Taxes and Peer Effects
Andrew Bird
Tepper School of Business
Carnegie Mellon University
[email protected]
Alexander Edwards
Rotman School of Management
University of Toronto
[email protected]
Thomas G. Ruchti
Tepper School of Business
Carnegie Mellon University
[email protected]
June 24, 2015
Keywords: effective tax rates, peer effects
JEL codes: H25, M41
Data Availability: Data used in this study are available from public sources identified in
the paper.
We thank Joanna Garcia and seminar participants at Carnegie Mellon University, Ohio University, the
University of Toronto, and the 2015 University of Waterloo Tax Conference. We gratefully acknowledge
financial support from the Tepper School of Business, Carnegie Mellon University, and the Rotman School
of Management, University of Toronto.
Taxes and Peer Effects
Abstract
Despite a significant literature investigating the determinants of corporate tax
avoidance, there remains much unexplained cross-sectional variation in GAAP
and Cash effective tax rates. In this paper, we look at one particular determinant of
such variation – the effect of a firm’s tax avoidance choices on the tax and financial
reporting behavior of peer firms. These choices are affected by many of the same
factors, such as industry-level tax policy changes or audit risk, so we make use
of exogenous–to the peer firms–shocks to tax rates. Following the methodology
of Dyreng et al. (2010), we estimate managerial tax avoidance fixed effects and
identify tax rate shocks associated with executive turnover. We find that peer firms
respond to these shocks by changing their GAAP tax rates in the same direction. The
magnitude of the effect corresponds to a 50% response to the average change in peer
group GAAP effective tax rates. Our evidence suggests that these peer effects occur
only for book, rather than cash, effective tax rates. These findings are consistent
with the primacy of earnings-based measures to managers and other capital market
participants.
1. Introduction
“We were one of the last ones in our sector to do this [corporate inversion]. So it’s
not like I was blazing the trail. If you put on your business hat, you can’t maintain
competitiveness by staying at a competitive disadvantage. I mean you just can’t. The odds
are just not in your favor.”
–Heather Bresch, CEO of Mylan1
Studies in accounting (Arya and Mittendorf (2005), Tse and Tucker (2010),
Reppenhagen (2010), Albuquerque (2009)), economics (Evans et al. (1992)), and
corporate finance (John and Kadyrzhanova (2008), Leary and Roberts (2014), Foucault
and Fresard (2014)) have uncovered many roles for peer groups in explaining individual
and firm behavior. Despite a large and expanding body of empirical research on tax
behaviors and the associated financial accounting rules, little is understood about how a
firm’s peers impact their tax planning and associated financial reporting behavior. In this
study, we investigate the impact of an exogenous shock to a firm’s reported tax expense,
and amount of taxes paid, on the firm’s industry peers. With a few exceptions which
we discuss below, networks of firms have not been employed when modeling firm level
tax planning and financial reporting for taxes. We present an approach which considers
learning, norms, competition a firm faces from its peers, managerial incentives, and what
effects these concerns have on a firm’s tax planning and financial reporting behavior.
Following the framework of Dyreng et al. (2010), in which the authors posit that a firm’s
tax avoidance will differ depending on whether it is run by one executive or another,
we use executive turnover, and executive tax planning ability, to identify shocks to the
tax rates of firms. Using these shocks, we examine if, and how, peer firms in the same
industry respond to these shocks. Specifically, we examine if industry peers (referred to
as peer firms or peers from herein) change their tax planning and/or associated financial
1 Andrew
Ross Sorkin, DealBook: NYT, July 14, 2014
1
reporting behavior, controlling for firm characteristics, as a result of a similar firm in
the same industry (referred to as focal firms from herein) employing a tax aggressive
executive. We separately examine positive (i.e., increase in tax expense and payments
by a focal firm) and negative (i.e., decreases in tax expense and payments by a focal
firm) tax rate shocks to observe if the reaction to shocks by peer firms is asymmetric. We
document that peer firms react to negative shocks (i.e., decreases in focal firm ETRs) by
reporting lower GAAP effective tax rates. The magnitude of the response approximately
corresponds to a 50% response to the change in peer’s effective tax rate and the effect
persists for several years into the future. We do not observe a statistically significant
response by industry peers to positive tax shocks. We also examine the response of peer
firms in terms of cash effective tax rates. We do not observe a statistically significant
response to either positive or negative cash effective tax rate shocks. Taken together, our
results are consistent with peer firms responding to tax shocks through financial reporting
behavior but not real changes in tax payments.
While prior research has documented managerial effects on tax avoidance coming
from a ‘tone from the top’ mechanism for a given firm, in this study we are interested
in the impact of these changes on that firm’s industry peers.2 If a firm hires a CEO who
is either skilled at, or focused on, tax avoidance, it may change the emphasis placed on
functional areas of the firm. The executive may emphasize foreign operations more,
may change compensation of the tax director or devote resources toward hiring different
advisers for the firm. These underlying changes are at least partially observable to peers,
while the outcome—greater tax avoidance—may serve as a benchmarking tool for
compensation and evaluation of the firm (Armstrong et al. (2012), Powers et al. (2013)).
Observing this change in the behavior of a firm with a new executive, peer firms within
2 We
define tax avoidance as in Dyreng et al. (2008) as anything that lowers a firm’s taxes relative to pretax
accounting income and so are agnostic about specifically what strategies may be used to lower tax rates, and
do not attempt to say anything about tax shelters, tax evasion, tax risk, or tax aggressiveness, per se. We use
the standard measure from the literature, the effective tax rate (ETR) calculated using GAAP and do further
tests using cash taxes paid from the statement of cashflows (cash ETR).
2
the same industry could react in several ways. Peer firms could learn about tax planning
strategies and attempt to implement those strategies themselves, reducing their cash
outflows for taxes. It is also possible that industry peers could react in their reporting
behavior, decreasing GAAP effective tax rates without changes in the underlying
cashflows. A non-cash reaction would be consistent with managers fixating on reported
tax expense and financial statement earnings (Armstrong et al. (2012)) and provide
evidence on the primacy of the firm’s GAAP effective tax rate as the most important tax
metric to top management (Graham et al. (2014)).
There is an emerging literature highlighting the role of individual executives in
corporate tax avoidance (Gallemore et al. (2014), Chyz (2013), Graham et al. (2014)).
In this study, we use executive turnover at a firm to capture shocks in tax planning and
reporting which may elicit a response from peer firms. We estimate the focal firm’s
executive effects on tax avoidance as in Dyreng et al. (2010) using a technique developed
by Bertrand and Schoar (2003) that tracks individuals who were top executives at more
than one firm. This kind of methodology is now in wide use in finance and accounting,
with other recent applications to disclosure choices (Bamber et al., 2010) and the
accounting choices of CFOs (Ge et al., 2011). Our aim is to observe whether particular
executives appear to have consistent effects on their firms’ tax avoidance and observe
how that effect impacts industry peers (measuring the effect of focal firm shocks on
peer firm tax behavior). We track CEOs, CFOs, and other top 5 executives listed in the
ExecuComp database, and identify those firms that experienced significant changes in
tax avoidance ability among their executive teams.3 We call these tax rate shocks, and
distinguish whether they involve a substantial increase or decrease in the effective tax
rate. We then generate an industry peer group for each firm, by finding, within industry,
the five most similar firms, measured by size. Using these shocks, we model the tax
3 We
acknowledge that the impact on taxes of executives in various top 5 position will vary. For example,
a CFO likely has a great impact on taxation than a Director or VP of Marketing. Our empirical strategy
deals with this concern by identifying "big shocks" to effective tax rates around executive turnover based on
statistical significance and magnitude of the change.
3
planning and related financial reporting of peer firms, controlling for firm characteristics,
and firm, executive, and industry-year fixed effects. Our identifying assumption is that
after including this set of controls, the tax rate shock at a peer firm i is exogenous to the
tax planning and related financial reporting decision of firm j. In essence, this means that
we assume the executive turnover decision that gave rise to the tax rate shock at firm i is
driven by either factors idiosyncratic to firm i, or by the common observable factors that
we control for in our models. The presence of industry-year effects in our some of our
specifications is designed to alleviate any remaining endogeneity concerns.
Our results are consistent with the argument that tax rate shocks have an
asymmetric, statistically significant effect on peer firm financial reporting for taxes
but not the underlying tax cash flows. While a positive tax rate shock (a firm hires an
executive team that is relatively bad at tax avoidance, resulting in a substantial increase
in effective tax rate) appears to have no effect on peer behavior, industry peer firms
respond by lowering their reported GAAP effective tax rate in response to a negative tax
rate shock at the focal firm (a firm hires an executive team that is relatively good at tax
avoidance, resulting in a substantial decrease in effective tax rate). We fail to observe
a significant response in industry peer firms’ cash effective tax rates to either a positive
or negative tax rate shock. These results are intuitive and in line with both academic
(Brown and Drake (2013), Kubick et al. (2015)) and anecdotal evidence. If tax planning
strategies or norms are to be considered seriously, an industry peer firm will respond, in
a competitive environment, to a firm receiving a negative tax rate shock more readily
than a firm paying higher taxes. If firms are unable to respond in real terms (i.e., reduce
actual tax payments), or managers are incentivized to focus on reported GAAP effective
tax rates, they will respond through financial reporting choices.4
4 Note,
when a firm undertakes tax planning strategies to reduce tax payments, this can impact both the
CASH ETR and the GAAP ETR (i.e., when the tax strategy increases permanent book-tax differences).
Decreases in GAAP ETR without an associated decrease in CASH ETR are less likely to be the result of
reduced tax payments and more likely to be the result of financial reporting choices (e.g., hold foreign
earnings and taxes constant, a firm can decrease GAAP ETR by declaring a larger amount of foreign
earnings as indefinitely reinvested (Krull (2004)).
