Is the auditor’s industry specialization a reliable indicator of tax avoidance?
Andrew M. Bauer
Department of Accountancy
University of Illinois at Urbana-Champaign
ambauer@illinois.edu
Miguel Minutti-Meza
School of Business Administration
University of Miami
mminutti@bus.miami.edu
Andreya Marie Silva
School of Business Administration
University of Miami
andreyamarie@miami.edu
August 2012
Is the auditor’s industry specialization a reliable indicator of tax avoidance?
ABSTRACT: This paper examines whether purchasing tax services from an auditor (APTS) with a large
within-industry market share at city-level, a proxy for auditor expertise, affects the client’s level of tax
avoidance. First, we examine cross-sectional differences in tax avoidance between clients of specialist
and non-specialist auditors. Second, we demonstrate that, after matching clients of specialist and nonspecialists along a number of client characteristics, there are no consistent differences in tax avoidance
between clients of specialist and non-specialist auditors. Third, we perform a number of additional tests
that confirm the results of our matched sample analyses, such as: using the FIN48-related reserves as a
proxy for aggressive tax avoidance, including client fixed effects in our main models, and using
alternative definitions of expertise at the office-level. Overall, we do not observe a consistent pattern of
results indicating that purchasing APTS from an auditor with high market share results in comparatively
greater tax avoidance. The evidence provided in this study suggests that an auditor’s within-industry
market share is not a reliable indicator of tax avoidance. Nevertheless, we caution the reader that our
results must be interpreted with due regard to their limitations and to the caveats of our methodology and
highlight that our findings do not imply that industry knowledge is not important for auditors and tax
advisors.
Keywords: tax avoidance, auditor industry specialization; effective tax rate; cash effective tax rate;
matching.
Data
availability:
Data
are
publicly
available
from
sources
identified
in
the
article.
Is the auditor’s industry specialization a reliable indicator of tax avoidance?
I. INTRODUCTION
As a response to concerns regarding independence, the Sarbanes-Oxley (SOX) Act
restricts the types of non-audit services that a public company can purchase from its auditor. The
auditor cannot provide most consulting services to the client, but SOX still permits the auditor to
provide tax services (APTS) if the client’s audit committee grants approval. The passage of SOX
motivated a sizeable number of clients to stop purchasing tax services from their external
auditors (e.g., Omer et al. 2006; Maydew and Shackelford 2007); nevertheless, over 58 percent
of public companies purchased tax services from their external auditors after SOX and, on
average, tax fees were close to 20 percent of audit fees.1
The audit and tax literature has examined the consequences of purchasing tax services
from the external auditor in addition to whether certain auditor’s characteristics, such as
expertise, matter incrementally when purchasing joint audit and tax services. This paper focuses
on whether purchasing tax services from an auditor with a large within-industry market share at
the office level, a proxy for auditor expertise, affects the client’s level of tax avoidance.2
Examining the benefits of receiving joint audit and tax services is important given the potential
costs in terms of auditor independence. Moreover, asserting the effects of auditor industry
specialization in the context of APTS is relevant for audit committees approving tax services
from the auditor, to regulators concerned with issues of auditor independence, and to audit firms
aiming to perform high-quality audits while maintaining their competitive position in each
industry.
1
These statistics are based on all clients with non-missing audit and tax fee data in the Audit Analytics database
with fiscal year-ends between 2005 and 2011.
2
We focus on office or city-level expertise following Francis et al. (2005), Francis and Yu (2009), Reichelt and
Wang (2010), and McGuire et al. (2012).
1
The extant literature examining the effects of APTS, suggests that there is a knowledge
spillover between the audit and tax functions, which may increase the quality of the jointly
provided services. Kinney et al. (2004) and Seetharaman et al. (2011), show that both accounting
restatements and tax restatements are less likely to occur for clients that receive APTS. Fortin
and Pittman (2008) show that bondholders reward companies that pay proportionately more tax
fees to their auditor with lower yield spreads. Gleason and Mills (2011) document that clients
with APTS estimate full reserves for IRS disputes while clients without APTS require additional
reserves in their estimate of tax expense. Conversely, there may be cases where APTS have
negative consequences. For example, Cook et al. (2008) find that earnings management, through
decreases in third-to-fourth quarter effective tax rate, is most prevalent in clients that pay
comparatively higher tax fees to auditors.
In a study closely related to this paper, McGuire et al. (2012) focus on differences
between auditors that provide tax services. They suggest that within-industry market share may
capture the size of the spillover effect between audit and tax services; and that this measure of
industry specialization is a proxy for the tax expertise of audit firms. McGuire et al. (2012) find
that the clients of industry specialist auditors have greater tax avoidance compared to the clients
of non-specialist auditors. This study relies on arguments derived from the experimental and
archival findings of the literature on auditor expertise and audit quality.
Experimental auditing research provides evidence that industry expertise enhances
auditors’ judgements. The findings of prior studies suggest that knowledge of the industry may
increase audit quality via improving the accuracy of error detection (Solomon et al. 1999;
Owhoso et al. 2002), enhancing the quality of the auditor’s risk assessment (Taylor 2000; Low
2004), and influencing the choice of audit tests and the allocation of audit hours (Low 2004).
2
Archival auditing research has also examined the effects of auditor industry expertise; however,
archival researchers cannot directly observe expertise at the firm, office, or auditor level.
Consequently, this area of the literature has used each audit firm’s within-industry market share
or auditor industry specialization as an indirect proxy for auditor expertise.3
In archival settings, measuring the effects of auditor industry expertise on tax avoidance
is problematic because the proxies for industry specialization and tax avoidance are associated
with underlying client characteristics. For example, large clients can have lower effective tax
rates and industry specialists often audit large clients. In addition, researchers examining the
impact of APTS face a significant data limitation. One such limitation is that disclosed tax fees
do not distinguish between amounts paid for tax planning and other services such as tax
compliance and M&A structuring. Moreover, clients are not required to disclose tax fees paid for
services provided by external non-auditor consultants or internal personnel. Thus, a potentially
significant portion of tax planning services remains unknown, raising questions about the
validity of cross-sectional comparisons among APTS and non-APTS clients.
Anecdotal evidence from individual tax experts argues that as long as the client and tax
consultant have strong sharing and communication of information the tax planning strategies will
be highly effective, regardless of whether those services are APTS or non-APTS (Hansen 2006).
Arguably, the focus on communication, due diligence, and controls has been heightened in the
post-SOX era, implying comparable tax planning strategies between APTS and non-APTS
clients. Furthermore, the bundling of audit and tax fees provides an audit firm the opportunity to
discount audit services while charging a premium on tax services, often perceived as a valueadded effort. Donohoe and Knechel (2009) provide evidence that tax aggressive clients
For example, a specialist is defined as a firm that has “differentiated itself from its competitors in terms of market
share within a particular industry” (Neal and Riley 2004, p. 170).
3
3
purchasing APTS pay a smaller audit premium than tax aggressive clients not purchasing APTS.
The existence of a bundle discount may result in a potential identification problem when using
market share, based on audit fees or joint fees, as a measure of accumulation of expertise.
Our study examines the association between tax avoidance and APTS provided by
industry specialist auditors, aiming to control for the impact of client characteristics and the
possibility of purchasing non-disclosed tax services from advisors that are not the external
auditor. Throughout our analyses there are two hierarchical categories of clients, those that
purchase APTS and a subgroup of clients that purchase APTS from a specialist auditor. In order
to determine the effect of auditor specialization, we seek to compare treated and control groups
that have similar client characteristics, ideally approximating experimental conditions. The
objective is to match treatment and control observations on all relevant observable dimensions
except for the treatment and outcome variables. Thus, the clients of specialist auditors have to be
compared to a control group of other clients. This control group may consist of other clients that
purchased APTS from non-specialist auditors or may consist of all other clients in the sample.
In the context of APTS, matching is useful for two primary reasons. First, we do not
observe any tax fees paid to non-auditor consultants by both APTS and non-APTS clients. If it is
more likely that a comparable peer-client will engage in similar tax planning, the adequate
comparison or counterfactual for a client of a specialist auditor is not all other clients, but rather
a peer-matched client with a similar level of APTS. Second, the linear control approach,
including client size, performance, growth, and other control variables in multivariate regression
analyses, has significant potential problems such as selection bias and model misspecification.
The literature on matching has documented that this technique is useful to mitigate both selection
bias and model misspecification (e.g., Rosenbaum and Rubin 1983; Ho et al. 2007).
4
Our study unfolds in three dimensions. First, we examine cross-sectional differences in
tax avoidance between clients of specialist and non-specialist auditors. We employ four proxies
for tax avoidance: effective tax rate (ETR), cash effective tax rate (CETR), book-tax differences
(BTD), and discretionary tax (DTAX); and use two definitions of specialization based on tax
fees and joint audit and tax fess as proxies for expertise. Second, we demonstrate that, after
matching clients of specialist and non-specialists along a number of client characteristics, there
are no consistent differences in tax avoidance between clients of specialist and non-specialist
auditors. Third, we perform a number of additional tests that confirm the results of our matched
sample analyses, such as: using the FIN48-related reserves as a proxy for aggressive tax
avoidance, including client fixed effects in our main models, and using alternative definitions of
expertise based on the size and importance of the tax function at the office-level.
Throughout our analyses we do not observe a consistent pattern of results indicating that
purchasing APTS from an auditor with high market share results in comparatively greater tax
avoidance. The combined evidence provided in this study suggests that an auditor’s withinindustry market share is not a reliable indicator of tax avoidance. Moreover, the extant empirical
methodology may not fully parse out the confounding effects of client characteristics in tests of
auditor industry specialization and tax avoidance.
We caution the reader that our results must be interpreted with due regard to their
limitations and to the caveats of our methodology. Although they are appropriate for estimating
causal effects, matching models have intrinsic restrictions, resulting from a trade-off between
internal and external validity. In addition, our conclusions are based on the proxies for expertise
and tax avoidance used in this study. Finally, the findings documented in this study do not imply
that industry knowledge is not important for auditors and tax advisors.
5
Section II briefly discusses prior research. Section III explains potential sources of bias in
cross-sectional models of tax avoidance and auditor industry specialization. Section IV describes
our methodology. Section V describes our sample selection and provides descriptive statistics on
our samples. Section VI provides our results. Section VII includes a number of additional and
sensitivity analyses. Section VIII concludes.
II. PRIOR LITERATURE ON TAX AVOIDANCE, AUDITOR PROVIDED TAX
SERVICES AND AUDITOR INDUSTRY SPECIALIZATION
Researchers and the public alike have a keen interest in corporate tax avoidance,
specifically, the amount of explicit taxes a company pays (Hanlon and Heitzman 2010).
Corporate tax avoidance raises questions about companies paying “a fair share” of tax revenues,
managers providing value to shareholders through tax savings, and, more generally, the various
incentives and factors contributing to the cross-sectional variation in corporate taxes paid. In
examining cross-sectional variation in tax avoidance, researchers commonly focus on variables
such as size, return on assets, leverage, and foreign activities. Typically, these studies rely on
book effective tax rates, cash effective tax rates, book-tax differences, and the residuals of a tax
rate model as proxies for a client’s level of tax avoidance (e.g., Dyreng et al. 2008; Frank et al.
