Electronic copy available at: http://ssrn.com/abstract=1124082

Audit Quality, Alternative Monitoring Mechanisms,

and Cost of Capital: An Empirical Analysis

Anwer S. Ahmed*

Ernst & Young Professor of Accounting

Stephanie J. Rasmussen

PhD Student

Senyo Tse

KPMG Professor of Accounting

Texas A&M University

August 2008

* Corresponding author; Tel: (979) 845-1498; Fax: (979) 845-0028;

E-mail: aahmed@mays.tamu.edu

Texas A&M University

Mays Business School

Department of Accounting

4353 TAMU

College Station, TX 77843-4353

We thank Mike Drake, Steve Fortin, Michel Magnan, James Myers, Tom Omer, Mike Shaub, Mike Wilkins, and

Chris Wolfe for helpful comments and suggestions. We also appreciate the comments and suggestions from

workshop participants at McGill University, Texas A&M University, the 2008 AAA FARS Conference, and the

2008 AAA Annual Meeting.

Electronic copy available at: http://ssrn.com/abstract=1124082

2

Audit Quality, Alternative Monitoring Mechanisms,

and Cost of Capital: An Empirical Analysis

Abstract

Prior studies document that firms using a Big 4 auditor have a lower cost of capital than other

firms. We extend this literature by examining whether using an industry specialist auditor

reduces cost of capital for clients of Big 4 audit firms. We document that firms that use Big 4

auditors that are industry specialists have significantly lower cost of both equity and debt than

firms that use non-specialist Big 4 auditors. We further investigate whether the benefits of using

an industry specialist auditor vary with the strength of alternative monitoring mechanisms. We

show that using an industry specialist auditor is especially important when alternative monitoring

mechanisms, such as boards of directors or institutional shareholders, are relatively weak. In

other words, the benefits of using an industry specialist auditor dissipate when alternative

monitoring mechanisms are strong. This evidence suggests some degree of substitutability

between audit quality and alternative monitoring mechanisms.

Keywords: Audit quality; Industry specialization; Financial reporting credibility; Cost of

capital.

JEL classification: G3; M42

Data availability: Data are available from sources identified in the paper.

3

1. Introduction

Prior studies document that firms that use a Big 4 auditor have a lower cost of capital

than other firms (Khurana and Raman [2004]; Mansi et al. [2004]; Pittman and Fortin [2004]).1

This line of research assumes that audit quality is homogeneous across Big 4 and Non-Big 4

auditors. Given that over 90 percent of publicly listed firms are audited by Big 4 auditors, the

extent of differences in audit quality across Big 4 firms and the causes of those differences are

important. Accordingly, we extend this literature by examining whether using an industry

specialist auditor reduces cost of capital for clients of Big 4 audit firms. We find that among Big

4 clients, the use of an industry specialist auditor significantly reduces firms’ cost of equity and

cost of debt. Moreover, these effects are driven by firms that have relatively weak alternative

monitoring mechanisms such as boards of directors or institutional shareholders.

Economic theory suggests that auditing plays a vital role in the presence of information

asymmetry and moral hazard by assuring capital providers that the financial statements prepared

by insiders (managers) are credible (Jensen and Meckling [1976]; Watts and Zimmerman [1981];

Simunic and Stein [1987]). In other words, without auditing, outsiders would be skeptical of the

information provided by managers and would therefore either refuse to invest capital or demand

an extremely high rate of return to compensate them for the risk of potential expropriation of

their capital by managers. This suggests that, ceteris paribus, the higher the audit quality, the

lower would be the rate of return required by capital providers.

We expect industry specialists to provide higher quality audits than non-specialists for

three reasons. First, industry specialists are likely to have a better understanding of the client’s

business and audit risks (Craswell et al. [1995]; Hogan and Jeter [1999]; Solomon et al. [1999];

1 By Big 4, we mean studies that examine the Big 4/5/6 auditors. In this study, our data pertains to the Big 5

auditors prior to Arthur Andersen’s demise and the Big 4 auditors after Andersen’s demise.

4

Gramling and Stone [2001]). This superior knowledge should enable specialists to perform

higher quality audits than non-specialists. Second, prior work suggests that industry specialists

perform higher quality audits than non-specialists. For example, Balsam et al. [2003] find that

using an industry specialist reduces the likelihood of earnings management (proxied by

discretionary accruals) and increases the informativeness of earnings (proxied by earnings

response coefficients, or ERCs). Third, consistent with the notion that specialists perform higher

quality audits, audit fees for specialists are higher than for non-specialists (Craswell et al. [1995];

Ferguson et al. [2003]; Mayhew and Wilkins [2003]).

It is important to document the effects of audit quality on cost of capital because of the

widespread perception that investors have little confidence in companies’ financial reports. For

example, DeFond and Francis [2005] state that “…popular opinion seems almost unanimous in

concluding that the auditing profession is broken, so much so that regulators have taken away the

profession’s ability to set its own standards.” While this is an important presumption underlying

the Sarbanes Oxley Act and the associated institutional changes, capital market participants may

not necessarily share this view. If capital providers do subscribe to this view then they are

unlikely to perceive auditing as a valuable monitoring or governance mechanism and thus there

should be no effect of audit quality differences on firms’ cost of capital.

Following Dhaliwal et al. [2006] and Hail and Leuz [2006], we derive our cost of capital

measure by averaging four implied cost of equity estimates described in Gebhardt et al. [2001],

Claus and Thomas [2001], Gode and Mohanram [2003], and Easton [2004]. Similar to Khurana

and Raman [2006], we include several controls for other determinants of cost of equity in our

model. We measure cost of debt using credit ratings, as in Mansi et al. [2004]. Furthermore, our

control variables are also based on the specification Mansi et al. [2004] use in their tests.

5

We classify an auditor as an industry specialist using two alternative market-share

measures proposed by Neal and Riley [2004] and Mayhew and Wilkins [2003]. Both measures

are based on the market share of an auditor in a given industry compared to all other Big 4

auditors in that industry. The first measure, NRMkt, classifies an auditor as a specialist if that

auditor’s industry market share is at least 20 percent higher than a within-industry market share

average (Neal and Riley [2004]). This method allows more than one auditor to be classified as a

specialist for each industry. The second measure, MWMkt, classifies an auditor as a specialist if

that auditor’s industry market share is the highest in a given industry and is at least 10 percentage

points higher than the next Big 4 competitor (Mayhew and Wilkins [2003]). This method allows

at most one industry specialist per industry, and is therefore more conservative than the NRMkt

approach.

Using a sample of 8,740 firm-year observations that have the required data items on

CRSP, COMPUSTAT, I/B/E/S, and Thomson Financial over 1999-2005, we find that after

controlling for cost of equity determinants, the use of an industry specialist auditor is associated

with a significantly lower cost of equity. On average, use of an industry specialist auditor is

associated with a reduction of 10 to 20 basis points in the cost of equity for our full sample of

firms, depending on which industry specialist classification we use. Considering that the mean

and median market values of equity for our sample are $7.4 billion and $1.2 billion, respectively,

these reductions in cost of equity are economically significant. Moreover, this effect is driven by

firms that have relatively weak monitoring mechanisms and is larger for these firms (use of an

industry specialist by firms with weak monitoring mechanisms is associated with a 20-30 basis

point reduction in cost of capital). Audit quality is not significantly associated with cost of

capital for firms with strong monitoring.

6

Our cost of debt analysis includes 8,434 firm-year observations with required data items

on CRSP, COMPUSTAT, I/B/E/S, and Thomson Financial over 1999-2005. Consistent with the

cost of equity results, we find that using an industry specialist is associated with a significantly

lower cost of debt. On average, the odds of firms that use an industry specialist auditor having a

higher cost of debt (rather than a lower cost of debt) are approximately 20 percent less than the

corresponding odds for firms that use a non-specialist auditor. The average debt rating for firms

in our sample is BBB-. Similar to the cost of equity analysis, the effect of an industry specialist

on the cost of debt is driven by firms with relatively weak monitoring mechanisms.

To summarize, we contribute to the literature in a number of ways. First, we extend prior

studies, such as Khurana and Raman [2004] and Mansi et al. [2004], by documenting that, within

Big 4 audits, using industry specialist auditors is significantly negatively associated with both

cost of equity and cost of debt. Second, we show that the benefits of using an industry specialist

vary with the strength of alternative monitoring mechanisms. In particular, we show that using an

industry specialist auditor is especially important when other monitoring mechanisms are weak.

This suggests that audit quality can be a substitute for other monitoring mechanisms such as

board of directors and/or institutional shareholder monitoring.

