THE ACCOUNTING REVIEW
Vol. 85, No. 3
2010
pp. 1035–1064
American Accounting Association
DOI: 10.2308/accr.2010.85.3.1035
Performance Measurement of Corporate
Tax Departments
John R. Robinson
The University of Texas at Austin
Stephanie A. Sikes
University of Pennsylvania
Connie D. Weaver
Texas A&M University
ABSTRACT: We investigate why firms choose to evaluate a tax department as a profit
center 共“contributor to the bottom line”兲 as opposed to as a cost center and the association between this choice and effective tax rates 共ETRs兲. Using data from a confidential survey taken in 1999 of Chief Financial Officers, we develop and test a theory for
choosing between these two methods of evaluating a tax department. We find that the
likelihood of evaluating the tax department as a profit center is increasing in firm decentralization characteristics and tax-planning opportunities. We then employ instrumental variables to investigate whether evaluating a tax department as a profit center
provides an effective incentive for the tax department to contribute to net income
through lower ETRs. We find that our instrument for profit center firms is associated
with significantly lower ETRs than cost center firms.
Keywords: tax department; effective tax rates; profit centers; managerial incentives.
JEL Classifications: H25; L33; M41; M43.
I. INTRODUCTION
n this study, we focus on the choice between two performance measurement models for tax
departments. Many corporate administrative departments 共i.e., accounting, finance, communications, and human resources兲 are evaluated using a cost center model, a measurement system
designed to minimize departmental costs. Traditionally, tax departments were also evaluated using
I
The authors thank Ernst & Young for providing data necessary to complete this project, and appreciate constructive
comments from Ben Ayers, Michelle Hanlon, Ross Jennings, Lillian Mills, Tom Omer, Sonja Rego, Doug Shackelford,
Terry Shevlin, David Weber, Ryan Wilson, and workshop participants at Notre Dame University, The University of Texas
at Austin, the National Tax Association’s 99th Annual Conference on Taxation, the 2007 American Taxation Association’s
Midyear Meeting, the 2007 American Accounting Association Annual Meeting, and the 2008 Lone Star Conference. The
authors gratefully acknowledge research support provided by their respective institutions: Professor Robinson 共Red McCombs School of Business and C. Aubrey Smith Professorship兲 and Professor Weaver 共Mays Research Fellowship兲.
Editor’s note: Accepted by Dan Dhaliwal.
Submitted: April 2008
Accepted: September 2009
Published Online: May 2010
1035
1036
Robinson, Sikes, and Weaver
cost center models, with an emphasis on minimizing the cost of tax compliance 共Ernst & Young
2004兲. However, during the 1990s, some firms began to use profit centers as the performance
measurement model for tax departments. Broadly speaking, the performance of a profit center
department is evaluated by the difference between costs and revenue associated with the department. For these firms, the performance of the corporate tax department is judged by its contributions to reported financial income rather than cost minimization.1 It is unlikely that one particular
measurement system is optimal for all firms; rather, firm characteristics 共e.g., industry and production technology兲 determine what performance measurement model is best for each firm 共Jensen
and Meckling 1998兲.
Our objectives are twofold. First, based upon the organizational design literature, we develop
and test a model for predicting whether firms choose to evaluate their tax departments as profit or
cost centers. This model fills a void in the literature by addressing the question of which factors
contribute to the design of performance measurement systems and, more specifically, which factors lead firms to choose different methods for evaluating administrative functions. Second, we
investigate whether a specific tax-related financial attribute, the effective tax rate 共ETR兲, is associated with the expected choice of performance measurement model, namely profit or cost center.2
Specifically, we test whether firms with characteristics conducive to a profit center measurement
system are associated with lower ETRs.
We base our empirical tests on a unique data set constructed from confidential survey responses from more than 200 Chief Financial Officers 共CFOs兲 of Fortune 1000 companies. The
confidential survey, conducted in 1999 at the behest of Ernst & Young, was designed to explore
the role tax plays in the corporate strategic decision-making process and how CFOs measure the
success of their tax departments. In this study, we focus on the survey question that asks CFOs to
describe whether their firm manages and measures the tax department as a “contributor to the
bottom line” 共i.e., profit center兲 or as a cost center.
To investigate the determinants of this choice, we estimate a logistic regression that models
the performance measurement choice as a function of firm variables representing four constructs:
firm decentralization, the degree of coordination between the tax department and operating divisions within the firm, firm growth and tax-planning opportunities, and the importance of financial
tax management to the firm. We find that the likelihood of evaluating the tax department as a profit
center is positively related to attributes associated with decentralized firms—firm size, number of
product lines and geographic segments, and the degree of coordination between the tax department
and operating units. We also find that the likelihood of evaluating the tax department as a profit
center is associated with tax-planning opportunities and resources 共R&D activity, amount of intangible assets, and proportion of the tax department budget allocated to tax planning兲. These
results suggest that firms are more likely to adopt profit center models to motivate additional tax
planning when the firm is large, diverse, and has tax-planning opportunities that can affect a
number of business units.
To address our second objective, we estimate a regression that tests whether effective tax rates
are associated with the underlying characteristics that determine the profit or cost center choice. In
this ETR regression, we utilize an instrumental variable to represent a firm’s choice to evaluate its
1
2
Certain strategies allow managers to increase financial income but do not change taxable income. For example, designating foreign earnings as “permanently” reinvested abroad increases reported earnings by reducing reported tax expense yet does not affect taxable income or taxes paid 共Krull 2004兲.
Lower ETRs do not always imply effective tax planning. Jennings et al. 共2010兲 describe how implicit taxes reduce
pre-tax returns by the amount of any explicit tax savings. However, ETRs will capture the financial accounting effects
of tax planning 共i.e., financial tax management兲. We capture the financial accounting effects of tax planning by measuring ETR as the ratio of total tax expense to reported pre-tax income.
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tax department as a profit center, the likelihood of profit center choice predicted by our logistic
profit center choice regression. We use this two-stage approach to address the potential for endogeneity between the choice of performance measurement model and ETR.3 We find that firms
predicted to utilize profit centers have, on average, lower ETRs. This finding is consistent with the
conjecture that a profit center performance measurement model provides an effective incentive to
reduce ETRs.
Lower ETRs can result from planning strategies designed to either reduce tax payments
and/or manage tax accruals 共Hanlon and Heitzman 2009兲. In supplemental analyses, we test
whether we can attribute our profit center results to cash tax planning or tax accrual planning or
both. We substitute a measure of real tax planning, cash-ETR, for our measure of financial ETR
and find that managing and evaluating a tax department as a profit center has no incremental effect
on cash tax savings. Taken together with our main results, this result suggests that evaluation of
the tax department as a profit center encourages tax departments to focus on tax-planning activities
that reduce reported financial ETRs rather than generate incremental cash tax savings.
This study contributes to the literature in several ways. We identify how the determinants of
organizational architecture/design influence firms’ choices of performance evaluation systems for
the tax department. This research provides the first documentation of the factors contributing to
the choice of performance measurement in the context of an administrative function of a firm, such
as a tax department. Second, we provide evidence that through these factors, firms expected to
evaluate their tax departments by their contributions to the “bottom line” have lower effective tax
rates, which increases their reported financial earnings. We do not find that the performance
measurement system choice affects cash ETRs.
Our findings should be of primary interest to researchers and investors who seek to understand the causes and financial reporting consequences of the design of performance measurement
and evaluation. In particular, our results should be of interest to researchers who study the design
of incentives and choice of performance measurement systems. Regulators such as the Securities
and Exchange Commission, the Financial Accounting Standards Board, and the Internal Revenue
Service should be interested that the method of evaluating specific administrative units, such as the
tax department, has specific and predictable effects on results presented for financial reporting
purposes. Finally, policy makers should be interested in results indicating that performance measurement choices can encourage financial reporting aggressiveness and are, in part, responsible for
lower ETRs.
Section II develops our hypotheses. Section III describes our data and research design and
Section IV discusses our empirical results. Section V summarizes the results and offers conclusions.
II. HYPOTHESES DEVELOPMENT
Research that examines tax implications of managerial incentives generally focuses on how
compensation provides an incentive for aggressive tax reporting. For example, prior research has
found a positive association between tax aggressiveness and annual bonus compensation 共Hanlon
et al. 2007兲, equity-based compensation 共Desai and Dharmapala 2006兲, and compensation levels
of CFOs and CEOs 共Rego and Wilson 2008兲. Additionally, Dyreng et al. 共2010兲 examine the effect
of individual managers on corporate tax planning and report that corporate tax planning is a
function of individual tax managers’ characteristics. Phillips 共2003兲 finds that compensating busi3
As described by Larcker 共2003兲, studies of management choice are typically confronted by endogeneity, and this study
is no exception. Hanlon et al. 共2003兲 address similar endogeneity issues in their study of executive stock options. We
describe our approach in more detail in the research method section.
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Robinson, Sikes, and Weaver
ness unit managers 共but not CEOs兲 on an after-tax basis reduces ETRs.4 In contrast to this
literature, we investigate the firm characteristics associated with a choice to measure and evaluate
the tax department as a profit or cost center. In addition, we examine whether the performance
measurement system encourages tax managers to utilize available tax-planning strategies to reduce
ETRs.
Evaluating Tax Departments as Profit or Cost Centers
Organizational design theory depicts performance measurement as one of three “rules of the
game” that determine how individuals behave in an organization.5 Divisional performance measurement consists of a system of measuring and evaluating the performance of the firm’s subunits
共e.g., departments兲. The suitability and effectiveness of divisional and individual performance
measurement systems relate to the cost of acquiring and transferring knowledge in the organization 共Jensen and Meckling 1998兲. Information transfer costs depend on the type of knowledge
required to be acquired and/or transferred where knowledge can be characterized along a continuum where one end is general knowledge and the other end is specific knowledge. General
knowledge is easily aggregated and transferred at a relatively low cost, whereas specific knowledge is idiosyncratic information that is difficult to aggregate and is costly to transfer. Knowledge
that resides in tax departments can be characterized along this same continuum. Some tax knowledge could be viewed as general because tax accounting relies on general financial principles and
often corresponds to financial accounting precepts. However, most tax knowledge is specific
because tax rules 共1兲 vary from accounting principles in peculiar ways, 共2兲 vacillate over time, and
共3兲 are often contextual and difficult to interpret and apply.
When specific knowledge is located in a subunit 共i.e., the tax department兲, the performance
measurement system should correspond to the costs of transferring the knowledge within the firm.
The literature describes several different performance measurement systems, but for purposes of
this study we compare only two: cost centers and profit centers 共Brickley et al. 1995; Jensen and
Meckling 1998兲. A cost center is a performance measurement system based on minimizing costs
for a given output, maximizing output for a given cost, or minimizing average cost. In contrast, a
profit center is a system based on evaluating the department by the difference between its costs
and revenue 共as defined by the system兲.
