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 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 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).

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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).

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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 value-

added effort. Donohoe and Knechel (2009) provide evidence that tax aggressive clients

3 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).

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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).

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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 within-

industry 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.

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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

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(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).

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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

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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.

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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.

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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 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;

CETR = Cash effective rate, defined as cash taxes paid (TXPD) divided by pre-

tax book income (PI) less special items (SPI); CETRs with negative

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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 within-

industry market share at the city-level.8

TAXEXPERT = Indicator variable equal to 1 if an audit firm is a tax expert, and 0

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 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.

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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:

EXPERT = Indicator variable equal to 1 is the auditor is designated as a tax or

combined tax and industry expert, and 0 otherwise;

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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

(AT);

CASH = Cash holding at the end of year t (CHE) divided by total assets at the

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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

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

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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 propensity-

score 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 non-

specialist 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 non-

specialist auditors.9

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

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 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.

16

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

for alternative treatments.11

Client fixed effects

If the full sample regressions are biased as a result of omitted client characteristics that 10 All results are qualitatively similar if we use all control variables in Equation (1) as predictors of the propensity score. 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.

17

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 time-

invariant 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

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

18

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.

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 market-

19

share 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.

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.

20

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

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.

21

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

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

22

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.

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:

23

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_TAXFOCU

S

= 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

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

24

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

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

25

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 cross-

sectional 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 within-

industry 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.

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.

26

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APPENDIX AVariable Definitions

Tax avoidance proxies

ETR = 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 1 st

and 99th percentiles;CETR = 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;

BTD = 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,

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

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;

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;

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;

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;

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,

31

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 = 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 APTS_H.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 X 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 0FI = 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 X 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 (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.

32

Table 1Choice Models for Auditor Expert

Panel A: TAXEXPERT

(I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX) (X)VARIABLES Dep. Var. = TAXEXPERT

LOGASSETS 0.305*** 0.364*** 0.334***(15.30) (18.53) (20.02)

APTS 1.722*** 1.850***(22.56) (27.01)

FI 1.034 1.852** 5.978***(1.13) (2.10) (6.95)

RD 1.592*** 1.500*** -1.146***(2.60) (2.65) (-2.59)

LEV -0.007 -0.092 1.116***(-0.04) (-0.48) (6.85)

BTM -0.187** -0.221*** -0.252***(-2.50) (-3.03) (-4.32)

PPE -0.225 -0.230 0.096(-1.32) (-1.39) (0.94)

PTAXROA -0.502* -0.553** -0.976***(-1.90) (-2.25) (-4.62)

Intercept -1.639** -1.167 -2.231*** -1.571*** -0.271*** -0.117*** -0.340*** -0.029 -0.188*** -0.039(-2.31) (-1.13) (-21.40) (-25.18) (-7.85) (-3.20) (-8.24) (-0.63) (-4.26) (-0.97)

Observations 12,971 12,971 12,987 12,987 12,987 12,987 12,987 12,987 12,987 12,987Area under ROC 0.77 0.71 0.68 0.66 0.57 0.50 0.57 0.53 0.53 0.51Pseudo R2 0.17 0.10 0.08 0.10 0.01 0.00 0.01 0.00 0.00 0.00

33

Panel B: OVERALLEXPERT

(I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX) (X)VARIABLES Dep. Var = OVERALLEXPERT

LOGASSETS 0.370*** 0.422*** 0.367***(16.88) (19.37) (20.18)

APTS 1.471*** 1.676***(18.47) (23.50)

FI 0.659 1.348 6.212***(0.69) (1.44) (7.29)

RD 2.079*** 1.982*** -0.728(3.20) (3.20) (-1.59)

LEV 0.028 -0.031 1.162***(0.13) (-0.15) (7.04)

BTM -0.212*** -0.240*** -0.268***(-2.59) (-2.99) (-4.40)

PPE -0.255 -0.258 -0.059(-1.40) (-1.44) (-0.56)

PTAXROA -0.700** -0.736*** -1.165***(-2.37) (-2.62) (-5.09)

Intercept -1.882** -1.482 -2.812*** -1.798*** -0.612*** -0.464*** -0.682*** -0.353*** -0.473*** -0.349***(-2.44) (-1.39) (-24.02) (-27.32) (-16.82) (-12.03) (-15.67) (-7.40) (-10.23) (-8.16)

Observations 12,971 12,971 12,987 12,987 12,987 12,987 12,987 12,987 12,987 12,987Area under ROC 0.78 0.74 0.70 0.64 0.58 0.49 0.57 0.53 0.48 0.52Pseudo R2 0.18 0.13 0.09 0.08 0.01 0.00 0.01 0.00 0.00 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.

