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1. Introduction

This paper examines empirically the effect of auditor-provided non-audit tax services (NATS) on

stock price crash risk, and the mechanisms through which the relationship, if any, manifests

itself. Also examined, is whether tax service-induced crash risk varies according to the particular

business strategy pursued by firms. The study is motivated by a long-standing debate in the

history of auditing as to whether external auditors should provide non-audit services (NASs) to

clients, and the recent regulatory ban on auditors’ providing a variety of NASs excluding the

provision of NATS.

There are competing arguments regarding the implications of NASs for audit quality.

Opponents of NASs argue that auditor independence and, consequently, audit quality is impaired

when auditors also provide NASs. This idea was formally developed by DeAngelo (1981) who

relates auditor independence with client-specific future quasi–rents, defined as “...the excess of

revenues over avoidable costs, including the opportunity cost of auditing the next-best

alternative client” (italics in original) (p.116). She develops a two-dimensional definition of

audit quality that sets the standard for addressing the auditor independence issue. An auditor will

be considered independent if he/she can detect a material misstatement and then report it to the

audit service consumers. If no client-specific quasi-rents are expected from a given client

relationship, then the auditor should have no economic incentive to conceal financial

misstatements and, therefore, should be considered perfectly independent from the client.

However, if the client-specific future quasi-rents are high then auditors may compromise

independence in order to retain them. It has been argued that NASs provide more quasi-rents

than do audit services because of the higher margin derived from the former and, therefore,

NASs are more likely to impair auditor independence [SEC, 2000], particularly independence in

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appearance (Lindberg and Beck 2004). Proponents of auditor-provided NASs, however, argue

that, NASs provide considerable economies of scope. These economies of scope are broadly

categorized into knowledge spillover benefits (benefits from transferring information and

knowledge), and contractual economies (making better use of assets and/or safeguards already

developed when contracting and ensuring quality in auditing) (Simunic 1984; Beck et al. 1988;

Arrunada 1999).

The regulatory response to NASs, at least in the USA, favors the ‘impairment of auditor

independence’ argument. This response can be attributed to an independence concerns about big

audit firms arising from the provision of NASs to existing clients which, among other factors,

has been identified as one of the reasons for the massive corporate collapses experienced by the

US economy at the beginning of this century. Congress ratified the Sarbanes-Oxley Act (SOX)

in 2002. Section 201 of this act prohibits auditors from providing a variety of NASs for their

audit clients. However, the SEC determined that it would not prohibit auditor-provided NATS: a

specific type of NAS that auditors frequently provide to their audit clients. However significant

restrictions have been imposed on the provision of such services. In particular, (a) all auditor-

provided NATS are to be specifically pre-approved by the client’s audit committee; (b) fees paid

to auditors for NATS are to be identified separately in the company’s annual proxy statements;

(c) the auditor cannot audit his own work, function as a part of management, or serve in an

advocacy role for the audit client; and (d) SOX violations are subject to expanded criminal and

civil liabilities and penalties.

To address regulatory concerns, recent empirical studies have examined the relation

between auditor-provided NATS and earnings management, and find support for knowledge

spillover benefits from auditor-provided NATS outweighing an independence impairment

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concerns. For example, auditor-provided NATS reduce the incidence of financial restatements

(Kinney et al. 2004) as well as tax-related restatements (Seetharaman et al. 2011). Paterson and

Valencia (2011) confirm the Kinney et al.’s (2004) result, but only for recurring NATS. Non-

recurring tax engagements, on the other hand, increase the probability of future restatements (a

threat to auditor independence). Also consistent with the knowledge spillover argument, Omer et

al. (2006) and Krishnan and Visvanathan (2011) find a negative association between auditor-

provided NATS and discretionary accruals. Gleason and Mills (2011) find that companies

purchasing tax services from their auditors do not manage tax reserves to meet/beat analyst

forecasts any more than other companies.

These studies, however, did not examine a more extreme form of adverse outcome

having direct economic consequences for investors: stock price crash risk. Stock price crash risk

at the firm level refers to the likelihood of observing extreme negative values in the distribution

of firm-specific returns after adjusting for the return portion that co-move with common factors

(Jin and Myers 2006; Kim et al. 2011a; 2011b). Conceptually, stock price crash risk is premised

on the notion that managers have a tendency to withhold bad news for an extended period of

time, allowing bad news to stockpile. When the accumulation of bad news passes a threshold, it

is revealed to the market at once, leading to a large negative drop in price for the stock (Jin and

Myers, 2006). Crash risk is an important characteristic of the return distribution that is relevant

to portfolio theories, asset-pricing, and option-pricing models. Sunder (2010) argues that crash

risk is undiversifiable, unlike the risk from symmetric volatilities. Harvey and Siddique (2000)

suggest that conditional skewness (one proxy for crash risk) is a priced factor.1 Because investors

1 Crash risk, a third moment of stock returns capturing negative skewness, is distinct from other measures studied in prior research, such as the average return (first moments), and the variance of stock returns (the

second moments). Several studies (e.g. Gabaix 2012; Kelly and Jiang 2014; Huang et al. 2012) have both

hypothesized, and empirically documented, that crash risk increases the required return of investors. Conrad et

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are averse to stocks with greater crash risk due to the undiversifiable nature of those stocks,

determining whether auditor-provided NATS mitigate (exacerbate) crash risk by deterring

(aiding) managerial bad news hoarding behavior, would help investors better allocate their

resources.

In a recent empirical study, Kim et al. (2011a) document a positive association between

corporate tax avoidance and future crash risk. They argue that “Tax avoidance activities can

create opportunities for managers to pursue activities that are designed to hide bad news and

mislead investors…Perhaps more importantly, managers are able to justify the opacity of tax

avoidance transactions by claiming that complexity and obfuscation are necessary to minimize

the risk of tax avoidance arrangements being detected by the [taxing authorities]… To some

extent, these avoidance activities are shielded from the investigations of audit committees and

external auditors” (p. 640, italics added). Whereas Kim et al. (2011a) consider external auditor

monitoring of client tax avoidance to be lacking, we argue that the strength of the relationship

between tax avoidance and crash risk depends on auditor-provided NATS.

However, competing arguments exist as to whether auditor-provided NATS impair

auditor independence or generate spillover financial reporting quality benefits: i.e. insights

acquired from providing tax services can enhance audit effectiveness and client financial

reporting quality. In the former case external auditors could assist their clients by devising

complex tax avoidance strategies, thereby increasing the probability of a future crash. In the

latter case, however, the knowledge spillover created by auditor-provided NATS can enhance

overall audit effectiveness via better communication between the audit and tax sides, thus

mitigating auditor information asymmetry (Krishnan et al. 2013). Considered from this

al. (2013) find that more negatively skewed returns generate subsequent higher returns: a compensation for

greater crash risk. From an asset pricing perspective, Chang et al. (2013) find that negative skewness in market

returns is associated with a positive risk premium.

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perspective, future crash risk is likely to be lower for firms with incumbent auditors providing

NATS. We test these competing arguments by including two channels, namely, tax expense-

based earnings management and tax avoidance, through which the association between auditor-

provided NATS and crash risk could be more meaningfully tested.2

We use a two-stage model to control for potential endogeneity associated with the

selection of the auditor for tax services. Our first-stage model includes determinants of auditor-

provided NATS identified in prior research (McGuire et al. 2012). We then examine the impact

of auditor-provided NATS on future crash risk and inquire whether tax expense earnings

management and tax avoidance moderate the relationship. We use earnings management in tax

expenses as opposed to the commonly used discretionary accruals as our earnings management

proxy. Using negative conditional skewness (NCSKEW) and down-up volatility (DUVOL) as our

crash risk measures, we find that auditor-provided NATS mitigates future crash risk by reducing

earnings management in tax expenses, and deterring tax avoidance behavior. We, therefore,

provide empirical support for the knowledge spillover benefits emanating from the provision of

NATS. We also find that auditor-provided NATS constrain tax avoidance and, hence, reduce the

probability of a crash for firms following innovator business strategies, a finding that has not

been documented before. We consider the strategy-tax avoidance link, as Higgins et al. (2014)

show that innovators engage more in tax avoidance than their defender counterparts.

2 Zang et al. (2013) find that auditors are more likely to resign from firms with aggressive ax strategies (high

tax avoidance). Relevant to our study, they also document a weaker effect of tax aggressiveness on auditor

resignation if the incumbent auditors provide tax services. They interpret this finding as NATS-induced

impairment of auditor independence. However, an alternative interpretation may suggest that knowledge

spillover benefits from tax services allow incumbent auditors to be more efficient in auditing financial and tax

accounting, reducing the chance of reporting irregularities which, in turn reduces auditor exposure to costly

litigation. This line of reasoning supports the contention that tax services provision lowers the chance of

auditor resignation.

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We contribute to the literature in a number of ways. First, we provide evidence on the

desirability of allowing, further constraining, or banning auditor-provided NATS (PCAOB

2004). We do that by evaluating the extent to which provision of tax services increases or

decreases crash risk, a more acute form of outcomes having direct economic consequences for

investors. Unlike other studies that directly examine the effect of auditor-provided NATS on

earnings quality (Krishnan and Visvanathn 2011), we consider the joint effect of NATS and

earnings quality on crash risk. Second, we extend the growing literature on the determinants of

crash risk by investigating the effect of external auditors: an essential governance mechanism; on

crash risk. Since external auditors are directly responsible for verifying the authenticity of the

tax-related transactions reported in financial statements, it is logical to assume that a dominant

role is played by auditors. Robin and Zhang (forthcoming) find that industry specialist auditors

reduce future crash risk. They argue that industry specialist auditors have the expertise required

to reduce the managerial propensity to hoard bad news. Our study differs from Robin and Zhang

because of our focus on auditor-provided NATS and the role of tax expense-related earnings

management in moderating the relationship between NATS and future crash risk. Finally, we

contribute to the interface of strategy and crash risk research. An understanding of the

relationship between business strategy and crash risk, and of auditor-provided NATS moderating

such relationship, would assist investors in allocating resources carefully among companies with

differing business strategies.

The remainder of the paper proceeds as follows. Section 2 reviews the related literature

and develops testable hypotheses. Section 3 explains research design issues. The following

section provides our sample selection procedure and descriptive statistics. The main test results

are reported in section 5. The final section concludes the paper.