2009; Wilson 2009; Chen et al. 2010; Dyreng et al. 2010; and McGuire et al. 2012)
Related streams of research focus on the effect of APTS on tax restatements (Kinney et
al. 2004; Seetharaman et al. 2011), the impact of APTS on audit efficiency (Knechel et al. 2009),
and the relation between APTS and debt pricing (Fortin and Pittman 2008). The literature has
also examined the relation between APTS and tax outcomes, such as tax accrual management
(Cook et al. 2008), tax reserves (Gleason and Mills 2011), and more recently, tax avoidance
6
(McGuire et al. 2012). Generally APTS studies find benefits consistent with potential knowledge
spillover; however, Cook et al. (2008) and Knechel et al. (2009) find that APTS is associated
with higher earnings management and less audit efficiency, respectively.
Extant research in auditing focuses on the role of the auditor as an expert or specialist.
Evidence suggests that industry specialists, at the national and city-level, receive an audit
premium for their services (Ferguson et al. 2003; Francis et al. 2005) and that audit quality is
higher when an audit is performed by industry specialists (Balsam et al. 2003; Reichelt and
Wang 2010). Recent studies also examine the interaction between APTS and industry
specialization. Donohoe and Knechel (2009) find evidence consistent with an audit premium
paid when industry specialists provide joint audit and tax services. McGuire et al. (2012) present
evidence consistent with higher levels of tax avoidance in clients that purchase APTS from tax
industry specialists compared to clients that purchase APTS, but do not use such specialists.
In contrast, we examine the relation between industry specialization and tax avoidance
including non-APTS clients in our analyses. As discussed in the following sections, our analysis
attempts to control for the influence of client characteristics and unobserved tax planning fees, to
strengthen the inferences drawn from this line of research. Consistent with the prior literature, we
rely on ETR, CETR, BTD and DTAX as proxies for tax avoidance. Moreover, we extend our
analysis of APTS and industry specialization to examine tax aggressiveness. The literature has
yet to link auditor specialization to more aggressive forms of tax avoidance. We use the level of
unrecognized tax benefits (UTBSC) as a measure of tax aggressiveness, consistent with
Lisowsky et al. (2012).
7
III. POTENTIAL SOURCES OF BIAS IN CROSS-SECTIONAL MODELS OF TAX
AVOIDANCE AND AUDITOR INDUSTRY SPECIALIZATION
There are two main reasons as to why controlling for confounding factors is particularly
important for studying the effects of auditor industry specialization. First, an audit firm may have
extensive industry expertise even when its within-industry market share is small relative to other
audit firms. Industry knowledge could be gained through other means; for instance, by the
number of years an audit team has audited clients in the industry, by providing training to
individual auditors, by auditing private clients in the same industry, by providing consulting
services, or by hiring experts from within the industry or from other audit firms. Thus, it is not
obvious that auditors with larger market share will have higher quality. Second, the evidence in
Boone et al. (2010) and Lawrence et al. (2011) shows that the previously documented association
between auditor size and audit quality could be attributed to differences in client characteristics,
particularly to differences in client size. The separation of specialist and non-specialist auditors
by within-industry market share also creates two groups of auditors with different client
characteristics. For example, specialist auditors have larger clients compared to non-specialist
auditors. In Table 2, Panel B we show that the mean size of the clients of specialist auditors
(SIZE = 6.822 Column I) is 1.24 times the mean size of the clients of non-specialist auditors
(SIZE = 5.487 Column IV).
Cross-sectional regression models with linear controls may result in inappropriate
inferences due to two problems that affect the research design simultaneously, selection bias and
model misspecification. To overcome the selection bias, some studies (e.g., McGuire et al. 2012)
use econometric designs that explicitly model the mechanism that results on differences in client
characteristics between auditors, such as the Heckman (1979) self-selection model or two-stage
8
instrumental variable models. A limitation of these research designs is that they require
identifying appropriate exogenous instrumental variables or exclusion restrictions in the first
stage, which is a difficult condition to meet in models predicting auditor choice (Lennox et al.
2012).4 The variables that predict the choice of an audit specialist are strongly associated with all
tax avoidance proxies. Matching constitutes an alternative to determine the auditor treatment
effects, balancing the effect of client characteristics between specialist and non-specialist
auditors.5
To illustrate this point, in Table 1 we show a comparison of several possible choice
models for auditor specialist.6 The choice of specialist is modelled as driven by client size
(LOGASSETS), whether the client purchases APTS (APTS), foreign income (FI), leverage (LEV),
book-to-market ratio (BTM), property plant and equipment (PPE), and pre-tax ROA
(PTAXROA). Panel A shows the models for the tax specialist and Panel B for audit and tax
specialist. A comprehensive way to evaluate the relative performance of these models in
predicting the choice of auditor specialist is by examining the receiver operating characteristic
curve (ROC) for each model. This curve represents the performance of a binary classifier as its
discrimination threshold is varied. It is created by plotting the fraction of true positives out of the
positives (TPR) versus the fraction of false positives out of the negatives (FPR) at various
probability cutoffs. TPR is also known as sensitivity and FPR is one minus the specificity or true
negative rate. The best possible classifier is one with TPR equal to one and FPR equal to zero. In
comparing different choice models, a useful indicator is the area under the ROC curve; as this
4
Exclusion restrictions are not strictly necessary but without exogenous variables in the first stage, identification
relies on meeting the strong distributional assumptions of the Heckman two-stage estimator (Li and Prabhala 2007).
5
Heckman (2005) discusses extensively the advantages and disadvantages of matching versus explicit modeling of
the selection process. Both approaches are acceptable for estimating treatment effects; however, the matching
approach does not require identification of exclusion restrictions. Conversely, matching relies on the assumption that
selection is strictly based on observables or that treatment assignment is “strongly ignorable,” and also requires
some degree of overlap or “common support” between treatment and control observations.
6
The sample selection, model specification, and definition of variables are described in the next Section.
9
area approaches one, TPR increases and FPR decreases. Additionally, another way to evaluate
these models is the pseudo R2. The two most important explanatory variables in the specialist
choice model, both in terms of the area under the ROC curve and R2, are whether the client
purchases APTS and the client size.7
In addition to selection bias, cross-sectional regressions may suffer from misspecification
in the form of non-linearity. Important variables such as client size and the level of APTS are
nonlinear in the tax avoidance models. An F-test of powers two and three of size and APTS
reveals that these variables have non-linear relationships with the proxies for tax avoidance.
Moreover, potential non-linearity is indicated by the Ramsey RESET test, using powers of the
fitted values of the dependent variables. The null of no omitted variables is rejected in our tax
avoidance models.
A linear regression model may increase bias in the estimation of treatment effects when
there are even moderately nonlinear relations between the dependent and independent variables.
This problem is exacerbated when there are significant differences in means and variances in the
independent variables between treated and control groups (see Rubin 1979; Heckman et al. 1998;
Rubin and Thomas 2000; and Rubin 2001). The aforementioned conditions appear in our
analyses, (1) client size has a non-linear relationship with the tax avoidance proxies and (2) client
size is different between specialist and non-specialist auditors. The specialist variable may be
capturing the effect of size non-linearity due to the correlation between the specialist choice and
client size. Matching on client characteristics may mitigate the non-linearity problem as it aims
to balance characteristics between treated and control groups (e.g., average size is the same for
specialist and non-specialist auditors) and to eliminate the correlation between the treatment
7
The overall fit of the full model is comparable to the choice model in McGuire et al. (2012). The area under the
ROC curve for the McGuire et al. (2012) model is 0.73.
10
variable and other variables (e.g., after matching the specialist variable and client size are
uncorrelated).
Finally, cross-sectional regressions may also suffer from misspecification in the form of
omitted variables. The omitted variables could be either transformations of the variables
currently in the model, such as omitting the square of client size or other unobservable variables.
If the omitted variables are correlated with the matching variables the matching process may
mitigate this problem; however, this is an inherently difficult problem to address. Another
potential way to mitigate the effect of correlated omitted variables is including client fixed
effects in the main regression model. In our setting, a main omitted variable is the fees paid to
tax consultants other than the auditor.
IV. METHODOLOGY
Tax avoidance and tax aggressiveness proxies
This study employs four proxies of tax avoidance from the extant literature and an
additional proxy for tax aggressiveness. The first four proxies, effective tax rate, cash effective
tax rate, book-to-tax differences, and discretionary tax are similar to those used in McGuire et al.
(2012). We employ the level of unrecognized tax benefits scaled by total assets (UTBSC) as a
measure of tax aggressiveness, consistent with Lisowsky et al. (2012). Our proxies are defined as
follows:
ETR = Effective tax rate, defined as total tax expense (TXT) divided by pretax book income (PI) less special items (SPI); ETRs with negative
denominators are deleted; the remaining non-missing ETRs are
winsorized (reset) at the 1st and 99th percentiles;
CETR = Cash effective rate, defined as cash taxes paid (TXPD) divided by pre-
11
tax book income (PI) less special items (SPI); CETRs with negative
denominators are deleted; the remaining non-missing CETRs are
winsorized (reset) at the 1st and 99th percentiles;
BTD = Book-tax difference, calculated as pre-tax income less estimated
taxable income, scaled by total assets at the beginning of the year
(AT); pre-tax book income is defined as pre-tax income (PI) less
minority interest (MII); taxable income is defined as the sum of
current federal tax expense (TXFED) and current foreign tax expense
(TXFO) divided by the top U.S. statutory tax rate less the change in
net operating loss carryforward (TLCF); if current federal or foreign
tax expense is missing, then we calculate tax expense as the difference
between total tax expense (TXT) and the sum of deferred tax expense
(TXDI), state tax expense (TXS), and other tax expense (TXO); and,
DTAX = Discretionary permanent book-tax differences, calculated following
Frank et al. (2009) where DTAX is the residual from a regression of
total book-tax differences (total BTD) on proxies for intangible assets,
equity income, minority interest income, state taxes, change in tax
losses and prior-year total BTD; DTAX is winsorized (reset) at the 5th
and 95th percentiles; and,
UTBSC = Unrecognized tax benefit, calculated as the ending balance of the
unrecognized tax benefit accrual (TXTUBEND) scaled by total assets
at the beginning of the year (AT).
Measures of auditor expertise
This study uses two main measures of auditor expertise based on the auditor’s withinindustry market share at the city-level.8
8
In our sensitivity analyses we report the results of using two alternative measures, based on the auditor with the
largest market share and 10 percent greater market share than the closest competitor. Francis et al. (2005) and
Reichelt and Wang (2010) also use MSA definitions to identify city level specialists. Cities with less than three
12
TAXEXPERT =
Indicator variable equal to 1 if an audit firm is a tax expert, and 0
otherwise; tax expertise is defined as a tax service market share in a
given MSA (city) and industry (two-digit SIC) market that is greater
than or equal to 30 percent; market share is defined as total tax fees
paid to the audit firm divided by total tax fees paid to all other audit
firms in the same industry and MSA; and,
OVERALLEXPERT =
Indicator variable equal to 1 if an audit firm is both an audit and tax
expert, and 0 otherwise; audit (tax) expertise is defined as an audit
(tax) market share in a given MSA (city) and industry (two-digit SIC)
market that is greater than or equal to 30 percent; market share is
defined as total audit (tax) fees paid to the audit firm divided by total
audit (tax) fees paid to all other audit firms in the same industry and
MSA.
Comparing our two proxies for industry specialization, OVERALLEXPERT is less
directly related to tax-specific expertise than TAXEXPERT, because OVERALLEXPERT captures
both audit and tax fees. Thus, any association between OVERALLEXPERT and tax avoidance has
to be interpreted with care.
