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Reputation and Corporate Tax Planning: A Moral Licensing View

Prepared for the 2017 Lone Star Accounting Research Conference

Yu Bai*

Gerald J. Lobo*

Yuping Zhao*

*University of Houston

Abstract: This study examines how a firm’s overall reputation status (reputation hereafter) affects its tax planning. Drawing on the moral licensing theory, we posit that managers’ and other stakeholders’ perception of a firm’s questionable behavior may be affected by the firm’s reputation and that a good reputation may help a firm to justify, or “license”, such behavior. This licensing effect may reduce a firm’s concerns about its tax avoidance behavior and incentivize reputable firms to engage in more tax reduction activities that have ambiguities in transgression. The empirical findings support our conjecture. Specifically, we test the association between a firm’s established reputation and its tax planning using multiple tax avoidance measures, which capture different tax reduction technologies that either fall into the gray area or violate tax and financial reporting rules. Relative to less reputable firms, more reputable firms on average avoid more taxes by using tax reduction technologies that have ambiguity in transgression, but are less likely to engage in tax-related activities that are blatant transgressions. We further investigate whether the licensing effect of reputation is more pronouced under the more principles-based or rules-based standards. Our findings suggest that the licensing effect is more pronounced under the more principle-based standards.

Keywords: Tax avoidance, reputation, moral licensing, rules-based standards.

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Reputation and Corporate Tax Planning: A Moral Licensing View

“To superficial minds, the vices of the great seem at all times agreeable. They connect them, not only with the splendour of fortune, but with many superior virtues, which they ascribe to their superiors; with the spirit of freedom and independency, with frankness, generosity, humanity and politeness. The virtues of the inferior ranks of people, on the contrary, their parsimonious frugality, their painful industry, and rigid adherence to rules, seem to them mean and disagreeable. They connect them, both with the meanness of the station to which those qualities commonly belong, and with many great vices, which, they suppose, usually accompany them; such as an abject, cowardly, ill-natured, lying, pilfering disposition.”

Adam Smith, The Theory of Moral Sentiments, p. 388–389.

I. Introduction

Do reputable firms have more concerns for tax avoidance because they have more at stake

to lose? Or do they have fewer concerns for tax avoidance since their good reputations, once

established, can be hard to lose? This study examines these two possibilities by testing the

association between a firm’s reputation status and its tax planning activities. It also investigates

how such association varies with standards.

Recent years have witnessed a growing public interest in naming and shaming corporate

tax avoidance behavior. The behavior of firms that legally minimize taxes has been increasingly

criticized by the media and governments. Since such “tax shaming” may significantly affect the

corporate tax planning environment, better understanding this phenomenon is potentially

important to investors, regulators and other stakeholders. It is however unclear whether, or how

effectively, “tax shaming” deters tax avoidance behavior. For the “tax shaming” to be effective,

corporate tax avoidance should be perceived negatively by stakeholders, who then impose

reputational penalties to firms engaging in tax avoidance activities. Such potential reputational

costs, if large enough, would then raise a firm’s ex-ante concerns and deter the firm from avoiding

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taxes. Nevertheless, how people perceive tax avoidance and what are the non-tax costs of tax

avoidance are still open questions (Hanlon and Heitzman 2010).

Theoretically, the reputational effect arises wherever information asymmetry exists.1 Prior

studies have demonstrated that a firm’s reputation has a significant impact on its business and

reporting strategies, such as financing choices and financial reporting (Diamond 1991; Cao, Myers

and Omer 2012). Therefore, reputation supposedly should play an important role in corporate tax

planning area, where severe information asymmetry widely exists between firms and stakeholders.

As such, researchers recently have started to examine the relationship between reputation and tax

avoidance. Graham, Hanlon, Shevlin and Schroff (2013) surveyed 595 corporate tax executives

and found that 69 percent of survey participants would decline tax strategies that would

“potentially harm to the company reputation”. This evidence supports the notion that reputation

plays an important role in corporate tax planning. In addition, they found that the levels of

reputational concerns for aggressive tax planning are higher for public firms than for private firms.

In contrast, Gallemore, Maydew and Thornock (2014) showed that reputational penalties for tax

sheltering to both the participating firms and their top executives are rather minimal. One

explanation for the findings in Gallemore, et al. (2014), as the authors suggested, is that only the

firms and managers who would not suffer from the reputational loss will participate in tax

sheltering activities, i.e, firms who refrain from tax sheltering activities due to ex ante reputation

concerns are not observable.

1When there exists information asymmetry between firms and stakeholders, high-quality and low-quality goods can coexist in the market. As such, information asymmetry imposes costs to the valuation of a firm. Reputation performs as a potential remedy for information asymmetry. It serves as an implicit contract and is enforced by the firm’s concern about its future performance. Once acquired it can be used again and again to signal the quality of the goods and increase stakeholders’ confidence. For example, Kreps and Wilson (1982) show that a little amount of imperfect information about players’ payoff is sufficient to give rise to the reputational effect.

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Overall, the existing literature suggests that firms have reputational concerns for tax

avoidance, but such concerns may be different among firms. Some firms may even be able to get

away with the reputational penalties for their tax avoidance. However, what drives the cross-

sectional variation in this reputational effect remains relatively unexplored in the literature.

We offer an explanation for this variation by drawing on the moral licensing view (or the

licensing view) in the psychology literature. This view emphasizes the role of perception in moral-

relevant decisions, which is a non-trivial factor in determining corporate tax planning, as

highlighted by Forbes magazine in quoting tax experts who participated in the 2013 SYNERGY

conference, “the clear consensus among all of the participants in our panel was that perception

has become a serious factor driving corporate tax strategy.” In particular, this view suggests that

the established reputation may affect the perception of the current action, especially when this

action is ambiguous in its nature. In this regard, the reputational cost of an action is determined

not only by the current action itself but also by the established reputation. Therefore, we posit that

a firm’s established reputation may affect managers’ and other stakeholders’ perception of

ambiguous corporate actions, and a good reputation may “license” and hence reduce the firm’s

concerns for reputational consequences of tax avoidance.

The purpose of this study is thus to test whether, when, and how such licensing effect arises.

We posit that the licensing effect is determined by the aggressive nature of the tax reduction

technologies a firm adopts and is expected to arise if these technologies have ambiguity in

transgression. Following prior research (e.g. Slemrod and Yitzhaki 2002; Hanlon and Heitzman

2010), we view tax avoidance as representing a continuum of tax planning technologies and

differentiate the ambiguity in transgression of each technology by its legality. We run multivariate

tests using a firm’s established reputation to predict six tax-related variables, which capture

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different tax technologies with various levels of transgressions. The conventional wisdom is that

more reputable firms have more reputation to lose and thus have larger reputational concerns. We

challenge this wisdom by conditioning our prediction based on the legality of tax avoidance.

Specifically, we predict that firms with a better reputation, as measured by three different proxies,

engage in more tax reduction activities that have ambiguity in transgression but are also less likely

to participate in tax-related activities that are blatant transgressions. Our findings support this

prediction.

Moreover, we argue that the licensing effect of reputation on corporate tax avoidance is

two-layered. First, established reputation may facilitate firms to avoid more taxes and increase net

income, we label this effect as the “income effect”. Second, established reputation may influences

a firm’s choice among different tax avoidance technologies, we label this effect as the “substitution

effect”. Specifically, we posit that the existence of bright-line rules in standards may restrict the

role of perception and limit the opportunity set facing managers. Therefore, the licensing effect of

reputation on tax avoidance can be more pronounced when there are fewer bright-line rules in

standards. As such, rule-makers may not only regulate the income effect of moral licensing by

changing the legality of tax planning technologies, but also alter the substitution effect of moral

licensing by imposing bright-line rules in standards.

To build testable hypothesis, we focus on the difference in rules that govern the accrual-

and cash-based income tax reporting in the United States. Our predictions are grounded on the

consensus that the U.S. tax laws are rules-based (Maines, Bartov, Fairfield, Hirst, Iannaconi,

Mallett, Schrand, Skinner and Vincent 2003). In comparison, U.S. GAAP is relatively more

principles-based, to the extent that it offers significant discretions to managers on accounting

treatment for the purpose of “providing comparable, relevant and reliable financial reporting”

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(Schipper 2003; Folsom, Hribar, Mergenthaler, and Peterson 2016).2 We find that the licensing

effect is more pronounced under the more principle-based U.S. GAAP relative to under the more

rules-based tax laws.

This study contributes to the literature on the effects of reputation on tax avoidance

(Graham et al. 2014; Gallemore et al. 2014), which usually adopts a citizenship view and

conjectures that reputational concerns motivate firms with good reputation to engage in less tax

avoidance. We propose the moral licencing view as an alternative framework to understand the

relaionship between reputation and tax avoidance. Under the moral license view, more reputable

firms could exploit their reputation and engage in more tax avoidance, particularly when such

behavior is ambiguous in its nature. It is often presumed that reputation complements regulatory

mechanisms in curtailing corporate misconduct (e.g. Barro and Gordon 1983). However, our

findings suggest that reputation could also motivate socially questionable behavior in gray areas

and cast doubts on significant reputational effects of publically shaming legal corporate tax

reduction behavior. When non-regulatory monitoring is not as effective as presumed, how to

regulate in order to constrain such undesirable effect is an issue for rule-makers.

