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Does a Firm’s Business Strategy Influence its Level of Tax Avoidance?

Danielle M. Higgins

University of Connecticut

Thomas C. Omer

Texas A&M University

John D. Phillips*

University of Connecticut

February 14, 2011

We appreciate helpful comments received from Amy Dunbar, Dave Weber, participants at the American Accounting Association Northeast Regional Meeting, the discussant, Michaele Morrow, and workshop participants at the University of Connecticut. Danielle Higgins and John Phillips appreciate the research support received from the University of Connecticut School of Business. Tom Omer appreciates the research support received from Ernst & Young.



Abstract: A firm’s business-level strategy dictates how the firm competes in its chosen

line of business. Porter (1996) argues that the best way for a firm to achieve a sustainable

competitive advantage in its chosen market is to reinforce its business-level strategy with

a host of activities, including functional policies, organization structure, etc. The choices

made by each firm are likely to determine, to some extent, the level of tax avoidance

because the firm strategies are, in part, based on firms’ willingness to deal with risk and

uncertainty. Thus, we examine whether a firm’s business strategy, as defined by the

Miles and Snow typology (1978), influences its level of tax avoidance. We find that firms

engaging in a strategy that focuses on minimizing and reducing the uncertainty of costs

(defenders) avoid fewer taxes than firms that not only follow a strategy focusing on

product differentiation and the aggressive pursuit of opportunities, but are more willing to

deal with uncertainty (prospectors). Our results suggest that even though defenders could

engage in tax avoidance activities to lower costs, prospectors are more willing to engage

in tax planning efforts that have uncertain outcomes.


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1. Introduction

A firm’s business-level strategy dictates how the firm will compete in its chosen

market.1 Porter (1996) argues that the best way for a firm to achieve a sustainable

competitive advantage in its chosen market is to reinforce its business-level strategy with

a host of activities, including functional policies, organization structure, etc. The choices

made by each firm are likely to determine, to some extent, the level of tax avoidance

because the firm strategies are, in part, based on firms’ willingness to deal with risk and

uncertainty. Thus, we investigate whether a firm’s business-level strategy influences it

tax planning activities (i.e., tax avoidance).2

Investigating whether a firm’s business strategy influences its level of tax avoidance

is interesting for at least two reasons. First, prior literature finds that there is substantial

variation in firms’ propensities to avoid income taxes (Dyreng et al. 2008). This variation

not only remains largely unexplained but also may be highly idiosyncratic and

determined by a number of factors and their interactions (Hanlon and Heitzman 2010).

Prior literature has documented an association between tax avoidance and several firm-

level characteristics (i.e., research and development activities, growth, capital intensity,

etc). The theoretical work of Miles and Snow (1978) identifies several of these firm-

level characteristics as reflective indicators of a firm’s business strategy. Therefore,

examining the association between tax avoidance and a combination of these firm-level

characteristics, reflective of a firm’s overall business strategy, should lead to a more

comprehensive understanding of firms’ tax avoidance activities.

Second, it remains unclear in the literature why seemingly similar firms engage in

varying degrees of tax avoidance. For example, firms within the same industry and with

similar incentives (and potentially similar opportunities) to engage in tax planning,

1 Business-level strategy is not the same as corporate strategy. Corporate strategy is concerned with the overall purpose and scope of the business (Hambrick 1983) whereas business-level strategy is concerned with how the firm competes in its chosen field of business. 2 Following Hanlon and Heitzman (2010), we view tax avoidance as a continuum that ranges from clearly legal (e.g. investments in municipal bonds) to those of questionable legality (e.g. tax shelters). Thus, tax avoidance refers to any activity that reduces a firm’s explicit tax liability relative to its pretax income.

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exhibit substantial variation in their efforts to avoid income taxes. Snow and Hrebiniak

(1980) and Hambrick (1983) document that business strategies can vary within a single

industry. Therefore, if there is a relation between firms’ business strategy and tax

avoidance, it may explain the intra-industry variation in firms’ tax avoidance activities.

Although several influential business strategy typologies exist, (e.g., Abell 1980;

Porter 1980), we use the Miles and Snow (1978) typology to define firms’ business

strategies because of the advantages it possesses over other typologies.3 Specifically,

employing the Miles and Snow (1978) typology is advantageous because of its detailed

theoretical orientation and its generalizability across industry settings. Miles and Snow

(1978) contend that four basic patterns or strategies emerge as firms attempt to best adapt

to their competitive environment, and that three of these strategies may be fitted on a

continuum. At one end of the continuum are defenders, which are firms that follow a

cost leadership strategy. Defenders are firms that tend to have a narrow product domain, a

focus on efficiency and avoiding uncertainty, and a stable organizational structure. At

the opposite end of the spectrum are prospectors, which are firms that follow a

differentiation strategy.4 Prospectors have a very broad product domain, a focus on

innovation and change, a more flexible organization structure, and less emphasis on

avoiding uncertainty. Although Miles and Snow (1978) do not consider the tax avoidance

behavior of their topology of firms, the attributes of these groups may lend themselves to

an increased or decreased emphasis on tax planning.

To determine the extent of their tax avoidance activities, firms trade off the marginal

benefits against the marginal costs of managing tax expense. Tax avoidance activities not

only increase cash flow, but increase accounting earnings in certain circumstances (e.g.,

tax credits and permanent book-tax differences). Tax avoidance costs include planning

and implementation expenses, potential penalties imposed by tax authorities and

3 For example, Porter’s (1980) strategy has been criticized as for its lack of detailed theoretical orientation and its inability to generalize to large firms, while Abell’s (1980) typology has been criticized for its inability to differentiate among firms that follow cost leadership strategies, firms that fall in the middle of the strategy spectrum and firms lacking any apparent strategy (Chrisman et al. 1988, Smith et. al. 1989). 4 Miles and Snow (1978) also identify two additional strategies, analyzers and reactors. Analyzers have attributes of both defenders and prospectors and fall somewhere in between these two strategies on the continuum. Reactors do not follow any consistent strategy but rather only respond to environmental change (Miles and Snow 1978).

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reputation costs if the firm’s tax avoidance activities are deemed to be aggressive and

subject to public scrutiny. The difference between defender and prospector firms’ tax

avoidance levels depends on the impact of the differential characteristics of these two

strategies on the benefits and costs of tax avoidance. For example, because defenders are

interested in minimizing costs, defender firms may benefit more from the increased tax

savings generated by tax avoidance activities than prospector firms. Yet, at the same

time, the cost of devising and implementing tax planning strategies can be expensive and

thus, for cost conscious defender firms, the benefit of additional tax savings may not

outweigh the costs of implementing the tax avoidance strategies. In addition, defenders’

emphasis on stability and their need to reduce uncertainty suggests that these firms could

be more concerned with 1) the uncertainty associated with tax avoidance that makes cost

minimization more difficult, 2) the potential penalties imposed by tax authorities and 3)

the reputation damage associated with public disclosure of involvement with particularly

egregious avoidance activities.

Relative to defenders, prospectors are less concerned with minimizing costs, and

more focused on growth and innovation, possibly making tax minimization less of a

concern for these types of firms. Nevertheless, prospectors are known for aggressively

pursuing new opportunities and it is possible that this aggressive culture influences their

level of tax avoidance. In addition, prospectors tend to have a greater propensity for risk,

and thus may be more willing to engage in more aggressive tax positions, relative to

defenders. Thus, because it is unclear whether defenders engage in more or less tax

avoidance activities than prospectors, we investigate this issue empirically.

To address our research question we begin by creating a measure (STRATEGY)

designed to provide a score that reflects the most likely business strategy adopted by

sample firms. The score is based on variables identified in prior literature as

characteristics of firms’ business strategies. Because Miles and Snow (1978) suggests

that competitive strategy can be classified as a continuum between two different strategic

orientations, with prospectors at one end and defenders at the other end, we identify

defenders as those firms having the lowest value and prospectors as those having the

highest value of our STRATEGY measure. All other firms are classified as analyzers.