4
The financial reporting result is consistent with evidence on the importance and
salience of bottom line net earnings to executives. Extant financial accounting studies
provide evidence that managers fixate on financial reporting income and are even willing
to forego positive net present value projects in order to meet or beat earnings targets such
as analysts’ forecasts (Graham et al. (2005)). In a tax setting, Graham et al. (2014) survey
corporate tax executives and find that approximately half of the managers in their sample
of publicly-traded firms indicate that their firm’s GAAP effective tax rate is the most
important tax metric to top management, whereas only 15 percent state that cash taxes
paid is the most important tax metric. Armstrong et al. (2012) provide further evidence
consistent with the primacy of GAAP ETRs to managers. They find a strong negative
association between tax director incentive compensation and GAAP ETRs, but no relation
between incentive compensation and cash ETRs. Collectively, these studies suggest that
if firms benchmark there tax avoidance against industry peers, it is likely that reported tax
numbers are more important than actual tax payments. It is possible that an industry peer
firm could respond by devoting more resources toward other endeavors, but our results
indicate that peer firms react only to the hiring of tax avoiding executive teams, and not
to those which place less of an emphasis on tax planning, possibly due to a ratchet effect.
A simple rationale for this result is the observation that investors and journalists appear
to put most if not all emphasis, when considering tax rates, on particularly low tax rates,
either in absolute terms, or within industry.
We conduct a variety of robustness tests by varying the procedure to estimate
the tax rate shocks and construct the peer groups. The peer effects associated with a
downward tax rate shock appear to occur quite quickly and persist over several years. In
all specifications we do not find evidence of peer effects on cash effective tax rates, even
after a number of years.
There is an emerging related literature on network and peer firm effects on tax
planning. Related to the notion of peers and learning, prior research has examined the role
5
of contagion of specific tax strategies among firms with network ties. Contagion could
represent a form of learning in which firms become aware of the choices of other firms
due to some interconnections, and act on this knowledge. Looking at board interlocks,
in which a board member of one firm is also a board member at another firm, Brown
(2011) finds that these ties increase the likelihood of the adoption of the corporate owned
life insurance (COLI) shelter. Brown and Drake (2013) uses these same interlocks to
examine the behavior of low tax firms, finding board membership does play a similar
role in perpetuating tax avoidance strategies. These prior studies differ from ours in that
they examine specific tax strategies and real connections of personnel between firms. Our
focus is on how firms respond, in terms of reporting choices and cash flows, when a peer
alters their tax avoidance behavior.
The prior research most closely related with our study examines the impact
of product market competition on is tax avoidance (Kubick et al. (2015), Brown et al.
(2014)). These studies focus on how a firm’s competitive environment, or their leadership
within their product market, impacts their tax avoidance. We differ these prior studies in
several important ways.5 First, while prior research focuses on the cause of higher levels
of tax avoidance behavior of product market leaders and in more competitive markets, our
study focuses on how financial reporting for taxes and tax payments change in response
to exogenous shocks to a peer firm’s tax avoidance. The source of the change in tax
avoidance at the focal firm is not important in our setting (as long as it is exogenous
to the peer firms), whereas this is the primary question at issue in these prior studies.
Our hypotheses and tests relate to how similar firms (i.e., peers) respond to changes
in tax avoidance at the focal firm; we do not restrict the focal firm to being a product
market leader or not, being large or small, or any other criteria. We also do not focus
on particularly competive (or not) industries. Alternatively stated, our study examines
the dynamics within a group of peer firms of financial reporting and payment of taxes,
5 We
discuss the similarities and differences in between our study, Kubick et al. (2015), and Brown et al.
(2014) in greater detail in section 4.4.
6
not the impact of a “leader.” Our focus is simply on how firms respond to the actions of
their closest peers. In supplemental tests we document that our results are strongest in less
competitive industries (i.e., industries with a Herfindahl index value above median).
The remainder of the paper is organized as follows: Section 2 develops our
testable hypotheses, Section 3 describes the data and empirical methodology in detail,
Section 4 presents the results and Section 5 concludes.
2. Hypothesis Development
2.1. Peer Firm Responses to Focal Firm Shocks
Peer firms are often in competition for the same customers, supply chains, employees,
investors, and, in particular, managerial talent. At these firms, management engages in
various activities, from marketing, to operations, to tax, and must learn from or succeed
the competition, or face market reaction to unexploited opportunities. If a firm adopts
a strong tax avoidance strategy, then its peers may learn from this behavior, and devote
resources toward meeting similar strategies. Alternatively, if a firm is unwilling or unable
to respond in terms of cash flow effects to the newly adopted tax avoidance strategy at
a peer firm, then it may attempt to respond in terms of reported tax expense. In either
case, it naturally follows that firms learn from and respond to behaviors of those closest to
them, as innovations among peers may be both the most salient and the most applicable.
When a firm hires a new executive, the firm could seek to hire an executive team
member who is good at tax avoidance strategies. Alternatively, the firm could look to
executives with other strengths and talents who may not be as skilled in avoiding tax
payments (Dyreng et al. (2010)). This means that a CEO, for example, could orient
firm strategy toward tax avoidance, or a CEO could focus on other factors, leaving tax
avoidance less important. This behavior is not necessarily inconsistent with effective tax
planning. As emphasized in the Scholes and Wolfson framework (Scholes et al. (2015)),
the goal of effective tax planning is to maximize after tax cash flows and not simply
7
minimize tax payments. Whether a new manager implements a move is toward a lower
tax rate or a higher tax rate, peer firms will evaluate the situation has should respond
accordingly. This is should be true if firms are matching overall strategies, or if tax
planning is important for competitive reasons, but is not profitable. This hypothesis would
not hold however, if there are preexisting tax norms that continue to hold, if firms are
unable to observe the new tax avoidance strategies or determine a suitable substitute, or
if firms fail to respond due to fears of regulator/IRS responses and reputational concerns
(Dyreng et al. (2014)).
Hypothesis 1. Tax rate shocks are positively associated with tax rate changes at peer
firms.
2.2. Peer Firm Asymmetric Responses
Our first hypothesis predicts a symmetric reaction to positive and negative tax rate
shocks. If firms are learning about tax avoidance from their peers, waiting for other firms
to "test the waters" with more aggressive tax strategies, or are driven by norms about
tax avoidance and devote resources toward strategies as other firms ramp up their tax
avoidance, then an asymmetric response is possible. If this were the case, industry peer
firms would only respond to negative tax shocks. If a focal firm has a positive tax shock,
then a peer firm would have little incentive to change their financial reporting behavior
to show lower bottom line net income or change their tax planning to pay higher taxes.
Whether the focal firm is using overall corporate strategies that limit tax avoidance, or
are failing to consider tax avoidance as a valuable part of overall corporate strategy,
the peer firm has nothing to learn from a tax perspective from the new behavior of the
focal firm. A peer firm looks for competitive advantage, and while the other firm could
be doing something new, it would not necessarily effect their tax avoidance strategy. If,
however, the focal firm exhibits a negative tax shock, then it may be evidence they found
a new tax strategy, or that the competitive environment drives them to lower taxes. A
8
peer firm observes this, and must respond in some way. Either the peer firm learns of
something new, or realizes the opportunity to remain competitive. Brown and Drake
(2013) document just this kind of asymmetry.
Hypothesis 2. Negative tax rate shocks have stronger effects on tax rate changes at peer
firms than positive tax rate shocks.
2.3. CASH ETR Peer Firm Responses
There are many ways to measure tax avoidance, and this choice is particularly important
to our study. The two primary measures used in prior literature are a measure based
on book tax expense (GAAP ETR) or a measure based on cash taxes paid (cash ETR).
We use GAAP ETR in our main tests because it appears to be the measure most salient
to investors and other capital market participants. Since managerial compensation is
typically based on GAAP measures, such as earnings per share, one might also expect
the GAAP ETR to be the subject of competition across firms or managers. Finally, for
the same reasons why cash-based measures are often preferred in the literature (Dyreng
et al. (2008), Edwards et al. (2014)), GAAP measures offer more opportunities for
manipulation or ‘window-dressing’ in response to tax rate shocks at focal firms. As a
result, peer firms who are unwilling or unable to respond to tax rate shocks in real terms
(i.e., tax related cash flows) are still incentivized and could still respond in terms of
reported GAAP ETRs.
Prior studies on financial reporting incentives provide evidence that managers
are fixated on bottom line reported financial accounting income and are even willing to
forego positive net present value projects in order to meet or beat earnings targets such
as analysts’ forecasts (Graham et al. (2005)). More directly related to this study, in a
tax specific setting, Graham et al. (2014) survey corporate tax executives and document
that almost half of the surveyed managers of publicly-traded firms indicate that the most
important tax metric to their firm’s top executives in the firm’s reported GAAP effective
9
tax rate. The importance of the GAAP effective tax rate can be viewed in contrast to cash
taxes paid, which only 15 percent state of surveyed managers indicate is the firm’s most
important tax metric. Armstrong et al. (2012) provide further evidence consistent with the
primacy of GAAP ETRs to managers. They find a strong negative association between
tax director incentive compensation and GAAP ETRs, but no relation between incentive
compensation and cash ETRs. Collectively, these studies suggest that if firms benchmark
there tax avoidance against industry peers, it is likely that reported tax numbers are more
important than actual tax payments.
Hypothesis 3. GAAP ETR responses by peer firms to tax rate shocks are stronger than
CASH ETR responses by peer firms to tax rate shocks.
3. Data and Methodology
3.1. Sample and Descriptive Statistics
We use the same sample of firm years to both identify focal firm shocks and measure
peer firm responses. Our sample selection procedure begins with all executives listed
in the ExecuComp database from 1992 through 2012. We look at executive tenures
across firms and years, looking for executives that work across several firms. Our data
requirements are minimal, requiring that the firm-year be listed in the Compustat database
with non-missing values of total assets and other required firm characteristics available.
The resulting sample is 24,800 firm-year observations. Table 1 presents descriptive
statistics and Table 2 presents correlations. The GAAP effective tax rate, (GAAP ETR)
is measured as total tax expense (current and deferred) divided by pre-tax income (less
special items).6 Consistent with prior research, the mean GAAP ETR in the sample is
29.7%, with a median of 32.6%, which are both below the U.S. statutory rate of 35%. We
6 Following
Dyreng et al. (2010), we winsorize the ETR measures at 0 and 1, and use raw values of the
control variables. In an untabulated robustness check, we repeat our analysis winsorizing all continuous
variables at the 1 percent and 99 percent levels, results are similar to those reported in the tables and
inferences are unchanged.