Main regression model
Our main regression model is similar to the one in McGuire et al. (2012); we model tax
avoidance as follows:
TAX_AVOIDANCEi.t= ω0 + ω1EXPERTi,t + ω2APTSi,t + ω3APTS_HDi,t
+ ω4OPPORTUNITYi,t + ω5SIZEi,t + ω6DACCi,t + ω7NOLi,t
+ ω8∆NOLi,t + ω9EQINQi,t + ω10FIi,t + ω11R&Di,t + ω12LEVi,t
+ ω13BTMi,t + ω14PPEi,t + ω15PTAXROAi,t + ω16CASHi,t
+ ω17DEPi,t + ω18BIG4i,t + ω19SEC_TIERi,t
+ ω20YEAR and INDUSTRY F.E. ++ vi,t
(1)
where for client i and fiscal year-end t:
observations are deleted from the sample. MSA definitions are available at the U.S. Census Bureau’s website:
http://www.census.gov/population/www/metroareas/metrodef.html.
13
EXPERT = Indicator variable equal to 1 is the auditor is designated as a tax or
combined tax and industry expert, and 0 otherwise;
APTS = Indicator variable equal to 1 if the firm purchased tax services from
their external auditor, and 0 otherwise;
APTS_H = Indicator variable equal to 1 if the firm’s ratio of tax service fees to
total combined audit and tax fees is greater than the median, and 0
otherwise;
APTS_HD = Interaction term of APTS and APTSH
OPPORTUNITY = Market value of a client divided by the sum of the market value of all
clients in the same industry at the same MSA city;
SIZE = Natural log of market value of equity (PRCC_F * CSHO) at the
beginning of year t;
DACC = Absolute value of discretionary accurals based on Kothari et al. (2005)
including ROA;
NOL = Indicator variable equal to 1 if there is a tax loss carryforward (TLCF
is positive) during year t, and 0 otherwise;
 NOL = Change in tax-loss carryforward (TLCF) from year t-1 to t scaled by
total assets at the beginning of the year (AT)
EQINC = Equity income for year t (ESUB) scaled by total assets at the
beginning of the year (AT) and the missing EQINCs are reset to 0
FI = Pre-tax foreign income for year t (PIFO) scaled by total assets at the
beginning of the year (AT) and the missing FIs are reset to 0;
R&D = R&D expense for year t (XRD) scaled by total assets at the beginning
of the year (AT) and the missing XRDs are reset to 0;
LEV = Long-term-debt-to-asset ratio at the end of year t (DLTT) scaled by
total assets at the end of the year (AT);
BTM = Book-to-market ratio for the end of year t, measured as book value of
equity (CEQ) divided by market value of equity (PRCC_F * CSHO);
PPE = Net PPE for year t (PPENT) scaled by total assets at the beginning of
the year (AT);
PTAXROA = Pre-tax net income divided by total assets at the beginning of the year
14
(AT);
CASH = Cash holding at the end of year t (CHE) divided by total assets at the
beginning of the year (AT);
DEP = Depreciation and amortization expense for year t (DP) divided by total
assets at the beginning of the year (AT);
BIG4 = Indicator variable equal to 1 if audited by a Big 4 firm, and 0
otherwise; and,
SEC_TIER = Indicator variable equal to 1 if audited by a second-tier accounting
firm, namely, Grant Thornton LLP and BDO Seidman LLP, and 0
otherwise.
Consistent with prior research (e.g., Gupta and Newberry 1997; Mills et al. 1998; Rego
2003; Dyreng et al. 2008; Frank et al. 2009; Wilson 2009; Chen et al. 2010; Dyreng et al. 2010),
we control for a number of firm-level determinants of tax avoidance. Financial reporting
aggressiveness (DACC), net operating losses (NOL and  NOL), foreign income (FI), R&D and
capital intensity (R&D and PPE), leverage (LEV), firm stability (BTM), profitability
(PTAXROA), and resource availability (CASH) are typically associated with an increase in tax
avoidance. We also control for firm size (SIZE), although tax avoidance could increase or
decrease with size due to either benefits from opportunities or political costs, respectively.
Finally, we control for equity income (EQINC), depreciation (DEP), and the use of Big-4 or
second tier auditors (BIG4 and SEC_TIER), consistent with McGuire et al. (2012). Increases in
tax avoidance will be represented by negative coefficients in our ETR and CETR models and
positive coefficients in our BTD and DTAX (and UTBSC) models.
Matching approach
To investigate the difference in audit quality between specialists and non-specialist
auditors, researchers must ascertain that the observed differences between the auditors’ clients
15
are the result of the auditors’ effect. When the number of matching variables is small, the
researcher can directly match on the variables of interest or within a specified distance from each
variable of interest without requiring a weighting approach to aggregate across dimensions.
Another approach that can be used to find comparable firms is the propensity-score matching
methodology proposed by Rosenbaum and Rubin (1983). Propensity-score matching is a widely
used methodology to find a group of comparable cases and control observations to mitigate the
effect of self-selection in observational causal studies. In general, this approach can be used to
pair-match observations that belong to two different regimes; in the context of this study, to find
comparable clients audited by specialist and non-specialist auditors. A potential drawback of this
approach is that it depends on the specification of the choice model, known as the “strongly
ignorable treatment assignment assumption.” Nevertheless, the main advantage of propensityscore matching is that it is typically effective at selecting observations that are closely matched
in the predefined covariates.
For each tax avoidance proxy and specialization measure, clients of specialist and nonspecialist auditors are matched using propensity scores. The propensity of choosing specialist
auditors is predicted using a logistic regression where the dependent variable is the specialist
indicator variable and the independent variables are as in the model in Table 1, Column (I),
including industry and year-indicator variables. Observations are matched by propensity score,
within common support, without replacement, using a caliper distance of 0.01. Next, the main
multivariate model is estimated in the matched sample of clients of industry specialist and nonspecialist auditors.9
9
These propensity score settings are consistent with Lawrence at al. (2011) and generally result in balanced
covariates between auditor groups. Qualitatively similar results are obtained by reducing the caliper distance,
although this reduces the sample size further. Using the logarithm of total assets in the propensity score calculation
16
We also investigate whether the results are sensitive to including clients with and without
APTS in the matching process. As a second alternative, the propensity of choosing specialist
auditors is predicted using a logistic regression where the dependent variable is the specialist
indicator variable and the independent variables are as in the model in Table 1, Column (II),
including industry and year-indicator variables. Finally, following our argument described in
Section III and the findings of Lawrence et al. (2011), we also investigate whether using client
size as a matching variable produces similar results to a multivariate matching. Under this
alternative approach, clients of specialist and non-specialist auditors are matched by year,
industry, and total assets. Individual observations are matched using propensity score, estimated
using the logarithm of total assets, and industry and year indicator variables as predictors in the
logistic regression.10
An advantage of matching is that it imposes weak stationarity or linearity conditions on
the relation between the matched firm characteristics and the proxies for audit quality. Although
the matched sample reflects the relative quality between peer firms, idiosyncratic differences
should be mitigated in large samples, allowing researchers to assess the average treatment effects
of specialist auditors. Nonetheless, using matched samples comes at a cost due to a trade-off
between internal and external validity. Four underlying threats to matching approaches are (1)
firms deemed to be economically similar may not be truly comparable, (2) the results from
matched samples may not be immediately extended to the entire population, (3) matching
reduces sample sizes, and (4) it is not possible to match on pre-treatment attributes or to control
as a size variable, instead of the logarithm of market value, produces more balanced results in the matched samples,
and thus we use total assets as the size matching variable.
10
All results are qualitatively similar if we use all control variables in Equation (1) as predictors of the propensity
score.
17
for alternative treatments.11
Client fixed effects
If the full sample regressions are biased as a result of omitted client characteristics that
are stable over time, an alternative to matching is including client fixed effects in the model. The
fixed effects model assumes that time-invariant individual characteristics may impact or bias the
predictor or outcome variables. The fixed effects model eliminates the influence of those timeinvariant characteristics from the predictor variables so we can assess the predictors’ net effect.
In the context of this study, the coefficient on the industry specialization variable in a fixed
effects model can be compared to examining a sample of clients switching between specialist
and non-specialist auditors.
V. SAMPLE SELECTION AND DESCRIPTIVE STATISTICS
Data and Sample
For our analyses, we use firm-year observations from the years 2002 to 2009. We collect
financial statement data from Compustat and supplemental audit and financial information from
Audit Analytics. In our full sample, we use 12,987 firm-year observations, of which 5,978
observations represent client firms that use tax specialists (TAXEXPERT=1) and 7,009
observations represent client firms that do not use tax specialists (TAXEXPERT=0). The overall
sample is reduced to 12,422 for BTD and to 7,228 for DTAX due to additional data constraints in
these proxies. Use of tax specialists is represented by 5,754 and 3,300 observations and absence
of tax specialists is represented by 6,668 and 3,928 observations with non-missing BTD and
11
The fourth threat could result in a bias if the matching variables are affected by the auditor choice. It is not
possible to fully rule out concerns that inferences based on matched samples are affected by ex post matching or
alternative treatments.
18
DTAX, respectively. These numbers decrease further in our additional analysis of tax
aggressiveness, where FIN 48 data to construct UTBSC is only available in years 2007 through
2009 and is non-missing for less than 55% of the observations in this time period.
Descriptive Statistics
Table 2, Panel A presents descriptive statistics of our variables for the full sample of
12,987 observations and Panel B presents a comparison of the mean, standard deviation, and
median
of
these
variables
for
the
specialist
(TAXEXPERT=1)
and
non-specialist
(TAXEXPERT=0) subsamples. According to the mean of APTS in Panel A, Column (I), 72.5
percent of our sample firms purchase some form of tax services from their auditor; however,
only 25 percent of the clients in the sample have a high-level of these services based on the mean
of APTS_HD. Of the firm-year observations, 77.4 of clients have a Big 4 auditor and 9.2 have a
second tier, non-Big 4 auditor.
The descriptive statistics in Panel B imply that the client characteristics of specialist and
non-specialist tax service-providers are different. By construction, 89.7 and 35.9 percent of
clients using a tax expert have any or high APTS, respectively, while only 57.9 and 15.7 percent
of firms not using a tax expert have any or high APTS, respectively. Many additional control
variables also suggest differences. The clients of tax experts are larger, have higher leverage, and
are more likely to use a Big 4 auditor. With these differences in mind, we shift to our analyses
that include investigation of specialists and non-specialists in full samples, matched samples, and
other sensitivity tests.
19
VI. RESULTS
Full samples
Our baseline tests of tax avoidance use full samples of firm-year observations where
clients may or may not hire a tax expert or use APTS. These baseline cross-sectional tests do not
control for self-selection issues and other biases related to differences in client characteristics.
Table 3, Panels A and B provide some evidence that tax avoidance is associated with our marketshare measure of tax expertise. We only find a negative and statistically significant association
between ETR and TAXEXPERT (Panel A, Column I, TAXEXPERT = -0.009**); a negative and
statistically significant association between ETR and OVERALLTAXEXPERT (Panel B, Column
I, OVERALLTAXEXPERT = -0.010**), and a positive and statistically significant association
between DTAX and OVERALLTAXEXPERT (Panel B, Column IV, OVERALLTAXEXPERT =
0.030**). Furthermore, tax aggressiveness does not appear to be associated with tax expertise,
based on the regressions using our UTBSC proxy (Panels A and B, Column V)
This evidence is consistent with McGuire et al. (2012) and in favor of higher levels of tax
avoidance for clients that use tax specialists. Finally, the coefficients on our APTS variables
APTS and APTS_HD are not significantly associated with tax avoidance across our models. Our
control variables, including SIZE, DACC, NOL, FI, R&D, LEV, PTAXROA, CASH, BIG4 and
SEC_TIER, load in our models as we expect them to or, where no prediction is stated, load
consistently across the different proxies. Our BTD proxy of tax avoidance increases with  NOL
and BTM but our ETR and CETR proxies of tax avoidance decrease with respect to these
variables. Finally, EQINC and PPE generally show consistent associations with our tax
avoidance and aggressiveness proxies, except for DTAX and UTBSC.