We also contribute to the literature on principles- versus rules- based standards. In this

literature, empirical research in a U.S. setting has been rare, partially due to data limitations. More

importantly, existing studies mostly focus on litigation costs or effects of regulatory changes (e.g.

Ahmed, Neel and Wang 2013; Donelson, McInnis and Mergenthaler 2012; 2016). No study, to the

2 For example, based on U.S. GAAP, managers accrue for bad debt expense and warranty expense at the time of sale, and exercise discretion on the amount of the accrued expense based on the matching principle. In contrast, U.S. tax rules allow the deduction of bad debt and warranty expense only when uncollectible accounts are actually written off and when the warranty liability is actually honored.

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extent of our knowledge, has examined the relationship between rules-based standards and

reputation. Nevertheless, reputation loss, along with litigation costs and regulation change, have

been identified as three major business risks (Hackenbrack and Nelson 1996). Our study helps to

draw a more complete picture over the desirability of rules-based standards.

The rest of the paper proceeds as follow, Section II reviews prior literature and develops

hypotheses, Section III outlines research design, Section IV discusses empirical results, Section V

presents additional analyses, and Section VI concludes.

II. Background and hypotheses development

2.1 Reputation and tax avoidance

A firm’s reputation can be viewed as the summary measure of its public recognition and

social approval (e.g., Fombrun and Shanley 1990; Pfarrer, Pollock, and Rindova 2010). With its

value defined by stakeholders’ assessment of the firm’s past behaviors, reputation is a unique

intangible asset that allows stakeholders to efficiently differentiate among different firms.

Game theorists model the reputation of a player as the perception others have for the

player’s type based on the player’s past actions (e.g. Kreps and Wilson 1982). Since others use a

player’s past actions to form beliefs about the player’s type and choose reactions to the player’s

current action, the player internalizes this reputational effect and chooses actions accordingly to

maximize the overall payoff. Thus, a player’s action may be shaped by reputational concerns, but

the player’s strategic actions can have a greater long-run payoff than in the absence of reputational

concerns.

In other words, reputation can be both a benefit and a burden. Research on corporate

reputation shows that firms with a better reputation have better performances on average (e.g.,

Fombrun and Shanley 1990; Roberts and Dowling 2002) and that a firm’s behavior is constrained

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by reputational concerns (e.g. Atanasov, Ivanov and Litvak 2012; Cao et al. 2012). Recent research

also shows that a firm’s reputation affects investors’ decision-making, as captured by better market

performance and lower cost of equity capital for firms with good reputation (Cao, Myers, Myers

and Omer 2015; Raithel and Shwaiger 2015). In addition, there is suggestive evidence that a firm’s

reputation may affect its stakeholders’ action against the firm’s opportunistic conduct (Atanasov,

Ivanov and Litvak 2012).

The extant literature on the relationship between reputation and tax avoidance provided

evidence that firms’ reputational concerns may vary and that firms may forgo profitable actions

due to reputational concerns (e.g. Chen, Chen, Cheng and Shevlin 2009; Graham et al. 2013). This

literature mostly draws on the citizenship view in the sociology literature and focuses on the moral

burden of reputation (e.g. Graham et al. 2013; Gallemore et al. 2014; Chen, et al. 2009). Under

this view, a firm should pay its “fair share” of taxes, otherwise it would be deemed as a “poor

corporate citizen” (Bankman 2004). However, while most would agree that everyone should pay

a fair amount of taxes, what constitutes the fair amount is subjective.

Moreover, while the citizenship view may explain why firms with a poor reputation avoid

taxes – because they are no longer afraid of being labeled as a poor citizen, it is unclear whether

firms with a good reputation may also avoid taxes. Although more reputable firms may feel more

obliged to engage in socially desirable actions, they may not necessarily choose tax payments as

the way to show their contributions to the society. In a related literature, recent research in

corporate social responsibility (CSR) found mixed results regarding the association between CSR

and tax avoidance. For example, while Hoi, Wu and Zhang (2013) found that socially irresponsible

(CSiR) firms are more likely to engage in aggressive tax planning activities, Davis, Guenther,

Krull and Williams (2015) found that socially responsible firms do not pay more taxes than other

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firms. These findings suggest that firms who engage in moral activities in other domains may not

necessarily pay more taxes.

2.2 Moral licensing

The moral licensing view originated from the moral balance model (Nisan 1990), where it

is observed that morally uptight people sometimes choose to engage in a little amount of immoral

behavior. In this model, each individual is not fixated to his or her moral ideal but rather has both

an assessment of the current moral self-standing (moral self-regard) and a moral equilibrium point

of the moral self-standing that one would like to remain over time. An actor makes moral-relevant

decisions not only by how the moral act moves his or her moral self-regard but also by the distance

between the self-regard and the moral equilibrium. Nisan (1990) argues that individuals make

moral-relevant decisions that will move the moral self-regards towards the moral equilibriums.

Therefore, an actor is incentivized to act morally when the moral self-regard is below the moral

equilibrium.

However, when the moral self-regard has been pushed by previous good deeds to a level

above the moral equilibrium, an actor is incentivized to engage in bad deeds that will drive the

moral self-regard back to the moral equilibrium. Sachdeva, Iliev and Medin (2009) argued that

prosocial activities are often costly so that people who have established their reputation as a moral

person may not feel the need to engage in altruistic behavior. Moral licensing describes such

negative effect, where an actor is licensed by previous good deeds to act immorally. In particular,

Miller and Effron (2010, 162-163) reviewed relevant literature and defined the moral licensing

phenomenon as “people can call to mind previous instances of their own socially desirable or

morally laudable behaviors,” and “(being) more comfortable taking actions that could be seen as

socially undesirable or morally questionable”.

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The moral licensing theory has made significant progress in the past decade and drawn

increasing attention from researchers in other research domains such as accounting, economics,

and management (e.g. Klotz and Bolino 2013; Griffin 2014; Rose, Rose, Norman and Mazza 2014).

Recent studies on moral licensing provide not only supportive evidence that individuals do engage

in moral licensing but also that observers are also influenced by the moral license (e.g. Effron,

Cameron and Monin 2009; Effron and Monin 2010; Miller and Efron 2010). As such, a moral

license may allow an actor to engage in bad deeds without negative consequences, because

observers may also license such behavior. For example, Effron and Monin (2010) found that an

actor’s good deeds in the past license his or her morally dubious behaviors, which are more likely

to be excused by observers. Effron and Monin (2009) and Miller and Effron (2010) provide

evidence that a moral license changes how misconducts are perceived. Merrit, Effron and Monin

(2010) suggest that previous good deeds make an actor’s ambiguous behavior appear like less of

a transgression. However, past good deeds would license only ambiguous behavior, but not blatant

transgressions, in the eyes of observers. In addition, the current bad deeds and past good deeds do

not have to be in the same domain to give rise to the licensing effect. Indeed, there is evidence

suggesting that an observer is more likely to license bad deeds that are in a different domain from

past good deeds, probably because they are less likely to raise concerns for hypocrisy or damage

the actor’s identity (Effron and Monin 2010).

The moral licensing theory has important implications for the potential effect of a firm’s

reputation on stakeholders’ perception of tax avoidance. Specifically, it suggests that a reputable

firm who has gained social approval from its various past actions may be entitled to a license to

engage in tax avoidance without harming its reputation. This lack of consequences provides an

incentive for reputable firms to avoid more taxes. Tax avoidance frequently involves large

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ambiguity in its transgression. Research on the perception of corporate tax avoidance suggested

that, although it is not clear how people view tax avoidance (Hanlon and Heitzman 2010), it is

perceived as relatively innocuous compared with tax evasion (e.g. Kirchler, Maciejovsky and

Schneider 2003). There is also evidence that tax avoidance could incur minimal reputational

penalties and little tax risk if it is compliant with tax codes and regulations (Dyreng, Hanlon, and

Maydew 2008, 2014; Gallemore, et al. 2014).

However, although tax avoidance per se may not be deleterious, the dividing line between

tax avoidance and tax evasion is sometimes blurry. Tax avoidance activities may also loom more

atrocious misconducts, such as management appropriation of corporate assets (e.g. Desai and

Dharmapala 2006; Desai, Dyck and Zingales 2007). Due to the information asymmetry between

the firm and outsiders, oftentimes the aggressive nature of the tax avoidance behavior is ambiguous

in the eyes of outsiders. Previous research suggests that investors may refer to a firm’s past

behavior to make judgment on its tax avoidance activities (e.g. Hanlon and Slemrod 2009). A good

reputation may enable the firm to engage in tax avoidance behavior without being second guessed

by stakeholders as to whether tax avoidance is used to conceal managerial rent extraction activities

or to benefit all shareholders. It is thus possible that that the firm is less incentivized to pay more

taxes to alleviate its stakeholders’ suspicion.

Moreover, the moral licensing effect may not only make observers’ more likely to condone

a firm’s tax avoidance behavior but also influence managers’ subjective assessment of the tax risk.

