krishnan 2000

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Auditing:

A Journal of Practice

& Theory

Vol. 19, No. 2

Fall 2000

The Differentiation of Quality among Auditors:Evidence from the Not-for-Profit Sector

Jagan Krishnan and Paul C. Schauer

SUMMARY

The association between auditor size and audit quality is examined for a sample of

not-for-profit (NFP) entities. The audit quality measure is based on the entities’ compli-

ance with eight GAAP reporting requirements. The numbers on compliance supports

policymakers’ contention (prior to the recent issuance of three new statements) that

reporting by NFPs was inconsistent. Of the eight reporting requirements examined,

noncompliance is highest for those that pertain specifically to NFPs, for example, disclo-

sures about pledges and donated materials. However, the extent of noncompliance

decreases as one moves from the small non-Big 6 to the large non-Big 6 and from the

large non-Big 6 to the then Big 6. This positive association between auditor size and

audit quality is borne out in multivariate regression analyses, after controlling for other

correlates of audit quality. Another measure of audit firm size, based on the number of

professionals employed by the firm, further confirms this finding. In addition, the results

indicate that there are other factors, i.e., client size, financial health, client wealth, and

participation in a peer-review process, that impact audit quality.

Key Words: Audit quality, Not-for-profit organizations, Voluntary health and welfareorganizations.

Data Availability: Data used in this study will be available from the second author upon

completion of a subsequent research project.

INTRODUCTION

This study examines the association be-

tween audit firm size and audit quality in

the context of an audit environment that

has not been studied by prior work, that is, the

not-for-profit (NFP) sector. The perception of a

positive association between auditor size and au-

dit quality has been a subject of concern for sometime (e.g., National Commission on Fraudulent

Financial Reporting 1987; AICPA 1993). While

regulators (AICPA 1980) have maintained that

audit quality is independent of firm size, account-

ing researchers (e.g., DeAngelo 1981b) have ar-

gued that large accounting firms produce a higher

quality audit than small accounting firms. Empiri-

cal work in auditing has generally assumed a posi-

tive association between audit quality and audit

Jagan Krishnan is an Associate Professor

at Temple University and Paul C. Schauer is

an Assistant Professor at Bowling Green State

University.

The authors would like to thank the participants at

the Temple University Accounting Research Workshop, the

1997 American Accounting Association Mid-Atlantic Re-

gional Meeting, and two anonymous reviewers for their

insightful comments.

firm size, with firm size usually measured by the

brand name of the auditor. Actual tests of the

association have been hampered by the lack of

observable measures of audit quality. Some stud-

ies use indirect measures such as audit fees or

auditor litigation or users’ perceptions about audit

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10 Auditing, Fall 2000

quality. Two recent studies use more directly ob-

servable measures, based on regulators’ assess-

ment of audits (Deis and Giroux 1992) and errorsin accounting estimates (Petroni and Beasley

1996), which are likely to be positively correlated

with elements of audit quality. However, these

studies yield different results about the associa-

tion between audit quality and auditor size.1

The not-for profit organizations provide a

unique setting for a study of audit quality, par-

ticularly for the sample period. The sample pe-

riod covers fiscal year-ends prior to June 1995,

which precedes the effective date of adoption of

three major statements pertaining to not-for-profit

entities issued by the Financial Accounting Stan-

dards Board (FASB), Statement of Financial Ac-counting Standards (SFAS) No. 116 (FASB

1993a), SFAS No. 117 (FASB 1993b), and SFAS

No. 124 (FASB 1995).2 The issuance of these

statements was in response to the increasing real-

ization that reporting by NFPs was inconsistent.

Thus Probst (1997, 24) notes:

The FASB has indicated that financial state-

ments of not-for-profit (NFP) organizations

cannot be compared because of inconsisten-

cies in reporting. Numerous examples exist

of two different organizations reporting the

same event in two different ways. The FASBcommitted itself to obtain uniformity in fi-

nancial reporting among not-for-profit enti-

ties. Such uniformity enhances comparabil-

ity, thereby providing interested parties with

information useful to evaluate organizations

competing for support.

Prior to the issuance of the new standards,

Generally Accepted Accounting Principles

(GAAP) for the NFPs were contained in a vari-

ety of official pronouncements, most of which

were not specifically identified as standards for

NFPs. The absence of specifically identified ac-

counting standards for NFPs, and the major dif-

ferences in the nature and purpose of financial

reporting between NFPs and for-profit entities,

meant that auditors played an important role in

ensuring adherence with GAAP. Moreover, the

need for specific industry knowledge coupled

with the lack of uniformly stated standards is

likely to have engendered considerable varia-

tion in audit quality prior to the issuance of the

new standards.3

The NFP setting also provides a directly

observable measure that is likely to be positively

correlated with audit quality. The agencies used

in the sample are required to present their finan-

cial statements in accordance with GAAP. As

noted, the rationale for the issuance of the new

standards was the inconsistent reporting by

NFPs. In fact, the FASB identified key areas

such as the treatment of depreciation, contribu-

tions, and investments as problematic reporting

areas (Probst 1997, 24). Since audit assurance

(and therefore audit quality) is likely to be posi-

tively associated with compliance with standards(Copley et al. 1994), variations in disclosure in

these and other important areas are likely to be

the result of variations in audit quality. Thus,

the measure of audit quality used in this study is

the extent of compliance with GAAP reporting

requirements.

This study makes two contributions to stud-

ies of the association between audit quality and

audit firm size. First, this study examines a

unique sample of entities, consisting of 164 vol-

untary health and welfare organizations. Sec-

ond, it employs a directly measurable proxy for

audit quality, a score (ranging over values 0through 8) summarizing the presence of key re-

quired disclosures in the financial statements of

these entities. Thus, by examining a hitherto un-

explored audit environment of not-for-profit or-

ganizations, this study contributes to the

generalizability of results concerning the relation

between audit quality and audit firm size. After

controlling for factors identified in previous work

as determinants of audit quality, the size of the

audit firm is found to be positively associated

1 Copley et al. (1994) use a measure of audit quality based

on quality reviews of entities participating in federalassistance programs, but do not examine the effect of auditor size on audit quality.