10
will also examine cash effective tax rates (CASH ETR), which is calculated as the cash
payment of taxes during the fiscal year divided by pre-tax income (less special items).
We observe CASH ETR that are several percentage points lower than GAAP ETRs, with
a mean value of 27.0% and median of 25.4%. The table also includes statistics for the
standard set of control to be included in regressions explaining firm level effective tax
rates.
3.2. Estimating Tax Shocks
The first step of developing our focal firm shocks to tax rates is to estimate the effects
of individual executives on firm tax avoidance, as in Dyreng et al. (2010). While some
of the previous literature has emphasized the homogeneity of executives, we appeal to
the alternative view, as discussed in Bertrand and Schoar (2003), that executives have
ample discretion within firms, and that they use this discretion to affect corporate policy,
particularly tax avoidance strategies in our setting.
We follow Bertrand and Schoar (2003) and Dyreng et al. (2010) and estimate the
following regression model over our panel of data.
ET Rit = α0 + ∑ αkCONT ROLitk + ∑ αt Y EARt + ∑ αi FIRMi
t
k
j
+ ∑ αm EXECUT IV Em + εit .
m
Here ET Rit is GAAP ETR (or CASH ETR for later comparison tests of Hypothesis
3), and FIRMi , Y EARt are firm and year fixed effects respectively. Following
Dyreng et al. (2010) we include control variables for firm profitability (EBIT DA),
research and development activities (R&D), advertising expenses (ADV ERT ISING),
selling general and administrative expenses (SG&A), capital expenditures
(CAPITALEXPENDITURES), sales growth (CHANGEINSALES), capital
structure (LEV ERAGE), cash holdings (CASHHOLDINGS), foreign activity
11
(1)
(FOREIGNOPERAT IONS), size (SIZE), the presence of loss carryforwards
(NET OPERAT INGLOSS), stock based compensation (OPT IONEXPENSE),
intangibility (INTANGIBLEASSET S), and property, plant, and equipment
(GROSSPP&E). Detailed variable definitions are included in Table 1. We are interested
in uncovering the statistically significant parameters from among the EXECUT IV Em
fixed effects. We employ panel data so that variation in executive tenure, or more
specifically, executive turnover, can be used to identify the effects executives have vs.
stationary firm characteristics that could have tangible effects on tax avoidance. As
a result, we can only estimate managerial fixed effects for managers who worked at
multiple firms in our sample.
By pure chance, one would expect that some executives will have a significant
effect, in a statistically sense. However, Dyreng et al. (2010) find that the number of
observed significant managers is significantly different from a ‘noise’ benchmark, a
fact that we verify for our slightly larger sample (due to our longer sample period) in
untabulated results.
A firm can hire or fire several executives at once, leading to a possible wholesale
change in tax avoidance. To estimate tax shocks at the firm-year level, we employ a
parsimonious procedure of summing only those executive effects within a firm and year
that are statistically significant to get a fixed effect for the top executive team in that
year. Alternative procedures, such as using an F-test to determine the joint significance
of executive fixed effects in a particular year yield similar results. A firm-year is coded
as a focal firm tax shock, which we denote as BIGSHOCK, if the sum of significant
fixed effects is, in absolute value, above a threshold of 0.05, corresponding to a five
percentage point difference in effective tax rate. In further tests, this variable is split into
BIGSHOCKUP, if the tax rate increased due to the shock, and BIGSHOCKDOW N if the
rate fell. In summary, the procedure for defining BIGSHOCK is as follows:
12
BIGSHOCKt =




1

















-1



















 0
if the sum of significant executive fixed effects in year t,
subtracting the sum of significant executive fixed effects in
year t − 1, is greater than 0.05
if the sum of significant executive fixed effects in year t,
subtracting the sum of significant executive fixed effects in
year t − 1, is less than -0.05
otherwise
and, for later specifications, to break down the direction of the shock,
BIGSHOCKUPt = 1
if BIGSHOCKt = 1
BIGSHOCKDOW Nt = −1
if BIGSHOCKt = −1
For the estimation of peer effects in Section 3.4, we would like these tax shocks
to be exogenous in the sense that they are uncorrelated with the error terms in the tax rate
regressions for peer firms, conditional on observables. Note that this does not preclude
a board replacing an executive who is relatively poor at tax avoidance with one who is
relatively good, or vice versa, for unobservable reasons, or based on omitted variables
that we cannot include in our tax rate regressions. Alternatively stated, for our purposes,
it does not matter if a board chooses its executives particularly for their tax avoidance
abilities, or even does so anticipating future tax rate changes. Our empirical strategy relies
on the tax shocks being exogenous to the peer firm but, the shock need not be exogenous
to the focal firm actually undergoing the executvie turnover. This is an important point
given recent work by Fee et al. (2013), documenting that boards seem to anticipate
apparent changes in managerial style so that such changes in, for example, tax avoidance
behavior, could reflect a purposeful choice by the board. For this reason, in our empirical
strategy for estimating peer effects, we are particularly concerned with controlling for
13
external drivers of such a decision.
3.3. Determining Peers
We define a firm’s peers as those firms that are most similar in industry and size. Firms
define their own peer groups for compensation in their proxy statements (Lewellen
(2014), Albuquerque et al. (2013)) or for product market purposes in their annual reports
but we wish to avoid any firm-level biases in determining peer groups. Analysts also
determin and disclose peer groups in their reports but this selection process is also subject
to discretion and impacted by strategic and opportunistic factors (De Franco et al. (2014)).
In all these cases, such peer groups also vary in number of firms included (making results
less comparable across firms), and could contain cherry-picked firms which are neither
industry-comparable nor otherwise relevant to a firm’s operational choices.
Choosing industry specifications for firms is non-trivial in determining the
peers of a firm. We wish to correctly control for any industry trends that may occur,
while also allowing for there to be enough peers within each industry in each year. If an
industry specification is too granular, then we face the problem of not properly modeling
industry trends that potentially drive tax behavior. If, however, we make our industry
specification too fine, there might not be enough firm observations in each industry and
year to construct a peer group. We choose the Fama-French 17 industry definition (Fama
and French (1988)), with its focus on capital market considerations, as a balance between
these two competing drivers. The mean number of firms in each industry and year is 74,
while the median is 61. The associated tenth and twenty-fifth percentiles are 16 and 23
so that even in relatively smaller industries, we have a meaningful number of potential
sample firms from which to pull out size-comparable peers.7
In addition to selecting the set of peer firms within industry, we must also select
firms that are comparable. If one firm is to learn from or benchmark against another, or
7 In
contrast, 2-digit SIC codes have a median of 10 in each industry and year in our sample, with a tenth
percentile of 3, and a twenty-fifth percentile of 5.
14
for the firms to be compared in the marketplace, then they might also need be similar in
characteristics other than industry. For our tests, we choose to select peer firms that are
also of similar size. When selecting similar sized firms from an industry-year to form the
peer group, the choice of number of peer firms is also subject to a tradeoff. If we choose
a number of peer firms which is too small, then measuring the effect of shocks to a focal
firm will very rarely significantly effect the peer group. If, however, we choose a number
of peer firms which is too large, then the effect of a shocks to the focal firm on a peer
should fall as that peer firm becomes less and less comparable. When selecting firms for
similar size, we measure firm size as total assets. In in main analysis we select an ad-hoc
number of five industry-year peer firms. We investigate alternative ways of defining peer
groups in Section 5.
3.4. Estimating Peer Effects
The goal of our study is to measure the effect of shocks, in the form of a large change
to the executive fixed effects of tax avoidance at a focal firm, on the effective tax rate of
peer firms. Using BIGSHOCK, as defined above, and the industry-size peer groups for
each firm, we model the effective tax rates of peer firms using a specification similar to
Equation 1. We use this model to isolate discretionary choices over effective tax rates
that peer firms take in response to shocks. We measure the effect of the shock at the focal
firm, on the peer firms, one year after the shock first occurs. We later investigate possible
timing differences in effects using the level of the shock and a series of lags.
ET Rit = α0 + ∑ αkCONT ROLitk + ∑ αt Y EARt + ∑ αi FIRMi + ∑ αm EXECUT IV Em
k
t
i
m
+BIGSHOCKi,t−1 + εit
Despite the many controls we include for evaluating a business’s opportunities
15
(2)
for tax avoidance, we also include year and firm fixed effects. The inclusion of year fixed
effects accounts for macroeconomic changes effect tax payment or financial reporting
choices across firms. The inclusion of firm fixed effects accounts for the impact of tax
payment and the related financial reporting behavior of any firm specific characteristic
that is fixed over time, which may be unobservable or difficult to model. This could
still leave possible endogeneity between focal firm shocks and the respective peer firm
behavior, however. To draw stronger causal inferences, we need focal firm shocks that are
exogenous to the strategy of the peer firm. There could be some sort of within-industry
trend or shock that leads firms in an industry to reduce their tax burden or otherwise
engage in more or less tax avoidance behavior. Such phenomena include industry-level
changes in tax policy from the government or changes in IRS audit frequency and/or
intensity. To alleviate this concern, and to make the effects of these shocks exogenous to
industry trends or strategies, we also utilize a regression model for the effective tax rates
that includes separate year effects by industry (i.e., industry-year fixed effects).