20
Matched samples
Our matched samples allow us to control for the aforementioned biases and to more
appropriately assess the influence that specialists could have on their client’s level of tax
avoidance. Each panel in Table 4 shows the results for three different matched samples – match
on all control variables, match on all control variables except for APTS, and match only on size,
industry, and year. Each panel also shows results for both of our market-share expertise
variables, TAXEXPERT and OVERALLEXPERT.
In Table 4, Panel A, only our ETR proxy of tax avoidance is associated with expertise
using the joint audit and tax market share OVERALLEXPERT, in the “All control variables”
matched sample (Column IV, OVERALLEXPERT = -0.008*). The negative association in this
regression model is consistent with our baseline model; however, the general insignificance of
the expertise variables across the various matched samples implies that the use of a specialist is
not consistently associated with tax avoidance. None of the APTS variables are significant across
these regressions.
In Table 4, Panels B and C, our CETR and BTD tax avoidance proxies are not
significantly associated with either measure of expertise. Generally, these tax avoidance proxies
are not significantly associated with the APTS variables, with the exception of APTS_HD in
Panel C, Column II (APTS = -0.004**).
In Table 4, Panel D, our DTAX proxy of tax avoidance is associated with expertise using
the joint audit and tax market share OVERALLEXPERT, in the “All control variables” matched
sample
(Column
IV,
OVERALLEXPERT
=
0.029*).
The
positive
coefficient
on
OVERALLEXPERT suggests higher levels of tax avoidance. The APTS variables in this panel are
21
also generally insignificant, with the exception of positive coefficients on APTS in Column III
(APTS = 0.039**) and Column IV (APTS = 0.049**)
Finally, Table 4, Panel D shows that our proxy for tax aggressiveness UTBSC is not
significantly associated with expertise in any of our matched samples. The control variables are
also generally insignificant, although we find a statistically negative coefficients on APTS in
Column V (APTS = -0.003*). This coefficient suggests lower tax aggressiveness in firms that use
their auditor for tax services.
As a whole, the results in Table 4 show a lack of consistent evidence that tax avoidance
and tax aggressiveness are associated with market-share measures of tax expertise. Specifically,
we find no association between TAXEXPERT and tax avoidance in any of our matched samples.
In addition, little evidence exists to support an association between tax avoidance or tax
aggressiveness and the market-share measure of overall expertise OVERALLEXPERT. There are
two important issues concerning these results. First, the weak results using the
OVERALLEXPERT measure and the statistically insignificant results using the TAXEXPERT
measure are difficult to interpret, given that TAXEXPERT is expected to have a more direct
impact on tax avoidance. Second, the difference in results between the book-tax rate ETR proxy
and the cash-tax rate CETR proxy using the OVERALLEXPERT measure casts doubt on whether
there is a true pattern of tax avoidance in the data.
VII. ADDITIONAL AND SENSITIVITY ANALYSES
Full samples including client fixed effects
As an alternative to our matched sample tests, we estimate our models using the full
sample of observations with client fixed effects included. This approach mitigates bias in our
22
baseline tests that could result from the omission of static client characteristics. As mentioned in
Section III, these fixed effects models can be interpreted as the effect of clients alternating
between specialist and non-specialist auditors.
Consistent with our results from the matched samples, in Table 5 we find no evidence of
an association between our tax avoidance proxies and TAXEXPERT and no association between
our CETR and BTD tax avoidance proxies and OVERALLEXPERT. Furthermore, our tax
aggressiveness proxy UTBSC also lacks an association with our expertise measures. The
negative association between ETR and OVERALLEXPERT (Column VI, OVERALLEXPERT = 0.007*) and the positive association between DTAX and OVERALLEXPERT (Column VIII,
OVERALLEXPERT = 0.031**) is consistent with our matched results. A potential limitation of
fixed-effect models is that they may have low power when the underlying variables vary slowly
over time (Li and Prabhala 2007); however, these results are incremental to our match sample
analyses and appear to confirm the results in Table 4.
Alternative measures of expertise based on the size and importance of the tax function at the
office-level
Francis and Yu (2009) provide evidence that large auditor offices have higher audit
quality than small auditor offices. Their evidence suggests that large offices may have more
collective experience in auditing public companies. We propose two alternative measures of tax
expertise, related to the size and importance of the tax function at the city level. Francis and Yu
(2009) argue that the engagement office that contracts with the client has primary responsibility
for the audit, including overseeing work performed by other offices. The same logic is likely to
apply to tax services.
23
The first measure, OFFICESIZE_TAX is similar to the office size variable in Francis and
Yu (2009). We expect offices providing comparatively more tax services to have more resources
and
trained
personnel
to
deal
with
complex
tax
issues.
The
second
measure,
OFFICE_TAXFOCUS is intended to capture the importance of the tax function compared to the
importance of the audit function within each office. We also expect offices providing
comparatively more tax services, measured by the ratio of tax revenue to audit revenue, to have
more resources and trained personnel to deal with complex tax issues. The definition of these
variables is as follows:
OFFICESIZE_TAX =
Natural logarithm of one plus the sum of total tax fees for each
audit firm in a given a given MSA (city) and industry (two-digit
SIC) market; and,
OFFICE_TAXFOCUS =
Total tax fees divided by total audit fees for for each audit firm in a
given a given MSA (city) and industry (two-digit SIC) market.
Using these alternative definitions, we find mixed results. Our tax avoidance proxies
CETR, BTD, and DTAX are not associated with OFFICESIZE_TAX or OFFICE_TAXFOCUS,
consistent with the majority of our matched and fixed-effects tests. Conversely, ETR is positively
associated with both of these expertise alternatives, implying that tax avoidance is lower in firms
that acquire services from offices with relatively more tax services offered. This evidence is
inconsistent with the results of our cross sectional analysis in Table 3, Panels A and B, which
suggest that tax avoidance is higher for clients of specialist auditors.
Furthermore, the positive association between UTBSC and OFFICESIZE_TAX, which
implies more tax aggressiveness with the use of specialists, is inconsistent with our prior results
using the UTBSC proxy. The conflicting evidence from these sensitivity analyses also suggests
24
that the empirical proxies for auditor expertise are not a reliable indicator of the client’s tax
avoidance.
Alternative measures of expertise based on a different market-share cut-off
All of our main analyses are also repeated using an alternative market share cut-offs for
auditor expertise. Specifically, we conduct all our tests using the measures TAXLEADER and
ALLLEADER, defined as follows:
TAXLEADER =
Indicator variable equal to 1 if an audit firm is a tax expert, and 0
otherwise; tax expertise is defined as the top tax service market share
in a given MSA (city) and industry (two-digit SIC) market, and 10
percent greater market share than the closest competitor; market
share is defined as total tax fees paid to the audit firm divided by
total tax fees paid to all other audit firms in the same industry and
MSA; and,
ALLLEADER =
Indicator variable equal to 1 if an audit firm is both an audit and tax
expert, and 0 otherwise; audit (tax) expertise is defined as the top
(audit) tax service market share in a given MSA (city) and industry
(two-digit SIC) market, and 10 percent greater market share than the
closest competitor; market share is defined as total (audit) tax fees
paid to the audit firm divided by total (audit) tax fees paid to all
other audit firms in the same industry and MSA.
Using these alternative definitions, we find similar results as those presented in the main
analyses. The untabulated evidence supports a lack of consistent associations between our
proxies for tax avoidance and tax aggressiveness and these alternative measures of expertise.
Large positive book-to-tax differences
Following Blaylock et al. (2012), we also examine whether having an expert auditor
changes the probability of having large positive BTDs, a proxy for tax avoidance (and/or
25
earnings management). From our full sample we delete firms with negative BTDs and then
create an indicator variable equal to 1 if the client has a BTD above the median, and 0 otherwise.
Using this indicator variable instead of the continuous BTD measure, we obtain similar results to
those documented in the main analyses supporting a lack of association between this proxy and
our measures of specialization. The results are qualitatively similar when we change the cut-off
to 1 if the client has a BTD in the top quartile, and 0 otherwise.
VIII. CONCLUSION
The audit and tax literature has examined the consequences of purchasing tax services
from the external auditor in addition to whether certain auditor’s characteristics, such as
expertise, matter incrementally when purchasing joint audit and tax services. This paper focuses
on whether purchasing tax services from an auditor with a large within-industry market share at
the city-level, a proxy for auditor specialization or expertise, affects the client’s level of tax
avoidance.
We compare the results of cross-sectional analyses of tax avoidance and auditor
specialization to three alternative analyses: first, we conduct various matched-sample analyses
comparing the clients of specialist and non-specialist auditors; second, we modify the crosssectional models of tax avoidance including client fixed-effects; and third, we employ alternative
proxies for expertise based on the size and importance of the tax function at the office-level.
Throughout our analyses we do not observe a consistent pattern of results indicating that
purchasing APTS from an auditor with high market share results in comparatively greater tax
avoidance. The combined evidence provided in this study suggests that an auditor’s withinindustry market share is not a reliable indicator of tax avoidance. Moreover, the extant empirical
26
methodology may not fully parse out the confounding effects of client characteristics in tests of
auditor industry specialization and tax avoidance.
Our results may be of interests to audit committee members, regulators, and auditors.
Moreover, we hope the results of this study will encourage other researchers to explore
alternative research designs to identify the effect of tax and audit expertise on tax avoidance.
27
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31
APPENDIX A
Variable Definitions
Tax avoidance proxies
ETR
=
CETR
=
BTD
=
DTAX
=
UTBSC
=
Effective tax rate, defined as total tax expense (TXT) divided by pre-tax
book income (PI) less special items (SPI); ETRs with negative denominators
are deleted; the remaining non-missing ETRs are winsorized (reset) at the 1st
and 99th percentiles;
Cash effective rate, defined as cash taxes paid (TXPD) divided by pre-tax
book income (PI) less special items (SPI); CETRs with negative
denominators are deleted; the remaining non-missing CETRs are winsorized
(reset) at the 1st and 99th percentiles;
Book-to-tax difference, calculated as pre-tax income less estimated taxable
income scaled by total assets at the beginning of the year (AT); pre-tax book
income is defined as pre-tax income (PI) less minority interest (MII); taxable
income is defined as the sum of current federal tax expense (TXFED) and
current foreign tax expense (TXFO) divided by the top U.S. statutory tax
rate less the change in net operating loss carryforward (TLCF); if current
federal or foreign tax expense is missing, then we calculate tax expense as
the difference between total tax expense (TXT) and the sum of deferred tax
expense (TXDI), state tax expense (TXS), and other tax expense (TXO);
and,
Discretionary permanent book-tax differences, calculated following Frank et
al. (2009) where DTAX is the residual from a regression of total book-tax
differences (total BTD) on proxies for intangible assets, equity income,
minority interest income, state taxes, change in tax losses and prior-year
total BTD; DTAX is winsorized (reset) at the 5th and 95th percentiles; and,
Unrecognized tax benefit, calculated as the ending balance of the
unrecognized tax benefit accrual (TXTUBEND) scaled by total assets at the
beginning of the year (AT).