34

Table 2 Descriptive Statistics

Full Sample

Panel A: Full Sample

Variable Mean Std. Dev. 25th Pctl. Median 75th Pctl.TAXEXPERT 0.460 0.498 0 0 1OVERALLEXPERT 0.380 0.485 0 0 1ETR 0.269 0.179 0.142 0.311 0.370CASHETR 0.202 0.201 0.027 0.169 0.308BTD 0.038 0.105 -0.001 0.024 0.059DTAX 0.222 0.509 -0.009 0.051 0.229UTBSC 0.014 0.019 0.003 0.007 0.017APTS 0.725 0.446 0 1 1APTSHD 0.250 0.433 0 0 1OPPORTUNITY 0.298 0.365 0.013 0.092 0.549SIZE 6.102 2.229 4.676 6.26 7.557DACC 0.005 0.074 -0.034 0.002 0.037NOL 0.416 0.493 0 0 1ΔNOL -0.006 0.080 0 0 0EQINC 0.001 0.005 0 0 0FI 0.019 0.038 0 0 0.023RD 0.037 0.063 0 0 0.05LEV 0.161 0.176 0 0.116 0.268BTM 0.521 0.424 0.270 0.445 0.680PPE 0.30 0.292 0.087 0.194 0.420PTXROA 0.123 0.117 0.047 0.091 0.162CASH 0.218 0.252 0.033 0.118 0.319DEP 0.047 0.032 0.026 0.039 0.058BIG 0.774 0.418 1 1 1SECTIER 0.092 0.289 0 0 0

35

Panel B: Partition by TAXEXPERT

TAXEXPERT = 1 TAXEXPERT = 0

Variable MeanStd. Dev. Median Mean Std. Dev. Median

ETR 0.274 0.168 0.309 0.265 0.188 0.312CASHETR 0.213 0.199 0.187 0.192 0.203 0.149BTD 0.035 0.09 0.024 0.041 0.117 0.024DTAX 0.230 0.513 0.051 0.216 0.506 0.051UTBSC 0.015 0.019 0.009 0.013 0.019 0.006APTS 0.897 0.304 1 0.579 0.494 1APTSHD 0.359 0.48 0 0.157 0.364 0OPPORTUNITY 0.405 0.386 0.253 0.206 0.32 0.038SIZE 6.822 2.046 6.867 5.487 2.195 5.671DACC 0.001 0.064 0 0.007 0.082 0.004NOL 0.415 0.493 0 0.417 0.493 0ΔNOL -0.001 0.066 0 -0.011 0.091 0EQINC 0.001 0.005 0 0.001 0.005 0FI 0.024 0.042 0 0.015 0.035 0RD 0.034 0.059 0 0.039 0.067 0LEV 0.180 0.176 0.152 0.146 0.174 0.08BTM 0.497 0.384 0.436 0.542 0.455 0.456PPE 0.304 0.281 0.208 0.296 0.3 0.181PTXROA 0.116 0.102 0.090 0.129 0.127 0.093CASH 0.199 0.236 0.108 0.233 0.264 0.132DEP 0.045 0.029 0.039 0.048 0.034 0.040BIG 0.935 0.246 1 0.637 0.481 1SECTIER 0.028 0.164 0 0.146 0.354 0

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.

36

Table 3Tax Avoidance, Tax Aggressiveness and Auditor Expertise, Full Samples

Panel A: TAXEXPERT

(I) (II) (III) (IV) (V)VARIABLES ETR CETR BTD DTAX UTBSC

TAXEXPERT -0.009** 0.002 0.002 0.020 0.000(-2.31) (0.51) (1.10) (1.44) (0.02)

APTS 0.000 0.006 0.000 0.013 -0.001(0.01) (1.13) (0.10) (0.86) (-0.88)

APTS_HD 0.002 0.006 -0.003 0.005 0.002(0.55) (1.22) (-1.57) (0.33) (1.46)

OPPORTUNITY -0.002 -0.009 0.003 -0.007 -0.002(-0.28) (-1.22) (1.19) (-0.35) (-1.49)

SIZE 0.007*** 0.009*** -0.005*** 0.000 0.001(5.29) (5.76) (-7.15) (0.01) (1.60)

DACC -0.356*** 0.051* 0.133*** 0.141 -0.005(-13.43) (1.86) (7.57) (1.49) (-0.64)

NOL -0.020*** -0.037*** 0.018*** 0.030** 0.001(-4.90) (-8.13) (10.22) (2.20) (0.66)

ΔNOL 0.128*** 0.144*** 0.951*** 0.458*** -0.007(5.26) (6.89) (62.08) (6.55) (-0.82)

EQINQ -1.500*** -1.325*** 0.338* -4.300*** 0.074(-3.27) (-2.65) (1.67) (-3.86) (0.68)

FI -0.206*** 0.059 -0.056 -0.003 0.092***(-3.33) (0.87) (-1.25) (-0.02) (5.70)

R&D -0.354*** -0.341*** 0.167*** 0.646*** 0.055***(-8.20) (-7.45) (6.80) (4.28) (2.80)

LEV -0.036** -0.098*** 0.036*** 0.158*** 0.003(-2.53) (-6.29) (6.56) (3.86) (0.67)

BTM 0.039*** 0.023*** 0.005** 0.004 -0.002*(6.25) (3.81) (2.03) (0.22) (-1.81)

PPE -0.016 -0.098*** 0.033*** -0.239*** -0.010***(-1.19) (-6.69) (5.19) (-6.73) (-3.30)

PTAXROA 0.206*** 0.017 0.349*** 0.432*** 0.005(10.01) (0.78) (17.56) (5.88) (0.73)