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Analyzers have attributes of both prospectors and defenders and fall somewhere in

between these two strategies on the continuum.

To examine firms’ tax avoidance activities, we use two different effective tax rate

measures drawn from prior literature. Our first measure, the book effective tax rate

defined as total tax expense divided by pre-tax book income (adjusted for special items),

captures tax avoidance activities that directly affect net income through total tax expense.

Our second measure, the cash effective tax rate, defined as cash taxes paid divided by

pre-tax book income (adjusted for special items), reflects the level of cash taxes paid to

tax authorities. Lower effective tax rates reflect increased tax avoidance.

Using data from 1998 to 2008, we regress each of our measures of tax avoidance

on our continuous measure, STRATEGY, and control for other factors that prior literature

suggests are associated with tax avoidance. We find that defender firms engage in less tax

avoidance than prospector firms, as demonstrated by higher book and cash effective tax

rates. These results suggest that defender firms’ concern with the costs and uncertainties

associated with tax avoidance outweighs the benefit of additional tax savings.

Our study makes several contributions to the literature. First, our results

contribute to the stream of research that investigates the variation in firms’ tax avoidance

activities. Prior research has documented an association between tax avoidance and firm-

level characteristics, executive characteristics, and managerial incentives (e.g., Dyreng et

al. 2009; Phillips 2003; Rego 2003; Rego and Wilson 2009). We extend this line of

research with evidence that a firm’s business strategy influences its level of tax

avoidance. In doing so, we respond to Hanlon and Heitzman’s (2010) conjecture that tax

avoidance is most likely explained by a number of factors and interactions by providing

evidence that a firm’s strategy, which represents a broader view of the firm and is

identified based on a range of determinants, is a determinant of level of tax avoidance.

Furthermore, our results contribute toward a better understanding of the impact of

different competitive strategies on firms’ tax planning and reporting practices.

Our study also links two streams of literature: organizational theory from the

management literature and tax avoidance from the accounting literature. Our study

contributes to these two streams of literature in several ways. First, we develop a proxy of

organizational business strategy that is constructed using publicly available data, and is

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more comprehensive than existing proxies in accounting5 or management. Thus, our

measure can be used in future research in both management and accounting settings.6

Second, we contribute to the management literature by providing evidence that firms’ tax

planning activities are associated with their organizational strategy. Thus, our results

empirically support Porter’s (1996) argument that firms should reinforce their business-

level strategy with support activities in order sustain a competitive advantage.

The remainder of the paper is organized as follows: Section two discusses prior

research and develops the hypotheses, Section three presents the research design, Section

four explains the sample selection process and provides descriptive statistics, Section five

provides the results of the main analyses, and Section 6 presents our sensitivity analysis

and Section 7 concludes.

2. Prior Literature and Hypothesis Development 2.1 Prior Literature on the Determinants of Tax Avoidance

Prior tax avoidance studies generally focus on firm-level characteristics, including

but not limited to an association between tax avoidance and firm-level characteristics

such as size, the magnitude of foreign operations, capital intensity, leverage, and research

and development expense (e.g. Armstrong et al. 2009; Dyreng et al. 2009; Phillips 2003;

Rego 2003). 7 In addition, several studies provide evidence of differing investments in tax

planning to lower effective tax rates. For example, Mills et al. (1998) provide evidence

that firms’ tax planning expenditures are associated with lower effective tax rates.8 Cook

et al. (2008) provide evidence that the magnitude of tax fees paid to the external auditor

5 The exception is a concurrent working paper (Bentley, Omer and Sharp 2010) that tests whether certain business strategies are associated with more financial reporting irregularities. They use similar measures to construct their measure of firms’ business strategies. They find that prospector firms are more likely to experience financial reporting irregularities. Thus, given the findings in Frank et al. (2009), their results are complementary to our results. 6 Thus far, the accounting literature has examined organizational strategy as a determinant of compensation (Ittner et al. 1997), the outsourcing of the corporate tax function (Dunbar and Phillips 2001), and audit fees and accounting irregularities (Bentley, Omer and Sharp 2010). 7 See Zimmerman (1983), Gupta and Newberry (1997), Mills et al. (1998), Rego (2003), Stickney and McGee (1982), and Shevlin and Porter (1992). In addition, see Callihan (1994) for a review of the annual effective tax rate literature. 8 Mills et al. measures ETR as the ratio of current tax expense to pre-tax income

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is associated with greater reductions in fourth quarter effective tax rates. Furthermore,

McGuire et al. (2010) find that firms that engage auditors with industry expertise

continuously focus more on tax avoidance that lowers book effective tax rates.

In addition to firm-level characteristics, prior studies also examine whether

managerial incentives are associated with tax avoidance. After controlling for selection

bias, Phillips (2003) finds that business-unit manager, but not chief executive officer

(CEO), after-tax bonus compensation is associated with lower effective tax rates. Rego

and Wilson (2008) finds a positive relation between tax avoidance and CEO and chief

financial officer (CFO) compensation. Meanwhile, Armstrong et al. (2010) find that tax

directors’ incentive compensation is related to lower ETRs, but not cash ETRs suggesting

that tax directors focus on tax strategies that lower income tax expense and thus increase

earnings. Finally, Robinson et al. (2010) find that firms organizing their tax departments

as profit centers have lower book ETRs, but not lower cash ETRs.

Although prior studies have linked tax avoidance to firm-level characteristics and

managerial incentives, the literature is still unable to explain much of the variation in

measures of tax avoidance. For example, it remains unclear why firms within the same

industry, with seemingly similar incentives and opportunities, engage in different levels

of tax avoidance. Hanlon and Heitzman (2010) suggest that tax avoidance may be highly

idiosyncratic and determined by a number of factors and their interactions. Accordingly,

prior literature has documented an association between tax avoidance and several firm-

level characteristics (i.e., research and development activities, growth, capital intensity,

etc). The theoretical work of Miles and Snow (1978) identifies several of these firm-

level characteristics as reflective indicators of a firm’s business strategy. Examining the

association between tax avoidance and a combination of these firm-level characteristics

should therefore lead to a more comprehensive understanding of firms’ tax avoidance

activities. Therefore, we provide evidence on whether business-level strategy influences

firms’ tax avoidance activities.

2.2 Business Strategy and Tax Avoidance Within the strategic management literature, the Miles and Snow (1978) typology

is one of the most popular and well cited theories of strategic types. Miles and Snow

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(1978) argue that different competitive strategies arise from the way companies decide to

address three fundamental problems: entrepreneurial, engineering, and administrative

problems.9 The entrepreneurial problem relates to how a firm should manage its market

share. The engineering problem involves how a company should implement its solution

to the entrepreneurial problem. The administrative problem considers how a company

should structure itself to manage the implementation of the solutions to the first two

problems. Miles and Snow (1978) contend that four basic patterns or strategies emerge as

firms attempt to solve these problems: prospector, defender, analyzer, and reactor

strategies. Of these four strategies, three are viable and can be fitted on a continuum.10 At

one end of the continuum are defenders. Defenders are firms that stress cost efficiency

and certainty as the basis of competition. Defenders tend to have a narrow product

domain, a focus on efficiency, and a stable organizational structure. At the opposite end

of the continuum are prospectors. Prospectors have a very broad product domain, a focus

on innovation and change, and more flexible organization structure. Analyzers are an

intermediate type, and tend to exhibit traits of both prospectors and defenders. See

Appendix A for a detailed description of the key features of each strategic type.

Several studies have examined the association between business strategies and

certain firm characteristics (e.g., Snow and Hrebiniak 1980; Ittner et al. 1997; Simons

1987). Hambrick (1983) classifies firms as either prospectors or defenders using product-

market data and examines the relation between organizational strategy and firm

performance. He finds that defenders outperformed prospectors in terms of current

profitability and cash flows. Ittner et al. (1997) examine whether firms place more weight

on non-financial measures in chief executive officer (CEO) bonus contracts when they

follow a prospector strategy. They construct a measure of firm strategy and use this

measure to classify firms as either prospectors or defenders. They find that CEOs of

9 Miles and Snow (1978) refer to the process by which firms align themselves with their environment as the adaptive cycle 10 Miles and Snow (1978) identify a fourth group of firms, reactors. These firms do not follow any consistent strategy but rather only respond to environmental change and thus are expected to change over time. Miles and Snow (1978) note that this strategy is not viable in the long-run. Organizations are often forced into this strategy when top management is unwilling (or unable) to develop distinctive competencies, organizational structures or management processes required by a particular strategy.