2 SFAS Nos. 116 and 117 were effective for financialstatements issued for fiscal years beginning after De-cember 15, 1994, except for smaller NFPs for which theeffective date was later. SFAS No. 124 was effective for fiscal years beginning after December 15, 1995.

3 For a review of the development of standards for the NFP sector, see Anthony (1995).

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Krishnan and Schauer 11

with audit quality. In addition, the results point

to other factors that have an impact on audit

quality, in particular client size, financial health,client wealth, and participation in a peer-review

process.

The remainder of this paper is organized as

follows. The next section develops the audit qual-

ity measure based on the reporting requirements

for the sample entities. This is followed by sec-

tions containing a description of the data and

model specification and the empirical results.

The final section presents the conclusions of the

study.

REPORTING REQUIREMENTS FOR

SAMPLE ENTITIES AND THE MEA-SURE OF AUDIT QUALITY

The NFPs examined in this study are termed

Voluntary Health and Welfare Organizations

(VHWOs), which include NFPs that “derive their

revenue primarily from voluntary contributions

from the general public to be used for general or

specific purposes connected with health, wel-

fare, or community services” (AICPA 1992, v).

Details about the data collection for these enti-

ties are presented in the next section. Fiscal year-

ends for these entities range over June 1994 to

June 1995, and precedes the issuance of three

recent FASB pronouncements on not-for-profitorganizations. GAAP for not-for-profit entities

for the sample period are found in FASB state-

ments SFAS No. 93 (FASB 1987) and SFAS

No. 99 (FASB 1988), although other pronounce-

ments that did not specifically discuss not-for-

profit organizations were also relevant. Specific

guidelines for the industry are set out in the

AICPA guide Audits of Voluntary Health and

Welfare Organizations (AICPA 1992).4 These

guidelines cover the following issues: the valua-

tion of investments, the valuation of fixed as-

sets, the recording of cash donations and pledges,

the recording of donated materials and services,

presentation of statement of functional expenses,

and the format of the financial statements and

the auditor’s report. Guidelines are provided both

for the recording and the disclosure of account-

ing information. Because the financial statements

provide evidence primarily of the disclosure

function, the focus here is on compliance with

the disclosure requirements. Table 1 describes

the major accounting issues and the correspond-ing correct accounting disclosures required of

VHWOs. While some issues listed are relevant

to other kinds of entities as well (for example,

the valuation of fixed assets and investments),

others such as the proper disclosure of pledges,

donated materials and services, functional ex-

penses, and the format of financial statements

are peculiar to NFPs and require specific audi-

tor expertise in this area.

Audit Quality Measure

Audit quality is defined as the probability

an auditor will both discover and report a breachin the client’s accounting system (DeAngelo

1981a). Because both aspects of the probability

are unobserved, researchers have taken one of

two approaches to measuring audit quality in

empirical work. The first approach is an indirect

one, and looks at correlates of audit quality, such

as audit fees, auditor litigation, and users’ per-

ceptions of quality. A number of studies (e.g.,

Craswell et al. 1995) report finding that the Big

6 accounting firms charge a premium for their

services, and infer that this could be due to higher

quality of audits.5 Palmrose (1988) examines

auditor litigation as a measure of audit qualityand reports significantly higher rates of litiga-

tion for non-Big 8 auditors as compared to Big

8 auditors. However, she reports finding no sig-

nificant difference between Big 8 and non-Big 8

auditors in the type of resolution.

The second set of studies takes the more di-

rect approach that the probability of discovery

4 Another industry accounting guide, Standards of Ac-counting and Financial Reporting for Voluntary Healthand Welfare Organizations, prepared by the NationalHealth Council and the National Social Welfare Assem-

bly, sets forth principles compatible with the AICPAguide (AICPA 1992, v).

5 Other studies that report similar results include Ander-son and Zeghal (1994), Francis (1984), Francis andSimon (1987), Francis and Stokes (1986), and Palmrose(1986) in the private sector, and Baber et al. (1987),Rubin (1988) and Ward et al. (1994) in the governmentsector. Studies that report lower underpricing for initial

public offerings audited by the Big 6 include Balvers etal. (1988) and Elder (1994).

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TABLE 1

Disclosure Requirements for Voluntary Health and Welfare Organizations

Accounting Issue Required GAAP Treatment

(1) Disclosure whether the investments are carried at cost or

market; (2) if investments are carried at cost (market), disclo-

sure about market (cost); and (3) disclosure of unrealized appre-

ciation (or depreciation).

Disclosure concerning the basis for valuation of fixed assets.

Disclosure of (1) depreciation expense; (2) balances of major

depreciable assets, by nature or function; (3) accumulated de-

preciation; and (4) methods used for computing depreciation.

Compliance with the reporting requirements of Statement on

Auditing Standards No. 58 (AICPA 1988).

Disclosure about allowance for uncollectible pledges and netamount of pledges receivable.

Disclosure about (1) value of materials and the basis of valua-

tion; and (2) methods followed in evaluating, recording and

reporting donated services making a distinction between do-

nated services that have been recorded and those that have not

been recorded.

Disclosure about (1) functional expenditures: e.g., program ex-

penditures and other expenditures; (2) description of functions;

and (3) statement of functional expenses.

Required number of statements and the statements presented in

recommended formats.

Source: Statement of Financial Accounting Standards No. 93 (FASB 1987) and 99 (FASB 1988), and

AICPA guide Audits of Voluntary Health and Welfare Organizations (AICPA 1992).