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2. Review of related literature and development of hypotheses

Auditor-provided NASs have been, and still remain, a matter of serious regulatory and investor

concern. While all fees create economic bonds between the auditor and client, debate has

typically alleged that the provision of NASs provide incentives for audit firms to accept clients’

questionable accounting choices, thus reducing auditor independence and ultimately the quality

of financial reporting. The provision of NASs has been the focus of most recent attention,

presumably reflecting the widely accepted view that NASs are typically provided at a higher

profit margin than audit services (Ruddock and Taylor 2005). Such concerns prompted the SEC

to prohibit most types of NASs in Section 201 of the SOX-2002 act. However, SEC allowed

auditor-provided NATS subject to the pre-approval of audit committees.3

Auditors evaluate the validity of accrued taxes payable and tax contingent liabilities on

the balance sheet, income tax expenses on the income statement, and the related note disclosures,

in order to provide adequate assurance to the investing public about the appropriateness of these

items and disclosures (Barrett 2004). Although many accruals facilitate earnings management,

managers can use valuation allowances (Frank and Rego 2006), tax contingency reserves (Gupta

et al. 2011), estimates of accrued taxes (Dhaliwal et al. 2004), and the designation of

permanently reinvested earnings (Krull 2004) to achieve earnings targets. Because any material

information about risky tax transactions tends to be hidden in these accounts and disclosures,

auditors also have to assess whether their clients engage in potentially abusive tax transactions.

Whether auditor-provided NATS help auditors to discover these risky tax transactions

(knowledge spillover), or impair independence by aiding clients in developing abusive taxation

3 For a representative list of NATS that were routinely provided by auditing firms to their audit clients but are either banned by SOX, no longer offered by some auditing firms to their clients, or are unlikely to be pre-

approved by audit committees, see Appendix B in Seetharaman et al. (2013).

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strategies (impairment of independence), has become an issue of immense importance for

regulators and investor community alike.

In a study of restatements involving GAAP violations, Kinney et al. (2004) find a

negative association between restatements of financial statements and tax services, but

Seetharaman et al. (2009) fail to find any such evidence. However, they do find a significant

negative association between auditor-provided tax services and tax-related restatements, which

would be consistent with knowledge spillover. Further evidence in support of the knowledge-

spillover benefits from NATS has been documented for the following: the propensity for auditors

to issue going-concern opinions (Robinson 2008); the maintenance of adequate tax reserves by

firms procuring tax services from incumbent auditors (Gleason and Mills 2010); lower debt

pricing costs for clients procuring tax services from the same auditor (Fortin and Pitman 2008);

and lower earnings management in tax expenses for firms having auditors providing NATS

(Lisic 2014). Krishnan and Visvanathn (2011) also find support for auditor-provided tax

services, constraining earnings management, but their proxy for earnings management is loss

avoidance, a proxy that which fails to capture the tax-related earnings management practices. In

a follow-up study, Krishnan et al. (2013) find that earnings are more value-relevant for firms

where incumbent auditors provide tax services. However, their study does not investigate the

possible channels that strengthen or weaken this relationship (e.g., information quality, external

monitoring and so on).

Evidence in support of a threat to auditor independence from auditor-provided NATS has

been documented by Cook et al. (2008). They find that incumbent auditors who also provide tax

services help clients to reduce 4th quarter effective tax rates (ETR), thereby increasing earnings

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management. Harris and Zhou (2013) find that auditor-provided NATS reduce non-tax internal

control weaknesses, but not tax-related internal control weaknesses.

Since the decision to use an incumbent auditor for tax services is endogenously

determined, some studies examine the rationale for procuring tax services from incumbent

auditors. Albring et al. (2014) find that corporate governance attributes, such as board

independence, the audit committee’s accounting expertise, and separation of the CEO and

chairman of the board, play a role in a firm’s decision to switch to a non-auditor provider for tax

services. Lassila et al. (2010) provide evidence that firms with strong corporate governance and

relatively high levels of tax and operational complexity are more likely to retain their auditor for

tax services.

In a recent empirical work Kim et al. (2011a) find convincing evidence that tax

avoidance increases future stock price crash risk supporting the contention that aggressive tax

strategies and planning provide managers with a means to conceal negative information that

increases crash risk. Kim et al. (2011a), however, did not investigate whether auditor-provided

NATS aid or deter tax-related earnings management as well as tax avoidance behavior.

Therefore external auditors’ involvement in the tax avoidance-crash risk link remains

unexplored. Instead Kim et al. (2011a) considered strength of external monitoring, proxied by

analyst coverage and institutional shareholdings, as potential mechanisms for reducing crash

risk. We fill this void by arguing that incumbent auditors providing tax services are better able to

differentiate between value-enhancing versus value-destroying tax planning strategies. Firms that

engage in tax aggressiveness have a higher chance of issuing misstatements and restatements

because managers can use various accounts, such as valuation allowances, tax contingency

reserves, and estimates of accrued taxes to manipulate earnings (e.g., Hanlon and Heitzman

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2010; Frank and Rego 2006; Gupta et al. 2011; Dhaliwal et al. 2004). Aggressive tax positions

involve complex and risky techniques, which require additional research, specialized audit

procedures, documentation, and consultations with tax professionals to audit (Donohoe and

Knechel 2013).4

Audit firms providing tax services to their clients package their reputation or technical

expertise in the provision of such services and, simultaneously, optimize their unique ability as

auditors to leverage these benefits into higher financial reporting quality (Seetharaman et al.,

2013). Insights gained from performing tax services enable auditors to be more familiar with

clients’ strategic decisions regarding tax planning: a feature that benefits the auditors in

uncovering tax expense-related earnings management and tax avoidance policies. That

familiarity also facilitates management’s consideration of the financial reporting implications of

various alternatives, and results in more certainty in the financial reporting of the company’s tax

positions, while constraining opportunities for management to use complex transactions for

obfuscating financial reporting opaqueness. Since financial reporting opaqueness has been

identified to be an important determinant crash risk (Jin and Myers 2006; Hutton et al. 2009), the

above argument on knowledge spillover suggests a negative relationship between auditor-

provided NATS and crash risk.

A competing argument exists, however. Auditor-provided NATS could threaten auditor

independence by increasing auditors’ economic dependence on their clients. This has become

4 For example, complex tax shelters created by Enron allowed managers to manipulate earnings while

preventing investors from understanding the sources (Desai and Dharmapala 2009). The tax avoidance

activities arranged by Tyco facilitated the centralization of power by management, and enabled them to

obscure their rent diversion through means such as unauthorized compensation, abuse of corporate funds for

personal uses, and insider trading (Desai 2005). In line with this view, studies document that aggressive tax reporting leads to lower earnings quality, a higher likelihood of managerial fraud, and higher stock price crash

risk (e.g., Badertscher et al. 2009; Ettredge et al. 2008; Frank et al. 2009; Hanlon 2005; Kim et al. 2011a; Zang

et al. 2013).

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more of a concern after SEC’s simultaneous prohibition on other NASs. Seetharaman et al.

(2013) conjecture that this may have the unintended consequence of shifting the source of

independence problems from other categories of NASs to NATS. Such lack of independence will

reduce the incentives for incumbent auditors to detect and report tax-related earnings

manipulation, with the resulting consequence of a greater probability of future crash. Our first

hypothesis on the association between NATS and future crash risk is as follows:

H1: Auditor-provided NATS are associated with tax-related earnings management and client tax

avoidance with an implication for future stock price crash.

Our second hypothesis is built on the premise that there is an association between firm-

level business strategies, corporate tax avoidance, and subsequent crash risk. We argue that these

firm-level determinants of crash risk are determined, to a large extent, by the unique business

strategies pursued by individual firms. For example, firms following aggressive business

strategies may have to engage more in earnings manipulation in order to sustain overly optimistic

expectations by investors about the future performance of these firms (Higgins et al., 2014;

Skinner and Sloan 2002; Lakonishok et al. 1994).

Miles and Snow (1978, 2003) detail three viable business strategies that may exist

simultaneously within industries—Prospectors, Defenders, and Analyzers— because of

differences in the magnitude and direction of change regarding their particular products and

markets (Hambrick 1983). These strategies are positioned along a continuum, with prospectors at

one end and defenders at the other. Prospectors change their product market mix rapidly in order

to be innovative market leaders, while defenders focus more on a narrow and stable product base

in order to compete on the basis of price, service, or quality. Firms that constitute the middle of

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the continuum are termed analyzers, and possess the attributes of both prospectors and defenders

(Miles and Snow 1978, 2003). Prior research on organization theory has demonstrated that

prospectors have a higher level of outcome uncertainty, are plagued with more information

asymmetry, and structure executive compensation that is primarily long-term and incentive-

based (Rajagopalan 1997; Balsam et al. 2011; Kothari et al. 2009). Higgins et al. (2014)

document an increasing propensity for innovators to engage in more tax avoidance compared to

their defender counterparts. Their finding is explained by the fact that firm strategies are, in part,

based on firms’ willingness to deal with risk and uncertainty, with prospectors being subject to

more uncertainty and, hence, requiring more tax planning. Habib and Hasan (2014) provide

empirical evidence that prospectors are more prone to crash. These two streams of literature,

therefore, suggest that the tax avoidance propensities of prospectors may contribute to future

crash. Auditor-provided NATS may strengthen (weaken) this relationship depending on whether

the independence impairment or the knowledge spillover argument dominates. Our second

hypothesis is as follows:

H2: There is an association between auditor-provided NATS and future crash for firms

pursuing differing business strategies.

3. Research design and sample selection

3.1 Stock price crash risk

In this study, two measures of firm-specific crash risk are used, as in Chen et al. (2001). Both

measures are based on firm-specific weekly returns estimated as the residuals from the market

model. This ensures that our crash risk measures reflect firm-specific factors rather than broad

market movements. Specifically, we estimate the following expanded market model regression:

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)1.......(,,,,, ,2,51,4,31,22,1, jmjmjmjmjmjjj rrrrrr

where r,j,τ is the return of firm j in week τ, and rm,τ is the return on the CRSP value-

weighted market return in week τ. The lead and lag terms for the market index return is included

to allow for non-synchronous trading (Dimson, 1979). The firm-specific weekly return for firm j

in week τ (W j,τ) is calculated as the natural logarithm of one plus the residual return from Eq. (1)

above. In estimating equation (1), each firm-year is required to have at least 26 weekly stock

returns. Our first measure of crash risk is the negative conditional skewness of firm-specific

weekly returns over the fiscal year (NCSKEW). NCSKEW is calculated by taking the negative of

the third moment of the firm-specific weekly returns for each year, and normalizing it by the

standard deviation of firm-specific weekly returns raised to the third power. Specifically, for

each firm j in year τ, NCSKEW is calculated as:

NCSKEW= 2/3,

2,

32/3 ))(2)(1(/)1( jj wnnwnn ……..(2)

Our second measure of crash risk is the down-to-up volatility measure (DUVOL) of the crash

likelihood. For each firm j over a fiscal-year period τ, firm-specific weekly returns are separated

into two groups: ‘‘down’’ weeks when the returns are below the annual mean, and ‘‘up’’ weeks

when the returns are above the annual mean. The standard deviation of firm-specific weekly

returns is calculated separately for each of these two groups. DUVOL is the natural logarithm of

the ratio of the standard deviation in the ‘‘down’’ weeks to the standard deviation in the ‘‘up’’

weeks:

Up

j

Downdjuj wnwnDUVOL ,

2,

2

, )1/()1(log ……………….(3)

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A higher value of DUVOL indicates greater crash risk. As suggested in Chen et al. (2001),

DUVOL does not involve third moments and, hence, is less likely to be overly influenced by

extreme weekly returns.