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prospector firms are compensated more on the basis of non-financial measures than

defender firm CEOs.

Phillips and Dunbar (2001) is the only study that examines the association

between business strategy and corporate tax planning. The authors classify firms as either

defenders (nongrowth firms) or prospectors (growth firms), and examine the relation

between these two strategies and the outsourcing of the corporate tax function activities.

The authors argue that prospectors (growth firms) focus less on minimizing income tax

expense, and thus will outsource more of their tax-planning and -compliance activities.

Their results are consistent with this hypothesis. However, this study does not explicitly

address whether prospectors engage in more or less tax avoidance than defender firms.

Furthermore, Phillips and Dunbar classify firms as prospectors and defenders based on

single variable (i.e.,, growth). Prior theoretical and empirical research in management

provides evidence that strategy is multidimensional, however, and should not be defined

on the basis of a single variable (e.g. Miles and Snow 1978, Hambrick 1983).

Although Miles and Snow (1978) do not consider the tax avoidance behavior of

their topology of firms, the attributes of these groups should lead to an increased or

decreased emphasis on tax avoidance. Thus, we examine whether the business strategies

defined by the Miles and Snow topology are associated with more or less tax avoidance,

which depends on the impact of the differential characteristics of these two strategies on

the benefits and costs of tax avoidance. The benefits of tax avoidance include greater tax

savings, while the costs include planning and implementation costs, potential penalties

imposed by the IRS and reputation costs (Chen et al. 2010).

On one hand, the Miles and Snow (1978)’s topology suggests that the potential

benefits of tax avoidance are greater for defender firms than prospector firms because

defender firms emphasize cost efficiency as the cornerstone of competitive advantage

whereas prospectors are focused more on innovation and growth and less on cost

minimization. Therefore, because defenders are primarily interested in minimizing costs,

and because income tax expense is a major cost for most firms, firms following a

defender strategy should benefit more from the increased tax savings generated by

increased tax avoidance activities than firms following a prospector strategy.

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Alternatively, although defenders should benefit more from tax planning than

prospectors, the costs and uncertainty associated with tax avoidance may be greater than

the tax benefits for defender firms. Although tax planning strategies may create

additional tax savings, the cost of devising and implementing these strategies can be

extensive. For example, Mills et al. (1998) contend that while all firms may engage in

some baseline amount of planning to comply with tax laws, some firms may find that the

cost of additional investments in tax planning outweigh the benefits. Thus, for cost

conscious defender firms, the benefit of additional tax savings may not outweigh the cost

of implementation.

Moreover, another important cost of tax avoidance is due to uncertainty regarding

the long-term success of sustaining aggressive tax strategies and the potential penalties

imposed by tax authorities. This cost is more likely to have a substantial effect on

defender firms’ competitive position relative to prospectors. The structure and processes

of the defender organization are all designed to reduce uncertainty within the

organization. Therefore, the inherent uncertainty associated with sustainability, an audit

by a tax authority and potential penalties threatens defender firms’ need for stability and

predictability (Miles and Snow 1978). Specifically, these penalties create an additional

and potentially significant unexpected cost for defender firms, which could threaten their

competitive position because cost efficiency is an important prerequisite for their success.

Alternatively, the prospector strategy is more conducive to uncertainty and thus taking on

more risk. Because these firms often aggressively pursue new opportunities before

detailed planning is completed, outcome certainty is not central to their competitive

strategy. Thus, for these firms, engaging in particularly risky tax avoidance strategies

would not, based on the Miles and Snow description of these firms, appear to threaten

their competitive position as significantly as defender firms.

Finally, another potential non-tax cost for defender firms is the reputation damage

from being associated with a particular egregious tax avoidance strategy. Defender firms’

operate in very narrow product domains, offer limited products (relative to prospectors),

and compete through cost leadership and quality (Miles and Snow 1978). Because these

firms compete on the basis of cost and not innovation, substitutes most likely exist for

these firms’ products or services. Thus, the negative publicity associated with being a

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“bad” corporate citizen could impose a greater cost on defender firms’ competitive

position because customers can easily find substitutes for their product or services.

Alternatively, because prospector firms generate more unique products, it may be

difficult for consumers of such products to find an appropriate substitute.

Overall, both the benefits and costs of tax avoidance appear to be greater for

defender firms than for prospector firms. Thus, it is unclear whether defenders will

exhibit higher or lower levels of tax avoidance relative to prospectors. Based on these

arguments, we state the following null hypothesis:

H1: Organizational business strategies are not associated with firms’ levels of tax

avoidance

3. Research Methodology 3.1 Measure of Strategy

To categorize firms into their business strategy groups, we create a measure,

STRATEGY, designed to provide a score that reflects the most likely business strategy

adopted by sample firms. To construct the STRATEGY score, we follow prior literature

and identify variables which reflect different facets of this unobserved construct. We first

follow Ittner et al. (1997) and include the following variables: (1) the ratio of research

and development to sales, (2) the ratio of employees to sales and (3) the market-to-book

ratio.11, 12 We then expand upon this construct to include the following additional

measures based on the theoretical work of Miles and Snow (1978) and the empirical work

of Hambrick (1983): (4) the ratio of advertising expenses to total sales and (5) fixed asset

intensity. Consistent with Ittner et al. (1997), all variables are computed using a rolling

average of the respective yearly ratios over the prior five years.

11 We also consider an alternative measure of growth in order to capture the sporadic growth of prospectors and the steady growth of defenders. To capture the rate of growth, we compute the coefficient of variation on the change in total assets. We use this measure in place of market-to-book in the STRATEGY score. Our results are robust to this measure. 12 Ittner et al. (1997) use a fourth measure, the number of new products and services, which requires access to a proprietary database. Our goal in creating the STRATEGY score is to create a measure the can be easily reproduced using publicly available data.

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Each of the five measures is intended to capture different elements of a firm’s

organizational strategy. The ratio of research and development to sales (RD/SALES5)

serves as a measure of a firm’s propensity to seek new products. Because prospectors

engage in greater amounts of innovative activity, they are expected to have higher

research and development costs than defenders (Hambrick, 1983). The ratio of employees

to sales (EMP/SALE5) is a proxy for a firm’s ability to produce and distribute its goods

and services efficiently (Thomas et al. 1991). Because defenders focus on organizational

efficiency, we expect firms following this strategy to have fewer employees per dollar of

sales. The ratio of market value to book value (MtoB5) is a proxy for a firm’s growth or

investment opportunities (e.g., Smith and Watts 1992; Bushman et. al 1996). Consistent

with Ittner et al. (1997), we expect prospectors to have greater growth opportunities than

defenders. We include the ratio of advertising expense to total sales (MARKET5) as a

proxy for firms’ emphasis on marketing and sales.13 Prospectors spend more time

motivating, educating and informing their customers, thus we expect them to have higher

advertising expense than defenders. Furthermore, Hambrick (1983) finds that prospectors

have higher marketing expenses relative to defenders. Last, we include a measure of

fixed asset intensity (PPEINTENSITY5), measured as the ratio of gross property, plant

and equipment to lagged total assets. This measure is designed to capture a firms’ focus

on production assets, and thus higher ratios would be representative of defender firms

(Hambrick, 1983).