CASH -0.095*** -0.072*** 0.026*** -0.173*** 0.004(-9.14) (-6.45) (4.39) (-4.79) (1.13)

DEP -0.280*** 0.376*** 0.009 0.936*** 0.023(-3.33) (3.95) (0.21) (3.60) (1.12)

BIG4 0.036*** 0.019** -0.017*** -0.044* 0.001(4.29) (2.12) (-4.02) (-1.89) (0.47)

SEC_TIER 0.020** 0.009 -0.013*** -0.068*** -0.000(2.02) (0.87) (-3.03) (-2.61) (-0.08)

Intercept 0.204*** 0.083* 0.027 0.003 -0.019***(3.33) (1.66) (1.61) (0.05) (-3.23)

Observations 12,987 12,987 12,422 7,228 2,230Adj. R-squared 0.13 0.11 0.60 0.20 0.18

37

Panel B: OVERALLEXPERT

(I) (II) (III) (IV) (V)VARIABLES ETR CETR BTD DTAX UTBSC

OVERALLEXPERT -0.010** 0.001 0.002 0.030** 0.001(-2.45) (0.15) (1.22) (2.23) (0.92)

APTS -0.000 0.007 0.000 0.011 -0.001(-0.06) (1.23) (0.14) (0.79) (-1.09)

APTS_HD 0.002 0.006 -0.003 0.006 0.002(0.39) (1.29) (-1.51) (0.38) (1.42)

OPPORTUNITY -0.001 -0.008 0.003 -0.011 -0.002*(-0.20) (-1.15) (1.14) (-0.54) (-1.66)

SIZE 0.007*** 0.009*** -0.005*** -0.000 0.001(5.36) (5.76) (-7.17) (-0.09) (1.57)

DACC -0.356*** 0.052* 0.133*** 0.139 -0.005(-13.41) (1.87) (7.57) (1.47) (-0.66)

NOL -0.020*** -0.037*** 0.018*** 0.030** 0.001(-4.90) (-8.14) (10.23) (2.19) (0.69)

ΔNOL 0.127*** 0.144*** 0.951*** 0.460*** -0.007(5.23) (6.91) (62.06) (6.59) (-0.82)

EQINQ -1.500*** -1.321*** 0.337* -4.318*** 0.070(-3.27) (-2.64) (1.66) (-3.88) (0.64)

FI -0.206*** 0.059 -0.056 -0.006 0.091***(-3.33) (0.88) (-1.25) (-0.03) (5.67)

R&D -0.353*** -0.341*** 0.167*** 0.642*** 0.055***(-8.17) (-7.45) (6.78) (4.25) (2.78)

LEV -0.036** -0.098*** 0.036*** 0.157*** 0.003(-2.51) (-6.29) (6.56) (3.83) (0.64)

BTM 0.039*** 0.023*** 0.005** 0.003 -0.002*(6.26) (3.81) (2.03) (0.22) (-1.85)

PPE -0.016 -0.098*** 0.033*** -0.239*** -0.010***(-1.20) (-6.69) (5.20) (-6.74) (-3.27)

PTAXROA 0.206*** 0.017 0.349*** 0.433*** 0.005(10.00) (0.77) (17.54) (5.90) (0.76)

CASH -0.095*** -0.072*** 0.026*** -0.173*** 0.004(-9.18) (-6.45) (4.41) (-4.77) (1.11)

DEP -0.279*** 0.376*** 0.009 0.935*** 0.023(-3.32) (3.95) (0.21) (3.59) (1.13)

BIG4 0.036*** 0.019** -0.017*** -0.046* 0.001(4.27) (2.18) (-4.07) (-1.96) (0.40)

SEC_TIER 0.020** 0.009 -0.013*** -0.068*** -0.000(2.01) (0.86) (-3.03) (-2.60) (-0.08)

Intercept 0.204*** 0.083* 0.027 -0.000 -0.020***(3.34) (1.65) (1.61) (-0.00) (-3.30)

Observations 12,987 12,987 12,422 7,228 2,230Adj. R-squared 0.13 0.11 0.60 0.20 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 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.

38

Table 4Tax Avoidance, Tax Aggressiveness and Auditor Expertise

Matched Samples

Panel A: ETR, TAXEXPERT and OVERALLEXPERT

(I) (II) (III) (IV) (V) (VI)All Vars. No APTS Size All Vars. No APTS Size

VARIABLES Dep. Var. = ETR

TAXEXPERT -0.005 -0.005 -0.006(-1.18) (-1.27) (-1.41)

OVERALLEXPERT -0.008* -0.005 -0.006(-1.76) (-1.23) (-1.52)

APTS -0.001 -0.008 0.002 -0.001 -0.002 -0.007(-0.25) (-1.41) (0.33) (-0.16) (-0.34) (-1.22)

APTS_HD 0.004 0.004 -0.002 0.005 0.003 0.000(0.73) (0.77) (-0.45) (1.06) (0.51) (0.00)

OPPORTUNITY 0.000 -0.001 0.002 -0.002 -0.001 0.001(0.05) (-0.22) (0.33) (-0.32) (-0.21) (0.12)

