Dunn & Mayhew 2000

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Auditor Industry Specialization and Client Disclosure Quality

Kimberly A. Dunn

Baruch College

Brian W. MayhewUniversity of Wisconsin Madison

Suzanne G. MorsfieldBaruch College

February 18, 2000

Abstract

We examine the relation between audit firm industry specialization and client disclosurequality. Our motivation for conducting this research arises from the claims made by each of theBig5 public accounting firms that industry specialization enables each to provide superior serviceand quality to clients in its target industries.

We document a positive association between industry-specialized audit firms andanalysts rankings of disclosure quality in unregulated industries, but no relation in regulatedindustries. Alternative measures of auditor industry specialization support our conclusions. Ourresults suggest industry-specialized audit firms add value to clients in unregulated industriesthrough improved disclosure quality.

Please send correspondence to:Brian W. Mayhew975 University AvenueMadison, WI [email protected]

This paper was previously titled Disclosure Quality and Auditor Choice. We appreciate the

comments of the referee, Hollis Ashbaugh, Ramji Balakrishnan, Martin Benis, Sudipta Basu,Larry Brown, Michael Calegari, Douglas Carmichael, Don Jones, Greg Geisler, Norman Godwin,Karla Johnstone, Richard Leftwich, Terry Warfield, Joe Weintrop, Christine Tan and workshopparticipants at the 1999 International Symposium on Audit Research, 1999 American AccountingAssociation Annual Meeting, Auburn University and Georgia State University.

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Introduction

We examine the association between industry-specialized audit firms and their clients

disclosure quality. A visit to all of the Big5 web-sites and review of the popular press suggest

that each firm claims it is increasingly focusing on specific industries and that it provides superior

service and value to its selected industries [Berton 1995]. We utilize standard measures of

disclosure quality and audit firm industry specialization to test for an association between the two

constructs. We use analysts disclosure quality evaluations reported in the annual AIMR

(Association for Investment Management and Research) Corporate Information Committee

Reports as a proxy for disclosure quality (Lang and Lundholm [1993, 1996], and Sengupta

[1998]), and the proportion of industry sales audited by each auditor as a proxy for auditor

industry specialization (Craswell, Francis and Taylor [1995]). Big6 auditors audit all of our

sample firms, so the industry specialization measures do not reflect Big6/ NonBig6 dichotomies.

Cross-sectional analyses employing control variables from Lang and Lundholm [1993]

indicate a positive association between industry-specialized auditors and AIMR overall and

annual report rankings. A partition of the data into regulated and unregulated industries suggests

that a strong positive association between auditor industry specialization and disclosure quality in

unregulated industries drives the overall results. There does not appear to be any such association

in regulated industries. The results do not appear to be impacted by individual Big6 audit firms.

Sensitivity tests also generally support our findings using alternative measures of industry

specialization. An examination of AIMR Disclosure Quality Awards given by the same analyst

committees to the top firms in each industry further corroborates our conclusions.

We organized the remainder of this paper as follows. The next section provides a

discussion of the empirical proxies we employed and our hypotheses. Section three presents the

research design and data. Section four describes the results. Section five presents tests based on

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alternative measures of audit firm industry specialization. The final section summarizes our

findings and points to avenues for future research.

Empirical Proxies and Hypotheses

Industry Specialization as a Proxy for Audit Quality

We define industry specialization based on the portion of the industry served by an

auditor. The reported results focus on two measures of audit firm industry specialization - a

continuous ranking of the industry sales audited, and a dichotomous measure based on whether

the auditor audits 15% of industry sales. Each industry consists of all companies within each

two-digit Standard Industry Classification (SIC) code included in the Compustat database. We

recalculated the specialization measure for each of the years in our sample.1We also report the

results of robustness tests using 10%(15%)(20%) of an industrys sales, assets, and number of

firms audited in an industry in addition to the auditors self-reported industry specializations from

their web-sites.

Prior research has generally defined an audit firm as an industry specialist if it audited

more than 10% of firms or sales in an industry (Craswell, et al. [1995], and DeFond [1992]).

Researchers used the 10% cut-off prior to the consolidation of the Big8 into the Big6. We moved

our measure of industry specialization to 15% of sales audited to reflect the consolidation. After

the consolidation, a Big6 accounting firm would have more than an equal share of an industry if it

audited approximately 16% or more of the industry (96%/6).2

We define industry specialization as the proportion of industry sales audited in two-digit

SIC codes for two reasons. First, prior research shows a correlation between audit fees and client

size measured in sales or assets (Simunic [1980], Palmrose [1986]). We assume the total fees

earned by an auditor within an industry serves as a proxy for an auditors incentive to invest in

industry specialization.

Second, Craswell, Francis, and Taylor [1995] argue that audit quality increases with an

auditor's market share. However, Krishnan [1998] states that this is not always the case. Citing

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microeconomic theory of specialization, Krishnan notes that specialization evolves to serve

market niches, and that these niches need not be very large. Our robustness checks consider the

possibility of small market share specialization by partitioning auditor specialization into fewer

than 10%, 10-20%, and more than 20% of market share.

Analysts evaluations as a proxy for disclosure quality

Each year in our sample, the AIMR Corporate Information Committee (CIC) selected

buy and sell-side analysts to form industry-specific committees that evaluated the disclosure

quality of firms in selected industries.3 Each industry subcommittee determined its own criteria

and scoring system and then selected the firms to be evaluated. Subcommittees evaluated the

adequacy of disclosure among three categoriesannual published information, quarterly and

other published information, and investor relations. Within the categories, each industry

subcommittee identified the important aspects of disclosure for that industry and then assigned a

score to each firm. The subcommittees then weighted each category to arrive at an overall score.

Industry subcommittees differed on what they discussed in their reports. A majority of

subcommittees reported scores in each of the three categories and an overall score. However,

some industries reported only an overall score, some ranked only the firms within the industry

and a minority provided only qualitative evaluations.

After evaluating the firms, the subcommittees summarized their scores, prepared a

written justification for their results, and considered whether to recommend that the top

companies in their industry receive an Award for Excellence or Letter of Commendation in

Corporate Reporting. The AIMR subcommittees selected up to two firms from each industry to

be recognized for excellence in corporate reporting.