Andreoni, Erard, and Feinstein (1998) review relevant literature and suggest that subjectively

perceived audit probabilities are important determinants of tax reporting decisions. They further

conclude that the subjective assessment of the audit probability can be mediated by psychological

variables, such as the subjective view of moral responsibilities to pay taxes. In this respect, a firm’s

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good reputation may alleviate its manager’s guiltiness for tax avoidance and hence affect the

manager’s subjective assessment of the audit probability. In other words, even if the actual

consequences of tax avoidance remain unchanged, a moral license may still encourage managers

to engage in more tax avoidance behavior.

Overall, a well-respected reputation may provide a moral license to the firm and lead

managers to believe that the firm will be under less scrutiny or suffer fewer consequences for

avoiding taxes. We thus expect more reputable firms to avoid more taxes if the licensing effect

exists. For the licensing effect to be activated, it is crucial that a firm’s tax planning have certain

level of ambiguity in transgression. Intuitively, people are much more likely to condone or find

excuse for morally dubious behaviors than for behaviors that clearly cross the line. Research on

moral licensing also shows that observers are more likely to license bad deeds that have ambiguity

in transgression than blatant transgressions (Effron and Monin 2010). Miller and Effron (2010)

suggested that the licensing effect may be activated when an action is framed as means rather than

goals. The ambiguity in the tax avoidance behavior thus likely gives rise to the licensing effect,

because such behavior may be framed as benefiting all stockholders.

We posit that the level of ambiguity in transgression is dictated by the legality of the tax

reduction technologies a firm adopts. Legality is the classic distinction that differentiates the level

of tax aggressiveness (Slemrod and Yitzhaki 2002; Hanlon and Heitzman 2010). Research on

reputational consequences also suggests that the legality of a corporate conduct critically

determines the magnitude of its reputational penalties. On one hand, the reputational penalties can

be huge and much larger than the direct costs of lawsuits for clearly illegal behaviors such as

options backdating and non-GAAP earnings management (e.g. Ertimur, Ferri and Mabor 2012;

Karpoff, Lee and Martin 2008). On the other hand, reputational penalties are weaker for corporate

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conducts that fall into gray areas, such as within-GAAP accrual management and environmental

pollution (Burgstahler and Dichev 1997; Karpoff, Lott, and Wehrly 2005).

To summarize, we predict that a firm’s tax planning technologies are influenced by its

established reputation. We expect a firm with a better reputation to be associated with a higher

level of tax avoidance activities that have ambiguity in transgression. However, if a tax planning

activity signals illegal violation with high precision, it is less excusable and thus less likely to be

licensed. In such a scenario, firms with a better reputation may have more reputational concerns

because they have more to lose. Therefore, we expect a firm with better reputation to be associated

with lower propensity to engage in illegal tax-related activities. We test the following hypotheses

stated in the alternative form.

Hypothesis 1a: Ceteris paribus, more reputable firms on average engage in more tax reduction

activities that have ambiguity in transgression than less reputable firms do.

Hypothesis 1b: Ceteris paribus, more reputable firms on average engage in less tax-related

activities that are clear transgressions than less reputable firms do.

2.3 Reputation and bright-line rules in standards

Reputation and standards are two closely related concepts in that both of them influence

corporate decisions by altering the benefits and costs of corporate conducts. Because standards are

ex-ante and cannot cover all dimensions, stakeholders base their judgments of corporate behavior

on the context, and the context includes reputation. Since stakeholders’ judgment is based on

available information, a firm’s action can interact with its reputation only if the relevant

information is publically available. Because accounting for income taxes (AFIT) is arguably the

sole source of public information that outsiders can use to assess the firm’s tax planning activities

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(Graham, Raedy and Shackelford 2012), accounting standards thus can be crucial to the

reputational effects of tax avoidance,.

Rule-makers may be able to tailor the reputational effects of tax avoidance by altering the

precision of the rule and thus the legality of tax activities. One important dimension that

differentiates standards is “the existence of exceptions and bright-lines which lead to very detailed

implementation guidance” (SEC 2003, 23). The existence of bright-line rules can reduce the

demand for professional judgment and reduce controversy by drawing a line between right and

wrong. However, it may also create opportunities for firms to structure transactions in order to

bypass the rules (Nelson, Elliott and Tarpley 2002). Notwithstanding, it is difficult to classify a

specific standard as either purely rules-based or purely principles-based (Schipper 2003).

In a monetary policy setting, Barro and Gordon (1983) analytically show that reputation

substitutes regulation in governing behaviors and that the optimal policy is the combination of the

two. In an international study, Ball, Robin and Wu (2003) suggest that researcher should not ignore

market-oriented reputational incentive and classify markets solely by accounting standards.

Several empirical studies investigated the consequences of rules-based standards in the U.S.

setting. Prior research has found that the rules-based standards affect litigation risks and facilitate

enforcement (Donelson, et al. 2012, 2016). Prior research has also found that managers use the

discretion allowed by the principles-based standards to either convey private information or to

manipulate earnings (Folsom, Hribar, Mergenthaler, and Peterson 2016).

Previous experimental research provides evidence that managers exploit the flexibility

inherent in accounting rules to make accounting choices in favor of their own incentives. For

examples, Nelson et al. (2002) finds that managers use accrual management when standards are

imprecise and use real activities manipulations when standards are precise. Cuccia, Hackenbrack

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and Nelson (1995) show that executives use the judgment as an excuse for aggressive tax planning

when a standard is “verbal”. When a standard is “numerical”, they turn to evaluate the evidence in

a manner compatible with their own incentives.

We expect that the licensing effect of reputation can be more pronounced under the

principle-based standards for the following reasons. First, the existence of bright-line rules both

curtails managers’ discretion and reduces stakeholders’ reliance on contexts in judging a firm’s

behavior. Kadous and Mercer (2011) examine the effects of precise standards on jurors’ decisions

in the auditor’s negligence case. They find that the decision of juries largely depends on the

compliance with standards when standards are precise, and depends on compliance with the

industry norm when standards are imprecise. Second, avoiding taxes through transactions

structuring could be hard to detect. Given the information disadvantage and the complexity of

AFIT, outsiders may not be able to distinguish between transactions carried as normal business

activities and those orchestrated for tax purposes. In such a scenario, a firm has little reputational

concerns and does not need to rely on its reputation to license such transactions. In addition,

avoiding taxes by transactions structuring could be costlier than the use of discretion.

When choosing tax reduction technologies from available options, a firm considers not

only the income effect of how much taxes can be reduced but also the substitution effect of which

technology to use. Holding the magnitude of tax reduction constant, a firm with a good reputation

may prefer a technology that is contingent on standards that allows discretion over standards that

stipulate bright-line rules. We state the formal hypothesis in its alternative form as follows.

Hypothesis 2: Keeping the amount of tax savings fixed, compared with less reputable firms, more

reputable firms relatively use more tax reduction technologies that are contingent on

principles-based standards.

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III. Methods

3.1 Measures of tax-related variables

We rely on information available in the financial reports to build our measures. To test our

hypothesis on the association between reputation and corporate tax planning (H1a and H1b), we

rely on the extensive list of measures of tax avoidance in the tax literature. Each measure captures

the effects of a certain group of tax reduction technologies with estimation errors. Our first two

tax avoidance measures are GAAP effective tax rate (GAAP_ETR) and cash effective tax rate

(CASH_ETR), which capture a broad range of tax avoidance activities. Prior literature has shown

that a lower level of GAAP_ETR or CASH_ETR suggests a higher level of tax avoidance (e.g.

Hanlon and Heitzman 2010). To mitigate the effects of influential observations, we follow prior

literature (e.g. Gallemore, et al. 2014; Dyreng, et al. 2014) to winsorize both ETR variables to the

range [0, 1].

Our next two measures are tax sheltering (TAX_SHELTER) and uncertain tax benefits

(UTB) (Lisowsky et al. 2013; Gallemore et al. 2014). If we conceive the set of tax technologies as

a continuum with different levels of transgression, these two measures tend to capture more

aggressive tax avoidance technologies (Hanlon and Heitzman 2010). Nevertheless, the

participation of tax sheltering or the recognition of UTBs can be totally compliant to the tax and

accounting rules.3 We estimate the Wilson Score developed by Wilson (2009) to measure the

propensity for tax shelter participation. UTB captures uncertain tax benefits associated with open

tax positions that will unlikely pass an IRS audit or sustain in a litigation, and its amount increases

with the level of uncertainty about a tax position. Due to this qualitative nature, UTB has a strong

3 Tax shelter participation itself does not necessarily imply illegality. The legality of a tax shelter is determined in a court of law and relatively few tax shelter cases are litigated (Lisowsky et al. 2013).