Two concurrent working papers also examine the effect of using an industry specialist

auditor on cost of equity. Li and Wang [2008] find that clients audited by city-level industry

specialists have a significantly lower cost of equity than other clients. Fernando et al. [2008] also

document a negative relation between cost of equity and use of an industry specialist in a sample

of firms audited by both Big 4 and Non-Big 4 auditors. Our study differs from these studies on

several important dimensions. First, we examine the effect of using an industry specialist on both

firms’ cost of equity and cost of debt, while these studies only examine cost of equity effects.

7

Second, we use a more comprehensive approach to measuring cost of equity than the PEG ratio

(Easton [2004]), used in these studies. Third, we focus on the effect of using industry specialists

within the Big 4 firms. In other words, we ask whether using an industry specialist matters given

that a client is using a Big 4 auditor. Fernando et al. [2008] examine both Big 4 and Non-Big 4

clients, and Li and Wang [2008] add clients audited by some Non-Big 4 firms. Finally, we

provide evidence on the substitutability of audit quality (proxied by the choice of a specialist

auditor) and alternative monitoring mechanisms. We find that for firms audited by Big 4

auditors, industry specialists only have a significant effect on cost of equity and debt capital

when alternative monitoring mechanisms are weak. Li and Wang [2008] and Fernando et al.

[2008] do not examine alternative monitoring mechanisms.

The remainder of this paper is organized as follows. Section 2 presents a discussion of

prior research and hypothesis development. Section 3 presents the research design and Section 4

presents the empirical analysis. The conclusion is presented in Section 5.

2. Prior Research and Hypothesis Development

Jensen and Meckling [1976] argue that the separation of ownership and control results in

information asymmetry and moral hazard problems. In other words, managers have better

information than shareholders and also have the opportunity to take self-serving actions at the

expense of outside capital providers (such as shareholders and bondholders). Thus, outside

capital providers demand monitoring or governance mechanisms that assure them that managers

will not expropriate their capital. Economic theory suggests that auditing plays a vital role in

capital markets by assuring outside capital providers that the financial statements prepared by

managers are credible (Watts and Zimmerman [1981]; Simunic and Stein [1987]; Titman and

8

Trueman [1986]; Slovin et al. [1990]; Datar et al. [1991]). Credible financial reporting in turn

helps to reduce costs arising from information asymmetry and moral hazard. In other words, it

facilitates the monitoring of managerial actions (Bushman et al. [2004]; Ahmed and Duellman

[2007]).

Although all audits are mandated to meet certain minimal standards, audit quality can

vary in practice (DeAngelo [1981]). This variation may be driven by investors’ and managers’

demand for levels of audit quality appropriate for the firm’s monitoring needs or the degree of

information asymmetry between insiders and outsiders.2 High quality audits are likely to reduce

the costs arising from information asymmetry (e.g. transaction costs, lower market liquidity) by

reducing the risk of insider expropriation of outsiders’ wealth.

Prior evidence suggests that industry specialist auditors provide higher quality audits than

non-industry specialists. Use of industry specialists is associated with smaller discretionary

accruals and higher ERCs (Balsam et al. [2003]). Furthermore, industry specialists tend to earn

higher fees than non-specialists (Craswell et al. [1995]; Ferguson et al. [2003]; Mayhew and

Wilkins [2003]). This suggests that industry specialist auditors provide a higher quality audit

than non-specialists. However, there are at least two reasons why some firms may choose not to

seek the higher audit quality that a specialist auditor offers. First, Hogan [1997] finds that firms

engaging in an initial public offering (IPO) trade off higher audit quality and higher audit fees.

While she finds that higher quality audits are associated with lower IPO underpricing for her

sample of firms, clients appear to attempt to minimize the sum of stock underpricing and higher

audit fees. Second, Kwon [1996] finds that the competitive nature of an industry affects auditor

selection. In highly competitive industries, clients prefer to use a different auditor than their

2 Francis and Wilson [1988], DeFond [1992], and Francis et al. [1999] find that firms with strong monitoring needs

demand higher quality audits than other firms. Beatty [1989] and Willenborg [1999] find that high-quality audits

mitigate information asymmetry problems to a greater degree than low quality audits.

9

competitors because they are concerned about transfer of proprietary information. In summary,

clients consider many costs and benefits of using an auditor beyond the auditor’s industry

specialization.

Without auditing, outsiders would be skeptical of information provided by managers and

would therefore either refuse to invest capital or demand an extremely high rate of return to

compensate them for the risk of potential expropriation of their capital by managers. This

suggests that, ceteris paribus, the higher the audit quality, the lower would be the rate of return

required by capital providers (Khurana and Raman [2004]; Mansi et al. [2004]; Pittman and

Fortin [2004]).

Prior studies generally examine the relation between audit quality (proxied by the use of a

Big 4 versus a Non-Big 4 auditor) and the cost of capital. They find compelling evidence that

Big 4 audits are of higher quality than Non-Big 4 audits. For example, Becker et al. [1998] and

Francis et al. [1999] find that use of a Big 4 auditor is associated with smaller discretionary

accruals. Teoh and Wong [1993] document that clients of Big 4 auditors have higher ERCs than

clients of Non-Big 4 auditors. Balvers et al. [1988], Beatty [1989], and Willenborg [1999] show

that employing a Big 4 auditor reduces IPO under-pricing. Building on this line of research,

Khurana and Raman [2004] hypothesize and find that firms that use a Big 4 auditor have a lower

cost of equity than firms that use a Non-Big 4 auditor. Furthermore, Mansi et al. [2004] and

Pittman and Fortin [2004] show that firms that use a Big 4 auditor have a lower cost of debt.

Drawing on prior research that suggests industry specialization is associated with higher

quality audits, we predict that use of an industry specialist auditor is associated with a lower cost

of capital for the audit client. We relax prior studies’ implicit assumption that audit quality is

10

homogeneous across Big 4 firms and provide evidence on the effect of audit quality differences

across Big 4 clients on the cost of capital. Our first hypothesis is stated as follows:

Hypothesis 1. Firms that use an industry specialist auditor have a lower cost of capital

than firms that do not use a specialist auditor.

While we expect industry specialists to provide higher quality audits than non-specialists,

the potential benefits of using a specialist auditor are likely to be lower if a firm has strong

alternative monitoring or governance mechanisms. In particular, two other potential monitoring

mechanisms are the board of directors and institutional shareholders (Fama and Jensen [1983];

Shleifer and Vishny [1986]; Bhojraj and Segupta [2003]; Ahmed and Duellman [2007]).

Beasley [1996] and Dechow et al. [1996] find that board independence, measured by the

percentage of outside directors, is negatively related to the likelihood of financial statement

fraud. Farber [2005] finds similar results regarding board characteristics. Peasnell et al. [2005]

find that the percentage of outsiders on the board is negatively related to income-increasing

abnormal accruals for UK companies. Klein [2002] finds similar results for US firms using

absolute abnormal accruals as a proxy for earnings management. Dechow et al. [1996] also

document a negative relation between outsider block holders and the likelihood of fraud. Chung

et al. [2002] document evidence consistent with large institutional shareholders deterring

managers from using income-increasing discretionary accruals. Koh [2003], using a sample of

Australian firms, finds that high levels of institutional ownership are negatively related to

income-increasing discretionary accruals.

Collectively, these studies suggest that board and institutional-shareholder monitoring

constrain managers’ accounting choices. Consequently, the incremental benefit of using an

11

industry specialist auditor is likely to be smaller when alternative monitoring mechanisms are

strong. This leads to our second hypothesis:

Hypothesis 2. The effect of using an industry specialist auditor on cost of capital is

smaller for firms with strong governance than for firms with weak governance.

3. Research Design

3.1 MEASUREMENT OF COST OF EQUITY CAPITAL

Prior accounting research uses a variety of methods to estimate ex ante cost of equity

capital, but the association between cost of equity measures and risk proxies can vary

significantly (e.g. Botosan and Plumlee [2005]; Guay et al. [2005]). To avoid relying on

individual cost of equity measures, recent studies calculate a representative cost of equity

measure by averaging several measures (Dhaliwal et al. [2006]; Hail and Leuz [2006]). All cost

of equity measures are calculated with error, so an average measure is likely to be less noisy than

any individual measure. We follow Dhaliwal et al. [2006] and Hail and Leuz [2006] and average

four implied cost of equity estimates to derive our estimate. We estimate the annual firm-

specific cost of equity measures described in Gebhardt et al. [2001], Claus and Thomas [2001],

Gode and Mohanram [2003], and Easton [2004]. Detailed descriptions of these calculations are

contained in Appendix A. Our cost of equity proxy for each firm (CoE) is the average of the

firm’s annual cost of equity measures.

