shelter: Empirically modeling tax shelters using financial statement
information. The Accounting Review 85 (5): 1693–1720.
Lisowsky, P., L. Robinson, and A. Schmidt. 2013. Do publicly disclosed tax reserves tell us
about privately disclosed tax shelter activity? Journal of Accounting Research 51 (3):
583–629.
Mills, L., 1998. Book-tax differences and internal revenue service adjustments. Journal of
Accounting Research 36 (2): 343-356.
39
Mills, L. F., and K. J. Newberry. 2005. Firms’ off-balance sheet and hybrid debt financing:
evidence from their book-tax reporting differences. Journal of Accounting Research 43
(2): 251–282.
Rajgopal, S., T. Shevlin, and V. Zamora. 2006. CEOs’ outside employment opportunities and the
lack of relative performance evaluation in compensation contracts. The Journal of
Finance 61 (4): 1813–1844.
Rego, S. O. 2003. Tax-avoidance activities of U.S. multinational corporations. Contemporary
Accounting Research 20 (4): 805–833.
Rego, S. O., and R. Wilson. 2012. Equity risk incentives and corporate tax aggressiveness.
Journal of Accounting Research 50 (3): 775–810.
Skaife, H. A., D. Veenman, and D. Wangerin. 2013. Internal control over financial reporting and
managerial rent extraction: Evidence from the profitability of insider trading. Journal of
Accounting and Economics 55 (1): 91–110.
Wilson, R. J. 2009. An examination of corporate tax shelter participants. The Accounting Review
84 (3): 969–999.
Wooldridge, J. M. 2002. Econometric analysis of cross section and panel data. MIT Press.
Boston, MA.
40
Table 1: Number of Observations by Year
Fiscal year N %
1994 2,841 6.37
1995 2,898 6.50
1996 3,229 7.24
1997 3,190 7.15
1998 2,836 6.36
1999 2,732 6.12
2000 2,507 5.62
2001 2,214 4.96
2002 2,350 5.27
2003 2,456 5.50
2004 2,706 6.07
2005 2,746 6.15
2006 2,708 6.07
2007 2,503 5.61
2008 2,363 5.30
2009 2,089 4.68
2010 2,248 5.04
Total 44,616 100
Notes: This table reports the frequency of observations in our sample by fiscal year.
41
Table 2: Descriptive Statistics
N Mean P50 SD P25 P75
Dependent variables
CASHETR 44,616 0.2751 0.2402 0.2434 0.0881 0.3694
CASHETR3 32,736 0.3167 0.2744 0.2539 0.1496 0.3796
CASHETR5 23,803 0.3417 0.2855 0.2667 0.1756 0.3859
ΔCASHETR3 1,114 -0.0193 -0.0075 0.2947 -0.1035 0.0816
PRED_SHELTER 35,326 0.7165 0.9119 0.3511 0.4929 0.9892
UTB 5,506 0.0131 0.0068 0.0184 0.0020 0.0162
PRED_UTB 20,786 0.0119 0.0114 0.0062 0.0068 0.0153
Independent variable of interest
MA_SCORE 44,616 0.0300 0.0169 0.1397 -0.0600 0.1072
ΔMA_SCORE3 1,114 -0.0226 -0.0288 0.2937 -0.1578 0.1237
Control variables
R&D 44,616 0.0309 0.0000 0.0604 0.0000 0.0303
AD 44,616 0.0090 0.0000 0.0231 0.0000 0.0050
CAPX 44,616 0.1471 0.1093 0.1223 0.0656 0.1877
LEV 44,616 0.1709 0.1241 0.1822 0.0047 0.2764
FOREIGN 44,616 0.3759 0.0000 0.4844 0.0000 1.0000
SIZE 44,616 5.6843 5.6268 2.0158 4.2489 7.0498
INTANG 44,616 0.1410 0.0666 0.1745 0.0007 0.2226
NOL_DECREASE 44,616 0.1594 0.0000 0.3660 0.0000 0.0000
Notes: This table reports descriptive statistics for our regression variables. All variables are defined in the Appendix
and all continuous variables are winsorized at the 1st and 99
th percentiles (pooled).