ET Rit = α0 + ∑ αkCONT ROLitk + ∑ αt Y EARt ∑ α j FIRM j + ∑ αm EXECUT IV Em
k
t
j
m
+BIGSHOCKi,t−1 + ∑ α` INDUST RY` ×Y EARt + εit
(3)
`,t
If firms are merely copying the behavior of their peers, then positive shocks to
tax rates at focal firms will induce peer firm responses as much as negative shocks to
focal firm tax rates. This would be consistent with a story in which tax avoidance is
arbitrary, and firms engage in it merely to appear like their peers, rather than to remain
competitive. However, it is more natural to consider learning, or competition, as drivers
of tax avoidance behavior. If a focal firm receives a positive shock to its tax rate, then
peers will not respond in a statistically significant way. The reason is that the peer firm
is not learning of new tax avoiding strategies, how these strategies may be perceived as
the norm in the market, or how to respond competitively with tax avoidance. The focal
16
firm with the positive tax shock could have chosen management without considering
tax avoidance concerns. The selection of new management could be to address any of
several other within-firm concerns. If a focal firm receives a negative tax rate shock, then
peer firms will be more likely to respond. A peer firm can learn about the tax avoiding
strategy, better understand the regulatory response, or attempt to respond to the change
in tax avoidance behavior through financial reporting choices. To meet these asymmetric
concerns of the underlying tax avoidance story, we introduce a split shock, with the same
signs as before, BIGSHOCKUP and BIGSHOCKDOWN. This allows for different effects
for shocks upward to focal firm tax rates and those shocks downward.
ET Rit = α0 + ∑ αkCONT ROLitk + ∑ αt Y EARt + ∑ α j FIRM j + ∑ αm EXECUT IV Em
t
k
j
m
+BIGSHOCKUPi,t−1 + BIGSHOCKDOW Ni,t−1
+ ∑ α` INDUST RY` ×Y EARt + εit
(4)
`,t
4. Results
4.1. Peer Firm Responses to Focal Firm Shocks
When combining the estimated focal firm tax rate shocks with the panel data for
estimating peer effects, we find that about two percent of our sample of firms are subject
to a tax shock (from its own executive turnover) of at least five percentage points in
GAAP ETR. This results in approximately ten percent of firm-years being indirectly
affected by such a shock (i.e., ten percent of peer firm-year observations experience
a shock to the focal firm’s GAAP ETR)—a function of our choice of peer group size.
We investigate the impact of our selection of peer group size below. Conditional on
being impacted by at least one focal firm tax shock in a given year, a peer firm faces on
average 1.1 shocks in that year, so the procedure of averaging across focal firm shocks to
a particular peer firm-year is not important to the results that follow. This also suggests
17
that the tax shocks are not arising due to an industry-wide response to a change in the
benefits or costs of tax avoidance.
To test our first hypothesis, we start with the basic model of tax avoidance
presented in Table 3, including firm characteristics and year fixed effects, as well as
dummy variables for a direct (own) shock or indirect (peer shock). The coefficient on
BIGSHOCK is positive, as expected, which means that a firm responds to shocks at
a focal firm by adjusting their GAAP effective tax rate in the same direction by 0.35
percentage points. However, this result is merely suggestive, since the t-statistic of 1.52
is below traditional levels of significance. This same pattern of results holds after adding
firm fixed effects, in column (2), and executive fixed effects, in column (3), which yield
the equivalent empirical model to the model that was used to estimate the tax shocks in
section 3.2. In column (4), we add industry-year fixed effects.
Note that a firm’s own shock has a strong effect in the predicted direction, of just
under six percentage points of ETR, in columns (1) and (2), which is a mechanical result
of our procedure for estimating focal firm tax shocks. After adding executive fixed effects
in columns (3) and (4), this effect mechanically goes away because of the near-perfect
collinearity between the tax shock and the executive fixed effects.8
4.2. Peer Firm Asymmetric Responses
The specifications in Table 3 assume a symmetric effect of tax rate shocks (i.e., positive
and negative shocks are both included in the BIGSHOCK indicator variable). To examine
is the response to focal firm tax rate shocks is asymmetric we proceed to test our second
hypothesis by splitting BIGSHOCK into two mutually exclusive indicator variables
according to whether the tax rate shock was positive or negative. Table 4 employs
this classification and presents the same four specifications as above. An asymmetric
8 The
reason these coefficients can be estimated at all is that the shock is a nonlinear transformation of a
linear combination of the executive fixed effects, though the variation induced by this transformation is
small.
18
response is clear from the simple model presented in column (1), where the coefficient
on BIGSHOCKUP is very close to zero and not close to statistical significance, whereas
BIGSHOCKDOW N is positive and statistically significant. This means that when a
focal firm (i.e., a firm’s peer firm) experiences a negative tax shock (an increase in tax
avoidance), the firm itself also experiences a decline in GAAP effective tax rate. If the
firm itself experiences a shock, its own tax rate moves in the predicted direction. In terms
of magnitude, the peer effect is about 0.6 percentage points, relative to an own effect of
five percentage points.
In column (2), we add firm fixed effects so that identification of tax effects comes
from changes in tax rates within firms over time. This yields somewhat larger effects
from the shock though the relative magnitude of the peer effect and own effect is similar.
Column (3) adds in executive fixed effects to parallel the model used to estimate tax
shocks in section 3.2. The peer effect for the downward shock remains positive and
significant. As above, this specification removes the effect of OW NSHOCK through its
collinearity with the executive fixed effects.
The fourth column adds industry-year fixed effects for 17 Fama-French industries,
which control for such factors as industry-level tax policy changes, changes in IRS audit
risk, or changes in public perceptions or opinions about tax avoidance activity. This
addition yields substantially similar results. In untabulated results, we verify that the
choice of industry aggregation level at which to construct the industry-year controls
does not meaningfully impact the results. In fact, the results are robust to controlling for
variation at a much lower level of aggregation.
Ideally, we would preform this estimation using a richer model that determines
tax shocks simultaneously with peer effects. This would allow for multiple levels of
interaction between firms, with peer firms responding to a shock at the focal firm, then
the focal firm itself responding to the peer firms, and so on. Such a procedure would be
computationally taxing and econometrically complex. However, because the magnitude
19
of peer effects is smaller than the size of the tax shocks by approximately an order of
magnitude, we do not think that feedback from peer firms back to the focal firms would
significantly affect our results or inferences.
Table 5 investigates the robustness of the evidence for asymmetric peer effects
along three different dimensions: the peer group industry choice, the number of peers per
firm, and the choice of cutoff for determining tax shocks at focal firms from executive
turnover. We consider two digit NAICS industries and GIC groups in addition to the
baseline 17 Fama-French industries. Results obtain using all industry definition are
quantitatively and qualitatively similar. Next, we vary the size of the peer group from
three to seven (note, in Table 2 and 3 we report results using peer groups of five);
qualitatively, the results are similar though there is some evidence of an increase in peer
effect as the group size increases. Expanding the peer groups further yields progressively
smaller (untabulated) effects, which retain statistical significance until a peer group
size of ten. This is suggestive of the tradeoff discussed above between the necessity of
having enough shocks to actually estimate an effect and expanding the group so much that
comparability between firms declines too much to see any effect.
Lastly, we consider different choices for the cutoff in determining tax shocks.
Varying the cutoff for effective tax rate change from all statistically significant changes
greater than zero to all those greater than ten percent yields broadly similar results. In
sum, the significant peer effects for downward shocks to effective tax rates, and lack
thereof for upward shocks, appear to be robust to a variety of changes to our estimation
procedure.
In all of the preceding results, we assume that the effects of a focal firm tax rate
shock on peer firms take place with a one year lag. We now relax this assumption by
adding the contemporaneous and three lags of the shock. We continue to separate positive
and negative focal firm tax rate shocks. Because this specification requires more years of
lagged data for each firm, the sample size in the regression models that follow are lower.
20
Table 6 presents the four specifications of the tax rate regression using the additional lags
of the focal firm tax rate shock. BIGSHOCKUP is never close to significant in any of
these specifications, while the pattern of coefficients for BIGSHOCKDOW N adds some
nuance to the peer effects story. In each case, the effect increases from the year of the
shock to the subsequent two years (though the increase itself is not always significant) and
then becomes not significantly different from zero after that. The peak effect two years
after the focal firm shock corresponds to a more than one percentage point fall in GAAP
effective tax rate from a downward focal firm shock. The immediate effect is actually
statistically significant in the final specification, which suggests rather fast dissemination
of information and peer firm response, perhaps through quarterly reports or informal
information flows across managers, employees or advisers.
To illustrate the change in tax rates induced by focal firm tax rate shocks, we also
add terms to the regression for the first three leads of tax shocks – indicator variables
equal to one in year t if a shock occurs in year t + 1, t + 2 or t + 3. We run this regression
in the specification including firm controls and both year and firm fixed effects, and
graph the resulting coefficients and 95% confidence intervals in Figure 1. These results
are slightly different from the preceding table for two reasons. First, we here allow for
GAAP ETR changes to anticipate the focal firm tax rate shock by up to three years, both
to properly measure the magnitude of changes in GAAP ETRs over time and as a test of
the mechanism. More importantly, the additional three leads required means that we are
effectively only using data through the end of 2009.
Figure (1a) provides further evidence on the lack of peer firm impact of focal
firm positive tax rate shocks. There appears to be little anticipation of the shock by peer
firms and still no effect in the years following the focal firm positive shock. However,
Figure (1b) provides some insight into the effects discussed above. None of the leads are
significantly different from zero, providing no evidence of anticipation of the (future)
focal firm negative shocks. This is reassuring supportive evidence for the exogeneity
21
of these focal firm shocks to peer firms. In contrast with the results in Table 6, we
observe a more level effect of the negative shock, which appears to persist more strongly
through three years following the focal firm shock. As the time after the shock increases,
it becomes harder to attribute the change in GAAP ETR only to the shock; for this
reason, we do not continue to investigate further lags or leads. The mean effects pre- and
post-shock are significantly different at the 1% level and this result is maintained even
when excluding the year of the shock, or even including it in the pre-shock period.
For comparison purposes, and to illustrate the magnitude of peer effects, we
combine and graph the coefficients delineating the estimated peer effect over time with
those for the shocked focal firm itself, in Figure 2. The top figure again presents the effect
of an upward shock to the GAAP effective tax rate, while the bottom figure present the
same for a downward shock. In both figures, the direct effect of the shock on the focal
firm (i.e., the firm actually experiencing the executive turnover) is approximately the
same size and indicates a particularly large difference in tax rates in the years just before
and after the shock. This could reflect purposeful behavior on the part of the firm or the
executive but more likely results from the estimation procedure. Executive tenures which
feature this pattern, whether behavioral or noise, are mechanically more likely to make
it through the procedure and become embodied in our tax shocks. The magnitude of the
focal firm change appears to decline over time, even after the year of the shock.