Consistent with Gebhardt et al. [2001], Gode and Mohanram [2003], and Dhaliwal et al.

[2006], we estimate cost of equity in June of each year (t). Financial statement information is

obtained for the most recent fiscal-year ending during or before March of year t. Data required

for the cost of equity estimates are obtained from I/B/E/S and Compustat. Each of the four cost

12

of equity models require one-year-ahead and two-year-ahead earnings-per-share forecasts

(FEPSt+1 and FEPSt+2) and common stock share price in June of year t (Pt). In addition, we

require either three-year-ahead earnings-per-share forecast (FEPSt+3) or a long-term consensus

growth forecast (LTG) for each firm-year observation.3 Additional model-specific data

requirements are explained in Appendix A.

3.2 MEASUREMENT OF COST OF DEBT

Consistent with Mansi et al. [2004], Ghosh and Moon [2005], and Ashbaugh-Skaife et al.

[2006], we use Standard and Poor’s long-term domestic debt ratings to proxy for the cost of debt.

(CoD) The debt ratings (Compustat #280) range from AAA+ to D. We assign a CoD value of

one for debt ratings of AAA+, and increase the value of CoD by one unit for each decline in the

debt rating. Under this coding scheme, low values of CoD proxy for low cost of debt, and high

values of CoD proxy for high cost of debt.

To assess the robustness of our results with respect to the choice of the cost of debt

measure, we conduct sensitivity tests using the implied interest rate on debt as our proxy for cost

of debt. We use this alternative measure because Standard and Poor’s debt ratings are

unavailable for many Compustat firms.4 We obtain similar results in our cost of debt analysis

that uses the implied interest rate on debt.

3.3 MEASUREMENT OF AUDITOR INDUSTRY SPECIALIZATION

3 If FEPS3 is available for an observation but LTG is missing, we estimate the long-term growth rate with

(FEPSt+3/FEPSt+2) – 1. 4 We estimate the implied interest rate by dividing interest expense by the average of short-term and long-term debt

during the year. We do not use this measure in our main tests because interest rates depend on borrowing and

payment patterns during the year as well as on the loan term, collateral and other conditions of the loan, none of

which are readily available. Therefore, we report detailed results using debt ratings and discuss sensitivity results

using the implied interest rate on debt in Section 4.

13

Prior studies typically measures auditor specialization with a dichotomous variable based

on a market share approach or a portfolio share approach (Neal and Riley [2004]). Market share

measures indicate how an audit firm is differentiated from its competitors within an industry. An

audit firm is classified as a specialist in an industry if its audit clients’ assets, audit fees, or sales

revenues in that industry exceed the levels for other audit firms’ clients. Portfolio share

measures reflect the distribution of an audit firm’s services across industries. An audit firm is

classified as a specialist in an industry if its clients’ assets in a given industry exceed a

benchmark, such as equal distribution of client firms’ assets across all industries.

We believe that a market-share measure of industry specialization is more appropriate for

our study for two reasons. First, Neal and Riley [2004] argue that the market share measure is

more appropriate in settings where the focus is on the actions of the audit client (e.g. the decision

to hire a specialist) because this measure is better aligned with the client’s primary within-

industry focus than is the portfolio measure. Second, an audit firm’s market share across

industries is likely to be more readily observable by investors than a firm’s portfolio share. That

is, investors are more likely to be aware of, and respond to, a classification of an auditor as a

specialist based on its market share than to a classification based on its portfolio share.

We utilize two national market share measures of industry specialization, both of which

estimate an auditor’s market share by taking the square root of client assets audited by the

auditor in a two-digit SIC industry and dividing this by the sum of the square root of assets

audited by all Big 4 auditors in that industry.5 The first measure of industry specialization,

MWMkt, classifies an auditor as an industry specialist if the auditor has the highest market share

in the industry and its market share is at least 10 percentage points higher than the next closest

5 This approach is consistent with Mayhew and Wilkins [2003] and Neal and Riley [2004]. We also require at least

10 client firm observations for each two-digit SIC industry-year combination in order to estimate market share for

auditors in that industry.

14

Big 4 competitor (Mayhew and Wilkins [2003]). MWMkt equals one if an auditor meets this

criterion, and it equals zero otherwise. The second measure of industry specialization, NRMkt,

classifies the auditor as a specialist if its industry market share is greater than an industry cutoff

(Neal and Riley [2004]). The industry cutoff equals (1 firm/N firms)*1.20.6 This cutoff requires

that an auditor have a market share of at least twenty percent higher than the market share that

would exist if all auditors had an equal share in the industry. NRMkt equals one for a given

auditor if that auditor meets or exceeds the industry cutoff, and it equals zero otherwise.7

3.4 MEASUREMENT OF THE STRENGTH OF ALTERNATIVE MONITORING MECHANISMS

As discussed in Section 2, we expect the effect of using an industry specialist to be

greatest when alternative monitoring mechanisms are relatively weak. To measure the strength of

alternative monitoring mechanisms, we use three variables: board independence, board size and

institutional ownership. For each firm, we compare each variable to the annual sample median

and assign a score of one for each variable if its level relative to the median value suggests strong

monitoring. Based on prior research (e.g. Jensen [1993]; Beasley [1996]; Dechow et al. [1996];

Chung et al. [2002]; Klein [2002]; Hermalin and Weisbach [2003]), strong monitoring is

suggested by above-median board independence, above-median institutional ownership, and

below-median board size. Thus, the maximum monitoring strength score is 3 and the minimum is

zero. We classify firms as having weak monitoring if the strength score is 0 or 1 (LowMon=1),

and we classify firms as having strong monitoring if the overall strength score is 2 or 3

(LowMon=0).

6 During 1999-2001, N equals 5. After Andersen’s demise (2002-2005), N equals 4.

7 Although the industry specialist measures are determined each year, they are relatively consistent over time. For

instance, under the MWMkt measure, 40 of 51 industries (78 percent) have only one audit firm classified as a

specialist during our sample period. The average number of years this auditor is classified as a specialist within an

industry is 4.35 (out of 7).

15

3.5 EMPIRICAL SPECIFICATION: COST OF EQUITY

We begin with the following OLS regression model, based on Khurana and Raman

[2006], to test for the association between cost of equity and use of an industry specialist:

CoE = α + β1Beta + β2BTM + β3Lev + β4Rec_Ret + β5RMSE + β6Size + β7Var

+ β8(Specialist Measure) + ΣηIndustry and Year Indicators, (1)

where

CoE = the ex ante firm-specific cost of equity capital;

Beta = the systematic risk calculated for the 36 month period preceding the cost of

equity estimation;

BTM = the natural log of the ratio of book value of equity to market value of equity

at fiscal-year end;

Lev = the natural log of the ratio of total debt to total assets at fiscal-year end;

Rec_Ret

= firm-specific stock return for the 12 month period preceding the cost of

equity estimation;

RMSE

= the standard deviation of residuals from the market model (used to calculate

Beta) over the 36 month period preceding the cost of equity estimation;

Size

= the natural log of a firm’s market value of common equity at fiscal-year end;

Var = dispersion in one-year ahead analysts’ earnings forecasts at June of year t;

and

Specialist

Measure

= either MWMkt or NRMkt, both of which are indicator variables set equal to 1

if the firm is classified as an industry specialist, and zero otherwise.

We predict associations between cost of equity and its determinants (indicated by

coefficients β1 through β7) that are consistent with Khurana and Raman [2006]. Beta measures

the firm’s stock price volatility with respect to the overall market, and we expect this risk

measure to be positively associated with the cost of equity [Sharpe 1964]. We include the book-

16

to-market ratio (BTM) because Fama and French [1995] suggest that BTM may proxy for

financial distress. We expect a positive association between BTM and cost of equity. Because

investors are likely to perceive increased firm risk as leverage increases (Modigliani and Miller

[1958]), we expect Lev to be positively associated with cost of equity. Rec_Ret is included to

correct for any sluggishness in forecast revisions by analysts whose earnings forecasts are used

to calculate the cost of equity measure. If analysts’ forecast revisions are sluggish, their

forecasts would be low if recent stock returns have been high and vice versa (Guay et al. [2005]).

We expect a positive association between Rec_Ret and cost of equity. Stice [1991] and Khurana

and Raman [2006] use dispersion of abnormal stock returns (RMSE) to proxy for litigation risk.

Since cost of equity should increase as litigation risk increases, we expect RMSE to be positively

associated with cost of equity. Size, measured as the natural log of a firm’s market value of

common equity, and is a proxy for the firm’s information environment and liquidity. Consistent

with prior research (Botosan and Plumlee [2005]), we expect Size to be negatively associated

with cost of equity. Because perceived firm risk increases with forecast dispersion (Gebhardt et

al. [2001]), we expect a positive association between Var and cost of equity.