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Hypotheses

Our main question concerns whether or not disclosure quality is associated with industry-

specialized auditors. The AIMR overall score captures analysts perception of a firm's overall

disclosure policy. We test for a positive relationship between the overall scores and auditor

industry specialization. This leads to our first and main hypothesis.

H1(alternative form): Analysts overall scores are higher for firms employing industry

specialist auditors than for firms employing non-specialist auditors.

Analysts evaluate annual reports including the financial statements as part of their annual

published information score. The auditor's close affiliation with the annual report leads to an

expectation that the auditor's greatest impact will be on the annual published information score.

Accordingly, our second hypothesis predicts a positive relationship between analysts scores for

annual published information and auditor industry specialization.

H2(alternative form): Analysts scores for annual published information will be higher for

firms employing industry specialist auditors than for firms employing non-specialist

auditors.

We also use the Awards of Excellence and Letters of Commendation as evidence of high

disclosure quality. We expect a higher proportion of firms receiving the Awards of Excellence or

Letters of Commendation to be audited by an industry-specialized auditor. This leads to our third

hypothesis:

H3 (alternative form): Industry-specialized auditors are associated with firms receiving the

AIMR Award of Excellence or Letter of Commendation in Corporate Reporting.

Prior research has documented a difference in the market concentration measures for

regulated and unregulated industries (Hogan and Jeter [1999], Danos and Eichenseher [1982]).

This research implies a difference in the nature of specialization in regulated versus unregulated

industries. In addition, specific financial reporting and disclosure rules apply to most regulated

industries. The specific requirements may restrict the ability of clients and auditors to

differentiate themselves in disclosure quality. To explore the possibility that the disclosure

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quality and auditor industry specialization relation differs across these two dimensions, we

conducted a second set of analyses for samples of regulated and unregulated industries.

H4 (alternative form): Analysts scores for overall and annual published information are

higher (the same) for firms employing industry specialist auditors than for firms employing

non-specialist auditors in unregulated (regulated) industries.

Research Design and Data

Research Design

We use rank regression methods similar to Lang and Lundholm [1993]. We ranked

dependent and independent variables within each AIMR industry and then converted to fractions:

(rank-1)/(number of firms 1).4 The conversion yields the fraction of a firms rank in the

industry, so that the highest ranked firm receives a one and the lowest ranked firm receives a

zero. The rank regressions allow us to pool data cross-sectionally even though separate analyst

committees evaluated the firms using different scoring systems. We employed cross-sectional

comparisons to test the above hypotheses. We could not explore a time-series relationship

between industry specialization and disclosure quality because our sample includes only a limited

number of auditor switches.

Control Variables

Lang and Lundholm [1993] examined the relation between AIMR scores and firm

characteristics over the 1985-89 period. They provided evidence that AIMR scores increase with

firm size and firm performance measured by current returns. Firms issuing securities in the

current and subsequent two periods also received higher AIMR scores. Conversely, Lang and

Lundholms [1993] evidence suggested that analysts award lower scores for firms with high

earnings/ returns correlations. They provided weak evidence of a negative association of the

volatility of returns with AIMR scores. We include Lang and Lundholms variables (as defined

below) in our multivariate analyses to control for potential alternative explanations to our

hypotheses.

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We test H1 using the SPECIALIST variable in following model:

RANKi = 1+ 1MKTVALi+ 2ERCORRi+ 3STDRETi+ 4SURPRISEi+ 5MKTRETi+

6OFFERi+ 7SPECIALISTi

RANKi = The sample firm's AIMR rank for overall (annual) disclosure qualitywithin its industry. We hand collected this data from the AIMRCorporate Information Committee Reports. AIMR labels the reports1990-1991, 1991-1992, etc. which we categorize as 1990, 1991, etc.

MKTVALi = The market value of outstanding equity at the beginning of the fiscalyear, calculated by Compustat Data Item 25 multiplied by CompustatData Item 24.

ERCORRi = The correlation between annual stock returns calculated from the CRSPMonthly Stock File and annual earnings as reported in the CompustatAnnual Tapes, Data Item 58, each computed for the ten years prior to thecurrent year. We also included firms with fewer than ten years of data,as long as they had at least four years of earnings and returns

information.STDRETi = The standard deviation of annual market-adjusted stock returns

calculated from the CRSP Monthly Stock File by subtracting the annualequal-weighted market returns from annual firm returns, computed overthe ten years prior to the current fiscal year. We also included firmswith fewer than ten years of data, as long as they had at least four yearsof earnings and returns information.

SURPRISEi = I/B/E/S reported actual earning per share less the I/B/E/S reported meanconsensus forecast earning per share at the beginning of the fiscal year,divided by the I/B/E/S reported price per share at the beginning of thefiscal year.

MKTRETi = The annual firm return less the annual equal-weighted market return for

the fiscal year calculated from the CRSP Monthly Stock file.OFFERi = An indicator variable equal to one if the firm files a debt or equity

registration statement in the current fiscal year or in the next two fiscalyears, and zero otherwise. We hand collected the data from the CapitalChanges Reporter.

SPECIALISTi = Industry specialist as defined by PERC_SLS or SLS_15.PERC_SLSi = The percentage of sales audited by the firm's auditor in the firm's two-

digit SIC code as reported by Compustat. Compustat does not reportauditor codes for companies in the financial industries; therefore,PERC_SLS for financial companies is the percentage of sales audited bythe firm's auditor in the firm's industry for the companies included in oursample.

SLS_15i = An indicator variable equal to one if the firm's auditor audits at leastfifteen percent of the sales in the firm's two-digit SIC code as reportedby Compustat, and zero otherwise. SLS_15 for financial companiesequals one if the company's auditor audits at least fifteen percent of thesales in the firm's industry for companies included in our sample.

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To determine the relationship between auditor industry specialization and annual

published information (H2), we substitute the rank fraction of the annual published information

score as the dependent variable. We also use the model to test the association between disclosure

quality and auditor specialization in sub-samples of regulated and unregulated industries (H4).

To determine the relationship between auditor industry specialization and receipt of an

Award for Excellence or Letter of Commendation in Corporate Reporting (H3), we construct a

two-by-two frequency table for the overall sample and both the regulated and unregulated

samples.