3.2 Earnings management

Earnings management for this study is proxied by earnings management in tax expenses

following Dhaliwal et al. (2004). Specifically, we compare changes in 4th quarter ETR from 3rd

quarter ETR to discern the presence of earnings management. A more negative difference

implies higher earnings management in tax expenses. ETR is defined as accumulated tax

expenses (Compustat quarterly #6) divided by accumulated pre-tax income (quarterly #23). We

believe the ETR-based earnings management proxy is more relevant for our study as we are

interested in auditors’ monitoring role over tax-based earnings management practices. We

include discretionary accruals as an additional control variable in our model to rule out the

possibility that discretionary accruals subsume the effect of tax expense earnings management.

3.3 Business strategy composite measure

Our composite strategy score is derived from Bentley et al. (2013). A higher (lower) score

represents companies with prospector (defender) strategies. Bentley et al. (2013) constructed

their index using a wide variety of firm characteristics intended to capture the differences in the

magnitude and direction of change regarding its products and markets (Hambrick 1983).

Characteristics included are: (a) the ratio of R&D to sales (measure of a firm’s propensity to seek

new products); (b) the ratio of employees to sales (firm’s ability to produce and distribute its

goods and services efficiently); (c) a measure of employee fluctuations (standard deviation of

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total employees); (d) a historical growth measure (one-year percentage change in total sales)

(proxy for a firm’s historical growth); (e) the ratio of marketing (SG&A) to sales (a proxy for

firms’ emphasis on marketing and sales); and (f) a measure of capital intensity (net PPE scaled

by total assets) (designed to capture a firms’ focus on production).

All variables are computed using a rolling average over the prior five years. Each of the

six individual variables is ranked by forming quintiles within each two-digit SIC industry-year.

Within each company-year, those observations with variables in the highest quintile are given a

score of 5, in the second-highest quintile are given a score of 4, and so on, and those

observations with variables in the lowest quintile are given a score of 1. Then for each company-

year, the scores across the six variables are summed such that a company could receive a

maximum score of 30 (prospector-type) and a minimum score of 6 (defender-type).5

3.4 Empirical model

Before formally developing empirical models for testing our hypotheses we estimate the

likelihood of a firm’s procuring tax services from incumbent auditors. Prior research finds that

the decision to use the incumbent auditor for tax services is driven by several factors (Omer et al.

2006; Lassila et al. 2010; McGuire et al. 2012). To control for the influence of the observable

and unobservable determinants associated with the decision to hire the auditor for tax services,

we use a two-stage model (Heckman 1979). Our first-stage model includes likely determinants of

the decision to acquire tax services from incumbent auditors following McGuire et al. (2012).

We estimate the following probit model (firm and year subscripts are not reported):

5 See Appendix 2 in Bentley et al. (2013) for a detailed discussion on the estimation of the STRATEGY score.

The scoring for the capital intensity measure is inverted because defenders are expected to have the highest

capital intensity.

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)4.......(..............................4&

1615

1413121110987

6543210

SECTIERBIGCASHROAPPEBTMLEVDRFIEQINC

NOLDACSIZELAFAUDINDMERGERATSD

Where ATSD is an indicator variable set equal to 1 if the firm purchased tax services from their

incumbent auditors, and 0 otherwise; MERGER is an indicator variable set equal to 1 if the firm

participated in any merger activity during the year, and 0 otherwise; AUDIND is auditor

independence from the client, measured as non-audit fees less tax fees divided by total audit fees

received from the client; LAF is natural log of audit fees received from the client; SIZE is natural

log of market value of equity; |DAC| is the absolute value of Modified Jones (1995)

discretionary accruals; NOL is an indicator variable set equal to 1 if there is a tax loss carry

forward during year t, and 0 otherwise; EQINC is equity income for year t scaled by total assets

at the beginning of the year; FI is pre-tax foreign income for year t scaled by total assets at the

beginning of the year; R&D is R&D expense for year t scaled by total assets at the beginning of

the year; LEV is long-term debt for year t scaled by total assets at the beginning of the year; BTM

is book-to-market ratio for the end of year t, measured as book value of equity divided by market

value of equity; PPE is net PPE for year t scaled by total assets at the beginning of the year; ROA

is return on assets for year t, measured as the ratio of income before extraordinary items to the

average of total assets for the year; CASH is cash holding at the end of year t divided by total

assets at the beginning of the year; BIG4 is an indicator variable set equal to 1 if the firm is

audited by one of the Big 4 auditors, and 0 otherwise; and SECTIER is an indicator variable set

equal to 1 if the firm is audited by a second-tier accounting firm; namely, Grant Thornton LLP

and BDO Seidman LLP, and 0 otherwise. As is consistent with prior research, positive

coefficients are expected on MERGER, LAF, SIZE, NOL, FI, LEV, PPE, ROA, and BIG4, and

negative coefficients on AUDIND, |DAC|, EQINC, R&D, BTM, CASH, and SECTIER. We

17 | P a g e

estimate IMR from the first-stage model and use this as an additional independent variable in the

subsequent regressions in order to address endogeneity.

To investigate the association between auditor-provided NATS and future crash risk we

develop the following regression model.

)5(........................................4||

3*

15114113112

11111019181716

15141312110,

IMRBIGROADACLEVMTBSIZESDRETRETTURN

ETRETRTAXETRTAXCRASHCRASH

ttt

tttttt

tttttti

where CRASH risk is proxied by the NCSKEW and DUVOL measures following

equations (2) and (3) above. The independent variables are calculated using data from the

preceding year, as is consistent with the crash risk literature. We first control for the lag value of

CRASH_RISK in order to account for the potential serial correlation of NCSKEW or DUVOL for

the sample firms. TAX is the dollar value of tax services in thousands (in a sensitivity analysis we

also report the primary results using tax fee ratio). The coefficient on TAX could be either

positive or negative with respect to crash risk, depending on whether auditor-provided NATS

threaten auditor independence or generate financial reporting quality spillover benefits. ∆ETR is

change in ETR from the 3rd to the 4th quarter, and is our proxy for earnings management in tax

expenses. We expect the coefficient to be negative. Our variable of primary interest is

TAX*∆ETR which captures the effect of NATS-induced earnings management and its effect on

future crash risk. We expect the coefficient to be negative (positive) if the knowledge spillover

(impairment of independence) argument dominates. ETR3 is included in the model to control for

possible mean reversion in ETR (Lisic, 2014).

Inclusion of the control variables follows prior literature on the determinants of crash

risk. TURNt-1 is the average monthly share turnover for the current fiscal year minus the average

18 | P a g e

monthly share turnover for the previous fiscal year, where monthly share turnover is calculated

as the monthly trading volume divided by the total number of shares outstanding during the

month. Chen et al. (2001) indicate that this variable is used to measure differences of opinion

among shareholders, and it is significantly, positively related to crash risk proxies. Chen et al.

(2001) show that negative skewness is larger in stocks that have had positive stock returns over

the prior 36 months. To control for this possibility, we include past one-year weekly returns

(RETt-1). SDRET t-1 is the standard deviation of firm-specific weekly returns over the fiscal year,

and denotes stock volatility as more volatile stocks are likely to be more crash prone. Chen et al.

(2001) also demonstrate that negative return skewness is higher for larger firms. To control for

the size effect, we add SIZEt-1 measured as the natural log of total assets. The variable MTBt-1 is

the market value of equity divided by the book value of equity. Both Chen et al. (2001) and

Hutton et al. (2009) show that growth stocks are more prone to future crash risk. LEVERAGEt-1

is the total long-term debt divided by total assets, and is has been shown to be negatively

associated with future crash risk (Kim et al. 2011a, b). DAC t-1 are the absolute discretionary

accruals calculated using the Modified Jones model (1995) and should be positively associated

with crash risk (Hutton et al., 2009). ROA is included in the model to control for the profitability

effect on crash risk. Finally, we include BIG 4 in the regression equation to control for differing

audit quality which might have implications for the managerial propensity to withhold bad news:

the underlying determinant of crash risk.

H1 also predicts that auditor-provided NATS will affect future crash risk by deterring or

aiding clients’ abusive tax avoidance practices. The following regression specification is

developed to test this proposition.

19 | P a g e

)6.........(..............................4||

*

14113112

11111019181716

15141312110,

IMRBIGROADACLEVMTBSIZESDRETRET

TURNAVOIDTAXAVOIDTAXCRASHCRASH

tt

tttttt

tttttti

We use three proxies to operationalise tax avoidance behavior. Lisowsky et al. (2012)

suggest that the probability of engaging in tax sheltering, discretionary permanent book-tax

difference, permanent book-tax difference, book-tax difference, and cash ETR all capture the

varying degree of tax aggressiveness, from most to least aggressive. We consider tax sheltering

and permanent book-tax differences as the two most aggressive form of tax avoidance and also

use ETR as the least aggressive form of tax avoidance. We first use the tax shelter prediction

score developed by Wilson (2009) as follows:

SHELTER = -4.86 + 5.20 * BTD + 4.08 * DAC - 1.41 * LEV + 0.76* SIZE + 3.51 * ROA + 1.72

* FI + 2.43 * R&D…………………………………(7)

where BTD is book income less taxable income scaled by lagged total assets, DAC is the

discretionary accruals from the performance-adjusted modified cross-sectional Jones Model,

LEV is long-term debt divided by total assets; SIZE is the log of total assets, ROA is pre-tax

earnings divided by total assets, FI is foreign pre-tax earnings divided by lagged total assets,

R&D is research and development expenditure divided by lagged total assets. A high score

implies more sheltering and, hence, more tax avoidance. This proxy of tax sheltering captures a

more complicated form of sheltering that allows managers to conceal bad news, and increases

the probability of a crash in the future (Kim et al., 2011a).