As noted previously, we modify Khurana and Raman’s [2006] model to include a

measure of industry specialization. Our main variable of interest in equation (1) is industry

specialization, represented by MWMkt or NRMkt, both of which are defined above as a

dichotomous variable based on a market share measure of industry specialization. Because we

expect a negative relation between audit quality and cost of equity, we predict a negative

coefficient for the specialist measure (β8). Industry specialization is our main variable of

interest, so we use MWMkt and NRMkt in place of the audit fee measures that Khurana and

Raman [2006] examine in their study.

17

We also control for industry and year-specific effects with two-digit SIC industry

indicators and year indicators.

3.6 EMPIRICAL SPECIFICATION: COST OF DEBT

Following Mansi et al. [2004], we use the following specification for cost of debt:

Pr(CoD) = exp(α + β1Tenure + β2Bankdebt + β3FirmAge + β4O-score + β5Qspread

+ β6RMRF + β7SMB + β8HML + β9(Specialist Measure) + ΣβIndustry

and Year Indicators) (2)

where

CoD = the firm-specific cost of debt capital proxy based on debt ratings (Compustat

# 280);

Tenure = the number of years a client has been audited by the auditor identified in the

financial statements, beginning in 1986;

Bankdebt = an indicator variable equal to one if the firm has notes payable; else indicator

equals zero;

FirmAge = the natural log of the number of years since the firm's initial public offering,

as identified by CRSP;

O-score = the Ohlson [1980] default risk measure for the firm;

Qspread

= the spread between BAA and AAA corporate bond indexes (Federal Reserve

of St. Louis FRED);

RMRF

= the CRSP value-weighted market index return less the one month Treasury

bill return;

SMB

= the return for a portfolio of small stocks less the return for a portfolio of

large stocks;

HML = the return for a portfolio of high book-to-market stocks less the return for a

portfolio of low book-to-market stocks; and

Specialist

Measure

= either MWMkt or NRMkt, both of which are an indicator variable equal to 1 if

the firm is classified as an industry specialist, and otherwise 0.

18

Our specification differs from that in Mansi et al. [2004] in three ways. First, our sample

consists entirely of Big 4 audit clients so we omit a Big 4 auditor variable. Second, we include

either MWMkt or NRMkt as a measure of audit quality. Third, Mansi et al. [2004] include a firm-

specific measure of outstanding debt duration. We exclude this measure because we do not have

access to firm specific debt issuance data. Although our definition of CoD is consistent with

Mansi et al. [2004], they use OLS regression to estimate their regression model. We use an

ordered logistic regression since the proxy for cost of debt is an ordinal variable.8

We predict associations between cost of debt and the determinants (indicated by

coefficients β1 through β8) that are consistent with the prior literature. Tenure equals the number

of years the auditor has been retained by the client. We expect a negative association between

Tenure and cost of debt based on the findings of Mansi et al. [2004]. Bankdebt is an indicator

variable that equals one if the firm has notes payable, and is zero otherwise. We expect a

negative association between the presence of bank debt and cost of debt because bank debt

provides evidence of external monitoring (Diamond [1984]). FirmAge is the natural log of the

number of years since the firm's initial public offering, as identified by CRSP. We expect a

negative association between cost of debt and firm age because surviving firms are by definition

relatively successful and also because the cost of capital is likely to decline as investors become

increasingly familiar with a firm’s operations over time.

O-score is the Ohlson [1980] default-risk measure for the firm. We expect that as default

risk increases, the cost of debt will also increase. Qspread equals the spread between BAA and

AAA corporate bond indexes and is obtained from the Federal Reserve of St. Louis’s Federal

Reserve Economic Data (FRED) website. This is a risk measure that increases in economic

8 As a sensitivity test, we estimate the cost of debt model using OLS regression, and our inferences are unchanged.

19

downturns and controls for macroeconomic factors that could impact the cost of debt (Chen,

Roll, and Ross [1986]). We expect it to be positively associated with cost of debt. We also

include the Fama and French [1993] risk factors following Mansi et al. [2004]: RMRF, SMB,

and HML. While Elton et al. [2001] find a positive and significant association between the Fama

and French [1993] factors and cost of debt, Mansi et al. [2004] do not find these factors to be

significant when they use credit ratings as a proxy for cost of debt. Therefore, we do not predict

a sign for the association between these risk factors and cost of debt.

As in the cost of equity analysis, we include a specialist measure, either MWMkt or

NRMkt, to capture the effect of industry specialization. Because we expect a negative relation

between audit quality and cost of debt, we expect a negative coefficient on the specialist measure

(β9). We also control for industry and year-specific effects with two-digit SIC industry

indicators and year indicators.

4. Evidence

4.1 SAMPLE SELECTION

Table 1 presents the steps that we use to arrive at our final sample. We begin our sample

selection process by obtaining all Big 4 audit client observations from the Compustat Annual

database for 1999 through 2005. Following Khurana and Raman [2006], we retain non-financial

firm observations (SIC codes other than 6000-6999). After these restrictions, 31,076 firm-year

observations remain. This set of observations serves as the base for both our cost of equity

analysis and our cost of debt analysis.

In order to arrive at our cost of equity sample, we exclude observations missing necessary

Compustat data (8,513 observations) and observations missing necessary CRSP data (4,655

20

observations). We exclude 9,168 observations due to I/B/E/S restrictions for the four cost of

equity estimation methods. Our final cost of equity sample contains 8,740 firm-year

observations.

In order to arrive at our cost of debt sample, we once again begin with our base sample of

31,076 observations and exclude observations missing necessary Compustat data (22,387

observations) and observations missing necessary CRSP data (255 observations). Our final cost

of debt sample contains 8,434 firm-year observations.

4.2 DESCRIPTIVE STATISTICS

Table 2 presents descriptive statistics and correlations for variables used in both the cost

of equity model and the cost of debt model. With the exception of dichotomous indicator

variables and variables that are transformed with a natural log, all variables are winsorized at the

top and bottom one percent to mitigate any potential effects of outliers.

Panel A of Table 2 presents descriptive statistics for variables used in the cost of equity

analysis. The sample consists of large firms with mean and median assets of $5.4 billion and

$1.3 billion, respectively, and mean and median market value of equity of $6.6 billion and $1.3

billion, respectively. The mean cost of equity is 10 percent and the median is 9 percent, in line

with Khurana and Raman’s [2004, 2006] findings. The firms have average risk with a mean and

median Beta of 1.05 and 0.91, mean BTM of 0.55, and a mean leverage ratio of 0.22. We report

mean and median analyst forecast dispersion (Var) of 0.06 and 0.04. These risk measures are

similar to the findings of Khurana and Raman [2006]. Our sample firms have mean annual stock

returns (Rec_Ret) of 0.18 and median stock returns of 0.11. These data are similar to the

21

univariate statistics for Khurana and Raman’s [2006] observations for 2001. The mean RMSE

for our sample firms is 0.12 and the median is 0.11.9

Sixteen percent of our firm-year observations are classified as using an industry specialist

auditor under the Mayhew and Wilkins [2003] market share approach, MWMkt, and forty-three

percent are classified as using an industry specialist auditor under the Neal and Riley [2004]

market share approach, NRMkt. These differences are consistent with our expectations because

the Mayhew and Wilkins [2003] measure is more conservative than the Neal and Riley [2004]

measure. Overall, the descriptive statistics indicate that the average firm in our sample is large

and profitable, and has average risk.

Panel B of Table 2 presents descriptive statistics for variables used in the cost of debt

analysis. The firms in this sample are even larger firms than those in the cost of equity analysis,

with mean and median assets of $8.3 billion and $2.5 billion, respectively, and mean and median

market value of equity of $8.9 billion and $2.0 billion, respectively. The mean cost of debt is

10.43, which translates to a rating of BBB-. This is consistent with Mansi et al. [2004] who

report a mean debt rating of 9.52. Our sample firms have mean auditor tenure of 8.53 years,

while the mean age of our sample firms is 22.52 years. These findings are similar to the statistics

reported by Mansi et al. [2004]. Forty-six percent of our firms have bank debt, and the average

O-score is 1.82. Compared to Mansi et al. [2004], fewer of our firms have bank debt and our

sample mean for O-score is higher.

Eighteen percent of our firm-year observations for our cost of debt analysis are classified

as using an industry specialist auditor under the Mayhew and Wilkins [2003] market share

9 Our univariate statistics differ from Khurana and Raman [2006] with respect to this variable. Khurana and Raman

[2006] define RMSE as the standard deviation of residuals from the market model. We use this definition and arrive

at a sample mean RMSE of 0.12. However, when we instead measure RMSE as the variance of residuals from the

market model, we obtain a mean RMSE value of 0.017, which is very close to the sample median of 0.015 reported

by Khurana and Raman [2006] for 2000.