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conceptual link to the more aggressive end of tax avoidance continuum. Lisowsky et al. (2013)

find that for tax shelter participating firms in their sample, 12%-48% of the aggregate UTB balance

reflects tax shelter activities. Following Lisowsky et al. (2013) and Hoi et al. (2013), we measure

uncertain tax benefits (UTB) disclosed under FIN 48 by the natural logarithm of fiscal-year ending

balance of total tax reserves.4

We use the following two measures to capture tax-related activities beyond the gray area,

where the legality of the corporate action is largely questionable. The first one is the incidence of

tax penalties and interest related to a firm’s uncertain tax benefits in the income statement

(TAX_PENALTY) (e.g. Abernathy, Beyer, Gross, and Rapley 2014). FIN 48 requires all public

firms to disclose these tax positions in the income statements and balance sheets. It also requires

the disclosure of the accrued interest and penalty expense on unrecognized tax benefits. This

measure captures one of the most controversial tax positions that are exposed to the public

(Frischmann, Shevlin and Wilson 2008). The second measure is the incidence of tax-related

financial report misstatements (TAX_REST) based on the non-reliant restatement database in Audit

Analytics (Lennox 2016). Accounting misstatements have a significantly negative impact on a

firm’s reputation and often result in legal consequences (e.g. Karpoff, et al. 2008; Chakravarthy,

De Haan, and Rajgopal 2014). Following prior literature (e.g. Lennox 2016), we use the coding in

Audit Analytics to determine whether a misstatement is tax-related.

To test the moderating effect of accounting standard precision on the relationship between

reputation and tax avoidance (H2), we use the following measure to capture the differences in the

4 COMPUSTAT reports a large number of missing values on the ending balance of tax reserves, though many of them actually have a non-zero ending balance (Lisowsky, Robinson and Schmidt 2013). In our main tests involving UTBs, we remove all observations with missing values. In untabulated tests, we set the value of the variable UTB to zero if the value is missing in the Compustat, and the results are qualitatively similar to our main results.

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accounting standards with respect to the existence of bright lines rules. As mentioned earlier, the

measure relies on the observation that the U.S. GAAP is more principles-based and contains fewer

bright-line rules than the IRS tax law (Maines et al. 2003). Specifically, the measure is the

difference between GAAP and CASH ETRs (ETR_DIFF=GAAP_ETR-CASH_ETR). Similar to

Nelson’s (2003) “incremental” perspective, income tax expense can be viewed as incremental to

the cash tax payments in that AFIT uses tax accruals to adjust the recognition of cash tax inflows

and cash tax outflows. The accrual process involves the use of discretion in compliance with the

GAAP, whereas the cash tax payments are more contingent on the IRS rules. If a firm largely relies

on tax technologies that exploit the ambiguities in accounting standards, we would observe a

negative ETR_DIFF.

3.2 Measures of reputation

Our main measure of reputation is based on Fortune Magazine’s Most Admired Companies

lists (FMAC), which is an indicator variable that is coded as one if a firm is selected into the FMAC

lists in a particular year, and zero otherwise. As previously discussed, a firm’s reputation is

subjective to its stakeholders’ discretion. FMAC is based on the perception of a relatively broad

range of audience of the firm. As such, it is viewed as conceptually close to the underlying

construct of corporate reputation and is the most used measure in business research fields (e.g. Cao

et al. 2012; Fombrun and Shanley 1990; Gallemore et al. 2014; Kim et al. 2012).5

In additional analyses, we use two other measures of reputation in order to triangulate our

evidence and ensure the robustness of our results. Our first alternative measure is the total number

of analysts covering the firm (COVERAGE). One of the benefits for firms with higher reputation

5 In untabulated tests, the results are qualitatively similar when we measure reputation using a continuous variable which equals to the reputation scores of the FMAC list.

18

is the “status ordering” effect that allows the firms to attract more analyst attention (Merton 1968;

Shen, Tang and Chen 2013). Reputation can be an important determinant for analyst coverage

above other variables documented in the literature. Compared with media coverage, analyst

coverage as a measure of reputation has the advantage that more analyst coverage is always good

for a firm, whereas negative news coverage in media can be bad for a firm’s reputation (Lang and

Lundholm 1996). Our last measure for reputation is an indicator variable of investment grade based

on the S&P long-term domestic issuer credit rating (RATING).6 A firm’s credit reputation is

arguably closely linked with its credit rating. Similar to analyst coverage, firms with higher

reputation are more likely to have higher credit ratings. One disadvantage of these two measures

is that they only capture certain portions of stakeholders (a.k.a. equity and debt analysts).

3.3 Research Design

We model the managers’ decision-making on tax avoidance as a function of reputation and

other firm-specific factors that affect tax planning. Specifically, we estimate the following model

to test H1a and H1b:

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝐷𝐷𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑅𝑅𝐷𝐷𝐷𝐷𝑅𝑅𝐷𝐷𝑣𝑣𝐷𝐷𝑣𝑣𝑅𝑅𝐷𝐷 + ∑𝐶𝐶𝑅𝑅𝐷𝐷𝐷𝐷𝑣𝑣𝑅𝑅𝑣𝑣𝑠𝑠𝑖𝑖𝑖𝑖 + 𝑌𝑌𝐷𝐷𝑣𝑣𝑣𝑣 𝐷𝐷𝑅𝑅𝑑𝑑𝑑𝑑𝑣𝑣𝐷𝐷𝑠𝑠 +

𝐼𝐼𝐷𝐷𝐷𝐷𝑅𝑅𝑠𝑠𝐷𝐷𝑣𝑣𝐼𝐼 𝐷𝐷𝑅𝑅𝑑𝑑𝑑𝑑𝑣𝑣𝐷𝐷𝑠𝑠 + 𝜀𝜀𝑖𝑖𝑖𝑖 , (1)

where the dependent variable is one of our measures of tax-related variables and the independent

variable of interest is our measure of a firm’s reputation. We draw on prior research to control for

other important determinants of corporate tax avoidance that are potentially correlated with a

firm’s reputation. One drawback of FMAC, our main measure of reputation, is the “financial halo”

effect in that a firm’s concurrent financial performance significantly affects its FMAC score and

6 In untabulated tests, we repeat our analysis using a continuous variable for credit rating ranging from 1 to 22, with 0 for no credit rating , 1 for level D , and 22 for level AAA. The results are qualitatively similar.

19

whether it is selected into the list. To mitigate this effect, we follow prior literature (e.g. Brown

and Perry 1994) to control for the return on assets (ROA) and the stock return (RET) during the

fiscal year. We also control for the log of company total assets (SIZE) and company age (AGE),

because larger and older firms may have more resources, not necessarily reputation, for them to

be more effective tax planners. We also construct an indicator variable that identifies firms who

have tax-related law suits in recent years (Legit) by searching the “legal proceedings” sections of

10-K or 10-Q filings, since recent tax lawsuits likely affect a firm’s current tax planning activities.

Gupta and Newberry (1997) found that company size, capital structure, asset mixes, and financial

performance are important determinants of tax avoidance. Thus, we also control for financial

leverage (LEVERAGE), capital intensities (CAPINT), investment intensities (INVINT), and R&D

intensities (RDINT).

We include the market to book ratio (MTB) to control for the effect of growth. We also

include discretionary accrual (DCA) to control for the effects of earnings management. A foreign

income indicator is included (PIFO_DI) to control for the tax effect of operations in foreign

jurisdictions. In addition, we allow for tax avoidance to vary for firms with tax loss by including

an indicator variable for the presence of tax loss carryforward (TLC). We include intangible assets

(INTANG) because firms are alleged to shift income from high-tax to low-tax jurisdictions by

transferring intangible assets (Dyreng, et al. 2008). We include Big 4 auditor status (BIG4) and tax

service fees paid to the auditor (TAXFEE) to control for the effects of auditors on corporate tax

planning. We include year and industry indicators based on the Fama-French 30 Industries

classification. For continuous and dichotomous dependent variables, we estimate the respective

models using generalized least square (GLS) and logistic regressions, respectively. We choose

20

GLS over pooled-OLS method to correct for potential autocorrelations in residuals. Standard errors

are robust-clustered at the firm level. Appendix A provides detailed variable definitions.

We estimate model (2) to test the effect of bright-line rules in accounting standards on the

relationship between a firm’s reputation and tax avoidance (H2). Prior literature shows that book-

tax differences reflect tax avoidance. For example, Mills (1998) shows that the differences between

book income and taxable income are positively correlated with the IRS’s audit adjustments. In

order to mitigate the concern that the level of tax avoidance affects the strength of the association

between reputation and ETR_DIFFit, we augment our equation by including CASH_ETR to control

for the level of tax avoidance.

𝐸𝐸𝐸𝐸𝑅𝑅_𝐷𝐷𝐼𝐼𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑅𝑅𝐷𝐷𝐷𝐷𝑅𝑅𝐷𝐷𝑣𝑣𝐷𝐷𝑣𝑣𝑅𝑅𝐷𝐷 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶_𝐸𝐸𝐸𝐸𝑅𝑅 + ∑𝐶𝐶𝑅𝑅𝐷𝐷𝐷𝐷𝑣𝑣𝑅𝑅𝑣𝑣𝑠𝑠𝑖𝑖𝑖𝑖 + 𝑌𝑌𝐷𝐷𝑣𝑣𝑣𝑣 𝐷𝐷𝑅𝑅𝑑𝑑𝑑𝑑𝑣𝑣𝐷𝐷𝑠𝑠 +

𝐼𝐼𝐷𝐷𝐷𝐷𝑅𝑅𝑠𝑠𝐷𝐷𝑣𝑣𝐼𝐼 𝐷𝐷𝑅𝑅𝑑𝑑𝑑𝑑𝑣𝑣𝐷𝐷𝑠𝑠 + 𝜀𝜀𝑖𝑖𝑖𝑖 , (2)

A negative coefficient on the independent variable Reputation would suggest that more reputable

firms are more likely to exploit the licensing effect of reputation under the principles-based

standards than under rules-based standards and support H2.