Much prior research 共both theoretical and empirical兲 focuses on the importance of performance measures in compensation contracts; however, relatively little empirical evidence exists to
identify the determinants of performance measure choices. The empirical evidence tends to focus
on performance measurement for firm executives 共Lambert and Larcker 1987; Sloan 1993; Bushman et al. 1996兲 or general workers 共Ittner and Larcker 2002兲 rather than on the performance
measurement of business units or departments. There are exceptions. A handful of studies 共Govindarajan and Gupta 1985; Simons 1987; Bushman et al. 1995; Keating 1997兲 investigate the factors
associated with the choice of performance measures for business units. However, business units
are typically operating divisions rather than administrative functions with responsibilities permeating the entire firm. Nonetheless, these studies provide insight into the determinants of perfor-
4
5
Mills et al. 共1998兲 investigate investments in tax planning and find that effective tax planning is a function of taxplanning costs and opportunities to engage in tax planning; however, they do not investigate the incentives in place to
encourage tax planning.
The other two rules are the reward system and the method of partitioning decision rights. The three rules are related. For
example, rewards must be coordinated with performance measurement to have the intended effect on behavior. Our
survey data limit us to an investigation of performance measurement only. As we discuss below, to the extent that the
three rules are contradictory, the choice of performance measurement system could be ineffectual.
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mance measures for business units. We draw upon these studies to develop our performance
measurement model for tax departments.
Prior research finds that firm decentralization, diversification, and growth opportunities are
associated with the use of corporate performance measures in division manager compensation
共Bushman et al. 1995; Keating 1997兲.6 In our study, the corporate “bottom line” is a firm-wide or
corporate performance measure, implying that a profit center performance measure is equivalent to
a firm-wide measure. In contrast, a cost center performance measure is more closely aligned with
a divisional- or departmental-level performance measure.
Decentralization
Firms may be decentralized in a number of ways, such as along product lines, by legal
entities, or across geographic regions. In decentralized firms, the manager of each business unit or
division is granted the right to make business decisions for the division 共Brickley et al. 1995兲. In
contrast, business decisions at centralized firms are made at the corporate level. Regardless of
whether their operating divisions are centralized or decentralized, in most firms, administrative
functions such as the tax department operate at the corporate level.7 For example, Johnson &
Johnson is a decentralized company divided by product lines; however, the tax function for
Johnson & Johnson is highly centralized.8 Tax departments of centralized firms are likely to
consider the overall firm effects of their tax-planning decisions even without an explicit performance measurement system incentive. However, tax managers at decentralized firms might need a
firm-wide performance measure 共i.e., profit center兲 to encourage them to consider overall firm
performance in making their tax-planning decisions. Consequently, firm diversification and decentralization are likely to affect the choice of tax department performance measure. Based on this
discussion, we posit the following hypothesis 共stated in alternative form兲:
H1a: The likelihood that a firm will measure its tax department as a profit center increases
with firm size and decentralization.
Departmental Interdependencies and Coordination
Information problems vary with the degree of firm centralization 共Matejka and DeWaegenaere
2005兲, and tax plans must often integrate specific tax knowledge with specific knowledge from
operating units. Because effective tax plans must consider the economic environment and business
strategies across different operating units, managers of large, decentralized firms may have difficulty determining if all tax-planning opportunities have been identified or effectively implemented. Therefore, even within decentralized firms, the optimal choice of performance measure
may depend on the coordination between the divisions or departments. In the case of a firm that is
decentralized but has no coordination among its divisions, measuring the performance of the tax
department as a profit center might influence a tax manager to engage in tax saving strategies that
will not only reduce tax expense, but also reduce overall firm profitability 共Jensen and Meckling
1998兲.
To illustrate this point, assume that a tax department determines that it is optimal for the firm
to operate a manufacturing facility in a specific low tax jurisdiction. If labor or materials for the
6
7
8
Keating 共1997兲 finds that the use of division-level metrics is positively related to the correlation between division
earnings and value in the division’s industry. However, we do not incorporate this result in our study because we are
unable to assess either the tax department’s earnings or value.
We do not intend to imply that a decentralized firm is likely to have either a centralized or decentralized tax department.
Instead, we expect that, all else equal, a decentralized firm is more likely to have a tax department evaluated using a
profit center model.
We thank an anonymous reviewer for this example.
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Robinson, Sikes, and Weaver
facility are not readily available in this jurisdiction, additional costs to obtain these critical production inputs might exceed any tax savings from a reduced tax rate. Operating managers might be
aware of the lack of resource availability, but might not communicate this information to the tax
department in the absence of coordination between the tax department and operating units. Consequently, net income could actually decrease rather than increase. Additionally, without coordination, many tax strategies developed by profit center tax departments might be impossible to
implement. With coordination, profit center tax departments can modify tax plans to include
constraints identified by the operating units to enhance overall firm profits. In contrast, cost center
tax departments lack any explicit incentive to either identify or engage in costly coordination of
tax-planning strategies because they seek to minimize costs. Accordingly, we hypothesize that the
degree of coordination can affect the performance measurement choice 共stated in alternative
form兲:
H1b: The likelihood that a firm will measure its tax department as a profit center increases
with the degree of coordination between the tax department and operating units.
Firm Growth and Tax-Planning Opportunities
Financial performance metrics do not always reflect the expected future performance effects
of present actions or decisions. A critical factor is the time horizon from initiation to fruition. For
example, investment in R&D may not result in increases in profitability for several years, yet the
expenditures associated with those investments reduce current profits. Prior research provides
evidence that the use of firm-wide accounting metrics is negatively associated with firm growth
opportunities 共Keating 1997兲. Consistent with this research, we expect a negative association
between firm growth opportunities and the likelihood of measuring the tax department as a profit
center.
Jensen and Meckling 共1998兲 caution that the choice of performance measurement system
must fit the circumstances of the firm. A cost center performance measure is optimal when corporate management can easily assess the quantity and quality of departmental output. In contrast,
“if the knowledge required to make the product mix, quantity, and quality decisions is specific to
the division and therefore costly or impossible for managers at higher levels to obtain, the profit
center can be an effective performance measurement system” 共Jensen and Meckling 1998, 352兲.
As tax-planning opportunities become more evident, it is likely that managers will lack the specific tax knowledge necessary to assess either the quantity or quality of the tax department’s
tax-planning efforts. In this situation, a firm-wide performance metric, such as contribution to net
income, might be a more useful performance measure than a departmental measure such as
quantity or quality of tax-planning efforts. Therefore, as tax planning becomes a greater focus of
the tax department, a profit center performance measurement system is likely to provide a better
measure of a tax department’s performance.
However, a performance measurement system based upon a firm-wide metric will not be
effective unless: 共1兲 the tax department has potential tax-planning opportunities and 共2兲 corporate
management can measure efforts expended on tax planning. Prior research documents crosssectional variation in investments in tax planning and finds that this variation is related to firm
characteristics such as firm size, growth, extent of foreign operations, capital intensity, and other
characteristics associated with tax-planning opportunities 共Mills et al. 1998; Dunbar and Phillips
2001兲. Wilson 共1995兲 explains that the tax terrain 共i.e., the underlying tax opportunities兲 affects the
value that the tax department can add to stakeholders.
In addition, corporate managers must be able to determine the contribution of tax planning to
the corporate bottom line. In general, the time horizon for evaluating tax-planning opportunities is
fairly short. Jennings et al. 共2010兲 investigate the persistence of tax preferences and document that
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most of the benefits of tax planning are reflected in current earnings. Thus, the outcome of tax
planning ought to be measurable using current earnings. In addition, Mills et al. 共1998兲 find that a
$1 investment in tax-planning results in a $4 decrease in tax liabilities. This suggests that taxplanning efforts will be at least partially revealed in effective tax rates and by extension, net
income. Based on this discussion, we posit the following hypothesis 共stated in alternative form兲:
H1c: The likelihood that a firm will measure its tax department as a profit center increases
with tax-planning opportunities.
Importance of Financial Tax Management
In addition to tax-planning opportunities, firm management must also place value on tax
department functions, and this value can be revealed in various managerial actions or incentives.
For example, management’s choice to compensate officers and business unit managers on a pretax or after-tax basis could reveal tax function value 共Phillips 2003兲. Additionally, a competitive
industry might push managers to seek innovative ways 共including tax strategies兲 to increase firm
value 共Hou and Robinson 2006兲. Finally, firms engaging in earnings management to meet or beat
earnings targets might use tax accounts to manage their book earnings 共Phillips et al. 2003;
Dhaliwal et al. 2004; Badertscher et al. 2009兲. Thus, to the extent that firm managers view taxes
as a tool to meet earnings targets, managers would view the tax function as valuable. We posit the
following hypothesis 共stated in alternative form兲:
H1d: The likelihood that a firm will measure its tax department as a profit center increases
with the value firm management places on the tax function.
ETR Consequences of Evaluating Tax Departments as Profit Centers
In this section we describe how the choice of a performance measurement model for a tax
department impacts firm profitability through its effect on a firm’s effective tax rate.9 Jensen and
Meckling 共1998兲 suggest that cost centers work best for evaluating units where departmental
output is externally determined and when firm management can easily and inexpensively measure
the quality and quantity of the output of the department. In the context of a tax department, this
occurs when the tax department’s primary function is tax compliance.10 Compliance tasks are
relatively easy for firm managers to monitor, as external agencies 共state, federal, and international
enforcement agencies兲 require firms to file returns with fixed deadlines and enforce these requirements. Thus, compliance-oriented tax departments evaluated as cost centers would maximize firm
value if compliance activities are certain and/or management can ascertain and evaluate the specified output externally. If no external incentive exists for tax planning in cost center tax departments, tax planning would only occur to the extent that either tax planning is inseparable from tax
compliance or tax personnel are intrinsically motivated to engage in such activities 共Osterloh and
Frey 2000兲.
9
10
Although tax managers of both profit and cost centers can improve net income by engaging in cost-saving measures, we
expect that these cost savings will be smaller in magnitude than the potential savings from tax planning. As a result, we
focus our discussion on the tax-planning activities of tax departments as revealed in firms’ effective tax rates. We
recognize that using effective tax rates as a measure of the consequences of the different performance measures has
limitations. For example, effective tax rates will not capture benefits from deferral tax-planning techniques nor will it
reflect tax-planning strategies if implicit taxes offset the benefits 共Halperin and Sansing 2005; Jennings et al. 2010兲.
Nevertheless, effective tax rates are reported in published financial statements and represent the observed output from
the tax department.
We assume that tax departments’ tasks are twofold: tax compliance and tax planning. We acknowledge that the tasks are
related and, at times, inseparable. However, we proceed, as has prior literature, under the premise that the two tasks are
separable 共Mills et al. 1998兲.
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As tax planning becomes more important for tax departments, operating as a cost center
becomes more challenging and can result in reduced firm profits. Firms sacrifice profits because,
ceteris paribus, a department evaluated as a cost center will seek to operate at a level that
minimizes average costs for tasks that do not have fixed output requirements 共e.g., tax planning兲
rather than operate at the optimal level of output 共Jensen and Meckling 1998兲. We use Figure 1
共modified from Jensen and Meckling 1998兲 to illustrate how the performance measurement model
for a tax department might affect firm profitability. The figure depicts a U-shaped cost function for
a firm’s tax department, where cost is represented on the Y-axis and quantity of tax-planning
services is represented on the X-axis.