Next, we rank each of the five variables by forming quintiles within each 2-digit

SIC industry-year.14 For each of the first four variables (RD/SALE5, EMP/SALE5,

MTOB5, MARKET5), firms in the top quintile receive a score of 5, those in the next

quintile receive a score of 4, etc. The scoring for PPEINTENSITY5 is inverted because

defenders are expected to have greater fixed asset intensity. Therefore, firms in the top

PPEINTENSITY5 quintile receive a score of 1, those in the next quintile receive a score

of 2, etc.15 Then, for each firm-year, we sum the scores across the five variables such that

13 Following Dyreng et al. (2008, 2009), we replace advertising expense with zero if it is missing. 14 Hambrick (1983) notes that strategy is a relative phenomenon and should be defined within industry. 15 In constructing the STRATEGY variable, we assume equal weighting of each of the five variables. This methodology sacrifices precision for the simplicity necessary to build a strategy index, and thus is similar to the approach taken by Gompers et al. (2003) in constructing their governance index.

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the maximum STRATEGY score a firm could receive is 25, and the minimum score a firm

could receive is 5. Higher STRATEGY scores represent prospector firms, while lower

STRATEGY scores represent defender firms.

3.2 Measures of Tax Avoidance The tax avoidance literature has developed several proxies of tax avoidance.16 To

proxy for firms’ tax avoidance activities, we estimate firms’ book and cash effective tax

rates. We use two different proxies of tax avoidance because the proxies, although

correlated, capture different types of tax avoidance.

Our first measure of tax avoidance is the book effective tax rate (BOOK_ETR).

Following Dyreng et al. (2009), we define BOOK_ETR as total tax expense divided by

pre-tax book income (adjusted for special items).17 The ETR is a commonly used

measure of a firm’s tax burden (e.g. Phillips 2003; Rego 2003; Rego and Wilson 2009;

Robinson et al. 2010). It is designed to capture avoidance activities that directly affect net

income via the tax expense.18 Prior literature suggests that a lower ETR reflects higher

incidences of tax avoidance (e.g., Gupta and Newberry 1992; Rego 2003).

Our second measure of tax avoidance is the cash effective tax rate (CASH_ETR).

Following Dyreng et al. (2008, 2009), we define CASH_ETR as cash taxes paid divided

by pre-tax book income (adjusted for special items). The cash ETR captures all tax

avoidance activities that reduce cash taxes paid to the authorities. Therefore, the cash

ETR reflects avoidance activities that defer income taxes to later periods (i.e., activities

that create temporary differences and do not affect net income), as well as those that

avoid tax entirely (i.e., activities that create permanent differences and affect net income).

This measure’s numerator excludes changes in the tax contingency reserve and the

deferred tax asset valuation allowance, which affect current and deferred tax expense,

16 Hanlon and Heitzman (2010) identify 12 proxies of tax avoidance. 17 Dyreng et al. (2008, 2009) adjust pretax income for special items because these items can be quite large in magnitude and introduce volatility in one-year ETR measures. 18 The book ETR has several limitations. In addition to tax avoidance activities, the book ETR also reflects non-avoidance activities such as changes in the valuation allowance and changes in tax reserves. Furthermore, the numerator is total tax expense, which represents the sum of current and deferred taxes. Deferred taxes represent future tax payments or refunds that are associated with temporary book-tax differences. Therefore, tax avoidance activities that generate temporary differences are not reflected in ETR (Hanlon and Heitzman 2010).

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respectfully. Cash payments relating to prior years, however, are included in CASH_ETR.

Dyreng et al. (2008) suggest that lower values of CASH_ETR represent higher levels of

tax avoidance.

Several measurement issues arise when using ETRs to measure tax avoidance.

One particular measurement issue regarding ETRs concerns firms with negative pretax

income or tax refunds (i.e., negative tax expense). Although we retain loss firms and

firms with negative tax expense in our sample, their ETRs are distorted and, thus,

difficult to interpret.19 To address this problem, we follow Gupta and Newberry (1997)

and set the ETR: (1) to zero for firms with negative tax expense, and (2) to 100 percent

for firms with positive tax expense and negative (or zero) pretax income.20 Another

measurement issue concerns the effect of relatively small values in the denominator of

the ETR, thus giving rise to unreasonably large ETRs. To address this problem, we

follow Gupta and Newberry (1997) and constrain ETRs to lie between zero and 100

percent.

3.3 Empirical Model To examine the association between tax avoidance and organizational strategy,

we estimate the following model using ordinary least squares (OLS):

, , , , ,

, , & , , ,

, , ,

Where:

ETR = BOOK_ETR or CASH_ ETR as defined above;

19 For example, the ETR of a firm with negative tax expense and negative pretax income will be positive, even though the firm paid no taxes. Similarly, the ETR of a firm with positive tax expense and negative pretax income will be negative, even though the firm paid taxes. 20 There is no clear solution for dealing with the measurement issues created by the inclusion of these firms. Therefore, we perform a sensitivity analysis (discussed later) where we exclude loss firms. The results are qualitatively similar.

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STRATEGY = Continuous strategy score, ranging from 5 (defender-type

firm) to 25 (prospector-type firm);

SIZE = natural log of total assets (Compustat AT) for year t;

ROA = the ratio of pre-tax income (Compustat PI) for year t to total assets (Compustat AT) at the beginning of the year;

DEBT

= Long term debt (Compustat DLCC) for year t scaled by total assets (Compustat AT) at the beginning of the year;

FOREIGN = Indicator variable equal to 1 if a firm had positive pretax foreign income (Compustat PIFO) in year t, and 0 otherwise;

INTAN = Intangibles (Compustat INTAN) for year t scaled by total assets (Compustat AT) at the beginning of the year;

R&D = Total research and development expense (Compustat XRD) in year t, scaled by beginning of the year total assets (Compustat AT);

PPEINTENISTY = Gross property, plant and equiptment (Compustat PPEGT) in year t, scaled by beginning of the year total assets (Compustat AT) in year t;

NOL = indicator variable equal to one if there was a tax-loss carryforward (Compustat TLCF) as of the end of year t, zero otherwise;

We estimate equation (1) for each measure of tax avoidance (BOOK_ETR and

CASH_ETR). Firms that engage in more tax avoidance will have lower book ETRs

and/or cash ETRs. A negative coefficient on STRATEGY would suggest that prospector

firms engage in more tax avoidance than defender firms, while a positive coefficient

would suggest than defender firms engage in more tax avoidance than prospectors.

In addition to our variable of interest, we also include several control variables

drawn from the effective tax rate literature. We include a variable for firm size (SIZE)

because prior literature documents that it is an important determinant of ETRs (Gupta and

Newberry 1997; Mills et al. 1998; Rego 2003). Prior research finds mixed results

between size and ETRs, and thus, we do not predict a sign for , the coefficient on SIZE.

15

We include a control for the presence of foreign operations (FOREIGN) because

multinational firms may have more opportunities to avoid income taxes than domestic

firms (Mills et al. 1998; Rego 2003). Multinational firms also face taxation on profits in

multiple jurisdictions or may have foreign tax credit limitations (Markle and Shackelford

2009). Therefore, because prior research finds mixed results between the presence of

foreign operations and ETRs, we do not predict a sign for , the coefficient on

FOREIGN. We include a firms’ financial leverage (DEBT) as a control for the

complexity of firms’ financial transactions because firms have the ability to minimize

taxes through financing transactions (Mills et al. 1998). Yet, firms with higher leverage

may have less need for other non-debt tax shields, and thus engage in less tax planning to

reduce taxes (Graham and Tucker, 1996). Because prior research finds mixed results

between debt and the ETR, we do not predict a sign for , the coefficient on DEBT. In

addition, we also control for firm profitability (ROA) and the presence of a net operating

loss (NOL) to proxy for a firm’s need to avoid taxes (Rego 2003; Wilson 2009).

Consistent with Chen et al. (2010), we control for firms’ levels of intangible

assets (INTAN) due to the differing book and tax treatments of intangible assets.

However, opportunities to shift income could also be represented by INTAN (Grubert and

Slemrod, 1998; Hanlon et al. 2007). Thus, we do not predict a sign for , the coefficient

on INTAN. Finally, we include industry and year fixed effects because prior research

documents that tax avoidance varies by industry and year (Rego, 2003). All variables are

defined in Appendix C.