SIZE 0.007*** 0.005*** 0.006*** 0.003* 0.004** 0.003*(4.50) (3.07) (3.98) (1.87) (2.52) (1.77)

DACC -0.339*** -0.351*** -0.352*** -0.353*** -0.346*** -0.383***(-9.81) (-10.35) (-10.44) (-9.16) (-9.31) (-10.36)

NOL -0.019*** -0.013*** -0.009** -0.009* -0.013*** -0.013***(-4.06) (-2.87) (-2.07) (-1.80) (-2.75) (-2.72)

ΔNOL 0.084*** 0.110*** 0.118*** 0.089** 0.111*** 0.106***(2.67) (3.30) (3.39) (2.34) (2.96) (2.65)

EQINQ -1.296** -1.483*** -1.512*** -1.129* -1.099** -1.492***(-2.16) (-2.95) (-2.89) (-1.92) (-2.07) (-2.74)

FI -0.208*** -0.188*** -0.196*** -0.177*** -0.238*** -0.228***(-3.08) (-2.92) (-2.91) (-2.58) (-3.76) (-3.54)

R&D -0.396*** -0.361*** -0.392*** -0.403*** -0.411*** -0.383***(-8.08) (-7.17) (-7.53) (-7.78) (-7.79) (-6.68)

LEV -0.043*** -0.042*** -0.043*** -0.036** -0.043*** -0.043***(-2.60) (-2.67) (-2.76) (-2.12) (-2.69) (-2.63)

BTM 0.040*** 0.029*** 0.033*** 0.037*** 0.030*** 0.035***(5.43) (3.88) (4.62) (4.18) (3.74) (4.17)

PPE -0.006 -0.019 -0.026* -0.020 -0.009 -0.024(-0.39) (-1.24) (-1.75) (-1.19) (-0.53) (-1.55)

PTAXROA 0.230*** 0.242*** 0.238*** 0.282*** 0.242*** 0.260***(8.85) (8.90) (8.97) (9.50) (8.36) (8.72)

CASH -0.100*** -0.106*** -0.098*** -0.099*** -0.098*** -0.090***(-8.39) (-9.19) (-8.20) (-7.93) (-7.52) (-6.90)

DEP -0.295*** -0.261*** -0.192** -0.349*** -0.351*** -0.296***(-2.98) (-2.71) (-2.04) (-3.30) (-3.32) (-2.89)

BIG4 0.032*** 0.028*** 0.033*** 0.038*** 0.030** 0.043***(3.25) (2.65) (3.15) (3.18) (2.38) (3.37)

SEC_TIER 0.020 0.015 0.021* 0.025 0.021 0.032**(1.56) (1.14) (1.70) (1.64) (1.35) (2.04)

Intercept 0.302*** 0.326*** 0.229*** 0.323*** 0.229*** 0.301***(18.06) (15.94) (2.97) (16.45) (3.06) (12.35)

39

Observations 7,840 9,148 9,182 7,404 8,088 8,144Adj. R-squared 0.14 0.12 0.13 0.13 0.13 0.12

Panel B: CETR, TAXEXPERT and OVERALLEXPERT

(I) (II) (III) (IV) (V) (VI)All Vars. No APTS Size All Vars. No APTS Size

VARIABLES Dep. Var. = CETR

TAXEXPERT 0.003 0.007 0.006(0.53) (1.32) (1.22)

OVERALLEXPERT 0.004 0.004 0.004(0.70) (0.80) (0.72)

APTS 0.001 0.002 0.002 0.003 0.004 -0.001(0.16) (0.37) (0.40) (0.43) (0.53) (-0.11)

APTS_HD 0.007 0.005 0.005 0.008 0.007 0.009(1.24) (0.87) (0.83) (1.26) (1.14) (1.57)

OPPORTUNITY -0.006 -0.009 -0.012 -0.012 -0.012 -0.004(-0.75) (-1.14) (-1.58) (-1.35) (-1.44) (-0.43)

SIZE 0.010*** 0.008*** 0.010*** 0.008*** 0.008*** 0.007***(5.93) (4.96) (5.60) (4.24) (4.86) (4.01)

DACC 0.077** 0.085** 0.081** 0.076* 0.077* 0.077*(2.03) (2.33) (2.21) (1.79) (1.87) (1.92)

NOL -0.034*** -0.035*** -0.030*** -0.026*** -0.032*** -0.028***(-6.29) (-6.67) (-5.99) (-4.66) (-5.98) (-5.10)

ΔNOL 0.156*** 0.155*** 0.154*** 0.134*** 0.154*** 0.184***(5.67) (5.31) (5.18) (3.89) (4.62) (5.33)

EQINQ -1.130* -1.147* -1.413** -0.676 -0.781 -1.408**(-1.78) (-1.93) (-2.36) (-0.96) (-1.25) (-2.29)

FI 0.041 0.056 0.065 0.062 0.024 -0.022(0.58) (0.83) (0.93) (0.83) (0.35) (-0.32)

R&D -0.376*** -0.335*** -0.398*** -0.360*** -0.362*** -0.351***(-6.91) (-5.87) (-7.09) (-5.67) (-6.35) (-5.59)