Figure 1 models a firm that has many tax-planning opportunities, with the optimal demand
condition requiring tax-planning output Q*. However, assume that the knowledge necessary to
obtain the optimal amount of tax planning is specific to the tax department and inaccessible to
those higher in the firm hierarchy. Because the optimal level of tax planning and its cost are not
externally determined, a department evaluated as a cost center will seek to minimize average costs
共quantity QC in Figure 1兲. Actual firm profitability is lower at point QC because the tax department
operates at a suboptimal level of tax planning to minimize costs. At the same time, firm manage-
FIGURE 1
Desired Output of Manager Evaluated as a Cost Center with No Quantity Constraint
Cost
Average Cost
QC
Q*
Quantity
Source: Jensen and Meckling (1998).
The vertical axis represents the cost of tax services and the horizontal axis represents the quantity of taxplanning services for a specific demand condition. QC represents tax-planning output when the tax department
is evaluated using a cost center model, and Q* represents the optimal tax-planning output for a firm with unused
tax-planning opportunities.
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ment is unable to correct the tax department’s suboptimal choice because it lacks the specific
knowledge to detect the effort and assess the quality of tax-planning activities.11
In contrast, suppose that the firm in Figure 1 uses a profit center to evaluate the tax department. Although firms can operationalize the contribution to firm profits in different ways, we posit
that firms evaluate profit center tax departments by the incremental reduction in income tax
expense because this metric directly contributes to the firm’s bottom line, reported after-tax financial income. Rather than minimizing costs, profit center tax departments will engage in taxplanning activities until marginal costs equal marginal benefits. Thus, in contrast to the cost center
department, a tax department evaluated as a profit center will increase tax-planning activities that
contribute to reported income 共a reduction in ETR兲 until reaching Q*.12
Hence, a profit center can be a more effective performance measurement system when knowledge is necessary to determine the optimal output and this knowledge is specific to the department
共i.e., it is very costly for higher-level managers to ascertain the optimal level of the department’s
output兲. In the case of a tax department, the application of the tax law 共specific knowledge兲 cannot
be measured, but its output 共tax savings兲 can be assessed and compensated according to its value
to the firm 共Osterloh and Frey 2000兲. Consequently, the effectiveness of the decision to measure
and evaluate a tax department as a profit or a cost center is dependent on firm characteristics
related to whether tax-planning opportunities exist and the extent to which the tax function is
valued by the firm.
Of course, several factors could preclude profit center tax departments from reducing ETRs
by increasing tax-planning activities. First, a lack of coordination could prevent the required
information transfers between the tax department and operating units, thereby diminishing the
likelihood of identifying or implementing optimal tax plans for the firm as a whole. If tax departments find it difficult to identify or implement tax strategies because of a lack of information
transfer and coordination between divisions, then the marginal costs of tax-planning strategies
increase. As a result, the likelihood of pursuing a beneficial tax strategy decreases. Second, implicit taxes can constrain tax planning designed to reduce ETRs. Using the example of locating a
facility in a low tax jurisdiction, if demand for labor or materials increases operating costs, then
any tax savings on the investment could be offset and possibly dominated by increasing costs. The
overall effect could be a decrease in net income rather than the expected increase. To the extent
that implicit taxes erode explicit tax benefits, we would not expect to observe any difference in
ETRs between profit and cost center firms. Finally, recall that the performance measurement
system is one of three “rules” of organizational design. If the other two rules, the reward system
and the method of partitioning decision rights, are incompatible or inconsistent with the performance measurement system, then there might not be any difference between firms using profit
centers versus those using cost centers. Based on this discussion, we predict the following hypothesis 共stated in alternative form兲:
H2: Firms more likely to use a profit center model to evaluate their tax departments will be
associated with lower effective tax rates than firms more likely to use a cost center
model.
11
12
Note that if a firm does not have many tax-planning opportunities 共or resources兲, the firm may be operating optimally
by only investing in QC level of planning.
Recall that tax departments must provide 共directly or indirectly兲 a minimum amount of tax compliance services. Hence,
quantity Q in Figure 1 could also be interpreted as the minimum cost of tax compliance. This assumes that the firm has
unutilized tax-planning opportunities that make the profit center system a viable option.
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III. RESEARCH METHOD
Survey Data and Sample
We obtain data about firms’ tax functions from a survey of Fortune 1000 companies commissioned by Ernst & Young in 1999.13 The survey was conducted via phone conversations with
participating CFOs and was designed to explore the role of tax departments in the corporate
decision-making process. The survey also included questions about how CFOs view and measure
their tax departments. The survey consisted of 35 formal and follow-up questions. CFOs from 214
firms responded to the survey, and the results have a margin of error of approximately ⫾6.7
percent. Of the 214 survey respondents, we were able to identify all but nine firms that we
eliminate from our sample, leaving a total of 205 respondents.
Table 1 compares survey respondents to the remaining Fortune 1000 firms in 1999 共707 firms
with available data on Compustat兲. Panel A shows that, in general, the industry composition of the
survey is representative of the remaining Fortune 1000 firms. The survey includes a somewhat
larger percentage of firms in the food, textile, and chemical industries 共26 percent兲 compared to
the Fortune 1000 共18 percent兲 and a slightly smaller concentration of firms in the transportation
and utility industries 共10 percent versus 15 percent for the Fortune 1000兲. Panel B of Table 1
provides descriptive statistics of the survey respondents and the remaining Fortune 1000 firms in
1999. Although the survey firms are large in terms of average total assets 共$10.9 billion兲, sales
共$6.12 billion兲, and market value of equity 共$8.52 billion兲, they are smaller both in terms of means
and medians than the remaining Fortune 1000 firms in 1999. On average, the survey firms have
less long-term debt than the remaining Fortune 1000 firms; however, their debt-to-asset ratio is
greater than that of the remaining Fortune 1000. Additionally, the survey firms have lower net
income and pre-tax income, yet we find no difference in profitability between the survey firms and
the Fortune 1000. Importantly, we find no difference in the effective tax rates between the survey
firms and the remaining Fortune 1000 firms.
Our study primarily focuses on the response to question 9A posed in the Ernst & Young
survey. Specifically, CFOs answered the following question:
Overall, would you say that your tax department is managed and measured as more of a cost center
or as more of a contributor to the bottom line?
Possible responses include “Cost Center,” “Contributor to Bottom Line,” or “Both.” All but
one of the 205 CFOs in the data set responded to question 9A. To provide a clear distinction
between the evaluations, we eliminate 15 firms 共7 percent兲 that responded “both” in the survey. In
addition, we exclude firms with 共1兲 missing survey responses to other questions used in our study
共13 firms兲, 共2兲 missing effective tax rate data for 1999 共37 firms兲, and 共3兲 missing data for our
other regression variables 共18 firms兲. Our final sample includes 121 firms.14
As described in Panel A of Table 2, a majority of the sample firms 共56 percent兲 responded that
the tax department was managed and measured as a profit center. This response indicates that the
CFO believes that the employees in the tax function have incentives 共explicit or implicit兲 to
increase reported income.15 However, the survey did not request or collect specific compensation
or incentive information relating to the tax department.
13
14
15
Although Ernst & Young 共EY兲 commissioned the survey, Clark, Martire & Bartolomeo, Inc. conducted the survey,
ensuring participants’ anonymity. EY sent a letter of introduction to all Fortune 1000 firms as a starting point for survey
participants. Clark, Martire & Bartolomeo, Inc. then called for appointments and conducted the interviews. EY granted
us permission to obtain the identifying information for participants in the 1999 survey from the marketing firm.
We also conduct our analyses after classifying the firms responding “both” as profit centers. Regression results are
qualitatively similar to those presented in the tables.
The survey does not define “bottom line.” Given the nature of the survey, it is most likely that CFOs would interpret this
term to mean contribution to reported or accounting profits, and we develop our measure of tax-planning effectiveness
with this in mind. If this assumption is incorrect 共e.g., if CFOs interpret “bottom line” to mean economic profits兲, then
ETR may not adequately represent how tax departments are evaluated.
The Accounting Review
American Accounting Association
May 2010
Panel A: Industry Affiliation of Survey Sample and Fortune 1000a
Survey Sample
Fortune 1000
SIC
Industry Affiliation (SIC)
Number
Percent
Number
Percent
0000
1000
2000
3000
Agricultural and forestry
Metal and construction
Food, textile, and chemicals
Rubber, metal, and machine
products
Transportation and utilities
Wholesale and retail trade
Financial services
Hotel and other services
Health and other services
Utilities
Total
2
14
53
39
1
7
26
19
2
19
129
150
1
3
18
21
20
28
31
10
6
2
205
10
13
15
5
3
1
100
107
128
108
45
16
3
707
15
18
15
6
2
1
100
4000
5000
6000
7000
8000
9000
Panel B: Descriptive Statistics for Survey Sample and Fortune 1000
Survey Sample
n ⴝ 205
Assets 共$ in millions兲b,c
Sales 共$ in millions兲b,c
Market value 共$ in millions兲b,c
Long-term debt 共$ in
millions兲b,c
Debt-to-asset 共ratio in percent兲c
Income 共$ in millions兲b,c
Pretax income 共$ in millions兲b,c
Fortune 1000
n ⴝ 707
n
Mean
25%
Quartile
Median
75%
Quartile
n
Mean
25%
Quartile
Median
75%
Quartile
180
180
169
180
10,881
6,122
8,524
2,001
1,271
1,733
512
252
2,662
2,735
1,691
729
7,123
5,874
4,587
1,473
629
631
603
628
21,961
8,365
15,285
3,501
1,682
1,903
1,071
311
4,393
3,375
3,401
854
13,441
8,582
9,544
2,552
180
180
180
24.67
340
546
12.49
39
72
23.6
124
196
33.85
289
462
628
631
630
22.83
529
842
8.78
57
94
21.7
185
293
32.61
501
778
(continued on next page)
1045
May 2010
American Accounting Association
Characteristic
Performance Measurement of Corporate Tax Departments
The Accounting Review
TABLE 1
Comparison of Survey Firms and Other Fortune 1000 Firms
Characteristic
Return on assets 共percentage兲
Effective tax rate 共percentage兲
Capital intensity 共ratio兲
Research intensity 共ratio兲b
Foreign intensity 共ratio兲
a
b
c
1046
The Accounting Review
American Accounting Association
Panel B: Descriptive Statistics for Survey Sample and Fortune 1000
Survey Sample
n ⴝ 205
Fortune 1000
n ⴝ 707
n
Mean
25%
Quartile
Median
75%
Quartile
n
Mean
25%
Quartile
Median
75%
Quartile
180
180
176
175
180
7.57
36.92
30.68
1.08
20.6
3.12
32.76
14.87
0
0
6.96
36.72
28.11
0
0
11.45
40.13
44.66
0.73
23.19
628
628
611
609
629
6.77
36.97
30.21
1.54
18.64
2.23
31.81
12.08
0
0
5.98
36.88
26.72
0
0
11.63
39.85
45.71
1.14
18.83
The survey sample consists of 205 Fortune 1000 firms 共nine of the 214 respondents to the survey were unidentified兲. The Fortune 1000 consists of the remaining members of the
Fortune 1000 in 1999 that have available information on Compustat.
Indicates that the difference in the means between the survey sample and the remaining Fortune 1000 firms is significant at p ⬍ 0.10 using a two-tailed t 共Wilcoxon兲 test.
Indicates that the difference in the medians between the survey sample and the remaining Fortune 1000 firms is significant at p ⬍ 0.10 using a two-tailed t 共Wilcoxon兲 test.