42
Table 3: Pairwise Correlations
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 CASHETR -
2 CASHETR3 0.4868 -
3 CASHETR5 0.3938 0.6710 -
4 PRED_SHELTER 0.0339 0.0426 0.0425 -
5 UTB -0.0605 -0.0483 0.0000 0.0765 -
6 PRED_UTB -0.0418 -0.0301 -0.0488 0.2371 0.3581 -
7 MA_SCORE -0.0300 -0.0364 -0.0413 0.0165 0.0776 0.0927 -
8 R&D -0.0821 -0.1001 -0.1141 -0.0181 0.2926 0.6335 0.0377 -
9 AD 0.0188 0.0084 -0.0076 0.0373 0.0591 0.0667 0.0180 -0.0145 -
10 CAPX -0.0254 -0.0246 -0.0408 0.0001 -0.0055 0.0240 0.2258 0.1204 0.0381 -
11 LEV -0.0111 -0.0340 -0.0347 0.2273 -0.0672 -0.1032 -0.0912 -0.2357 0.0004 -0.1419 -
12 FOREIGN 0.0445 0.0393 0.0349 0.3458 0.1928 0.5819 -0.0549 0.1833 0.0346 -0.1189 -0.0198 -
13 SIZE 0.0512 0.0517 0.0527 0.8114 0.0857 0.1938 -0.0525 -0.0647 0.0668 -0.1404 0.2620 0.3812 -
14 INTANG 0.0330 0.0163 -0.0006 0.2149 -0.0132 0.0608 -0.0247 -0.0274 0.0833 -0.0766 0.2314 0.1376 0.2758 -
15 NOL_DECREASE -0.1117 -0.0736 -0.0661 -0.0444 0.0519 0.0848 -0.0042 0.0722 0.0073 -0.0427 -0.003 0.0727 -0.0183 0.0574
Notes: This table reports Pearson product-moment correlations. Correlation coefficients in bold are significant at greater than the 0.10 level. All variables are defined in
the Appendix and all continuous variables are winsorized at the 1st and 99
th percentiles (pooled).
43
Table 4: Relation between Tax Avoidance and Managerial Ability
Panel A: One-year measure of CASHETR
Dependent variable: (1) (2) (3)
CASHETR Baseline model with
Pred. controls for tax avoidance Including firm fixed effects Including MA_SCORE
Sign Coeff. t-stat Coeff. t-stat Coeff. t-stat
MA_SCORE - -0.1892*** -11.01
R&D - -0.3485*** -12.58 0.4877*** 5.52 0.4883*** 5.70
AD + 0.2054*** 3.03 0.1829 1.25 0.1577 1.07
CAPX - -0.0724*** -5.88 0.0194 1.19 0.0628*** 3.77
LEV - -0.0846*** -8.23 0.0248 1.62 0.022 1.45
FOREIGN - 0.0276*** 7.55 0.0117* 1.92 0.0112* 1.82
SIZE ? 0.0054*** 5.30 0.0273*** 7.36 0.0276*** 7.41
INTANG - 0.0670*** 6.45 -0.0022 -0.11 -0.0109 -0.52
NOL_DECREASE - -0.0677*** -18.79 -0.0471*** -10.75 -0.0470*** -10.77
Intercept ? 0.2272*** 28.84 0.0696*** 2.93 0.0696*** 2.93
Firm fixed effects Excluded Included Included
Year fixed effects Included Included Included
Adjusted R2 0.0126 0.2449 0.2494
N 44,616 44,616 44,616
44
Table 4: Relation between Tax Avoidance and Managerial Ability (cont.)
Panel B: Long-run measures of CASHETR
(1) (2)
Pred. Y = CASHETR3 Y = CASHETR5
Sign Coeff. t-stat Coeff. t-stat
MA_SCORE - -0.2442*** -6.64 -0.3059*** -4.90
R&D - 0.5915*** 3.74 0.6691*** 2.96
AD + 0.3281 1.24 0.1616 0.39
CAPX - -0.0241 -0.61 -0.2110*** -3.03
LEV - 0.0181 0.65 -0.0218 -0.46
FOREIGN - 0.0122 1.05 -0.0034 -0.23
SIZE ? 0.0289*** 3.95 0.0374*** 3.44
INTANG - 0.0043 0.11 0.0322 0.55
NOL_DECREASE - -0.0696*** -6.44 -0.0873*** -4.53
Intercept ? 0.1216*** 2.67 0.1240* 1.84
Year fixed effects Included Included
Firm fixed effects Included Included
Adjusted R2 0.3954 0.5125
N 32,736 23,803
Notes: This table presents the results from estimating OLS regressions where the dependent variable is the cash-based ETR. In Panel A, we use a one-year
measure of CASHETR and long-run measures in Panel B. In Column 1 (2) of Panel B, the dependent and independent variables have been averaged over the
time period t through t-2 (t through t-4) so the dependent and independent variables are measured contemporaneously. All variables are defined in the Appendix
and all continuous variables are winsorized at the 1st and 99
th percentiles (pooled). Standard errors are adjusted for heteroskedasticity and clustered at the firm
level. ***, **, and * indicate significance at the 1%, 5%, and 10% respectively.