1. Investments

2. Fixed Assets Valuation

3. Fixed Assets Depreciation

4. Format of Auditor’s Report

5. Pledges

6. Donated Material and Services

7. Program and Supporting Services

8. Format of Financial Statements

and reporting of breaches would be reflected in

certain features of the audit, such as in the audit

work of auditors, the errors made by auditors,

or compliance with Generally Accepted Audit-

ing Standards. Deis and Giroux (1992) use as-

sessments of auditors’ working papers by the

controlling government agency as the audit qual-

ity proxy for a study of audits of school districts

submitted to the Texas Education Agency. Theyreport a positive association between audit qual-

ity and the size of the audit firm. Hardiman et al.

(1987, 30) report a higher occurrence of non-

compliance with standards for smaller firms than

larger firms in an analysis of 120 audits received

by the GAO in 1984. Petroni and Beasley (1996)

examine errors in accounting estimates of claim

loss reserves for a sample of insurance compa-

nies. They report finding no significant differ-

ence in the claim reserve estimates for Big 6

and non-Big 6 firms, except for financially

troubled insurers, for which the Big 6 auditors

show more conservative estimates than non-Big

6 auditors.6

This study adopts the approach in the sec-

ond set of studies, but looks at a different aspectof the audit process, the extent to which the au-

dits of the VHWOs results in reporting that is in

6 However, Petroni and Beasley (1996) find that the rela-tively more conservative reserve estimates for bigger auditors arise when estimation errors are measured rela-tive to total assets, but not when expressed in material-ity units.

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compliance with GAAP . As was noted earlier,

GAAP for these entities was (prior to the issu-

ance of the new standards) spread out over avariety of official pronouncements and probably

required specific auditor expertise for their cor-

rect application. Since GAAP is the standard

used to identify breaches in a client’s account-

ing system, the extent of compliance with GAAP

is likely to be directly correlated with the prob-

ability of discovering and reporting a breach in

the accounting system, or audit quality. A simi-

lar argument is made by Copley et al. (1994,

248) for their use of official reviews of compli-

ance with professional standards: “[i]f a posi-

tive relation exists between the level of audit

assurance and the propensity to be in compli-ance with professional standards, then audit firms

specializing in lower quality levels will exhibit

higher frequencies of non-compliance with pro-

fessional standards than firms specializing in

higher quality levels.”7

The audit quality measure (AUDQUAL) is

based on the entities’ compliance with the dis-

closure requirements listed in Table 1. Accord-

ingly, the financial statements of the sample en-

tities were examined to ascertain whether the

required accounting disclosure was made in eight

areas. Eight indicator variables were constructed

to indicate the presence (1) or absence (0) of the proper accounting disclosures for each of the

eight areas below (see Table 1 for descriptions

of required disclosures):

1. Proper disclosure about investments (In-

dicator variable I1).

2. Proper disclosure about valuation of

fixed assets (I2).

3. Proper disclosure about depreciation of

fixed assets (I3).

4. Proper form of audit report (I4).

5. Proper disclosure about cash donationsand pledges (I

5).

6. Proper disclosure about donated materi-

als and services (I6).

7. Proper presentation of statement of func-

tional expenses (I7).

8. Balance sheet and other statements pre-

sented in appropriate format (I8).

“Proper” accounting treatment can indicatethat a particular condition was present and re-

ceived the correct accounting treatment or the

absence of a particular condition. For example,

if entity A had investments reported on the bal-

ance sheet but did not make the required disclo-

sures, entity A was coded 0 for I1. If entity B

had investments and made the proper disclo-

sures, it was treated as a correct accounting treat-

ment and coded 1 for I1. If entity C had no

investments, and therefore had no disclosures,

it was treated as a correct accounting treatment

and coded 1 for I1.8 Audit quality (AUDQUAL),

defined as the sum of the eight indicator vari-ables, is a score indicating the extent of compli-

ance in the eight areas. Thus, the audit quality

for an entity can vary between 0 and 8, with a 0

indicating the lowest audit quality and an 8 indi-

cating the highest audit quality. It is important

to emphasize that the measure does not capture

the actual number of disclosures. Consider two

entities, A and B, with AUDQUAL scores of 6

and 8, respectively. The numbers do not indi-

cate the entity A has 6 disclosures and entity B

has 8 disclosures. Instead, they indicate that en-

tity A has accorded the correct accounting treat-

ment in 6 out of the 8 areas, and entity B hasaccorded the correct accounting treatment in all

8 areas. The actual number of disclosures made

by both entities depends on whether each dis-

closure is applicable, and may be less than their

AUDQUAL scores.

DATA AND MODEL SPECIFICATION

The data consists of the audited financial

7 Despite the fact that these measures (in Copley et al.[1994], and Petroni and Beasley [1996], as well as thatused in this study) are relatively more “direct” than

previous measures of audit quality such as audit fees or audit litigation, it should be noted that these measuresare also essentially indirect because the two elements inDeAngelo’s (1981a) definition of audit quality are notobserved.

8 Other areas were coded similarly. If an entity did notreport fixed assets, it was assumed that it did not havefixed assets, and I

2 and I

3 were coded 1.

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statements of Voluntary Health and Welfare Or-

ganizations requesting or receiving funding from

six chapters of the United Way located in South-eastern Pennsylvania and Southern New Jersey.9

For five of the six chapters, all of the audits sub-

mitted to the chapter for the current funding cycle

were included in the sample.10 A time constraint

placed on the data-collection process by the sixth

chapter prevented obtaining all audits for the cur-

rent funding cycle. In this chapter, all audits per-

formed by the Big 6 and a random sample of

other audits were obtained. The six United Way

chapters yielded 164 unique observations.11

The association between auditor size and

audit quality is examined by estimating regres-

sion models with AUDQUAL as the dependentvariable. Independent variables include auditor

size and control variables. The control variables

are those that prior work has identified as deter-

minants of audit quality. Two measures of audit

firm size are used in the study. The first mea-

sure is based on a three-tier classification of

auditors: Big 6, (large) non-Big 6 with more

than ten professionals, and other (small) non-

Big 6 firms with ten or less professionals.12

Dummy variables for Big 6 (BIGSIX) and large

non-Big 6 with more than ten professionals

(LNBIGSIX) are used in the model to segment

the audit firm population. The second measureis the number of professionals in the local office

of the audit firm (AUDSIZE).