Variable Definitions:
Market value ⫽ market value of common equity;
Debt-to-asset ⫽ long-term debt divided by total assets at the beginning of the year;
Income ⫽ income before extraordinary items;
Return on assets ⫽ pre-tax income divided by total assets at the beginning of the year;
Effective tax rate ⫽ total tax expense divided by pre-tax income;
Capital intensity ⫽ gross property, plant, and equipment divided by gross assets at the beginning of the year;
Research intensity ⫽ research and development expense divided by sales; and
Foreign intensity ⫽ foreign assets expressed as a percentage of total assets.
Unless otherwise noted, variables are measured at the end of fiscal year 1999.
Robinson, Sikes, and Weaver
May 2010
Performance Measurement of Corporate Tax Departments
1047
TABLE 2
Regression Sample Derivation and Distribution
Panel A: Regression Sample Derivation
Survey
Sample
Sample Restriction
Cost
Centers
Profit
Centers
Firm responding to Survey
Firm unidentified
Firm missing response to question 9A
Failed to designate profit or cost
214
⫺9
⫺1
⫺15
Survey Sample
Other survey information unavailable
Effective tax rate data unavailable for 1999
Other regression data unavailable
189
⫺13
⫺37
⫺18
83
⫺6
⫺17
⫺6
106
⫺7
⫺20
⫺12
121
54
共44兲
67
共56兲
Regression Sample
共percentage兲
Panel B: Industry Affiliation of Regression Sample
Cost Centers
Profit Centers
SIC
Industry Affiliation
Number
Percent
Number
Percent
1000
2000
3000
4000
5000
6000
7000
8000
9000
Metal and construction
Food, textile, and chemicals
Metal, and machine products
Transportation and utilities
Wholesale and retail trade
Financial services
Hotel and other services
Health and other services
Utilities
Total
5
18
9
4
12
2
2
2
0
54
9
33
17
7
22
4
4
4
0
100
4
24
20
7
3
2
4
2
1
67
6
36
30
10
5
3
6
3
1
100
Table 2, Panel B reports the industry composition of our sample firms partitioned by profit
versus cost center. We find differences in the percentage of firms with profit center tax departments
compared to firms with cost center tax departments in only two industries. Specifically, a larger
percentage of firms in the metal and machine products industry are evaluated as profit centers,
whereas a larger percentage of tax departments in wholesale and retail trade firms are evaluated as
cost centers. Untabulated results indicate that in the metal and machine products industry, a greater
percentage of profit center firms spend more on tax planning as a percentage of their total budget
and are more involved in tax shelters than are the cost center firms.16 We do not find similar
differences in the wholesale and retail trade industry. Additionally, we do not find differences in
the percentage of the tax department budget that is outsourced for profit and cost center firms in
these industries.
16
We determine tax shelter participation by matching our firms to the firms identified as using tax shelters in Graham and
Tucker 共2005兲. We were able to identify seven firms using tax shelters: five profit center firms and two cost center firms.
In addition, we also conducted a key term search through Factiva and verified that four profit center firms and one cost
center firm were associated with tax shelters.
The Accounting Review
May 2010
American Accounting Association
1048
Robinson, Sikes, and Weaver
Determinants of Profit/Cost Center Choice
We begin our analysis by operationalizing the model that represents the choice of performance measure for a tax department 共i.e., profit or cost center兲 as a function of decentralization,
departmental coordination, firm growth, and tax-planning opportunities, and the emphasis the firm
places on tax planning. Equation 共1兲 provides the empirical specification:
PROFIT = ␤0 + ␤1SIZE + ␤2FOR + ␤3LINE + ␤4GEO + ␤5COOR + ␤6GROW + ␤7RDI
+ ␤8LEV + ␤9CAPI + ␤10INVI + ␤11INTAN + ␤12PLAN + ␤13HERF + ␤14TOOL
+ ␷.
共1兲
All variables other than those that are generated from answers to the survey questions
共PROFIT, COOR, PLAN, and TOOL兲 are measured in fiscal year 1998. We include a summary of
variable definitions in Table 3.
Decentralization
Hypothesis 1a predicts that the likelihood that a firm will measure its tax department as a
profit center increases with firm size and decentralization. Because we do not have a direct
measure of firm decentralization, we rely on prior literature to identify characteristics associated
with decentralized firms. Palmer et al. 共1993兲 posit that decentralization occurs when firms expand
either the scope or the size of their operations beyond a point where a central management can
coordinate operations efficiently. Brickley et al. 共1995兲 also note that larger companies, especially
those facing stiff foreign competition, are more likely to decentralize decision rights. Decentralized firms are associated with departmental interdependencies, as measured by the degree of
product line and geographic diversification 共Bushman et al. 1995兲. Consistent with this literature,
we include firm size 共SIZE兲, amount of foreign operations 共FOR兲, product line diversification
共LINE兲, and geographic diversification 共GEO兲 as proxies for firm decentralization. We expect
positive coefficients on these variables and measure them as follows:
SIZE ⫽ natural logarithm of total assets at year-end;
FOR ⫽ ratio of foreign assets to total assets at year-end;
LINE ⫽ number of reported product line segments; and
GEO ⫽ number of reported geographic segments.
Departmental Coordination
Coordination between the tax department and business operations is important in decentralized firms to facilitate information transfer of both specific tax and general business knowledge.
We include COOR to capture the degree of coordination between the tax department and operating
units and expect a positive coefficient. We obtain this variable from question 2 of the Ernst &
Young survey, which asks:
To what degree is your tax department involved in business operations? By business operations I
mean both the day-to-day activities of your company as well as the more long-range or strategic
functions.
CFOs responded using a five-point scale, with 5 共1兲 indicating the highest 共lowest兲 level of
involvement. The variable COOR equals 1 if a CFO’s response is 4 or 5, and equals 0 otherwise.
Firm Growth and Tax-Planning Opportunities
Keating 共1997兲 finds that firms with greater growth opportunities are less likely to use a
firm-wide performance. Consistent with Keating 共1997兲, we measure firm growth using a firm’s
The Accounting Review
American Accounting Association
May 2010
Panel A: Continuous Variables
Variable
ETR1 (ETR2)
⫽
ETRC1 (ETRC2)
⫽
SIZE
FOR
LINE
GEO
GROW
RDI
LEV
CAPI
INVI
INTAN
HERF
PLAN
ROA
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
Total tax expense for fiscal year 1999 共1999 plus 2000兲 divided by pre-tax income for 1999 共1999 plus 2000兲 with extreme
values truncated at 0 and 1.
Total cash taxes paid for fiscal year 1999 共1999 plus 2000兲 divided by pre-tax income for 1999 共1999 plus 2000兲 with extreme
values truncated at 0 and 1.
Natural log of total assets at beginning of 1999.
Foreign assets expressed as a percentage of total assets.
Number of reported product line segments.
Number of reported geographic segments.
One-year percentage growth in sales.
Research and development expense divided by sales.
Year-end long-term debt divided by total assets as of the beginning of the year.
Year-end gross property, plant, and equipment divided by beginning total assets.
Year-end inventory divided by beginning total assets.
Year-end intangible assets divided by beginning total assets.
Herfindahl index computed as sum of squared market shares of all firms in three-digit SIC industry.
The survey response indicating the percentage of the tax budget that is allocated to tax planning.
Pre-tax income divided by beginning total assets.
⫽
COOR
⫽
TOOL
⫽
Definition
1 if the response to question 9A of the survey 共“Overall, would you say that your tax department is managed and measured as more of
a cost center or as more of a contributor to the bottom line?”兲 indicates a profit center; 0 if the response indicates a cost center.
1 if the response to question 2 of the survey 共“To what degree is your tax department involved in business operations?”兲 is at least 3
on a five-point scale with 5 indicating that the tax department is highly involved with operational decisions; 0 otherwise.
1 if the response to question 11 of the survey 共“How important are taxes as a tool to meeting earnings expectations?”兲 is at least a four
on a five-point scale with five indicating “extremely important;” 0 otherwise.
Except for survey information and unless otherwise noted, variables are measured for fiscal year 1998.
1049
May 2010
American Accounting Association
Panel B: Indicator Variables
Variable
PROFIT
Definition
Performance Measurement of Corporate Tax Departments
The Accounting Review
TABLE 3
Variable Definitions
1050
Robinson, Sikes, and Weaver
one-year percentage growth in sales and expect a negative coefficient on GROW. Firms with few
tax-planning opportunities are unlikely to measure their tax departments as profit centers. We
include several variables to capture firms’ tax-planning opportunities. Prior literature documents
an association between tax planning and various firm characteristics such as firm size, growth,
extent of foreign operations, capital intensity, R&D intensity, leverage, intangibles, and industry
共Gupta and Newberry 1997; Grubert and Slemrod 1998; Hanlon et al. 2007; Dyreng et al. 2008兲.
We include the following variables to proxy for tax-planning opportunities: R&D intensity 共RDI兲,
leverage 共LEV兲, capital intensity 共CAPI兲, inventory intensity 共INVI兲, and intangibles 共INTAN兲. We
expect a positive coefficient for each of these variables and we measure them as follows:
RDI ⫽ R&D expense divided by sales;
LEV ⫽ year end long-term debt divided by beginning total assets;
CAPI ⫽ gross property, plant, and equipment divided by beginning total assets;
INVI ⫽ year end inventory divided by beginning total assets; and
INTAN ⫽ intangible assets divided by beginning total assets.
Brickley et al. 共1995兲 conclude that companies in the same industry tend to develop similar
organizational architectures. In addition, prior research controls for industry membership in tax
burden regressions 共Zimmerman 1983; Wilkie 1988; Collins and Shackelford 1996; Jacob 1996;
Phillips 2003; Rego 2003; Dyreng et al. 2008兲; thus, we control for industry by clustering by
one-digit SIC code.
Importance of Financial Tax Management
We include three variables to represent the emphasis that firm management places on tax
management. One measure of the importance that a firm places on tax planning is the percentage
of its total tax budget that it allocates to tax planning, as opposed to compliance. We include a
variable from the survey, PLAN, which captures the percentage of the tax budget that is allocated
to tax planning as opposed to tax compliance 共Mills et al. 1998兲. We obtain this variable from
question 5A of the 1999 Ernst & Young survey, which asks:
Thinking now of your total tax department budget, what proportion is spent on tax-planning
activities and what percentage on compliance?
We use a Herfindahl Index, calculated as the sum of squared market shares of all firms in an
industry, to measure industry concentration 共Hou and Robinson 2006兲. The variable HERF is an
inverse measure of the level of competition in an industry. We measure a firm’s market share as its
sales divided by the sum of the sales of all firms in its industry, which we define using three-digit
SIC codes. TOOL captures the importance that a firm places on using taxes to meet earnings
targets. We expect that firms that view taxes as a tool to meet earnings expectations are more likely
to organize their tax departments as profit centers to encourage this behavior. Question 11 in the
1999 Ernst & Young survey asks:
How important are taxes as a tool to meeting earnings expectations?
Responses vary from 5 ⫽ Extremely Important to 1 ⫽ Not Important at All. TOOL equals 1
if a firm responded with a 5 or 4, and 0 otherwise. We expect positive coefficients on PLAN and
TOOL, and a negative coefficient on HERF.