Data

We examine the relation between the AIMR scores and auditor specialization over a six

year period, 1990-95. The sample intentionally omits data prior to the 1989 consolidation of the

Big8 into the Big6. By employing only post-consolidation data we avoid any shifts in industry

specialization associated with the consolidation and provide a more powerful test of association.

Table 1 lists all of the industries included in our analysis separated into regulated and

unregulated industries. We define regulated industries based on the same categorization of two-

digit SIC codes used by Hogan and Jeter [1999]. Our sample starts with all firms rated by the

analyst subcommittees included in the AIMR reports. A small number of industries covered by

these reports provide only qualitative evaluation disclosures and accordingly we omitted them

from the analyses. We use the firm years that include all the data necessary to calculate our

control variables on Compustat, CRSP, and I/B/E/S databases from the remaining AIMR

industries.

Compustat does not include auditor codes for the full sample period for the

insurance, banking, savings institutions and financial services industries. We hand

collected auditor codes for these industries from Laser Disclosure. We based auditor

industry specialization measures for these industries on their market share of our sample

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firms, since Compustat did not provide the information to calculate them based on their

larger industry population.

Results

Table 2 lists the descriptive statistics for the overall sample (panel A) and for two sets of

sub-samples unregulated (panel B) and regulated (panel C) industries, and firms audited by

industry specialist auditors (panel D) and industry non-specialist auditors (panel E) based on a

15% of industry sales audited. Not surprisingly, given our focus is on firms followed by analysts,

the typical firm in our sample has a fairly large market capitalization with an inter-quartile range

of $723 M to $4,930 M.

The median auditor in the sample audits 17% of the firms in each two-digit SIC code

based on industry sales and assets and 15% (not reported) based on number of firms in the

industry. The median auditor industry coverage supports our use of a 15% cut-off for defining

industry specialization. The descriptive statistics for industry specialization measures represent

only industries with auditor codes included on Compustat.5

The descriptive statistics for the sub-samples generally match the descriptive statistics for

the overall sample, especially when looking at the inter-quartile ranges for each variable. The

difference in size between the auditor specialist, and non-specialist samples reflects the definition

of industry specialization based on the portion of sales audited by the auditor. We control for

client size by including market value in the multivariate analyses.

We first investigate H1 by conducting t-tests on mean, unadjusted analysts overall score

for each industry partitioned into specialist and non-specialist auditors. We use 15% of sales as

the cut-off for specialist and non-specialist auditors for this test. We also provide some

preliminary evidence about H4 on industry specialization being associated with disclosure quality

in unregulated industries but not regulated industries, by partitioning table 1 into regulated and

non-regulated industries.

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The univariate comparisons in Table 1 suggest weak support for hypotheses 1 and 4.

Auditor industry specialization corresponds to significantly higher overall disclosure quality

ratings in five out of seventeen unregulated industries and one out of eighteen regulated

industries. We now turn to the multivariate analysis to control for the factors associated with

disclosure quality identified by Lang and Lundholm [1993].

Table 3 summarizes the multivariate results with the overall score as the dependent

variable for all industries, unregulated industries and regulated industries. The all industries

model shows a positive association between auditor industry specialization and disclosure quality

in support of H1. However, when we break the sample into regulated and unregulated industries,

it becomes clear that the results are driven entirely by unregulated industries, in support of H4.

Auditor industry specialization appears to be strongly related to disclosure quality in the

unregulated industry sample, but not at all related to the disclosure quality in the regulated

industry sample.

Table 4 substitutes the analysts annual report score for the overall score as the dependent

variable to test H2. The overall sample provides weak support (t=1.57, p

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specialization variables remain significant in the unregulated sample and insignificant in the

regulated sample with overall (annual) disclosure quality as the dependent variable and indicator

variables for each of the Big6 firms.

We also examine Awards of Excellence awarded by each analyst industry committee for

evidence of an association between disclosure quality and auditor industry specialization. Table 7

shows a two-by-two contingency table to test the association. The table suggests industry

specialization is associated with disclosure quality (p=.002) for the firms in our sample. The

table documents that firms receiving the award are audited by industry specialist more often than

by non-specialists. Once again, when we divide the table into individual tables for unregulated

(Panel B) and the regulated industries (Panel C) we see unregulated industries drive the overall

results. This further supports the multi-variate results.

Alternative Measures of Auditor Industry Specialization

We consider a number of alternative definitions of auditor industry specialization. The

industry specialization literature typically uses measures of specialization based on sales, assets

or number of firms audited. Industry specialization measures based on portion of sales or assets

audited are highly correlated (r=.95) and yield almost identical results in our multivariate

analyses (results not reported). However, industry concentration based on the number of firms

audited is not as highly correlated with sales or asset measures (r=.56) and generally does not

generate the same results as our multivariate analyses. Hogan and Jeter [1999] note a similar

difference in results when they use number of clients instead of assets audited to measure industry

specialization in tests for an association between auditor and client industry concentration levels.

We also consider different cut-off levels for industry specialization. Our basic finding of

an association between industry specialization and disclosure quality (both overall and annual

scores) holds at a 20% market share cut-off when based on sales or assets but not firms. Results

at 15% of assets mirror those reported for sales, but again results based on proportion of firms

audited is insignificant. The results also hold, but are weaker, at a 10% market share for sales,

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assets and firms. We also used two indicator variables to explore how a dual cut-off (greater than

20%, and fewer than 10%) performed. We observed a significantly positive coefficient on the

greater than 20% variable (t=2.83, p

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measures of audit firm industry specialization captures audit firms who employ more industry

specialists auditors than firms classified as non-specialist, our measure provides evidence that

audit quality impacts client disclosure quality. In addition, audit firms may impact disclosure

quality in a more direct manner by sharing disclosure best practices with clients in their

specialized industries. Prior to its merger with Price Waterhouse, Coopers & Lybrand issued a

report on an annual basis titled Anticipating Question at Shareholders Meetings. This report

included specific insights and hot topics related to annual shareholder meetings for 15 industries

(Coopers & Lybrand, LLP, [1998]).