LEV -0.099*** -0.112*** -0.114*** -0.095*** -0.104*** -0.115***(-5.37) (-6.19) (-6.19) (-4.54) (-5.29) (-5.87)

BTM 0.033*** 0.024*** 0.020*** 0.026*** 0.013* 0.020***(4.09) (3.32) (2.76) (3.03) (1.80) (2.59)

PPE -0.111*** -0.109*** -0.116*** -0.115*** -0.110*** -0.121***(-6.34) (-6.76) (-7.30) (-6.33) (-6.25) (-6.92)

PTAXROA 0.017 0.016 0.010 0.032 0.003 0.034(0.62) (0.57) (0.38) (1.01) (0.12) (1.13)

CASH -0.072*** -0.085*** -0.080*** -0.086*** -0.089*** -0.091***(-5.25) (-6.64) (-6.00) (-5.68) (-6.20) (-6.39)

DEP 0.461*** 0.453*** 0.525*** 0.475*** 0.480*** 0.514***(3.76) (3.94) (4.58) (3.64) (3.79) (4.10)

BIG4 0.005 0.015 0.007 0.016 -0.005 0.014(0.45) (1.35) (0.63) (1.14) (-0.33) (1.01)

SEC_TIER 0.001 0.005 -0.001 0.021 -0.003 -0.005(0.11) (0.43) (-0.09) (1.25) (-0.16) (-0.28)

Intercept 0.029 0.043 0.038 0.040 0.066** 0.165(1.21) (1.57) (1.25) (1.51) (2.50) (1.55)

Observations 7,840 9,148 9,182 7,404 8,088 8,144Adj. R-squared 0.11 0.11 0.11 0.10 0.11 0.11

40

Panel C: BTD, TAXEXPERT and OVERALLEXPERT

(I) (II) (III) (IV) (V) (VI)All Vars. No APTS Size All Vars. No APTS Size

VARIABLES Dep. Var. = BTD

TAXEXPERT -0.000 -0.001 0.000(-0.10) (-0.48) (0.06)

OVERALLEXPERT -0.001 -0.001 0.000(-0.87) (-0.89) (0.07)

APTS 0.000 0.001 0.000 -0.001 -0.001 -0.001(0.18) (0.66) (0.12) (-0.56) (-0.34) (-0.35)

APTS_HD -0.003 -0.004** -0.003 -0.002 -0.001 -0.001(-1.64) (-2.19) (-1.52) (-1.15) (-0.41) (-0.80)

OPPORTUNITY 0.004 0.003 0.003 0.005* 0.005* 0.001(1.19) (0.99) (1.29) (1.67) (1.94) (0.52)

SIZE -0.005*** -0.005*** -0.005*** -0.005*** -0.005*** -0.004***(-7.81) (-7.95) (-7.14) (-7.08) (-7.37) (-6.04)

DACC 0.123*** 0.130*** 0.124*** 0.111*** 0.124*** 0.109***(5.59) (6.78) (6.48) (5.34) (5.97) (5.08)

NOL 0.015*** 0.014*** 0.014*** 0.012*** 0.012*** 0.013***(7.77) (8.18) (8.01) (6.57) (6.76) (7.04)

ΔNOL 0.956*** 0.952*** 0.955*** 0.949*** 0.950*** 0.950***(48.82) (50.46) (56.53) (50.78) (55.54) (52.26)

EQINQ -0.005 0.143 0.098 0.204 0.113 -0.019(-0.02) (0.65) (0.43) (0.86) (0.47) (-0.07)

FI -0.026 -0.007 -0.011 0.023 0.045 0.022(-0.72) (-0.20) (-0.31) (0.63) (1.32) (0.67)

R&D 0.140*** 0.136*** 0.139*** 0.135*** 0.106*** 0.157***(4.97) (5.15) (4.91) (4.63) (3.74) (5.17)

LEV 0.033*** 0.037*** 0.031*** 0.034*** 0.031*** 0.033***(5.18) (6.27) (5.33) (5.63) (5.20) (5.31)

BTM 0.003 0.007*** 0.005* 0.004 0.008*** 0.007**(1.06) (2.78) (1.76) (1.60) (2.94) (2.47)

PPE 0.034*** 0.025*** 0.030*** 0.024*** 0.022*** 0.037***(5.09) (4.29) (5.10) (3.90) (3.93) (6.18)

PTAXROA 0.319*** 0.332*** 0.298*** 0.296*** 0.293*** 0.304***(13.47) (15.08) (13.75) (12.36) (13.07) (12.94)

CASH 0.028*** 0.029*** 0.029*** 0.027*** 0.029*** 0.023***(4.28) (4.84) (4.89) (4.19) (4.78) (3.55)

DEP -0.013 0.037 0.043 0.025 0.073 0.015(-0.28) (0.80) (1.00) (0.49) (1.54) (0.33)

BIG4 -0.009* -0.010** -0.013*** -0.005 -0.003 -0.009(-1.87) (-2.36) (-2.82) (-0.88) (-0.61) (-1.51)