Our second tax avoidance proxy, which is also considered to be relatively aggressive, is

discretionary permanent book-tax differences (DTAX), a subset of BTD.6 Following Frank et al.

6 BTDs can arise from the unbiased application of financial and tax reporting . However, managerial incentives

to report higher book income but lower taxable income may motivate managers to apply reporting discretions

to manage book income in ways that do not increase tax income or using tax-planning and sheltering strategies

20 | P a g e

(2009) we first compute the permanent book-to-tax difference (PERMDIFF) as total book-tax

differences (BTD) less temporary book-tax differences (TXDI/STR). DTAX is defined as the

residuals from the regression of permanent differences on several determinants of

nondiscretionary permanent differences unrelated to tax planning, and estimated by year and two

digit SIC code, with at least 20 firms in each industry:

PERMDIFF = α0 + α1(1/ATLAG) + α2INTANG + α3UNCON + α4MI + α5CSTE + α6ΔNOL

+α7LAGPERM + ε )……………………..(8)

ATLAG refers to lagged total assets (AT), INTANG refers to goodwill and other

intangibles (INTAN), UNCON refers to income/loss reported under the equity method (ESUB),

MI refers to income/loss attributable to minority interest (MII), CSTE refers to current state tax

expense (TXS), ΔNOL refers to the change in net operating loss carried forwards (TLCF) and

LAGPERM is the lagged PERMDIFF. All variables are scaled by lagged total assets.7

Our third measure of tax avoidance is proxied by firm’s ETR calculated as the ratio of

total tax expenses divided by Pre-tax income. The lower the ETR, the higher the tax avoidance

propensity will be. Of the three tax avoidance proxies used, ETR is considered to be least

aggressive and, therefore, is the proxy least likely to capture the effect of egregious tax

avoidance on crash risk. The coefficient on the interaction variable TAX*AVOID will indicate

whether auditor-provided NATS creates spillover benefits mitigating future crash risk, or

to reduce taxable income without decreasing book income. BTDs have been shown to be associated with

earnings management activities (Phillips, Pincus, and Rego 2003), growth in future earnings (Lev and Nissim

2004), and earnings persistence (Hanlon 2005). 7 Both SHELTER and DTAX have their share of limitation, though. Both these measures rely on a very small

sample of firms (59 and 211 shelter-engaging firms in Wilson and Lisowsky respectively) whose shelter

behavior was detected by the tax authorities and litigated. Frank et al. (2009) DTAX measure relies on the

premise that permanent differences are more aggressive than timing differences. Although the shelter

probabilities and DTAX are likely correlated with aggressive tax planning, they may fail to satisfactorily

captures all potential aggressive tax planning strategies.

21 | P a g e

threatens auditor independence and, hence, accentuates future crash risk. In the former (latter)

case the coefficient on the interaction variable should be negative (positive).

Finally we test tax provision by incumbent auditors on crash risk for firms following

differing business strategies. The following regression equation is developed.

)9.........(..............................4||

****

*

18117116115

11411311211111019

181716

15141312110,

IMRBIGROADACLEVMTBSIZESDRETRETTURN

AVOIDSTRATEGYTAXAVOIDSTRATEGYSTRATEGYTAX

AVOIDTAXAVOIDSTRATEGYTAXCRASHCRASH

ttt

tttttt

ttt

tttttti

where, STRATEGY is categorized into prospector/innovator strategies (PROSPECT) and

defender (DEFEND) strategies. Details of STRATEGY score composition are given in 3.3 above.

All other variables are defined as before. Higgins et al. (2014) document an increasing

propensity for innovators to engage in more tax avoidance compared to their defender

counterparts. If auditor-provided NATS mitigate future crash risk then this effect should be more

pronounced for innovators willing to engage in more tax avoidance. Therefore, we expect a

negative coefficient on the three-way interaction variable TAX*STRATEGY*AVOID if

knowledge spillover benefits dominate.

To control for unobservable industry and year characteristics associated with firm tax

service provision and crash risk, we include year and industry dummy variables in all our

regression specifications. To take into account the time series and cross-sectional dependence in

the error terms of our regressions, we calculate t-statistics using standard errors that are clustered

by both firm and year (Petersen 2009).

22 | P a g e

4. Sample selection and descriptive statistics

Our sample selection procedure begins with a total of 163,266 firm-year observations with

available auditor-provided NATS and relevant audit-related data retrieved from Audit Analytics

from the 2000-2012 sample period. We begin our sample period in 2002, the year that Congress

ratified SOX. We then excluded firm-year observations from the regulated industries (two digit

SIC 49) and financial institutions (two-digit codes 60-69). This eliminated a total of 55,720 firm-

year observations. We further eliminated 15,879 non-US firm-year observations, yielding a US

sample with the required audit data of 88,897 firm-year observations. We then matched CIK

codes from COMPUSTAT with the Audit Analytics CIK codes and found a matched sample of

45,573 firm-year observations. Not all of these observations had the requisite crash risk measures

and related control variables, and this reduced the sample to 21,950 firm-year observations. Our

sample size further reduces for tax avoidance analysis because of missing tax avoidance data.

Panel A in Table 1 provides descriptive statistics for the variables used in the regression

analyses. The mean values of the crash risk measures, NCSKEW and DUVOL, are -0.07 and -

0.37 respectively. About 67% of the firm-year observations procure tax services from their

incumbent auditors. In terms of dollar values, average firms pay about $303,000 in tax fees

although there is substantial variation among companies as is evident from the high standard

deviation. Average tax avoidance measures are 0.42, 0.02, and 0.28 for SHELTER, DTAX, and

ETR measures respectively. The average STRATEGY score is 16.91 on a scale of 6 to 30, with

prospectors and defenders constituting 5% and 11% respectively of the sample observations

respectively. Seventy seven percent of the firm-year observations are audited by Big 4 audit

firms. Firm-year observations come from a wide variety of industries with two digit SIC codes

35-39 and 70-79 commanding the largest industry representation in our sample. Panel C in Table

23 | P a g e

1 presents the correlation analysis. NATS and the two measures of crash risk are correlated

positively. Both crash risk proxies as well as NATS are significantly positively related to firm

size.

[TABLE 1 ABOUT HERE]

5. Multivariate regression results

We begin our multivariate analysis by modeling the determinants of firms’ decision to procure

tax services from their incumbent auditors, in order to alleviate the endogeneity concern, since

the choice of incumbent auditors over other providers is not a random selection. Regression

specification (4) is a probit model whereby auditor-provided NATS is regressed on a set of

variables likely to determine a firm’s decision to engage the incumbent auditor as tax service

provider (McGuire et al. 2012). The model includes a number of audit-related variables, e.g.

BIG4 and second tier auditor indicators, audit fees, and a variable that captures auditor

independence. Also included are financial variables likely to explain a firm’s decision to choose

the incumbent auditor as NATS provider. We find that larger firms, firms audited by Big 4, firms

paying higher audit fees, and firms with more foreign income are more likely to procure NATS

from incumbent auditors compared to firms audited by second tier audit firms, and firms with

more equity income. We computed IMR from this regression model and used it as an additional

independent variable in all the subsequent regressions to address the self-selection concern.

[TABLE 2 ABOUT HERE]

24 | P a g e

Table 3 reports our results for H1, which investigates the association between auditor-

provided NATS and future crash risk (Models 1& 2), and whether tax expense earnings

management (change in ETR from 3rd to 4th quarter) acts as a conduit through which NATS

accentuates or attenuates future crash risk (Models 3 & 4). The baseline regression model

reveals that NATS, proxied by the dollar amount of tax service costs, is negatively related to

future crash risk for both the crash risk measures (coefficients of -0.000017 and -0.000014 for

NCSKEW and DUVOL respectively). These negative and significant coefficients suggest that

auditor-provided NATS reduce future crash risk, lending support to the argument that knowledge

spillover benefits are being derived from NATS. Among the control variables, the coefficient on

average returns (RET) and ROA is positive and that on return volatility is negative suggesting

that firms with better stock and accounting performance and lower volatility are more likely to

experience crashes. This also suggests that crashes are unlikely to be a manifestation of declining

business conditions, continuation of poor stock performance (i.e., negative stock momentum),

and/or high stock volatility. Larger firms and high M/B firms are more prone to crash risk. BIG4

audit firms appear to constrain future crash risk but the coefficient is significant in the baseline

model.

We then extend our baseline regression model by incorporating two additional

moderating variables, namely change in 4th quarter ETR (∆ETR) and the interaction between TAX

and ∆ETR (TAX*∆ETR). Since a decrease in 4th quarter ETR compared to 3rd quarter ETR is used

as a proxy for earnings management, the coefficient on the interaction variables is expected to be

negative (positive) if the knowledge spillover (impairment of independence) argument

dominates. The negative (positive) coefficient, in turn, would indicate a lower (higher)

probability of a crash. Our empirical result is consistent with the knowledge spillover argument

25 | P a g e

as the interaction variables for both crash risk measures are negative and significant at better than

the 5% level. The coefficient on the baseline TAX variable continues to be negative and

significant.

Overall, the test of H1 supports the auditors’ constraining effect on tax expense

management, courtesy of their more intimate NATS-derived knowledge, which has the potential

to reduce subsequent crash risk.

[TABLE 3 ABOUT HERE]

Next we examine the association between auditor-provided NATS and crash risk, and ask

whether clients’ tax avoidance strategies strengthen or weaken the relationship. As argued

before, aggressive tax positions involve complex and risky techniques providing management

with the tools and justifications for opportunistic managerial behavior, such as earnings

manipulations, related party transactions, and other resource-diverting activities (e.g., Chen et al.

2010; Desai and Dharmapala 2006; Kim et al. 2011a). Auditor-provided NATS could threaten

auditor independence, should auditors devise tax avoidance strategies for their clients. Since tax

avoidance allows managers to hoard bad news, the combined effect of auditor-provided NATS

and tax avoidance would increase the probability of a crash (Kim et al., 2011a). On the other

hand, the knowledge spillover argument would suggest that incumbent auditors who are also

providing tax services would be better able to deter opportunistic tax avoidance strategies, thus

reducing future crash risk.