IV. Empirical results

4.1 Sample selection

The full sample includes annual financial reports of U.S. public firms with fiscal years

between 2008 and 2014. FASB Interpretation No. 48, which is commonly regarded as a “game

changer” for AFIT, became effective for fiscal years beginning after December 15, 2006. Our

sample starts from 2008, one year after the implementation of FIN 48, so we can conduct this study

over a period when the regulatory environment is relatively stable.

We first start with all 100,431 firm-year observations from the Compustat North America

database during the sample period. We then merge with Audit Analytics to get the auditor-related

21

variables, tax fees, and restatement variable, reducing the sample size to 41,001 firm-year

observations. Consistent with most prior research on AFIT, we exclude 9,142 observations for

foreign firms, utility firms (SIC codes 4800-4999) or financial firms (SIC codes 6000-6999)

because of their different tax rules and tax avoidance incentives. We then remove observations

with any missing value in variables required for the estimation, further reducing the sample size

to 14,419 observations for 3,750 unique firms.

Table 1 presents the descriptive statistics of the full sample. Other than GAAP_ETR and

CASH_ETR, all other continuous variables in the analysis are winsorized at the 1 and 99 percent

level. Among the full sample, about 6 percent of the firm-years have been selected into the FMAC

lists, which is comparable to the 4 percent in the main sample of Gallemore et al. (2014). The

means of GAAP_ETR and CASH_ETR are 0.22 and 0.16, respectively, similar to the numbers in

Figure 9 of Dyreng, et al. (2014). The mean (median) of the natural log of UTB is 1.92 (1.90),

smaller than the mean (median) of 2.90 (2.80) in Lisowsky et al. (2012), probably due to the sample

difference as our sample includes more firms and more recent fiscal years than theirs. The higher

mean of GAAP_ETR than that of CASH_ETR mechanically drives the average of ETR_DIFF to

be positive and implies that firms on average have positive net tax accruals.

Table 2 presents the correlations of key variables in our sample. The highly positive

correlations among measures of effective tax rates suggest that they capture some common

dimensions of tax avoidance. The correlation between FMAC, COVERAGE, and RATING, are all

larger than 0.4, lending confidence that the three variables potentially capture similar underlying

construct. The univariate results show that more reputable firms have slightly higher effective tax

rates and probability of restatements. However, untabulated results show that these firms are also

larger and older. They also have better performance on average, as suggested by the higher returns

22

on assets. These characteristics are normally associated with higher tax duties, suggesting that the

univariate analysis may be subject to potential bias of omitted correlated variables.

4.2 Test of H1a and H1b

We next turn to the multivariate analysis for more insights. Hypothesis 1a predicts that

more reputable firms avoid more taxes on average. In Table 3, Columns 1 – 4 present the results

on the association between reputation and tax avoidance using GAAP_ETR, CASH_ETR, Tax

Shelter, and UTB as the alternate dependent variable, respectively. In Columns 1 and 2, the

coefficient on reputation (FMAC) is -0.046 and -0.022 and statistically significant at the 1 percent

when GAAP_ETR and CASH_ETR serve as the dependent variables. On average, firms that are

selected into the Fortune’s Most Admired Companies lists have about 4.6 percent lower

GAAP_ETRs and 2.2 percent lower CASH_ETRs than firms not on the list.

Column 3 shows that the coefficient on FMAC is 0.246 and significant at the 1 percent

level, suggesting that more reputable firms are more likely to participate in tax sheltering activities.

On average, firms on the Fortune’s Most Admired Companies lists are 24.6 percent more likely to

participate in tax sheltering. Lisowsky et al. (2013) find that firms with higher tax reserves are

more likely to have reportable transactions, suggesting a publicly available FIN 48 tax reserves

serve as a reliable proxy for tax sheltering activities.7 In Column 4, the coefficient on FMAC is

0.255 and significant at the 1 percent level, indicating that more reputable firms have higher UTBs

on average. Everything else equal, a firm that is selected into the Fortune’s Most Admired

Companies lists has on average about 1.29 million US dollars more uncertain tax positions than

other firms. Overall, these results are consistent with our expectations and lend support to H1a in

7 In fact, Lisowsky et al. (2013) report that among several commonly used tax avoidance measures, FIN 48 tax reserve is the most robust empirical proxy for tax shelters.

23

that more reputable firms pay lower taxes than less reputable firms by using legal tax reduction

technologies.

H1b predicts that more reputable firms are less likely to participate in illegal tax-related

activities. Columns 5 and 6 of Table 3 are models for testing the association between reputation

and tax-related activities that more likely violate the tax law or GAAP. When Tax Penalty is the

dependent variable, the estimated coefficient for FMAC is -0.43 and statistically significant at the

1 percent level, suggesting that more reputable firms are less likely to incur tax penalties and

interests associated with non-compliance of tax laws. Column 6 shows that the coefficient on

FMAC is -0.52 and statistically significant at the 5 percent level, suggesting that more reputable

firms are also less likely to have tax-related accounting misstatements. Overall, these results are

consistent with our expectations and suggest that more reputable firms have a lower propensity to

participate in illegal or GAAP-violating tax-related activities and lend support to H1b. Taken

together, these findings show support to the moral licensing theory and suggest that managers take

advantage of the firm’s established reputation to strategically make tax planning decisions.

4.3 Test of H2.

Having documented that the managers put different weights on different tax techniques

based on the firm’s reputation, we next examine the effect of bright-line rules in the accounting

standards on the relationship between a firm’s reputation and tax avoidance.

Table 4 shows that the coefficient on FMAC is negative and statistically significant at the

1% level. Compared with less reputable firms, more reputable firms on average have lower

GAAP_ETRs relative to CASH_ETRs. Overall, these results lend support to Hypothesis 2 and

suggest that, after keeping the level of tax avoidance fixed, more reputable firms have a tendency

24

to use more tax avoidance technologies that involve the use of discretion. These technologies are

likely to be guided by accounting standards that have fewer bright-line rules.

V. Additional Analyses

5.1 Endogeneity analysis

The Fortune magazine selects firms into the list based on several observable firm

fundamentals. Although we include an extensive set of firm attributes in an attempt to isolate the

effect of reputation from the effect of other firm fundamentals on tax avoidance, it is plausible that

our test variable (FMAC) still captures part of the explanatory power of firm attributes for tax

avoidance. To address this concern, we employ a Heckman two-stage selection model that allows

us to correct for possible selection bias. The first stage probit model is specified below (firm and

year subscript suppressed):

Pr(𝐷𝐷𝐹𝐹𝐶𝐶𝐶𝐶 = 1) = 𝛼𝛼0 + 𝛼𝛼1𝐶𝐶𝑅𝑅𝑑𝑑𝐷𝐷𝐶𝐶𝑅𝑅𝑣𝑣𝑣𝑣 + 𝛼𝛼2𝐹𝐹𝑣𝑣𝐷𝐷𝑣𝑣𝐶𝐶𝑣𝑣𝑣𝑣𝑣𝑣 + 𝛼𝛼3𝐿𝐿𝑣𝑣𝐿𝐿𝐶𝐶𝑣𝑣𝐷𝐷𝐶𝐶𝑣𝑣𝑠𝑠ℎ_𝐸𝐸𝐸𝐸𝑅𝑅 +

𝛼𝛼4𝐶𝐶𝑣𝑣𝑆𝑆𝐷𝐷 + 𝛼𝛼5𝐹𝐹𝐸𝐸𝑀𝑀 + 𝛼𝛼6𝑅𝑅𝑅𝑅𝐶𝐶 + 𝛼𝛼7𝑅𝑅𝐷𝐷𝐷𝐷 + 𝛼𝛼8𝐿𝐿𝐷𝐷𝑣𝑣𝐷𝐷𝑣𝑣𝑣𝑣𝐿𝐿𝐷𝐷 + 𝐼𝐼𝐷𝐷𝐷𝐷𝑅𝑅𝑠𝑠𝐷𝐷𝑣𝑣𝐼𝐼 𝐷𝐷𝑅𝑅𝑑𝑑𝑑𝑑𝑣𝑣𝐷𝐷𝑠𝑠 +

𝑌𝑌𝐷𝐷𝑣𝑣𝑣𝑣 𝐷𝐷𝑅𝑅𝑑𝑑𝑑𝑑𝑣𝑣𝐷𝐷𝑠𝑠 + 𝜀𝜀 (3)

In the first stage estimation, we include common fundamental variables such as SIZE, ROA,

MTB, RET, and LEVERAGE. We also include LAGAVE_CASH_ETR, the lagged 3-year average

Cash ETRs from fiscal year t-3 to t-1, in the first stage estimation, because Hanlon and Slemrod

(2009) found that the perception of current tax avoidance behavior is affected by the past tax

payments. We also include ManaAbil (managerial ability) and ComCurr (compensation) in the

first-stage estimation to account for the differences in the managerial ability, because they may be

correlated with reputation and are important determinants of tax outcomes (Saavedra 2013;

Koester, Shevlin and Wangerin 2016). We obtain the inverse Mills ratio from the Model (3) and

re-estimate Models (1) and (2) after including the inverse-Mills ratio.