22

approach, MWMkt, and 47 percent are classified as using an industry specialist auditor under the

Neal and Riley [2004] market share approach, NRMkt.

Panel C of Table 2 presents Pearson and Spearman correlations for all variables used in

the cost of equity analysis. Pearson and Spearman correlations indicate that all the independent

variables are significantly correlated with CoE in the predicted direction (p < 0.05). Specifically,

Beta, BTM, Lev, RMSE, and Var are positively correlated with the cost of equity while Rec_Ret,

Size, MWMkt, and NRMkt are negatively associated with CoE. MWMkt and NRMkt are also

positively correlated (ρ = 0.50; p-value < 0.001).

Panel D of Table 2 presents Pearson and Spearman correlations for all variables used in

the cost of debt analysis. Pearson and Spearman correlations indicate that most of the

independent variables are significantly correlated with CoD in the predicted direction (p < 0.05).

Tenure, Bankdebt, FirmAge, MWMkt, and NRMkt are negatively correlated with the cost of debt

while O-score is positively associated with CoD. In addition, SMB is negatively associated with

CoD, but the correlations between CoD and Qspread, RMRF, and HML are insignificant.

Consistent with the cost of equity analysis, MWMkt and NRMkt are also positively correlated (ρ

= 0.49; p-value < 0.001).

4.3 EFFECTS OF USING AN INDUSTRY SPECIALIST ON COST OF EQUITY

Table 3 presents the results of the cost of equity regression. As mentioned above, our

specification follows Khurana and Raman [2006]. All t-statistics are calculated using Rogers

[1993] standard errors, which corrects for within-firm clustered standard errors.

Panel A of Table 3 presents results for the cost of equity regression using the Mayhew

and Wilkins [2003] and Neal and Riley [2004] market share based measures of industry

specialization in separate columns. The adjusted R2 is 0.39 in both columns. The coefficients on

23

the risk related explanatory variables (β1 through β8) are all significantly associated with cost of

equity and directionally consistent with prior literature (Khurana and Raman [2004, 2006]).10

The coefficients on the two specialist measures, MWMkt and NRMkt, are negative and

significant (p < 0.05 and p < 0.10, respectively) consistent with the predicted negative

association between using an industry specialist and cost of equity (H1). The MWMkt coefficient

is -0.002, which indicates that on average, use of an industry specialist auditor decreases the cost

of equity by 20/100ths of one percent, or 20 basis points. The NRMkt coefficient is -0.001, which

indicates that this relatively conservative specialist measure is associated with a 10-basis-point

reduction in the cost of equity. Considering that the mean and median market values of equity for

the sample are $7.4 and $1.2 billion, respectively, a 10-20 basis point decrease in the cost of

equity is economically significant.

Panel B of Table 3 presents results for the cost of equity regressions allowing for the

coefficient on MWMkt and NRMkt to differ across firms with strong versus weak alternative

monitoring mechanisms as defined in Section 3. Requiring data on alternative monitoring

mechanisms reduces the cost of equity sample from 8,740 firm-year observations to 5,231 firm-

year observations. The coefficients on MWMkt and NRMkt are both insignificantly different

from zero, indicating that use of an industry specialist auditor does not affect the cost of equity

for firms with strong alternative monitoring mechanisms. On the other hand, the coefficients on

LowMon*MWMkt and LowMon*NRMkt are negative and significant, indicating that the strength

of alternative monitoring mechanisms affects the association between use of an industry

specialist and the cost of equity. In addition, the MWMkt and NRMkt coefficients for firms with

10

The magnitude of the RMSE coefficients do differ from those reported by Khurana and Raman [2006]. As

mentioned in the descriptive section, we believe Khurana and Raman [2006] may actually measure RMSE as the

variance, and not the standard deviation of stock returns. This measurement difference would cause differences in

the regression coefficient for RMSE.

24

weak alternative monitoring mechanisms are negative and significant

(MWMkt+LowMon*MWMkt and NRMkt+LowMon*NRMkt). These results indicate that the

reduction shown in Panel A in the cost of equity for using an industry-specialist auditor only

applies to firms with weak alternative monitoring mechanisms. The magnitudes of the MWMkt

and NRMkt coefficients for firms with weak alternative monitoring mechanisms imply a

reduction in cost of equity of 30 and 20 basis points, respectively. This result is consistent with

our predictions under H2.

4.4 EFFECTS OF USING AN INDUSTRY SPECIALIST ON COST OF DEBT

Table 4 presents the results for the cost of debt regression. As mentioned earlier, our cost

of debt specification follows Mansi et al. [2004]. All t-statistics are calculated using Rogers

[1993] standard errors, which corrects for within-firm clustered standard errors.

Panel A of Table 4 presents results for the cost of debt analysis using the Mayhew and

Wilkins [2003] and Neal and Riley [2004] market share based measures of industry

specialization in separate columns. The Pseudo R2 are 0.15 based on the ordered logit regressions

for both specialization measures. If we instead estimate the model using OLS regression, the

adjusted R2 is 0.54, which is similar to the results reported by Mansi et al. [2004].11

The

coefficient signs for the risk-related explanatory variables (β1 through β8) are generally

consistent with Mansi et al. [2004].

The coefficients on MWMkt and NRMkt measures are negative and significant (p < 0.05

and p < 0.01, respectively) consistent with the predicted negative association between using an

industry specialist and the cost of debt (H1). The odds ratio of 0.80 (0.81) for MWMkt (NRMkt)

11

The inferences discussed below are unchanged if the OLS regression model is used instead of an ordered logistic

regression.

25

indicates that the odds of firms that use an industry specialist auditor having a higher cost of debt

are approximately 20 (19) percent lower than the corresponding odds for firms that use a non-

specialist auditor. Panel B of Table 4 presents results for the cost of debt regressions allowing

for the coefficient on the specialist measures to differ across firms with strong versus weak

alternative monitoring mechanisms as defined in Section 3. Requiring data on alternative

monitoring mechanisms reduces the cost of debt sample from 8,434 firm-year observations to

4,677 firm-year observations. The coefficients on MWMkt and NRMkt for firms with strong

alternative monitoring mechanisms are not significantly different from zero. On the other hand,

both of the coefficients on the specialist variables for firms with weak alternative monitoring

mechanisms are negative and significant (MWMkt+LowMon*MWMkt and

NRMkt+LowMon*NRMkt). These results indicate that use of an industry specialist auditor

significantly reduces the cost of debt for firms with weak alternative monitoring mechanisms

when compared to firms with strong alternative monitoring mechanisms. These findings support

H2.

4.5 SENSITIVITY ANALYSIS

We perform a number of sensitivity checks. First, we use an alternative cost of debt

estimate based on the implied interest rate on debt. Specifically, we estimate the cost of debt by

scaling interest expense by the average short- and long-term debt during the fiscal-year (Pittman

and Fortin [2004]; Francis et al. [2005]). Use of this alternative cost of debt measure increases

our main sample from 8,434 firm-year observations to 23,716 firm-year observations and our

alternative monitoring sample from 4,677 firm-year observations to 7,506 firm-year

observations. This estimated cost of debt is likely to be noisy because it ignores within-year

26

fluctuations in debt levels as well as debt covenants that may affect interest rates. Nevertheless

the Pearson and Spearman correlations between debt ratings and the estimated cost of debt for

the 8,326 firm-year observations with both measures are 0.38 and 0.45, respectively.

Untabulated results indicate that our inferences with respect to H1 and H2 are unchanged when

we use this alternative cost of debt proxy.

Second, we use an alternative industry specialization measure in our analyses. Recent

studies find that city-level industry specialization is more important than national-level industry

specialization for explaining audit fee premiums and earnings quality for a sample of Big 5 audit

clients (Francis et al. [2005a,b]). A concurrent study of both Big 4 and non-Big 4 audit clients

finds that cost of equity is lower for clients using city-level specialists but not national level

specialist (Li and Wang [2007]). We classify the Big 4 auditor with the highest market share in a

city as the city specialist. Clients are assigned to cities based on the city specified by the auditor

who signed the audit opinion, per Audit Analytics. Because Audit Analytics reports audit

opinion data beginning in 2000, we assume that the client auditor did not change cities from

1999 to 2000 and assign the 1999 city to be the same as the 2000 city. Client assets are used to

calculate the city market share, consistent with our use of assets to calculate our two national

market share specialization measures, MWMkt and NRMkt. Consistent with Francis et al.