Table 4 reports results for regression equation (6). We use three proxies for tax

avoidance, with SHELTER (ETR) being considered as the most (least) aggressive form of tax

avoidance. For SHELTER and DTAX proxies, we find the coefficient on SHELTER to be positive

26 | P a g e

and significant for both the crash risk proxies. The insignificant coefficient on AVOID using the

ETR measure lends support to the less aggressive nature of ETR. Our primary interest, however,

is the coefficient on the interaction variable TAX*AVOID which should be negative and

significant if NATS provides knowledge spillover benefits. The coefficient on the interaction

variable is negative and significant for both crash measures when SHELTER and DTAX are used

as the tax avoidance measures. For example, the coefficient on TAX*AVOID for the SHELTER

measure is -0.000017 and -0.000013 for the NCSKEW and DUVOL risk measures respectively.

Both these coefficients are statistically significant at better than the 5% level. Overall, our

analysis in Table 4 again supports the argument that auditor-provided NATS generate knowledge

spillover benefits which mitigate future crash risk.

[TABLE 4 ABOUT HERE]

Our final empirical analysis examines the combined effect of NATS and tax avoidance

on crash risk for firms pursuing differing business strategies. This test is motivated by the

observation that many of the firms’ tax-related decisions are influenced to a certain extent by

their business strategies. Following the Miles and Snow (1978, 2003) strategy typology, which

places prospectors and defenders at the extremes of a spectrum, Higgins et al. (2014) document

an increasing propensity for innovators to engage in more tax avoidance compared to their

defender counterparts. We argue that if auditor-provided NATS provide knowledge spillover

benefits then the mitigating effect on future crash risk would be more pronounced for firms

following innovator business strategies.

Table 5 reports result for this analysis. We include auditor-provided NATS (TAX), tax

avoidance (AVOID), and business strategy continuous score, and their interactions in table 5. We

are interested in the three-way interaction variable TAX*AVOID*STRATGEY. If auditor-provided

27 | P a g e

NATS create knowledge spillovers and if such expertise allows auditors to constrain tax

avoidance behavior for firms with innovator business strategies, then we should expect a

negative and significant coefficient on the three-way interaction terms. Our results are consistent

with H2 as we find the coefficient on the three-way interaction term to be negative and

significant for both SHELTER and DTAX proxies. The coefficient is insignificant for the ETR

measure, which supports the less aggressive nature of this tax avoidance measure. Because of the

three-way interaction, all the two-way interactions and main effects are also included. However,

the signs on the two-way interactions and main effects can no longer be easily interpreted after

the inclusion of the three-way interaction.

Additional analyses

(i) Discretionary accruals as earnings management: In our main test we have used the tax-

specific earnings management technique, i.e., changes in ETR from the 3rd to the 4th quarter. To

rule out the possibility that earnings management, as captured by the Modified Jones model

(1995), subsumes the effect of tax-specific earnings management for future crash risk, we

include an interaction variable TAX*DAC in Table 4. Un-tabulated results reveal that the

coefficient on the interactive variable is insignificant.

(ii) Tax fee ratio: We also conducted an additional analysis using the fee ratio (total tax

fees/total fees) as an alternative NATS proxy. The coefficients on the interaction variables FEEt-

1*∆ETRt-1 is -0.04 and -0.024, statistically significant at better than the 5% and 10% level

respectively, for NCSKEW and DUVOL measures. The coefficients on the interaction variable

FEEt-1*AVOIDt-1 is also negative and significant for both crash risk measures. Finally, the

coefficients on the three-way-interaction variable FEE*STRATEGY*AVOID is negative and

28 | P a g e

significant at better than the 5% consistent with the results reported using TAX (in ,000)

variable.

(iii) External monitoring by financial analysts, NATS, and crash risk: As discussed earlier,

the agency theory view of managerial incentives for withholding bad news, whether through

earnings management and/or tax avoidance, is based on the agency tension between managers

and shareholders. Strong external monitoring mechanisms can act as complements or substitutes

to auditor monitoring. Kim et al. (2011a) find that the positive relation between tax avoidance

and stock price crash risk is diminished for firms with strong external monitoring mechanisms.

We use analyst coverage as one such external monitoring mechanism. We retrieve the number of

analysts following a firm from I/B/E/S and include it as an additional independent variable in

Tables 3 and 4. Our primary result: the negative effect of auditor-provided NATS and the

interaction variables TAX*∆ETR and TAX*AVOID on crash risk; remains unchanged.

6. Conclusion

The possible impairment of external auditor independence resulting from the joint provision of

audit and NASs has long been a concern for regulators and the investment community. SOX-

2002 prohibits auditors from providing most NASs to their clients’ firms, with the exception of

NATS. The regulatory mandate for this choice may have been motivated by the presumption that

auditor-provided NATS generate spillover benefits for financial reporting that outweigh the

related independence concern. To assess whether this is indeed the case, recent empirical studies

have examined the relation between auditor-provided NATS and different facets of financial

reporting quality, and provide some support for continuing the provision of tax services by

incumbent auditors. However, tax avoidance activities can create opportunities for managers to

29 | P a g e

pursue activities that are designed to hide bad news and mislead investors. Complex and risky

tax avoidance arrangements, therefore, may increase the crash risk, a direct economic

consequence for investors. Consistent with this conjecture, Kim et al. (2011a) find that tax

avoidance indeed increases future crash risk. Kim et al. (2011a), however, did not investigate

whether auditor-provided NATS aid or deter tax-related earnings management as well as tax

avoidance behavior. Therefore external auditor involvement in the tax avoidance-crash risk link

remains unexplored. We fill this void in the literature.

Our empirical findings reveal that when auditors also provide NATS financial reporting

quality improves, at least from the perspective of reporting tax transactions, and the probability

of a future crash reduces. We, therefore, find support for spillover benefits, and concur with the

regulatory decision to allow audit firms to provide NATS. We also find that, auditor-provided

NATS constrain tax avoidance and, hence, reduce crash risk for firms following innovator

business strategies.

We contribute to the literature by providing evidence on the desirability of allowing,

further constraining, or banning auditor-provided NATS (PCAOB 2004). We also extend the

growing literature on the determinants of crash risk by investigating the effect of external auditor

monitoring through the provision of NATS on crash risk. Finally, our study contributes to the

interface of organizational theory, corporate governance, and crash risk discipline by informing

investors of the characteristics of stocks that are relatively more likely to experience a future

crash; and of the potential reduction in crash risk for those stocks from financial reporting quality

spillover benefits gained in providing NATS.

30 | P a g e

References

Albring, S., D. Robinson, and M. Robinson. 2014. Audit committee financial expertise, corporate

governance, and the voluntary switch from auditor-provided to non-auditor-provided tax services.

Advances in Accounting, 30 (1): 81-94, 2014.

Arrunada, B. 1999. The provision of non-audit services by auditors: let the market evolve and decide.

International Review of Law and Economics, 1999, 19(4), 513-531.

Badertscher, B., J. D. Phillips, M. Pincus, and S. O. Rego. 2009. Earnings management strategies and the

trade-off between tax benefits and detection risk: To conform or not to conform? The Accounting

Review 84 (1): 83–97.

Balakrsihnan, K., Blouin, J., and Guay, W. 2011. Does tax aggressiveness reduce financial

reporting transparency? Working paper, University of Pennsylvania.

Balsam, S., Fernando, G.D., and Tripathy, A. (2011). The impact of firm strategy on performance

measures used in executive compensation. Journal of Business Research 64 (2): 187-193.

Barrett, M. J. 2004. “Tax services” as a Trojan horse in the auditor independence provisions of

Sarbanes-Oxley. Michigan State Law Review 2004: 463-504.

Beck, P. J., T. J. Frecka, and 1. Solomon. 1988. A model of the market for NAS and audit

services: Knowledge spillovers and auditor-auditee bonding. Journal of Accounting

Literature 1: 50-64.

Bentley, K. A, Thomas C Omer, and Nathan Y Sharp. 2013. "Business Strategy, Financial Reporting

Irregularities, and Audit Effort." Contemporary Accounting Research 30 (2): 780-817.

Bentley, K. A., Thomas C Omer, and Brady J Twedt. 2013. "Does Business Strategy Impact a Firm’s

Information Environment?" Available at SSRN 2162240, Working paper, University of New

South Wales.

Chang, B. Y., P. Christoffersen, and K. Jacob. 2013. Market skewness risk and the cross section of stock

returns. Journal of Financial Economics 107 (1): 46–68.

Chen, Joseph, Harrison Hong, and Jeremy C Stein. 2001. "Forecasting Crashes: Trading Volume, Past

Returns, and Conditional Skewness in Stock Prices." Journal of Financial Economics 61 (3):

345-381.

Conrad, J. S., R. F. Dittmar, and E. Ghysels. 2013. Ex ante skewness and expected stock returns. Journal

of Finance 68 (1): 85–124.

Cook, K., G. R. Huston, and T. Omer. 2008. Earnings management through effective tax rates: The

effects of tax planning investment and the Sarbanes-Oxley Act of 2002. Contemporary

Accounting Research 25 (2): 447–71.

DeAngelo, L.E. 1981. Auditor independence, ‘low balling’, and disclosure regulation. Journal of

Accounting and Economics, 3, 113-127.

Dechow, Patricia M., Richard G. Sloan, and Amy P. Sweeney. 1995. "Detecting Earnings Management."

The Accounting Review 70 (2): 193-225.

Desai, M., 2005. The degradation of reported corporate profits. Journal of Economic Perspectives 19,

171-195.

Desai, M., and D. Dharmapala. 2009. Earnings management, corporate tax shelters, and book-tax

alignment. National Tax Journal 62: 169-186.

Dhaliwal, D., C. Gleason, and L. Mills. 2004. Last-chance earnings management: Using the tax expense

to meet analysts‟ forecasts. Contemporary Accounting Research 21 (2): 431-459.

Dimson, Elroy. 1979. "Risk Measurement When Shares Are Subject to Infrequent Trading." Journal of

Financial Economics 7 (2): 197-226.

Donohoe, M. P., and W. R. Knechel. 2013. Does corporate tax aggressiveness influence audit

pricing? Contemporary Accounting Research, 31(1), 284-308.

31 | P a g e

Ettredge, M., L. Sun, P. Lee, and A. Anandarajan. 2008. Is earnings fraud associated with high

deferred tax and/or book minus tax levels? Auditing: A Journal of Practice & Theory 27 (1): 1–

33.

Fortin, S., and J. Pittman. 2008. The impact of auditor-related tax services on corporate debt pricing. The

Journal of the American Taxation Association 30 (Fall): 79–106.

Frank, M., Lynch, and L., Rego, S., 2009. Tax reporting aggressiveness and its relation to

aggressive financial reporting. The Accounting Review 84, 467–496.

Gabaix, X. 2012. Variable rare disasters: An exactly solved framework for ten puzzles in macro-finance,

Quarterly Journal of Economics 127(2): 645–700.