Performance Consequences of Profit/Cost Center Choice
Hypothesis 2 predicts a negative relation between a firm’s likelihood of managing its tax
department as a profit center and the firm’s effective tax rate. An important econometric issue is
that the profit/cost center choice may be correlated with unobservables that affect firm perfor-
The Accounting Review
American Accounting Association
May 2010
Performance Measurement of Corporate Tax Departments
1051
mance. That is, if firms with lower ETRs are more likely to be profit centers, then, ceteris paribus,
failing to control for the endogeneity will yield profit center coefficient estimates that are inconsistent. For instance, it is possible that firms choosing to evaluate tax departments as profit centers
also value tax planning and may provide any number of incentives to reduce ETRs, only one of
which we test. Indeed, anecdotal evidence suggests that in the 1990s 共the same time period in
which profit center tax departments developed兲, firms stressed the value of tax planning through
ETRs 共Douglas et al. 1996; Krueger 1999; Martucci 2001兲. These potential alternative incentives
to reduce ETRs may be correlated with the profit/cost center choice thereby creating an endogeneity issue.
To address potential endogeneity, we test H2 using a two-stage approach. We recognize that
a two-stage model may be inadequate to control for potential endogeneity 共Larcker 2003兲, and this
is an inherent limitation of our methodology.17 The first stage is the profit/cost center choice from
Equation 共1兲. This equation depicts the profit/cost center choice as a function of firm decentralization and departmental coordination, firm growth, tax-planning opportunities, and the importance
of tax planning. In the second stage, we explain firm performance as a function of the predicted
and residual components of the profit/cost center choice and other controls. The predicted component of PROFIT captures the systematic determinants of the profit/cost center choice that may
affect firm performance. The residual component of PROFIT represents the unobservable or unexplained incentives that may contribute to firm performance. We test H2 with the following
equation:
ETR = ␤0 + ␤1 PROFIT* + ␤2RESIDUAL + ␤3COOR + ␤4 PLAN + ␤5TOOL + ␤6LEV
+ ␤7CAPI + ␤8FOR + ␤9RDI + ␤10INTAN + ␤11INVI + ␤12ROA + ␤13GROW + ␤14SIZE
+ ␧.
共2兲
Prior research demonstrates that ETRs reflect various tax-planning strategies used to manage
earnings 共Mills et al. 1998; Phillips 2003; Dhaliwal et al. 2004; Krull 2004兲.18 We adopt ETR as
our dependent variable because it is arguably the most direct way to assess the consequences of
the profit center measurement system. If the systematic determinants of PROFIT drive the effect
of the performance measurement system on ETRs, then we would expect a negative coefficient on
PROFIT*, the predicted profit component 共 ␤1兲. If the unobservable firm characteristics or incentives that lead firms to their profit/cost center choice also affect their ETRs, then we would expect
a negative coefficient on RESIDUAL, the residual PROFIT component 共 ␤2兲.
Dependent Variables
We measure effective tax rates in two ways: 1999 ETR 共ETR1兲 and, as an alternative measure,
a 1999–2000 average ETR (ETR2兲. Consistent with Phillips 共2003兲, our measure of ETR1 is the
ratio of total tax expense to pre-tax income in 1999. We use total tax expense in the numerator of
ETR1 because only actions that reduce total tax expense will decrease firms’ reported effective tax
rate and thereby increase reported financial earnings. To control for unexplained year-to-year
fluctuations in annual ETRs, we also estimate Equation 共1兲 using an average ETR calculated over
17
18
We also estimate Equations 共1兲 and 共2兲 simultaneously using a maximum likelihood method and find results 共untabulated兲 that are qualitatively similar to those reported in the tables.
It is unlikely that alternative measures of tax aggressiveness would suit our research design as these measures do not
always directly affect net income. For example, prior research uses book-tax differences or cash ETRs as alternative
measures of tax aggressiveness 共Dyreng et al. 2008; Rego and Wilson 2008兲. However, the deferral strategies reflected
in these measures will not affect net income.
The Accounting Review
May 2010
American Accounting Association
1052
Robinson, Sikes, and Weaver
1999 and 2000 共ETR2兲.19 We restrict the period over which we average ETR2 to 1999 through
2000 because we want to test the effect of designating the tax function as a profit center in 1999
on tax-planning effectiveness. Therefore, we do not use years prior to 1999. We do not include
years after 2000 because of the dramatic changes beginning in 2001, including extension of tax
shelter regulations, market fluctuations after September 11, 2001, and the enactment of the
Sarbanes-Oxley Act of 2002.
Consistent with prior literature, we constrain ETR1 and ETR2 to lie between 0 and 100
percent to prevent estimation problems and unreasonable values of ETRs due to small denominators 共Gupta and Newberry 1997兲.20 We set ETR1 and ETR2 to 0 percent for firms with tax refunds
and to 100 percent for firms with positive taxes and negative 共or zero兲 income. Several recent
studies document limitations regarding measuring tax burdens with financial statement data 共Mills
and Plesko 2003; Hanlon 2003; McGill and Outslay 2004兲. For our study, given that the incentive
is to “contribute to the bottom line,” which we interpret as reported net income, it is important for
us to use the measure of ETR used for financial reporting purposes regardless of these measurement issues. References to ETR in the following discussion refer to both ETR1 and ETR2.
Tax-Planning Resources
We include three variables to control for firms’ tax-planning resources in model 共2兲. We
control for the amount of coordination between the tax department and operations by including the
variable COOR. We do not sign COOR because effective coordination may work either to reduce
the tax expense or to rein in infeasible tax-planning ideas. To control for tax department planning
resources, we include PLAN, which captures the percentage of the tax budget that is allocated to
tax planning as opposed to tax compliance. We expect PLAN to be negatively associated with
ETRs. We include TOOL to capture the importance management places on using taxes to meet
earnings targets. We expect that firms that view taxes as a tool to meet earnings expectations will
have lower ETRs.
Tax-Planning Opportunities
Consistent with prior literature, we include several variables in Equation 共2兲 to proxy for a
firm’s tax-planning opportunities. Only if the firm has opportunities to engage in tax planning will
tax saving strategies be developed and implemented. We previously discussed these variables in
developing the PROFIT equation as they also relate to the profit or cost center decision. We
include these variables in the ETR equation to account for their direct effect on ETRs. Specifically,
we include variables for leverage 共LEV兲, capital intensity 共CAPI兲, foreign operations 共FOR兲, R&D
intensity 共RDI兲, intangible intensity 共INTAN兲, and inventory intensity 共INVI兲.
Prior research yields mixed results on the relation between leverage and ETRs; therefore, we
do not predict a sign on LEV 共Gupta and Newberry 1997; Mills et al. 1998; Phillips 2003; Rego
2003兲.21 Firms with multinational operations 共FOR兲 have more opportunities to avoid income
taxes than domestic firms 共Phillips 2003; Rego 2003兲; however, multinational firms also face
taxation on profits in multiple jurisdictions or may have foreign tax credit limitations 共Markle and
Shackelford 2009兲. Therefore, we do not predict a sign on FOR. We include variables for capital
intensity 共CAPI兲, R&D intensity 共RDI兲, and inventory intensity 共INVI兲 expecting negative coeffi19
20
21
To calculate this variable, we sum total tax expense for years 1999 and 2000 for the numerator and divide by the sum
of pretax income in 1999 and 2000. In supplemental analyses, we limit our sample to firms with a positive sum for
pretax income, and the results are qualitatively similar to those presented in the tables.
As a sensitivity test, we constrain ETR to a maximum of 50 percent. The results are qualitatively the same as those we
report in Table 7.
The sign between LEV and ETR could be positive if firms with high tax rates are more likely to use debt financing.
However, a negative relation between LEV and ETR would be consistent with interest expense deductibility.
The Accounting Review
American Accounting Association
May 2010
Performance Measurement of Corporate Tax Departments
1053
cients on these variables consistent with Gupta and Newberry 共1997兲.22 Consistent with Chen et al.
共2008兲, we include INTAN to control for differing book and tax treatments of intangible assets.
However, opportunities to shift income could also be represented by INTAN 共Grubert and Slemrod
1998兲. Thus, we do not predict a sign on INTAN.
Controls
We include several additional variables because prior research has linked such factors as
profitability, growth, size, and industry to ETRs 共Zimmerman 1983; Wilkie 1988; Bankman 1994;
Gupta and Newberry 1997; Mills et al. 1998; Phillips 2003兲. We include a firm’s profitability
共ROA兲 to proxy for changes in book income. Bankman 共1994兲 finds that high-growth firms generally place less emphasis on tax planning, suggesting a positive relationship between firm growth
and ETRs. We proxy for growth with a firm’s one-year percentage sales growth 共GROW兲 for 1998.
We expect ROA and GROW to be positively related to ETR. We control for firm size 共SIZE兲 and
industry because prior research documents that they are important determinants of ETRs. Because
prior research finds mixed results on the relation between firm size and ETRs, we do not predict
a sign on SIZE. We control for industry effects by clustering the standard errors by one-digit SIC
codes.
IV. RESULTS
Descriptive Statistics
Table 4 presents descriptive statistics for our continuous regression variables partitioned by
measurement scheme. Our comparison reveals several differences. Our sample consists primarily
of large firms: mean 共median兲 values of the natural log of total assets 共SIZE兲 is 7.55 共7.32兲 for cost
centers and 8.04 共8.02兲 for profit centers. This translates into average total assets of $2.21 共$3.53兲
billion, untabulated, for cost 共profit兲 center firms and indicates that profit center firms are larger
than cost center firms. Sample firms are profitable 共by construction兲, as shown by the inter-quartile
range for return on assets 共ROA兲, which runs from 4.4 to 13.0 percent for cost centers and from 6.4
to 14.7 percent for profit centers.
The median ETR for cost center firms 共0.387 for ETR1 and 0.382 for ETR2兲 is higher than the
median for profit center firms 共0.36 for ETR1 and 0.362 for ETR2兲. Firms that evaluate their tax
departments as profit centers also spend a larger proportion of their tax budget on planning, are
more R&D- and intangible-intensive, have more geographic segments, and are less levered than
firms evaluating their tax departments as cost centers. We find no significant differences between
profit and cost center firms in terms of foreign assets, number of product lines, percentage growth
in sales, capital intensity, and industry competitiveness.
In untabulated tests, we find that a greater proportion of profit center firms have more coordination 共COOR兲 between their tax department and business operations than do cost center firms
共52 percent to 21 percent兲. In addition, a larger proportion of firms that evaluate their tax departments as profit centers as opposed to cost centers 共48 percent to 33 percent兲 believe that taxes are
an important tool for meeting earnings expectations 共TOOL兲.23 On the whole, these univariate
22
23
During the sample period of Gupta and Newberry 共1997兲, firms could take advantage of investment tax credits, which
reduced their total tax expense. Although firms in our sample period cannot take advantage of investment tax credits and
accelerated depreciation for tax purposes does not affect total tax expense, we still use capital intensity to proxy for
tax-planning opportunities because we expect that firms that are more capital intensive can strategically locate their
assets.
The proportion of profit center firms that report coordination between their tax department and business operations
共COOR兲 and use taxes as an earnings management tool 共TOOL兲 is significantly greater than the proportion of cost center
firms, p ⬍ 0.10 using a two-tailed Chi-square test 共untabulated兲.