The following variables identified in prior

work as determinants of audit quality are included

as controls in the empirical analyses: client size,

financial health, client wealth, the presence or

absence of peer review, and auditor tenure. The

arguments for inclusion of one or more of these

variables (discussed below) pertain mainly to the

for-profit sector, and may be only partly appli-

cable to the NFP entities examined here. Never-

theless they are included as controls to ensure

robustness in the basic results regarding the im-

pact of auditor size on audit quality.

Client size is hypothesized to influence au-

dit quality in several ways. First, some studies

have argued that audit quality is positively asso-

ciated with client size (Simunic and Stein 1987),

because client size is posited to be positively

associated with agency costs.13 Second, large

clients may be associated with lower audit qual-

ity because they are likely to have “complex

transactions that require transaction-specificknowledge” (O’Keefe et al. 1994, 48). Third,

client size is hypothesized to be negatively as-

sociated with audit quality if such clients are

able to pressure the auditor into resolving any

audit differences in the client’s favor (e.g., Deis

and Giroux 1992). Because of the conflicting

predictions, no sign is predicted for the size vari-

able.14 Following prior work (O’Keefe et al.

1994), the natural log of annual revenue

(CLSIZE) is used as the client size measure,

because it is more appropriate for not-for-profit

entities than are the assets used in studies of for-

profit companies.15 Revenue is defined as con-tributions, interest income, funds from local, state

and federal government, United Way funds, and

revenues from other sources.

Financial condition has also been posited

in prior work to be associated with audit qual-

ity, but its effect is ambiguous. On the one hand,

9 The data was hand-collected during visits to each UnitedWay office. Special permission was obtained from themanagement of each United Way Chapter to collect thedata, since such financial statements are not publiclyavailable. It was agreed that the identity of the VHWOsand their auditors would be kept confidential.

10 An audit is not required by the United Way in general.

However, all of the chapters included in this study re-quired an audit for the entity to be considered for fund-ing. Also, all of the entities included in this sample werethose that did receive funding from the United Way.

11 Several agencies were funded by more than one of thechapters of the United Way. Duplicate observations wereeliminated.

12 Information on the number of professionals and participation in the peer-review process was obtained from thelocal offices of the audit firms.

13 Agency costs are usually expected to arise in the contextof publicly traded companies due to conflict betweendebtholders/stockholders and management. While thisis not directly applicable in the NFP context, NFPs oper-ate through delegation of responsibility to management

by contributors, and to the extent that management’sconsumption of perquisites is a function of entity size,

the demand for audit quality may increase with entitysize.

14 Client size may also control for factors such as clientcomplexity (Copley et al. 1994) that may be correlatedwith both auditor choice and pre-audit financial state-ment quality. Other researchers (Beasley and Petroni1998) use more precise measures such as the Herfindahlconcentration index, but such measures were not avail-able for the current sample.

15 Sensitivity analysis was conducted using log (assets) in place of CLSIZE. The results are reported later.

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it is expected to have a negative effect on audit

quality because, it is argued, better financial

health can give a client more bargaining power vis-à-vis the auditor in resolving audit conflicts

(Deis and Giroux 1992). On the other hand, fi-

nancial distress has been shown to be associ-

ated with “window dressing” by management

(Kinney and McDaniel 1989) and therefore is

likely to lead to misleading pre-audit financial

statements, increasing the likelihood of audit fail-

ure. By this argument, audit assurance and audit

quality may be positively associated with finan-

cial health. Financial condition is proxied by

FINSTAB, defined as the entity’s days cash and

investments. It is calculated by dividing the to-

tal of cash and investments by the total annualexpenses and multiplying that quotient by 365.

Because it measures the ability of an entity to

continue providing its service if its funding source

is eliminated, this measure is the equivalent of

measures of the probability of bankruptcy for for-

profit companies. No sign is predicted for

FINSTAB.

Deis and Giroux (1992) report a negative

effect of client wealth on audit quality. They

argue that, like client size, client wealth may be

positively associated with the client’s ability to

prevail over the auditor in the event of an audit

conflict. The auditor may seek to avoid suchconflict by lowering the quality of the audit.

WEALTH is defined as the entity’s operating

fund balance divided by its annual revenue. The

fund balance is the equivalent of net assets in a

for-profit balance sheet, so this variable cap-

tures the entity’s net assets relative to its size.

WEALTH is predicted to have a negative sign.

Peer review (PEER) is a dummy variable

indicating that the audit firm participates in an

AICPA peer-review process. The AICPA be-

lieves peer reviews are an important means to

assure the high level of audit quality demanded

by users of financial statements (White et al.

1988). Deis and Giroux (1992) report a positive

effect of peer review on their audit quality mea-

sure. The coefficient of PEER is expected to be

positive.

Auditor tenure is hypothesized in some prior

work to be negatively associated with audit qual-

ity. DeAngelo (1981a) argues that an auditor

may lower quality beyond the initial engage-

ment year in order to protect its quasi-rents. Oth-

ers (for example, Shockley 1981) argue that asaudit tenure increases, prior experience with the

client may affect the objective assessment of the

client. Deis and Giroux’s (1992) results indicate

a negative association between auditor tenure

and audit quality. However, other research (St.

Pierre and Anderson 1984) has argued that the

auditor’s client-specific knowledge increases

with tenure, thus increasing audit quality. Fol-

lowing Stice (1991), TENURE is defined as a

dummy variable indicating that the firm has au-

dited the client for at least three years.16 No sign

is predicted for TENURE.