Measures of auditor expertise
TAXEXPERT
=
OVERALLEXPERT
=
OFFICESIZE_TAX
=
OFFICE_TAXFOCUS
=
TAXLEADER
=
Indicator variable equal to 1 if an audit firm is a tax expert, and 0
otherwise; tax expertise is defined as a tax service market share in a given
MSA (city) and industry (two-digit SIC) market that is greater than or
equal to 30 percent; market share is defined as total tax fees paid to the
audit firm divided by total tax fees paid to all other audit firms in the
same industry and MSA;
Indicator variable equal to 1 if an audit firm is both an audit and tax
expert, and 0 otherwise; audit (tax) expertise is defined as an audit (tax)
market share in a given MSA (city) and industry (two-digit SIC) market
that is greater than or equal to 30 percent; market share is defined as total
audit (tax) fees paid to the audit firm divided by total audit (tax) fees paid
to all other audit firms in the same industry and MSA;
Natural logarithm of one plus the sum of total tax fees for each audit firm
in a given a given MSA (city) and industry (two-digit SIC) market;
Total tax fees divided by total audit fees for for each audit firm in a given
a given MSA (city) and industry (two-digit SIC) market;
Indicator variable equal to 1 if an audit firm is a tax expert, and 0
otherwise; tax expertise is defined as the top tax service market share in a
given MSA (city) and industry (two-digit SIC) market, and 10 percent
greater market share than the closest competitor; market share is defined
as total tax fees paid to the audit firm divided by total tax fees paid to all
other audit firms in the same industry and MSA; and,
32
ALLLEADER
=
Indicator variable equal to 1 if an audit firm is both an audit and tax
expert, and 0 otherwise; audit (tax) expertise is defined as the top (audit)
tax service market share in a given MSA (city) and industry (two-digit
SIC) market, and 10 percent greater market share than the closest
competitor; market share is defined as total (audit) tax fees paid to the
audit firm divided by total (audit) tax fees paid to all other audit firms in
the same industry and MSA.
Control Variables
APTS
=
APTS_H
=
APTS_HD
OPPORTUNITY
=
=
SIZE
=
DACC
=
NOL
=
 NOL
=
EQINC
=
FI
=
R&D
=
LEV
=
BTM
=
PPE
=
PTAXROA
CASH
=
=
DEP
=
BIG4
SEC_TIER
=
=
Indicator variable equal to 1 if the firm purchased tax services from their
external auditor, and 0 otherwise;
Indicator variable equal to 1 if the firm’s ratio of tax service fees to total
combined audit and tax fees is greater than the median, and 0 otherwise;
Interaction term of APTS and APTS_H.
Market value of a client divided by the sum of the market value of all clients
in the same industry at the same MSA city;
Natural log of market value of equity (PRCC_F X CSHO) at the beginning
of year t;
Absolute value of discretionary accurals based on Kothari, et al(2005)
including ROA;
Indicator variable equal to 1 if there is a tax loss carryforward (TLCF is
positive) during year t, and 0 otherwise;
Change in tax-loss carryforward (TLCF) from year t-1 to t scaled by total
assets at the beginning of the year (AT)
Equity income for year t (ESUB) scaled by total assets at the beginning of
the year (AT) and the missing EQINCs are reset to 0
Pre-tax foreign income for year t (PIFO) scaled by total assets at the
beginning of the year (AT) and the missing FIs are reset to 0;
R&D expense for year t (XRD) scaled by total assets at the beginning of the
year (AT) and the missing XRDs are reset to 0;
Long-term-debt-to-asset ratio at the end of year t (DLTT) scaled by total
assets at the end of the year (AT);
Book-to-market ratio for the end of year t, measured as book value of equity
(CEQ) divided by market value of equity (PRCC_F X CSHO);
Net PPE for year t (PPENT) scaled by total assets at the beginning of the
year (AT);
Pre-tax net income divided by total assets at the beginning of the year (AT);
Cash holding at the end of year t (CHE) divided by total assets at the
beginning of the year (AT);
Depreciation and amortization expense for year t (DP) divided by total assets
at the beginning of the year (AT);
Indicator variable equal to 1 if audited by a Big 4 firm, and 0 otherwise; and,
Indicator variable equal to 1 if audited by a second-tier accounting firm,
namely, Grant Thornton LLP and BDO Seidman LLP, and 0 otherwise.
33
Table 1
Choice Models for Auditor Expert
Panel A: TAXEXPERT
(I)
(II)
(III)
(IV)
VARIABLES
LOGASSETS
APTS
FI
RD
LEV
BTM
PPE
PTAXROA
Intercept
Observations
Area under ROC
Pseudo R2
0.305***
(15.30)
1.722***
(22.56)
1.034
(1.13)
1.592***
(2.60)
-0.007
(-0.04)
-0.187**
(-2.50)
-0.225
(-1.32)
-0.502*
(-1.90)
-1.639**
(-2.31)
12,971
0.77
0.17
0.364***
(18.53)
(V)
(VI)
Dep. Var. = TAXEXPERT
(VII)
(VIII)
(IX)
(X)
0.334***
(20.02)
1.850***
(27.01)
1.852**
(2.10)
1.500***
(2.65)
-0.092
(-0.48)
-0.221***
(-3.03)
-0.230
(-1.39)
-0.553**
(-2.25)
-1.167
(-1.13)
12,971
0.71
0.10
5.978***
(6.95)
-1.146***
(-2.59)
1.116***
(6.85)
-0.252***
(-4.32)
0.096
(0.94)
-2.231***
(-21.40)
12,987
0.68
0.08
-1.571***
(-25.18)
12,987
0.66
0.10
-0.271***
(-7.85)
12,987
0.57
0.01
-0.117***
(-3.20)
12,987
0.50
0.00
-0.340***
(-8.24)
12,987
0.57
0.01
-0.029
(-0.63)
12,987
0.53
0.00
-0.188***
(-4.26)
12,987
0.53
0.00
-0.976***
(-4.62)
-0.039
(-0.97)
12,987
0.51
0.00
34
Panel B: OVERALLEXPERT
(I)
(II)
(III)
(IV)
VARIABLES
LOGASSETS
APTS
FI
RD
LEV
BTM
PPE
PTAXROA
Intercept
Observations
Area under ROC
Pseudo R2
0.370***
(16.88)
1.471***
(18.47)
0.659
(0.69)
2.079***
(3.20)
0.028
(0.13)
-0.212***
(-2.59)
-0.255
(-1.40)
-0.700**
(-2.37)
-1.882**
(-2.44)
12,971
0.78
0.18
0.422***
(19.37)
(V)
(VI)
Dep. Var = OVERALLEXPERT
(VII)
(VIII)
(IX)
(X)
0.367***
(20.18)
1.676***
(23.50)
1.348
(1.44)
1.982***
(3.20)
-0.031
(-0.15)
-0.240***
(-2.99)
-0.258
(-1.44)
-0.736***
(-2.62)
-1.482
(-1.39)
12,971
0.74
0.13
6.212***
(7.29)
-0.728
(-1.59)
1.162***
(7.04)
-0.268***
(-4.40)
-0.059
(-0.56)
-2.812***
(-24.02)
12,987
0.70
0.09
-1.798***
(-27.32)
12,987
0.64
0.08
-0.612***
(-16.82)
12,987
0.58
0.01
-0.464***
(-12.03)
12,987
0.49
0.00
-0.682***
(-15.67)
12,987
0.57
0.01
-0.353***
(-7.40)
12,987
0.53
0.00
-0.473***
(-10.23)
12,987
0.48
0.00
-1.165***
(-5.09)
-0.349***
(-8.16)
12,987
0.52
0.00
This table presents the results of estimating a logistic regression model predicting the choice of auditor specialist. In Panel A, the dependent variable is
TAXEXPERT and in Panel B the dependent variable is OVERALLEXPERT. Variable definitions are included in Appendix A . *, **, *** indicate significance at
the 0.10, 0.05 and 0.01 levels, respectively, using two-tailed tests. T-statistics and p-values are calculated using clustered standard errors by company. Year and
industry-specific intercepts are included in Columns I and II only, but for brevity the intercepts are not reported.
35
Table 2
Descriptive Statistics
Full Sample
Panel A: Full Sample
Variable
TAXEXPERT
Mean
0.460
Std. Dev.
0.498
25th Pctl.
0
Median
0
75th Pctl.
1
OVERALLEXPERT
0.380
0.485
0
0
1
ETR
0.269
0.179
0.142
0.311
0.370
CASHETR
0.202
0.201
0.027
0.169
0.308
BTD
0.038
0.105
-0.001
0.024
0.059
DTAX
0.222
0.509
-0.009
0.051
0.229
UTBSC
0.014
0.019
0.003
0.007
0.017
APTS
0.725
0.446
0
1
1
APTSHD
0.250
0.433
0
0
1
OPPORTUNITY
0.298
0.365
0.013
0.092
0.549
SIZE
6.102
2.229
4.676
6.26
7.557
DACC
0.005
0.074
-0.034
0.002
0.037
NOL
0.416
0.493
0
0
1
ΔNOL
-0.006
0.080
0
0
0
EQINC
0.001
0.005
0
0
0
FI
0.019
0.038
0
0
0.023
RD
0.037
0.063
0
0
0.05
LEV
0.161
0.176
0
0.116
0.268
BTM
0.521
0.424
0.270
0.445
0.680
PPE
0.30
0.292
0.087
0.194
0.420
PTXROA
0.123
0.117
0.047
0.091
0.162
CASH
0.218
0.252
0.033
0.118
0.319
DEP
0.047
0.032
0.026
0.039
0.058
BIG
0.774
0.418
1
1
1
SECTIER
0.092
0.289
0
0
0
36
Panel B: Partition by TAXEXPERT
Variable
ETR
TAXEXPERT = 1
Mean
Std. Dev.
0.274
0.168
CASHETR
0.213
0.199
0.187
0.192
0.203
0.149
BTD
0.035
0.09
0.024
0.041
0.117
0.024
DTAX
0.230
0.513
0.051
0.216
0.506
0.051
UTBSC
0.015
0.019
0.009
0.013
0.019
0.006
APTS
0.897
0.304
1
0.579
0.494
1
APTSHD
0.359
0.48
0
0.157
0.364
0
OPPORTUNITY
0.405
0.386
0.253
0.206
0.32
0.038
SIZE
6.822
2.046
6.867
5.487
2.195
5.671
DACC
0.001
0.064
0
0.007
0.082
0.004
NOL
0.415
0.493
0
0.417
0.493
0
ΔNOL
-0.001
0.066
0
-0.011
0.091
0
EQINC
0.001
0.005
0
0.001
0.005
0
FI
0.024
0.042
0
0.015
0.035
0
RD
0.034
0.059
0
0.039
0.067
0
LEV
0.180
0.176
0.152
0.146
0.174
0.08
BTM
0.497
0.384
0.436
0.542
0.455
0.456
PPE
0.304
0.281
0.208
0.296
0.3
0.181
PTXROA
0.116
0.102
0.090
0.129
0.127
0.093
CASH
0.199
0.236
0.108
0.233
0.264
0.132
DEP
0.045
0.029
0.039
0.048
0.034
0.040
BIG
0.935
0.246
1
0.637
0.481
1
SECTIER
0.028
0.164
0
0.146
0.354
0
Median
0.309
TAXEXPERT = 0
Mean
Std. Dev.