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TABLE 4
Regression Sample Descriptive Statistics
Cost Centers
(n ⴝ 54)
Variable
b,c
ETR1
ETR2a,c
ETRC1a
ETRC2a
SIZEb,c
FOR
LINE
GEOc
GROW
RDIb,c
LEVb,c
CAPI
INVIb,c
INTANb,c
HERF
PLANb,c
ROAb,c
a
c
Mean
Std. Dev.
25% Quartile
Median
75% Quartile
Mean
Std. Dev.
25% Quartile
Median
75% Quartile
0.378
0.366
0.310
0.309
7.554
0.044
8.056
5.407
0.151
0.270
0.284
0.358
0.215
0.085
0.487
0.313
0.093
0.094
0.105
0.236
0.201
0.995
0.125
5.093
5.417
0.356
0.679
0.167
0.241
0.212
0.102
0.454
0.171
0.060
0.350
0.350
0.145
0.184
6.877
0.000
3.000
2.000
0.001
0.000
0.174
0.180
0.051
0.000
0.217
0.200
0.044
0.387
0.382
0.323
0.309
7.324
0.000
8.000
3.000
0.071
0.000
0.266
0.300
0.159
0.033
0.336
0.300
0.089
0.413
0.412
0.383
0.396
8.044
0.000
12.000
7.000
0.169
0.000
0.363
0.511
0.306
0.154
0.528
0.400
0.130
0.344
0.356
0.258
0.299
8.040
0.089
9.403
6.478
0.067
2.419
0.231
0.366
0.128
0.136
0.460
0.376
0.124
0.084
0.118
0.134
0.180
1.125
0.184
5.497
4.150
0.196
4.435
0.159
0.212
0.140
0.158
0.353
0.189
0.110
0.321
0.324
0.176
0.200
7.178
0.000
5.000
3.000
⫺0.011
0.000
0.107
0.224
0.048
0.000
0.195
0.250
0.064
0.360
0.362
0.260
0.252
8.016
0.000
9.000
6.000
0.041
0.139
0.225
0.342
0.099
0.084
0.321
0.330
0.106
0.391
0.388
0.331
0.361
8.722
0.000
12.000
9.000
0.109
2.643
0.304
0.437
0.148
0.213
0.697
0.500
0.147
Statistics for ETR2, ETRC1, and ETRC2 were calculated using all non-missing values. The number of observations for ETR2, ETRC1, and ETRC2 equals 51, 53, and 50,
respectively, for Cost Center firms and 62, 65, and 60 for Profit Center firms, respectively. Variables are defined in Table 3.
Indicates that the difference in the means between cost centers and profit centers is significant at p ⬍ 0.10 using a two-tailed t test.
Indicates that the difference in the medians between cost centers and profit centers is significant at p ⬍ 0.10 using a two-tailed median test.
May 2010
Robinson, Sikes, and Weaver
b
Profit Centers
(n ⴝ 67)
Performance Measurement of Corporate Tax Departments
1055
results suggest that firms that measure and evaluate their tax departments as profit centers are
likely using taxes as a tool to contribute to bottom line earnings.
Table 5 presents the correlations among our continuous variables. The profit center choice,
PROFIT, is positively correlated with SIZE, RDI, and INTAN and negatively correlated with ETR1
and INVI. The effective tax rate for 1999 共ETR1兲 is highly correlated with the two-year effective
tax rate 共ETR2兲. Most correlations among the remainder of the variables are small, thereby mitigating possible multicollinearity concerns.
Logistic Regression Results for Tests of Hypothesis 1
Table 6 provides the logistic regressions results for testing our first set of hypotheses. Model
1 tests H1a and H1b, Model 2 tests H1c, Model 3 tests H1d, and Model 4 tests all three hypotheses. We estimate these regressions using robust standard errors adjusted for clustering across
industry 共one-digit SIC兲. In Model 1, we observe the positive relationship predicted by H1a
between the profit center choice and three of the four proxies for firm decentralization 共SIZE,
FOR, and LINE兲. In addition, the positive and significant coefficient on COOR 共1.593, p ⬍ 0.01兲
supports H1b. Together, our proxies for firm decentralization and interdepartmental coordination
explain about 15 percent of the variation in the measurement system choice.
Model 2 in Table 6 tests H1c, which predicts that the likelihood that a firm will measure its
tax department as a profit center increases with tax-planning opportunities. Together, the taxplanning variables explain slightly more than 17 percent of the variation in the choice to manage
and measure the tax department as a profit center. However, only one of the tax-planning opportunity coefficients is significant 共RDI ⫽ 0.512, p ⬍ 0.01兲.
Model 3 in Table 6 tests H1d, which predicts that the likelihood that a firm will measure its
tax department as a profit center increases with the value that firm management places on the tax
function.24 As predicted, we find that the choice to manage and measure the tax department as a
profit center is positively associated with the degree of importance a firm places on taxes as a tool
to meet earnings expectations 共TOOL ⫽ 0.619, p ⬍ 0.01兲 and with the percentage of the total tax
budget that the firm allocates to tax planning as opposed to compliance 共PLAN ⫽ 1.76, p ⫽
0.037兲. Together, the variables that proxy for the firm’s emphasis on tax planning explain only 4
percent of the variation in firms’ performance measurement system choice.
In Model 4 the combination of independent variables explain almost 31 percent of the variation in a firm’s choice to manage and measure its tax department as a profit center. This model is
relatively well fitted 共Hosmer-Lemeshow ␹2 = 9.26, p ⫽ 0.32兲 and has substantial predictive
power 共over 77 percent predicted accurately at a 50 percent cutoff兲. The regression results for the
full model are consistent with firm decentralization and interdepartmental coordination results
from Model 1, with the exception that FOR is insignificant and the estimated coefficient for GEO
is negative. The tax-planning opportunity variable 共RDI兲 from Model 2 remains significant in the
full model and another variable 共INTAN ⫽ 5.976, p ⫽ 0.013兲 becomes significant in Model 4. The
estimated coefficient for TOOL is no longer significant in Model 4; however, PLAN remains
marginally significant.
To provide a sense of magnitude we estimate the average marginal effects 共at the sample
means兲 of several coefficient estimates in Table 6 共untabulated兲. We find that an increase of an
additional product line segment 共from an average of 8.8 product lines to 9.8 product lines兲 increases the likelihood of a sample firm measuring tax department performance via a profit center
24
As sensitivity tests we alternately include a variable equal to the percentage of the tax department budget that is
outsourced and a variable equal to sales, general, and administrative expense expressed as a percentage of sales. While
our sample size decreases with the inclusion of either of these variables, the results remain qualitatively unchanged and
the coefficient on the included variable is insignificant.
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TABLE 5
Pearson Correlation Matrix
ETR1
ETR2
ETRC1
ETRC2
SIZE
FOR
LINE
GEO
GROW
RDI
LEV
CAPI
INVI
INTAN
HERF
PLAN
ROA
PROFIT
ETR1
ETRC1
ETR2
ETRC2
SIZE
FOR
LINE
GEO
GROW
RDI
LEV
CAPI
INVI
INTAN
PLAN
ROA
ⴚ0.189
⫺0.045
⫺0.139
⫺0.025
0.222
0.140
0.126
0.112
⫺0.149
0.307
⫺0.163
0.018
ⴚ0.241
0.185
⫺0.034
0.172
0.171
1.000
0.754
0.265
0.091
⫺0.141
0.055
ⴚ0.225
0.057
0.078
ⴚ0.196
0.065
⫺0.098
0.109
0.175
0.150
⫺0.112
0.074
1.000
0.225
0.093
0.020
⫺0.004
ⴚ0.224
0.098
⫺0.139
⫺0.103
0.028
⫺0.006
⫺0.025
0.131
0.087
⫺0.102
0.084
1.000
0.752
⫺0.039
⫺0.027
0.119
0.189
0.109
⫺0.009
⫺0.135
⫺0.178
0.014
0.144
0.232
⫺0.024
0.076
1.000
⫺0.015
⫺0.084
0.135
⫺0.022
⫺0.014
⫺0.081
⫺0.163
⫺0.164
0.008
0.115
0.054
⫺0.066
0.053
1.000
⫺0.053
0.211
0.266
ⴚ0.238
0.322
0.060
0.261
ⴚ0.329
0.033
⫺0.102
0.027
⫺0.097
1.000
ⴚ0.239
0.259
⫺0.129
0.197
⫺0.153
0.101
⫺0.048
⫺0.035
0.061
0.100
0.078
1.000
0.133
⫺0.017
0.084
0.062
0.011
ⴚ0.228
⫺0.055
0.002
0.048
⫺0.059
1.000
⫺0.135
0.153
⫺0.099
0.041
ⴚ0.208
0.184
0.115
0.152
⫺0.046
1.000
⫺0.047
0.197
ⴚ0.206
0.330
⫺0.007
⫺0.024
⫺0.097
⫺0.004
1.000
ⴚ0.215
⫺0.129
⫺0.127
0.025
⫺0.125
0.288
0.185
1.000
0.253
0.226
0.127
0.045
⫺0.077
⫺0.104
1.000
ⴚ0.360
ⴚ0.197
0.049
⫺0.048
0.004
1.000
⫺0.158
⫺0.023
⫺0.025
0.084
1.000
0.003
0.130
0.043
1.000
0.071
0.106
1.000
0.301
Bold indicates that the Pearson correlation is significant at p ⬍ 0.05. Variables are defined in Table 3.
Robinson, Sikes, and Weaver
May 2010
Performance Measurement of Corporate Tax Departments
1057
TABLE 6
Logistic Regression Results
(n ⴝ 121)
Variables
Decentralization
SIZE
Prediction
⫹
FOR
⫹
LINE
⫹
GEO
⫹
Departmental Coordination
COOR
⫹
Model 1
⫹
LEV
⫹
CAPI
⫹
INVI
⫹
INTAN
⫹
⫹
PLAN
⫹
Constant
Pseudo R2
Pearson ␹2
p-value
Model 4
0.487**
共2.26兲
1.189
共0.74兲
0.098***
共2.39兲
⫺0.077**
⫺2.29
1.593***
共4.43兲
2.003***
共4.12兲
⫺0.600
共⫺0.69兲
0.512***
共4.00兲
⫺1.569
共⫺0.63兲
0.921
共0.63兲
⫺1.324
共⫺0.89兲
3.151
共1.25兲
Emphasis on Tax Planning
HERF
⫺
TOOL
Model 3
0.447***
共5.03兲
2.073**
共1.60兲
0.074**
共1.85兲
0.003
共0.30兲
Firm Growth and Tax-Planning Opportunities
GROW
⫺
RDI
Model 2
⫺4.629***
共⫺4.45兲
0.149
117.10
共0.43兲
⫺0.199
共⫺0.40兲
0.173
113.48
共0.50兲
0.035
共0.03兲
0.494***
共2.87兲
⫺2.714
共⫺1.16兲
0.882
共0.51兲
⫺0.287
共⫺0.21兲
5.976**
共2.25兲
⫺0.336
共⫺0.69兲
0.619***
2.64
1.76**
共1.82兲
⫺0.473
共⫺1.24兲
0.039
115.34
共0.42兲
⫺0.042
共⫺0.06兲
0.353
共0.82兲
1.271*
共1.30兲
⫺5.817***
共⫺4.39兲
0.307
100.18
共0.64兲
*, **, *** Robust t-statistics in parentheses denote statistical significance at p ⬍ 0.10, p ⬍ 0.05, and p ⬍ 0.01,
respectively, at the one-tailed level for variables with predictions, and two-tailed otherwise.