SEC_TIER -0.007 -0.007 -0.012** -0.002 -0.004 -0.005(-1.20) (-1.32) (-2.30) (-0.36) (-0.63) (-0.79)

Intercept 0.019*** 0.014** 0.023*** 0.007 -0.001 0.014(2.91) (2.46) (3.64) (0.78) (-0.06) (0.73)

Observations 7,510 8,750 8,808 7,096 7,742 8,606Adj. R-squared 0.60 0.60 0.60 0.59 0.60 0.58

41

Panel D: DTAX, TAXEXPERT and OVERALLEXPERT

(I) (II) (III) (IV) (V) (VI)All Vars. No APTS Size All Vars. No APTS Size

VARIABLES Dep. Var. = DTAX

TAXEXPERT 0.015 0.008 0.014(0.95) (0.53) (0.95)

OVERALLEXPERT 0.029* 0.012 0.018(1.81) (0.74) (1.22)

APTS 0.022 0.024 0.039** 0.049** 0.004 0.015(1.05) (1.33) (2.15) (2.23) (0.22) (0.83)

APTS_HD 0.005 -0.017 -0.001 -0.015 0.013 0.005(0.29) (-0.96) (-0.07) (-0.81) (0.74) (0.27)

OPPORTUNITY -0.007 0.007 0.012 -0.018 -0.007 0.002(-0.30) (0.31) (0.53) (-0.75) (-0.30) (0.10)

SIZE 0.003 0.001 -0.000 0.007 0.000 -0.000(0.55) (0.12) (-0.08) (1.35) (0.02) (-0.07)

DACC 0.067 0.100 -0.032 0.096 0.085 0.007(0.51) (0.78) (-0.27) (0.70) (0.65) (0.05)

NOL 0.033* 0.036** 0.023 0.037** 0.038** 0.032**(1.87) (2.26) (1.41) (2.01) (2.09) (1.99)

ΔNOL 0.489*** 0.421*** 0.560*** 0.512*** 0.521*** 0.474***(5.25) (4.42) (6.45) (4.95) (5.17) (5.15)

EQINQ -5.560*** -4.372*** -5.192*** -4.664*** -4.514*** -4.285***(-3.28) (-2.95) (-3.37) (-2.67) (-2.67) (-2.97)

FI -0.021 0.030 0.038 -0.131 -0.055 -0.063(-0.09) (0.13) (0.17) (-0.57) (-0.23) (-0.31)

R&D 0.351* 0.654*** 0.538*** 0.302 0.679*** 0.650***(1.95) (3.54) (2.96) (1.51) (3.43) (3.41)

LEV 0.169*** 0.123** 0.135*** 0.136** 0.134*** 0.104**(3.23) (2.41) (2.75) (2.43) (2.58) (2.16)

BTM 0.026 0.026 0.020 0.022 0.016 0.023(1.12) (1.15) (0.95) (0.88) (0.69) (1.10)

PPE -0.212*** -0.233*** -0.175*** -0.179*** -0.181*** -0.187***(-4.85) (-5.99) (-4.60) (-3.86) (-3.94) (-4.79)

PTAXROA 0.445*** 0.429*** 0.430*** 0.357*** 0.375*** 0.338***(4.37) (4.47) (4.68) (3.44) (3.65) (3.67)

CASH -0.171*** -0.218*** -0.198*** -0.195*** -0.198*** -0.232***(-3.69) (-4.75) (-4.70) (-3.93) (-4.01) (-5.01)

DEP 0.823** 0.841*** 0.421 0.696* 0.656* 0.157(2.49) (2.62) (1.47) (1.89) (1.86) (0.55)

BIG4 -0.026 -0.039 -0.020 -0.058 -0.045 -0.064*(-0.78) (-1.19) (-0.70) (-1.49) (-1.10) (-1.75)

SEC_TIER -0.080** -0.076** -0.063* -0.108** -0.070 -0.125***(-2.06) (-2.00) (-1.76) (-2.42) (-1.47) (-2.89)

Intercept 0.196*** 1.533*** 0.066 -0.125** -0.169*** 0.025(2.98) (25.71) (1.15) (-2.14) (-3.17) (0.30)

Observations 4,300 4,984 5,030 4,064 4,398 4,972

42

Adj. R-squared 0.22 0.21 0.22 0.22 0.23 0.23

Panel E: UTB, TAXEXPERT and OVERALLEXPERT

(I) (II) (III) (IV) (V) (VI)All Vars. No APTS Size All Vars. No APTS Size

VARIABLES Dep. Var. = UTBSC

TAXEXPERT -0.000 -0.001 0.000(-0.13) (-0.73) (0.03)

OVERALLEXPERT 0.001 0.000 0.000(1.02) (0.35) (0.39)

APTS -0.001 -0.001 -0.002 0.000 -0.003* -0.001(-0.43) (-1.07) (-1.36) (0.23) (-1.66) (-1.12)

APTS_HD 0.003* 0.002 0.003 0.002 0.002 0.002(1.76) (1.58) (1.51) (1.17) (1.26) (1.32)