[2005a], we exclude observations in cities where there is only one auditor in a city-industry

combination.

Untabulated cost-of-equity results for this sensitivity test indicate that H1 only holds for

firms that use an auditor who is an industry specialist at the national level. H2 holds for firms

that use either a national-only industry specialist or an auditor who is both a national and a city-

level industry specialist. Untabulated cost of debt results for this sensitivity test indicate that H1

27

holds for firms that use an auditor who is a city-only industry specialist or one who is both a

national- and a city-level industry specialist. H2 holds for firms that use an auditor who is both a

national and a city-level industry specialist. Firms with both low and high alternative monitoring

mechanisms experience a reduced cost of debt from using a city-only industry specialist. In

summary, these results corroborate our main findings that using an industry specialist auditor

significantly reduces a firm’s cost of equity and debt, particularly for firms with weak alternative

monitoring mechanisms.12

Third, because our sample period includes firm-year observations before and after the

enactment of the Sarbanes Oxley Act (SOX), we examine whether the relations we document

change after SOX. In general, SOX imposed stricter auditing and reporting rules on all firms, and

may have increased the credibility of financial reports, all else equal. If so, the incremental

benefits of using a specialist auditor may diminish after SOX. We define the pre-SOX period as

1999-2002 and the post-SOX period as 2003-2005. We include an indicator variable for the

post-SOX period in our cost of equity and cost of debt models and also interact that indicator

variable with all of our other independent variables. Untabulated results indicate that H1 and H2

hold only in the pre-SOX period. These results suggest that use of an industry specialist reduces

the cost of equity only in the pre-SOX period and only for firms with low alternative monitoring

mechanisms. Untabulated results indicate that in the cost of debt analysis, H1 holds during both

the pre- and post-SOX periods, and H2 holds only in the post-SOX period. These results suggest

12

We believe our cost of equity results differ from Li and Wang [2008] for a variety of reasons. First, Li and Wang

[2008] use a sample of both Big 4 and non-Big 4 auditors and do not interact the industry specialist measures with

the Big 4 auditor classification variable. Thus, it is impossible to directly compare our results to theirs. Second, Li

and Wang [2008] exclude any observations where the audit report city is not the same as the client headquarters city.

This reduces their sample size by 67 percent. Third, we use a more comprehensive cost of equity measure than the

PEG ratio based measure used un in their study.

28

that clients using an industry specialist auditor benefit from cost of debt reductions when using

an industry specialist auditor, particularly in the post-SOX period.

In untabulated results, we find that the use of auditors who are city-only, national-only,

and both city and national specialists is associated with a lower cost of equity in the pre-SOX

period. In the post-SOX period, the use of auditors who are national-only specialists (MWMkt)

is associated with a lower cost of equity, but there is no effect on cost of equity for other types of

specialist. These results are driven by clients with low alternative monitoring mechanisms, as

suggested by our primary tests. With respect to cost of debt, using auditors who are city-only

specialists or who are both city and national specialists is associated with a lower cost of debt in

the pre- and post-SOX time periods. These results obtain across high and low alternative

monitoring mechanisms in the pre-SOX period. In the post-SOX period, high monitoring firms

with city only specialists and low monitoring firms with either city only or auditors that are both

city and national specialists realize lower cost of debt.

Finally, we drop all Arthur Andersen clients from our entire sample and re-estimate the

cost of equity and cost of debt models. The early years in our sample period include

observations for Big 4 and Arthur Andersen clients, but recent research suggests that there were

significant cost of capital differences between these groups (Botosan, Kinney, and Palmrose

[2007]). When we remove observations for firms that retained Arthur Andersen at any point in

our sample period, our cost of equity sample drops from 8,740 firm-year observations to 6,826

firm-year observations, and our main cost of debt sample drops from 8,434 firm-year

observations to 6,473 firm-year observations. Although the statistical significance of results

using this sample (untabulated) are weaker than the results reported in Tables 4 and 5, our

29

overall inferences with respect to the effect of the use of an industry specialist and of alternative

monitoring mechanisms on the cost of capital are unchanged.

5. Conclusion

Conflicts between investors and managers can introduce frictions in capital markets that

increase the cost of capital that investors demand. Firms can ameliorate these conflicts by

issuing credible financial statements. In this study, we investigate the role of audit quality in

increasing the credibility of financial statements. Prior research finds that firms audited by Big 4

auditors have significantly lower cost of capital than other firms, suggesting that investors

perceive Big 4 audits to be of higher quality than non-Big 4 audits. This result raises the

question of whether investors also perceive differences in audit quality across Big 4 firms. This

question is important because Big 4 auditors provide audits for over 90 percent of publicly traded

firms, and any quality differences among Big 4 auditors could therefore affect a substantial

number of firms.

We investigate the relation between audit quality and cost of both equity and debt capital.

We use auditor industry specialization as our proxy for audit quality. Our results indicate that

firms that use industry-specialist Big 4 auditors have a significantly lower cost of equity and debt

capital than firms that use non-specialist Big 4 auditors. This result extends prior work by

showing that investors perceive audit quality differences among Big 4 firms, and not just

between Big 4 and non-Big 4 firms. We also find that the association between audits by industry

specialists and lower cost of capital is particularly strong when alternative governance structures,

such as institutional ownership and the independence of the board of directors, are weak. This

30

finding indicates that auditor industry specialization is a substitute for alternative monitoring

mechanisms that reduce information asymmetries between investors and managers.

31

Appendix A – Cost of Equity Models

Data definitions

Pt = share price reported by I/B/E/S in June of year t

BVt = book value of equity at the beginning of the year scaled by common shares

outstanding reported by I/B/E/S in June of year t

DPS0 = dividends per share paid in year t-1

EPS0 = year t-1 earnings-per-share reported by I/B/E/S

FEPSt+i = analyst forecasted earnings-per-share for year. FEPS1, FEPS2, and FEPS3 are

the one-, two-, and three-year ahead mean consensus forecasted reported by

I/B/E/S in June of year t. If FEPS3 is not available in I/B/E/S, it is replaced

with FEPS2*(1+I/B/E/S consensus long-term growth rate).

LTG = consensus long-term growth forecast reported by I/B/E/S in June of year t if

available; else FEPS3/FEPS2 – 1

k = dividend payout ratio, which equals DPS0/EPS0. When EPS0 ≤ 0, DPS0 is

scaled by 6 percent of total assets per share at the beginning of the year.

rgls, rct, rojn, rpeg = implicit cost of equity estimates derived from the four models described

below

rrf = 10-year treasury note yield in June of year t

______________________________________________________________________________

Gebhardt, Lee, and Swaminathan [2001]

where:

FROEt+i = forecasted ROE. This variable equals FEPSt+i/Bt+i-1 for years one, two,

and three. Beginning in year four, FROEt+i is a linear interpolation to

the industry median over the prior 10 years. In order to determine the

industry median, we use Fama and French [1997] 48 industry

classifications and the full universe of Compsutat firm-year

observations, excluding those firm-year observations reporting losses.

BVt+i = BVt+i-1+FEPSt+i*(1 – k)

T = 12 year forecast horizon

______________________________________________________________________________

32

Claus and Thomas [2001]

where:

aet+1 = expected abnormal earnings, which equals FEPSt+I – rct*BVt.

BVt+1 = BVt+i-1+k*FEPSt+1, where k = 0.50 following Claus and Thomas [2001]

gae = abnormal earnings growth, which equals rrf – 0.03

______________________________________________________________________________

Gode and Mohanram [2003]

where:

g2 = the average of the short-term growth rate [(FEPS2 – FEPS1)/FEPS1] and the long-term growth rate (LTG), which is previously defined

DPSt+1 = DPS0

This model requires FEPSt+1 > 0 and FEPSt+2 > 0

______________________________________________________________________________

Modified PEG ratio by Easton [2004]

where:

DPSt+1 = DPS0

This model requires FEPSt+2 ≥ FEPSt+1 > 0

33

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36

Compustat firm-years during 1999-2005

Excluding financial services firms

and Non-Big N audit clients

Base sample 31,076

Missing Compustat data (8,513)

Missing CRSP data (4,655)

Missing I/B/E/S data (9,168)

Final sample 8,740

Compustat firm-years during 1999-2005

Excluding financial services firms

and Non-Big N audit clients

Base sample 31,076

Missing Compustat data (22,387)

Missing CRSP data (255)

Final sample 8,434

TABLE 1

Sample Selection

Panel A: Cost of Equity Sample

Panel B: Cost of Debt Sample

37

Panel A: Descriptive Statistics - Cost of Equity Model

Variable Mean Median Min Q1 Q3 Max Stdev N

Assets 5,445 1,342 51 488 4,462 72,518 11,074 8,740

MVE 6,566 1,342 53 501 4,555 112,835 16,322 8,740

CoE 0.10 0.09 0.05 0.08 0.11 0.19 0.03 8,740

Beta 1.05 0.91 -0.36 0.48 1.44 3.77 0.82 8,740

BTM 0.55 0.47 0.05 0.28 0.71 2.12 0.38 8,740

Leverage 0.22 0.21 0.00 0.10 0.32 0.64 0.15 8,740

Rec_ret 0.18 0.11 -0.70 -0.13 0.37 2.54 0.51 8,740

RMSE 0.12 0.11 0.04 0.08 0.15 0.30 0.05 8,740

Var 0.06 0.04 0.00 0.02 0.07 0.49 0.08 8,740

MWMkt 0.16 0.00 0.00 0.00 0.00 1.00 0.36 8,740

NRMkt 0.43 0.00 0.00 0.00 1.00 1.00 0.49 8,740

TABLE 2

Descriptive Statistics and Correlations

This table presents descriptive statistics and correlations for variables used in the analyses as well as othe variables of interest. Assets

equals the total fiscal-year end assets (millions of dollars). MVE equals the fiscal-year end market value of equity (millions of dollars).