Variables are defined in Table 3.
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Robinson, Sikes, and Weaver
by about 2 percent. The initiation of a research initiative by a sample firm that costs about 1
percent of sales would increase the likelihood of a profit center by over 10 percent. An increase in
intangible assets from the sample average of 11 percent of total assets to 21 percent increases the
likelihood of a profit center by almost 13 percent. Finally, a sample firm that significantly increased coordination with a tax department 共COOR from 0 to 1兲 would increase the likelihood of
a profit center by almost 38 percent. Together these results suggest that decentralization, departmental coordination, tax-planning opportunities, and firm emphasis on tax planning are important
determinants in the choice of measuring and evaluating firms’ tax departments as profit or cost
centers.
Results of Tests of Hypothesis 2
Hypothesis 2 posits that firms expected to adopt a profit center measurement system will have
relatively lower effective tax rates. Table 7 reports the results of our tests of H2, estimating ETR
regressions with robust standard errors adjusted for clustering across industry 共one-digit SIC兲. We
report the results of two models after eliminating influential observations.25 The first model uses
ETR1 as the dependent variable and the second model uses ETR2. Consistent with prior research,
both ETR regressions exhibit significant explanatory power 共36.1 percent of the variation in ETR1
and 31.9 percent in ETR2兲. Because the results for both ETR1 and ETR2 are largely consistent,
below we discuss the ETR1 results and note where the ETR2 results differ.
The estimated coefficient in Table 7 for profit center status, PROFIT*, is negative and statistically significant 共p ⬍ 0.01 for ETR1 and p ⬍ 0.05 for ETR2兲, as predicted. Although this result
must be viewed cautiously given the potential for endogeneity in the ETR regression, the negative
coefficient on PROFIT* 共⫺0.148兲 is consistent with the contention that, conditional on the remaining independent variables, firms predicted to use a profit center to evaluate the tax department
are associated with lower ETRs. That is, based on this regression, we would expect to observe a
reduction in ETRs of 1.5 percent, on average, for every 10 percent increase in the likelihood that
a firm would adopt a profit center measurement model. To further quantify this result, we compare
the predicted ETR for a firm with a 55 percent likelihood of choosing a profit center model 共the
median for PROFIT*兲 with the predicted ETR for a firm whose characteristics increase the
likelihood of choosing a profit center by one standard deviation of PROFIT* 共29 percent兲. A one
standard deviation increase in the likelihood of using a profit center model to evaluate the tax
department is associated with a reduction in the predicted ETR from 35.8 to 31.5 percent.26
The estimated coefficient on RESIDUAL in Table 7 is not statistically significant, suggesting
that the potential unobservable characteristics or unmodeled incentives that lead firms to their
profit/cost center choice do not separately affect their ETRs. In other words, the systematic determinants of PROFIT such as decentralization, firm size, and tax-planning opportunities and resources are the important factors that drive ETRs lower for profit center firms.
In addition to these primary results, several of the estimated coefficients for control variables
are statistically significant. Under tax-planning resources, the estimated coefficient for TOOL is
negative and significant. This result is consistent with firms having lower ETRs when their CFOs
believe taxes are an important earnings management tool. With respect to tax-planning opportunities, the estimated coefficients for LEV, CAPI, FOR, INTAN, and INVI are statistically signifi-
25
26
The ETR1 共ETR2兲 regression excludes six 共17兲 potentially influential observations identified using the procedures in
Belsley et al. 共1980兲. Inclusion of these observations produces similar results.
We estimate the median sample firm’s reduction in ETR using the coefficients from the ETR1 model of Table 7 and the
median values of all variables except indicator variables, which we set to zero.
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Performance Measurement of Corporate Tax Departments
1059
TABLE 7
ETR Regression Results
Construct
Tax Department Evaluation
Prediction
Variables
⫺
PROFIT*
RESIDUAL
Tax-Planning Resources
Tax-Planning Opportunities
Controls
?
COOR
⫺
PLAN
⫺
TOOL
?
LEV
⫺
CAPI
?
FOR
⫺
RDI
?
INTAN
⫺
INVI
⫹
ROA
⫹
GROW
?
SIZE
Constant
Observations
R2
ETR1
ETR2
⫺0.148***
共⫺3.73兲
⫺0.005
共⫺0.48兲
⫺0.107**
共⫺2.44兲
⫺0.018
共⫺1.53兲
0.033
共1.56兲
⫺0.011
共⫺0.64兲
⫺0.015**
共⫺1.86兲
0.019
共0.99兲
⫺0.004
共⫺0.15兲
⫺0.021***
共⫺4.57兲
0.047*
共1.95兲
⫺0.043**
共⫺1.87兲
0.041**
共2.81兲
⫺0.001
共⫺0.52兲
0.152***
共3.91兲
⫺0.078***
共⫺3.67兲
0.066**
共2.98兲
⫺0.085***
共⫺3.32兲
0.056**
共2.48兲
⫺0.002*
共⫺1.60兲
0.097**
共3.00兲
⫺0.088***
共⫺3.42兲
0.048***
共2.63兲
0.002
共0.13兲
0.009***
共4.29兲
0.375***
共21.88兲
115
0.361
0.070**
共1.87兲
0.015
共1.08兲
0.010***
共2.87兲
0.352***
共11.37兲
104
0.319
*, **, *** Robust t-statistics in parentheses denote statistical significance at p ⬍ 0.10, p ⬍ 0.05, and p ⬍ 0.01,
respectively, at the one-tailed level for variables with predictions, and two-tailed otherwise. Variables are defined in
Table 3.
cant. Additionally, ROA and SIZE are both positive and significant. The estimated coefficients for
the remaining variables are not significant.27
27
We conduct multiple alternative tests to check sensitivity. We include a variable in the ETR equation indicating whether
firms have negative pretax income to control for differences in incentives for firms with possible NOLs 共Beaver et al.
2007兲. Next, we alternatively substitute different measures for variables such as size 共market value of equity or sales兲,
extent of foreign operations 共proportion of foreign income or sales兲, and growth 共market-to-book ratio兲. The results we
report in Table 7 are robust to these alternate specifications.
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Robinson, Sikes, and Weaver
Supplemental Analyses
Hanlon and Heitzman 共2009兲 partition tax planning into two components: cash tax planning
and tax accrual planning. Many cash tax-planning strategies reduce firms’ cash tax payments
through deferral techniques. These techniques, however, will not reduce the ETR reported in the
financial statements because of the accrual concept of accounting for income taxes. A profit center
designation may lead firms to engage in tax accrual planning that is designed to reduce the
financial ETR and not cash tax payments 共e.g., designating foreign earnings as permanently
reinvested兲. Our primary tests cannot distinguish which of these two planning components contributes to our profit center results. In this section, we extend our analyses to test whether measuring and evaluating tax departments as profit or cost centers affects real tax planning that is
reflected in cash ETRs.
To test this proposition, we alternatively substitute ETRC1 共1999 cash ETR兲 and ETRC2
共average 1999–2000 cash ETR兲 for our dependent variable in Equation 共2兲.28 We measure ETRC1
as the ratio of income taxes paid per the 1999 statement of cash flows to pretax income 共Dyreng
et al. 2008兲. Consistent with our measure of effective tax rate, we measure ETRC2 as the ratio of
the sum of taxes paid per the 1999 and 2000 statements of cash flows to the sum of pretax income
in 1999 and 2000. Table 4 Panel A shows that the mean median ETRC1 and ETRC2 are 25.8 共26.0兲
and 29.9 共25.2兲 percent, respectively, for profit center firms and 31.0 共32.3兲 and 30.9 共30.9兲
percent, respectively, for cost center firms. We find no statistical difference in mean and median
ETRC1 or ETRC2 between profit and cost center firms. A negative coefficient on PROFIT* would
suggest that firms with underlying characteristics of profit centers have lower cash ETRs. Table 8
presents the results from estimating these cash-ETR regressions.
The regressions estimated in Table 8 exhibit significant explanatory power, but we find that
PROFIT* and RESIDUAL are not significant in explaining either one- or two-year cash ETRs.
These results are not entirely unexpected, given the inherent instability in short-term cash ETRs as
documented by Dyreng et al. 共2008兲. In addition, discussions with tax professionals indicate that,
during the time period of our survey, many firms were not interested in tax-planning strategies that
did not generate a financial statement benefit. Consistent with this observation, the 1999 E & Y
survey finds that 38 percent of survey respondents indicated that ETR is a “very important” metric
for measuring the performance of tax departments, whereas only 6 percent indicated that “cash
savings” was a very important metric for measuring the performance of tax departments. The
results are also consistent with Armstrong et al. 共2009兲, who find little evidence that firms incentivize tax directors to lower cash tax burdens. Combined with our main results reported in Table 7,
the results in Table 8 suggest that a profit center performance model is effective in motivating tax
departments to reduce financial ETRs, but ineffective in motivating tax departments to reduce cash
ETRs. This indirect evidence underscores the difference between incentives to encourage real tax
planning 共cash tax savings兲 versus tax accrual planning 共financial tax management of ETRs兲.29
V. CONCLUSION
We use confidential survey data from 1999 that indicates whether firms manage and evaluate
their tax function as a profit center 共“contributor to the bottom line”兲 or as a cost center to develop
28
29
Because cash ETRs tend to be quite volatile from year to year, we repeated the cash ETR regressions using average cash
ETRs over the three-year period 1998–2000 and the four-year period 1997–2000. The results are qualitatively similar to
those reported in Table 8.
The percentage of profit center firms in our sample that disclose information about permanently reinvested earnings and
valuation allowance accounts for years 1998–1999 is greater than the percentage of cost center firms that do so. The
higher frequency of these disclosures among profit center firms is consistent with the contention that profit center firms
focus on financial ETRs.
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Performance Measurement of Corporate Tax Departments
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TABLE 8
Cash ETR Regression Results
Variables
Tax Department Evaluation
PROFIT*
RESIDUAL
Tax-Planning Resources
COOR
PLAN
TOOL
Tax-Planning Opportunities
LEV
CAPI
FOR
RDI
INTAN
INVI
Controls
ROA
GROW
SIZE
Constant
Observations
R2
ETRC1
ETRC2
⫺0.095
共⫺1.11兲
0.014
共1.26兲
⫺0.068
共⫺0.63兲
0.011
共0.51兲
0.008
共0.42兲
⫺0.065
共⫺1.50兲
⫺0.074***
共⫺4.15兲
0.002
共0.15兲
⫺0.130**
共⫺2.45兲
⫺0.060**
共⫺2.35兲
⫺0.254***
共⫺3.82兲
0.021
共0.39兲
⫺0.031
共⫺1.05兲
0.002
共1.32兲
0.310***
共3.51兲
0.031
共0.53兲
⫺0.236***
共⫺3.53兲
0.013
共0.25兲
0.028
共0.48兲
0.001
共0.08兲
0.290**
共2.87兲
0.086
共1.00兲
0.310
共1.55兲
0.213***
共4.16兲
0.003
共0.14兲
0.306**
共2.35兲
113
0.335
0.254
共1.59兲
0.091**
共2.74兲
⫺0.004
共⫺0.28兲
0.390**
共2.29兲
104
0.236
*, **, *** Robust t-statistics in parentheses denote statistical significance at p ⬍ 0.10, p ⬍ 0.05, and p ⬍ 0.01,
respectively, at the one-tailed level for variables with predictions and two-tailed otherwise.
