Internal Control Risk and Initial Audit Pricing

Patricia Navarro

Ph.D. Student

University of Nevada, Las Vegas

Patricia.Navarro-Velez@unlv.edu

Robin W. Roberts

Al and Nancy Burnett Eminent Scholar

University of Central Florida

RRoberts@ucf.edu

Dana Wallace*

Assistant Professor

University of Central Florida

Dana.Wallace@ucf.edu

August 2019

ABSTRACT

We examine whether the practice of initial audit fee discounting persists for high internal control risk

clients. Auditing standards require auditors to use a risk-based approach to conduct and price their audits,

yet we predict the intense price competition for new audits dominates control risk pricing effects. Consistent

with our prediction, we find initial audits of clients that disclose a prior year material weakness in internal

control over financial reporting (MW) receive significant fee discounts relative to ongoing engagements.

Further, the fee discounts do not impair audit quality, measured using accruals quality and restatements.

Additional tests reveal that prior year MW-initial audit fee discounting is strongest among clients: with no

current MW, audited by Big 4 firms, in high audit competition markets, audited by auditor specialists, and

during non-crisis periods. We contribute to both the regulatory debate regarding initial audit fee cutting and

the initial engagement literature by shedding light on the prevalence and consequences of initial audit fee

cutting for high control risk clients.

Keywords: internal control; material weakness; audit fees; audit quality

We thank Lisa Baudot, Kristina Demek, James R. Moon, Elizabeth Poziemski, Gregory Trompeter, and

Arnold Wright for their helpful comments and suggestions.

*Corresponding author

1

Internal Control Risk and Initial Audit Pricing

I. INTRODUCTION

It is common practice for auditors to price initial audits below market value.1 Both past and present

audit regulators have expressed concerns regarding initial audit fee discounting and its potential adverse

effects on audit quality (AICPA 1978; Turner 2001; Cohn 2014; PCAOB 2014; Ferguson 2015). In this

study, we examine whether the practice of discounting initial audit engagements persists for a specific type

of client—one that reports a material weakness in internal control over financial reporting (“MW”) in the

year prior to the auditor change. We also assess whether fee discounts for high internal control risk clients

impact audit quality and investigate explanations for the initial audit fee discounting we document. Our

study contributes to the ongoing regulatory debate regarding initial engagement fee cutting and offers

insights into the limits of audit market competition and the pressure it places on audit fees.

Considerable pressure and competition exist among audit firms on the basis of fees (Doty 2015a).

The Sarbanes-Oxley Act of 2002 (SOX) sought to combat some of these pressures by, for example, shifting

auditor hiring from management to the audit committee. Still, evidence suggests management often has the

most influence on auditor hiring and termination (Cohen, Krishnammorthy, and Wright 2010). Further, the

Public Company Accounting Oversight Board (PCAOB) continues to express concerns that audit

committees “see their job as negotiating the lowest audit fee, not championing auditor objectivity and

independence from management” (Doty 2011, p. 2), which may establish “implicit incentives for

auditors…[that]…may not all promote quality” (Doty 2015b, p. 6). Former BDO International CEO Jeremy

Newman raises similar concerns, stating, “There is increasing evidence that fees are being forced down to

such an extent that one worries this will encourage audit firms to ‘cut corners’ to reduce their own costs

and thereby reduce audit quality” (Newman 2010, p. 1).

1 This practice is often referred to as “lowballing”. Throughout the paper, however, we refer to the practice as “initial

audit fee discounting” or “initial audit fee cutting”.

2

Academic researchers have documented the existence of significant fee discounts for initial audit

engagements dating back to the early 1980s (Simon and Francis 1988) and continuing well into the post-

SOX period (Desir, Casterella, and Kokina 2013). The general concern is that by charging lower fees for a

first-time client, the audit firm has strong incentives to retain that client for several years in order to recover

its initial loss. Such pressure to retain a client may pose threats to auditor independence and audit quality

(Stanley, Brandon, and McMillan 2015), although classic collateral bond arguments dispute this conclusion

(DeAngelo 1981).

Audit fees are a function of client size, client complexity, and risk (Simunic 1980). Auditing

Standard (AS) No. 1101 requires auditors to consider audit risk (including control risk) when determining

the extent and nature of audit procedures (PCAOB 2010). Indeed, several studies provide evidence

consistent with auditors adjusting fees in response to increased control risk. Specifically, clients disclosing

a material weakness in internal control over financial reporting (material weakness, or MW) pay

significantly higher audit fees in the year of a MW and lower subsequent audit fees if the internal control

problem is remediated (Raghunandan and Rama 2006; Hogan and Wilkins 2008; Hoitash, Hoitash, and

Bedard 2008; Hoag and Hollingsworth 2011; Munsif, Raghunandan, Rama, and Singhvi 2012).2 The

results of these studies suggest auditors increase fees for clients with MWs because of either additional

audit effort in response to increased control risk or as a form of fee premium to compensate auditors for

potential future litigation costs (Jiang and Son 2015).

Our study investigates the intersection of these two well-documented practices—lower audit fees

for initial engagements and higher audit fees for high control risk engagements. Since the existence of a

MW increases control risk, auditing standards suggest auditors should price this risk, even when pricing an

2 Sarbanes-Oxley Act (SOX) Section 302 requires management to report on the effectiveness of its company’s internal

controls for years ended after August 29, 2002. SOX Section 404 requires U.S. accelerated filers (public float

exceeding $75M) to disclose both their assessment of internal controls over financial reporting and an auditor’s

attestation in electronic filings for years ended after November 15, 2004. Prior studies examining the relation between

internal control deficiencies and audit fees utilize MWs reported under Section 302 exclusively, Section 404

exclusively, or both. The general conclusion under each approach remains—MWs are associated with higher audit

fees. We utilize auditor-reported SOX 404 MW disclosures in our study.

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initial engagement. The power of audit market competition, however, coupled with the longstanding

practice of initial audit fee discounting, suggest auditors may continue to discount initial audits despite

increased control risk. Thus, how auditors price initial audits for clients with internal control deficiencies

is an empirical question. Given the intense commercial pressures on audit firms, we predict that clients with

prior MWs will continue to receive initial audit discounts. Alternatively, higher control risk and heightened

regulatory scrutiny may offset the tendency for auditors to provide a fee discount.

Evidence of initial audit fee cuts for clients with prior MWs raises the possibility that such fee cuts

negatively impact the audit. Regulatory concerns regarding the practice of initial engagement discounting

focus on its potentially adverse impact on audit testing and auditor independence and thus, audit quality.

Prior research is mixed regarding whether initial audit fee cuts impact audit quality. Using mainly accruals

quality and restatements to assess audit quality, some studies find a positive or no relation between initial

audit fee discounts and audit quality (Deis and Giroux 1996; Gul, Fung, and Shaggi 2009). Other studies,

however, suggest initial audit fee discounts are associated with poorer audit quality (Gul, Jaggi and

Krishnan 2007; Stanley and DeZoort 2007; Stanley et al. 2015). Given heightened regulatory concerns and

some research linking initial audit fee cuts to lower audit quality, we predict poorer audit quality (measured

using accruals quality and financial restatements) for prior MW clients that receive an initial audit fee

discount.

We conduct our investigation using a sample of audit clients with the necessary audit and financial

data from Audit Analytics and Compustat over the 2004-2017 sample period. We first examine descriptive

statistics related to initial audit fee discounting among 1,162 initial audit engagements over our sample

period. Within this sample, 116 observations laterally change auditors and disclose a prior year MW.3 We

find that 66% (53%) of prior year-MW observations that laterally change Big 4 (non-Big 4) auditors pay

lower audit fees in the year of auditor change than in the prior year. These proportions are similar to those

3 As described in more detail in Section 3, we focus the majority of our analyses on lateral auditor changes (as opposed

to all auditor changes) in order to remove the effect of audit pricing changes due to the direction of the auditor change

(i.e., Big 4 to non-Big 4 and vice versa).

4

of the sample of observations that change auditors and do not disclose a prior year MW (64% and 47% for

Big 4 and non-Big 4 lateral auditor changes, respectively). Additionally, the median decline in audit fees is

20% (2%) for clients that laterally change Big 4 (non-Big 4) auditors and disclose a prior year MW. Again,

the fee discounts are similar for clients that do not disclose a prior year MW but laterally change auditors

(11% and 0% for Big 4 and non-Big 4 lateral auditor changes, respectively). While observing total and

median changes in audit fees provides initial evidence that initial audit fee cutting persists for clients who

disclose a prior year MW, this analysis does not allow us to examine audit fee discounts relative to ongoing

audit engagements.

Thus, we next examine a sample of 26,083 firm-year observations that includes all audit

engagements with available data from 2004-2017. Using a multivariate regression approach, we first find

an average 19% fee discount for initial audit engagements during our sample period, consistent with prior

research that documents initial audit fee discounting (Simon and Francis 1988; Desir et al. 2014). We then

regress audit fees on an interaction between an indicator for a prior year MW (PMW) and an indicator for

an auditor change (AUDCH). We find the coefficient on the PMW*AUDCH interaction is negative and

significant (p = 0.06), resulting in an average 10% audit fee discount for clients with prior year MWs that

change auditors. Since this test does not control for the directionality of the auditor change and prior

research suggests this is an important determinant of the size of the fee cut (Ettredge and Greenberg 1990),

we next regress audit fees on an interaction between PMW and an indicator for a lateral auditor change

(LAT). Here, we continue to find a significantly (p = 0.07) negative interaction (PMW*LAT), resulting in

an average 13% audit fee discount for clients that laterally change auditors and disclose a prior year MW.4

Thus, both our descriptive and regression analyses support our prediction that initial audit fee discounting

exists for clients who disclose an internal control deficiency in the prior year.

Next, we examine the audit quality implications of new engagement fee cuts for clients with prior

MWs to assess whether regulator concerns over audit quality implications of discounted initial engagements

4 For ease of exposition, we will refer to firms with prior year MWs that laterally change auditors in the current year

as PMW-LAT firms.

5

are substantiated. Using both accruals quality and financial restatements to proxy for audit quality, we find

no association between audit quality and an indicator for PMW-LAT clients that receive a fee discount.

Thus, although there may be auditor independence issues associated with fee cutting that our empirical

proxies are unable to capture, our evidence suggests initial audit fee discounting for clients with prior year

MWs does not impair audit quality. It appears auditors conduct sufficient audit testing despite initial lower

audit fees.

We conduct several robustness tests to confirm our main results. First, prior research suggests that

due to significant audit risk differences between clients with auditor resignations versus auditor dismissals,

audit fees are unusually high (low) following auditor resignations (dismissals) (Krishnan and Krishnan

1997; Griffin and Lont 2011). Following prior research on initial engagement pricing (Huang,

Raghunandan, and Rama 2009; Desir et al. 2014), we remove roughly 200 auditor-change observations that

result from auditor resignations and re-estimate our audit fees regression. The PMW*LAT interaction

remains significantly negative after removing auditor resignation observations.