CoE equals the average of four implied cost of equity estimates dervied from the models in Appendix A. Beta equals the systematic

risk calculated for the 36 month period preceding the cost of equity estimation. BTM equals the ratio of book value of equity to market

value of equity. Leverage equals the ratio of total debt to total assets. Rec_Ret equals the firm-specific stock return for the 12 month

period preceding the cost of equity estimation. RMSE equals the standard deviation of residuals from the market model (used to

calculate Beta ) over the 36 month period preceding the cost of equity estimation. Var equals dispersion in one-year ahead analysts’

earnings forecasts at June of year t. MWMkt is an indicator variable equal to 1 an auditor has the highest market share in a two-digit

SIC industry and the market share of the next closest competitor is at least 10 percentage points lower; else the indicator equals 0.

NRMkt is an indicator variable equal to 1 if the square root of assets audited by an auditor in a two-digit SIC industry, scaled by the

sum of the square root of assets audited by all auditors in that industry, is great than 1/K; else the indicator equals 0. K equals the

market-share cutoff point, which is set to 20 percent more than equal partition by Big 4/5 auditors in an industry. CoD equals the firm-

specific cost of debt capital proxy based on debt ratings. Tenure equals the number of years a client has been audited by the auditor

identified in the financial statements, beginning in 1986. Bankdebt is an indicator variable equal to one if the firm has notes payable;

else indicator equals zero. Age equals the number of years since a firm's initial public offering. O-Score equals the Ohlson [1980]

default risk measure for the firm. Qspread equals the spread between BAA and AAA corporate bond indexes. RMRF equals the CRSP

value-weighted market index return less the one month Treasury bill return. SMB equals the return for a portfolio of small stocks less

the return for a portfolio of large stocks. HML equals the return for a portfolio of high book-to-market stocks less the return for a

portfolio of low book-to-market stocks. The upper (lower) diagonal of Panels B and C contains Spearman (Pearson) correlations.

Bolded correlations are significant at the 0.05 level.

38

Panel B: Descriptive Statistics - Cost of Debt Model

Variable Mean Median Min Q1 Q3 Max Stdev N

Assets 8,251 2,496 176 1,017 7,844 104,457 15,829 8,434

MVE 8,851 1,980 16 631 6,838 133,636 20,442 8,334

CoD 10.43 10.00 2.00 8.00 13.00 19.00 3.52 8,434

Tenure 8.53 7.00 1.00 4.00 14.00 20.00 5.59 8,434

Bankdebt 0.46 0.00 0.00 0.00 1.00 1.00 0.50 8,434

Age 22.52 13.93 0.01 6.74 32.69 80.05 20.90 8,434

O-Score 1.82 1.74 -2.42 0.67 2.83 7.65 1.79 8,434

Qspread 0.93 0.95 0.64 0.73 1.22 1.32 0.23 8,434

RMRF 1.26 1.63 -10.14 0.03 4.34 7.95 4.18 8,434

SMB 1.28 0.16 -6.48 -0.53 4.29 7.51 3.29 8,434

HML 0.51 0.47 -9.09 -0.38 3.50 7.94 4.29 8,434

MWMkt 0.18 0.00 0.00 0.00 0.00 1.00 0.38 8,434

NRMkt 0.47 0.00 0.00 0.00 1.00 1.00 0.50 8,434

TABLE 2 (continued)

Panel C: Pearson (Spearman) Correlations for Cost of Equity Model

1 2 3 4 5 6 7 8 9 10

1 CoE 0.20 0.38 0.12 -0.21 0.24 -0.35 0.26 -0.05 -0.05

2 Beta 0.16 -0.08 -0.18 -0.03 0.33 -0.07 0.07 -0.11 -0.11

3 BTM 0.40 -0.09 0.21 -0.10 0.02 -0.37 0.18 0.03 0.07

4 Leverage 0.14 -0.16 0.19 -0.02 -0.10 0.00 0.16 0.08 0.08

5 Rec_ret -0.19 0.04 -0.11 -0.04 -0.03 0.01 -0.08 0.01 -0.01

6 RMSE 0.22 0.40 0.05 -0.08 0.14 -0.48 -0.10 -0.16 -0.12

7 Size -0.34 -0.08 -0.37 -0.04 -0.07 -0.44 0.16 0.11 0.11

8 Var 0.23 0.03 0.12 0.09 -0.03 -0.07 0.15 0.04 0.04

9 MWMkt -0.04 -0.12 0.02 0.08 -0.02 -0.14 0.11 0.02 0.50

10 NRMkt -0.05 -0.11 0.05 0.07 -0.03 -0.11 0.11 0.02 0.50

39

Panel D: Pearson (Spearman) Correlations for Cost of Debt Model

1 2 3 4 5 6 7 8 9 10 11

1 CoD -0.18 -0.41 -0.42 0.52 0.01 -0.01 -0.03 0.01 -0.07 -0.10

2 Tenure -0.20 0.07 0.36 -0.13 -0.01 -0.02 0.02 -0.03 -0.04 -0.06

3 Bankdebt -0.40 0.08 0.28 -0.07 -0.01 -0.01 0.03 0.01 0.06 0.08

4 FirmAge -0.38 0.39 0.25 -0.15 0.00 -0.01 -0.03 -0.02 0.08 0.09

5 O-score 0.53 -0.14 -0.08 -0.15 0.05 0.00 0.05 0.02 0.00 0.02

6 Qspread 0.02 -0.02 0.00 0.00 0.07 -0.40 -0.24 0.38 0.02 0.00

7 RMRF -0.02 0.00 -0.01 -0.01 -0.02 -0.49 0.32 -0.54 -0.01 0.02

8 SMB -0.03 0.00 0.03 -0.03 0.06 -0.09 0.37 -0.52 -0.06 0.04

9 HML 0.02 0.00 0.00 0.00 -0.01 0.36 -0.57 -0.62 0.01 0.00

10 MWMkt -0.08 -0.04 0.06 0.08 -0.01 0.02 -0.02 -0.06 0.02 0.49

11 NRMkt -0.10 -0.07 0.08 0.07 0.01 0.00 0.02 0.04 -0.01 0.49

TABLE 2 (continued)

40

Panel A: Full Sample

Variable

Predicted

Sign Coeff. t-Stat Coeff. t-Stat

Beta + 0.002 (4.25) *** 0.002 (4.26) ***

BTM + 0.007 (11.55) *** 0.007 (11.54) ***

Lev + 0.002 (10.40) *** 0.002 (10.39) ***

Rec_Ret - -0.011 (-21.13) *** -0.011 (-21.14) ***

RMSE + 0.087 (10.27) *** 0.087 (10.29) ***

Size - -0.004 (-14.44) *** -0.004 (-14.37) ***

Var + 0.059 (11.22) *** 0.059 (11.25) ***

MWMkt - -0.002 (-2.00) **

NRMkt - -0.001 (-1.34) *

N 8,740 8,740

Adjusted R2

0.39 0.39

This table presents cost of equity regression results for a sample of Big N audit firm clients during 1999-2005.