0.265
0.188
Median
0.312
This table presents the descriptive statistics of the data used in the main analyses. Panel A shows descriptive
statistics for the full sample of 12987 observations. Panel B shows descriptive statistics for a partition by
TAXEXPERT. Variable definitions are included in Appendix A. The ETR and CASHETR samples have 12,987
observations. The BTD sample has 12,422 observations. The DTAX sample has 7,228 observations. The UTBSC
sample has 2,230 observations.
37
Table 3
Tax Avoidance, Tax Aggressiveness and Auditor Expertise, Full Samples
Panel A: TAXEXPERT
VARIABLES
TAXEXPERT
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
(I)
ETR
(II)
CETR
(III)
BTD
(IV)
DTAX
(V)
UTBSC
-0.009**
(-2.31)
0.000
(0.01)
0.002
(0.55)
-0.002
(-0.28)
0.007***
(5.29)
-0.356***
(-13.43)
-0.020***
(-4.90)
0.128***
(5.26)
-1.500***
(-3.27)
-0.206***
(-3.33)
-0.354***
(-8.20)
-0.036**
(-2.53)
0.039***
(6.25)
-0.016
(-1.19)
0.206***
(10.01)
-0.095***
(-9.14)
-0.280***
(-3.33)
0.036***
(4.29)
0.020**
(2.02)
0.204***
(3.33)
12,987
0.13
0.002
(0.51)
0.006
(1.13)
0.006
(1.22)
-0.009
(-1.22)
0.009***
(5.76)
0.051*
(1.86)
-0.037***
(-8.13)
0.144***
(6.89)
-1.325***
(-2.65)
0.059
(0.87)
-0.341***
(-7.45)
-0.098***
(-6.29)
0.023***
(3.81)
-0.098***
(-6.69)
0.017
(0.78)
-0.072***
(-6.45)
0.376***
(3.95)
0.019**
(2.12)
0.009
(0.87)
0.083*
(1.66)
12,987
0.11
0.002
(1.10)
0.000
(0.10)
-0.003
(-1.57)
0.003
(1.19)
-0.005***
(-7.15)
0.133***
(7.57)
0.018***
(10.22)
0.951***
(62.08)
0.338*
(1.67)
-0.056
(-1.25)
0.167***
(6.80)
0.036***
(6.56)
0.005**
(2.03)
0.033***
(5.19)
0.349***
(17.56)
0.026***
(4.39)
0.009
(0.21)
-0.017***
(-4.02)
-0.013***
(-3.03)
0.027
(1.61)
12,422
0.60
0.020
(1.44)
0.013
(0.86)
0.005
(0.33)
-0.007
(-0.35)
0.000
(0.01)
0.141
(1.49)
0.030**
(2.20)
0.458***
(6.55)
-4.300***
(-3.86)
-0.003
(-0.02)
0.646***
(4.28)
0.158***
(3.86)
0.004
(0.22)
-0.239***
(-6.73)
0.432***
(5.88)
-0.173***
(-4.79)
0.936***
(3.60)
-0.044*
(-1.89)
-0.068***
(-2.61)
0.003
(0.05)
7,228
0.20
0.000
(0.02)
-0.001
(-0.88)
0.002
(1.46)
-0.002
(-1.49)
0.001
(1.60)
-0.005
(-0.64)
0.001
(0.66)
-0.007
(-0.82)
0.074
(0.68)
0.092***
(5.70)
0.055***
(2.80)
0.003
(0.67)
-0.002*
(-1.81)
-0.010***
(-3.30)
0.005
(0.73)
0.004
(1.13)
0.023
(1.12)
0.001
(0.47)
-0.000
(-0.08)
-0.019***
(-3.23)
2,230
0.18
38
Panel B: OVERALLEXPERT
VARIABLES
OVERALLEXPERT
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
(I)
ETR
(II)
CETR
(III)
BTD
(IV)
DTAX
(V)
UTBSC
-0.010**
(-2.45)
-0.000
(-0.06)
0.002
(0.39)
-0.001
(-0.20)
0.007***
(5.36)
-0.356***
(-13.41)
-0.020***
(-4.90)
0.127***
(5.23)
-1.500***
(-3.27)
-0.206***
(-3.33)
-0.353***
(-8.17)
-0.036**
(-2.51)
0.039***
(6.26)
-0.016
(-1.20)
0.206***
(10.00)
-0.095***
(-9.18)
-0.279***
(-3.32)
0.036***
(4.27)
0.020**
(2.01)
0.204***
(3.34)
12,987
0.13
0.001
(0.15)
0.007
(1.23)
0.006
(1.29)
-0.008
(-1.15)
0.009***
(5.76)
0.052*
(1.87)
-0.037***
(-8.14)
0.144***
(6.91)
-1.321***
(-2.64)
0.059
(0.88)
-0.341***
(-7.45)
-0.098***
(-6.29)
0.023***
(3.81)
-0.098***
(-6.69)
0.017
(0.77)
-0.072***
(-6.45)
0.376***
(3.95)
0.019**
(2.18)
0.009
(0.86)
0.083*
(1.65)
12,987
0.11
0.002
(1.22)
0.000
(0.14)
-0.003
(-1.51)
0.003
(1.14)
-0.005***
(-7.17)
0.133***
(7.57)
0.018***
(10.23)
0.951***
(62.06)
0.337*
(1.66)
-0.056
(-1.25)
0.167***
(6.78)
0.036***
(6.56)
0.005**
(2.03)
0.033***
(5.20)
0.349***
(17.54)
0.026***
(4.41)
0.009
(0.21)
-0.017***
(-4.07)
-0.013***
(-3.03)
0.027
(1.61)
12,422
0.60
0.030**
(2.23)
0.011
(0.79)
0.006
(0.38)
-0.011
(-0.54)
-0.000
(-0.09)
0.139
(1.47)
0.030**
(2.19)
0.460***
(6.59)
-4.318***
(-3.88)
-0.006
(-0.03)
0.642***
(4.25)
0.157***
(3.83)
0.003
(0.22)
-0.239***
(-6.74)
0.433***
(5.90)
-0.173***
(-4.77)
0.935***
(3.59)
-0.046*
(-1.96)
-0.068***
(-2.60)
-0.000
(-0.00)
7,228
0.20
0.001
(0.92)
-0.001
(-1.09)
0.002
(1.42)
-0.002*
(-1.66)
0.001
(1.57)
-0.005
(-0.66)
0.001
(0.69)
-0.007
(-0.82)
0.070
(0.64)
0.091***
(5.67)
0.055***
(2.78)
0.003
(0.64)
-0.002*
(-1.85)
-0.010***
(-3.27)
0.005
(0.76)
0.004
(1.11)
0.023
(1.13)
0.001
(0.40)
-0.000
(-0.08)
-0.020***
(-3.30)
2,230
0.18
This table presents the pooled analyses using the full samples. Variable definitions are included in Appendix A. *,
**, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, using two-tailed tests. T-statistics and pvalues are calculated using clustered standard errors by company. Year and industry-specific intercepts are included
in all Columns, but for brevity the intercepts are not reported.
39
Table 4
Tax Avoidance, Tax Aggressiveness and Auditor Expertise
Matched Samples
Panel A: ETR, TAXEXPERT and OVERALLEXPERT
(I)
All Vars.
(II)
No APTS
(III)
Size
-0.005
(-1.18)
-0.005
(-1.27)
-0.006
(-1.41)
VARIABLES
TAXEXPERT
(IV)
All Vars.
Dep. Var. = ETR
OVERALLEXPERT
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
-0.001
(-0.25)
0.004
(0.73)
0.000
(0.05)
0.007***
(4.50)
-0.339***
(-9.81)
-0.019***
(-4.06)
0.084***
(2.67)
-1.296**
(-2.16)
-0.208***
(-3.08)
-0.396***
(-8.08)
-0.043***
(-2.60)
0.040***
(5.43)
-0.006
(-0.39)
0.230***
(8.85)
-0.100***
(-8.39)
-0.295***
(-2.98)
0.032***
(3.25)
0.020
(1.56)
0.302***
(18.06)
7,840
0.14
-0.008
(-1.41)
0.004
(0.77)
-0.001
(-0.22)
0.005***
(3.07)
-0.351***
(-10.35)
-0.013***
(-2.87)
0.110***
(3.30)
-1.483***
(-2.95)
-0.188***
(-2.92)
-0.361***
(-7.17)
-0.042***
(-2.67)
0.029***
(3.88)
-0.019
(-1.24)
0.242***
(8.90)
-0.106***
(-9.19)
-0.261***
(-2.71)
0.028***
(2.65)
0.015
(1.14)
0.326***
(15.94)
9,148
0.12
0.002
(0.33)
-0.002
(-0.45)
0.002
(0.33)
0.006***
(3.98)
-0.352***
(-10.44)
-0.009**
(-2.07)
0.118***
(3.39)
-1.512***
(-2.89)
-0.196***
(-2.91)
-0.392***
(-7.53)
-0.043***
(-2.76)
0.033***
(4.62)
-0.026*
(-1.75)
0.238***
(8.97)
-0.098***
(-8.20)
-0.192**
(-2.04)
0.033***
(3.15)
0.021*
(1.70)
0.229***
(2.97)
9,182
0.13
-0.008*
(-1.76)
-0.001
(-0.16)
0.005
(1.06)
-0.002
(-0.32)
0.003*
(1.87)
-0.353***
(-9.16)
-0.009*
(-1.80)
0.089**
(2.34)
-1.129*
(-1.92)
-0.177***
(-2.58)
-0.403***
(-7.78)
-0.036**
(-2.12)
0.037***
(4.18)
-0.020
(-1.19)
0.282***
(9.50)
-0.099***
(-7.93)
-0.349***
(-3.30)
0.038***
(3.18)
0.025
(1.64)
0.323***
(16.45)
7,404
0.13
(V)
No APTS
(VI)
Size
-0.005
(-1.23)
-0.002
(-0.34)
0.003
(0.51)
-0.001
(-0.21)
0.004**
(2.52)
-0.346***
(-9.31)
-0.013***
(-2.75)
0.111***
(2.96)
-1.099**
(-2.07)
-0.238***
(-3.76)
-0.411***
(-7.79)
-0.043***
(-2.69)
0.030***
(3.74)
-0.009
(-0.53)
0.242***
(8.36)
-0.098***
(-7.52)
-0.351***
(-3.32)
0.030**
(2.38)
0.021
(1.35)
0.229***
(3.06)
8,088
0.13
-0.006
(-1.52)
-0.007
(-1.22)
0.000
(0.00)
0.001
(0.12)
0.003*
(1.77)
-0.383***
(-10.36)
-0.013***
(-2.72)
0.106***
(2.65)
-1.492***
(-2.74)
-0.228***
(-3.54)
-0.383***
(-6.68)
-0.043***
(-2.63)
0.035***
(4.17)
-0.024
(-1.55)
0.260***
(8.72)
-0.090***
(-6.90)
-0.296***
(-2.89)
0.043***
(3.37)
0.032**
(2.04)
0.301***
(12.35)
8,144
0.12
40
Panel B: CETR, TAXEXPERT and OVERALLEXPERT
(I)
All Vars.
(II)
No APTS
(III)
(IV)
Size
All Vars.