Variables are defined in Table 3.
a model to identify determinants of corporate tax departments’ performance measurement systems.
Our logit test results indicate that the extent of decentralization and departmental coordination are
important considerations in the decision to utilize a profit center to measure the performance of a
tax department. We also find evidence that the availability of certain tax-planning opportunities,
such as the level of R&D activity and the relative level of intangible assets, are important considerations in the choice of performance measurement technique. Finally, we provide weak evidence that the choice of performance measure for a tax department is related to the level of
resources committed to tax planning.
The Accounting Review
May 2010
American Accounting Association
1062
Robinson, Sikes, and Weaver
Next, we analyze the effect of a firm’s choice to manage and measure its tax department as a
profit center on its effective tax rate. To address potential endogeneity, we use the likelihood of
profit center measurement as predicted from a logit analysis. As expected, we find that firms
expected to adopt profit center performance measures for their tax departments have lower effective tax rates. In supplemental analyses, we investigate whether the profit center designation
affects real tax planning 共cash ETR兲 in addition to tax accrual planning. We find no association
between the method of evaluating the tax function and the magnitude of cash ETRs, suggesting
that the profit center designation only affects financial tax management and not cash tax savings.
The E&Y survey took place in 1999. Subsequent to this survey, several important events
transpired 共tax shelter regulations and Sarbanes-Oxley兲 that reduced the predominance of profit
center tax departments. Many tax departments are focused once again on compliance issues
including tax and SFAS No. 109 issues. This trend is consistent with our findings. More
compliance-oriented tax departments may be acting optimally as cost centers because compliance
costs are relatively more externally driven than tax planning. As a firm’s opportunities for tax
planning and its ability to focus on tax planning shrink, a profit center performance measurement
system may no longer be optimal, thereby explaining a shift back to cost centers.
The results in this study are important because they illustrate associations between organizational design theory and methods of evaluating administrative functions that were previously
undetected and untested in the literature. The findings are also important in that they indicate that
performance measurement can act as an incentive for administrative units, such as a tax department, to contribute to reported earnings.
REFERENCES
Armstrong, C. S., J. L. Blouin, and D. F. Larcker. 2009. The incentives for tax planning. Working paper,
University of Pennsylvania.
Badertscher, B., J. Phillips, M. Pincus, and S. Rego. 2009. Earnings management strategies and the trade-off
between tax benefits and detection risk: To conform or not to conform? The Accounting Review 84 共1兲:
63–97.
Bankman, J. 1994. The structure of Silicon Valley start-ups. UCLA Law Review, University of California, Los
Angeles, School of Law 41 共September兲: 1737–1768.
Beaver, W. H., M. F. McNichols, and K. Nelson. 2007. An alternative interpretation of the discontinuity in
earnings distributions. Review of Accounting Studies 12 共4兲: 525–556.
Belsley, D. A., E. Q. Kuh, and R. E. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and
Sources of Collinearity. New York, NY: John Wiley & Sons.
Brickley, J., C. Smith, and J. Zimmerman. 1995. The economics of organizational architecture. Journal of
Applied Corporate Finance 8 共2兲: 19–31.
Bushman, R. M., F. J. Indjejikian, and A. Smith. 1995. Aggregate performance measures in business unit
manager compensation: The role of intrafirm interdependencies. Journal of Accounting Research 33
共Supplement兲: 101–128.
——–, R. J. Indjejikian, and A. Smith. 1996. CEO compensation: The role of individual performance evaluation. Journal of Accounting and Economics 21 共April兲: 161–193.
Chen, S., X. Chen, Q. Cheng, and T. Shevlin. 2008. Are family firms more or less tax aggressive than
non-family firms? Journal of Financial Economics 共forthcoming兲.
Collins, J., and D. Shackelford. 1996. Corporate domicile and average effective tax rates: The cases of
Canada, Japan, the United Kingdom, and the United States. In The Taxation of Multinational Corporations, edited by Slemrod, J., 51–79. Boston, MA: Kluwer Academic publishers.
Desai, M. A., and D. Dharmapala. 2006. Corporate tax avoidance and high-powered incentives. Journal of
Financial Economics 79: 145–179.
Dhaliwal, D. S., C. A. Gleason, and L. F. Mills. 2004. Last chance earnings management: Using the tax
expense to meet analysts’ forecasts. Contemporary Accounting Research 21 共2兲: 431–459.
The Accounting Review
American Accounting Association
May 2010
Performance Measurement of Corporate Tax Departments
1063
Douglas, R. F., P. J. Ellingsworth, and J. P. McAndrews, III. 1996. What management expects: Evaluation of
the tax department and its executives. Tax Executive 共Jan/Feb兲: 29–32.
Dunbar, A. E., and J. D. Phillips. 2001. The outsourcing of corporate tax function activities. The Journal of
the American Taxation Association 23 共2兲: 35–49.
Dyreng, S., M. Hanlon, and E. Maydew. 2008. Long-run corporate tax avoidance. The Accounting Review 83
共1兲: 61–82.
——–, ——–, and ——–. 2010. The effects of executives on corporate tax avoidance. The Accounting Review
共forthcoming兲.
Ernst & Young. 2004. Tax risk management: The evolving role of tax directors. Available at: http://
www.ey.com/global/download.nsf/International/EY_-_Tax_-_Tax_Risk_Management/$file/
EY_Tax_Risk_Survey_Report.pdf.
Govindarajan, V., and A. K. Gupta. 1985. Linking control systems to business unit strategy: Impact on
performance. Accounting, Organizations and Society 10 共1兲: 51–66.
Graham, J., and A. Tucker. 2005. Tax shelters and corporate debt policy. Journal of Financial Economics 81
共3兲: 563–594.
Grubert, H., and J. Slemrod. 1998. The effect of taxes on investment and income shifting to Puerto Rico. The
Review of Economics and Statistics 80 共3兲: 365–373.
Gupta, S., and K. Newberry. 1997. Determinants of the variability in corporate effective tax rates: evidence
from longitudinal data. Journal of Accounting and Public Policy 16: 1–34.
Halperin, R., and R. Sansing. 2005. Is the effective tax rate an effective performance measure? Working
paper, Dartmouth College.
Hanlon, M. 2003. What can we infer about a firm’s taxable income from its financial statements? National
Tax Journal 56 共4兲: 831–863.
——–, and S. Heitzman. 2009. A review of tax research. Working paper, Massachusetts Institute of Technology.
——–, L. Mills, and J. Slemrod. 2007. An empirical examination of big business tax noncompliance. In
Taxing Corporate Income in the 21st Century, edited by A. Auerbach, J. R. Hines, Jr., and J. Slemrod,
171–210. Cambridge, U.K.: Cambridge University Press.
——–, S. Rajgopal, and T. Shevlin. 2003. Are executive stock options associated with future earnings?
Journal of Accounting and Economics 36: 3–43.
Hou, K., and D. T. Robinson. 2006. Industry concentration and average stock returns. The Journal of Finance
61 共4兲: 1927–1956.
Ittner, C. D., and D. F. Larcker. 2002. Determinants of performance measure choices in worker incentive
plans. Journal of Labor Economics 20 共2兲: S58–S90.
Jacob, J. 1996. Taxes and transfer pricing: Income shifting and the volume of intrafirm transfers. Journal of
Accounting Research 34: 301–312.
Jennings, R., W. Mayew, and C. Weaver. 2010. The effect of TRA86 on the extent of implicit taxes at the
corporate level. Working paper, The University of Texas at Austin, Duke University, and Texas A&M
University.
Jensen, M. C., and W. H. Meckling. 1998. Divisional Performance Measurement. Foundations of Organizational Strategy. Cambridge, MA: Harvard University Press.
Keating, A. S. 1997. Determinants of divisional performance evaluation practices. Journal of Accounting and
Economics 24: 243–273.
Krueger, M. 1999. AICPA workshop identifies five management areas where controllers can generate value.
The Controller’s Report 共Jan兲: 1, 15–19.
Krull, L. 2004. Permanently reinvested earnings, taxes, and earnings management. The Accounting Review
79 共3兲: 745–767.
Lambert, R. A., and D. F. Larcker. 1987. An analysis of the use of accounting and market measures of
performance in executive compensation contracts. Journal of Accounting Research 25 共Supplement兲:
179–203.
Larcker, D. F. 2003. Discussion of: Are executive stock options associated with future earnings. Journal of
Accounting and Economics 36: 91–103.
The Accounting Review
May 2010
American Accounting Association
1064
Robinson, Sikes, and Weaver
Markle, K., and D. Shackelford. 2009. Does the domicile of a multinational affect its worldwide corporate
income tax liability? Working paper, University of North Carolina.
Martucci, S. 2001. Fortune 1000 tax departments streamlining to handle strategy role. Global Finance
共November兲.
Matejka, M., and A. DeWaegenaere. 2005. Influence costs and implementation of organizational changes.
Journal of Management Accounting Research 17: 43–52.
McGill, G., and E. Outslay. 2004. Lost in translation: Detecting tax shelter activity in the financial statements. National Tax Journal 57 共3兲: 739–756.
Mills, L., M. Erickson, and E. Maydew. 1998. Investments in tax planning. The Journal of the American
Taxation Association 20 共1兲: 1–20.
——–, and G. Plesko. 2003. Bridging the reporting gap: A proposal for more informative reconciling of book
and tax income. National Tax Journal 56 共4兲: 865–893.
Osterloh, M., and B. S. Frey. 2000. Motivation, knowledge transfer, and organizational forms. Organization
Science 11 共5兲: 538–550.
Palmer, D., P. D. Jennings, and X. Zhou. 1993. Late adoption of the multidivisional form by large U.S.
corporations: Institutional, political, and economic accounts. Administrative Science Quarterly 38:
100–131.
Phillips, J. D. 2003. Corporate tax-planning effectiveness: The role of compensation-based incentives. The
Accounting Review 78 共3兲: 847–874.
——–, M. Pincus, and S. Rego. 2003. Earnings management: New evidence based on deferred tax expense.
The Accounting Review 78 共2兲: 491–521.
Rego, S. 2003. Tax avoidance activities of U.S. multinational corporations. Contemporary Accounting Research 20: 805–833.
——–, and R. Wilson. 2008. Executive compensation, tax reporting aggressiveness, and future firm performance. Working paper, The University of Iowa.
Simons, R. 1987. Accounting control systems and business strategy: An empirical analysis. Accounting,
Organizations and Society 12: 357–374.
Sloan, R. G. 1993. Accounting earnings and top executive compensation. Journal of Accounting and Economics 16: 56–100.
Wilkie, P. 1988. Corporate average effective tax rates and inferences about relative tax preferences. The
Journal of the American Taxation Association 10 共Fall兲: 75–88.
Wilson, G. P. 1995. Corporate tax effectiveness: Adding value through the tax function. Working paper,
Harvard University.
Zimmerman, J. 1983. Taxes and firm size. Journal of Accounting and Economics 5: 119–149.
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American Accounting Association
May 2010
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