Gleason, C., and L. Mills. 2011. Do auditor-provided tax services improve the estimate of tax

reserves? Contemporary Accounting Research 28 (5): 1484–1509.

Gupta, S. R. Laux, and D. Lynch. 2011. Do firms use tax cushion reversals to meet earnings targets?

Evidence from the pre- and post-FIN 48 periods. Working paper, Michigan State University.

Habib, A., and Hasan. M. (2014). Business strategy, overvalued equities, and stock price crash risk.

Working paper, Massey University and Curtin University.

Hambrick, Donald C. 1983. "Some Tests of the Effectiveness and Functional Attributes of Miles and

Snow's Strategic Types." Academy of Management journal 26 (1): 5-26.

Hanlon, M., and S. Heitzman. 2010. A review of tax research. Journal of Accounting and Economics 50

(2/3): 127–178.

Hanlon, M., and J. Slemrod. 2009. What does tax aggressiveness signal? Evidence from stock price

reactions to news about tax shelter involvement. Journal of Public Economics 93, 126-141.

Hanlon, M. 2005. The persistence and pricing of earnings, accruals, and cash flows when firms have large

book-tax differences. The Accounting Review 80(1): 137-166.

Harris, D., and Zhou, J. (2013). Auditor-provided tax consulting, knowledge spillovers, and

reported weaknesses in internal control. Working paper, Syracuse University and University of

Hawaii at Manoa

Harvey, Campbell R, and Akhtar Siddique. 2000. Conditional skewness in asset pricing tests. The Journal

of Finance 55 (3): 1263-1295.

Heckman, James J, (1979). Sample Selection Bias as a Specification Error, 47(1), 153-61.

Higgins, Danielle, Thomas C Omer, and John D Phillips. 2014. The Influence of a Firm's Business

Strategy on Its Tax Aggressiveness. Contemporary Accounting Research.

Huang, W., Q. Liu, G. Rhee, and F. Wu. 2012. Extreme downside risk and expected stock returns.

Journal of Banking and Finance 36 (5): 1492–1502.

Hutton, Amy P., Alan J. Marcus, and Hassan Tehranian. 2009. "Opaque Financial Reports, R2, and Crash

Risk." Journal of Financial Economics 94 (1): 67-86.

Ittner, Christopher D., David F. Larcker, and Madhav V. Rajan. 1997. "The Choice of Performance

Measures in Annual Bonus Contracts." The Accounting Review 72 (2): 231-255.

Jin, Li, and Stewart C. Myers. 2006. "R2 around the World: New Theory and New Tests." Journal of

Financial Economics 79 (2): 257-292.

Kelly, B., and H. Jiang. 2014. Tail risk and asset prices. Review of Financial Studies, forthcoming.

Kim, J-B., Y. Li, and L. Zhang. 2011a. Corporate tax avoidance and stock price crash risk: Firm-level

analysis. Journal of Financial Economics 100: 639-662.

Kim, J-B, Y. Li, and L. Zhang. 2011b. CFOs versus CEOs: equity incentives and crashes. Journal of

Financial Economics 101 (3): 713-730.

Kinney, W.R., Z. Palmrose, and S. Scholz. 2004. Auditor independence, non-audit services, and

restatements: was the U.S. government right? Journal of Accounting Research 42: 561- 588.

Koester, A. 2012. Investor valuation of tax avoidance through uncertain tax positions, Working paper,

Georgetown University.

Kothari, S.P., Shu, S., and Wysocki, P.D. (2009). Do managers withhold bad news? Journal of

Accounting Research 47 (1): 241-276.

32 | P a g e

Krishnan, G., G. Visvanathan, and Wu, Y. (2013). Do Auditor-Provided Tax Services Enhance or

Impair the Value Relevance of Earnings? The Journal of the American Taxation Association

35(1) (Spring: 1-19.

Krishnan, G., and G. Visvanathan. 2011. Is there an association between earnings management and

auditor provided tax services? The Journal of the American Taxation Association 33 (Fall): 111–

135.

Lakonishok, J., Shleifer, a., and Vishny, R.V. (1994). Contrarian Investment, Extrapolation, and Risk.

The Journal of Finance 49 (5): 1541-1578.

Lassila, D. R., T. C. Omer, M. K. Shelley, and L. M. Smith. 2010. Do complexity, governance, and

auditor independence influence whether firms retain their auditors for tax services? The Journal

of the American Taxation Association 32 (Spring): 1–23.

Lev, B., and D. Nissim. 2004. Taxable income, future earnings, and equity values. The Accounting

Review 79 (4): 1039–74.

Lisic, L.L. 2014. Auditor-provided tax services and earnings management in tax expense: The

importance of audit committees. Journal of Accounting Auditing & Finance, 29(3), 340- 366.

Lisowsky, P., Robinson, L., Schmidt, A., 2012. Do publicly disclosed tax reserves tell us about

privately disclosed tax shelter activity? Journal of Accounting Research, forthcoming.

McGuire S. T., T. C. Omer, and D. Wang. 2012. Tax avoidance: Does tax-specific industry

expertise make a difference? The Accounting Review 87 (3): 975-1003.

Miles, R. E., and C. C. Snow. 1978. Organizational Strategy, Structure, and Process. New York:

McGraw-Hill.

———. 2003. Organizational Strategy, Structure, and Process: Stanford, CA: Stanford University Press.

Omer, T., J. Bedard, and D. Falsetta. 2006. Auditor-provided tax services: The effects of a

changing regulatory environment. The Accounting Review 81 (October): 1095–1117.

Paterson, J. S. and A. Valencia. 2011 The effects of recurring and nonrecurring tax, audit related, and

other nonaudit services on auditor independence. Contemporary Accounting Research 28(5):

1510-1536.

Phillips, J., M. Pincus, and S. Rego. 2003. Earnings management: New evidence based on

deferred tax expense. The Accounting Review 78 (2): 491–521.

Public Company Accounting Oversight Board (PCAOB). 2004. Auditor independence tax services

roundtable. Available at http://pcaobus.org/ standards/standards_and_related_rules/2004-07-

4_roundtable_transcript.pdf

Rajagopalan, N. (1997). Strategic orientations, incentive plan adoptions, and firm performance: evidence

from electric utility firms. Strategic Management Journal 18 (10): 761-785.

Rego, S., and R. Wilson, 2012. Equity risk incentives and corporate tax aggressiveness, Journal of

Accounting Research, 50(3), 775-809.

Robin, A., and Zhang, H. Do Industry-Specialist Auditors Influence Stock Price Crash Risk?

Forthcoming in Auditing: A Journal of Practice & Theory.

Robinson, D. 2008. Auditor independence and auditor-provided tax service: Evidence from going-

concern audit opinions prior to bankruptcy filings. Auditing: A Journal of Practice & Theory 27

(November):31–54.

Ruddock, C., S. J. Taylor and S. L. Taylor. (2005). Nonaudit Services and Earnings Conservatism: Is

Auditor Independence Impaired?’ Contemporary Accounting Research, 23(3): 701-746.

Securities and Exchange Commission (SEC), Final rule: Revisions of the Commission’s Auditor

Independence Requirements, Government Printing Office, 2000.

Seetharaman, A., Y. Sun, and W. Wang. 2011. Tax-related financial statement restatements and auditor

provided tax services. Journal of Accounting, Auditing and Finance 26 (4): 677– 698.

Simunic, D. (1984). Auditing, consulting, and auditor independence. Journal of Accounting

Research, 22 (2), 679-702.

33 | P a g e

Skinner, D., and Sloan, R.G. (2002). "Earnings Surprises, Growth Expectations, and Stock Returns or

Don't Let an Earnings Torpedo Sink Your Portfolio." Review of Accounting Studies 7 (2-3): 289-

312.

Sunder, S., 2010. Riding the accounting train: from crisis to crisis in eighty years. Presentation at the

Conference on Financial Reporting, Auditing and Governance, Lehigh University, Bethlehem,

PA.

Wilson, R., 2009. An examination of corporate tax shelter participants. The Accounting Review 84:

969-999.

34 | P a g e

Appendix 1: Variable definitions

Variables Explanation

NCSKEW

Negative conditional skewness of firm-specific weekly returns over the fiscal year. NCSKEW is calculated by taking the

negative of the third moment of firm-specific weekly returns for each year and normalizing it by the standard deviation of

firm-specific weekly returns raised to the third power [See text for the detailed formula].

DUVOL Down-to-up volatility measure of the crash likelihood. For each firm j over a fiscal-year period t, firm-specific weekly returns

are separated into two groups: ‘‘down’’ weeks when the returns are below the annual mean, and ‘‘up’’ weeks when the returns

are above the annual mean. The standard deviation of firm-specific weekly returns is calculated separately for each of these

two groups, and DUVOL is the natural logarithm of the ratio of the standard deviation in the ‘‘down’’ weeks to the standard

deviation in the ‘‘up’’ weeks. For both crash risk measures the firm-specific weekly return for firm j in week τ (W j,τ) is

calculated as the natural logarithm of one plus the residual return from the following expanded market model regression:

)1.......(,,,,, ,2,51,4,31,22,1, jmjmjmjmjmjjj rrrrrr

where r,j,τ is the return of firm j in week τ, and rm,τ is the return on CRSP value-weighted market return in week τ. The lead and

lag terms for the market index return is included to allow for non-synchronous trading (Dimson, 1979). In estimating equation

(1), each firm-year is required to have at least 26 weekly stock returns.

TURN Average monthly share turnover over the current fiscal year minus the average monthly share turnover over the previous fiscal

year, where monthly share turnover is calculated as the monthly trading volume divided by the total number of shares

outstanding during the month.

RET One-year weekly returns.

SDRET Standard deviation of firm-specific weekly returns over the fiscal year.

ATSD An indicator variable set equal to 1 if the firm purchased tax services from the auditor and 0 otherwise.

TAX (‘000) Total dollar amount in thousands paid to auditors for tax services.

FEERATIO Tax fees received by audit firms as a proportion of total fees (audit + audit-related + tax).

MERGER An indicator variable set equal to 1 if the firm participated in any merger activity during the year and 0 otherwise.

AUDIND Auditor independence from the client, measured as non-audit fees less tax fees divided by total audit fees received from the

client.

LAF Natural log of audit fees received from the client.

SIZE Natural log of market value of equity.

NOL An indicator variable coded 1 if the firm reported net operating loss carried forward and 0 otherwise.

EQINC Equity income for year t scaled by total assets at the beginning of the year.

FI Pre-tax foreign income for year t scaled by total assets at the beginning of the year.

LEV Long-term debt for year t scaled by total assets at the beginning of the year.