OPPORTUNITY -0.002 -0.003 -0.003 -0.004** -0.003 -0.002(-1.21) (-1.58) (-1.50) (-2.20) (-1.44) (-1.00)

SIZE 0.001 0.001* 0.001 0.001** 0.001 0.001(1.58) (1.94) (1.08) (2.47) (1.43) (1.23)

DACC 0.009 -0.010 -0.010 -0.006 -0.009 -0.005(1.02) (-1.09) (-1.05) (-0.66) (-1.08) (-0.51)

NOL -0.000 0.000 0.001 0.001 0.000 0.000(-0.22) (0.13) (0.66) (1.08) (0.01) (0.19)

ΔNOL -0.010 -0.016 -0.003 -0.019 -0.002 -0.014(-0.89) (-1.60) (-0.31) (-1.59) (-0.22) (-1.34)

EQINQ 0.099 0.090 0.043 0.224 0.123 0.129(0.51) (0.60) (0.29) (1.28) (0.75) (0.95)

FI 0.076*** 0.081*** 0.100*** 0.083*** 0.086*** 0.081***(4.82) (4.72) (5.20) (4.97) (4.90) (4.85)

R&D 0.054*** 0.049*** 0.064*** 0.045** 0.049*** 0.049***(3.04) (2.93) (3.03) (2.25) (2.84) (2.86)

LEV 0.004 -0.002 0.003 0.002 0.001 0.003(0.80) (-0.43) (0.60) (0.42) (0.24) (0.66)

BTM -0.005*** -0.004*** -0.004** -0.003** -0.004** -0.003**(-3.40) (-2.70) (-2.25) (-2.09) (-2.14) (-2.04)

PPE -0.011*** -0.009*** -0.010*** -0.012*** -0.011*** -0.010***(-4.16) (-2.77) (-3.01) (-3.82) (-2.74) (-3.29)

PTAXROA 0.001 -0.000 0.009 0.008 0.012 0.009(0.07) (-0.01) (0.98) (0.89) (1.47) (1.00)

CASH 0.002 0.004 0.002 0.005 0.006 0.005(0.46) (0.95) (0.54) (1.15) (1.40) (1.26)

DEP 0.003 0.021 0.009 0.041 0.045 0.026(0.16) (1.01) (0.39) (1.58) (1.63) (1.17)

BIG4 -0.002 0.005** 0.001 0.001 0.003 0.006***(-0.50) (2.41) (0.39) (0.23) (0.94) (3.15)

SEC_TIER 0.001 0.005** 0.001 0.004 0.003 0.007***(0.15) (2.00) (0.19) (0.93) (0.93) (2.61)

Intercept -0.007 -0.010* -0.009 0.001 -0.006 -0.007(-1.03) (-1.66) (-1.32) (0.06) (-0.80) (-1.03)

43

Observations 1,214 1,486 1,522 1,146 1,352 1,658Adj. R-squared 0.18 0.18 0.20 0.18 0.20 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. T-statistics 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 5Tax Avoidance, Tax Aggressiveness and Auditor Expertise

Full Samples Including Client Fixed Effects

(I) (II) (III) (IV) (V) (VI) (VII) (VIII) (VIII) (IX)VARIABLES ETR CETR BTD DTAX UTBSC ETR CETR BTD DTAX UTBSC

TAXEXPERT -0.006 0.003 0.001 0.016 -0.000(-1.44) (0.72) (0.64) (1.22) (-0.63)

OVERALLEXPERT -0.007* 0.002 0.001 0.031** 0.000(-1.72) (0.50) (0.34) (2.32) (0.08)

Observations 12,987 12,987 12,422 7,228 2230 12,987 12,987 12,422 7,228 2230Adj. R-squared 0.13 0.11 0.60 0.21 0.18 0.13 0.11 0.60 0.21 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 6Tax Avoidance, Tax Aggressiveness and Tax Expertise at the Office-Level

Full Samples

Panel A: OFFICESIZE_TAX

(I) (II) (III) (IV) (V)VARIABLES ETR CETR BTD DTAX UTBSC

OFFICESIZE_TAX 0.003*** 0.002 -0.001 -0.004 0.001**(2.69) (1.31) (-1.08) (-1.15) (2.23)

APTS -0.003 0.006 0.001 0.019 -0.001(-0.73) (1.18) (0.43) (1.35) (-1.06)

APTS_HD 0.000 0.006 -0.002 0.009 0.002(0.04) (1.19) (-1.34) (0.59) (1.20)

OPPORTUNITY 0.000 -0.006 0.003 -0.007 -0.001(0.08) (-0.78) (1.07) (-0.34) (-0.54)

SIZE 0.007*** 0.008*** -0.005*** 0.001 0.001(4.69) (5.44) (-6.84) (0.26) (1.19)

DACC -0.359*** 0.050* 0.133*** 0.143 -0.005(-13.56) (1.82) (7.60) (1.51) (-0.70)

NOL -0.020*** -0.037*** 0.018*** 0.030** 0.001(-4.91) (-8.15) (10.23) (2.19) (0.65)

ΔNOL 0.127*** 0.145*** 0.951*** 0.460*** -0.006(5.26) (6.92) (62.09) (6.57) (-0.80)