Dep. Var. = CETR
0.003
(0.53)
0.007
(1.32)
0.006
(1.22)
VARIABLES
TAXEXPERT
OVERALLEXPERT
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
0.001
(0.16)
0.007
(1.24)
-0.006
(-0.75)
0.010***
(5.93)
0.077**
(2.03)
-0.034***
(-6.29)
0.156***
(5.67)
-1.130*
(-1.78)
0.041
(0.58)
-0.376***
(-6.91)
-0.099***
(-5.37)
0.033***
(4.09)
-0.111***
(-6.34)
0.017
(0.62)
-0.072***
(-5.25)
0.461***
(3.76)
0.005
(0.45)
0.001
(0.11)
0.029
(1.21)
7,840
0.11
0.002
(0.37)
0.005
(0.87)
-0.009
(-1.14)
0.008***
(4.96)
0.085**
(2.33)
-0.035***
(-6.67)
0.155***
(5.31)
-1.147*
(-1.93)
0.056
(0.83)
-0.335***
(-5.87)
-0.112***
(-6.19)
0.024***
(3.32)
-0.109***
(-6.76)
0.016
(0.57)
-0.085***
(-6.64)
0.453***
(3.94)
0.015
(1.35)
0.005
(0.43)
0.043
(1.57)
9,148
0.11
0.002
(0.40)
0.005
(0.83)
-0.012
(-1.58)
0.010***
(5.60)
0.081**
(2.21)
-0.030***
(-5.99)
0.154***
(5.18)
-1.413**
(-2.36)
0.065
(0.93)
-0.398***
(-7.09)
-0.114***
(-6.19)
0.020***
(2.76)
-0.116***
(-7.30)
0.010
(0.38)
-0.080***
(-6.00)
0.525***
(4.58)
0.007
(0.63)
-0.001
(-0.09)
0.038
(1.25)
9,182
0.11
0.004
(0.70)
0.003
(0.43)
0.008
(1.26)
-0.012
(-1.35)
0.008***
(4.24)
0.076*
(1.79)
-0.026***
(-4.66)
0.134***
(3.89)
-0.676
(-0.96)
0.062
(0.83)
-0.360***
(-5.67)
-0.095***
(-4.54)
0.026***
(3.03)
-0.115***
(-6.33)
0.032
(1.01)
-0.086***
(-5.68)
0.475***
(3.64)
0.016
(1.14)
0.021
(1.25)
0.040
(1.51)
7,404
0.10
(V)
No APTS
(VI)
Size
0.004
(0.80)
0.004
(0.53)
0.007
(1.14)
-0.012
(-1.44)
0.008***
(4.86)
0.077*
(1.87)
-0.032***
(-5.98)
0.154***
(4.62)
-0.781
(-1.25)
0.024
(0.35)
-0.362***
(-6.35)
-0.104***
(-5.29)
0.013*
(1.80)
-0.110***
(-6.25)
0.003
(0.12)
-0.089***
(-6.20)
0.480***
(3.79)
-0.005
(-0.33)
-0.003
(-0.16)
0.066**
(2.50)
8,088
0.11
0.004
(0.72)
-0.001
(-0.11)
0.009
(1.57)
-0.004
(-0.43)
0.007***
(4.01)
0.077*
(1.92)
-0.028***
(-5.10)
0.184***
(5.33)
-1.408**
(-2.29)
-0.022
(-0.32)
-0.351***
(-5.59)
-0.115***
(-5.87)
0.020***
(2.59)
-0.121***
(-6.92)
0.034
(1.13)
-0.091***
(-6.39)
0.514***
(4.10)
0.014
(1.01)
-0.005
(-0.28)
0.165
(1.55)
8,144
0.11
41
Panel C: BTD, TAXEXPERT and OVERALLEXPERT
(I)
All Vars.
(II)
No APTS
(III)
Size
-0.000
(-0.10)
-0.001
(-0.48)
0.000
(0.06)
VARIABLES
TAXEXPERT
(IV)
All Vars.
Dep. Var. = BTD
OVERALLEXPERT
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
0.000
(0.18)
-0.003
(-1.64)
0.004
(1.19)
-0.005***
(-7.81)
0.123***
(5.59)
0.015***
(7.77)
0.956***
(48.82)
-0.005
(-0.02)
-0.026
(-0.72)
0.140***
(4.97)
0.033***
(5.18)
0.003
(1.06)
0.034***
(5.09)
0.319***
(13.47)
0.028***
(4.28)
-0.013
(-0.28)
-0.009*
(-1.87)
-0.007
(-1.20)
0.019***
(2.91)
7,510
0.60
0.001
(0.66)
-0.004**
(-2.19)
0.003
(0.99)
-0.005***
(-7.95)
0.130***
(6.78)
0.014***
(8.18)
0.952***
(50.46)
0.143
(0.65)
-0.007
(-0.20)
0.136***
(5.15)
0.037***
(6.27)
0.007***
(2.78)
0.025***
(4.29)
0.332***
(15.08)
0.029***
(4.84)
0.037
(0.80)
-0.010**
(-2.36)
-0.007
(-1.32)
0.014**
(2.46)
8,750
0.60
0.000
(0.12)
-0.003
(-1.52)
0.003
(1.29)
-0.005***
(-7.14)
0.124***
(6.48)
0.014***
(8.01)
0.955***
(56.53)
0.098
(0.43)
-0.011
(-0.31)
0.139***
(4.91)
0.031***
(5.33)
0.005*
(1.76)
0.030***
(5.10)
0.298***
(13.75)
0.029***
(4.89)
0.043
(1.00)
-0.013***
(-2.82)
-0.012**
(-2.30)
0.023***
(3.64)
8,808
0.60
-0.001
(-0.87)
-0.001
(-0.56)
-0.002
(-1.15)
0.005*
(1.67)
-0.005***
(-7.08)
0.111***
(5.34)
0.012***
(6.57)
0.949***
(50.78)
0.204
(0.86)
0.023
(0.63)
0.135***
(4.63)
0.034***
(5.63)
0.004
(1.60)
0.024***
(3.90)
0.296***
(12.36)
0.027***
(4.19)
0.025
(0.49)
-0.005
(-0.88)
-0.002
(-0.36)
0.007
(0.78)
7,096
0.59
(V)
No APTS
(VI)
Size
-0.001
(-0.89)
-0.001
(-0.34)
-0.001
(-0.41)
0.005*
(1.94)
-0.005***
(-7.37)
0.124***
(5.97)
0.012***
(6.76)
0.950***
(55.54)
0.113
(0.47)
0.045
(1.32)
0.106***
(3.74)
0.031***
(5.20)
0.008***
(2.94)
0.022***
(3.93)
0.293***
(13.07)
0.029***
(4.78)
0.073
(1.54)
-0.003
(-0.61)
-0.004
(-0.63)
-0.001
(-0.06)
7,742
0.60
0.000
(0.07)
-0.001
(-0.35)
-0.001
(-0.80)
0.001
(0.52)
-0.004***
(-6.04)
0.109***
(5.08)
0.013***
(7.04)
0.950***
(52.26)
-0.019
(-0.07)
0.022
(0.67)
0.157***
(5.17)
0.033***
(5.31)
0.007**
(2.47)
0.037***
(6.18)
0.304***
(12.94)
0.023***
(3.55)
0.015
(0.33)
-0.009
(-1.51)
-0.005
(-0.79)
0.014
(0.73)
8,606
0.58
42
Panel D: DTAX, TAXEXPERT and OVERALLEXPERT
(I)
All Vars.
(II)
No APTS
0.015
(0.95)
0.008
(0.53)
VARIABLES
TAXEXPERT
(III)
(IV)
Size
All Vars.
Dep. Var. = DTAX
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
0.022
(1.05)
0.005
(0.29)
-0.007
(-0.30)
0.003
(0.55)
0.067
(0.51)
0.033*
(1.87)
0.489***
(5.25)
-5.560***
(-3.28)
-0.021
(-0.09)
0.351*
(1.95)
0.169***
(3.23)
0.026
(1.12)
-0.212***
(-4.85)
0.445***
(4.37)
-0.171***
(-3.69)
0.823**
(2.49)
-0.026
(-0.78)
-0.080**
(-2.06)
0.196***
(2.98)
4,300
0.22
0.024
(1.33)
-0.017
(-0.96)
0.007
(0.31)
0.001
(0.12)
0.100
(0.78)
0.036**
(2.26)
0.421***
(4.42)
-4.372***
(-2.95)
0.030
(0.13)
0.654***
(3.54)
0.123**
(2.41)
0.026
(1.15)
-0.233***
(-5.99)
0.429***
(4.47)
-0.218***
(-4.75)
0.841***
(2.62)
-0.039
(-1.19)
-0.076**
(-2.00)
1.533***
(25.71)
4,984
0.21
(VI)
Size
0.012
(0.74)
0.004
(0.22)
0.013
(0.74)
-0.007
(-0.30)
0.000
(0.02)
0.085
(0.65)
0.038**
(2.09)
0.521***
(5.17)
-4.514***
(-2.67)
-0.055
(-0.23)
0.679***
(3.43)
0.134***
(2.58)
0.016
(0.69)
-0.181***
(-3.94)
0.375***
(3.65)
-0.198***
(-4.01)
0.656*
(1.86)
-0.045
(-1.10)
-0.070
(-1.47)
-0.169***
(-3.17)
4,398
0.23
0.018
(1.22)
0.015
(0.83)
0.005
(0.27)
0.002
(0.10)
-0.000
(-0.07)
0.007
(0.05)
0.032**
(1.99)
0.474***
(5.15)
-4.285***
(-2.97)
-0.063
(-0.31)
0.650***
(3.41)
0.104**
(2.16)
0.023
(1.10)
-0.187***
(-4.79)
0.338***
(3.67)
-0.232***
(-5.01)
0.157
(0.55)
-0.064*
(-1.75)
-0.125***
(-2.89)
0.025
(0.30)
4,972
0.23
0.014
(0.95)
OVERALLEXPERT
APTS
(V)
No APTS
0.039**
(2.15)
-0.001
(-0.07)
0.012
(0.53)
-0.000
(-0.08)
-0.032
(-0.27)
0.023
(1.41)
0.560***
(6.45)
-5.192***
(-3.37)
0.038
(0.17)
0.538***
(2.96)
0.135***
(2.75)
0.020
(0.95)
-0.175***
(-4.60)
0.430***
(4.68)
-0.198***
(-4.70)
0.421
(1.47)
-0.020
(-0.70)
-0.063*
(-1.76)
0.066
(1.15)
5,030
0.22
0.029*
(1.81)
0.049**
(2.23)
-0.015
(-0.81)
-0.018
(-0.75)
0.007
(1.35)
0.096
(0.70)
0.037**
(2.01)
0.512***
(4.95)
-4.664***
(-2.67)
-0.131
(-0.57)
0.302
(1.51)
0.136**
(2.43)
0.022
(0.88)
-0.179***
(-3.86)
0.357***
(3.44)
-0.195***
(-3.93)
0.696*
(1.89)
-0.058
(-1.49)
-0.108**
(-2.42)
-0.125**
(-2.14)
4,064
0.22
43
Panel E: UTB, TAXEXPERT and OVERALLEXPERT
(I)
All Vars.
(II)
No APTS
(III)
(IV)
Size
All Vars.