The difference in association between industry specialists and disclosure quality in

regulated and unregulated industries provides insight into the nature of audit firm industry

specialization. The answers to two fundamental, but not necessarily mutually exclusive

questions, have remained elusive to theorists attempting to understand auditor industry

specialization. Do audit firms specialize to capture economies of scale and become low cost

providers of audit services for the industry? Or, do audit firms specialize to build the expertise

necessary to produce higher quality audits and capture a fee premium? Our results suggest that

the answers to these questions may depend on client industry characteristics.

It appears that audit quality may be a motivating factor for industry specialization in

unregulated industries while economies of scale may explain industry specialization in regulated

industries. Prior research identified higher auditor concentration ratios in regulated versus

unregulated industries (Hogan and Jeter [1999], Danos and Eichenseher [1982]). Palmrose

[1986] and Pearson and Trompter [1994] supplied evidence that industry specialists do not earn

fee premiums in regulated industries. This prior evidence along with the evidence presented here

that disclosure quality is not associated with industry specialization in regulated industries,

suggests that industry specialization in regulated industries may result from economies of scale.

To probe further into auditor industry specialization our results suggest that a

reinvestigation of audit fees and industry specialization using U.S. data is warranted. Palmrose

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[1986] concluded there was no association between audit fees and industry specialization.

However, nine out of twelve industries in her sample would be regulated industries in our sample

for which we would not expect a premium. Consistent with our expectations, Pearson and

Trompeter [1994] also found that industry specialists in the regulated insurance industry do not

earn fee premiums. However, Ward, Elder, and Kattelus [1994] provide evidence of a fee

premium for auditor industry specialization in the municipal audit market. The municipal audit

market introduces another potential determinant of industry specialization whether the industry

consists of for-profit or not-for-profit entities. Our results imply the characteristics of auditor

specialization differ depending on the industry being examined.

1 The 15% of industry sales audited definition of industry specialization produces an average of 2.6 auditfirms defined as industry specialist in the 54 two-digit SIC codes we evaluate using Compustat data forthe years included in our sample. The inter-quartile range of specialization per industry is two to three

audit firms. Each Big6 audit firm averages between 16 and 33 industry specializations per year. Foreach Big6 firm the range in number of industry specializations between years is only one to fivespecializations.

2 Over our sample timeframe, Big6 auditors audit on average 96% of sales reported by Compustat.3 See Lang and Lundholm [1993] for a complete discussion of the scoring process.4 The rank of the largest firm in the industry equals n and the smallest equals one.5 In the multivariate analyses, we convert these percentages into rank percentages, at which time we add

rank percentages derived from firms in our sample for the other industries that we hand collectedauditor codes. This explains why the number of observations for the auditors percentage of industryaudited measures are less than the number of observations in our multivariate analyses.

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References

Berton, L. 1995. Price Waterhouse Managers Realign to Cover Specialized Industry Lines. The

Wall Street Journal(June 28).

Coopers & Lybrand, LLP. 1998. Anticipating Questions at Shareholders Meetings 1998.

Coopers and Lybrand LLP. New York, NY.

Craswell, A.T. J.R. Francis, and S.L. Taylor. 1995. Auditor Brand Name Reputations and

Industry Specializations.Journal of Accounting and Economics20: 297-322.

Danos, P. and Eichenseher, J. 1982. Audit Industry Dynamics: Factors Affecting Changes in

Client-Industry Market Shares. Journal of Accounting Research20 (Autumn): 604-616.

DeFond, M.L. 1992. The Association Between Changes in Client Firm Agency Costs and

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Hogan C. E. and D. C. Jeter. 1999. Industry Specialization by Auditors.Auditing: A Journal of

Practice and Theory 18 (Spring): 1-17.

Krishnan, J. 1998. A Comparison of Auditors Self-Reported Industry Expertise and Alternative

Measures of Industry Specialization. Villanova University Working Paper.

Lang, M. and R. Lundholm. 1993. Cross-Sectional Determinants of Analyst Ratings of Corporate

Disclosures. Journal of Accounting Research 31 (Autumn): 246-271.

Lang, M. and R. Lundholm. 1996. Corporate Disclosure Policy and Analyst Behavior. The

Accounting Review71 (October): 467-492.

Owhoso, V.E., W.F. Messier and J. Lynch. September 1998. Risk Reduction and the Audit

Review Process: The Interaction of Review Hierarchy, Team Processing, and Industry

Specialization. Georgia State University Working Paper.

Palmrose, Z. 1986. Audit Fees and Auditor Size: Further Evidence.Journal of Accounting

Research24 (Spring) 97-110.

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Pearson, T. and G. Trompeter. 1994. Competition in the Market for Audit Services: The Effect of

Supplier Concentration on Audit Fees. Contemporary Accounting Research11 (Summer):

115-135.

Sengupta, P. 1998. Corporate Disclosure Quality and the Cost of Debt. The Accounting Review.

73 (October) 459-474.

Simunic, D. 1980. The Pricing of Audit Services: Theory and Evidence. Journal of Accounting

Research(Spring): 161-90.

Solomon, I. M. Shields, and O. R. Whittington. 1999. What Do Industry Auditors Know?Journal

of Accounting Research(Spring): 191-208.

Taylor, M. 1998. Bounded Rationality, Uncertainty, and Competence: The Effects of Industry

Specialization on Auditors Inherent Risk Assessments. Working Paper University of

Nebraska Lincoln.

Ward D. D., Elder R. J. , and Kattelus S.C. 1994. Further Evidence on the Determinants of

Municipal Audit Fees. TheAccounting Review(April): 399-411.