25

Results are presented on Table 5. Overall, the results show that even after addressing the

potential bias due to unobservable firm fundamentals, reputation still has a positive and significant

effect on corporate tax planning that falls into the grey area, and a negative and significant effect

on tax activities that violate the tax law or GAAP. Comparing the estimates in Table 5 with those

in Table 3 and 4, the Heckman two-stage coefficient estimates are significant but smaller in

absolute magnitude. The change could be due to either the correction of selection bias or reduced

testing power arising from the small sample size.

5.2 Alternative measures of reputation

In the main analyses, we use the selection into Fortune Most Admired Company list as a

comprehensive measure of a firm’s overall reputation. As robustness tests, we focus on a firm’s

reputation as perceived by sophisticated participants in the equity and debt market by using analyst

coverage (COVERAGE) and debt rating (RATING) as the alternative measures of reputation.

Table 6 Panels A and B present the results when measuring reputation with analyst coverage and

debt rating, respectively. Columns 1-4 report the results on the association between reputation and

four different measures of tax avoidance, and Columns 5 and 6 present the results on the

association between reputation and the propensity to have tax penalties and interests or tax-related

accounting misstatements, respectively. Columns 7 provides the results for on the effect of bright-

line rules on the association between reputation and tax avoidance. In general, the results are

consistent with the findings in the main tests, except that the coefficient on reputation is negative

but not significant in Column 5 of Panel B, where Tax_Penalty is the dependent variable. Overall,

these results support H1a and H1b that more reputable firms avoid more taxes through legal tax

avoidance technology on average but are less likely to engage in tax avoidance that violate tax

laws or GAAP. It also reinforces our conclusion that more reputable firms are more likely to

26

exploit the discretion afforded by the more principle-based accounting standards to avoid tax than

less reputable firms.

5.3 Reputation and the quality of tax accruals

An alternative explanation for the negative association between reputation and the

ETR_DIFF could be that more reputable firms are less aggressive in financial reporting (Cao et al.

2012). Although we control for the level of tax avoidance by controlling for CASH_ETR when

we test H2, we could not fully eliminate this alternative explanation.

Nevertheless, this explanation implies that more reputable firms have better tax accrual

quality, i.e., less mismapping and estimation error that arise from judgment and complexity in

estimating the tax accruals. We thus examine this possibility by regressing reputation on the tax

accrual quality. We follow Choudhary, Koester, and Shevlin (2016) and define tax accrual quality

as the unexplained difference between income tax expense and cash tax payments. Specifically,

we estimate the following equation (Eq.1b in Choudhary et al. 2016):

𝐸𝐸𝑣𝑣𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇𝑣𝑣𝑖𝑖 = 𝜃𝜃0 + 𝜃𝜃1𝐶𝐶𝐸𝐸𝑃𝑃𝑖𝑖−1 + 𝜃𝜃2𝐶𝐶𝐸𝐸𝑃𝑃𝑖𝑖 + 𝜃𝜃3𝐶𝐶𝐸𝐸𝑃𝑃𝑖𝑖+1 + +𝜃𝜃4𝛥𝛥𝐷𝐷𝐸𝐸𝐿𝐿𝑖𝑖 + 𝜃𝜃5𝛥𝛥𝐷𝐷𝐸𝐸𝐶𝐶𝑖𝑖 + 𝜀𝜀𝑖𝑖 , (4)

where TaxAccr is tax accruals, CTP is cash tax paid, and ΔDTLt and ΔDTAt are changes in deferred

tax liabilities and changes in deferred tax assets, respectively. The tax accrual quality is defined as

the negative standard deviation of the residuals from the firm-level rolling regression over an eight

year window. The descriptive statistics of the sample, as presented in Table 7, are qualitatively

similar to those in Choudhary et al. (2016).

Table 8 presents the testing results using three different measures of reputation. In all

models, the coefficients on reputation are negative and insignificant, suggesting that reputable

firms do not have better tax accrual quality. These results provide evidence that the negative

27

association between reputation and the book-tax gap cannot be caused by differential tax accrual

quality among firms with different reputations.

VI. Conclusion

This study draws on the moral licensing theory to explain the effect of a firm’s reputation

on its tax planning activities. Empirical results show that more reputable firms avoid more taxes

but are less likely to engage in illegal tax-related activities. We interpret these findings as

suggesting that a firm’s reputation may affect the perception and hence alter the costs and benefits

of its tax avoidance behavior. This study further shows evidence that the effects of reputation on

tax avoidance are dampened by the existence of bright-line rules in the accounting standards. Our

findings show support to the moral licensing theory, which predicts that past good deeds, by

altering the perceptions, reduce the penalties to current bad deeds. As such, this study challenges

the conventional wisdom that [quote from Benjamin Franklin] “it takes many good deeds to build

a good reputation, and only one bad one to ruin it”. These findings also suggest that the legal tax

reduction behaviors may not necessarily be punished by the public, casting a doubt over the

effectiveness of the public “tax shaming” strategy. Moreover, our findings suggest that firms with

a well-established reputation could avoid more taxes if there are fewer bright-line rules in the

standards. As such, this study has standard-setting implications and adds to the debate over the

desirability of rules-based standards.

Hanlon and Heitzman (2010) raised three consecutive questions regarding corporate tax

avoidance. Why do some firms do not avoid more taxes? How do people perceive tax avoidance?

What are the non-tax costs of tax avoidance? This study may shed lights on these questions. This

study may also help to explain certain conflicting findings in previous studies. For example,

although Davis, et al. (2015) attributed, at least in part, the difference between their results and

28

those of Hoi et al. (2013) to sample differences, our findings suggest that an alternative explanation

could be the difference in legality of CSR and CSiR behavior. Some CSiR activities may raise

legal concerns, some CSR activities might be conducted for the purpose of reputation repair. In

addition, the moral licensing theory may also be able to explain the mixed findings in studies that

investigate the association between reputation and earnings quality (Cao, et al. 2012; Francis,

Huang, Rajgopal and Zang 2008).

The findings in this study are subject to certain limitations. For examples, the association

study used in this paper is subject to potential bias from correlated omitted variables. Our tax-

related measures are all based on the information from the financial reports and thus can only

capture a certain proportion of all available tax avoidance technologies with noise (Hanlon and

Heitzman (2010). Our measures that capture the difference in the existence of bright-lines in

standards also capture other dimensions of the standards. Future research could explore other

alternative measures that better capture different discretions afforded under the principles-based

and rules-based standards in the tax setting and offers deeper insights about the implications of

bright-line rules for the tax avoidance behavior of firms.

29

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33

Appendix A: Variable definitions*

Dependent Variables

GAAP_ETR Total tax expense divided by pretax income [Compustat item TXT /

PI]

CASH_ETR Total cash tax paid divided by pretax income [Compustat item

TXPD / PI]

TAX_SHELTER The Wilson Score developed by Wilson (2009)

UTB The natural log of the total tax reserves at fiscal year-end

[Compustat item TXTUBEND]

TAX_PENALTY Equal to 1 if Total Interest & Penalties Recognized in the I/S

[Compustat item TXTUBXINTIS] is greater than zero, and equal to 0

otherwise

TAX_REST Equal to 1 if the current year financial report is subsequently restated

and the restatement category in the Audit Analytics is 18 (tax

expense /benefit /deferral /other (FAS 109) issues, and equal to 0

otherwise

ETR_DIFF = GAAP_ETR - CASH_ETR

Reputation

FMAC Equal to 1 if the on the Fortune’s Most Admired Companies List. If

the firm-year is not on the list, the variable is set to 0.

COVERAGE Total number of analyst following at the fiscal year end

RATING Equal to 1 if Standard & Poor’s Long-Term Domestic Issuer Credit

Rating [Compustat item SPLTICRM] is investment grade (above

BBB- threshold), and 0 otherwise.

Control Variables

AGE The current fiscal year minus the year of IPO [Compustat item

IPODATE]. If the IPO date is missing in Compustat, use the first

year in Compustat with non-missing Compustat item PRCC_F.

34

BIG4 Equal to 1 if the auditor is Big 4 auditor and zero otherwise

CAPINT Capital intensity, calculated as PP&E [Compustat item PPENT]

divided by total assets [Compustat item AT]

COMPCURR Current compensation (salary + bonus) of the CEO in fiscal year t.

(ExecuComp).

DCA Discretionary accruals based on modified Jones model in Dechow,

Sloan and Sweeney (1995).