Second, prior research finds firms that remediate their MWs receive larger fee discounts (Hoag and

Hollingsworth 2011). Thus, we partition the sample into subsamples based on whether the client discloses

a MW in the current year and re-estimate our audit fees model for each subsample. We find that audit fee

discounting for PMW-LAT firms is concentrated with the no-MW sample, suggesting auditors may be more

willing to offer initial audit fee discounts to clients that do not have ongoing internal control issues.

Third, we partition the sample into Big 4 vs. non-Big 4 subsamples based on prior research that

finds audit pricing and audit quality differs based on audit firm size (DeAngelo 1981; Palmrose 1986).

Here, we find that PMW*LAT is marginally significantly negative among Big 4 auditors and insignificant

among non-Big 4 auditors, suggesting Big 4 firms are potentially more willing or more able to offer audit

fee cuts for PMW-LAT firms.

Last, our sample period (2004-2017) includes the financial crisis years (2007-2008), which likely

impacted audit pricing decisions. Thus, we re-estimate our audit fees model in the pre-crisis, post-crisis,

crisis, and combined non-crisis periods within our sample period. We find significant audit fee cuts for

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PMW-LAT firms in the pre-, post-, and combined non-crisis years. Interestingly, we do not find a significant

PMW*LAT interaction during the crisis years, suggesting auditors may have been more constrained during

that period (i.e., auditors were potentially unable to offer typical audit discounts due to the inability of other

clients to pay them during times of financial hardship).

Next, we explore two potential explanations for audit fee discounts for PMW-LAT firms—audit

market competition and auditor expertise. Prior research finds initial audit fee discounting is more

extensive: (1) in markets where price competition is higher (Ghosh and Lustgarten 2006), suggesting

auditors in highly competitive markets have greater incentives to cut prices to generate new business, and

(2) for auditors with more industry expertise (Ettredge and Greenberg 1990), suggesting expert auditors

can conduct audits more efficiently, resulting in lower audit fees in a competitive setting. We divide our

large sample into high/low competition and specialist/non-specialist partitions. Consistent with prior

research, we find that audit fee discounting for PMW-LAT firms is associated with higher audit market

competition and auditor industry expertise. Our evidence lends support for regulator concerns that intense

audit competition propels new engagement fee cuts.

In our final analysis, we replace the dichotomous PMW with a continuous measure of internal

control quality developed from prior research (Ge, Koester, and McVay 2017; Navarro, Roberts, and

Wallace 2019) that uses firm characteristics (e.g., size, age, performance, distress, etc.) to measure the

likelihood of a MW. Prior research suggests this continuous measure of the prior year’s MW likelihood

(PISCORE) may better capture the underlying distribution of internal control risk among audit clients than

a simple binary PMW measure (Navarro et al. 2019). Contrary to our main results, we find the

PISCORE*AUDCH and PISCORE*LAT interactions are consistently significantly positive or insignificant,

suggesting auditors price initial audits higher or the same for clients with higher control risk as compared

to ongoing engagements. This evidence suggests that while the disclosure of a MW does not significantly

increase control risk enough to deter auditors from offering significant fee discounts, auditors do price the

underlying attributes of control risk.

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Our paper makes several contributions to both academic research and practice. First, we add to the

literature on initial audit engagement pricing, which consistently finds initial audit fee discounting (Simon

and Francis 1988; Desir et al. 2013) but has yet to examine the impact of control risk on this audit fee

practice.5 Likewise, we add to the internal control literature, which suggests auditors price the disclosure of

a MW (Raghunandan and Rama 2006; Hogan and Wilkins 2008; Hoitash et al. 2008), but does not address

whether this audit fee premium persists for new audit engagements.

Further, our study contributes to the ongoing regulatory debate regarding the existence and

implications of initial audit fee discounting (AICPA 1978; Turner 2001; Cohn 2014; PCAOB 2014;

Ferguson 2015). Specifically, our study addresses whether audit market competition is structured such that

audit firms will price initial audits unrealistically low even under circumstances when the potential client’s

poor internal control structure is known ex ante. We show this fee cutting practice continues for high control

risk clients, but we find no evidence that audit fee discounts for PMW-LAT firms adversely affect audit

quality.

Last, our study speaks to regulator concerns regarding the low incidence of MW disclosure

(Croteau, 2013). Prior research documents that MW disclosures are often triggered by a financial

restatement (Rice and Weber 2012; Rice, Weber, and Wu 2015), possibly due to low materiality thresholds

for reporting or various auditor and management incentives biasing auditors’ initial reporting decisions. We

find that while PMW-LAT firms receive fee discounts, firms with higher control risk based on the underlying

risk characteristics of the firm (PISCORE) receive no significant fee discount. Our evidence suggests

auditors pay less attention to MW disclosures when pricing initial audits, and potentially focus more on the

underlying risk characteristics of new clients.

5 Elliot, Ghosh, and Peltier (2013) examine initial audit pricing for firms that disclose certain information in their 8-K

auditor change filings. The overall conclusion in their study is that firms disclosing reportable events or auditor-client

disagreements pay an initial engagement fee premium compared to less risky initial engagements. The overall

conclusion from our study is that firms disclosing a prior year MW continue to receive an initial engagement fee

discount in the current year compared to all other engagements. We explain differences between our study and Elliot

et al. (2013) in greater detail in Section 2.

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II. BACKGROUND AND HYPOTHESIS DEVELOPMENT

Initial Audit Pricing

Initial audit fee discounting occurs when auditors charge fees below an expected audit fee based

on the client’s size, risk and complexity. The expectation is that auditors will substantially increase fees in

later years to recoup this initial loss (AICPA 1978). This arrangement can make an auditor more invested

in the client, and thus, more willing to acquiesce to client demands for fear of losing the client and their

investment (i.e., the initial discount). The American Institute of Public Accountants (AICPA) expressed

their concerns regarding this practice decades ago: “…when the preceding year's audit fee remains unpaid,

independence is impaired. This prohibition is based on the belief that such a receivable from the client gives

the auditor an interest in the financial success of the client.... We believe that accepting an audit engagement

with the expectation of offsetting early losses or lower revenues with fees to be charged in future audits

creates the same condition and represents the same threat to independence” (AICPA 1978, p.121). The

PCAOB echoes these early concerns when discussing how the regulator should “begin to create the upward

draft in the boardroom about how important it is for directors acting responsibly under SOX to keep

management out of the fee discussion, negotiate the quality of the audit before you negotiate the fee and

then finally to avoid what appears to be perhaps a trend toward a lowball fee” (PCAOB 2014, p. 205).

Since auditor costs are not publicly available, prior archival research uses initial audit fees to

capture the discounting phenomenon. Several studies provide evidence of initial audit fee discounts (Simon

and Francis 1988; Ettredge and Greenberg 1990; Roberts, Glezen, and Jones 1990; Whisenant,

Sankaraguruswamy, and Raghunandan 2003). While some research suggests initial audit fee discounts

were less prevalent following the Sarbanes-Oxley Act of 2002 (SOX) (Ghosh and Pawlewicz 2006; Huang

et al. 2009), other research suggests the practice continues through at least 2010 (Desir et al. 2014; Stanley

et al. 2015). Overall, regulatory concern regarding the practice of initial audit fee discounting, combined

with academic research highlighting this practice, support the prevalence of and continued concern over

the initial fee cutting phenomenon.

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Internal Control Risk and Audit Pricing

Auditors must plan and perform audits to obtain reasonable assurance that financial statements are

free of material misstatement (PCAOB 2010). Auditors obtain this assurance by reducing audit risk6, which

is comprised of (1) the risk of material financial statement misstatement and (2) auditor detection risk, to

an appropriately low level. First, risk of material misstatement consists of both inherent risk (susceptibility

of an assertion to misstatement before consideration of any related controls) and control risk (risk that a

misstatement will not be prevented on a timely basis by the company’s internal control) (PCAOB 2010).

Auditors use misstatement risk to determine an appropriate level of detection risk—the risk that auditor

procedures will not detect a material misstatement. The higher the risk of material misstatement, the lower

the level of detection risk needed to reduce audit risk to an appropriately low level. Auditors reduce

detection risk through the nature, timing, and extent of substantive testing. Hence, higher control risk

results in higher misstatement risk, resulting in more testing needed to reduce detection risk to an acceptable

level. More testing typically translates into higher audit fees.

Our study focuses specifically on control risk. While auditing standards have long required the

auditor to consider a client’s internal controls in audit planning, since 2004, Section 404 of SOX requires

management (SOX 404(a)) and the auditor (SOX 404(b)) to report on internal controls over financial

reporting. Prior research uses the disclosure of material weaknesses in internal control over financial

reporting (“MW”) as a proxy for the level of control risk. Extant literature supports a positive control risk-

audit fees relation, documenting significant increases in audit fees in the year of and/or following a MW

disclosure (Raghunandan and Rama 2006; Hoitash et al. 2008; Munsif et al. 2011; Jiang and Son 2015).

Hogan and Wilkins (2008) also find significant increases in audit fees in the year prior to a MW disclosure,

supporting an auditor effort response to increased control risk. While the authors of each of the

aforementioned studies examine the control risk-audit fees relation in their own nuanced fashion (e.g.,

6 The PCAOB (2010) defines audit risk as “the risk that the auditor expresses an inappropriate audit opinion when the

financial statements are materially misstated.”

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altering the timing of the MW disclosure, examining remediation or severity of the MW, and testing sample

periods), a consistent conclusion is that firms with increased control risk pay higher audit fees.

Internal Control Risk and Initial Audit Pricing

While regulators and prior research suggest that market competition drives audit fees below cost in

the initial year of an engagement, prior research does not address whether this practice persists for clients

with potentially higher control risk (i.e., those clients who report a MW in the prior year). Our study

addresses whether the ongoing practice of initial audit fee discounting continues when an auditor change

occurs in the year following MW disclosure, thus shedding light on the pervasiveness of fee cutting. One

study related to ours examines the role of auditor-client disagreements and other reportable events in auditor

change 8-K filings on audit fees (Elliot, Ghosh, and Peltier 2013). They find that, among firms that change

auditors, audit fees are significantly higher for firms that report a disagreement or other reportable event.7

Their evidence suggests that these types of risky clients are unable to reduce fees when switching auditors.

Based on regulator concern over the practice of initial audit fee cutting and academic research supporting

the pervasiveness of this practice, however, we expect audit fees will continue to be discounted, on average,

for initial audits of clients with prior MWs. Our formal hypothesis (stated in alternate form) is as follows:

H1: Audit clients that change auditors in the current year and disclose an internal control

deficiency in the prior year receive an audit fee discount.