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Table 1t-tests of Auditor Industry Specialization Partitioned by Regulated and Unregulated Industries

Panel A: Unregulated Industries

Specialist Non-Specialist

Industry n Score n Score t-statistic

Aerospace 3 63.77 8 73.99 -1.98+

Apparel 26 73.62 16 58.38 4.79**

Auto 7 69.02 7 62.41 1.52

Chemical 40 75.13 16 71.19 1.24

Construction 18 82.60 12 78.50 1.42

Container 15 143.63 14 131.06 1.55

Electrical Equipment 32 78.29 13 78.43 -0.05

Pharmaceuticals 66 84.88 37 80.76 3.24**

Home Building 8 72.48 2 71.00 0.40

Machinery 41 75.46 25 67.01 3.21**

Media 77 67.99 40 60.70 3.95**

Non-Metals 20 48.83 2 44.5 0.65Paper 65 61.43 50 62.11 -0.39

Retail 107 90.08 46 86.05 3.25**

Software 58 103.63 83 107.98 -1.08

Specialty Chemical 37 65.01 22 63.55 0.50

Textiles 20 64.80 4 73.75 -1.52

Panel B: Regulated Industries

Specialist Non-Specialist

Industry n Score n Score t-statistic

Airline 52 73.43 0 - -

Bank 137 .49 56 .55 -1.48Coal Mining 8 46.81 1 55.9 -

Environment 18 71.33 47 68.64 0.81

Financial Services 22 71.38 16 75.12 -1.62

Food and Beverage 74 67.36 91 67.82 -0.20

Health Care 66 84.88 37 80.76 3.24**

Insurance 144 274.35 10 244.66 0.42

Motor Carrier 5 76.36 4 78.45 -0.54

Natural Gas Distributors 37 843.80 14 821.89 1.63

Natural Gas Pipeline 24 767.00 24 817.83 -2.45*

Oil Drillers 4 6485.00 0 - -

Oil Producers 4 734.90 4 733.15 0.03

Oil Service and Drilling 10 5362.30 3 4982.00 0.15

Oil Domestic 23 78.24 16 82.01 -1.73+

Oil Refining 2 83.00 4 82.70 0.18

Precious Metals 30 63.09 38 59.04 1.52

Railroad 24 82.90 12 81.30 0.74

Savings Institutions 2 88.5 1 83 -

The t-statistic measures the statistical difference between specialist auditor overall scores andnon-specialist auditor overall scores. *(**) Significant at .05 (.01); + Significant at .10. Auditorspecialization measured by whether the auditor audits 15% or more of sales in the client firms

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two-digit SIC code. The industries listed represent the AIMR committee industries. Industrieswere defined as regulated or unregulated based on two-digit SIC codes used by Hogan and Jeter[1999]. For AIMR industries that included firms with more than one two-digit SIC code, wecategorized the AIMR industry based on the regulated / unregulated status of a majority of thefirms in that AIMR industry.

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Table 2Descriptive Statistics

Panel A: All Observations

Percentile

Variable N Mean 1% 25% 50% 75% 99%

MKTVAL 1932 4965.61 54.09 723.13 1989.42 4930.10 50095.26

ERRCORR 1712 .18 -.73 -.04 .20 .42 .89

STDRET 1729 .27 .11 .19 .24 .31 .67

SURPRISE 1877 -.04 -.48 -.02 .00 .00 .07

MKTRET 1901 .01 -.88 -.19 .02 .20 .97

OFFER 1998 .47 0 0 0 1 1

PERC_SLS 1610 .21 .03 .11 .17 .28 .72

SLS_15 1998 .63 0 0 1 1 1

Panel B: Unregulated Industries

PercentileVariable N Mean 1% 25% 50% 75% 99%

MKTVAL 1012 5490.37 50.43 695.19 1953.86 5061.65 51019.25

ERRCORR 882 .715 -.80 -.07 .17 .40 .85

STDRET 887 .27 .12 .20 .25 .31 .67

SURPRISE 988 -.02 -.28 -.02 .00 .00 .05

MKTRET 984 .03 -.82 -.19 .02 .23 1.06

OFFER 1037 .44 0 0 0 1 1

PERC_SLS 1037 .21 .04 .11 .18 .27 .68

SLS_15 1037 .62 0 0 1 1 1

Panel C: Regulated IndustriesPercentile

Variable N Mean 1% 25% 50% 75% 99%

MKTVAL 920 4388.38 58.55 742.43 2014.04 4800.34 48788.19

ERRCORR 830 .20 -.71 -.02 .24 .44 .89

STDRET 842 .27 .10 .19 .24 .30 .75

SURPRISE 889 -.06 -.73 -.02 .00 .00 .07

MKTRET 917 -.01 -.96 -.19 .01 .19 .87

OFFER 961 .52 0 0 1 1 1

PERC_SLS 573 .21 .02 .11 .17 .29 .98

SLS_15 961 .64 0 0 1 1 1

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Panel D: Firms Audited by Auditors with Greater than 15% Market Share of Two-Digit SICSales.

Percentile

Variable N Mean 1% 25% 50% 75% 99%

MKTVAL 1211 5376.23 52.93 815.54 2332.34 5411.77 50095.26

ERRCORR 1092 .19 -.71 -.06 .20 .44 .95

STDRET 1104 .26 .11 .19 .24 .30 .61

SURPRISE 1192 -.03 -.43 -.02 .00 .00 .06

MKTRET 1195 .01 -.91 -.19 .02 .20 .98

OFFER 1256 .47 0 0 0 1 1

PERC_SLS 951 .28 .15 .19 .25 .34 .98

SLS_15 1256 1 1 1 1 1 1

Panel E: Firms Audited by Auditors with Less than 15% Market Share of Two-Digit SIC Sales.

Percentile

Variable N Mean 1% 25% 50% 75% 99%

MKTVAL 721 4275.94 56.28 557.02 1554.54 4008.53 49651.15ERRCORR 620 .16 -.76 -.03 .20 .39 .80

STDRET 625 .28 .11 .20 .25 .32 .78

SURPRISE 685 -.05 -.82 -.02 .00 .00 .07

MKTRET 706 .02 -.88 -.19 .01 .22 .97

OFFER 742 .48 0 0 0 1 1

PERC_SLS 659 .10 .02 .07 .10 .13 .149

SLS_15 742 0 0 0 0 0 0

MKTVAL represents the market value of outstanding equity at the beginning of the fiscal year.ERCORR represents the correlation between annual stock returns and annual earnings as eachcomputed for the ten years prior to the current year. We also included firms with fewer than ten

years of data, as long as they had at least four years of earnings and returns information. STDRETrepresents the standard deviation of annual market-adjusted stock returns calculated over the tenyears prior to the current fiscal year. SURPRISErepresents the I/B/E/S reported actual earningper share less the I/B/E/S reported mean consensus forecast earning per share at the beginning ofthe fiscal year, divided by the I/B/E/S reported price per share at the beginning of the fiscal year.MKTRET represents annual firm return less the annual equal-weighted market return for thefiscal year. OFFER represents an indicator variable equals one if the firm files a debt or equityregistration statement in the current fiscal year or in the next two fiscal years, and zero otherwise.PERC_SLS represents the percentage of sales audited by the firm's auditor in the firm's two-digitSIC code as reported by Compustat. Compustat does not report auditor codes for financial firms;therefore, PERC_SLS does not include financial firms. SLS_15, for non-financial companies,represents indicator variable equal to one if the firm's auditor audits at least fifteen percent of the

sales in the firm's two-digit SIC code as reported by Compustat, and zero otherwise. SLS_15, forfinancial companies, equals one if the company's auditor audits at least fifteen percent of the salesin the firm's industry for companies included in our sample.