INTANG Intangible assets [Compustat item INTAN] scaled by total assets

INVINT Investment intensity [Compustat item INVT] scaled by total assets

LAG_AVE_CASH_ETR 3-year average of the Cash ETRs from fiscal year t-3 to t-1.

LEGIT Equal to 1 if a firm has disclosed in 10-K or 10-Q at least one tax-

related lawsuit in the most recent 8 years.

LEVERAGE Long-term debt [Compustat item DLTT]

MANAABIL The score of managerial ability from Demerjian, Lev and McVay

(2012).

MTB Market value of equity divided by book value of common equity.

[Compustat item CEQ / (CSHO*PRCC_F)]

PIFO_DI Equal to 1 if Pretax Income/Foreign [Compustat item PIFO] is

greater than zero, and zero otherwise

RDINT R&D intensity, calculated as R&D expenses scaled by sales

[Compustat item – XRD / SALE]

RET Stock market return of the fiscal year [Compustat item – PRCC_F at

period t divided by PRCC_F at period t-1]

ROA Return on assets, calculated as operating income after depreciation

[COMPUSTAT item OIADP] scaled by total assets

SIZE Log of total assets [Compustat item AT]

TAXFEE Tax fees from Audit Analytics

TLC Tax Loss Carry Forward [Compustat item TLCF]

Tax Accrual Quality Variables

CTP Total cash taxes paid [Compustat item TXPD] scaled by total assets

35

dDTA_LT Change in the long-term deferred tax asset [Compustat item TXDBAt

- TXDBAt-1], scaled by total assets. We reset missing values of TXDB

equal to net DTA/DTL [TXNDB] less short-term DTL [TXDBCL]

less short-term DTA [TXDBCA], with missing values of TXDBCL

(TXDBCA) reset to zero when TXDBCA (TXDBCL) is not equal to

missing. If TXDBAt is missing and TXDBt is not missing, TXDBAt is

reset to zero.

dDTL_LT Change in the long-term portion of the deferred tax liability

[Compustat item TXDBt - TXDBt-1], scaled by total assets. We reset

missing values of TXDB equal to net DTA/DTL [TXNDB] less short-

term DTL [TXDBCL] less short-term DTA [TXDBCA], with missing

values of TXDBCL (TXDBCA) reset to zero when TXDBCA

(TXDBCL) is not equal to missing. If TXDBt is missing and TXDBAt

is not missing, TXDBt is reset to zero.

TaxAccr Total tax accrual, calculated as total tax expense less cash taxes paid

[Compustat item TXT – TXPD] scaled by total assets

TaxAQ Standard deviation of the residuals from firm-specific estimates of

Equation (3), multiplied by -1 so larger values indicate higher tax

accrual quality.

36

Table 1: Descriptive statistics of the main sample (n=14,419) Variable Q1 Mean Median Q3 St.d.

GAAP_ETR 0.00 0.22 0.24 0.36 0.21

CASH_ETR 0.00 0.16 0.05 0.28 0.22

TAX_SHELTER 0.44 0.77 0.74 1.17 2.03

UTB 0.34 1.92 1.90 3.50 2.30

TAX_PENALTY 0.00 0.20 0.00 0.00 0.40

TAX_REST 0.00 0.03 0.00 0.00 0.16

ETR_DIFF 0.00 0.06 0.00 0.12 0.20

FMAC 0.00 0.06 0.00 0.00 0.24

COVERAGE 1.00 7.20 5.00 11.00 7.81

RATING 0.00 044 0.00 9.00 0.73

AGE 11.00 22.22 17.00 29.00 15.75

BIG4 0.00 0.65 1.00 1.00 0.48

CAPINT 0.07 0.28 0.17 0.39 0.29

DCA -0.06 -0.12 0.01 0.14 2.22

INTANG 0.01 0.20 0.10 0.31 0.25

INVINT 0.00 0.11 0.06 0.17 0.14

LEGIT 0.00 0.15 0.00 0.00 0.36

LEVERAGE 0.00 0.19 0.11 0.28 0.25

MTB 0.99 2.60 1.79 3.27 7.56

PIFO_DI 0.00 0.36 0.00 1.00 0.48

RDINT 0.00 0.49 0.00 0.09 2.57

RET 0.69 1.22 1.02 1.35 1.18

ROA -0.09 -0.19 0.04 0.11 1.11

SIZE 4.07 5.76 5.93 7.59 2.58

TAXFEE 0.00 0.42 0.06 0.40 0.89

TLC 0.00 1.22 0.00 0.22 4.80

Note: This table reports descriptive statistics of the full sample. See Appendix A for variable definitions.

37

Table 2: Correlations of major variables

1 2 3 4 5 6 7 8 9 10 1 GAAP_ETR

0.50 -0.08 0.04 0.15 0.03 0.47 0.09 0.22 0.18

2 CASH_ETR 0.50

-0.07 0.07 0.17 0.03 -0.52 0.10 0.18 0.13 3 TAX_SHELTER -0.08 -0.07

0.00 -0.03 -0.01 -0.01 -0.01 -0.06 -0.04

4 UTB 0.04 0.07 0.00

0.24 0.05 -0.03 0.42 0.59 0.58 5 TAX_PENALTY 0.15 0.17 -0.03 0.24

0.05 -0.03 0.13 0.25 0.21

6 TAX_REST 0.03 0.03 -0.01 0.05 0.05

0.00 0.01 0.04 0.02 7 ETR_DIFF 0.47 -0.52 -0.01 -0.03 -0.02 0.00

-0.02 0.04 0.05

8 FMAC 0.10 0.17 -0.02 0.39 0.13 0.01 0.00

0.42 0.44 9 COVERAGE 0.22 0.18 -0.06 0.59 0.25 0.04 0.04 0.42

0.54

10 RATING 0.20 0.20 -0.16 0.57 0.22 0.03 0.11 0.40 0.53

Note: This table reports the Pearson / Spearman correlation coefficients on the upper-right / lower-left triangle for the dependent and test variables. Correlation coefficients significant at the 5% or better are in bold. See Appendix A for variable definitions.

38

Table 3: Tests on the Associations Between Reputation and Tax-Avoidance Variables Test of H1a Test of H1b (1) (2) (3) (4) (5) (6)

Dep. Var.= GAAP ETR

CASH ETR

TAX SHELTER

UTB TAX PENALTY

TAX REST

Intercept 0.08 0.02 1.66*** -5.57*** -3.81*** -7.52*** (0.10) (0.05) (0.23) (0.41) (0.24) (1.50) FMAC -0.05*** -0.02*** 0.25*** 0.26*** -0.43*** -0.52**

(0.00) (0.01) (0.04) (0.09) (0.00) (0.00) AGE 0.00*** 0.01*** 0.00 0.00** 0.00 -0.00

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) BIG4 0.00 0.01* -0.18*** -0.03 0.44*** 0.34*

(0.01) (0.01) (0.07) (0.09) (0.08) (0.18) CAPINT 0.02 -0.02 -0.24** -1.74*** -0.83*** 0.12

(0.01) (0.01) (0.10) (0.17) (0.14) (0.30) DCA -0.00** -0.00 -0.01*** -0.00 -0.01 -0.02

(0.00) (0.00) (0.00) (0.01) (0.01) (0.03) INTANG 0.05*** 0.05*** 0.09 -1.20 *** -0.20* 0.74***

(0.01) (0.01) (0.07) (0.11) (0.11) (0.24) INVINT 0.13*** 0.12*** 0.10 -0.57* 0.19 -0.84

(0.02) (0.02) (0.18) (0.29) (0.25) (0.62) LEGIT -0.01 -0.00 0.02 0.05 0.22*** 0.13

(0.001) (0.01) (0.05) (0.10) (0.06) (0.14) LEVERAGE -0.05*** -0.06*** 0.04 0.10 -0.03 -0.17

(0.01) (0.01) (0.07) (0.15) (0.13) (0.30) MTB -0.00** -0.00 0.00 0.01* -0.00 -0.01

(0.00) (0.00) (0.00) (0.00) (0.00) (0.01) PIFO_DI 0.02*** 0.05*** 0.03 0.33*** 0.45*** 0.21

(0.01) (0.01) (0.05) (0.06) (0.05) (0.13) RDINT -0.00*** -0.00*** 0.00 0.06*** -0.21*** -0.06

(0.00) (0.00) (0.00) (0.02) (0.07) (0.07) RET -0.00** -0.00*** -0.00 0.04** -0.02 -0.07

(0.00) (0.00) (0.01) (0.02) (0.03) (0.06) ROA 0.01*** 0.01*** 0.01 -0.07 0.15 0.40

(0.00) (0.00) (0.01) (0.12) (0.11) (0.26) SIZE 0.02*** 0.02*** -0.06*** 1.07*** 0.35*** 0.14***

(0.00) (0.00) (0.01) (0.02) (0.02) (0.05) TAXFEE 0.00 0.00 0.01 0.06 0.02 0.11 (0.00) (0.00) (0.02) (0.04) (0.04) (0.08) TLC -0.00*** -0.00*** -0.01*** 0.13*** -0.06** -0.07

(0.00) (0.00) (0.00) (0.02) (0.03) (0.06) Yearly FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes N 14,419 14,419 14,419 8,104 14,419 14,419

R2/Pesudo R2 0.19 0.16 0.02 0.69 0.16 0.03 LR Ratio 2462.0 373.9

Note: This table reports the estimation results for Model (1). The full sample is used in the regression reported in all columns except Column (4), where the subsample with non-missing value for UTB is used. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. We calculate robust standard errors clustered at the firm-level. See Appendix A for variable definitions.