EQINQ -1.526*** -1.325*** 0.342* -4.277*** 0.082(-3.34) (-2.64) (1.69) (-3.81) (0.76)

FI -0.213*** 0.058 -0.055 0.011 0.091***(-3.42) (0.86) (-1.23) (0.06) (5.64)

R&D -0.358*** -0.342*** 0.168*** 0.656*** 0.054***(-8.34) (-7.50) (6.82) (4.35) (2.78)

LEV -0.037** -0.098*** 0.037*** 0.159*** 0.003(-2.57) (-6.27) (6.59) (3.87) (0.63)

BTM 0.039*** 0.023*** 0.005** 0.004 -0.002*(6.26) (3.82) (2.03) (0.25) (-1.86)

PPE -0.015 -0.098*** 0.033*** -0.241*** -0.009***(-1.11) (-6.67) (5.18) (-6.79) (-3.20)

PTAXROA 0.209*** 0.018 0.348*** 0.427*** 0.005(10.17) (0.82) (17.48) (5.83) (0.78)

CASH -0.097*** -0.072*** 0.026*** -0.172*** 0.004(-9.32) (-6.50) (4.45) (-4.74) (1.02)

DEP -0.278*** 0.377*** 0.009 0.929*** 0.023(-3.31) (3.96) (0.20) (3.59) (1.13)

BIG4 0.019* 0.011 -0.014*** -0.020 -0.002(1.87) (1.04) (-3.00) (-0.71) (-0.65)

SEC_TIER 0.014 0.005 -0.012*** -0.061** -0.001(1.39) (0.51) (-2.73) (-2.26) (-0.36)

Intercept 0.169*** 0.064 0.033* 0.053 -0.027***(2.70) (1.20) (1.86) (0.70) (-3.70)

Observations 12,987 12,987 12,422 7,228 2,230Adj. R-squared 0.135 0.111 0.601 0.202 0.184

46

Panel B: OFFICE_TAXFOCUS

(I) (II) (III) (IV) (V)VARIABLES ETR CETR BTD DTAX UTBSC

OFFICE_TAXFOCUS 0.034** 0.023 -0.010 -0.077 -0.004(2.02) (1.17) (-1.34) (-1.60) (-0.59)

APTS -0.002 0.007 0.001 0.018 -0.001(-0.52) (1.27) (0.36) (1.28) (-0.86)

APTS_HD -0.001 0.005 -0.002 0.011 0.002(-0.12) (1.07) (-1.16) (0.73) (1.60)

OPPORTUNITY -0.004 -0.008 0.003 -0.001 -0.002(-0.68) (-1.15) (1.41) (-0.06) (-1.54)

SIZE 0.007*** 0.009*** -0.005*** 0.000 0.001(5.20) (5.78) (-7.07) (0.07) (1.59)

DACC -0.357*** 0.052* 0.133*** 0.143 -0.005(-13.46) (1.87) (7.58) (1.51) (-0.64)

NOL -0.020*** -0.037*** 0.018*** 0.029** 0.001(-4.85) (-8.11) (10.21) (2.13) (0.66)

ΔNOL 0.127*** 0.145*** 0.951*** 0.459*** -0.007(5.25) (6.92) (62.12) (6.56) (-0.82)

EQINQ -1.525*** -1.326*** 0.344* -4.262*** 0.074(-3.34) (-2.66) (1.70) (-3.82) (0.68)

FI -0.207*** 0.062 -0.056 -0.002 0.092***(-3.33) (0.92) (-1.25) (-0.01) (5.67)

R&D -0.354*** -0.340*** 0.167*** 0.647*** 0.055***(-8.21) (-7.44) (6.79) (4.29) (2.81)

LEV -0.036** -0.098*** 0.036*** 0.159*** 0.003(-2.55) (-6.25) (6.56) (3.88) (0.67)

BTM 0.039*** 0.023*** 0.005** 0.004 -0.002*(6.25) (3.82) (2.02) (0.22) (-1.80)

PPE -0.016 -0.098*** 0.033*** -0.241*** -0.010***(-1.16) (-6.70) (5.19) (-6.77) (-3.29)

PTAXROA 0.208*** 0.017 0.349*** 0.428*** 0.005(10.08) (0.78) (17.53) (5.85) (0.74)

CASH -0.096*** -0.072*** 0.026*** -0.173*** 0.004(-9.19) (-6.45) (4.42) (-4.78) (1.09)

DEP -0.281*** 0.375*** 0.009 0.937*** 0.023(-3.34) (3.95) (0.22) (3.61) (1.12)

BIG4 0.031*** 0.018* -0.015*** -0.033 0.002(3.66) (1.95) (-3.80) (-1.40) (0.57)

SEC_TIER 0.020** 0.009 -0.013*** -0.069*** -0.000(2.04) (0.87) (-3.04) (-2.66) (-0.10)

Intercept 0.195*** 0.077 0.030* 0.028 -0.019***(3.16) (1.50) (1.76) (0.44) (-3.23)

Observations 12,987 12,987 12,422 7,228 2,230Adj. R-squared 0.134 0.111 0.601 0.202 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 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.

47

48