There were a number of other variablesincluded in the models developed by previous

researchers that were not appropriate in the cir-

cumstances. The Appendix provides a compari-

son of these models.

To summarize, the regression models esti-

mated are:

Model 1

AUDQUAL = α1+α

2(BIGSIX) +α

3(LNBIGSIX)

+ α4(CLSIZE) +α5(FINSTAB)

+ α6(WEALTH) + α

7(TENURE)

+ α8(PEER) + ε1 (1)

Model 2

AUDQUAL = β1 +β2(AUDSIZE) +β3 (CLSIZE)

+β4(FINSTAB) + β5

(WEALTH)

+ β6(TENURE) + β7(PEER)

+ ε2 (2)

where:

AUDQUAL = the measure of audit quality rang-

ing from 0 to 8;

BIGSIX = 1 if the auditor is a Big 6 firm, 0

otherwise;

LNBIGSIX = 1 if the auditor is a large non-Big 6

firm (i.e., with more than ten pro-

fessionals), 0 otherwise;

AUDSIZE = the number of professionals in the

local office of the audit firm;

CLSIZE = natural log of revenue;

16 Because of unavailability of sufficient past informationfor a greater number of years, it was not possible tocollect information on the actual tenure of the auditor.

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FINSTAB = days cash and investments;

WEALTH = fund balance divided by revenue;

TENURE = 1 if auditor tenure is three or moreyears, 0 otherwise;

PEER = 1 if auditor participates in a peer

review process, 0 otherwise; and

ε1

and ε2

= the error terms with normal

distributions.

EMPIRICAL RESULTS

The distribution of the variables that measure

audit firm size are presented in Table 2. The sample

consists of 35 clients of Big 6 firms, 65 clients of

non-Big 6 firms with more than 10 professionals,

and 64 clients of smaller non-Big 6 firms with 10

or less professionals. Looking at audit firm size interms of numbers of professionals employed by

the firm, the average firm size is 121. There is

considerable variation in firm size, ranging from

1 to 1,000 professionals. Not surprisingly, the av-

erage firm size increases as one moves from the

small non-Big 6 to the Big 6.

Table 3 indicates the breakdown of the ac-

counting treatment in each of the eight areas

that comprise the audit quality measure, for the

full sample and for each of the three auditor

type sub-samples. These numbers provide sup-

port for the concerns raised by policymakers

about inconsistent reporting by NFPs. The areasin which the compliance is lowest are those pe-

culiar to not-for-profit entities: disclosures in

the areas of pledges (I5), donated materials and

services (I6), and functional expenses reporting

(I7). This is true for the full sample as well as for

each auditor type. All of the sample organiza-

tions had received pledges. As Table 1 indi-cates, entities are required to disclose an allow-

ance for uncollectible pledges, and the net

receivable value of pledges. Table 3 shows that

73 percent (100 – 27) of the sample (see I5) did

not contain this information. Similarly, for do-

nated materials and donated services (I6), nearly

45 percent of this sample did not contain the

required information about the methods of re-

cording the amounts. The requirement that enti-

ties report functional expenses was violated by

15 percent of the sample entities. For more “tra-

ditional” accounting items, such as investments

(I1), valuation of fixed assets (I2), depreciationof fixed assets (I

3), and form of audit report (I

4),

the extent of noncompliance is small, ranging

between 2 and 7 percent. The extent of noncom-

pliance in the format of the financial statements

(I8) is about 4 percent. The numbers for each

auditor type indicate an increase in the extent of

compliance as one moves from the smaller non-

Big 6 firms to the larger non-Big 6 firms, and

from the larger non-Big 6 firms to the Big 6

firms. Chi-squared tests (reported in the last col-

umn of Table 3) that test the null hypothesis of

independence of auditor type and the extent of

compliance reject the null (at 10 percent level

or less) in 4 of the 8 categories.

Table 4 presents the distribution of

AUDQUAL for the full sample, and for the three

subsamples. The average audit quality for the

TABLE 2

Distribution of Audit Firm Size

Number of Audit Firm Sizea

Audit Firm Type Observations Mean Range

Small Non-Big 6 b 64 3.77 1–10

Large Non-Big 6 65 41.14 12–350

Big 6 35 482.66 150–1,000

Total 164 120.80 1–1,000

a Number of professionals in the local office of the audit firm. b Ten or less professionals.

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TABLE 3

Components of Audit Quality (AUDQUAL)

Sample in Compliance

Full Small Large

Indicator Sample Non-Big 6 Non-Big 6 Big 6 Chi-squaredb

Variablea # % # % # % # % (p-value)

I1

153 93.29 56 87.50 62 95.38 35 100.00 6.403 (0.041)

I2

158 96.34 58 90.63 65 100.00 35 100.00 9.731 (0.008)

I3

161 98.17 62 96.88 64 98.46 35 100.00 1.281 (0.527)

I4

162 98.78 62 96.88 65 100.00 35 100.00 3.164 (0.206)

I5

45 27.44 8 12.50 14 21.54 23 65.71 34.064 (0.001)

I6

91 55.49 31 48.44 39 60.00 21 60.00 2.112 (0.348)

I7

139 84.76 49 76.56 56 86.15 34 97.14 7.580 (0.023)

I8

157 95.73 59 92.19 63 96.92 35 100.00 3.754 (0.153)

n 164 64 65 35

a The indicator variables correspond to the items in Table 1. They are coded 1 if the entity's financial statements contain proper disclosure, 0 otherwise:

I1 = proper disclosure about investments;

I2 = proper disclosure about valuation of fixed assets;

I3 = proper disclosure about depreciation of fixed assets;

I4 = proper form of audit report;

I5 = proper disclosure about cash donations and pledges;

I6 = proper disclosure about donated materials and services;

I7 = proper presentation of statement of functional expenses;

I8 = balance sheet and other statements presented in appropriate format;

b Tests for independence of auditor type and extent of compliance.

sample is 6.5. The variable value ranges be-

tween 3 and 8. In aggregate, the extent of com-

pliance is fairly high; 83 percent of the sample

accords the correct accounting treatment to at

least six items. Looking at the distribution of

AUDQUAL for each subsample, once again the

extent of compliance increases as one moves

from the small non-Big 6 to the Big 6 clients.