Prior research has documented an association between R&D, capital intensity and

tax avoidance (e.g. Mills et al. 1998), and therefore, we add two additional controls to

equation (1), the ratio of property, plant, and equipment to total assets (PPEINTENSITY)

and total research and development expense divided by beginning of the year total assets

(R&D). Although we use these two variables to construct our STRATEGY measure, we

include them as control variables in the main model to alleviate the concern that our

results may be driven by firms with high R&D expense or firms with significant property,

plant and equipment (PPE).

Last, in estimating equation (1), endogeneity is unlikely to be of critical concern

in our setting, as it is unlikely that a firm’s level of tax avoidance prompts the firm to

16

follow one strategy over the other. However, assuming that a firm’s strategy is not

entirely preordained by its operating environment and is partially shaped by management,

there is an issue of why some firms follow one strategy over the other.21 This self

selection bias introduces a potential omitted correlated variable problem as some of the

determinants may affect a firm’s level of tax avoidance. To address this problem, we

include a comprehensive list of controls in our empirical model.

4. Sample Selection and Descriptive Statistics

4.1 Sample

Table 1 outlines our sample selection process. The sample selection begins with

all Compustat firm-years beginning in 1998 and extends to 2008. We require five years

of data to compute STRATEGY.

To justify the use of the Miles and Snow typology (1978), which is most

applicable at the business unit level, we exclude firms that earned less than 70 percent of

their sales from a single industry. This criterion was originally suggested by Rumelt

(1974), and helps to establish comparability across firms. To identify firms with less than

70 percent of their sales in a single industry, we use Compustat Segment data to identify

the various industries in which the firm operates. We then total segment sales by industry,

and divide by the firm’s total sales. As indicated in Table 1, these criteria result in the

exclusion of 54,821 firm-year observations.

Next, we delete 14,092 firm-year observations associated with a subsidiary or

foreign incorporated unit, as these firms face different reporting incentives or regulatory

scrutiny. Consistent with prior literature, we also delete 13,932 firm-year observations

associated with firms in financial services and regulated industries (2-digit SIC codes 49,

60-69). All data used to construct the STRATEGY score requires a five-year rolling

average, therefore we delete 48,505 firm-year observations with insufficient data to

21 Management’s ability to determine a firm’s business-level strategy appears to be more of a concern in the early stages of the firm’s life cycle. Miles and Snow (1978) contend that the decisions made by management during one adaptive cycle constrain their choice in the next cycle. Furthermore, once a particular business-level strategy is adopted, it is often difficult to change (Hambrick 1983). Therefore, computing strategy over the current and prior four years should also address the concern regarding managerial discretion over business-level strategy

17

construct the 5-year rolling average. We delete 9,896 firm years with insufficient data,

leaving 24,952 observations to construct the STRATEGY score. Next, we exclude 5,153

firm-year observations with insufficient data to compute CASH_ETR or BOOK_ETR.

Finally, we exclude 202 firm-year observations with insufficient data to construct control

variables. Our final sample consists of 19,597 firm-year observations representing 4,184

unique firms over the period 1998 to 2008.

Because Miles and Snow (1978) suggests that competitive strategy can be

classified as a continuum between two different strategic orientations, with prospectors at

one end and defenders at the other end, we define defenders as those firms having a

STRATEGY score ranging from 5 to 10 and prospectors as those firms having a

STRATEGY score ranging from 20 to 25. We create an indicator variable, DEFEND,

which equals one if the firm is classified as a “defender,” and zero otherwise. We also

create an indicator variable, PROSPECT, which equals one if the firms is classified as a

“prospector,” and zero otherwise. Firms having STRATEGY scores 11 through 19 are

classified as analyzers. Under this classification scheme, 1,546 firm-years are classified

as “defenders,” 1,636 firm-years are classified as “prospectors,” and 16,415 firm-years as

“analyzers.”

[Insert Table 1 here]

Table 2, Panel A presents the full sample descriptive statistics for the variables

used to construct STRATEGY and Panel B of Table 2 presents statistics comparing

defenders to prospectors. As expected, the comparisons reported in Panel B indicate that

defenders have lower advertising expense, research and development expense, market-to-

book ratios and fewer employees to sales, but have greater fixed assets, Table 2, Panel C

reports the descriptive statistics for variables used in our regression analysis. Focusing on

panel C, the mean (median) BOOK_ETR for the full sample is 28.43 (29.14) percent,

while the mean (median) CASH_ETR of 31.73 (21.25) percent. Our median cash ETR is

lower than the median book ETR, which is consistent with prior research (e.g., Dyreng et

al. 2009; Robinson et al. 2010); however our mean cash ETR is higher than the mean

book ETR. This result appears to be driven by the inclusion of loss firms and firms with

negative tax expense. When we restrict the sample to strictly profitable firms (results not

18

tabulated), the mean book ETR (32.57 percent) is higher than the mean cash ETR (26.82

percent), which is consistent with prior research.

Table 2, Panel D presents the descriptive statistics for all variables included in

equation (1) for both prospectors and defenders. The mean (median) BOOK_ETR for

defenders is 27.99 (29.67) percent, which is higher than the mean (median) BOOK_ETR

for prospectors, which is 22.28 (0.00) percent, and thus is consistent with prospector

firms engaging in more tax avoidance. Defenders have a mean (median) CASH_ETR of

31.00 (20.51) percent, while prospectors have a mean (median) CASH_ETR of 26.24

(1.94) percent. This is consistent with prospectors engaging in more tax avoidance than

defenders when the cash effective tax rate is used as a proxy for tax avoidance. The 0.00

percent median for BOOK_ETR and the 1.94 percent median for CASH_ETR for

prospectors appears to be due to the presence of loss firms, and the method we use to

bound our ETRs between 0 and 1. When we restrict the sample to strictly profitable firms

(results not tabulated), the median book and cash effective tax rates for prospectors are

high, specifically, 34.85 percent for book, and 26.64 percent for cash. These medians are

still significantly lower than median book ETR (35.16 percent) and the median cash ETR

(27.25 percent) for defenders.

Table 2, Panel D also shows that defenders have more debt relative to

prospectors. Specifically, defenders have a mean (median) DEBT value of 0.2387

(0.1506), as compared with 0.1872 (0.0163) for prospectors, which is consistent with

defenders having more fixed assets in place, and the ability to utilize these assets as

collateral. We also find that defender firms are more profitable (ROA), have fewer

intangible assets (INTAN) and tend to be larger in size (SIZE), relative to prospector

firms.


In Table 3, Panel A, we present the correlations between the five variables used

to measure STRATEGY. MARKET5 is positively and significantly correlated with

RD/SALES5 and EMP/SALES5, which is consistent with the argument that firms that

incur costs to inform, educate and motivate their expanding customer base (i.e.,

prospectors), tend to spend more on research and development and tend to be less

19

efficient. The significant and negative correlation between PPEINTENSITY5 and

RD/SALES5 and MtoB is consistent with the argument that firms that place greater

emphasis on production assets (i.e., defenders) spend less money on research and

development, and have fewer investment opportunities. Firms with lower ratios of

employee to sales are assumed to be more efficient. The significant and positive relation

between RD/SALES5 and EMP/SALES suggests that the most efficient firms (i.e.,

defenders) spend less on research and development activities, as these activities are

viewed as an inefficient use of resources. The significant and positive association

between MtoB5 and EMP/SALES5 suggests that more efficient firms have more

investment opportunities, which is inconsistent with our expectations; however the

correlation is not very high (0.01). MtoB5 is also significantly and positively correlated

with RD/SALES5, which is consistent with our expectations. The remaining correlations

are not statistically significant.

Table 3, Panel B presents the correlations among our regression variables. As

expected, BOOK_ETR is significantly and positively correlated with CASH_ETR.