Figure (2b) illustrates how the significant peer effects from the preceding figure
compare to these direct effects for BIGSHOCKDOW N. The peer effect is approximately
10% of the focal firm effect, whether calculated as the immediate different in coefficients
around the shock or the mean difference over time. Since a shock comes on average
from only slightly more than one of a firm’s peers in a particular year (or 20% of its peer
group), this corresponds to about a 50% response to the average effective tax rate in the
peer group.
This effect size is rather large given the paucity of evidence on peer effects in tax
22
avoidance, and the fact that tax planning strategies are not typically ‘one-size-fits-all’.
Optimal tax strategies in both a static and a dynamic sense vary across firms so there is
likely a limit to the extent to which one firm would or could respond to its competitors
tax strategy (in terms of cash flows and/or reported tax expense). Underlying the large
tax effects estimated by Dyreng et al. (2010) and replicated in this paper, must be the idea
that the tax function is an area subject to managerial skill but also to managerial focus.
Very large observed changes in GAAP effective tax rates, even if specifically brought
about by a purposeful tax plan, are likely associated with declines in pre-tax profits
It is also worth noting that actual peer effects in tax avoidance could be larger
than we have estimated, for two key reasons. First of all, many tax planning strategies
would operate through the firm characteristics which we include as controls, such as the
indicator variable for foreign activity. Hence, if such strategies are learned from peers,
they would show up as a change in the underlying firm characteristic and so would not
give rise to any residual GAAP ETR difference. Second of all, the finance literature has
documented peer effects in choices such as firm capital structures (Leary and Roberts
(2014)) which have obvious tax consequences – these kinds of effects are also not
captured by our empirical strategy.
4.3. CASH ETR Peer Firm Responses
Next, we turn to our tests of our third hypothesis—that peer effects will be more
pronounced in GAAP effective tax rates than in cash effective tax rates, because of the
salience of the GAAP number to both managers and other capital market participants.
Table 7 replicates the same four specifications of the earlier results for use cash effective
tax rates (Cash ETRs) in lieu of GAAP ETRs. The choice of effective tax rate enters into
the procedure twice—first when estimating focal firm tax shocks and then a second time
when estimating the peer response (i.e., the peer effects). When we repeat our analysis
using Cash ETRs, for both estimating the focal firm shock and the peer response, we fail
23
to observe a significant peer effect in any of the four specifications.9 When viewed in
combination with the significant peer effects observed using GAAP ETRs and reported
in Table 3, the lack of significance on Cash ETR responses provides evidence consistent
hypothesis 3. The peer firm tax cash flow responses (i.e., changes in cash effective tax
rates), to positive and negative GAAP and cash effective tax rate shocks at focal firms,
are not statistically significant. While we are cautious not to accept the null hypothesis,
it appears as though firms do not respond by changing tax payments to changes in the
effective tax rates of their peers. This result is in contrast to the evidence presented above
consistently documenting responses through financial reporting for taxes (i.e., GAAP
ETRs). This discrepancy in peer effects could be due to GAAP-based compensation
incentives for executives or the primacy of earnings based measures in capital markets
that managers consider, and are incentivized by, when benchmarking corporate tax
behavior.
4.4. Supplemental Analysis
In this subsection we perform several supplemental analyses related to our main
findings presented and discussed above. First, we examine the role of product market
competition in our hypothsized relation. Several recent studies examine how product
market competition impacts tax avoidance. For example, Brown et al. (2014) examine
how increased product market competition impacts tax avoidance using two industry-level
shocks to competition (reductions in import tariffs and major deregulation events). They
observe that tax avoidance, measured using GAAP ETRs, increases following increases in
competition, particularily for income mobile firms and firms that have Big N auditors.
In a related study, Kubick et al. (2015) document that firms with high product market
9 In
untabulated tests, we repeat this analysis examining peer firm Cash ETR responses to focal firm GAAP
ETR shocks (i.e., using GAAP ETRs in the first stage to identify a tax rate shock and Cash ETRs in the
second stage for the peer effect regression). This specification examines whether peer firms respond in
terms of tax related cash flows to a GAAP effective tax rate shock at the focal firm. (i.e., What is the effect
of shocks to GAAP effective tax rates on the cash ETRs of peer firms?). Consistent with hypothesis 3, we
fail to observe significant coefficients on the BIGSHOCKUP and BIGSHOCKDOW N indicator variables.
24
power exhibit higher levels of tax avoidance than their product market competitors and
that investors view comparatively the higher cash tax avoidance of firms with greater
product market power as relatively more risky. Kubick et al. (2015) also document that
other firms in the same product market mimic the tax avoidance behavior of high product
market power firms. We differ from Brown et al. (2014) and Kubick et al. (2015) in
several important ways. First, while Brown et al. (2014) and Kubick et al. (2015) focus
on the impact of product market competition and product market leaders as a cause of
higher levels of tax avoidance, our focus is on how financial reporting for taxes and tax
payments change in response to exogenous shocks to a peer firm’s tax avoidance.10 The
source of the change in tax avoidance at the focal firm is not important in our setting (as
long as it is exogenous to the peer firms), whereas this is the primary question at issue in
Kubick et al. (2015). Second, when examining if non-product market leader firms mimic
the tax avoidance of product market leaders, Kubick et al. (2015) relate current levels
of tax avoidance of non-product market leaders to the lagged tax avoidance of product
market leaders. Our hypotheses and tests relate to how similar firms (i.e., peers) respond,
in both reporting choices and cash flows, to changes in tax avoidance at the focal firm; we
do not restrict the focal firm to being a product market leader or not, being large or small,
or any other criteria.
To examine the effect of product market competition on our findings, we begin by
partitioning our sample into groups based on high and low product market competition.
To do so, we calculate the Herfindahl index by industry-year and designate observation
with values of the Herfindahl index below median as facing high levels of product
market competition. We repeat our main analysis on both high- and low-product market
competition observations and observe that our results are generally strongest in less
competitive industries. Finally, we perform (untabulated) supplemental analysis to
10 Brown
et al. (2014) restrict their analysis to GAAP ETRs. The authors are unable to examine the,
potentially differential, impact on CASH ETRs as a significant number of the competitive shocks they
examine occure prior to 1987, when cash taxes paid became widely reported.
25
examine our findings in relation to the documented association between product market
competition and the level of tax avoidance in Kubick et al. (2015) and Brown et al.
(2014).
Next, we perform several additional untabulated analysis around our coding of
BIGSHOCK, BIGSHOCKUP, and BIGSHOCKDOW N. In the main analysis these
variables are coded based on existence of a significant executive fixed effect using the
Dyreng et al. (2010) methodology. Because this procedure uses the change in ETR not
explained by firm characteristics in a regression model, not the raw change in ETRs
themselves, it is possible that a positive shock is actually a negative change in raw ETR
(and vice versa). Managers at peer firms could respond to this unexplained changed
in ETR, or, they may respond to the more naive raw change in ETR. To ensure our
results are robust to the potentially more naive approach managers could take, we first
calculate the frequency that the sign of the raw change in GAAP ETR matches our
coding of BIGSHOCKUP and BIGSHOCKDOW N. We observe that the signs match
for approximately three quarters of observations.11 To further ensure the robustness of
our findings, we repeat our multivariate analysis i) requiring that the direction of the
raw change in ETR match the sign of the statistically significant shock obtained from
the executive fixed effects model, and ii) requiring that the raw change in GAAP is at
least five percent and in the same direction as the sign of the statistically significant
shock obtained from the executive fixed effect model, in order to code the indicator
variables, BIGSHOCKUP and BIGSHOCKDOW N, other than zero. Inferences from
both these specifications are unchanged from those reported in the tables, for GAAP
ETRs the coefficients on BIGSHOCKUP are not significantly different from zero and
11 Specifically,
for BIGSHOCKUP, 73% of the changes are in the same direction, and for
BIGSHOCKDOW N, 78% of the changes are in the same direction.
26
the coefficients on BIGSHOCKDOW N are significantly positive.12
5. Conclusion
In this paper, we provide a novel empirical strategy for estimating causal peer effects in
tax avoidance behavior across firms. Following the methodology of Dyreng et al. (2010),
we identify effective tax rate shocks to firms based on executive turnover, which involves
a significant change in the ‘tax avoidance ability’ of an executive team. Using these
shocks, we examine whether peer firms adjust their tax payments and/or their associated
financial reporting of tax expense in response to changes in the tax rates of peer firms. We
find strong evidence for asymmetric effects – shocks that decrease effective tax rates at
focal firms are associated with decreases in GAAP effective tax rates at peer firms, while
those which decrease tax avoidance (increase effective tax rates) induce no such peer
effect. The positive response seems to occur quite quickly and persists for several years.
We also examine peer firm response to tax rate shocks in terms of real effects, changes
in cash effective tax rates. Unlikely the peer responses through GAAP effective tax rates,
we fail to observe significant changes in tax related cash flows at peer firms to tax rate
shocks. Taken together this evidence is consistent with the primacy and salience of GAAP
effective tax rates to managers, consistent with survey evidence (Graham et al. (2014))
and tax manager compensation (Armstrong et al. (2012)).
12 For
comparability with the main results and the other robustness test reported in Table 5, the coefficients
and standard errors on BIGSHOCKUP (se) and BIGSHOCKDOW N (se) are -.0023 (.0038) and 0.0130***
(.0045) respectively when requiring the direction of the raw change in GAAP ETR to match, and -0.0016
(.0043) and .0121** (.0053) respectively when requiring at least a five percent change in the raw GAAP
ETR.
27
References
Ana Albuquerque. Peer firms in relative performance evaluation. Journal of Accounting
and Economics, 48(1):69–89, 2009.
Ana M Albuquerque, Gus De Franco, and Rodrigo S Verdi. Peer choice in ceo
compensation. Journal of Financial Economics, 108(1):160–181, 2013.