Dep. Var. = UTBSC
-0.000
(-0.13)
-0.001
(-0.73)
0.000
(0.03)
VARIABLES
TAXEXPERT
OVERALLEXPERT
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
-0.001
(-0.43)
0.003*
(1.76)
-0.002
(-1.21)
0.001
(1.58)
0.009
(1.02)
-0.000
(-0.22)
-0.010
(-0.89)
0.099
(0.51)
0.076***
(4.82)
0.054***
(3.04)
0.004
(0.80)
-0.005***
(-3.40)
-0.011***
(-4.16)
0.001
(0.07)
0.002
(0.46)
0.003
(0.16)
-0.002
(-0.50)
0.001
(0.15)
-0.007
(-1.03)
1,214
0.18
-0.001
(-1.07)
0.002
(1.58)
-0.003
(-1.58)
0.001*
(1.94)
-0.010
(-1.09)
0.000
(0.13)
-0.016
(-1.60)
0.090
(0.60)
0.081***
(4.72)
0.049***
(2.93)
-0.002
(-0.43)
-0.004***
(-2.70)
-0.009***
(-2.77)
-0.000
(-0.01)
0.004
(0.95)
0.021
(1.01)
0.005**
(2.41)
0.005**
(2.00)
-0.010*
(-1.66)
1,486
0.18
-0.002
(-1.36)
0.003
(1.51)
-0.003
(-1.50)
0.001
(1.08)
-0.010
(-1.05)
0.001
(0.66)
-0.003
(-0.31)
0.043
(0.29)
0.100***
(5.20)
0.064***
(3.03)
0.003
(0.60)
-0.004**
(-2.25)
-0.010***
(-3.01)
0.009
(0.98)
0.002
(0.54)
0.009
(0.39)
0.001
(0.39)
0.001
(0.19)
-0.009
(-1.32)
1,522
0.20
0.001
(1.02)
0.000
(0.23)
0.002
(1.17)
-0.004**
(-2.20)
0.001**
(2.47)
-0.006
(-0.66)
0.001
(1.08)
-0.019
(-1.59)
0.224
(1.28)
0.083***
(4.97)
0.045**
(2.25)
0.002
(0.42)
-0.003**
(-2.09)
-0.012***
(-3.82)
0.008
(0.89)
0.005
(1.15)
0.041
(1.58)
0.001
(0.23)
0.004
(0.93)
0.001
(0.06)
1,146
0.18
(V)
No APTS
(VI)
Size
0.000
(0.35)
-0.003*
(-1.66)
0.002
(1.26)
-0.003
(-1.44)
0.001
(1.43)
-0.009
(-1.08)
0.000
(0.01)
-0.002
(-0.22)
0.123
(0.75)
0.086***
(4.90)
0.049***
(2.84)
0.001
(0.24)
-0.004**
(-2.14)
-0.011***
(-2.74)
0.012
(1.47)
0.006
(1.40)
0.045
(1.63)
0.003
(0.94)
0.003
(0.93)
-0.006
(-0.80)
1,352
0.20
0.000
(0.39)
-0.001
(-1.12)
0.002
(1.32)
-0.002
(-1.00)
0.001
(1.23)
-0.005
(-0.51)
0.000
(0.19)
-0.014
(-1.34)
0.129
(0.95)
0.081***
(4.85)
0.049***
(2.86)
0.003
(0.66)
-0.003**
(-2.04)
-0.010***
(-3.29)
0.009
(1.00)
0.005
(1.26)
0.026
(1.17)
0.006***
(3.15)
0.007***
(2.61)
-0.007
(-1.03)
1,658
0.20
This table presents the pooled analyses using the matched samples. Clients of expert and non-expert auditors are
matched based on the multivariate models in Table 1, Columns I to III. Variable definitions are included in
Appendix A. *, **, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, using two-tailed tests. Tstatistics and p-values are calculated using clustered standard errors by company. Year and industry-specific
intercepts are included in all Columns, but for brevity the intercepts are not reported.
44
Table 5
Tax Avoidance, Tax Aggressiveness and Auditor Expertise
Full Samples Including Client Fixed Effects
VARIABLES
TAXEXPERT
(I)
ETR
-0.006
(-1.44)
(II)
CETR
0.003
(0.72)
(III)
BTD
0.001
(0.64)
(IV)
DTAX
0.016
(1.22)
(V)
UTBSC
12,987
0.13
12,987
0.11
12,422
0.60
7,228
0.21
(VII)
CETR
(VIII)
BTD
(VIII)
DTAX
(IX)
UTBSC
-0.000
(-0.63)
OVERALLEXPERT
Observations
Adj. R-squared
(VI)
ETR
2230
0.18
-0.007*
(-1.72)
0.002
(0.50)
0.001
(0.34)
0.031**
(2.32)
0.000
(0.08)
12,987
0.13
12,987
0.11
12,422
0.60
7,228
0.21
2230
0.18
This table presents the pooled analyses using the full samples including client fixed effects to Equation (1). Variable definitions are included in Appendix A. *,
**, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, using two-tailed tests. T-statistics and p-values are calculated using clustered standard
errors by company. Year and industry-specific intercepts are included in all Columns. For brevity, only the coefficients on the auditor expert variables are
reported.
45
Table 6
Tax Avoidance, Tax Aggressiveness and Tax Expertise at the Office-Level
Full Samples
Panel A: OFFICESIZE_TAX
VARIABLES
OFFICESIZE_TAX
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
(I)
ETR
(II)
CETR
(III)
BTD
(IV)
DTAX
(V)
UTBSC
0.003***
(2.69)
-0.003
(-0.73)
0.000
(0.04)
0.000
(0.08)
0.007***
(4.69)
-0.359***
(-13.56)
-0.020***
(-4.91)
0.127***
(5.26)
-1.526***
(-3.34)
-0.213***
(-3.42)
-0.358***
(-8.34)
-0.037**
(-2.57)
0.039***
(6.26)
-0.015
(-1.11)
0.209***
(10.17)
-0.097***
(-9.32)
-0.278***
(-3.31)
0.019*
(1.87)
0.014
(1.39)
0.169***
(2.70)
12,987
0.135
0.002
(1.31)
0.006
(1.18)
0.006
(1.19)
-0.006
(-0.78)
0.008***
(5.44)
0.050*
(1.82)
-0.037***
(-8.15)
0.145***
(6.92)
-1.325***
(-2.64)
0.058
(0.86)
-0.342***
(-7.50)
-0.098***
(-6.27)
0.023***
(3.82)
-0.098***
(-6.67)
0.018
(0.82)
-0.072***
(-6.50)
0.377***
(3.96)
0.011
(1.04)
0.005
(0.51)
0.064
(1.20)
12,987
0.111
-0.001
(-1.08)
0.001
(0.43)
-0.002
(-1.34)
0.003
(1.07)
-0.005***
(-6.84)
0.133***
(7.60)
0.018***
(10.23)
0.951***
(62.09)
0.342*
(1.69)
-0.055
(-1.23)
0.168***
(6.82)
0.037***
(6.59)
0.005**
(2.03)
0.033***
(5.18)
0.348***
(17.48)
0.026***
(4.45)
0.009
(0.20)
-0.014***
(-3.00)
-0.012***
(-2.73)
0.033*
(1.86)
12,422
0.601
-0.004
(-1.15)
0.019
(1.35)
0.009
(0.59)
-0.007
(-0.34)
0.001
(0.26)
0.143
(1.51)
0.030**
(2.19)
0.460***
(6.57)
-4.277***
(-3.81)
0.011
(0.06)
0.656***
(4.35)
0.159***
(3.87)
0.004
(0.25)
-0.241***
(-6.79)
0.427***
(5.83)
-0.172***
(-4.74)
0.929***
(3.59)
-0.020
(-0.71)
-0.061**
(-2.26)
0.053
(0.70)
7,228
0.202
0.001**
(2.23)
-0.001
(-1.06)
0.002
(1.20)
-0.001
(-0.54)
0.001
(1.19)
-0.005
(-0.70)
0.001
(0.65)
-0.006
(-0.80)
0.082
(0.76)
0.091***
(5.64)
0.054***
(2.78)
0.003
(0.63)
-0.002*
(-1.86)
-0.009***
(-3.20)
0.005
(0.78)
0.004
(1.02)
0.023
(1.13)
-0.002
(-0.65)
-0.001
(-0.36)
-0.027***
(-3.70)
2,230
0.184
46
Panel B: OFFICE_TAXFOCUS
VARIABLES
OFFICE_TAXFOCUS
APTS
APTS_HD
OPPORTUNITY
SIZE
DACC
NOL
ΔNOL
EQINQ
FI
R&D
LEV
BTM
PPE
PTAXROA
CASH
DEP
BIG4
SEC_TIER
Intercept
Observations
Adj. R-squared
(I)
ETR
(II)
CETR
(III)
BTD
(IV)
DTAX
(V)
UTBSC
0.034**
(2.02)
-0.002
(-0.52)
-0.001
(-0.12)
-0.004
(-0.68)
0.007***
(5.20)
-0.357***
(-13.46)
-0.020***
(-4.85)
0.127***
(5.25)
-1.525***
(-3.34)
-0.207***
(-3.33)
-0.354***
(-8.21)
-0.036**
(-2.55)
0.039***
(6.25)
-0.016
(-1.16)
0.208***
(10.08)
-0.096***
(-9.19)
-0.281***
(-3.34)
0.031***
(3.66)
0.020**
(2.04)
0.195***
(3.16)
12,987
0.134
0.023
(1.17)
0.007
(1.27)
0.005
(1.07)
-0.008
(-1.15)
0.009***
(5.78)
0.052*
(1.87)
-0.037***
(-8.11)
0.145***
(6.92)
-1.326***
(-2.66)
0.062
(0.92)
-0.340***
(-7.44)
-0.098***
(-6.25)
0.023***
(3.82)
-0.098***
(-6.70)
0.017
(0.78)
-0.072***
(-6.45)
0.375***
(3.95)
0.018*
(1.95)
0.009
(0.87)
0.077
(1.50)
12,987
0.111
-0.010
(-1.34)
0.001
(0.36)
-0.002
(-1.16)
0.003
(1.41)
-0.005***
(-7.07)
0.133***
(7.58)
0.018***
(10.21)
0.951***
(62.12)
0.344*
(1.70)
-0.056
(-1.25)
0.167***
(6.79)
0.036***
(6.56)
0.005**
(2.02)
0.033***
(5.19)
0.349***
(17.53)
0.026***
(4.42)
0.009
(0.22)
-0.015***
(-3.80)
-0.013***
(-3.04)
0.030*
(1.76)
12,422
0.601
-0.077
(-1.60)
0.018
(1.28)
0.011
(0.73)
-0.001
(-0.06)
0.000
(0.07)
0.143
(1.51)
0.029**
(2.13)
0.459***
(6.56)
-4.262***
(-3.82)
-0.002
(-0.01)
0.647***
(4.29)
0.159***
(3.88)
0.004
(0.22)
-0.241***
(-6.77)
0.428***
(5.85)
-0.173***
(-4.78)
0.937***
(3.61)
-0.033
(-1.40)
-0.069***
(-2.66)
0.028
(0.44)
7,228
0.202
-0.004
(-0.59)
-0.001
(-0.86)
0.002
(1.60)
-0.002
(-1.54)
0.001
(1.59)
-0.005
(-0.64)
0.001
(0.66)
-0.007
(-0.82)
0.074
(0.68)
0.092***
(5.67)
0.055***
(2.81)
0.003
(0.67)
-0.002*
(-1.80)
-0.010***
(-3.29)
0.005
(0.74)
0.004
(1.09)
0.023
(1.12)
0.002
(0.57)
-0.000
(-0.10)
-0.019***
(-3.23)
2,230
0.180
This table presents the pooled analyses using the full samples, employing two alternative definitions of auditor
expertise based on the size of the tax function at the city-level. Variable definitions are included in Appendix A. *,
**, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, using two-tailed tests. T-statistics and pvalues are calculated using clustered standard errors by company. Year and industry-specific intercepts are included
in all Columns, but for brevity the intercepts are not reported.
47