7 Our study differs from Elliot et al. (2013) in several ways. Foremost, they exclude observations with only internal

control problems and focus on clients with multiple problems. Our study, on the other hand, focuses exclusively on

observations with MWs. Second, their sample spans 2001-2011, which includes both pre- and post-SOX periods,

whereas our sample spans 2005-2017, removing the change in regulation. Third, while we conduct both small

(n=1,162) and large (n=26,083) sample analyses, Elliot et al. (2013) restrict their sample to a small number (n=2,396)

of auditor changes. The effects they document, therefore, capture the difference in audit pricing among only first-time

engagements, whereas our effects capture differences in audit pricing within the cross-section of all engagements.

Last, Elliot et al. (2013) conclude audit fees are significantly higher for initial audits firms with disagreements and

other reportable events, as compared to initial audits with no prior issues. We conclude audit fees are lower for initial

audit firms with prior MWs, as compared to all other audit engagements. While the tenor of the two studies are similar,

the motivation, empirical approach, and overall conclusions are vastly different.

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Initial Audit Fee Discounting and Audit Quality

At the forefront of regulator concerns regarding the practice of initial audit fee cutting is its impact

on auditor independence and audit quality. As described previously, regulators fear that, driven by price

competition, auditors offer an initial year discount to their clients that they expect to recoup in subsequent

years (AICPA 1978; PCAOB 2014, 2015). The problem with this arrangement, however, is that auditors

may be more influenced by audit time budget pressure or more willing to acquiesce to client demands in

order to maintain the client-auditor relationship and recover their initial investment. Such threats to auditor

independence potentially impair audit quality. The PCAOB describes, “One of the troubling things we hear

is that some audit committees focus mostly on reducing the fee when they hire auditors…it is not the audit

committee's job to save a few dollars on the fee…[they] should be hiring the best auditor for the job and

negotiating with them to have the right level of staffing and work to ensure a high quality audit” (PCAOB,

2015).

Little empirical research examines the impact of initial audit fee discounting, specifically, on audit

quality. A significant stream of literature does exist, however, examining the association between auditor

tenure (i.e., shorter tenure to some extent captures initial audit discounting) and audit quality, with mixed

evidence. Some prior studies document a relation between shorter auditor tenure and measures of poor audit

quality, including lower accruals quality (Gul et al. 2007) and more financial restatements (Stanley and

DeZoort 2009). These studies suggest initial audit fee discounting and/or lack of client-specific knowledge

result in reduced audit quality. Gul et al. (2009), on the other hand, find no evidence that initial audit fee

cutting is associated with lower earnings quality, and thus attribute the longer tenure-higher quality relation

to auditor knowledge and efficiencies with long-term clients. In a later study, Stanley et al. (2015) find a

relation between the magnitude of initial fee discounts and earnings management. They conclude the results

are consistent with regulators’ concerns that auditor time and budget pressures for first year audits,

exacerbated by discounted audit fees and a lack of client-specific knowledge, leads to lower quality audits.

In our study, we examine the audit quality impact of prior-MW firms receiving initial audit fee discounts,

rather than broadly examining the audit quality impact of all initial audit fee discounts. Following the most

12

recent line of this research, as well as regulator concerns, we predict that prior year MW firms that receive

fee cuts in the year of an auditor change likely have poorer audit quality. Our hypothesis, stated in

alternative form, is as follows:

H2: Audit quality is lower for clients that change auditors, disclose an internal control

deficiency in the prior year, and receive a fee discount in the current year than for other

clients.

III. RESEARCH DESIGN

Sample and Data

We conduct our analyses using a sample of firm-year observations spanning the 2004-2017 period

with available audit data from Audit Analytics and financial statement information from Compustat. We

begin our sample in 2004 because it is the first year SOX required auditors to report on the quality of

internal controls over financial reporting. Thus, we focus our analysis on auditor-reported SOX 404(b) MW

disclosures. We remove observations from the financial industry (SIC code 6xxx) due to the regulated

nature of this industry. The final sample consists of 26,083 firm-year observations, which includes both

ongoing and initial audit engagements and both firms with and without prior year MWs. This sample

consists of 131 firm-year observations that disclose an internal control deficiency in the prior year and

laterally change auditors in the current year.8

Table 1 provides descriptive statistics for our sample. Approximately 5% of the observations

disclose MWs in the current year, consistent with prior research (Rice and Weber 2012; Newton et al.

2016). Auditor changes (AUDCH) account for 4.5% of our sample, with over half of those changes

consisting of lateral auditor changes (LAT). Table 2 provides select Pearson correlations among our

variables of interest as well as control variables. Consistent with prior research documenting initial audit

fee discounting (Simon and Francis 1988; Desir et al. 2013), the correlation between audit fees (LNFEES)

and auditor changes (AUDCH) is significantly negative (Pearson ρ = -0.116). A negative correlation also

8 Prior research finds that the direction of the auditor change is an important determinant of the amount of the fee cut

such that Big N to non-Big-N (as compared to non-Big N to Big N) auditor changes lead to significantly higher fee

cuts (Ettredge and Greenberg 1990). Thus, we focus the majority of our analyses on lateral auditor changes.

13

exists between audit fees (LNFEES) and lateral auditor changes (LAT) (ρ = -0.066). We also show a

significantly positive correlation between audit fees (LNFEES) and MW (ρ = -0.013), supporting prior

research that documents audit fees increase in the year of a MW as a result of either additional auditor effort

or as risk premium (Raghunandan and Rama 2006; Hogan and Wilkins 2008; Hoitash et al. 2008).

Audit Fees Model

To examine initial audit pricing for firms with prior year internal control deficiencies, we estimate

the following OLS model:9

LNFEESi,t = α1 + β1AUDCHi,t + β2PMWi,t + β3PMW*AUDCHi,t + β4MWi,t + β5LNASSETi,t +

β6INVRECi,t + β7LEVi,t + β8ROA,t + β9GROWTHi,t + β10LNSEGi,t + β11FOREIGNi,t +

β12LOSSi,t + β13BIG4i,t + β14NEWFINi,t + β15YEi,t + β16GCi,t + β17ANC_RESTi,t +

Industry + Year + et

(1)

where

LNFEES = natural logarithm of audit fees;

AUDCH = indicator equal to one if an auditor change occurs in the current year, and zero otherwise;

PMW = indicator equal to one if the firm discloses a material weakness in internal control over

financial reporting in the prior fiscal year, and zero otherwise;

MW = indicator equal to one if the firm discloses a material weakness in internal control over

financial reporting in the current fiscal year, and zero otherwise;

LNASSET = natural logarithm of total assets;

INVREC = inventory plus accounts receivable divided by total assets;

LEV = total debt divided by total assets;

ROA = return on assets, measured as net income divided by average total assets;

9 We conduct our analyses using the level of audit fees, rather than the change in audit fees for two reasons. First, it is

likely that firms with a MW in year t-1 pay higher audit fees in that year compared to the year following a MW. Thus,

there may be bias in favor of finding a fee reduction in year t, particularly if the firm corrects the internal control

problem early in year t. Second, audit fee changes models have much lower explanatory power than audit fee levels

models (Huang et al. 2009). Still, we estimate an audit fee changes model to test H1 and find similar results to our

levels analysis. Specifically, the coefficients on PMW*AUDCH and PMW*LAT (untabulated) remain negative and

significant (p < .05 and p = 0.07, respectively).

14

GROWTH = percentage change in sales from year t-1 to year t;

LNSEG = natural logarithm of number of business segments;

FOREIGN = indicator equal to one if the firm is engaged in foreign operations (measured as foreign

currency translation not equal to zero), and zero otherwise;

LOSS = indicator equal to one if the firm’s net income is less than zero in the current fiscal year,

and zero otherwise;

BIG4 = indicator equal to one if the firm’s auditor is a Big 4 auditor, and zero otherwise;

NEWFIN = indicator equal to one if the firm issues equity (greater than $10M) or debt (greater than

$1M) in the current year, and zero otherwise;

YE = indicator equal to one if the firm’s fiscal year-end month is not December, and zero otherwise;

GC = indicator equal to one if the firm receives a going concern opinion in the current year, and

zero otherwise; and

ANC_REST = indicator equal to one if the firm announces a financial statement restatement in the

current period, and zero otherwise.

If, as predicted, initial audit fee discounting exists despite increased control risk, we will observe a

significantly negative β3 coefficient. If, alternatively, initial audit fee discounting diminishes when internal

control risk increases, we will observe an insignificant β3 coefficient. A second possible alternative is that

the increased control risk increases audit fees, on average, in which case control risk subsumes the practice

of initial audit fee discounting, and we will observe a significantly positive β3 coefficient.

We include control variables based on prior audit fee research (e.g., Hay, Knechel, and Wong 2006;

Hogan and Wilkins 2008; Whisenant, Sankaraguruswamy, and Raghunandan 2003;). We control for client

firm characteristics that influence audit pricing, including firm size (LNASSET), complexity (LNSEG,

FOREIGN, GROWTH), risk (LEV, INVREC), profitability (ROA), and financial health (LOSS, GC), as well

as certain audit features (BIG4, YE) (Whisenant et al. 2003; Hay et al. 2006). Certain business events require

more auditor effort, such as obtaining new financing (NEWFIN) and restating financial statements

(ANC_REST), so we control for each of these activities (Hay et al. 2006). We also include Fama-French

48-industry indicators and year indicators in the model. We winsorize all continuous variables at the 1st and

99th percentiles to mitigate the influence of outliers. We cluster standard errors by firm.

15

Audit Quality Model

Next, we test the relation between audit quality and firms that change auditors, disclose a prior year

MW, and receive a fee cut by estimating the following OLS model:

AUDQ = α1 + β1PMW + β2LATi,t + β3FEECUTi,t + β4PMW*LATi,t + β5PMW*FEECUTi,t +

β6LAT*FEECUTi,t + β7PMW*LAT*FEECUTi,t + β8LNASSETi,t + β9AGGLOSSi,t +

β10STDSALEi,t + β11STDCFOi,t + β12LNSEG,t + β13FOREIGNi,t + β14GROWTHi,t +

β15INVi,t + β16INTANG_INTi,t + β17ZRANKi,t + β18BIG4i,t + β19WRITEOFFi,t + Industry

+ Year + et (2)

where

AUDQ = audit quality, measured as accruals quality (ACCRQ) and financial restatement

(RESTATE);

ACCRQ = we derive ACCRQ from the McNichols (2002) modification of the Dechow and Dichev

(2002) accruals quality measure. Within each year and 2-digit SIC industry with at least 20

observations, we estimate the Dechow and Dichev (2002) model of total working capital accruals

regressed on lagged, concurrent, and future period’s cash from operations. We adjust the Dechow

and Dichev (2002) model by including change in revenues from year t-1 to year t and gross

property, plant, and equipment (McNichols 2002). All independent variables are scaled by average

total assets. We utilize the absolute value of the firm-specific residual from this model to measure

accruals quality. ACCRQ is then the standard deviation of this residual over the previous five years;

RESTATE = indicator equal to one if the firm misstates their financial statement in the current year

(which subsequently results in a restatement in a later period), and zero otherwise;

FEECUT = indicator equal to one if current year audit fees are lower than prior year audit fees, and

zero otherwise

AGGLOSS = indicator equal to one if net income for years t and t-1 is negative, and zero

otherwise;

STDSALE = standard deviation of sales (sales / total assets) over the past five fiscal years;

STDCFO = standard deviation of cash flows (cash flow from operations / total assets) over the past

five fiscal years;

INV = average inventory scaled by total assets;

INTANG_INT = intangible asset intensity; research and development plus advertising divided by

sales;

ZRANK = decile ranking of Altman’s (1968) Z-score for bankruptcy risk, such that higher values

represent higher bankruptcy risk; and

16

WRITEOFF = indicator equal to one if the firm reports a write-off in the current fiscal year, and

zero otherwise.