BTM Book-to-market ratio for the end of year t, measured as book value of equity divided by market value of equity

ROA Return on assets for year t, measured as the ratio of income before extraordinary items to the average of total assets for the

35 | P a g e

year

CASH Cash holding at the end of year t divided by total assets at the beginning of the year.

BIG4 An indicator variable set equal to 1if the firm is audited by one of the Big 4 auditors and 0 otherwise.

SECTIER An indicator variable set equal to 1 if the firm is audited by a second-tier accounting firm: namely, Grant Thornton LLP and

BDO Seidman LLP, and 0 otherwise.

ETR ETR is defined as accumulated tax expense (Compustat quarterly #6) divided by accumulated pre-tax income (quarterly #23).

SHELTER Tax shelter prediction score developed by Wilson (2009) using BTD, DAC, LEV, SIZE, ROA, FI, and R&D.

DTAX Discretionary permanent book-tax differences (DTAX): a subset of BTD. Following Frank et al. (2009) we first compute

permanent book-to-tax difference (PERMDIFF) as total book-tax differences (BTD) less temporary book-tax differences

(TXDI/STR). DTAX is defined as the residuals from the regression of PERMDIFF on several determinants of nondiscretionary

permanent differences unrelated to tax planning,

DAC Absolute discretionary accruals calculated using the performance-adjusted Modified Jones model (Kothari, Leone, and

Wasley, 2005). We estimate the following model for all firms in the same industry (using the SIC two-digit industry code)

with at least 8 observations in an industry in a particular year, to get industry-specific parameters for calculating the non-

discretionary component of total accruals (NDAC). DAC is then the residual from model (1), i.e., DAC=ACC-NDAC. Where

ACC = Net income operating cash flows (OCF)/Lagged total assets.

ttttttt ROAPPERECEIVABLESalesAssetsACC 132110 )/1(

Where, ACC is total accruals defined as the difference between net income before extraordinary items and operating cash

flows (OCF), PPE is gross property, plant & equipment, ROA is return on assets. All variables are deflated by lagged assets.

R&D5 Ratio of research and development expenditures [XRD] to sales [SALE] computed over a rolling prior 5 year average.

EMPLOYEE5 Ratio of the number of employees [EMP] to sales [SALE] computed over a rolling prior 5 year average.

REV5 One-year percentage change in total sales computed over a rolling prior five-year average.

SG&A5 Ratio of selling, general and administrative (SG&A) expenses to sales computed over a rolling prior five-year average.

SD EMPLOYEE5 Standard deviation of the total number of employees computed over a rolling prior five-year period.

CAP5 Capital intensity measured as net property, plant, and equipment scaled by total assets and computed over a rolling prior five-

year average.

STRATEGY Each of the above six individual variables is ranked by forming quintiles within each two-digit SIC industry-year. Within each

company-year, those observations with variables in the highest quintile are given a score of 5, in the second-highest quintile

are given a score of 4, and so on ((except capital intensity which is reversed-scored so that observations in the lowest (highest)

quintile are given a score of 5 (1)). Then for each company-year, the scores across the six variables are summed such that a

company could receive a maximum score of 30 (prospector-type) and a minimum score of 6 (defender-type).

36 | P a g e

Table 1: Panel A

Descriptive statistics

Variables Mean SD 25% 50% 75% Observations

Crash risk

proxies

CRASH_NCSKEW t -0.07 1.17 -0.74 -0.07 0.61 21,950

CRASH_NCSKEWt-1 -0.01 1.06 -0.68 -0.05 0.60 21,950

CRASH_DUVOL t -0.37 0.91 -0.85 -0.29 0.21 21,950

CRASH_ DUVOL t-1 -0.31 0.81 -0.80 -0.27 0.21 21,950

Tax service

variables

ATSD t-1 0.67 0.47 0.00 1.00 1.00 21,950

TAX (in ‘000 dollar) t-1 302.65 1017.67 0.00 36.75 201.07 21,950

FEERATIO 0.10 0.14 0.00 0.05 0.17 21,950

ETR4-ETR3 t-1 0.0007 0.09 -0.01 -0.0006 0.01 15,483

ETR3 t-1 0.32 0.12 0.29 0.35 0.38 15,483

AVOID (SHELTER) t-1 0.42 1.96 -0.71 0.51 1.69 12,149

AVOID(PERMDIFF) t-1 0.02 0.17 -0.02 0.01 0.06 11,114

AVOID (ETR) t-1 0.28 0.16 0.19 0.31 0.37 17,473

Business

strategy

variables

STRATEGY t-1 16.91 3.66 14 17 19 12,149

PROSPECT t-1 0.05 0.21 - - - 12,149

DEFEND t-1 0.11 0.32 - - - 12,149

Control

variables

for crash

risk

TURN t-1 0.00 0.10 -0.03 0.00 0.03 21,950

RET t-1 0.07 0.01 0.00 0.00 0.01 21,950

STDRET t-1 2.63 0.04 0.05 0.06 0.09 21,950

SIZE t-1 6.04 1.99 4.60 6.02 7.42 21,950

MTB t-1 2.63 3.38 1.17 1.91 3.17 21,950

LEV t-1 0.16 0.18 0.00 0.11 0.26 21,950

|DAC| t-1 0.31 0.59 0.04 0.10 0.29 21,950

ROA t-1 0.04 0.18 -0.01 0.06 0.13 21,950

1st stage

regression

variables

MERGER t 0.18 0.38 0.00 0.00 0.00 21,950

AUDIND t 0.10 0.15 0.00 0.04 0.12 21,950

LNAF t 13.47 1.55 12.55 13.55 14.41 21,950

NOL t 0.81 0.39 1.00 1.00 1.00 21,950

EQINC t 0.00 0.00 0.00 0.00 0.00 21,950

FI t 0.02 0.04 0.00 0.00 0.02 21,950

R&D t 0.04 0.08 0.00 0.01 0.06 21,950

PPE t 0.26 0.24 0.09 0.19 0.36 21,950

CASH t 0.19 0.22 0.04 0.12 0.28 21,950

BIG 4 t 0.77 0.42 - - - 21,950

SECTIER t 0.09 0.29 - - - 21,950

37 | P a g e

Panel B: Industry distribution

Industry code Industry Observations % distribution

1-14 Agriculture & mining 1,158 0.05

15-17 Building construction 163 0.01

20-21 Food & Kindred Products 682 0.03

22-23 Textile Mill Products & apparels 404 0.02

24-27 Lumber, furniture, paper, and printing 917 0.04

28-30 Chemical, petroleum, and rubber & Allied Products 2,243 0.10

31-34 Metal 1,063 0.05

35-39 Machinery, electrical, computer equipment 7,171 0.33

40-48 Railroad and other transportation 901 0.04

50-51 Wholesale goods, building materials 973 0.04

53-59 Store merchandise, auto dealers, home furniture stores 2,030 0.09

70-79 Business services 3,165 0.14

80-99 Others 1,080 0.05

Total 21,950 1.00

38 | P a g e

Panel C: Correlation analysis

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

NCSKEW (1) 1.00

NCSKEWt-1 (2) -0.00 1.00

DUVOL (3) 0.80 -0.00 1.00

DUVOL t-1 (4) 0.04 0.78 0.12 1.00

TAX t-1 (5) 0.02** 0.02** 0.08 0.08 1.00

TURN t-1 (6) 0.03 0.04 0.05 0.03 0.001 1.00

RET t-1 (7) 0.03 0.07 0.009 0.11 -0.02 0.12 1.00

STDRET t-1 (8) -0.05 -0.11 -0.14 -0.25 -0.15 0.11 0.16 1.00

SIZE t-1 (9) 0.08 0.10 0.26 0.30 0.39 0.05 -0.07 -0.38 1.00

MTB t-1 (10) 0.04 0.06 0.04 0.06 0.06 0.05 0.14 -0.07 0.05 1.00

LEV t-1 (11) -0.002 -0.01* 0.03 0.02** 0.06 0.05 -0.02** 0.001 0.31 -0.04 1.00

|DAC| t-1 (12) -0.01 -0.001 -0.02** -0.03 0.00 -0.02** 0.001 0.06 -0.08 0.03 -0.05 1.00

ROA t-1 (13) 0.05 -0.06 0.19 0.15 0.08 0.04 0.17 -0.34 0.28 0.09 -0.04 -0.06 1.00

BIG4 t-1 (14) 0.04 0.05 0.14 0.17 0.15 0.02** -0.001 -0.17 0.51 0.05 0.16 -0.07 0.15 1.00

Italicized and bold-faced correlations are significant at p<0.001. ** and * represent statistical significance at p<0.05 and p<0.01 respectively.

Variable definitions are in the text and in Appendix 1.

39 | P a g e

Table 2: Determinants of tax service purchase

)4.......(..............................4&

1615

1413121110987

6543210

SECTIERBIGCASHROAPPEBTMLEVDRFIEQINC

NOLDACSIZELAFAUDINDMERGERATSD

Variables Coefficient z-statistics

Constant -5.19***

-12.29

MERGER -0.001

-0.04

AUDIND -0.50***

-3.68

LAF 0.10***

-5.18

SIZE 0.20***

12.43

DAC -0.03

-0.87

NOL 0.06

1.25

EQINC -7.67*

-1.95

FI 2.86***

5.35

R&D -0.12

-0.45

LEV 0.09

0.80

BTM 0.003

0.65

PPE -0.07

-0.66

ROA 0.05

0.53

CASH -0.09

-0.97

BIG4 0.51***

9.57

SECTIER -0.18***

-2.92

Industry FE Yes

Year FE Yes

Pseudo R2 0.20

Observations 21,950

***, **, and * represent statistical significance at the 1%, 5%, and 10% level respectively (two-tailed test)

Variable definitions are in Appendix 1.