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Table 3Multivariate Analysis of AIMR Overall Score

Control Variables with Auditor Specialization Variable

Dependent Variable = AIMR Overall Score

All Industries Unregulated Industries Regulated IndustriesIndependent

Variables ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

INTERCEPT 0.300(10.72)**

0.302(11.18)**

0.283(7.73)**

0.284(7.94)**

0.323(7.64)**

0.336(8.17)**

MKTVAL 0.204(8.08)**

0.205(8.18)**

0.199(5.63)**

0.202(5.77)**

0.206(5.70)**

0.209(5.78)**

ERCORR 0.060(2.46)*

0.060(2.48)*

0.075(2.23)*

0.081(2.41)*

0.035(0.98)

0.036(1.02)

STDRET -0.029(-1.17)

-0.028(-1.13)

-0.050(-1.47)

-0.046(-1.37)

-0.015(-0.43)

-0.016(-0.45)

SURPRISE 0.077(2.88)**

0.079(2.91)**

0.056(1.47)

0.060(1.58)

0.096(2.50)*

0.093(2.42)*

MKTRET -0.003(-0.13)

-0.005(-0.18)

-0.014(-0.37)

-0.019(-0.51)

0.015(0.39)

0.016(0.41)

OFFER 0.028(1.85)+

0.028(1.82)+

0.038(1.79)+

0.035(1.65)+

0.014(0.64)

0.013(0.59)

PERC_SLS 0.068(2.76)**

.134(3.88)**

-0.001(0.03)

SLS_15 0.042(2.66)**

0.100(4.50)**

-0.018(-0.78)

N 1617 1617 840 840 777 777Adjusted R2 6.6% 6.6% 8.2% 8.8% 5.4% 5.5%

*(**) Significant at .05 (.01); + Significant at .10.We ranked all continuous independent and dependent variables within each industry and then

converted to fractions: (rank-1)/(number of firms 1). AIMR Overall Score is the sample firm's

overall disclosure quality score within its industry as reported in the AIMR Corporate

Information Committee Reports. MKTVAL represents the market value of outstanding equity at

the beginning of the fiscal year. ERCORR represents the correlation between annual stock returns

and annual earnings for the ten years prior to the current year. We also included firms with fewer

than ten years of data, as long as they had at least four years of earnings and returns information.

STDRET represents the standard deviation of annual market-adjusted stock returns calculated

over the ten years prior to the current fiscal year. SURPRISE represents the I/B/E/S reported

actual earning per share less the I/B/E/S reported mean consensus forecast earning per share atthe beginning of the fiscal year, divided by the I/B/E/S reported price per share at the beginning

of the fiscal year. MKTRET represents annual firm return less the annual equal-weighted market

return for the fiscal year. OFFER represents an indicator variable equal to one if the firm filed a

debt or equity registration statement in the current fiscal year or in the next two fiscal years, and

zero otherwise. PERC_SLS, for non-financial companies, represents the percentage of sales

audited by the firm's auditor in the firm's two-digit SIC code as reported by Compustat.

PERC_SLS, for financial companies, represents the percentage of sales audited by the firm's

auditor in the firm's two-digit SIC code for the companies included in our sample. SLS_15, for

non-financial companies, represents an indicator variable equal to one if the firm's auditor audits

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at least fifteen percent of the sales in the firm's two-digit SIC code as reported by Compustat, and

zero otherwise. SLS_15, for financial companies, represents an indicator variable equal to one if

the company's auditor audits at least fifteen percent of the sales in the company's two digit SIC

code for companies included in our sample.

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Table 4Multivariate Analysis of AIMR Annual Score

Control Variables with Auditor Specialization Variable

Dependent Variable = AIMR Annual Score

All Industries Unregulated Industries Regulated IndustriesIndependentVariables Parameter

Estimate(T-

Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

INTERCEPT 0.315(9.67)**

0.301(9.42)**

0.334(8.52)**

0.325(8.35)**

0.291(4.98)**

0.259(4.63)**

MKTVAL 0.248(8.25)**

0.243(8.18)**

0.222(5.78)**

0.222(5.84)**

0.278(5.78)**

0.271(5.64)**

ERCORR 0.035(1.21)

0.033(1.15)

0.027(0.74)

0.029(0.82)

0.032(0.68)

0.037(0.78)

STDRET -0.072(-2.50)*

-0.070(-2.44)*

-0.082(-2.27)*

-0.079(-2.19)*

-0.078(-1.64)+

-0.075(-1.58)

SURPRISE 0.106(3.33)**

0.113(3.53)**

0.054(1.32)

0.061(1.50)

0.180(3.50)**

0.186(3.60)**

MKTRET -0.030(-0.96)

-0.033(-1.03)

-0.030(-0.74)

-0.034(-0.84)

-0.011(-0.22)

-0.013(-0.26)

OFFER 0.034(1.82)+

0.035(1.90)+

0.025(1.07)

0.025(1.10)

0.054(1.67)+

0.056(1.73)+

PERC_SLS 0.046(1.57)

0.116(3.12)**

-0.054(-1.12)

SLS_15 0.058

(3.09)**

0.096

(4.07)**

0.002

(0.08)

N 1123 1123 719 719 404 404

Adjusted R2 9.6% 10.2% 8.7% 9.5% 12.9% 12.6%

*(**) Significant at .05 (.01); + Significant at .10.We ranked all continuous independent and dependent variables within each industry and then

converted to fractions: (rank-1)/(number of firms 1). AIMR Annual Score is the sample firm's

annual report disclosure quality score within its industry as reported in the AIMR Corporate





STDRET represents the standard deviation of annual market-adjusted stock returns calculatedover the ten years prior to the current fiscal year. SURPRISE represents the I/B/E/S reported

actual earning per share less the I/B/E/S reported mean consensus forecast earning per share at

the beginning of the fiscal year, divided by the I/B/E/S reported price per share at the beginning



debt or equity registration statement in the current fiscal year or in the next two fiscal years, and

zero otherwise. PERC_SLS, for non-financial companies, represents the percentage of sales

audited by the firm's auditor in the firm's two-digit SIC code as reported by Compustat.