All other variables are as defined previously.

When either ACCRQ or RESTATE is the dependent variable, we predict a positive coefficient on β4,

indicating audit quality (measured inversely) is on average lower for firms with poorer internal control

environments who change auditors and receive a fee cut.

We include control variables based on prior accruals quality and restatement research (Doyle et al.

2007b; Efendi, Srivastava, and Swanson 2007; Ashbaugh-Skaife et al. 2008). We control for firm

complexity (LNSEG, FOREIGN, INTANG_INT), financial stability (LNASSET, AGGLOSS, ZRANK), and

rapid growth (GROWTH, INV, STDCFO, STDSALE) because firms with more complex operations,

smaller/less stable firms, and rapidly growing firms have lower quality accruals and are more likely to

misstate their financials (Francis, LaFond, and Schipper 2005; Ashbaugh-Skaife et al. 2008). Prior research

suggests more conservative accounting may improve financial reporting quality (Ball and Shivakumar

2006); thus we include the presence of an asset impairment or write-down (WRITEOFF) to capture

accounting conservatism. Last, we control for auditor quality (BIG4) since prior research suggests the

quality of financial information differs between clients of large and small audit firms (Ashbaugh, LaFond,

and Mayhew 2003). We again winsorize all continuous variables at the 1st and 99th percentiles, include

Fama-French 48-industry indicators and year indicators in the model, and cluster standard errors by firm.

IV. RESULTS

Main Analyses

Preliminary Evidence

We first examine change in audit fee statistics for all auditor-change observations in Table 3. We

focus on the change in audit fees from year t-1 to year t, without control variables, in order to gain initial

insight into how audit fees change under various circumstances. We separate the statistics based on the

directionality of the auditor change (i.e., Big 4 to Big 4, non-Big 4 to non-Big 4, non-Big 4 to Big 4, and

Big 4 to non-Big 4) and whether the client reports a prior year MW (PMW). Foremost, we observe declines

17

in audit fees across all auditor changes except for those clients moving from non-Big 4 to Big 4 auditors.

Excluding this category of observations, we next note that within each of the remaining three categories,

the percentage of observations that receive fee cuts with a prior year MW (PMW=1) is roughly equal to or

greater than the percentage of observations that receive fee cuts without a prior year MW (PMW=0). This

provides preliminary evidence that the practice of initial audit discounting persists for clients with high

control risk as proxied for by prior year internal control deficiencies. Since prior research finds that the

direction of the auditor change is an important determinant of the amount of the fee cut (Ettredge and

Greenberg 1990), we control for this factor by focusing the remainder of this analysis on lateral auditor

changes.

We denote our specific sample of observations that disclose a MW in the prior year (PMW) and

laterally change auditors in the current year (LAT) PMW-LAT observations. Of the 116 PMW-LAT

observations, 80 observations switch among Big 4 firms and 36 observations switch among non-Big 4

firms. Of the 80 (36) PMW-LAT Big 4 (non-Big 4) changers, 66% (53%) pay lower audit fees in the year

of auditor change than in the previous year. As a comparison, of the 447 (166) Big 4 (non-Big 4) auditor

changers without prior year MWs, 64% (47%) pay lower audit fees in the year of auditor change than in the

previous year. The median percentage change in audit fees for Big 4 (non-Big 4) PMW-LAT observations

is -20% (-2%). Similarly, the median percentage audit fee change for Big 4 (non-Big 4) auditor change

observations without a prior year MW is -11% (0%).

Overall, the change in fees descriptive statistics provide preliminary evidence of initial audit fee

discounting for clients with prior year MWs. That is, among clients that change auditors, with or without a

prior year MW, the majority receive initial audit fee discounts. This suggests the practice of initial audit fee

cutting dominates the increased control risk associated with prior year internal control deficiencies.

Initial Audit Pricing

We next use a multivariate approach to assess whether, controlling for a host of determinants of

audit fees, clients with prior year MWs receive initial audit fee discounts relative to all other audit

18

engagements. Table 4 displays the results of our main analysis. We first provide evidence of initial audit

fee discounting for all auditor changes, consistent with prior research (Simon and Francis 1988; Desir et al.

2013). In column (1) of Table 4, the coefficient on AUDCH is significantly negative (β1 = -0.209; p-value

< 0.001), suggesting clients pay lower audit fees, on average, for initial audits as compared to ongoing

audits. This translates into a fee discount of approximately 19% for new engagements.10 Likewise, column

(1) shows a significantly positive coefficient (p < 0.001) on both PMW and MW, suggesting internal control

deficiencies increase control risk and, thus, increase audit fees, consistent with prior research (Raghunandan

and Rama 2006; Hogan and Wilkins 2008; Hoitash et al. 2008).

Next, in column (2) we add to the model an interaction between PMW and AUDCH. We find the

interaction is significantly negative, providing preliminary evidence of initial audit fee discounting for

clients with prior year MWs (β3 = -0.108; p = 0.06). This test, however, does not hold constant the

directionality of the auditor change. Auditor switches can occur from a Big 4 to non-Big 4 auditor, vice

versa, or laterally from Big 4 to Big 4 or non-Big 4 to non-Big 4 auditor. Prior research suggests that the

direction of the auditor change is an important determinant of the amount of the fee cut (Ettredge and

Greenberg 1990). As such, we remove the effect of auditor change directionality on audit fees by separating

AUDCH into three variables—an indicator for upward (UP), lateral (LAT), and downward (DOWN) auditor

changes—, and by focusing our interaction coefficient of interest on lateral auditor changes among clients

with prior year MWs (PMW*LAT).11

In column (3) of Table 4, we show that before adding the PMW*LAT interaction term, audit fees

are significantly lower for clients that make upward, lateral, and downward auditor changes (p < 0.001)

compared to ongoing audit engagements. In column (4), we add the PMW*LAT interaction term and find

10 Since the dependent variable in our model is a logarithmic transformation, we can obtain the semilog function of

the impact of initial engagements on audit fees by transforming the parameter estimate to measure the initial audit

fee discount (Whisenant et al. 2003).

11 We also remove BIG4 from equation (1) in all estimations that include the three directionality variables since we

use BIG4 to construct the directionality variables, and the inclusion of BIG4 induces bias in the estimates of UP,

DOWN, and LAT.

19

that audit fees are significantly lower for clients that change auditors in the current year and disclosed a

MW in the prior year (β5 = -0.134; p = 0.07). Thus, on average, initial audit engagements are discounted

13% for clients that report prior year MWs. Overall, our evidence in Table 4 provides support for H1 that

clients who laterally change auditors in the current year and disclose an internal control deficiency in the

prior year receive audit fee discounts.

Audit Quality

We next test our second hypothesis—whether audit quality differs for clients that change auditors,

disclose a prior period MW, and receive a fee cut, as compared to all other clients. We estimate equation

(2) and display the results in Table 5. We examine the relation between PMW*LAT*FEECUT and both

current (year t) and future (year t+1) accruals quality and current and future restatements. Overall, we find

no evidence that audit quality is worse in the current or future periods for PMW-LAT firms that receive a

fee cut.12 Our results suggest that despite initial audit fee cutting for prior-MW clients, audit firms appear

to put forth the effort required to conduct a quality audit. In contrast to regulatory concerns that initial audit

fee discounts impair audit quality, our evidence suggests that initial audit fee discounts for clients with prior

year MWs do not impair concurrent or subsequent year audit quality.

Additional Analyses

Auditor Resignations, Remediation, and Auditor Size

In this section, we examine key subsamples of our main analysis. First, prior research finds that

auditor resignations differ significantly from client dismissals of auditors with regards to litigation risk

(Krishnan and Krishnan 1997) and thus, audit pricing (Griffin and Lont 2011). Therefore, following the

approach taken by prior research similar to ours (Desir et al. 2013; Huang et al. 2009), we remove auditor

resignations from our sample and re-estimate equation (1). As shown in column (1) of Table 6, restricting

12 We examine whether our accruals quality results are robust to various measures of accruals quality, including the

Jones (1991) model of discretionary accruals, the Jones (1991) model of discretionary accruals with a performance

adjustment, income-increasing accruals, the absolute value of discretionary accruals, and we find evidence consistent

with no relation between PMW*LAT*FEECUT and accruals quality.

20

our sample to only auditor dismissals reduces the sample by roughly 200 observations. Using this sample

of non-auditor resignation observations, our results become slightly stronger (i.e., PMW*LAT p < 0.05) and

continue to suggest initial audit fee-cutting even for clients with internal control problems. The increased

strength of our results is likely due to successor audit firms’ aversion to offering initial audit fee discounts

to clients whose predecessor auditors resigned, typically a signal of high audit risk.

Second, we reexamine our main results considering whether firms remediate their internal control

deficiencies. Prior research suggests remediating companies receive greater fee reductions, though the cuts

are not immediate (Hoag and Hollingsworth 2011). Thus, we partition the sample into subsamples based

on whether the client discloses a MW in the current year and re-estimate equation (1).13 Here, we find that

the initial audit discounting effect is concentrated with the no-MW sample. Specifically, columns (2) and

(3) in Table 6 show the PMW*LAT interaction is significantly negative for the no-MW sample (p < 0.05),

and insignificant for the MW sample, respectively. Our evidence suggests that the practice of initial audit

fee discounting for clients with prior year MWs is most prevalent among clients that disclose no internal

control deficiencies in the current year and are presumably lower control risk clients. Since auditors follow

a risk-based approach, it is reasonable that auditors may be more willing to offer initial audit fee discounts

to clients that are less risky in the sense that their internal control problems are not ongoing.