The percentage of the small non-Big 6 with 6 or

more items of compliance is 67.2, while the cor-

responding percentages for the large non-Big 6

and the Big 6 are 90.8 and 100, respectively. The

Chi-squared statistic (not shown in the table) to

test for independence of AUDQUAL and audi-

tor type rejects the null hypothesis that there is

no relation, with a p-value of .001.

For the auditor size measure (using the

number of professionals), the univariate rela-

tion between auditor size and audit quality is

examined by looking at the correlation between

AUDSIZE and AUDQUAL. However, while this

correlation (0.32) is significantly different from

zero indicating a positive association between

audit quality and auditor size, the magnitude is

not very high.

Both Tables 2 and 3 indicate a positive as-

sociation between auditor size and audit qual-

ity. However, differences in audit quality across

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TABLE 4

Frequency Distribution of Audit Quality Variable

Frequency

Full Sample Small Non-Big 6 Large Non-Big 6 Big 6

AUDQUALa # % # % # % # %

0 0 0.0 0 0.0 0 0.0 0 0.0

1 0 0.0 0 0.0 0 0.0 0 0.0

2 0 0.0 0 0.0 0 0.0 0 0.0

3 1 0.6 1 1.6 0 0.0 0 0.0

4 5 3.0 4 6.2 1 1.5 0 0.0

5 21 12.8 16 25.0 5 7.7 0 0.0

6 46 28.1 20 31.3 21 32.3 5 14.3

7 66 40.3 18 28.1 31 47.7 17 48.6

8 25 15.2 5 7.8 7 10.8 13 37.1

Total 164 100.0 64 100.0 65 100.0 35 100.0Mean 6.50 6.02 6.58 7.23

a AUDQUAL is defined as the sum of eight indicator variables, I1 – I

8. See Table 3 for definitions of the indicator variables.

the auditor types can reflect differences in client

characteristics arising from self-selection by cli-

ents, rather than innate differences in auditor

expertise.17 The multivariate analysis reported

later is designed to control for some of these cli-

ent characteristics. To investigate if clients differ

significantly across auditor types, Table 5 pre-

sents comparisons of some client characteristicsother than those included in the regressions: cli-

ent revenues, assets, fund balances, and ratios of

investments and fixed assets to total assets. Be-

cause the distributions of some variables are highly

skewed, tests of differences of both means and

medians are presented. The numbers indicate sig-

nificant differences between the small non-Big 6

and the Big 6 in four out of the five characteris-

tics presented. The large non-Big 6 and the Big 6

differ on revenues, total assets, and the ratio of

investments to total assets. The small and large

non-Big 6 groups differ only on revenues. In gen-

eral, the results suggest the need to control for size differences across the three groups, and to

examine the possibility that the small non-Big 6

have a clientele that is very different from that of

the Big 6. The first concern is taken care of in the

regression by including a control for client size.

The second concern is addressed by estimating

regressions (in sensitivity analyses) after elimi-

nating the small non-Big 6 group.18

Multivariate regression analyses support the

positive association between auditor size and

audit quality reported in the univariate numbers

in Tables 3 and 4. The regressions were esti-

mated after eliminating outliers, defined as ob-

servations with absolute studentized residuals

greater than 2.19 Descriptive statistics for the

independent variables are presented in Table 6,and the regression estimates in Table 7.20 Both

models in Table 7 have significant F-statistics

with p-values less than .001. Model 1 has some-

what greater explanatory power than model 2,

with an adjusted R 2 of 0.34 compared to 0.29

for model 2. Both models support the hypoth-

esis that audit quality is positively associated

17 For example, greater accounting sophistication may re-sult in better pre-audit financial statements. Accountingsophistication may also increase the likelihood that aBig 6 auditor is hired, as the client may be more com-fortable with a Big 6 auditor. Therefore, the quality of disclosures may be due to client accounting sophistica-tion but correlated with Big 6 audits.

18 For the continuous auditor size measure, examination of the correlations between AUDSIZE and the client char-acteristics in Table 5 revealed low numbers, rangingfrom –0.07 to 0.28.

19 Nine (ten) observations were eliminated by this processin model 1 (2). Inclusion of the outliers resulted in lower adjusted R 2s, but left the results unchanged.

20 The descriptive statistics are for the 155 observationsused for the model 1 regression. The numbers for the154 observations in model 2 were very similar, and there-fore not reported.

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TABLE 5

Sample Profile, by Auditor Type

Absolute t-statistics/W

for Differences betSmall Non-Big 6 Large

Small Large and Non-Big 6

Variable Non-Big 6 Non-Big 6 Big 6 Large Non-Big 6 and Big 6

Total Revenues b Mean 1267.24 1844.76 19981.04 1.336 2.107**

Median 635.77 949.08 2757.55 1.722* 4.141***

Total Assets b Mean 1337.43 1604.61 32307.27 0.617 1.945*

Median 344.44 672.56 2837.20 1.496 4.228***

Fund Balance b Mean 386.13 433.64 9156.59 0.245 1.607

Median 63.82 107.33 320.42 1.213 1.344

Investments/ Mean 0.06 0.05 0.13 0.342 2.684***

Total Assets Median 0.00 0.00 0.04 0.489 2.943***

Fixed Assets/ Mean 0.34 0.34 0.32 0.038 0.322

Total Assets Median 0.32 0.33 0.30 0.102 0.307n = 164

*, **, ***Significant at the .10, .05, and .01 levels, respectively, two-tailed test.a t-statistics tests differences in means. Wilcoxon Z tests differences in medians. b In thousands.