PERC_SLS, for financial companies, represents the percentage of sales audited by the firm's

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auditor in the firm's two-digit SIC code for the companies included in our sample. SLS_15, for

non-financial companies, represents an indicator variable equal to one if the firm's auditor audits

at least fifteen percent of the sales in the firm's two-digit SIC code as reported by Compustat, and

zero otherwise. SLS_15, for financial companies, represents an indicator variable equal to one if

the company's auditor audits at least fifteen percent of the sales in the company's two digit SIC

code for companies included in our sample.

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Table 5Multivariate Analysis of AIMR Overall Score

Control Variables with Individual Auditor and Auditor Specialization Variables

Dependent Variable = AIMR Overall Score


Estimate(T-

Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

INTERCEPT 0.300(9.48)**

0.310(10.13)**

0.217(5.01)**

0.233(5.50)**

0.391(8.38)**

0.390(8.77)**

MKTVAL 0.206(8.14)**

0.208(8.24)**

0.194(5.51)**

0.200(5.71)**

0.221(6.13)**

0.221(6.15)**

ERCORR 0.059(2.42)*

0.059(2.40)*

0.084(2.50)*

0.090(2.69)**

0.025(0.61)

0.024(0.67)

STDRET -0.025(-1.02)

-0.024(-0.10)

-0.046(-1.35)

-0.041(-1.21)

-0.005(-0.13)

-0.005(-0.14)

SURPRISE 0.072(2.68)**

0.074(2.72)**

0.047(1.24)

0.052(1.36)

0.080(2.070)*

0.078(2.03)*

MKTRET -0.003(-0.11)

-0.004(-0.15)

-0.012(-0.31)

-0.018(-0.48)

0.021(0.55)

0.021(0.56)

OFFER 0.030(1.93)+

0.029(1.89)+

0.042(1.98)*

0.038(1.81)+

0.015(0.67)

0.015(0.65)

CL -0.019(-0.65)

-0.027(-0.96)

0.063(1.57)

0.051(1.26)

-0.113(-2.74)**

-0.107(-2.63)**

EY 0.023(1.04)

0.019(0.87)

0.095(2.94)**

0.081(2.52)*

-0.031(-1.01)

-0.029(-0.95)

DT -0.014(-0.53)

-0.021(-0.77)

0.089(2.52)*

0.075(2.13)*

-0.148(-3.45)**

-0.146(-3.41)**

KPMG 0.001(0.03)

-0.008(-0.29)

0.064(1.70)+

0.057(1.52)

-0.051(-1.35)

-0.043(-1.12)

PW -0.032(-1.28)

-0.036(-1.42)

0.015(0.45)

0.006(0.18)

-0.092(-2.41)*

-0.091(-2.39)*

PERC_SLS 0.066(2.63)**

0.148(4.23)**

-0.029(-0.80)

SLS_15 0.042(2.62)**

0.099(4.53)**

-0.025(-1.04)

N 1617 1617 840 840 777 777

Adjusted R2 6.7% 6.7% 9.1% 9.4% 7.0% 7.0%*(**) Significant at .05 (.01); + Significant at .10.

We ranked all continuous independent and dependent variables within each industry and then

converted to fractions: (rank-1)/(number of firms 1). AIMR Overall Score is the sample firm's

overall disclosure quality score within its industry as reported in the AIMR Corporate





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STDRET represents the standard deviation of annual market-adjusted stock returns calculated

over the ten years prior to the current fiscal year. SURPRISE represents the I/B/E/S reported

actual earning per share less the I/B/E/S reported mean consensus forecast earning per share at

the beginning of the fiscal year, divided by the I/B/E/S reported price per share at the beginning



debt or equity registration statement in the current fiscal year or in the next two fiscal years, andzero otherwise. CL represents an indicator variable equal to one if Coopers and Lybrand, LLP

audits the company and zero otherwise. EY represents an indicator variable equal to one if Ernst

and Young LLP audits the company and zero otherwise. DT represents an indicator variable

equal to one if Deloitte and Touche LLP audits the company and zero otherwise. KPMG

represents an indicator variable equal to one if KPMG Peat Marwick LLP audits the company and

zero otherwise. PW represents an indicator variable equal to one if Price Waterhouse LLP audits

the company and zero otherwise. PERC_SLS, for non-financial companies, represents the

percentage of sales audited by the firm's auditor in the firm's two-digit SIC code as reported by

Compustat. PERC_SLS, for financial companies, represents the percentage of sales audited by

the firm's auditor in the firm's two-digit SIC code for the companies included in our sample.

SLS_15, for non-financial companies, represents an indicator variable equal to one if the firm's

auditor audits at least fifteen percent of the sales in the firm's two-digit SIC code as reported byCompustat, and zero otherwise. SLS_15, for financial companies, represents an indicator variable

equal to one if the company's auditor audits at least fifteen percent of the sales in the company's

two digit SIC code for companies included in our sample.