Third, we partition the sample into Big 4 vs. non-Big 4 subsamples. Prior research finds audit

pricing and audit quality differ between Big 4 and non-Big 4 audit firms. As shown in columns (4) and (5)

in Table 6, we find that PMW*LAT is marginally significantly negative among Big 4 auditors and

insignificant among non-Big 4 auditors, respectively. Our results suggest the practice of initial audit fee

discounting for clients with higher internal control risk may be mostly concentrated with Big 4 auditors

who have more resources to leverage and thus a greater ability to offer such price cuts.

13 Since our sample includes observations without prior year MWs, we do not partition the sample based solely on

remediation but instead focus on whether firms disclose a MW in the current year.

21

Crisis Period

Our sample period spans 2004 through 2017, which includes the 2007-2008 financial crisis years.

While Desir et al. (2014) find that initial audit fee discounting continues through the 2007 and 2008 crisis

years, we are uncertain as to whether the practice of initial audit fee discounting for PMW-LAT firms that

we document in this study continues during crisis periods. Considering the financial position of many audit

clients during the crisis period, it is conceivable that auditors were compelled to offer audits at discounted

prices. On the other hand, however, auditors may have been constrained themselves during this period and

unable to provide the same audit fee discounts that they typically provide in non-crisis times.

Table 7 shows the results of re-estimating equation (1), restricting our sample to the pre-crisis years

(2004-2006) in Panel A columns (1) through (3), the post-crisis years (2009-2017) in Panel A columns (4)

through (6), the crisis years (2007-2008) in Panel B columns (7) through (9), and the combined non-crisis

years (2004-2006 and 2009-2017) in Panel B columns (10) through (12). We estimate the model three times

within each period—first, we include the full sample; then we remove auditor resignation observations from

the full sample; and then we remove MW observations from the full sample. In general, we find that the

practice of audit fee discounting for PMW-LAT firms exists in the pre- and post-crisis periods but disappears

during the crisis period. This is inconsistent with prior research that finds initial audit fee discounting

continues through the crisis years (Desir et al. 2014). Our results suggest auditors were constrained during

the crisis period and unable to offer the same initial audit fee discounts that they did during non-crisis years.

The lack of discounting is potentially due to other clients’ inability to pay their audit fees or hire the same

caliber of auditor or due to increased auditor clout during a time of financial distress for clients.14

Audit Market Competition and Auditor Specialist

We next explore whether the phenomenon we document in our main analysis (i.e., initial audit fee

discounting for clients with prior year MWs) is associated with audit market competition and/or audit

market specialization. Prior research finds initial year price-cutting is more extensive in markets where

14 Note that auditor switching did not significantly decline among our sample observations during this period.

22

price competition is higher (Ghosh and Lustgarten 2006). Audit firms in highly competitive markets have

greater incentives to cut prices to generate new business than auditors in less competitive markets.

Moreover, while prior research shows a premium for auditor industry expertise (Hay et al. 2006), prior

studies also find larger initial audit fee cuts for auditors with more industry expertise (Ettredge and

Greenberg 1990). This relation is likely attributable to the efficiency with which expert auditors can conduct

the audit, resulting in lower audit fees in a competitive setting.

We measure audit market competition and auditor specialist following prior research (Numan and

Willekens 2012; Netwon et al. 2015; Aobdia 2018; Lee, Nagy, and Zimmerman 2019). We define

competition as the absolute value of the difference between: (1) the incumbent audit office’s market share

of audit fees within its MSA-industry and (2) the market share of the audit office within that MSA-industry

that is closest to the incumbent auditor (Newton et al. 2015). We identify audit markets based on the Fama-

French 12 industry classifications within US Metropolitan Statistical Areas (MSA). We then dichotomize

the competition variable into high and low competition by first decile-ranking based on decreasing values

(since competition is decreasing in the measure), and then define the top three deciles as high audit market

competition and the bottom three deciles as low audit market competition. Next, we define auditor specialist

as an indicator equal to one if, in a particular year, the audit firm has the largest market share of audit fees

in the client’s industry and its market share is at least ten percent greater than the second industry leader,

and zero otherwise (Lee et al. 2019).

Columns (1) and (2) of Table 8 display the results of re-estimating equation (1), after partitioning

the sample into low and high audit market competition, respectively. We find that PMW*LAT is

significantly negative in the high competition partition (p < 0.05) and insignificant in the low competition

partition. This evidence supports the notion that the practice of initial audit pricing discounting for clients

with prior year MWs may be attributable to high audit market competition, a concern repeatedly expressed

by the PCAOB (Doty 2015a). Further, in columns (3) and (4) of Table 8, we show PMW*LAT is marginally

significantly negative (p = 0.07) among auditor specialists but insignificant among non-specialists. This

evidence suggests the practice of initial audit fee discounting for clients with prior internal control

23

deficiencies may be more prevalent among auditor specialists, who likely have more flexibility (i.e., greater

efficiencies in conducting their audit work) to offer fee discounts.

I-Score

In our analyses thus far, we utilize a dichotomous measure of internal control risk, reflecting either

the disclosure or non-disclosure of a MW in the year prior to an auditor change. Evidence that firms

frequently restate internal control opinions following a financial restatement announcement suggest the

MW reporting decision may be subject to manager and auditor bias (Rice and Weber 2012). Thus, prior

research develops a continuous measure of internal control quality using the likelihood of a disclosed MW

(Ge et al. 2017; Navarro et al. 2019). This continuous measure may better capture the true distribution of

internal control risk among audit clients, as it reflects many underlying control risk attributes of a firm (e.g.,

size, age, performance, distress, etc.). Thus, we re-estimate equation (1), replacing PMW with PISCORE, a

continuous measure of internal control risk that factors in various firm-related characteristics. We follow

prior research (Navarro et al. 2019) in constructing PISCORE.15 Table 9 displays the results of re-estimating

equation (1) using PISCORE. Since this analysis does not require the use of a MW indicator, we are able

to expand the sample to begin in 2001, increasing the number of observations from roughly 26,000 to

66,000. Contrary to our evidence using PMW, the PISCORE*AUDCH interaction is significantly positive

(p < .001), suggesting auditors price initial audits higher for clients with higher internal control risk. Also

contrary to earlier evidence that finds a negative PMW*LAT interaction, in columns (4) through (7) of Table

9, we generally find the PISCORE*LAT interaction is insignificant.16 This evidence suggests that auditors

may pay less attention to MW disclosures and instead focus more on the underlying risk characteristics of

new clients. Specifically, despite auditors continuing to offer initial audit fee discounts for clients that

15 Specifically, we compute I-scores for our 2001-2017 sample by applying the coefficient estimates generated from

estimating a MW determinants model during the 2006-2007 estimation period in Navarro et al. (2019).

16 The positive PISCORE*AUDCH interaction remains significant (p < .001) and PISCORE*LAT remains

insignificant in the post-2004 time period.

24

disclose a MW, our evidence suggests auditors do consider the underlying risk-based characteristics that

contribute to a higher control risk client, and price the audit accordingly.

V. CONCLUSION

Prior research (Ettredge and Greenberg 1990; Ghosh and Lustgarten 2006) as well as regulator

concerns over extreme audit price competition (Doty 2015a) suggest widespread initial audit fee

discounting and an associated impairment of audit quality. This study examines whether the practice of

initial audit fee discounting exists for clients with reported evidence of high control risk. Prior research

documents audit fee premiums for clients disclosing a material weakness in internal control over financial

reporting (“MW”) (Raghunandan and Rama 2006; Hogan and Wilkins 2008; Hoitash et al. 2008).

Nevertheless, we expect that the practice of initial audit fee discounting, fueled by intense auditor pressure

and price competition (Doty 2015a), results in initial audit fee discounts for high control risk clients. Using

a sample of roughly 26,000 audit engagements over the 2004-2017 period, we find that clients disclosing a

MW in the prior year and laterally changing auditors in the current year (PMW-LAT firms) receive

significant fee discounts, on average, of approximately 13% compared to ongoing audit engagements over

our sample period. Nonetheless, we find no evidence that fee discounts for PMW-LAT firms are associated

with lower audit quality in the initial or subsequent year of auditor change.

We conduct several additional analyses to bolster our results. Our fee discounting evidence is robust

to excluding auditor resignation observations, to clients that do not report current year MWs, to clients

audited by Big 4 auditors, and persists in the pre- and post-financial crisis years but disappears during the

crisis years. We also find PMW-LAT audit fee discounts are larger for clients in markets with higher auditor

competition and higher auditor expertise. Last, we replace the dichotomous PMW variable with a

continuous measure of internal control risk that uses firm characteristics to generate a score representing

the likelihood of a MW (PISCORE). Using this measure, we find that high control risk clients that change

auditors pay a fee premium. This evidence suggests auditors potentially place less weight on the MW

disclosure itself and instead focus on the underlying risk characteristics of clients when pricing initial audits.

25

Our study makes several contributions. First, we contribute to both the initial audit engagement

pricing and internal control literatures. The audit pricing literature provides consistent evidence of initial

audit fee discounting (Simon and Francis 1988; Desir et al. 2013) but does not assess the impact of control

risk on this audit fee practice. We also contribute to the internal control literature, which finds auditors price

MW disclosures for ongoing clients (Raghunandan and Rama 2006; Hogan and Wilkins 2008; Hoitash et

al. 2008) but does not address whether this audit fee premium persists for new clients. Our study also adds

to ongoing regulatory discussions related to the existence of initial audit fee discounting and its impact on

audit quality (AICPA 1978; Turner 2001; Cohn 2014; PCAOB 2014; Ferguson 2015). In total, our evidence

suggests audit firms price initial audits below expected fees, even in situations when a client’s poor internal

control structure is known ex ante. This audit pricing practice, however, has no adverse impact on audit

quality.

26

APPENDIX

Variable Definition

LNFEES Natural logarithm of audit fees.

ACCRQ

McNichols (2002) modification of the Dechow and Dichev (2002) accruals quality

measure. Within each year and 2-digit SIC industry with at least 20 observations, we

estimate the Dechow and Dichev (2002) model of total working capital accruals

regressed on lagged, concurrent, and future period’s cash from operations. We adjust

the Dechow and Dichev (2002) model by including change in revenues from year t-1

to year t and gross property, plant, and equipment (McNichols 2002). All independent

variables are scaled by average total assets. We utilize the absolute value of the firm-

specific residual from this model to measure accruals quality. ACCRQ is then the

standard deviation of this residual over the previous five years.

RESTATE Indicator equal to one if the firm misstates their financial statement in the current year

(which subsequently results in a restatement in a later period), and zero otherwise.

AUDCH

Indicator equal to one if an auditor change occurs in the current year, and zero

otherwise.

LAT

Indicator equal to one if AUDCH=1 and both the predecessor and current auditor are

Big 4 (or both non-Big 4) audit firms, and zero otherwise.

MW

Indicator equal to one if the firm discloses an internal control material weakness in

the current year, and zero otherwise.

PMW

Indicator equal to one if the firm disclosed a material weakness in internal control

over financial reporting in the prior fiscal year, and zero otherwise.