Christopher S. Armstrong, Jennifer L. Blouin, and David F. Larcker. The incentives for
tax planning. Journal of Accounting and Economics, 53(1):391–411, 2012.
Anil Arya and Brian Mittendorf. Using disclosure to influence herd behavior and alter
competition. Journal of Accounting and Economics, 40(1):231–246, 2005.
Linda Smith Bamber, John Jiang, and Isabel Yanyan Wang. What’s my style? The
influence of top managers on voluntary corporate financial disclosure. The Accounting
Review, 85(4):1131–1162, 2010.
Marianne Bertrand and Antoinette Schoar. Managing with style: The effect of managers
on firm policies. The Quarterly Journal of Economics, pages 1169–1208, 2003.
Jennifer Brown, Cláudia Custódio, Steve Hillgeist, and Eugenia Yeh. Competitive
pressure and tax avoidance. Working Paper, 2014.
Jennifer L. Brown. The spread of aggressive corporate tax reporting: A detailed
examination of the corporate-owned life insurance shelter. The Accounting Review,
86(1):23–57, 2011.
Jennifer L. Brown and Katharine D. Drake. Network ties among low-tax firms. The
Accounting Review, 89(2):483–510, 2013.
James A. Chyz. Personally tax aggressive executives and corporate tax sheltering. Journal
of Accounting and Economics, 56(2):311–328, 2013.
Gus De Franco, Ole-Kristian Hope, and Stephannie Larocque. Analysts’ choice of peer
companies. Review of Accounting Studies, 20(1):82–109, 2014.
Scott Dyreng, Jeffrey L Hoopes, and Jaron H Wilde. Public pressure and corporate tax
behavior. Working Paper, 2014.
Scott D. Dyreng, Michelle Hanlon, and Edward L. Maydew. Long-run corporate tax
avoidance. The Accounting Review, 83(1):61–82, 2008.
Scott D. Dyreng, Michelle Hanlon, and Edward L. Maydew. The effects of executives on
corporate tax avoidance. The Accounting Review, 85(4):1163–1189, 2010.
Alexander Edwards, Casey Schwab, and Terry Shevlin. Financial constraints and the
incentive for tax planning. 2014.
28
William N. Evans, Wallace E. Oates, and Robert M. Schwab. Measuring peer group
effects: A study of teenage behavior. Journal of Political Economy, pages 966–991,
1992.
Eugene F. Fama and Kenneth R. French. Permanent and temporary components of stock
prices. The Journal of Political Economy, pages 246–273, 1988.
C. Edward Fee, Charles J. Hadlock, and Joshua R. Pierce. Managers with and without
style: Evidence using exogenous variation. Review of Financial Studies, 26(3):
567–601, 2013.
Thierry Foucault and Laurent Fresard. Learning from peers’ stock prices and corporate
investment. Journal of Financial Economics, 111(3):554–577, 2014.
John Gallemore, Edward L Maydew, and Jacob R Thornock. The reputational costs of tax
avoidance. Contemporary Accounting Research, 31(4):1103–1133, 2014.
Weili Ge, Dawn Matsumoto, and Jenny Li Zhang. Do CFOs have style? An empirical
investigation of the effect of individual CFOs on accounting practices. Contemporary
Accounting Research, 28(4):1141–1179, 2011.
John R Graham, Campbell R Harvey, and Shiva Rajgopal. The economic implications of
corporate financial reporting. Journal of accounting and economics, 40(1):3–73, 2005.
John R. Graham, Michelle Hanlon, Terry Shevlin, and Nemit Shroff. Incentives for tax
planning and avoidance: Evidence from the field. The Accounting Review, 89(3):
991–1023, 2014.
Kose John and Dalida Kadyrzhanova. Peer effects in corporate governance. Available at
SSRN 1108860, 2008.
Linda K Krull. Permanently reinvested foreign earnings, taxes, and earnings management.
The Accounting Review, 79(3):745–767, 2004.
Thomas R. Kubick, Daniel P. Lynch, Michael A. Mayberry, and Thomas C. Omer.
Product market power and tax avoidance: Market leaders, mimicking strategies, and
stock returns. The Accounting Review, 90(2):675–702, 2015.
Mark T. Leary and Michael R. Roberts. Do peer firms affect corporate financial policy?
The Journal of Finance, 69(1):139–178, 2014.
Stefan Lewellen. Executive compensation and peer effects. Technical report, Working
Paper, 2014.
Kathleen Powers, John R. Robinson, and Bridget Stomberg. Do CEO performance
measures motivate tax avoidance? Working Paper, 2013.
David A Reppenhagen. Contagion of accounting methods: Evidence from stock option
expensing. Review of accounting studies, 15(3):629–657, 2010.
29
Myron S. Scholes, Mark A. Wolfson, Merle Erickson, Edward L. Maydew, and Terry
Shevlin. Taxes and business strategy: A planning approach. Prentice Hall Englewood
Cliffs, NJ, 2015.
Senyo Tse and Jennifer Wu Tucker. Within-industry timing of earnings warnings: do
managers herd? Review of Accounting Studies, 15(4):879–914, 2010.
30
.02
Residual ETR Change
-.01
.01
0
-.02
-3
-2
-1
0
1
Time Period Around Shock
2
3
-.02
Residual ETR Change
.01
-.01
0
.02
(a) ∆ET R over time: BIGSHOCKUP
-3
-2
-1
0
1
Time Period Around Shock
2
3
(b) ∆ET R over time: BIGSHOCKUP
Figure 1: This figure presents the GAAP ETR peer effect of a focal firm tax rate shock (figure 1a for
BIGSHOCKUP and figure 1b for BIGSHOCKDOW N). Each figure illustrates the effect for the three
years preceding the focal firm shock, the year of the shock, and three years following the shock. The
dashed lines correspond to 95% confidence intervals. None of the effects are statistically significant for
BIGSHOCKUP, while the contemporaneous and lags of BIGSHOCKDOW N are all significantly different
from zero. Contrary to the results presented in the tables, the coding of the focal firm shock in this figure
corresponds to the direction of the change in GAAP ETR, so that BIGSHOCKDOW N is associated with
decreases in GAAP ETRs. In this case, the mean coefficients pre- and post-shock are significantly different
from each other at the 1% level. This result is maintained when dropping the level and comparing only the
leads and lags.
31
.09
Residual ETR Change
-.03
0
.03
.06
-.06
-.09
-3
-2
-1
0
1
Time Period Around Shock
2
3
-.06
Residual ETR Change
0
.03
-.03
.06
(a) ∆ET R over time: BIGSHOCKUP and OW NSHOCKUP
-3
-2
-1
0
1
Time Period Around Shock
2
3
(b) ∆ET R over time: BIGSHOCKDOW N and OW NSHOCKDOW N
Figure 2: This figure presents the residual effect of a focal firm tax rate shock (figure 2a for upward,
figure 2b for downward) on the GAAP ETR of a peer firm as well as the residual tax rate for the firm
receiving the initial tax shock from its own executive turnover. Each figure presents the effects for three
years prior, the year of the tax rate shock, and three years following. The dashed lines correspond to 95%
confidence intervals. The changes for peer firms are not statistically significantly different from zero for
upward shocks, but are in the years following a downward shock. The statistically significant response is
roughly 10% of the own shocked firm’s residual tax rate change.
32
Table 1: Descriptive Statistics
Variable
GAAP ETR
CASH ETR
EBITDA
R&D
ADVERTISING
SG&A
CAPITAL EXPENDITURES
PERCENTAGE CHANGE IN SALES
LEVERAGE
CASH HOLDINGS
FOREIGN OPERATIONS
NET OPERATING LOSS
SIZE
ESTIMATED OPTION EXPENSE
INTANGIBLE TO TOTAL ASSETS
GROSS PP&E TO TOTAL ASSETS
BIGSHOCK
BIGSHOCKUP
BIGSHOCKDOWN
n
Mean
Std. Dev.
25th
50th
75th
24,800
23,179
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
24,800
0.297
0.270
0.174
0.032
0.011
0.196
0.116
0.126
0.225
0.134
0.540
0.366
7.607
0.009
0.176
0.535
-0.011
0.086
-0.098
0.154
0.186
0.106
0.143
0.028
0.165
0.081
0.517
0.190
0.155
0.498
0.482
1.584
0.046
0.186
0.388
0.429
0.282
0.297
0.232
0.148
0.107
0.000
0.000
0.067
0.062
0.008
0.068
0.022
0.000
0.000
6.467
0.000
0.019
0.225
0
0
0
0.326
0.254
0.153
0.000
0.000
0.172
0.095
0.083
0.212
0.071
1.000
0.000
7.462
0.000
0.115
0.450
0
0
0
0.374
0.345
0.218
0.028
0.007
0.294
0.147
0.182
0.339
0.193
1.000
1.000
8.611
0.003
0.278
0.788
0
0
0
Data comes from the Compustat universe of firms, with estimated option expense from Execucomp, for the years 1992-2012. The
following variable definitions are based on Dyreng et al. (2010).
GAAP ETR — the financial accounting effective tax rate, defined as total income tax expense divided by pre-tax book income before
special items;
CASH ETR — the cash effective tax rate, defined as cash tax paid divided by pre-tax book income before special items;
EBITDA — earnings before interest, taxes, depreciation, and amortization scaled by lagged total assets;
R&D — research and development expense divided by net sales; when missing, we count it as 0;
ADVERTISING — advertising expense divided by net sales; when missing, we count it as 0;
SG&A — selling, general, and administrative expense divided by net sales; missing values of SG&A are counted as 0 when used as a
control variable in Equation 1;
CAPITAL EXPENDITURES — reported capital expenditures divided by gross property, plant, and equipment;
CHANGE IN SALES — the annual percentage change in net sales;
LEVERAGE — the sum of long-term debt and long-term debt in current liabilities divided by total assets;
CASH HOLDINGS — cash and cash equivalents divided by total assets;
FOREIGN OPERATIONS — the firm has a non-missing, non-zero value for pre-tax income from foreign operations, zero otherwise;
SIZE — the natural log of total assets;
NET OPERATING LOSS — an indicator if the firm has a non-missing value of tax loss carry-forward, zero otherwise;
OPTION EXPENSE — calculated from ExecuComp as 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, scaled by average total assets;
INTANGIBLE ASSETS — the ratio of intangible assets to total assets;
GROSS PP&E — gross property, plant, and equipment divided by total assets
BIGSHOCK — statistically significant changes to managerial tax avoidance ability coded as 1 for significant positive shocks to GAAP
ETR, -1 for significant negative shocks to ETR, and zero otherwise
BIGSHOCKUP — an indicator variable coded as 1 for the positive portion of BIGSHOCK (i.e., increases in GAAP ETR), and zero
otherwise
BIGSHOCKDOWN — an indicator variable coded as -1 for the negative portion of BIGSHOCK (i.e., decreases in GAAP ETR), and
zero otherwise
33
34
(1) GAAP ETR
(2) CASH ETR
(3) EBITDA
(4) R&D
(5) ADVERT
(6) SG&A
(7) CAPX
(8) 4 SALES
(9) LEVERAGE
(10) CASH
(11) FOR. OPS.