40 | P a g e

Table 3: Auditor-provided non-audit tax services, financial reporting quality and crash

risk

)5(........................................4||

3*

15114113112

11111019181716

15141312110,

IMRBIGROADACLEVMTBSIZESDRETRETTURN

ETRETRTAXETRTAXCRASHCRASH

ttt

tttttt

tttttti

Baseline model Earnings management model

Variables Model (1) Model (2) Model (3) Model (4)

Coefficient [t-statistics]

Coefficient [t-statistics]

Coefficient [t-statistics]

Coefficient [t-statistics]

Constant -0.25

[-1.07]

-0.67***

[-3.61]

-0.93**

[-2.71]

-1.16***

[-4.67]

NCSKEW t-1 -0.02**

[-2.44]

- -0.01*

[-1.64]

-

DUVOL t-1 - 0.03***

[3.51]

- 0.02**

[2.38]

TAX t-1 -0.000017**

[-2.16]

-0.000014***

[-2.78]

-0.000019***

[-2.70]

-0.000019***

[-3.23]

∆ETRt-1 -0.0040

[-1.04]

-0.002

[-0.97]

TAXt-1*∆ETRt-1 - - -0.00001**

[-2.33]

-0.000013**

[-2.26]

ETR3t-1 - - -0.02***

[-3.09]

-0.02***

[-3.18]

TURN t-1 0.32***

[3.61]

0.27***

[4.12]

0.08

[0.68]

0.19**

[2.28]

RET t-1 1.68**

[1.97]

3.31***

[5.04]

1.21

[1.03]

4.01***

[4.47]

STDRET t-1 -0.14

[-0.48]

-1.50***

[-6.29]

0.66

[1.59]

-1.55***

[-4.96]

SIZE t-1 0.05***

[5.30]

0.10***

[11.80]

0.08

[8.08]

0.08***

[13.96]

MTB t-1 0.0082***

[3.34]

0.005***

[2.97]

0.01

[3.49]

0.007***

[4.36]

LEVt-1 -0.14**

[-2.55]

-0.11***

[-2.72]

-0.04

[-1.10]

-0.05

[-1.51]

|DAC| t-1 -0.001

[-0.07]

-0.02

[-1.29]

-0.009

[-0.48]

-0.013

[-0.91]

ROA t-1 0.10*

[1.80]

0.46***

[10.46]

0.00

[0.12]

0.002

[0.56]

BIG4 -0.09***

[-2.66]

-0.05*

[-1.79]

-0.02

[-0.55]

0.02

[0.61]

IMR -0.06

[-0.83]

-0.14**

[-2.39]

0.16

[1.53]

-0.02

[-0.29]

Industry FE Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Adjusted R2 0.02 0.10 0.02 0.11

Observations 21,950 21,950 15,483 15,483

***, **, and * represent statistical significance at the 1%, 5%, and 10% level respectively (two-tailed

test). Variable definitions are in Appendix 1.

41 | P a g e

Table 4: Auditor-provided non-audit tax services, tax avoidance, and crash risk

)6.........(..............................4||

*

14113112111110

1918171615141312110,

IMRBIGROADACLEV

MTBSIZESDRETRETTURNAVOIDTAXAVOIDTAXCRASHCRASH

tttt

tttttttttti

Avoidance=SHELTER Avoidance=DTAX Avoidance=ETR

Variables Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Constant -0.50**

[-2.01]

-0.44**

[-2.06]

-0.65

[-1.60]

-0.71**

[-2.46]

-0.54*

[-1.82]

-0.64***

[-3.10]

NCSKEW t-1 -0.03***

[-3.22]

- -0.04***

[-3.75]

- -0.013

[-1.56]

-

DUVOL t-1 - 0.01

[1.13]

- 0.003

[0.31]

- 0.03***

[3.44]

TAX t-1 -0.000072*

[-1.88]

-0.000016**

[-2.20]

-0.01

[-0.13]

-0.000020**

[-2.73]

0.000035

[1.53]

0.000023

[1.38]

AVOID t-1 0.02**

[1.85]

0.016*

[4.89]

0.15*

[1.87]

0.11**

[2.08]

0.04

[0.58]

0.01

[0.22]

TAX t-1*AVOID t-1 -0.000017**

[-2.05]

-0.000013**

[-2.45]

-0.000016*

[-1.63]

-0.000016**

[-2.13]

-0.00017

[-1.54]

-0.00015**

[-2.33]

TURN t-1 0.38***

[3.15]

0.30***

[3.25]

0.18

[1.38]

0.16*

[1.76]

0.23**

[2.29]

0.23***

[3.12]

RET t-1 2.10*

[1.78]

2.56***

[2.78]

1.75

[1.41]

4.67***

[4.94]

1.05

[1.04]

3.15***

[4.21]

STDRET t-1 -0.69*

[-1.84]

-1.72***

[-5.23]

0.02

[0.04]

-2.01***

[-6.12]

0.09

[0.27]

-1.42***

[-5.73]

SIZE t-1 0.01

[0.83]

0.06***

[6.29]

0.06***

[5.54]

0.09***

[10.22]

0.05***

[4.79]

0.08***

[9.62]

MTB t-1 -0.00

[-0.11]

0.0076***

[3.26]

-0.007

[-0.59]

0.04***

[2.96]

0.04***

[8.46]

0.004**

[2.03]

LEVERAGEt-1 0.07*

[1.68]

-0.15**

[-2.60]

0.04

[0.88]

0.03

[1.03]

-0.16***

[-2.67]

-1.11**

[-2.46]

|DAC| t-1 0.03

[1.32]

-0.009

[-0.53]

0.02

[0.90]

-0.01

[-0.66]

0.00

[0.06]

-0.02

[-1.20]

42 | P a g e

Avoidance=SHELTER Avoidance= DTAX Avoidance=ETR

Variables Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

ROA t-1 0.001

[0.42]

0.44***

[6.29]

0.002

[1.10]

0.003**

[2.13]

0.18***

[2.94]

0.48***

[10.93]

BIG4 -0.08*

[-1.79]

-0.02

[-0.59]

-0.05

[-1.14]

-0.05

[-1.47]

-0.05**

[-2.00]

0.0055

[0.29]

IMR -0.17*

[-1.77]

-0.25***

[-3.62]

-0.02

[-0.13]

-0.24**

[-2.37]

-0.04

[-0.46]

-0.18***

[-2.70]

Industry FE Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes

Adjusted R2 0.03 0.11 0.03 0.11 0.02 0.10

Observations 12,149 12,149 11,114 11,114 17,473 17,473

***, **, and * represent statistical significance at the 1%, 5%, and 10% level respectively (two-tailed test). Variable definitions are in Appendix 1.

43 | P a g e

Table 5: Business strategy, tax avoidance, auditor-provided non-audit tax services and crash risk

)7.........(..............................4||***

**

18117116115114

113112111110191817

1615141312110,

IMRBIGROADACLEVMTBSIZESDRETRETTURNAVOIDSTRATEGYTAXAVOIDSTRATEGY

STRATEGYTAXAVOIDTAXAVOIDSTRATEGYTAXCRASHCRASH

tttt

ttttttt

ttttttti

AVOIDANCE=SHELTER AVOIDANCE= DTAX AVOIDANCE=ETR

Variables Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Constant 0.14

[0.52]

-0.18

[-0.85]

-0.68

[-2.04]

-0.66

[-2.31]

-0.17

[-0.49]

-0.25

[-0.96]

NCSKEW t-1 -0.03***

[-3.00]

- -0.04

[-3.77]

-0.02**

[-2.19]

-

DUVOL t-1 - 0.02

[1.34]

0.004

[0.36]

- 0.03***

[3.38]

TAX t-1 -0.0002

[-1.25]

-0.00025**

[-2.09]

-0.000039

[-0.67]

-0.000079

[-2.18]

-0.000089

[-0.84]

-0.0000

[-0.20]

STRATEGY t-1 0.001*

[2.90]

-0.0001

[-0.38]

0.0042

[1.20]

-0.007

[-2.44]

0.009*

[1.87]

0.006

[1.43]

AVOID t-1 0.07**

[2.26]

0.11***

[4.53]

0.08

[1.13]

0.11

[2.01]

0.29

[1.10]

0.28

[1.28]

TAX*AVOID t-1 0.000078

[1.51]

0.000062*

[1.76]

0.00044

[1.99]

0.00077

[2.42]

0.0004

[1.15]

0.00005

[0.20]

TAX*STRATEGY t-1 -0.00009

[-0.90]

0.000014**

[2.33]

0.0000012

[1.10]

0.0000032

[1.68]

0.000006

[1.17]

-0.00001

[-0.76]

STRATEGY*AVOID t-1 -0.002

[-1.38]

-0.003**

[-2.52]

0.05

[2.95]

0.04

[2.65]

-0.02

[-1.19]

-0.02

[-1.44]

TAX*STRATEGY*AVOID t-1 -0.00004*

[-1.60]

-0.000038**

[-2.09]

-0.000022**

[-2.33]

-0.000043**

[-2.43]

-0.000035

[-1.58]

-0.000019

[-0.83]

TURN t-1 0.33**

[2.59]

0.35***

[3.74]

0.20

[1.50]

0.16

[1.67]

0.24**

[2.35]

0.23***

[3.01]

MEANRET t-1 3.23**

[2.51]

5.32***

[5.38]

1.92

[1.55]

4.49

[4.67]

1.99*

[1.95]

3.46***

[4.47]

STDRET t-1 -0.83**

[-2.03]

-2.09***

[-6.62]

-0.06

[-0.14]

-1.83

[-5.49]

-0.05

[-0.16]

-1.64***

[-5.84]

44 | P a g e

AVOIDANCE=SHELTER AVOIDANCE= DTAX AVOIDANCE=ETR

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

SIZE t-1 -0.003

[-0.25]

0.04***

[4.05]

0.06

[5.30]

0.09

[10.38]

0.04***

[3.54]

0.07***

[8.52]

MTB t-1 0.00

[0.03]

-0.04***

[-2.90]

-0.006

[-0.34]

0.04

[2.62]

-0.02

[-1.44]

0.01

[1.27]

LEVERAGEt-1 -0.06

[-1.04]

-0.23***

[-3.80]

0.04

[0.90]

0.03

[0.93]

-0.008

[-0.18]

-0.02

[-0.47]

|DAC| t-1 0.03

[1.16]

-0.00

[-0.02]

0.02

[0.83]

-0.01

[-0.71]

0.005

[0.28]

-0.02

[-1.05]

ROA t-1 0.003

[0.78]

0.005

[1.30]

0.002

[1.13]

0.003

[2.08]

0.22***

[3.22]

0.44***

[7.86]

BIG4 -0.07

[-1.51]

-0.01

[-0.38]

-0.05

[-1.14]

-0.04

[-1.21]

-0.10**

[-2.50]

-0.06*

[-1.94]

IMR -0.19**

[-2.11]

-0.24***

[-3.25]

-0.01

[-0.09]

-0.21

[-2.10]

-0.18

[-1.42]

-0.34***

[-3.89]

Industry FE Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes

Adjusted R2 0.03 0.11 0.03 0.10 0.03 0.10

Observations 12,149 12,149 11,114 11,114 17,118 17,118

***, **, and * represent statistical significance at the 1%, 5%, and 10% level respectively (two-tailed test). Variable definitions are in Appendix 1.