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Table 6Multivariate Analysis of AIMR Annual Score

Control Variables with Individual Auditor and Auditor Specialization Variables

Dependent Variable = AIMR Annual Score


Estimate(T-

Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

ParameterEstimate

(T-Statistic)

INTERCEPT 0.294(7.79)**

0.286(7.79)**

0.271(5.77)**

0.272(5.89)**

0.341(5.30)**

0.326(5.27)**

MKTVAL 0.255(8.51)**

0.253(8.52)**

0.223(5.80)**

0.225(5.93)**

0.305(6.41)**

0.301(6.36)**

ERCORR 0.037(1.28)

0.034(1.21)

0.033(0.92)*

0.036(1.01)

0.021(0.44)

0.025(0.53)

STDRET -0.063(-2.22)*

-0.060(-2.13)*

-0.071(-1.97)*

-0.066(-1.84)+

-0.081(-1.76)+

-0.082(-1.76)+

SURPRISE 0.093(2.91)**

0.099(3.12)**

0.046(1.13)

0.053(1.30)

0.150(2.94)**

0.150(2.93)**

MKTRET -0.030(-0.96)

-0.032(-1.04)

-0.029(-0.72)

-0.034(-0.84)

-0.004(-0.08)

-0.004(-0.08)

OFFER 0.035(1.89)+

0.036(1.97)*

0.028(1.21)

0.028(1.23)

0.051(1.58)

0.052(1.62)

CL -0.026(-0.75)

-0.029(-0.85)

0.046(1.04)

0.031(0.70)

-0.150(-2.73)**

-0.152(-2.74)**

EY 0.046(1.70)+

0.047(1.74)+

0.081(2.30)*

0.073(2.08)*

-0.010(-0.24)

-0.009(-0.22)

DT 0.023(0.75)

0.017(0.54)

0.073(1.91+)

0.061(1.61)

-0.056(-0.99)

-0.051(-0.90)

KPMG 0.069(2.18)*

0.063(1.99)*

0.092(2.92)*

0.084(2.13)*

0.058(1.08)

0.065(1.21)

PW -0.064(-2.23)*

-0.066(-2.30)*

-0.005(-0.14)

-0.016(-0.43)

-0.16(-3.45)**

-0.64(-3.45)**

PERC_SLS 0.066(2.24)*

0.134(3.56)**

-0.05(-0.99)

SLS_15 0.065(3.49)**

0.104(4.37)**

-0.019(-0.60)

N 1123 1123 719 719 404 404

Adjusted R2 11.1% 11.6% 9.6% 10.4% 17.0% 16.9%*(**) Significant at .05 (.01); + Significant at .10.We ranked all continuous independent and dependent variables within each industry and thenconverted to fractions: (rank-1)/(number of firms 1). AIMR Annual Score is the sample firm'sannual report disclosure quality score within its industry as reported in the AIMR CorporateInformation Committee Reports. MKTVAL represents the market value of outstanding equity atthe beginning of the fiscal year. ERCORR represents the correlation between annual stock returnsand annual earnings for the ten years prior to the current year. We also included firms with fewerthan ten years of data, as long as they had at least four years of earnings and returns information.

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STDRET represents the standard deviation of annual market-adjusted stock returns calculatedover the ten years prior to the current fiscal year. SURPRISE represents the I/B/E/S reportedactual earning per share less the I/B/E/S reported mean consensus forecast earning per share atthe beginning of the fiscal year, divided by the I/B/E/S reported price per share at the beginningof the fiscal year. MKTRET represents annual firm return less the annual equal-weighted marketreturn for the fiscal year. OFFER represents an indicator variable equal to one if the firm filed a

debt or equity registration statement in the current fiscal year or in the next two fiscal years, andzero otherwise. CL represents an indicator variable equal to one if Coopers and Lybrand, LLPaudits the company and zero otherwise. EY represents an indicator variable equal to one if Ernstand Young LLP audits the company and zero otherwise. DT represents an indicator variableequal to one if Deloitte and Touche LLP audits the company and zero otherwise. KPMGrepresents an indicator variable equal to one if KPMG Peat Marwick LLP audits the company andzero otherwise. PW represents an indicator variable equal to one if Price Waterhouse LLP auditsthe company and zero otherwise. PERC_SLS, for non-financial companies, represents thepercentage of sales audited by the firm's auditor in the firm's two-digit SIC code as reported byCompustat. PERC_SLS, for financial companies, represents the percentage of sales audited bythe firm's auditor in the firm's two-digit SIC code for the companies included in our sample.SLS_15, for non-financial companies, represents an indicator variable equal to one if the firm's

auditor audits at least fifteen percent of the sales in the firm's two-digit SIC code as reported byCompustat, and zero otherwise. SLS_15, for financial companies, represents an indicator variableequal to one if the company's auditor audits at least fifteen percent of the sales in the company'stwo digit SIC code for companies included in our sample.

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Table 7Awards for Excellence/Letters of Commendation

Panel A - All Industries

Specialist AuditorNumber(Percent)

Non-Specialist AuditorNumber(Percent)

TotalNumber(Percent)

Awards for Excellence/Letters ofCommendation

117(74.05%)

41(25.95%)

158(7.91%)

Non-Winners 1,139(61.90%)

701(38.10%)

1,840(92.09%)

n = 1,998, 2 = 9.199, df=1, p-value=.002

Panel B - Unregulated Industries

Specialist AuditorNumber(Percent)

Non-Specialist AuditorNumber(Percent)

TotalNumber(Percent)


66(82.50%)

14(17.50%)

80(7.71%)

Non-Winners 574(59.98%)

383(40.02%)

957(92.29%)

n = 1,037, 2 = 15.848, df=1, p-value=.001

Panel C - Regulated Industries

Specialist Auditor

Number(Percent)

Non-Specialist Auditor

Number(Percent)

Total

Number(Percent)


51(65.38%)

27(34.62%)

78(8.12%)

Non-Winners 565(63.98%)

318(36.02%)

883(91.88%)

n = 961, 2 = 0.061, df=1, p-value=.805

The AIMR analyst committees issue Awards for Excellence and Letters of Commendation to up

to two firms in each industry that they perceive to exhibit outstanding disclosure practices. We

test whether industry specialized auditors are associated with the Awards for Excellence and

Letters of Commendation.

For non-financial companies, an auditor is classified as a specialist if the company's auditor audits

at least fifteen percent of the sales in the firm's two-digit SIC code as reported by Compustat. For

financial companies, an auditor is classified as a specialist if the company's auditor audits at least

fifteen percent of the sales in the company's two digit SIC code for companies included in our

sample.

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Dunn & Mayhew 2000