LNAGE Natural logarithm of the number of years the firm has information on Compustat.

AGGLOSS Indicator equal to one if net income for years t and t-1 is negative, and zero otherwise.

ANC_REST Indicator equal to one if the firm announces a financial statement restatement in the

current period, and zero otherwise.

LNASSET Natural logarithm of total assets.

AUD_RESIGN

Inidicator equatl to one if the auditor resigned from the engagement in year t, and zero

otherwise.

BIG4 Indicator equal to one if the firm’s auditor is a Big 4 auditor, and zero otherwise.

DOWN

Indicator equal to one if AUDCH=1 and the firm changes from a Big 4 to a non-Big

4 auditor in the current year, and zero otherwise.

FEECUT

Indicator equal to one if current year audit fees are lower than prior year audit fees,

and zero otherwise

FOREIGN

Indicator equal to one if the firm is engaged in foreign operations (measured as foreign

currency translation not equal to zero) in the current year, and zero otherwise.

GC

Indicator equal to one if the firm receives a going concern opinion in the current year,

and zero otherwise.

GROWTH Percentage change in sales from year t-1 to year t.

27

APPENDIX (Continued)

Variable Definition

ISCORE The likelihood of the existence of a MW as computed in Navarro et al. (2019)

INTANG_INT Research and development plus advertising divided by sales.

INV Average inventory divided by total assets.

INVREC Inventory plus accounts receivable divided by total assets.

LEV Total debt divided by total assets.

LOSS

Indicator equal to one if the firm’s net income is less than zero in the current fiscal year,

and zero otherwise.

NEWFIN

Indicator equal to one if the firm issues equity (greater than $10M) or debt (greater than

$1M) in the current year, and zero otherwise.

ROA Return on assets, measured as net income divided by average total assets.

LNSEG Natural logarithm of number of business segments.

STDCFO

Standard deviation of cash flows (cash flow from operations / total assets) over the past

five fiscal years.

STDSALE Standard deviation of sales (sales / total assets) over the past five fiscal years.

UP Indicator equal to one if AUDCH=1 and the firm changes from a non-Big 4 to a Big 4

auditor in the current year, and zero otherwise.

WRITEOFF Indicator equal to one if the firm reports a write-off in the fiscal year, and zero otherwise.

YE Indicator equal to one if the firm’s fiscal year-end month is not December, and zero

otherwise.

ZRANK Decile ranking of Altman’s (1968) Z-score for bankruptcy risk, such that higher values

represent higher bankruptcy risk.

28

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32

TABLE 1

Descriptive Statistics

n Mean Med StDv

LNFEES 26,083 14.193 14.129 1.038

AUDCH 26,083 0.045 0.000 0.206

UP 26,083 0.006 0.000 0.075

LAT 26,083 0.028 0.000 0.165

DOWN 26,083 0.011 0.000 0.104

ISCORE 24,592 0.988 0.782 0.619

LNASSET 26,083 7.042 6.945 1.785

INVREC 26,083 0.227 0.197 0.168

LEV 26,083 0.546 0.523 0.37

ROA 26,083 -0.003 0.038 0.234

GROWTH 26,083 0.135 0.059 0.649

LNSEG 26,083 0.347 0.000 0.478

FOREIGN 26,083 0.992 1.000 0.087

LOSS 26,083 0.279 0.000 0.448

BIG4 26,083 0.846 1.000 0.361

NEWFIN 26,083 0.542 1.000 0.498

YE 26,083 0.727 1.000 0.446

MW 26,083 0.051 0.000 0.22

RESTATE 26,083 0.084 0.000 0.278

GC 26,083 0.019 0.000 0.135

All variable definitions are provided in the APPENDIX.

33

TABLE 2

Selected Pearson Correlations

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

(1) LNFEES 1.000

(2) AUDCH -0.116 1.000

(3) LAT -0.066 0.785 1.000

(4) MW 0.013 0.091 0.060 1.000

(5) RESTATE 0.037 0.065 0.048 0.209 1.000

(6) GC -0.082 0.024 0.011 0.091 0.011 1.000

(7) ISCORE -0.327 0.339 0.213 0.185 0.069 0.211 1.000

(8) FSCORE -0.019 0.011 0.008 0.001 -0.002 0.021 0.003 1.000

(9) LNASSET 0.803 -0.093 -0.040 -0.105 -0.012 -0.146 -0.495 -0.031 1.000

(10) ROA 0.206 -0.042 -0.030 -0.085 -0.020 -0.352 -0.405 -0.074 0.331 1.000

(11) LOSS -0.182 0.051 0.025 0.111 0.047 0.201 0.692 0.021 -0.318 -0.532 1.000

Bold correlation coefficients are significant at the 5 percent level.

All variable definitions are provided in the APPENDIX.

34

TABLE 3

Audit Fee Changes

n

Mean Raw

Change in

Fees

%

Observations

with Fee Cuts

Mean %

Change in

Fees

Median %

Change in

Fees

Big4 to Big4

PMW=0 447 -181,177 64% 16% -11%

PMW=1 80 -289,513 66% 5% -20%

Non Big4 to Non Big4

PMW=0 166 -7,610 47% 13% 0%

PMW=1 36 -71,010 53% 21% -2%

Big4 to Non Big4

PMW=0 223 -247,266 75% -6% -26%

PMW=1 64 -539,598 77% -19% -41%

Non Big4 to Big4

PMW=0 124 111,241 44% 34% 5%

PMW=1 22 173,817 32% 28% 12%

This table displays the change in audit fees for select partitions of our sample. PMW is an indicator equal to one if

the firm disclosed a material weakness in internal control over financial reporting in the prior fiscal year, and zero

otherwise.

35

TABLE 4

Test of Relation Between Auditor Changes with Prior MW and Audit Fees

AUDCH Test LAT Test

(1) (2) (3) (4)

Variables LNFEES LNFEES LNFEES LNFEES

AUDCH -0.209*** -0.192***

(-8.84) (-7.61)

UP

-0.260*** -0.261***

(-3.58) (-3.59)

LAT

-0.271*** -0.250***

(-9.11) (-8.18)

DOWN

-0.291*** -0.293***

(-6.75) (-6.79)

PMW 0.300*** 0.313***

0.275*** 0.284***

(15.38) (16.20)

(13.56) (13.94)

PMW*AUDCH

-0.108*

(-1.60)

PMW*LAT

-0.134*

(-1.47)

MW 0.286*** 0.286***

0.275*** 0.275***

(14.27) (14.29)

(13.17) (13.17)

LNASSET 0.496*** 0.496***

0.523*** 0.523***

(79.16) (79.16)

(84.50) (84.51)

INVREC 0.698*** 0.698***

0.687*** 0.687***

(9.35) (9.36)

(8.95) (8.95)

LEV 0.110*** 0.111***

0.125*** 0.126***

(3.22) (3.22)

(3.43) (3.44)

ROA -0.179*** -0.179***

-0.172*** -0.172***

(-3.20) (-3.20)

(-2.82) (-2.82)

GROWTH -0.019*** -0.019***

-0.021*** -0.021***

(-3.21) (-3.24)

(-3.27) (-3.30)

LNSEG 0.121*** 0.121***

0.113*** 0.113***

(6.83) (6.83)

(6.28) (6.28)

FOREIGN 0.026 0.026

0.019 0.019

(0.40) (0.40)

(0.28) (0.29)

LOSS 0.136*** 0.135***

0.140*** 0.140***

(7.38) (7.33)

(7.21) (7.15)

BIG4 0.326*** 0.326***

(14.25) (14.25)

36

TABLE 4 (Continued)

AUDCH Results LAT Results

(1) (2) (3) (4)

Variables LNFEES LNFEES LNFEES LNFEES

NEWFIN 0.013 0.013

0.006 0.006

(1.00) (1.00)

(0.46) (0.46)

YE 0.032 0.032

0.039* 0.039*

(1.62) (1.63)

(1.94) (1.94)

GC 0.101** 0.100**

0.090** 0.090**

(2.39) (2.38)

(2.01) (1.99)

ANC_REST 0.053*** 0.054***

0.067*** 0.068***

(3.75) (3.79)

(4.64) (4.70)

Constant 10.245*** 10.244***

10.351*** 10.350***

(111.53) (111.53)

(109.65) (109.58)

Industry FEs Yes Yes Yes Yes

Year FEs Yes Yes Yes Yes

Observations 26,083 26,083 26,083 26,083

Adjusted R² 0.754 0.754 0.744 0.744

R² 0.754 0.754 0.743 0.743

*, **, *** Indicate significance at p< 0.10, p< 0.05, and p< 0.01, respectively (two-tailed, except when direction is predicted)

All continuous variables are winsorized at the top and bottom 0.01 level. Robust t-statistics, clustered by firm, are reported in

parentheses.

All variable definitions are provided in the APPENDIX.

37

TABLE 5

Test of Relation Between Prior MW, Initial Audit Discounts, and Audit Quality

(1) (2) (3) (4)

Variables AccrQ AccrQ t+1 RESTATE RESTATE t+1

PMW 0.009* 0.010* 0.870*** 0.416***

(1.81) (1.92) (7.83) (2.78)

LAT 0.006 0.015** 0.626*** 0.317

(0.82) (2.08) (3.29) (1.33)

FEECUT -0.002* -0.003*** -0.221*** -0.181***

(-1.96) (-3.24) (-4.03) (-3.09)

PMW*LAT 0.005 -0.011 0.015 0.413

(0.42) (-0.79) (0.04) (0.81)

PMW*FEECUT -0.004 -0.007 -0.285 0.240

(-0.63) (-0.98) (-1.74) (1.25)

LAT*FEECUT 0.002 -0.007 -0.194 -0.020

(0.27) (-0.98) (-0.72) (-0.07)

PMW*LAT*FEECUT -0.030** -0.008 -0.010 -0.560

(-2.12) (-0.49) (-0.02) (-0.84)

LNASSET -0.012*** -0.012*** 0.025 0.037

(-10.71) (-10.01) (1.05) (1.43)

AGGLOSS 0.004** 0.003 0.239*** 0.145**

(2.16) (1.44) (3.62) (2.06)

STDSALE 0.000*** 0.000*** -0.000 -0.000**

(8.54) (8.02) (-1.37) (-2.06)

STDCFO 0.035*** 0.020*** 0.052* -0.022

(6.18) (3.42) (1.94) (-0.59)

LNSEG -0.000 0.000 0.084 0.150**

(-0.07) (0.08) (1.36) (2.28)

FOREIGN 0.010** 0.010** 0.193 -0.091

(2.07) (2.25) (0.65) (-0.33)

GROWTH 0.009*** 0.009*** -0.023 0.021

(4.16) (4.40) (-0.57) (0.51)

INV -0.000 0.000 -0.000** -0.000

(-0.20) (0.85) (-2.01) (-1.54)

INTAG_INT 0.001 0.002 0.002* -0.028**

(1.32) (1.77) (2.00) (0.08)

ZRANK -0.003*** -0.004*** -0.080*** -0.075***

(-4.76) (-5.82) (-6.18) (-5.15)

38

TABLE 5 (Continued)

(1) (2) (3) (4)

Variables AccrQ AccrQ t+1 RESTATE RESTATE t+1

BIG4 -0.012*** -0.010*** 0.196*** 0.157*

(-3.55) (-2.93) (2.62) (1.85)

WRITEOFF -0.034 0.014**

(-1.39) (2.57)

Constant 0.169*** 0.140*** -3.475*** -3.291***

(6.22) (10.17) (-7.39) (-6.52)

Industry fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

Observations 17,398 15,291 26,830 23,522

Adjusted R² 0.263 0.246

R² 0.26 0.242 . .