39

Table 4: Tests of the Effects of Bright-Line Rules in Accounting Standards on Tax Avoidance of Reputable Firms

Tests of H2

ETR_DIFF Intercept 0.07 (0.09) FMAC -0.04***

(0.00) CASH_ETR -0.55***

(0.01) AGE 0.00***

(0.00) BIG4 -0.00

(0.01) CAPINT 0.03***

(0.01) DCA -0.00**

(0.00) INTANG 0.03***

(0.01) INVINT 0.08***

(0.02) LEGIT -0.00 (0.00) LEVERAGE -0.03***

(0.01) MTB PIFO_DI

-0.00** (0.00) -0.01 (0.00)

RDINT -0.00*** (0.00)

RET -0.00 (0.00)

ROA 0.01*** (0.00)

SIZE 0.01*** (0.00)

TAXFEE 0.00 (0.00)

TLC -0.00*** (0.00)

Yearly FE Yes Industry FE Yes

R2 0.36 N 14,419

Note: *p<0.1; **p<0.05; ***p<0.01. Standard errors are cluster-robust at the firm level.

40

Table 5: Endogeneity Analysis – Heckman Two-Stage Model

Stage 1 Test of H1a Test of H1b Test of H2 (1) (2) (3) (4) (5) (6) (7)

GAAP ETR

CASH ETR

TAX SHELTER

UTB TAX PENALTY

TAX REST

ETR DIFF

CompCurr 0.074 (0.074)

ManaAbil -0.263 (0.390)

Lag_Ave_Cash_ETR

-0.077 (0.279)

FMAC -0.016** -0.022*** 0.188*** 0.112* -0.263*** -0.102* -0.013**

(0.007) (0.009) (0.040) (0.059) (0.076) (0.059) (0.006)

Other Controls

Yes Yes Yes Yes Yes Yes Yes Yes

Inv_Mills Yes Yes Yes Yes Yes Yes Yes N 6,288 6,288 6,288 3,634 6,288 6,288 6,288

Yearly FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes

R2 0.16 0.15 0.02 0.69 0.47 LR Ratio 2368.8 470.3 309.10

Note: This table reports the estimation results for H1a, H1b, and H2 using the Heckman two-stage regression. The column “Stage 1” reports the regression

results for the first stage in Model (3) using a probit estimation. The estimate models for Columns (1)-(6) are the same as the corresponding columns in Table 3, and the estimation model for columns (7) is the same as those in Table 4. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. We calculate robust standard errors clustered at the firm-level.

41

Table 6: Additional Tests of the Associations Between Reputation and Tax-Avoidance Related Variables Using Alternative Measures of Reputation (N=14,419)

Panel A – Tests using analyst coverage as the measure of reputation

Test of H1a Test of H1b Test of H2 (1) (2) (3) (4) (5) (6) (7)

GAAP ETR

CASH ETR

TAX SHELTER

UTB TAX PENALTY

TAX REST

ETR DIFF

Coverage -0.001** -0.001* 0.008*** 0.022*** -0.004* -0.027** -0.001* (0.000) (0.000) (0.003) (0.004) (0.002) (0.010) (0.000)

Yearly FE Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes

R2 0.19 0.15 0.02 0.69 0.35 LR Ratio 2431.9 374.4

Note: *: p<0.1; **: p<0.05; ***p<0.01. Standard errors of estimated coefficients are cluster-robust at the firm-level. Coefficients of other control variables are

not reported. Model 7includes CASH_ETR as the independent variable in the estimation. Panel B – Tests using debt ratings as the measure of reputation

Test of H1a Test of H1b Test of H2 (1) (2) (3) (4) (5) (6) (7)

GAAP ETR

CASH ETR

TAX SHELTER

UTB TAX PENALTY

TAX REST

ETR DIFF

Rating -0.025*** -0.013*** 0.154*** 0.171*** -0.024 -0.483*** -0.019*** (0.005) (0.005) (0.029) (0.046) (0.0325) (0.105) (0.003)

Yearly FE Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes

R2 0.18 0.15 0.02 0.69 0.36 LR Ratio 2337.0 392.30

Note: *p<0.1; **p<0.05; ***p<0.01. Standard errors of estimated coefficients are cluster-robust at the firm-level. Coefficients of other control variables are

not reported. Model 7 includes CASH_ETR as the independent variable in the estimation.

42

Table 7: Sample Used to Estimating the Tax Accrual Quality

Panel A: Descriptive Statistics

Variable Q1 Mean Median Q3 Std Dev

1 TaxAccr -0.0037 0.0002 0.0000 0.0073 0.0240

2 CTP 0.0001 0.0148 0.0044 0.0206 0.0242

3 CTP_lag1 0.0001 0.0148 0.0044 0.0206 0.0242

4 CTP_lead1 0.0001 0.0145 0.0042 0.0201 0.0240

5 dDTL_LT 0.0000 0.0014 0.0000 0.0001 0.0124

6 dDTA_LT 0.0000 0.0006 0.0000 0.0000 0.0075

Panel b: Correlation Coefficients

Variable 1 2 3 4 5 6

1 TaxAccr 1.00 -0.12 -0.01 0.14 0.27 -0.27

2 CTP -0.12 1.00 0.70 0.70 0.00 0.02

3 CTP_lag1 -0.01 0.70 1.00 0.58 0.03 0.02

4 CTP_lead1 0.14 0.70 0.58 1.00 0.02 0.01

5 dDTL_LT 0.27 0.00 0.03 0.02 1.00 -0.01

6 dDTA_LT -0.27 0.02 0.02 0.01 -0.01 1.00

Notes: This table reports the descriptive statistics and correlation coefficients for variables used in the tax accrual quality estimation regression in Model (4).

43

Table 8: Tests of the association between reputation and tax accrual quality (N=8,584)

1 2 3 4 5 6 Intercept -9.808*** -10.04*** -9.949*** -10.18*** -10.13*** -10.36*** (0.97) (0.98) (0.97) (0.98) (1.04) (1.05) FMAC

-0.044 -0.029 (0.295) (0.29)

COVERAGE -0.027 -0.026 (0.02) (0.02)

RATING -0.244 -0.235 (0.167) (0.17)

CASH_ETR 1.505*** 1.491*** 1.525*** (0.40) (0.40) (0.40)

AGE -0.007 -0.008 -0.009 -0.010 -0.006 -0.007 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

BIG4 0.338 0.302 0.313 0.277 0.369 0.332 (0.34) (0.34) (0.34) (0.34) (0.33) (0.33)

CAPINT 1.888*** 1.874*** 1.856*** 1.842*** 1.866*** 1.849*** (0.64) (0.64) (0.64) (0.64) (0.65) (0.64)

DCA -0.044 -0.043 -0.043 -0.042 -0.043 -0.042 (0.04) (0.04) (0.04) (0.04) (0.04) (0.04)

INTANG 1.953*** 1.827*** 1.903*** 1.779*** 2.013*** 1.882*** (0.51) (0.51) (0.51) (0.52) (0.51) (0.51)

INVINT 0.715 0.545 0.659 0.492 1.115 0.936 (1.28) (1.28) (1.28) (1.28) (1.24) (1.23) LEGIT -0.45* -0.45* -0.45* -0.45* -0.45* -0.45*

(0.25) (0.25) (0.25) (0.25) (0.25) (0.25) LEVERAGE -0.161 -0.009 -0.259 -0.105 -0.074 -0.076

(0.60) (0.59) (0.60) (0.60) (0.61) (0.61) MTB -0.018 -0.019 -0.016 -0.017 -0.017 -0.017

(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) PIFO_DI 0.050 -0.003 0.052 0.001 0.062 0.008

(0.23) (0.23) (0.23) (0.23) (0.23) (0.23) RDINT 0.134 0.138 0.141 0.144 0.134 0.137

(0.20) (0.20) (0.20) (0.20) (0.20) (0.20) RET -0.137 -0.122 -0.149 -0.133 -0.135 -0.120

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11) ROA 2.088** 1.989** 2.069** 1.971** 2.074** 1.974**

(0.93) (0.95) (0.92) (0.94) (0.93) (0.95) SIZE 0.582*** 0.573*** 0.676*** 0.663*** 0.621*** 0.611***

(0.10) (0.10) (0.12) (0.12) (0.11) (0.11) TAXFEE -0.165 -0.167 -0.147 -0.150 -0.154 -0.156

(0.22) (0.22) (0.23) (0.23) (0.23) (0.22) TLC 0.203* 0.206* 0.210* 0.213* 0.207* 0.210*

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11) Yearly FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes R2 0.09 0.09 0.09 0.09 0.09 0.09

Note: *: p<0.1; **: p<0.05; ***p<0.01. Standard errors are cluster-robust at the firm level.