STRATEGY is significantly and negatively correlated with both BOOK_ETR and

CASH_ETR, suggesting that firms classified as prospectors (i.e., those firms with the

highest STRATEGY scores) tend to have lower effective rate rates relative to firms

classified as defenders (i.e., those firms with the lowest STRATEGY scores). Additionally,

based on the Pearson correlation coefficients, lower book ETRs are also reported for

smaller firms (SIZE), firms with fewer intangible assets (INTAN), firms with greater

leverage (DEBT), firms without foreign operations (FOREIGN), firms with higher

research and development expenditures (R&D) and firms with greater capital intensity

(PPEINTENSITY). In comparison, lower cash ETRs are reported for smaller firms

(SIZE), firms with greater leverage (DEBT), and firms with higher research and

development expenditures (R&D).


20

5. Multivariate Results 5.1 Main Results

Table 4 presents the results of our multivariate analyses.22 In column (A), we

report the results of estimating equation (1) using BOOK_ETR as the dependent variable

using the full sample. We find that the coefficient on STRATEGY is -0.0032 (p-value =

<0.001), which suggests prospector firms avoid more taxes than defender firms. In

column (B), we report the results of re-estimating equation (1) using CASH_ETR as the

dependent variable. Again, we find that prospector firms avoid more taxes than

defenders. Specifically, the coefficient on STRATEGY is -0.0042 (p-value = <0.001).

The economic significance between firms’ organizational strategy and the book

and cash effective tax rates is based on the interpretation of the coefficient on

STRATEGY. The coefficient on a continuous independent variable reflects the effect of a

one-unit increase in that variable on the dependent variable. For example, for every one

point increase in STRATEGY, there is a .32 percent decrease in the book effective tax

rate. Therefore, moving from a score of 5 (defender-type firm) to a score of 25

(prospector-type firm) is associated with a 6.4 percent decrease in the book effective tax

rate. Similarly, for every one point increase in STRATEGY, there is a .42 percent

decrease in the cash effective tax rate. Therefore, moving from a score of 5 (defender-

type firm) to a score of 25 (prospector-type firm) is associated with a 8.4 percent

decrease in the cash effective tax rate. This evidence is consistent with the argument that

following a prospector strategy versus a defender strategy is not only statistically

associated with lower effective tax rates, but also results in economically significant tax

savings.

Overall, the above analyses indicate that firms that follow a defender strategy

exhibit a lower level of tax avoidance compared with firms that follow a prospector

strategy. These results suggest that even those tax avoidance activities generate tax

22 In both Tables 5 and 6, the p-values are two-tailed and based on standard errors that are clustered by firm. Petersen (2009) suggests clustering by firm to address potential serial dependence in the data. See Petersen (2009) for a more detailed discussion.

21

savings for these firms, the costs and uncertainty associated with increased avoidance

outweighs the benefit for defender firms.


In addition to these primary results, several of the estimated coefficients for the

control variables are statistically significant. Focusing on column (A), when the

BOOK_ETR is used as a measure of tax avoidance, the coefficients on SIZE, ROA,

INTAN and FOREIGN are positive and significant, while the coefficients on DEBT and

NOL are negative and significant. In column (B), when the CASH_ETR is used as a

measure of tax avoidance, only the coefficients on SIZE and ROA are positive and

significant, while only the coefficient on FOREIGN and negative and significant. The

coefficients on the remaining variables are not significant. The coefficients on R&D,

and PPEINTENSITY are negative, but insignificant, when both the BOOK_ETR and

CASH_ETR are as measures of tax avoidance, suggesting that our results are not being

driven by firms with higher research and development expenditures (e.g., prospectors)

or firms with more fixed assets in place (e.g., defenders).

To examine the robustness of the full sample results, and to ensure our results are

not being driven by loss firms, we restrict the sample to include only firms with strictly

positive income during the period from 1998 through 2008. We exclude loss firms

because these firms may have different incentives to engage in tax avoidance. We choose

to exclude loss firms, as opposed to loss firm-years, because loss years not only affect the

current year, but past and future years because of NOL carryfowards and carrybacks.

Therefore, only dropping the loss years could lead to the conclusion that a firm’s tax

burden was less than it was in reality. Restricting the sample to strictly profitable firms

reduces the sample to 6,334 firm-year observations.

Table 4, Columns (C) and (D) presents the results for the reduced sample. The

results are qualitatively similar to the full sample results, and thus lead to similar

inferences. Again, we find that prospectors engage in more tax avoidance than

defenders.

22

5.2. The Consistency of Firm Strategy As an additional test of our STRATEGY measure, we consider whether the

particular strategy adopted by a firm is consistent over time, as suggested by Miles and

Snow (1978). Therefore, we expect that prospector, defender or analyzer firms should

have relatively stable STRATEGY scores from year-to-year.23 Specifically, we expect

firms classified as defenders to exhibit low STRATEGY scores and firms classified as

prospectors to exhibit high STRATEGY scores over our sample period. To test the

consistency of firms’ strategies, we compute first differences for each firm-year

STRATEGY score to determine how many total times a firm changed its STRATEGY

score. We find that 24 percent of firms never changed their score from year-to-year while

36 percent of firms changed their score by one value (e.g. changing from 25 to 24). Only

5 percent of firms changed their score by more than 3 values. We thus conclude that our

measure of strategy is relatively consistent throughout time.

6. Sensitivity Analysis 6.1. Defender/Prospector Indicator Variables

In this section, we discuss the results from estimating equation (1) after replacing

our continuous STRATEGY measure in model (1) with indicator variables for defenders

and prospectors. To examine whether defenders exhibit more or less tax avoidance than

prospectors, we perform an F-test to compare the difference between the coefficients on

DEFEND and PROSPECT. In untabulated results, we find that prospectors avoid more

taxes than defenders when the BOOK_ETR is used as the dependent variable (F-Statistic

= 9.26; Prob>F = 0.0024) and when the CASH_ETR is used as the dependent variable (F-

Statistic = 4.16; Prob>F = 0.0414). Thus, our main results are robust to this specification.

6.2. Is STRATEGY Capturing Something Different?

In this section, we examine whether our measure of strategy captures something

different and over and above the other explanatory variables in our model. We begin by

examining the correlations between STRATEGY and the other explanatory variables in

23 We would expect that firms classified as “reactors” would not exhibit relatively stable STRATEGY scores. We do not attempt to classify reactors,nor do we make any predictions for these firms.

23

Table 3, Panel B. Although the correlations between STRATEGY and the other

explanatory variables are statistically significant, none of the correlations are greater than

0.05, with the exception of R&D (0.1199) and PPEINTENSITY (-0.1287), which are used

to construct STRATEGY.

Next, we compute the canonical correlations between the variables used to

construct the STRATEGY score and the other explanatory variables in the model.

Examining the correlations between the first set of variables (RD/SALES5, EMP/SALES5,

PPEINTENSITY5, MARKET5, MtoB5) and the second set of variables (SIZE, ROA,

INTAN FOREIGN, DEBT, R&D, PPEINTENSITY NOL), we find that of the resulting five

canonical correlation coefficients, four are statistically significant. The largest significant

coefficient is 61 percent, and the smallest significant coefficient is 2.7 percent. Although

the 61 percent appears high this is offset by a low redundancy index. Specifically, the

maximum shared variation between the two groups of variables is 11 percent, which is

quite low and considered poor by analysis standards. Furthermore, the strongest shared

variance observed (7 percent) is between PPEINTENSITY and the other canonical

variates, which is not surprising because property, plant and equipment is in both groups.

This suggests that the combined strategy variable set captures related, but not

substantially overlapping, information about the firm. We suggest this supports the

notion that our STRATEGY variable represents aspects of the firm that are not represented

by the additional variables in the model that have been documented as affecting firms’

levels of tax avoidance.

6.3. Is Size Driving Our Results?

We conduct and additional sensitivity tests to confirm that our STRATEGY variable is

not a proxy for firm size. We rank firms on our variable SIZE by forming quintiles

within each 2-digit SIC industry-year. We create two indicator variables, Large, which

equals one if a firm is in the top SIZE quintile and 0 otherwise, and Small, which equals

one if the firm is in the bottom quintile. We then interact each of these indicator variables

with our STRATEGY variable, and re-estimate equation (1) by including Large, Small,

LargexSTRATEGY and SmallxSTRATEGY. If STRATEGY is not proxying for size, then

the coefficients on the interaction terms should not be significant. In untabulated results,

24

we find that of the two interactions only the Small*STRATEGY coefficient is negative

and significant which is opposite of that expected if STRATEGY is a proxy for firm size.