(12) SIZE
(13) NOL
(14) OPT. EXP.
(15) INTAN
(16) GROSS PP&E
(1)
1
0.22
0.11
-0.16
0
-0.01
0.06
0.01
0
-0.07
-0.14
-0.05
-0.11
-0.01
-0.04
0.04
1
-0.07
-0.09
0.01
0
-0.04
-0.08
-0.02
-0.08
-0.02
-0.04
-0.08
-0.06
-0.03
-0.03
(2)
1
0.02
0.09
-0.01
0.33
0.23
-0.09
0.19
-0.07
-0.26
-0.08
0.22
-0.07
0.05
(3)
1
-0.02
0.04
0.13
0.03
-0.22
0.47
0.23
-0.12
0.11
0.1
0.01
-0.24
(4)
1
0.02
0.05
-0.02
0
0.06
0.06
0.02
0
0
0.08
-0.08
(5)
1
0.01
0
0
0.04
0.01
-0.03
0.01
0.01
0
-0.03
(6)
1
0.24
-0.17
0.25
-0.1
-0.23
-0.04
0.18
-0.05
-0.25
(7)
This table presents Pearson correlations for the control variables in the sample.
1
-0.03
0.06
-0.04
-0.07
-0.01
0.09
-0.02
-0.07
(8)
Table 2: Correlations
1
-0.39
-0.06
0.27
0.03
-0.12
0.06
0.23
(9)
1
0.09
-0.27
0.06
0.19
-0.07
-0.36
(10)
1
0.16
0.25
-0.07
0.09
-0.15
(11)
1
0.06
-0.23
0.4
0.05
(12)
1
-0.01
0.02
-0.1
(13)
1
-0.03
-0.08
(14)
1
-0.09
(15)
1
(16)
Table 3: Symmetric Peer Effects
This table presents results from the estimation of the effect of signed (positive or negative) shocks to a
focal firm’s GAAP effective tax rate on the GAAP effective tax rate of that firm’s peers. Data comes from
Compustat and Execucomp and covers 24,800 firms, year observations from 1992-2012. Following the
specification in Equation 2, we model the effects on GAAP effective tax rate shocks on peer firms’ GAAP
effective tax rates using various controls, year, firm, and industry fixed effects, to uncover the effects of
a large shock up or down—BIGSHOCK—to another firm’s reported tax expense. (1) includes controls
listed in Table 1, whether a firm received an executive tax rate shock itself, and year fixed effects. (2) also
controls for firm fixed effects. (3) adds in executive fixed effects. (4) also includes separate year effects for
17 Fama-French industries. ***, **, and * indicate significance at the 1%, 5% and 10% levels (two-sided
test). See Table 1 for variable definitions.
(1)
0.0035
(0.0023)
(2)
0.0038
(0.0023)
(3)
0.0033
(0.0025)
(4)
0.0026
(0.0025)
Controls, OwnShock, & Year FE
Firm FE
Executive FE
Industry*Year FE
0.0562***
(0.0051)
0.1143***
(0.0173)
−0.0573*
(0.0331)
0.0490
(0.0433)
−0.0000
(0.0002)
0.0564***
(0.0147)
−0.0048**
(0.0022)
−0.0217**
(0.0090)
−0.0785***
(0.0120)
−0.0268***
(0.0030)
0.0004
(0.0011)
−0.0150***
(0.0030)
−0.0622***
(0.0129)
−0.0000*
(0.0000)
−0.0122***
(0.0044)
Y
N
N
N
0.0578***
(0.0054)
0.0988***
(0.0281)
−0.0346
(0.0280)
−0.1869*
(0.1053)
−0.0003***
(0.0001)
0.0719***
(0.0191)
−0.0053***
(0.0017)
−0.0315**
(0.0124)
−0.0014
(0.0166)
−0.0047
(0.0050)
0.0129***
(0.0038)
−0.0097**
(0.0041)
−0.0414***
(0.0136)
−0.0000***
(0.0000)
0.0049
(0.0118)
Y
Y
N
N
−0.0008
(0.0060)
0.0893***
(0.0244)
−0.0260
(0.0217)
−0.2007*
(0.1219)
−0.0003***
(0.0001)
0.0682***
(0.0195)
−0.0056***
(0.0020)
−0.0255*
(0.0151)
−0.0042
(0.0187)
−0.0092
(0.0057)
0.0127***
(0.0046)
−0.0127***
(0.0047)
−0.0395***
(0.0132)
−0.0000**
(0.0000)
0.0035
(0.0146)
Y
Y
Y
N
−0.0006
(0.0059)
0.0826***
(0.0224)
−0.0280
(0.0218)
−0.1745
(0.1237)
−0.0005***
(0.0001)
0.0653***
(0.0198)
−0.0065***
(0.0021)
−0.0235
(0.0151)
0.0008
(0.0189)
−0.0074
(0.0058)
0.0105**
(0.0048)
−0.0122**
(0.0047)
−0.0386***
(0.0130)
−0.0000***
(0.0000)
0.0065
(0.0150)
Y
Y
Y
Y
Adj. R2
N
0.059
24,800
0.206
24,800
0.268
24,800
0.272
24,800
BIGSHOCKt−1
OW NSHOCKt−1
EBITDA
R&D Expense
Advertising Expense
SG&A Expense
Capital Expenditures
Change in Sales
Leverage
Cash Holdings
Foreign Operations
Size
Net Operating Loss
Option Expense
Intangible Assets
Gross PP&E
35
Table 4: Asymmetric Peer Effects
This table presents results from the estimation of the effect of asymmetric shocks—up or down—to a
focal firm’s GAAP effective tax rate on the GAAP effective tax rate of that firm’s peers. Data comes
from Compustat and Execucomp and covers 24,800 firms-years from from 1992-2012. Following
the specification in Equation 4, we model the effects on GAAP effective tax rates of peer firms using
various controls, year, firm, and industry fixed effects, to uncover the differential effects of a large shock
up—BIGSHOCKUP—to a focal firm’s GAAP effective tax rate (reporting higher taxes) or a large shock
down—BIGSHOCKDOW N—to a focal firm’s GAAP effective tax rate (reporting lower taxes). (1)
includes controls listed in Table 1, whether a firm received an executive tax rate shock itself, and year
fixed effects. (2) also controls for firm fixed effects. (3) adds in executive fixed effects. (4) also includes
separate year effects for 17 Fama-French industries. Note that BIGSHOCKUP = 1 for a positive shock
and BIGSHOCKDOW N =-1 for a negative shock so that peer effects in the expected direction would
yield positive coefficients in both cases. ***, **, and * indicate significance at the 1%, 5% and 10% levels
(two-sided test). See Table 1 for variable definitions.
(1)
−0.0003
(0.0035)
0.0068**
(0.0035)
(2)
−0.0008
(0.0036)
0.0079**
(0.0034)
(3)
−0.0015
(0.0038)
0.0077**
(0.0037)
(4)
−0.0024
(0.0038)
0.0072*
(0.0038)
Controls, OwnShock, & Year FE
Firm FE
Executive FE
Industry*Year FE
0.0496***
(0.0091)
0.0620***
(0.0070)
0.1142***
(0.0173)
−0.0572*
(0.0331)
0.0495
(0.0434)
−0.0000
(0.0002)
0.0563***
(0.0147)
−0.0048**
(0.0022)
−0.0215**
(0.0090)
−0.0782***
(0.0120)
−0.0268***
(0.0030)
0.0006
(0.0011)
−0.0149***
(0.0030)
−0.0621***
(0.0129)
−0.0000*
(0.0000)
−0.0121***
(0.0044)
Y
N
N
N
0.0707***
(0.0098)
0.0460***
(0.0071)
0.0990***
(0.0282)
−0.0345
(0.0279)
−0.1868*
(0.1052)
−0.0003***
(0.0001)
0.0722***
(0.0191)
−0.0053***
(0.0016)
−0.0317**
(0.0124)
−0.0016
(0.0166)
−0.0047
(0.0050)
0.0131***
(0.0037)
−0.0098**
(0.0041)
−0.0413***
(0.0136)
−0.0000***
(0.0000)
0.0048
(0.0118)
Y
Y
N
N
0.0088
(0.0100)
−0.0098
(0.0078)
0.0894***
(0.0245)
−0.0261
(0.0219)
−0.1980
(0.1216)
−0.0003***
(0.0001)
0.0683***
(0.0195)
−0.0056***
(0.0020)
−0.0260*
(0.0151)
−0.0045
(0.0187)
−0.0091
(0.0057)
0.0128***
(0.0046)
−0.0126***
(0.0047)
−0.0394***
(0.0132)
−0.0000**
(0.0000)
0.0033
(0.0146)
Y
Y
Y
N
0.0087
(0.0099)
−0.0094
(0.0078)
0.0827***
(0.0224)
−0.0280
(0.0219)
−0.1716
(0.1233)
−0.0005***
(0.0001)
0.0654***
(0.0198)
−0.0065***
(0.0021)
−0.0239
(0.0151)
0.0004
(0.0190)
−0.0074
(0.0058)
0.0106**
(0.0048)
−0.0122**
(0.0047)
−0.0385***
(0.0130)
−0.0000***
(0.0000)
0.0062
(0.0150)
Y
Y
Y
Y
Adj. R2
N
0.059
24,800
0.206
24,800
0.269
24,800
0.272
24,800
BIGSHOCKUPt−1
BIGSHOCKDOW Nt−1
OW NSHOCKUPt−1
OW NSHOCKDOW Nt−1
EBITDA
R&D Expense
Advertising Expense
SG&A Expense
Capital Expenditures
Change in Sales
Leverage
Cash Holdings
Foreign Operations
Size
Net Operating Loss
Option Expense
Intangible Assets
Gross PP&E
36
Table 5: Robustness of Peer Effects
This table presents robustness results from the estimation of the effect of asymmetric shocks—up or
down—to a focal firm’s GAAP effective tax rate on the GAAP effective tax rate of that firm’s peers.