45
Table 5: Robustness Tests
(1) (2) (3) (4) (5)
Y = Industry-size GOV = GOV = Controlling for Including executive
Pred. adjusted CASHETR INST_OWN G_INDEX Option Exercises fixed effects
Sign Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat
MA_SCORE - -0.1829*** -10.73 -0.1876*** -10.91 -0.1968*** -5.20 -0.1594*** -4.26 -0.2158*** -7.45
GOV ? 0.0329** 2.42 0.0008 0. 24
EST_OPTION - -0.2971*** -5.84
Firm-level controls Included Included Included Included Included
Firm fixed effects Included Included Included Included Included
Year fixed effects Included Included Included Included Included
Executive fixed effects Excluded Excluded Excluded Excluded Included
Adjusted R2 0.2298 0.2496 0.2499 0.2431 0.2426
N 44,616 44,616 11,276 10,366 17,929
Number of firms
2,279
Number of executives 1,522
Notes: This table presents the results of robustness tests estimating OLS regressions where the dependent variable is the cash-based ETR. In column 1, the
dependent variable is the industry-size adjusted CASHETR calculated as the difference between the cash ETR for firm i and the mean cash ETR for the
corresponding set of firms belonging to the same Fama-French 48 industry and asset-size quintile in year t. The dependent variable in columns 2 - 5 is
CASHETR as previously defined. INST_OWN is the %age of shares held by institutional shareholders (Thomson 13F Institutional Holdings). G_INDEX is the
Gompers et al. (2003) governance index. Following Dyreng et al. (2010), EST_OPTION is the average annual value realized from exercise of options for the top
executives grossed up by the fraction of options owned by the covered executives (from Execucomp), scaled by average total assets. All other variables are
defined in the Appendix and all continuous variables are winsorized at the 1st and 99
th percentiles (pooled). Standard errors are adjusted for heteroskedasticity
and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5%, and 10% respectively.
46
Table 6: CEO Turnover Analyses
Panel A: Modeling CEO Turnover to Calculate Propensity Scores
(1) (2)
Pred. Y = TURNOVER Propensity Matched Sample Means
Sign. Coeff. t-stat Treatment Control t-stat
SIZE ? 0.0978*** 5.00 7.6399 7.6913 -0.57
INDROA - 0.0458 0.18 0.0614 0.0640 -0.43
GROWTH - -0.8029*** -5.78 0.0768 0.0834 -0.55
LEV ? -0.1236 -0.75 0.1944 0.2010 -0.68
BTM + -0.0001 -0.16 0.4192 0.4522 -0.48
ANALYST ? -0.0122 -0.44 1.9717 1.9887 -0.27
INST_OWN ? -0.2584 -1.39 0.1582 0.1433 1.68*
MA_SCORE - -0.5669*** -2.80 0.0090 0.0154 -0.81
Intercept ? -1.6887*** -10.11
Year fixed effects Included
Pseudo R2 0.0323
Area under ROC curve 0.6344
N 3,920 557 557
47
Table 6: CEO Turnover Analyses (cont.)
Panel B: Regression Results
(1) (2)
Y = ΔCASHETR3 Y = ΔCASHETR3
Pred. PSM Sample Non-PSM Sample
Sign Coeff. t-stat Coeff. t-stat
ΔMA_SCORE3 ? 0.0409 0.82 0.0041 0.17
TURNOVER ? -0.0129 -0.67 -0.0142 -0.93
ΔMA_SCORE3*TURNOVER - -0.1169* -1.74 -0.0946* -1.76
ΔR&D3 - 0.1138 0.86 0.0066 0.13
ΔAD3 + 0.0287 0.19 -0.0062 -0.05
ΔCAPX3 - 0.0104 0.28 -0.014 -0.49
ΔLEV3 - -0.0064 -0.22 -0.0033 -0.22
ΔFOREIGN3 - 0.0096 0.84 -0.001 -0.17
ΔSIZE3 ? 0.0159** 2.10 0.0174*** 3.76
ΔINTANG3 - 0.0194 0.58 0.0101 0.50
ΔNOL_DECREASE3 - -0.018 -1.49 -0.0101 -1.46
Intercept ? -0.0295* -1.85 -0.0291*** -3.28
Year fixed effects
Included Included
Adjusted R2
0.0187 0.0432
N 1,114 3,928
Notes: This table presents the results of CEO turnover analyses using a difference-in-difference design. Panel A presents the results from estimating a probit
regression specification to calculate propensity score values and a comparison of variable means by treatment versus control status. Panel B presents the results
from estimating an OLS regression specification. All variables are defined in the Appendix and all continuous variables are winsorized at the 1st and 99
th
percentiles (pooled). Standard errors are adjusted for heteroskedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5%, and
10% respectively.