*, **, *** Indicate significance at p< 0.10, p< 0.05, and p< 0.01, respectively (two-tailed, except when direction is predicted)

All continuous variables are winsorized at the top and bottom 0.01 level. Robust t-statistics, clustered by firm, are reported in

parentheses.

All variable definitions are provided in the APPENDIX.

39

TABLE 6

Test of Relation Between Lateral Auditor Changes with Prior MW and Audit Fees

The Impact of Auditor Dismissals, MW Remediation, and Audit Firm Size

AUD_RESIGN=0 MW=0 MW=1 BIG4=1 BIG4=0

(1) (2) (3) (4) (5)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES

UP -0.238*** -0.324*** 0.078 -0.349***

(-3.45) (-3.95) (0.59) (-4.80)

LAT -0.245*** -0.259*** -0.142 -0.208*** -0.261***

(-7.97) (-8.32) (-1.07) (-6.15) (-4.39)

DOWN -0.301*** -0.275*** -0.357**

-0.063

(-7.09) (-6.26) (-2.57)

(-1.42)

PMW 0.286*** 0.301*** 0.248*** 0.338*** 0.231***

(14.27) (14.90) (4.95) (16.71) (5.42)

PMW*LAT -0.198** -0.239** -0.051 -0.152* -0.068

(-1.79) (-2.07) (-0.27) (-1.41) (-0.44)

MW 0.277***

0.322*** 0.216***

(13.46)

(14.96) (5.16)

LNASSET 0.522*** 0.522*** 0.533*** 0.495*** 0.486***

(86.56) (82.63) (28.39) (76.93) (22.95)

INVREC 0.693*** 0.706*** 0.423* 0.770*** 0.520***

(9.13) (9.06) (1.78) (9.15) (3.70)

LEV 0.133*** 0.159*** 0.152*** 0.172*** 0.069

(3.74) (4.86) (2.87) (5.96) (1.45)

ROA -0.165*** -0.267*** 0.082 -0.343*** -0.102

(-2.69) (-5.58) (0.94) (-7.53) (-1.54)

GROWTH -0.020*** -0.017*** -0.110*** -0.012* -0.033***

(-3.22) (-2.72) (-3.36) (-1.93) (-3.33)

LNSEG 0.115*** 0.119*** -0.040 0.131*** 0.033

(6.43) (6.58) (-0.64) (7.04) (0.68)

FOREIGN 0.016 0.016 0.076 0.008 0.087

(0.24) (0.24) (0.42) (0.10) (1.00)

LOSS 0.139*** 0.110*** 0.252*** 0.092*** 0.166***

(6.99) (6.95) (5.06) (5.99) (5.18)

NEWFIN 0.005 -0.000 0.026 0.011 -0.024

(0.38) (-0.01) (0.55) (0.82) (-0.78)

40

TABLE 6 (Continued)

AUD_RESIGN=0 MW=0 MW=1 BIG4=1 BIG4=0

(1) (2) (3) (4) (5)

Variable LNFEES LNFEES LNFEES LNFEES LNFEES

YE 0.040** 0.043** -0.046 0.036* -0.013

(1.99) (2.12) (-0.79) (1.68) (-0.28)

GC 0.101** 0.059 0.081 0.086** 0.072

(2.26) (1.42) (1.00) (2.32) (0.94)

ANC_REST 0.069*** 0.053*** 0.092** 0.053*** 0.016

(4.83) (3.56) (2.25) (3.63) (0.40)

Constant 10.359*** 10.341*** 10.569*** 10.539*** 10.306***

(109.54) (107.75) (43.39) (100.38) (68.47)

Industry fixed effects Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes

Observations 25,900 24,748 1,335 22,062 4,021

Adjusted R² 0.747 0.754 0.626 0.734 0.559

R² 0.746 0.754 0.605 0.733 0.551

*, **, *** Indicate significance at p< 0.10, p< 0.05, and p< 0.01, respectively (two-tailed, except when direction is predicted)

All continuous variables are winsorized at the top and bottom 0.01 level. Robust t-statistics, clustered by firm, are reported in parentheses.

All variable definitions are provided in the APPENDIX.

41

TABLE 7

Test of the Relation Between Lateral Auditor Changes with Prior MW and Audit Fees

Crisis Period Analysis

Panel A: Pre- and Post- Crisis Years

Pre-2007 Post-2008

All Obs AUD_RESIGN=0 MW=0 All Obs AUD_RESIGN=0 MW=0

(1) (2) (3) (4) (5) (6)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES

UP -0.253 -0.247 -0.299 -0.299*** -0.264*** -0.349***

(-1.02) (-0.94) (-1.07) (-3.66) (-3.89) (-3.91)

LAT -0.075 -0.044 -0.045 -0.279*** -0.276*** -0.289***

(-0.81) (-0.47) (-0.48) (-7.59) (-7.44) (-7.58)

DOWN -0.358*** -0.335*** -0.316*** -0.287*** -0.294*** -0.259***

(-4.02) (-4.42) (-4.28) (-5.39) (-5.34) (-4.48)

PMW 0.327*** 0.335*** 0.319*** 0.224*** 0.219*** 0.262***

(9.36) (9.81) (8.46) (7.80) (7.63) (9.71)

PMW*LAT -0.230* -0.291** -0.378** -0.176 -0.281* -0.358*

(-1.48) (-1.77) (-2.16) (-1.24) (-1.63) (-1.83)

MW 0.331*** 0.340***

0.234*** 0.236***

(8.22) (8.96)

(9.08) (9.04)

LNASSET 0.515*** 0.512*** 0.518*** 0.525*** 0.524*** 0.524***

(54.01) (57.26) (53.96) (83.61) (84.55) (82.04)

INVREC 0.551*** 0.593*** 0.618*** 0.741*** 0.742*** 0.751***

(4.63) (5.63) (5.76) (8.88) (8.91) (8.84)

LEV 0.214*** 0.257*** 0.165** 0.122*** 0.128*** 0.169***

(3.40) (5.13) (2.34) (3.56) (3.75) (5.72)

ROA -0.255*** -0.262*** -0.278*** -0.142** -0.133** -0.248***

(-3.44) (-3.61) (-3.51) (-2.25) (-2.14) (-4.39)

GROWTH -0.030** -0.029** -0.015 -0.023*** -0.023*** -0.021***

(-2.06) (-2.03) (-1.17) (-3.28) (-3.23) (-3.02)

LNSEG 0.124*** 0.134*** 0.150*** 0.117*** 0.117*** 0.118***

(4.50) (5.13) (5.63) (6.21) (6.23) (6.21)

FOREIGN -0.053 -0.056 -0.051 0.034 0.031 0.034

(-0.56) (-0.58) (-0.49) (0.44) (0.40) (0.43)

LOSS 0.118*** 0.107*** 0.103*** 0.162*** 0.161*** 0.133***

(3.36) (3.21) (2.84) (7.94) (7.82) (7.55)

NEWFIN -0.014 -0.025 -0.009 0.018 0.019 0.006

(-0.60) (-1.07) (-0.37) (1.19) (1.24) (0.43)

42

TABLE 7 (Continued)

Pre-2007 Post-2008

All Obs AUD_RESIGN=0 MW=0 All Obs AUD_RESIGN=0 MW=0

(1) (2) (3) (4) (5) (6)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES

YE 0.043 0.044* 0.043 0.044** 0.045** 0.047**

(1.63) (1.66) (1.57) (2.10) (2.13) (2.19)

GC 0.163* 0.137* 0.285*** 0.049 0.073 -0.005

(1.94) (1.68) (2.81) (0.87) (1.36) (-0.08)

ANC_REST 0.098*** 0.104*** 0.099*** 0.050*** 0.052*** 0.042**

(3.09) (3.28) (2.83) (2.88) (3.02) (2.36)

Constant 10.427*** 10.434*** 10.403*** 10.076*** 10.088*** 10.067***

(77.96) (80.11) (75.29) (105.91) (105.43) (104.03)

Industry FEs Yes Yes Yes Yes Yes Yes

Year FEs Yes Yes Yes Yes Yes Yes

Observations 3,821 3,768 3,461 17,507 17,408 16,808

Adjusted R² 0.714 0.723 0.733 0.761 0.763 0.768

R² 0.710 0.718 0.728 0.760 0.762 0.768

Panel B: Crisis and Non-Crisis Years

Crisis Years Non-Crisis Years

All Obs AUD_RESIGN=0 MW=0 All Obs AUD_RESIGN=0 MW=0

(7) (8) (9) (10) (11) (12)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES

UP -0.130 -0.134 -0.261 -0.303*** -0.274*** -0.342***

(-0.77) (-0.75) (-1.24) (-3.88) (-3.90) (-3.95)

LAT -0.250*** -0.255*** -0.263*** -0.250*** -0.243*** -0.257***

(-3.49) (-3.51) (-4.00) (-7.34) (-7.11) (-7.29)

DOWN -0.229** -0.275** -0.265** -0.308*** -0.306*** -0.277***

(-2.10) (-2.42) (-2.23) (-6.55) (-6.85) (-6.07)

PMW 0.324*** 0.333*** 0.346*** 0.270*** 0.270*** 0.287***

(8.66) (8.70) (8.20) (12.29) (12.49) (13.19)

PMW*LAT 0.039 0.049 0.048 -0.175** -0.255** -0.316**

(0.21) (0.22) (0.26) (-1.67) (-2.04) (-2.30)

MW 0.302*** 0.286***

0.270*** 0.276***

(6.63) (6.16)

(12.05) (12.51)

LNASSET 0.523*** 0.522*** 0.520*** 0.524*** 0.522*** 0.523***

(60.75) (60.52) (60.07) (84.99) (86.88) (83.31)

43

TABLE 7 (Continued)

Crisis Years Non-Crisis Years

All Obs AUD_RESIGN=0 MW=0 All Obs AUD_RESIGN=0 MW=0

(7) (8) (9) (10) (11) (12)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES

INVREC 0.615*** 0.617*** 0.632*** 0.706*** 0.714*** 0.726***

(6.57) (6.58) (6.72) (8.96) (9.13) (9.02)

LEV 0.140*** 0.141*** 0.129** 0.127*** 0.136*** 0.166***

(2.73) (2.77) (2.47) (3.51) (3.79) (5.25)

ROA -0.314*** -0.317*** -0.335*** -0.151** -0.141** -0.253***

(-5.29) (-5.27) (-5.41) (-2.41) (-2.24) (-4.87)

GROWTH -0.009 -0.008 -0.001 -0.024*** -0.023*** -0.021***

(-0.61) (-0.57) (-0.06) (-3.70) (-3.62) (-3.19)

LNSEG 0.094*** 0.097*** 0.101*** 0.117*** 0.119*** 0.123***

(3.85) (3.95) (4.16) (6.47) (6.61) (6.74)

FOREIGN -0.010 -0.012 -0.055 0.024 0.021 0.028

(-0.09) (-0.10) (-0.45) (0.34) (0.29) (0.39)

LOSS 0.055** 0.056** 0.041 0.156*** 0.155*** 0.126***

(2.07) (2.07) (1.49) (7.78) (7.57) (7.61)

NEWFIN -0.029 -0.031 -0.016 0.013 0.012 0.003

(-1.38) (-1.45) (-0.76) (0.93) (0.86) (0.23)

YE 0.024 0.023 0.030 0.043** 0.044** 0.046**

(0.94) (0.90) (1.13) (2.12) (2.16) (2.24)

GC 0.098* 0.101* 0.121** 0.079 0.094* 0.042

(1.71) (1.75) (1.98) (1.57) (1.91) (0.88)

ANC_REST 0.092** 0.088** 0.040 0.063*** 0.066*** 0.053***

(2.42) (2.31) (0.98) (4.06) (4.29) (3.39)

Constant 10.271*** 10.281*** 10.319*** 10.323*** 10.331*** 10.304***

(75.61) (75.61) (75.15) (105.73) (105.55) (103.40)

Industry FEs Yes Yes Yes Yes Yes Yes

Year FEs Yes Yes Yes Yes Yes Yes

Observations 4,755 4,724 4,479 21,328 21,176 20,269

Adjusted R² 0.718 0.718 0.726 0.750 0.754 0.761

R² 0.714 0.714 0.723 0.750 0.753 0.760 *, **, *** Indicate significance at p< 0.10, p< 0.05, and p< 0.01, respectively (two-tailed, except when direction is

predicted)

All continuous variables are winsorized at the top and bottom 0.01 level. Robust t-statistics, clustered by firm, are

reported in parentheses.

All variable definitions are provided in the APPENDIX.

44

TABLE 8

Test of the Relation between Auditor Changes with Prior MW and Audit Fees

Analysis of Audit Market Competition and Auditor Specialist

Lo Competition Hi Competition Specialist Non-Specialist

(1) (2) (3) (4)

Variables LNFEES LNFEES LNFEES LNFEES

UP -0.235* -0.223** -0.065 -0.286***

(-1.74) (-1.98) (-0.39) (-3.62)

LAT -0.237*** -0.267*** -0.042 -0.277***

(-3.67) (-5.04) (-0.56) (-8.39)

DOWN -0.381*** -0.330*** 0.312 -0.293***

(-4.43) (-3.59) (0.98) (-6.67)

PMW 0.320*** 0.354*** 0.385*** 0.275***

(9.45) (7.58) (7.65) (12.34)

PMW*LAT -0.180 -0.479** -0.312* -0.097

(-0.91) (-2.08) (-1.48) (-0.93)

MW 0.318*** 0.378*** 0.345*** 0.277***

(6.13) (7.50) (5.50) (12.25)

LNASSET 0.518*** 0.504*** 0.508*** 0.524***

(54.33) (59.87) (40.48) (81.56)

INVREC 0.727*** 0.762*** 0.728*** 0.700***

(6.01) (7.34) (4.15) (8.74)

LEV 0.238*** 0.172*** 0.182*** 0.108***

(5.71) (5.18) (3.13) (2.85)

ROA -0.196** -0.216*** -0.335*** -0.182***

(-2.30) (-3.68) (-3.69) (-2.89)

GROWTH -0.036** -0.017 0.011 -0.022***

(-2.37) (-1.59) (0.79) (-3.33)

LNSEG 0.163*** 0.134*** 0.162*** 0.114***

(5.98) (5.24) (4.43) (5.92)

FOREIGN 0.053 0.090 -0.174* 0.043

(0.41) (0.92) (-1.79) (0.58)

LOSS 0.137*** 0.091*** 0.044 0.151***

(5.51) (3.70) (1.10) (7.52)

NEWFIN -0.000 0.005 -0.004 0.011

(-0.02) (0.25) (-0.12) (0.80)

YE 0.007 0.055** 0.034 0.043**

(0.23) (2.04) (0.67) (2.08)

GC 0.069 0.072 0.069 0.114**

(0.84) (1.22) (0.85) (2.32)

45

TABLE 8 (Continued)

Low Competition Hi Competition Specialist Non-Specialist

(1) (2) (3) (4)

Variables LNFEES LNFEES LNFEES LNFEES]

ANC_REST 0.057** 0.007 0.062 0.069***

(2.23) (0.22) (1.63) (4.54)

Constant 9.964*** 10.162*** 10.578*** 10.126***

(63.32) (81.70) (69.54) (110.98)

Industry fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

Observations 6,537 6,110 3,604 22,154

Adjusted R² 0.757 0.750 0.759 0.741

R² 0.754 0.747 0.754 0.740 *, **, *** Indicate significance at p< 0.10, p< 0.05, and p< 0.01, respectively (two-tailed, except when direction is predicted)

All continuous variables are winsorized at the top and bottom 0.01 level. Robust t-statistics, clustered by firm, are reported in

parentheses.

All variable definitions are provided in the APPENDIX.

46

TABLE 9

Test of the Relation between Auditor Changes with Prior I-Score and Audit Fees

AUDCH Test LAT Test AUD_RESIGN=1 MW=0 MW=1

(1) (2) (3) (4) (5) (6) (7)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES

AUDCH -0.177*** -0.282***

(-11.99) (-12.30)

UP

-0.209*** -0.208*** -0.196*** -0.298*** 0.083

(-2.71) (-2.69) (-2.69) (-3.45) (0.62)

LAT

-0.205*** -0.228*** -0.262*** -0.285*** 0.124

(-6.09) (-3.89) (-4.26) (-4.59) (0.72)

DOWN

-0.188*** -0.187*** -0.216*** -0.191*** -0.193

(-3.92) (-3.88) (-4.37) (-3.77) (-1.44)

ISCORE 0.001 -0.001 -0.039** -0.040** -0.033* -0.044** -0.090

(0.11) (-0.19) (-1.99) (-2.00) (-1.69) (-2.20) (-1.62)

PISCORE 0.025*** 0.019*** 0.061*** 0.060*** 0.060*** 0.056*** 0.021

(5.64) (4.25) (5.85) (5.75) (5.67) (5.37) (0.54)

PISCORE*AUDCH

0.067***

(6.18)

PISCORE*LAT

0.019 0.034 0.040 -0.153*

(0.49) (0.83) (0.93) (-1.47)

LNASSET 0.509*** 0.508*** 0.533*** 0.533*** 0.532*** 0.534*** 0.531***

(126.10) (126.16) (78.51) (78.53) (81.16) (78.28) (23.93)

INVREC 0.417*** 0.418*** 0.820*** 0.821*** 0.818*** 0.831*** 0.589**

(11.13) (11.15) (10.71) (10.70) (10.76) (10.79) (2.57)

LEV 0.054*** 0.055*** 0.128*** 0.127*** 0.131*** 0.152*** 0.219***

(13.42) (13.84) (3.43) (3.38) (3.63) (4.32) (3.34)

47

TABLE 9 (Continued)

AUDCH interaction LAT interaction AUD_RESIGN=1 MW=0 MW=1

(1) (2) (3) (4) (5) (6) (7)

Variables LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES LNFEES

ROA -0.049*** -0.050*** -0.192*** -0.192*** -0.188*** -0.288*** 0.139

(-7.68) (-7.79) (-3.23) (-3.24) (-3.15) (-5.59) (1.52)

GROWTH -0.031*** -0.031*** -0.022*** -0.022*** -0.021*** -0.017** -0.112***

(-10.22) (-10.23) (-3.37) (-3.37) (-3.27) (-2.52) (-3.22)

LNSEG 0.139*** 0.139*** 0.115*** 0.115*** 0.117*** 0.121*** -0.045

(9.83) (9.84) (6.14) (6.14) (6.28) (6.51) (-0.69)

FOREIGN 0.023 0.025 -0.028 -0.028 -0.031 -0.031 0.062

(0.65) (0.70) (-0.50) (-0.51) (-0.56) (-0.54) (0.34)

LOSS 0.159*** 0.161*** 0.175*** 0.176*** 0.168*** 0.137*** 0.371***

(13.76) (13.93) (6.50) (6.46) (6.08) (5.50) (5.42)

BIG4 0.433*** 0.429***

(26.91) (26.63)

NEWFIN 0.009 0.009 -0.001 -0.000 -0.001 -0.008 0.012

(1.02) (0.99) (-0.04) (-0.04) (-0.10) (-0.65) (0.25)

YE 0.087*** 0.087*** 0.045** 0.045** 0.046** 0.053*** -0.058

(6.28) (6.28) (2.21) (2.22) (2.26) (2.59) (-0.94)

GC 0.170*** 0.171*** 0.145*** 0.145*** 0.151*** 0.075* 0.144*

(10.05) (10.12) (3.22) (3.23) (3.38) (1.76) (1.80)

ANC_REST 0.157*** 0.157*** 0.153*** 0.153*** 0.151*** 0.091*** 0.103**

(13.81) (13.81) (10.19) (10.18) (10.16) (6.09) (2.46)

Constant 8.898*** 8.907*** 10.228*** 10.229*** 10.230*** 10.207*** 10.659***

(195.78) (196.13) (112.93) (112.78) (113.18) (111.77) (40.64)

48

TABLE 9 (Continued)

Industry fixed effects Yes Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes Yes

Observations 66,188 66,188 24,453 24,453 24,273 23,164 1,289

Adjusted R² 0.840 0.840 0.751 0.751 0.754 0.768 0.620

R² 0.840 0.840 0.750 0.750 0.753 0.767 0.598 *, **, *** Indicate significance at p< 0.10, p< 0.05, and p< 0.01, respectively (two-tailed, except when direction is predicted)

All continuous variables are winsorized at the top and bottom 0.01 level. Robust t-statistics, clustered by firm, are reported in parentheses.

All variable definitions are provided in the APPENDIX.