Financial service firms are excluded. CoE equals the average of four implied cost of equity estimates dervied from

the models in Appendix A. Beta equals the systematic risk calculated for the 36 month period preceding the cost of

equity estimation. BTM equals the natural log of the ratio of book value of equity to market value of equity. Lev

equals the natural log of the ratio of total debt to total assets at fiscal-year end. Rec_Ret equals the firm-specific

stock return for the 12 month period preceding the cost of equity estimation. RMSE equals the standard deviation of

residuals from the market model (used to calculate Beta ) over the 36 month period preceding the cost of equity

estimation. Size equals the natural log of a firm’s market value of common equity at fiscal-year end. Var equals

dispersion in one-year ahead analysts’ earnings forecasts at June of year t. MWMkt is an indicator variable equal to 1

if the auditor has the highest market share in a two-digit SIC industry and the market share of the next closest

competitor is at least 10 percentage points lower; else the indicator equals 0. NRMkt is an indicator variable equal to

1 if the square root of assets audited by an auditor in a two-digit SIC industry, scaled by the sum of the square root of

assets audited by all auditors in that industry, is great than 1/K; else the indicator equals 0. K equals the market-share

cutoff point, which is set to 20 percent more than equal partition by Big 4/5 auditors in an industry. LowMon is an

indicator variable equal to 1 if the client is classified as having relatively weak alternative monitoring mechanisms;

else the indicator equals 0. Industry indicator variables based on the two-digit SIC code of the firm-year observations

and year indicator variables are included in the regression, but not reported. *, **, and *** indicate statistical

significance at the 0.10, 0.05, and 0.01 level, respectively, for a one-tailed test when specific signed predictions are

made or two-tailed test when prediction is not made. All t-statistics are calculated using Rogers standard errors. All

variables (except those that are logged or indicators) are winsorized at the top and bottom 1%.

TABLE 3

Cost of Equity and Auditor Industry Specialization

CoE = α + β1Beta + β2BTM + β3Lev + β4Rec_Ret + β5RMSE + β6Size

+ β7Var + β8(Specialist Measure) + ΣηIndustry and Year Indicators

MWMkt NRMkt

41

Panel B: Monitoring Subsample

Variable

Predicted

Sign Coeff. t-Stat Coeff. t-Stat

Beta + 0.001 (2.49) *** 0.001 (2.47) ***

BTM + 0.008 (11.25) *** 0.008 (11.28) ***

Lev + 0.002 (8.36) *** 0.002 (8.29) ***

Rec_Ret - -0.012 (-17.03) *** -0.012 (-16.99) ***

RMSE + 0.077 (6.42) *** 0.077 (6.43) ***

Size - -0.003 (-9.00) *** -0.003 (-8.90) ***

Var + 0.061 (7.61) *** 0.061 (7.72) ***

MWMkt - 0.001 (0.39)

NRMkt - 0.000 (-0.26)

LowMon + 0.000 (0.23) 0.000 (0.47)

LowMon*MWMkt - -0.003 (-2.05) **

LowMon*NRMkt - -0.002 (-1.36) *

F-Stat F-Stat

MWMkt + LowMon*MWMkt - -0.003 8.89 ***

NRMkt + LowMon*NRMkt - -0.002 7.89 ***

N 5,231 5,231

Adjusted R2

0.43 0.43

MWMkt NRMkt

CoE = α + β1Beta + β2BTM + β3Lev + β4Rec_Ret + β5RMSE + β6Size

+ β7Var + β8(Specialist Measure) + β9LowMon + ΣµLowMon*(Specialist Measure)

+ ΣηIndustry and Year Indicators

TABLE 3 (continued)

42

Variable Prediction

Odds

Ratio Coefficient Z-Stat

Odds

Ratio Coefficient Z-Stat

Tenure - 0.98 -0.020 (-2.60) *** 0.98 -0.021 (-2.64) ***

Bankdebt - 0.27 -1.314 (-15.84) *** 0.27 -1.314 (-15.83) ***

FirmAge - 0.64 -0.439 (-10.62) *** 0.64 -0.440 (-10.66) ***

O-Score + 2.22 0.799 (29.02) *** 2.22 0.798 (29.01) ***

Qspread - 0.80 -0.224 (-1.83) ** 0.80 -0.219 (-1.79) **

RMRF ? 1.00 0.000 (-0.04) 1.00 0.000 (-0.02)

SMB ? 0.99 -0.008 (-0.92) 0.99 -0.007 (-0.79)

HML ? 0.99 -0.008 (-0.88) 0.99 -0.008 (-0.89)

MWMkt - 0.80 -0.222 (-2.25) **

NRMkt - 0.81 -0.210 (-2.77) ***

N 8,434 8,434

Wald Chi-square 2,789 2,787

p-value 0.0000 0.0000

Pseudo R2

0.15 0.15

TABLE 4

Cost of Debt and Auditor Industry Specialization

Pr(CoD) = exp(α + β1Tenure + β2Bankdebt + β3FirmAge + β4O-Score + β5QSpread + β6RMRF + β7SMB + β8HML +

β9(Specialist Measure) + ΣηIndustry and Year Indicators)

Panel A: Full Sample

MWMkt

This table presents cost of debt ordered logistic regression results for a sample of Big N audit firm clients during 1999-2005. Financial service

firms are excluded. CoD equals the firm-specific cost of debt capital proxy based on debt ratings. Tenure equals the number of years a client has

been audited by the auditor identified in the financial statements, beginning in 1986. Bankdebt is an indicator variable equal to one if the firm has

notes payable; else indicator equals zero. FirmAge equals the natural log of the number of years since a firm's initial public offering. O-Score

equals the Ohlson [1980] default risk measure for the firm. Qspread equals the spread between BAA and AAA corporate bond indexes. RMRF

equals the CRSP value-weighted market index return less the one month Treasury bill return. SMB equals the return for a portfolio of small stocks

less the return for a portfolio of large stocks. HML equals the return for a portfolio of high book-to-market stocks less the return for a portfolio of

low book-to-market stocks. MWMkt is an indicator variable equal to 1 if the auditor has the highest market share in a two-digit SIC industry and

the market share of the next closest competitor is at least 10 percentage points lower; else the indicator equals 0. NRMkt is an indicator variable

equal to 1 if the square root of assets audited by an auditor in a two-digit SIC industry, scaled by the sum of the square root of assets audited by all

auditors in that industry, is great than 1/K; else the indicator equals 0. K equals the market-share cutoff point, which is set to 20 percent more than

equal partition by Big 4/5 auditors in an industry. LowMon is an indicator variable equal to 1 if the client is classified as having relatively weak

alternative monitoring mechanisms; else the indicator equals 0. Industry indicator variables based on the two-digit SIC code of the firm-year

observations and year indicator variables are included in the regression, but not reported. *, **, and *** indicate statistical significance at the 0.10,

0.05, and 0.01 level, respectively, for a one-tailed test when specific signed predictions are made or two-tailed test when prediction is not made.

All z-statistics are calculated using Rogers standard errors. All variables (except those that are logged or indicators) are winsorized at the top and

bottom 1%.

NRMkt

43

Variable Prediction

Odds

Ratio Coefficient Z-Stat

Odds

Ratio Coefficient Z-Stat

Tenure - 0.99 -0.014 (-1.37) * 0.99 -0.014 (-1.40) *

Bankdebt - 0.31 -1.175 (-10.07) *** 0.31 -1.173 (-10.04) ***

FirmMage - 0.47 -0.763 (-10.16) *** 0.47 -0.765 (-10.19) ***

O-Score + 2.21 0.793 (18.53) *** 2.20 0.791 (18.41) ***

Qspread - 0.94 -0.064 (-0.42) 0.95 -0.047 (-0.31)

RMRF ? 0.99 -0.007 (-0.46) 0.99 -0.007 (-0.48)

SMB ? 0.97 -0.027 (-2.12) ** 0.98 -0.025 (-2.01) **

HML ? 0.99 -0.007 (-0.58) 0.99 -0.008 (-0.66)

MWMkt - 0.83 -0.186 (-0.98)

NRMkt - 0.85 -0.164 (-1.21)

LowMon + 0.83 -0.181 (-1.87) 0.90 -0.105 (-0.88)

LowMon*MWMkt - 0.91 -0.097 (-0.46)

LowMon*NRMkt - 0.84 -0.172 (-1.04)

Chi Sq Chi Sq

MWMkt + LowMon*MWMkt - -0.283 3.37 *

NRMkt + LowMon*NRMkt - -0.336 5.87 **

N 4,677 4,677

Wald Chi-square 2,002 2,034

p-value 0.0000 0.0000

Pseudo R2

0.15 0.15

Pr(CoD) = exp(α + β1Tenure + β2Bankdebt + β3FirmAge + β4O-Score + β5QSpread + β6RMRF + β7SMB + β8HML +

β9(Specialist Measure) + β10LowMon + ΣµLowMon*(Specialist Measure) + ΣηIndustry and Year Indicators)

MWMkt NRMkt

Panel B: Monitoring Subsample

TABLE 4 (continued)