Given that our prospector firms are smaller on average than defender firms in the sample

the significant interaction is not unexpected. The coefficient on Large is negative and

significant indicating that large firms have lower effective tax rates while the

STRATEGY variable continues to be negative and significant in all estimates.

7. Conclusion

We examine whether a firm’s tax planning activities (i.e., tax avoidance) are

influenced by its business strategy. Our research question is motivated by the results of

prior literature, which finds that there is substantial unexplained variation in firms’

propensities to avoid income taxes.

To address our research question, we create a measure (STRATEGY) designed to

provide a score that reflects the most likely business strategy adopted by sample firms.

We use this score to categorize firms as prospectors, defenders or analyzers. Using data

from 1998 to 2008, we examine the association between firms’ business strategy and two

measures of tax avoidance, the book effective tax rate and the cash effective tax rate.

We find that defender firms engage in less tax avoidance than prospector firms, as

demonstrated by higher book and cash effective tax rates. These results suggest that

even though tax avoidance activities generate tax savings for these firms, the costs and

uncertainty associated with increased avoidance outweighs the benefit.

Our study contributes to both the tax avoidance literature in accounting and the

organizational theory literature in management in the following ways. First, we develop a

replicable proxy of a firm’s business strategy, which is constructed using publicly

available data, and is more comprehensive than existing proxies in the accounting

management literatures. Second, we contribute to the management literature by providing

evidence that firms’ tax planning activities are associated with their organizational

strategy. Thus, our results empirically support Porter’s (1996) argument that firms should

reinforce their business-level strategy with support activities in order sustain a

competitive advantage. Third, our results contribute to the stream of research that

investigates the variation in firms’ tax avoidance activities, by providing evidence that a

25

firm’s business strategy influences its level of tax avoidance. Furthermore, our results

contribute toward a better understanding of the impact of different competitive strategies

on firms’ tax planning and reporting practices.

26

Definition

Competitive Balances flexibility andAdvantage stability

Planning

Growth Cautious and incremental Growth occurs in spurtsgrowth and advances in through product and market productivity development

Research andDevelopment

Marketing

Capital Intensity

ManagementCharacteristics

This table was constructed based on a review of the three viable business strategies identified by Miles & Snow (1978).

Miles and Snow (1978) identify fourth strategy, Reactors, which are generally presented as a "residual" type

lacking any systematic strategy, design, or structure.

Analyzers

Appendix AStrategic Type Characteristics

Defenders Prospectors

A firm that has a very

narrow product-market

domain, a focus on

cost efficiency and a

stable organizational

structure.

A firm that has a very

broad product-market

domain, a focus on

innovation and a flexible

organizational structure.

A firm that has the attributes

of both a prospector and

a defender. The firm operates

in two types of product

market domains, one is

relatively stable and theis changing.

Cost efficiency and stability Innovation and flexibility

Marketing and R&Dexperts are the most powerful members.

Finance and productionexperts are the mostpowerful members.

Strong focus on marketing

Tenure of executives is not lengthy and managers may be hired from outsideor promoted from within.

Marketing experts, followed by production,and planning staff mostimportant members.Tenure of executives is

lengthy and managers arepromoted from within.

Strong emphasis is onfinancial and productionfunctions, and less onmarketing

opportunities

Strong focus on marketingin innovative sector

Focus on production assets

Technology is embeddedin people

Moderate focus on production assets

innovations from prospectors

Steady growth through bothmarket penetration and product and market development

Minimal R&D, which isusually related to currentproducts

Extensive R&D to identifynew products and market

Require certainty in future

outcomes, and often engage

in detailed planning before

undertaking new opportunities

Minimal R&D becausethey adopt most promising

Require some amount of

certainty in future outcomes

Adapt to uncertainty, and

often engage in new

opportunities before detailed

planning is complete

27

Variable

research and development expense (Compustat XRD) to total sales

of 2, etc. The scores are summed over the five measures per firm-year, such that the maximum score a firmcould receive is 25 (prospector), and the minimum score a firm could receive is 5 (defender).

The STRATEGY score is constructed based on the following five measures, drawn from Ittner et al. (1997)and Hambrick (1983). Each of the variables are computed using a rolling average of the respective yearly ratios over the previous five years. We then rank each of the five variables by forming quintiles based on 2-digitSIC code and year. For each of the first four variables, firms in the top quintile receive a score of 5,those in the second highest quintile receive a score of 4, etc. The scoring for PPEINTENSITY5 is inverted, so that observations in the highest quintile receive a score of 1, those in the next highest quintile receive a score

(Compustat AT)

MARKET5

PPEINTENSITY5

MtoB5

EMP/SALES5

RD/SALES5

value of equity (Compustat abs(PRCC_F)*CSHO) to book value of equity (Compustat CEQ)

Five year rolling average (year t-5 through year t-1) of the yearly ratio of total advertising expense (Compustat XAD) to total sales (Compustat SALE)

Five year rolling average (year t-5 through year t-1) of the yearly ratio of gross property, plant and equipment (Compustat PPEGT) to total assets

Definition

Appendix BSTRATEGY Score Composition and Example

Five year rolling average (year t-5 through year t-1) of the yearly ratio of total

(Compustat SALE)

Five year rolling average (year t-5 through year t-1) of yearly ratio of the total number of employees (Compustat EMP) to total sales (Compustat SALE)

Five year rolling average (year t-5 through year t-1) of the ratio of market

28

Variable Definition

Appendix C Regression Variable Definitions

CASH_ETR

STRATEGY

SIZE

ROA

PPEINTENSITY

NOL

FOREIGN

INTAN

R&D

BOOK_ETR

DEBT

Strategy score (for composition, see Appendix B)

Natural log of total assets (Compustat AT) in year t

Indicator variable equal to 1 if a firm had positive pretax foreign income(Compustat PIFO) in year t , and 0 otherwise

Total intangibles (Compustat INTAN) in year t scaled by beginning of the year total assets (Compustat AT)

Total tax expense (Compustat TXT) in year t divided by pre-tax book income (Compustat PI) in year t adjusted for special items (Compustat SPI)

Total cash taxes paid (Compustat TXPD) divided by pre-tax book income Compustat PI) adjusted for special items (Compustat item SPI). If total cash taxes paid are missing, total current taxes are used (Compustat item TXC)

Pre-tax income (Compustat PI) in year t divided by beginning of the year total assets (Compustat AT)

Total long term debt (Compustat DLCC) for year t divided by beginning of the year total assets (Compustat AT)

Indicator variable equal to one if the firm has a tax-loss carry forward(Compustat TLCF) during the year t

Total research and development expense (Compustat XRD) in year t , scaled by beginning of the year total assets (Compustat AT)

Gross property, plant and equipment (Compustat PPEGT) in year t , divided bytotal assets (Compustat AT) in year t

29

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149.