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TABLE 7

Results of Regression Analysis of the Audit Quality Models

Model 1 Model 2

Dummy Variables Used to Audit Firm Size Defined as

Specify Three Tiers of Audit Number of Professionals in

Expected Firm Size Audit Firm

Sign Coefficient (t-statistic) Coefficient (t-statistic)

Intercept 3.867 4.008

(6.047) (6.310)BIGSIX + 0.796***

(3.858)

LNBIGSIX + 0.556***

(3.415)

AUDSIZE + 0.001***

(2.395)

CLSIZE ? 0.143*** 0.144***

(3.143) (3.169)

FINSTAB ? 0.001*** 0.001***

(4.213) (4.133)WEALTH – –0.265*** –0.290***

(–2.952) (–3.181)

TENURE ? 0.099 –0.017

(0.712) (–0.121)

PEER + 0.211 0.541***

(1.155) (3.259)

F-Value 12.390 11.210

(p-value) (0.0001) (0.0001)Adjusted R 2 0.341 0.286

n 155 154

*, **, *** Significant at the .10, .05, .01 levels, respectively, one-tailed test where signs are predicted, two-tailed otherwise.The dependent variable is AUDQUAL defined as the sum of eight indicator variables, I

1 –I

8. See Table 3 for definitions of

indicator variables and Table 6 for definitions of independent variables.

TABLE 6

Descriptive Statistics for the Independent Variables

Standard

Mean Deviation Minimum Maximum

BIGSIX 0.219 0.415 0 1

LNBIGSIX 0.406 0.493 0 1

AUDSIZE 120.613 227.699 1 1000

CLSIZE 13.817 1.549 9.213 19.832

FINSTAB 133.458 239.359 0 1850WEALTH 0.366 0.771 –1.118 5.799

TENURE 0.665 0.473 0 1

PEER 0.800 0.401 0 1

n = 155

BIGSIX = 1 if the auditor is a Big 6 firm, 0 otherwise;LNBIGSIX = 1 if the auditor is a large non-Big 6 firm (i.e., with more than ten professionals), 0 otherwise;AUDSIZE = the number of professionals in the local office of the audit firm;

CLSIZE = natural log of revenue;FINSTAB = days cash and investments;WEALTH = fund balance divided by revenue;TENURE = 1 if auditor tenure is three or more years, 0 otherwise; and

PEER = 1 if auditor participates in a peer-review process, 0 otherwise.

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with the size of the audit firm, after controlling

for other correlates of audit quality.21 The coef-

ficients of BIGSIX, LNBIGSIX in model 1, andAUDSIZE in model 2, are all significant at the .01

level. In addition, Table 7 indicates that several of

the control variables are significant. CLSIZE has

a significant positive coefficient, suggesting that

a larger client is associated with a higher audit

quality. The client’s financial condition

FINSTAB and WEALTH are also significant at

the .01 level. Finally, participation in a peer-

review process is associated with a higher level

of audit quality in model 2 but not in model 1.

Sensitivity Tests

Several sensitivity analyses were conductedto confirm the basic findings.

Materiality

As described earlier, the audit quality com-

ponents I1 through I

8 treat the lack of a disclo-

sure as the correct policy if a particular condi-

tion requiring a disclosure is not present (for

example, an entity has no investments and so

does not need any disclosures). An audit quality

indicator is coded 0 (indicating noncompliance)

only when a particular condition is present (for

example, an entity has some investments) but

the required disclosures are not present. How-ever, one reason some disclosures may not have

occurred may be that the amounts, although posi-

tive, were not considered material by the audi-

tor. If this were true, the audit quality variable

could merely be reflecting the degree of materi-

ality in the underlying numbers. Note that, while

materiality could certainly be important, one

would expect it to be randomly distributed over

the different quality components. It is difficult

to explain the differential pattern of compliance

noted in Table 3 in terms of materiality. Why do

these entities display less compliance with is-

sues concerning pledges (particularly when they

were the primary source of funding for all of

these entities), than with investments or fixed

assets valuation? Note also that, if materiality

were the sole factor behind the disclosures, there

would be no reason to expect a positive associa-

tion between audit quality and auditor size, un-

less materiality thresholds are inversely related

to auditor size. However, as noted earlier (see

footnote 6), recent evidence (Petroni and Beasley

1996, 152) suggests no difference in the conser-

vatism of large and small audit firms when “es-timation error is stated in materiality units.”

Since one cannot observe auditors’ material-

ity thresholds, a rough test of the materiality issue

can be a comparison of the relevant amounts for

entities that do and do not make the required

disclosures. Materiality standards may be relevant

for the following quality components: investments,

fixed assets valuation and depreciation, pledges,

and donated materials and services. Unfortunately,

the data on the amounts is only available for in-

vestments and fixed assets. However, note that

pledges were the major source of funding for these

entities, and therefore the amounts were prob-ably material in all cases. To examine the possi-

bility that the entities not making the investment

(I1) and fixed assets (I

2 and I

3) disclosures did

not do so because the relevant amounts were im-

material, Wilcoxon tests were conducted to com-

pare the investment amounts and fixed assets

amounts as proportions of assets, for complying

and noncomplying entities. The results indicate

that, for entities complying and not complying

with the fixed assets disclosures there was no

significant difference (p-value = 0.64) in the fixed

assets/total assets ratio. Similarly, for entities com-

plying and not complying with investment dis-

closures, there was no significant difference in

the investment/total assets ratio (p-value = 0.44).22

Further sensitivity tests were conducted by

21 Pearson correlation coefficients (not presented) revealonly two correlations above 0.4: between auditor sizeand client size and between financial stability and wealth.However, the regression results (reported later) remainunchanged when wealth and client size are dropped (inturn) from the estimation models.