48
Table 7: Tax Risk, Uncertainty, and Managerial Ability
(1) (2) (3)
Y =
PRED_SHELTER Y = UTB Y = PRED_UTB
Pred.
Sign
Pred.
Sign
Pred.
Sign
Coeff. t-stat Coeff. t-stat Coeff. t-stat
MA_SCORE ? 0.0115 0.84 ? 0.0102*** 2.85 ? 0.0062*** 15.21
R&D − -0.3092*** -4.90 + 0.0142 0.68 + 0.0428*** 14.40
AD ? -0.0824 -0.53 ? 0.0318 0.78 ? -0.0038 -1.16
CAPX ? 0.0308** 2.17 ? -0.0075 -1.51 ? -0.0004 -1.00
LEV − -0.0870*** -6.36 + 0.0055* 1.89 + 0.0004 1.43
FOREIGN + 0.0246*** 4.27 + 0.0000 -0.04 + 0.0058*** 52.93
SIZE + 0.1348*** 26.77 + 0.0001 0.07 + -
0.0003*** -3.01
INTANG ? -0.0008 -0.05 ? -0.0004 -0.08 ? -0.0004 -0.96
NOL_DECREASE ? 0.0031 1.06 ? -0.0001 -0.11 ? 0.0001** 2.44
Intercept ? -0.1295*** -4.10 ? 0.0104 0.92 ? 0.0100*** 14.84
Firm fixed effects
Included Included Included
Year fixed effects
Included Included Included
Adjusted R2
0.9176 0.8343 0.9212
N
35,236 5,506 20,786
Notes: This table presents the results from estimating an OLS regressions using measures of tax sheltering and unrecognized tax benefits capturing tax risk and
uncertainty as the dependent variables. All variables are defined in the Appendix and all continuous variables are winsorized at the 1st and 99
th percentiles
(pooled). Standard errors are adjusted for heteroskedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5%, and 10%
respectively.
49
Table 8: Cross-sectional Tests of Managerial Ability and Mechanisms for Tax Avoidance
(1) (2)
Pred. Y = CASHETR Pred. Y = PRED_UTB
Sign Coeff. t-stat Sign Coeff. t-stat
MA_HIGH - -0.0293** -2.15 + 0.0016*** 5.55
R&D*MA_HIGH - -0.1260** -1.97 + 0.0075*** 4.79
AD*MA_HIGH + 0.0363 0.25 ? -0.0006 -0.22
CAPX*MA_HIGH - -0.0596** -2.02 ? -0.0001 -0.08
LEV*MA_HIGH - -0.0440** -2.07 + 0.0009*** 2.73
FOREIGN*MA_HIGH - -0.0155** -2.01 + -0.0001 -0.77
SIZE*MA_HIGH ? 0.0026 1.19 + -0.0002*** -4.42
INTANG*MA_HIGH - 0.0364* 1.74 ? 0.0001 0.36
NOL_DECREASE*MA_HIGH - 0.0022 0.29 ? -0.0001 -0.30
R&D - 0.5561*** 5.61 + 0.0381*** 12.27
AD + 0.159 0.97 ? -0.0039 -1.05
CAPX - 0.0836*** 3.07 ? 0.0004 0.87
LEV - 0.0454** 2.29 + -0.0002 -0.75
FOREIGN - 0.0189** 2.50 + 0.0058*** 52.15
SIZE ? 0.0248*** 6.22 + -0.0001 -1.41
INTANG - -0.0234 -0.97 ? -0.0007 -1.64
NOL_DECREASE - -0.0481*** -8.03 ? 0.0002** 2.41
Intercept ? 0.0886*** 3.54 ? 0.0088*** 13.32
Firm fixed effects Included Included
Year fixed effects Included Included
Adjusted R2 0.2481 0.9187
N 44,616 20,786
Notes: This table presents the results of cross-sectional tests estimating an OLS regressions with CASHETR as the
dependent variable in column (1) and PRED_UTB in column (2). MA_HIGH is an indicator variable set equal to
one when MA_SCORE is above the sample median, zero otherwise. All variables are defined in the Appendix and
all continuous variables are winsorized at the 1st and 99
th percentiles (pooled). Standard errors are adjusted for
heteroskedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5%, and 10%
respectively.