Data comes from Compustat and Execucomp and covers 24,800 firm-year observations from 1992-2012.
Following the specification as (2) in Table 4, we model the effects of shocks to peer firm GAAP effective
tax rates, controlling for the firm characteristics listed in Table 1, whether a firm received an executive
tax rate shock itself, and year and firm fixed effects. Coefficient on the control variables are not reported
for parsimony. We present various specifications, varying industry categorization of peers, varying
the number of peers for each shocked focal firm, and varying the cutoff for executive shocks. Bolded
entries reproduce the standard specification for comparison purposes. Column (1) presents the effects
and (2) the standard errors for BIGSHOCKUP. Column (3) and column (4) present respectively the
BIGSHOCKDOW N coefficient and its standard error. ***, **, and * indicate significance at the 1%, 5%
and 10% levels (two-sided test). See Table 1 for variable definitions.
BIGSHOCKUPt−1
(s.e.)
BIGSHOCKDOW Nt−1
(s.e.)
Industry = SIC (FF-17)
Industry = NAICS (2-digit)
Industry = GIC Group
-.0003
.0003
.0012
(.0035)
(.0036)
(.0037)
.0079**
.0070**
.0070**
(.0034)
(.0036)
(.0036)
#Peers = 3
#Peers = 4
#Peers = 5
#Peers = 6
#Peers = 7
−.0004
−.0030
-.0003
.0004
.0003
(.0043)
(.0038)
(.0035)
(.0034)
(.0034)
.0049
.0075**
.0079**
.0098**
.0087**
(.0043)
(.0037)
(.0034)
(.0032)
(.0031)
Shock cutoff = 0
Shock cutoff = .025
Shock cutoff = .05
Shock cutoff = .075
Shock cutoff = .10
−.0005
−.0009
-.0003
−.0016
.0009
(.0034)
(.0035)
(.0035)
(.0038)
(.0041)
.0084**
.0082**
.0079**
.0094**
.0065*
(.0033)
(.0034)
(.0034)
(.0037)
(.0038)
37
Table 6: Timing of Peer Effects
This table presents timing results from the estimation of the effect asymmetric shocks—up or down—to
a focal firm’s GAAP effective tax rate on the GAAP effective tax rate of that firm’s peers. Data comes
from Compustat and Execucomp and covers 20,510 firm-year observations from 1992-2012. The sample
size is smaller than in the previous tables because of the requirement of two extra lags for each firm year.
Following the specification in Equation 4, we model the effects on GAAP effective tax rates of peer
firms using various controls, year, firm, and industry fixed effects, to uncover the differential effects of
a large shock up—BIGSHOCKUP—to another firm’s tax rate (reporting higher taxes) or a large shock
down—BIGSHOCKDOW N—to another firm’s tax rate (reporting lower taxes). (1) includes controls
listed in Table 1, whether a firm received an executive tax rate shock itself, and year fixed effects. (2) also
controls for firm fixed effects. (3) adds in executive fixed effects. (4) also includes separate year effects for
17 Fama-French industries. Coefficient on the control variables are not reported for parsimony. Note that
BIGSHOCKUP = 1 for a positive shock and BIGSHOCKDOW N =-1 for a negative shock so that peer
effects in the expected direction would yield positive coefficients in both cases. ***, **, and * indicate
significance at the 1%, 5% and 10% levels (two-sided test). See Table 1 for variable definitions.
(1)
(2)
(3)
(4)
BIGSHOCKUPt
−0.0014
(0.0036)
−0.0002
(0.0038)
0.0017
(0.0043)
0.0007
(0.0044)
BIGSHOCKUPt−1
−0.0003
(0.0036)
−0.0017
(0.0039)
−0.0007
(0.0044)
−0.0019
(0.0044)
BIGSHOCKUPt−2
−0.0007
(0.0036)
−0.0018
(0.0038)
0.0013
(0.0043)
−0.0012
(0.0045)
BIGSHOCKUPt−3
0.0034
(0.0038)
0.0014
(0.0040)
0.0016
(0.0044)
0.0012
(0.0044)
BIGSHOCKDOW Nt
0.0030
(0.0036)
0.0068*
(0.0039)
0.0069
(0.0043)
0.0089**
(0.0044)
BIGSHOCKDOW Nt−1
0.0054
(0.0035)
0.0088**
(0.0038)
0.0104**
(0.0043)
0.0098**
(0.0044)
BIGSHOCKDOW Nt−2
0.0080**
(0.0034)
0.0099***
(0.0036)
0.0116***
(0.0043)
0.0111**
(0.0043)
BIGSHOCKDOW Nt−3
0.0053
(0.0036)
0.0057
(0.0038)
0.0045
(0.0043)
0.0040
(0.0044)
Controls, OwnShock, & Year FE
Firm FE
Executive FE
Industry*Year FE
Y
N
N
N
Y
Y
N
N
Y
Y
Y
N
Y
Y
Y
Y
Adj. R2
N
0.079
20,510
0.236
20,510
0.276
20,510
0.280
20,510
38
Table 7: Asymmetric Peer Effects - Cash
This table presents results from the estimation of the effect of asymmetric shocks—up or down—to a
focal firm’s CASH effective tax rate on the CASH effective tax rate of that firm’s peers. Data comes
from Compustat and Execucomp and covers 23,179 firms-years from from 1992-2012. Following
the specification in Equation 4, we model the effects on cash effective tax rates of peer firms using
various controls, year, firm, and industry fixed effects, to uncover the differential effects of a large shock
up—BIGSHOCKUP—to a focal firm’s cash effective tax rate (reporting higher taxes) or a large shock
down—BIGSHOCKDOW N—to a focal firm’s cash effective tax rate (reporting lower taxes). (1) includes
controls listed in Table 1, whether a firm received an executive tax rate shock itself, and year fixed effects.
(2) also controls for firm fixed effects. (3) adds in executive fixed effects. (4) also includes separate
year effects for 17 Fama-French industries. Note that BIGSHOCKUP = 1 for a positive shock and
BIGSHOCKDOW N =-1 for a negative shock so that peer effects in the expected direction would yield
positive coefficients in both cases. ***, **, and * indicate significance at the 1%, 5% and 10% levels
(two-sided test). See Table 1 for variable definitions.
Controls, OwnShock, & Year FE
Firm FE
Executive FE
Industry*Year FE
(1)
0.0014
(0.0042)
-0.0021
(0.0045)
0.0516***
(0.0105)
0.0559***
(0.0078)
-0.0942***
(0.0203)
-0.2200***
(0.0423)
0.1047*
(0.0628)
0.0004**
(0.0002)
-0.0661***
(0.0219)
-0.0231**
(0.0108)
-0.0382***
(0.0132)
-0.0626***
(0.0155)
0.0037
(0.0039)
-0.0071***
(0.0015)
-0.0240***
(0.0040)
-0.0673***
(0.0112)
-0.0000
(0.0000)
-0.0398***
(0.0064)
Y
N
N
N
(2)
-0.0013
(0.0042)
-0.0002
(0.0046)
0.0571***
(0.0104)
0.0656***
(0.0078)
-0.2609***
(0.0289)
0.2640***
(0.0766)
-0.0149
(0.1119)
-0.0000
(0.0003)
0.0543**
(0.0244)
-0.0109*
(0.0063)
0.0186
(0.0160)
-0.0466**
(0.0217)
0.0030
(0.0066)
0.0091*
(0.0048)
-0.0148***
(0.0050)
-0.0259**
(0.0105)
-0.0000
(0.0000)
0.0438***
(0.0145)
Y
Y
N
N
(3)
-0.0019
(0.0046)
0.0007
(0.0050)
0.0004
(0.0108)
0.0176*
(0.0090)
-0.2748***
(0.0335)
0.2637***
(0.0799)
-0.0347
(0.1460)
-0.0001
(0.0003)
0.0620**
(0.0263)
-0.0105
(0.0070)
0.0285
(0.0182)
-0.0557**
(0.0246)
0.0038
(0.0076)
0.0128**
(0.0056)
-0.0103*
(0.0057)
-0.0219*
(0.0124)
-0.0000
(0.0000)
0.0486***
(0.0171)
Y
Y
Y
N
(4)
0.0001
(0.0047)
0.0015
(0.0051)
-0.0150
(0.0110)
-0.0041
(0.0098)
-0.2723***
(0.0334)
0.2595***
(0.0810)
-0.0409
(0.1501)
0.0001
(0.0003)
0.0582**
(0.0257)
-0.0115*
(0.0060)
0.0273
(0.0180)
-0.0599**
(0.0244)
0.0039
(0.0075)
0.0082
(0.0058)
-0.0090
(0.0058)
-0.0269**
(0.0122)
-0.0000
(0.0000)
0.0400**
(0.0170)
Y
Y
Y
Y
Adj. R2
N
0.056
23,179
0.256
23,179
0.305
23,179
0.318
23,179
BIGSHOCKUPt−1
BIGSHOCKDOW Nt−1
OW NSHOCKUPt−1
OW NSHOCKDOW Nt−1
EBITDA
R&D Expense
Advertising Expense
SG&A Expense
Capital Expenditures
Change in Sales
Leverage
Cash Holdings
Foreign Operations
Size
Net Operating Loss
Option Expense
Intangible Assets
Gross PP&E
39