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Criteria

166,198

(54,821)

(14,092)

(13,932)

(48,505)

(9,896)

24,952

(5,153)

(202)

19,597

Panel A: Sample Selection ProcedureNumber of

Firm-Year Observations

Observations with insufficient data to measure BOOK_ETR or CASH_ETR

Observations with insufficient data for measure control variables

Final Sample for Fiscal Years 1998-2008

Observations with insufficient data to measure variables used in the STRATEGY score

Total Observations for STRATEGY Score

Less:

Observations associated with a foreign subsidiary or foreign incorporated units

Observations in regulated industries (SIC Codes 49, 60-69)

Firms without required five year rolling average data for STRATEGY variable

Table 1Sample Selection

All Compustat Firms for Fiscal Years 1993-2008

Less:

Firms that earn less than 70 percent of sales from a single industry

33

Variable N Mean Std. Dev. 25th Pctl Median 75th Pctl

MARKET5 19597 0.0208 0.6734 0.0000 0.0000 0.0080

PPEINTENSITY5 19597 0.5418 0.7577 0.2223 0.4169 0.7146RD/SALES5 19597 0.3821 1.7035 0.0000 0.0073 0.1211EMP/SALES5 19597 0.0178 0.1650 0.0040 0.0062 0.0099

MtoB5 19597 3.0331 3.8111 1.2932 2.2092 3.8698

*See Appendix B for variable definitions

Variable Mean Std. Dev. 25th Pctl Median 75th Pctl Mean Std. Dev. 25th Pctl Median 75th PctlMARKET5 a,b 0.0010 0.0049 0.0000 0.0000 0.0000 0.1151 2.3126 0.0000 0.0048 0.0310PPEINTENSITY5 a,b 0.8609 1.0221 0.4876 0.7376 1.0522 0.3429 0.3155 0.1375 0.2362 0.4526RD/SALES5 a,b 0.0164 0.0468 0.0000 0.0000 0.0145 1.8055 3.7636 0.0011 0.2105 1.1698EMP/SALES5 a,b 0.0066 0.0119 0.0027 0.0047 0.0070 0.0565 0.3203 0.0064 0.0109 0.0269MtoB5 a,b 0.9458 2.2204 0.6599 1.1651 1.7647 6.5022 5.1875 3.0743 5.1375 8.3890

a (b) indicates that the differences in the means (medians) between defenders and prospectors is significant at the .10 level using a t-test (Wilcoxon test) of differences in means (medians)


Panel A: STRATEGY Variables

Panel B: STRATEGY Variables by Prospectors and Defenders

Descriptive StatisticsTable 2

Defenders (N= 1546 ) Prospectors (N= 1636)

34

Variable N Mean Std. Dev. 25th Pctl Median 75th Pctl

BOOK_ETR 19597 0.2843 0.3014 0.0000 0.2914 0.3829

CASH_ETR 19597 0.3173 0.3565 0.0000 0.2125 0.4189STRATEGY 19597 15.0254 3.2443 13.0000 15.0000 17.0000SIZE 19597 5.0456 2.1411 3.5026 5.0655 6.5325ROA 19597 -0.3446 27.6541 -0.0886 0.0420 0.1287INTAN 19597 0.1549 0.7092 0.0000 0.0435 0.1866DEBT 19597 0.1989 0.4943 0.0000 0.0771 0.2721FOREIGN 19597 0.2447 0.4299 0.0000 0.0000 0.0000R&D 19597 0.0854 0.3335 0.0000 0.0057 0.0976PPEINTENSITY 19597 0.5735 1.1633 0.2149 0.4250 0.7454NOL 19597 0.3969 0.4893 0.0000 0.0000 1.0000

*See Appendix C for variable definitions

Variable Mean Std. Dev. 25th Pctl Median 75th Pctl Mean Std. Dev. 25th Pctl Median 75th Pctl

BOOK_ETR a,b 0.2799 0.2879 0.0000 0.2967 0.3873 0.2228 0.3272 0.0000 0.0000 0.3673

CASH_ ETR a,b 0.3100 0.3465 0.0028 0.2051 0.4151 0.2624 0.3753 0.0000 0.0194 0.3700

SIZE a,b 4.8502 1.9874 3.4296 4.8733 6.1520 4.2859 2.0325 2.8244 4.0998 5.7811

ROA a,b -0.0368 1.0578 -0.0347 0.0475 0.1132 -0.3881 2.0556 -0.4381 -0.0959 0.0878

INTAN b 0.1419 1.2839 0.0000 0.0167 0.1255 0.1947 0.8278 0.0000 0.0431 0.2137DEBT a,b 0.2387 0.3769 0.0105 0.1506 0.3312 0.1872 0.4346 0.0000 0.0163 0.2493

FOREIGN a,b 0.1986 0.3991 0.0000 0.0000 0.0000 0.1559 0.3628 0.0000 0.0000 0.0000

R&D a,b 0.0261 0.1244 0.0000 0.0000 0.0146 0.1753 0.3135 0.0000 0.0884 0.2322

PPEINTENSITY a,b 0.9115 2.3431 0.4395 0.7163 1.0742 0.3968 0.4051 0.1433 0.2671 0.5244

NOL a,b 0.3752 0.4843 0.0000 0.0000 1.0000 0.4401 0.4966 0.0000 0.0000 1.0000

Defenders (N= 1546) Prospectors (N=1636)

Panel C: Regression Variables

a (b) indicates that the differences in the means (medians) between defenders and prospectors is significant at the .10 level using a t-test (Wilcoxon test) of differences in means (medians)


Panel D: Regression Variables by Prospectors and Defenders

Table 2 (cont'd)Descriptive Statistics

Panel A: STRATEGY Variables

MARKET5 PPEINT5 RD/SALES5 EMP/SALES5 MtoB5

MARKET5 1.0000

PPEINTENSITY5 -0.0048 1.0000

RD/SALES5 0.0350 -0.0517 1.0000

EMP/SALES5 0.4711 -0.0061 0.3598 1.0000

MtoB5 0.0098 -0.0284 0.0457 0.0144 1.0000


Panel B: Regression Variables BOOK_ETR CASH_ETR STRATEGY SIZE ROA INTAN DEBT FOREIGN R&D PPEINT. NOL

BOOK_ETR 1.0000CASH_ETR 0.3752 1.0000STRATEGY -0.0414 -0.0437 1.0000SIZE 0.1396 0.0364 -0.0303 1.0000ROA 0.0108 0.0078 -0.0084 0.0292 1.0000INTAN 0.0208 0.0066 0.0182 0.0393 -0.0073 1.0000DEBT -0.0267 -0.0168 -0.0350 0.0315 -0.0114 0.2085 1.0000FOREIGN 0.0677 0.0078 -0.0016 0.3608 0.0088 0.0136 -0.0441 1.0000R&D -0.0777 -0.0499 0.1199 -0.1777 -0.0467 0.0343 0.0188 -0.0335 1.0000PPEINTENSITY -0.0272 -0.0089 -0.1287 -0.0763 -0.0083 -0.0482 0.1398 -0.0578 -0.0045 1.0000NOL -0.0564 -0.0220 0.0410 -0.0738 -0.0109 0.0307 0.0271 0.0470 0.0391 -0.0185 1.0000

Note: Bold indicates that the correlation is significant at p<0.05.


Table 3Correlations

36

(A) (D)

Variables p-value p-value p-value p-value

Intercept <0.001 <0.001 <0.001 <0.001

STRATEGY 0.001 <0.001 0.006 0.009

SIZE <0.001 0.003 0.024 0.116

ROA 0.001 0.001 0.129 0.740

INTAN 0.051 0.353 0.727 0.260

DEBT 0.001 0.117 0.179 0.003

FOREIGN 0.022 0.054 <0.001 0.355

R&D 0.135 0.109 <0.001 <0.001

PPEINTENSITY 0.435 0.855 <0.001 0.006

NOL <0.001 0.288 0.007 <0.001

Adjusted R-SquaredN (firm-years)

*p-values are two-tailed and based on standard errors clustered by firm (Petersen 2009).

*Coefficient estimates for fiscal year and industry dummies are not reported for brevity


0.025219,597

0.10576,334

CASH_ETRBOOK_ETR

-0.0032 -0.0042

Coeff. Est. Coeff. Est.

0.3761 0.3810

19,597

0.0156 0.0063

0.0000 0.0001

0.0082 0.0043

-0.0171 -0.0092

-0.0273 -0.0085

-0.0337 -0.0333

0.0013-0.0032

BOOK_ETR

Strictly Profitable Firms Only

0.0385

0.0155 -0.0160

CASH_ETR

-0.0024 -0.0028

0.0040 -0.0033

Coeff. Est. Coeff. Est.

0.3523 0.4065

-0.0193

-0.0167 -0.0543

-0.0175 0.0068

0.0921 -0.0073

0.0041 -0.0161

6,334

-0.0162 -0.0349

(C)(B)Full Sample

Table 4Regression Results

0.1022

-0.3639 -0.3482

-0.0315

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