22 As noted earlier, the audit-quality measure is based onthe assumption that, if an entity does not report a par-ticular item, for example fixed assets, it is assumed thatthe entity does not have fixed assets. This assumes inessence that the completeness assertion is true for eachitem. Sensitivity tests for this assumption were conducted

by estimating the models after dropping observations for which (1) investments were zero and (2) fixed assetswere zero. The results are qualitatively similar to thosereported in Table 7 with the exception of the SIZE vari-able in model 2, which is insignificant. In another sensi-tivity test, items with zero investments and fixed assetswere assumed to be treated incorrectly (instead of thecurrent assumption of correct treatment) and the modelswere reestimated. The results are qualitatively similar tothose reported in Table 7.

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dropping each audit-quality component in turn

from the construction of AUDQUAL, and then

estimating the regression equations. Theestimates (not reported) indicate that the results

with respect to auditor size are very robust to

changes in the dependent variable. In all cases,

the auditor size variables are significant. The

results for the other variables are very similar to

those in Table 7, and hold consistently across

the models.

Cli ent Size

Because client size is a potentially strong

confounding factor, the client size variable was

replaced by a more conventional measure of cli-

ent size, natural logarithm of assets, althoughthis may be less applicable to NFPs. The results

remain unchanged.

Sensitivity to Exclusion of One Auditor Type

Differences in the types of clientele serviced

by the different auditors could potentially bias

the results reported if some of these unobserved

client characteristics form omitted variables in

the models specified. To test for such possibili-

ties, the models were estimated after eliminat-

ing, in turn, the small non-Big 6 observations,

and the Big 6 observations. All the results re-

ported in Table 7 continue to hold in all cases.

Al ternative Models

One source of misspecification in the models

reported arises due to the categorical nature of the

dependent variable, and the relatively small range

of values it takes. To test for its effect, probit

models were estimated. The dependent variable

was defined as a 0/1 indicator variable, using two

different cutoffs of AUDQUAL. The first (sec-

ond) model defined the dependent variable as 1, if

AUDQUAL ≥ 7 (= 8), 0 otherwise. The results

were similar to the regression results.

CONCLUSIONS

The association between auditor size and

audit quality was examined for a sample of not-

for-profit entities. The audit quality measure is

based on the entity’s compliance with GAAP re-

porting requirements. The sample period precedes

the adoption date of three recent FASB statements

pertaining to NFPs. Examination of the compli-

ance with GAAP supports the FASB’s conten-

tion (prior to the issuance of the new statements)that reporting by NFPs was inconsistent. Of the

eight reporting requirements examined, noncom-

pliance is highest for those that pertain particu-

larly to NFPs. These include disclosures about

pledges and donated materials. However, the ex-

tent of noncompliance differs across auditor types.

Auditors are divided into three groups: Big 6, large

non-Big 6, and small non-Big 6. The extent of

compliance increases as one moves from the small

non-Big 6 to the large non-Big 6 and from the

large non-Big 6 to the Big 6. This positive asso-

ciation between auditor size and audit quality is

borne out in multivariate regression analyses, af-ter controlling for other correlates of audit qual-

ity. Another measure of audit firm size, based on

the number of professionals employed by the firm,

further confirms this finding. In addition to the

effect of audit firm size, the results also indicate

other factors that impact audit quality. In particu-

lar, client size and financial health impact posi-

tively on audit quality, while client wealth has a

negative impact on audit quality. The audit firm’s

participation in a peer-review process has a posi-

tive impact on audit quality in majority of the

models that were estimated.

To the extent that the audit-quality model usedcaptures important correlates of audit quality, the

results of this study provide some support for the

existence of a positive association between audi-

tor size and audit quality. However, as in all stud-

ies of this nature, omitted factors that are corre-

lated both with the extent of disclosures and the

likelihood of hiring a certain type of auditor can

provide alternative explanations for the result. For

example, the extent of client complexity and the

extent of client accounting sophistication may be

positively associated both with the likelihood of

hiring a larger auditor and the likelihood of pre-

paring better quality financial statements. While

variables such as client size may control for such

factors, it is not clear if they are adequate con-

trols. Interpretation of the results of this study must

be further tempered by the recognition that the

evidence pertains to one type of entity, not-for-

profit agencies, in one geographical location, the

greater Philadelphia metro region.

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APPENDIX

A Comparison of Audit Quality Models

Study Stice (1991) Craswell et al. (1995) Petroni and Beasley (1996) Deis and Giroux (1992) Copley et al. (1

Sample Lawsuits against Australian publicly Property casualty insurers Audits submitted to Texas Audits submittedauditors listed companies Education Agency (TEA) the General Acco

Office (GAO)

SamplePeriod 1960–1985 1987 1979–1983 1983–88 1985

Dependent Binary variable Audit fee Claim loss reserve TEA audit quality GAO audit qualitvariable for lawsuit/no estimation error measure measure

lawsuit

Auditor Big 6, non-Big 6 Big 6, non-Big 6 Big 6, non-Big 6 Number of TEA firmsaudited

Audit Receivables/total Current assets/ total Loss reserves as a Assessed property valuecomplexity/ assets, inventory/ assets, quick ratio, percentage of total per student, percent ofrisk total assets, percent leverage, ROI, liabilities new board members in

change in sales, proportion of foreign last two yearsvariance of subsidiaries, square

abnormal returns root of number of subsidiaries

Financial Altman Z-Score Dummy for loss Dummy for distress based oncondition number of unusual IRIS ratios

Tenure Dummy for tenure Number of years audited Number of years(three or more years)

Indepen- Client sales/totaldence sales of all clients

Size Market Value Assets Average student attendance Budgeted total rev

Audit Dummy for qualified Dummy for material weak-opinion opinion ness or noncompliance

with laws and regulations

Peer Dummy for peer review

review

Other Dummies for year-end Dummies for positive or Timeliness of report, audit Variables relatingvariables and industry negative error, premiums hours, industry specializa- auditor selection

specialization for malpractice insurance tion, year dummy process

Adjusted/Psuedo R 2 .44 .73 .53 .21 .13

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