ACCOUNTING HORIZONS
Supplement
2003
pp. 1–16
Does Big 6 Auditor Industry Expertise
Constrain Earnings Management?
Gopal V. Krishnan
SYNOPSIS: Earnings management remains a popular topic of debate and discussion
among investors, regulators, analysts, and the public. One mechanism that might mitigate earnings management is auditors’ industry expertise. Using a large sample of
clients of Big 6 auditors, this research examines the association between auditor industry expertise, measured in terms of both auditor market share in an industry and an
industry’s share in the auditor’s portfolio of client industries, and a client’s level of
absolute discretionary accruals, a common proxy for earnings management. Clients of
nonspecialist auditors report absolute discretionary accruals that are, on average, 1.2
percent of total assets higher than the discretionary accruals reported by clients of
specialist auditors. This finding is consistent with the notion that specialist auditors
mitigate accruals-based earnings management more than nonspecialist auditors and,
therefore, influence the quality of earnings.
Keywords: industry specialization; Big 6; specialist firms; earnings management; discretionary accruals; audit quality.
Data Availability: The data used in this study are publicly available from the sources
indicated in the text.
INTRODUCTION
arnings management is a concern for investors, regulators, analysts, and the public. In a
review of the earnings management literature, Healy and Wahlen (1999) call for research on
factors that limit earnings management. This study is a response that provides empirical
evidence on one mitigating factor: auditors’ industry expertise. Specifically, I examine the association between Big 6 auditor industry expertise and the level of firms’ absolute discretionary accruals—a common proxy for earnings management.
Bonner and Lewis (1990) find that, on average, more experienced auditors outperform less
experienced auditors. Similarly, Bedard and Biggs (1991) observe that auditors with more manufacturing experience are better able to identify errors in a manufacturing client’s data than auditors with
less manufacturing experience. This is consistent with the findings of Johnson et al. (1991) that
industry experience is associated with enhanced ability to detect fraud. Wright and Wright (1997)
conclude that significant experience in the retailing industry enhances hypothesis generation in
identifying material errors.
E
Gopal V. Krishnan is an Associate Professor at the City University of Hong Kong.
I appreciate helpful comments from Patricia Dechow, James Largay, and two anonymous reviewers.
1
2
Krishnan
Specialist auditors are likely to invest more in staff recruitment and training, information technology, and state-of-the art audit technologies than nonspecialist auditors (Dopuch and Simunic
1982). Solomon et al. (1999) find that specialist auditors have more accurate nonerror frequency
knowledge than nonspecialists. This finding is important because it is not unusual that client firms
suggest nonerror explanations for ratio fluctuations. Audit effectiveness thus depends on the accuracy of auditors’ nonerror frequency knowledge. All these findings support the conclusion that
auditors’ industry-specific knowledge is associated with audit effectiveness.
How does an auditor’s specialized industry knowledge help in detecting earnings management?
Maletta and Wright (1996) observe fundamental differences in error characteristics and methods of
detection across industries. This suggests that auditors who have a more comprehensive understanding of an industry’s characteristics and trends will be more effective in auditing than auditors without
such industry knowledge.
Auditors who specialize in the banking industry can assess the adequacy of loan loss provisions
better than nonspecialist auditors and, therefore, can improve the credibility of reported earnings.
Auditors with expertise in manufacturing can evaluate whether the client’s provision for warranty
obligations is in line with industry standards better than an auditor without this expertise. Specialist
auditors are also likely to develop databases detailing industry-specific best practices, industryspecific risks and errors, and unusual transactions, all of which serve to enhance overall audit
effectiveness.
Besides having the resources and the expertise to detect earnings management, specialist auditors who enjoy a brand-name reputation have particular incentives to deter and report questionable
or aggressive accounting practices. It has been accepted that Big 6 auditors are of higher quality than
non-Big 6 auditors (DeAngelo 1981).1 Given their larger client base, Big 6 auditors have more to
lose than non-Big 6 auditors in the event of a loss of reputation. Thus, Big 6 auditors should have
more incentive to protect their reputation than non-Big 6 auditors. MacDonald (1997), for example,
reports that between 1994 and 1997, Big 6 auditors dropped 275 publicly traded audit clients
because of concerns about harm to their reputations or litigation risk. In short, Big 6 auditors who are
also specialist auditors have both the expertise to detect earnings management and the incentives to
report it. This argument is consistent with the findings of O’Keefe et al. (1994) that specialist
auditors exhibit greater compliance with auditing standards (GAAS) than nonspecialist auditors.
RESEARCH QUESTION AND CONTRIBUTIONS
Taken together, findings of the above studies support the notion that specialist auditors have the
resources, the industry-specific expertise, and the incentives to detect and constrain earnings management, and therefore enhance the quality of earnings. This leads to the research question:
RQ:
The absolute value of discretionary accruals of firms audited by specialist auditors
is lower than the absolute value of discretionary accruals of firms audited by nonspecialist auditors.
Consistent with prior research, I use the absolute value of discretionary accruals to proxy for
accruals-based earnings management (Francis et al. 1999). The sample consists of 4,422 firms
audited by Big 6 auditors from 1989 through 1998. I focus on clients of Big 6 auditors because they
audit more than 80 percent of firms in the Compustat database. Confining the sample firms to those
audited by the Big 6 makes it possible to isolate differences due to industry expertise rather than
differences in audit quality between Big 6 auditors and other auditors.
1
Consistent with the prior research, I refer to the original Big 8 auditors (Big 5 after 1998 and now Big 4) as Big 6 auditors.
Accounting Horizons, Supplement 2003
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
3
Using sales as a measure of client size, I estimate auditors’ industry expertise using two common
proxies: the audit fees an auditor earns in an industry relative to the total audit fees earned by all
auditors serving that particular industry, and an auditor’s audit fees earned from an industry relative
to fees earned from clients across all industries served (Gramling and Stone 2001). I find that the
clients of nonspecialist auditors report higher absolute discretionary accruals than the clients of
specialist auditors. This finding is consistent with the notion that auditors’ industry expertise moderates earnings management.
My study contributes to two aspects of the literature. The first is the literature on audit quality.
My contribution is to demonstrate that audit quality varies even among Big 6 auditors. Research like
Becker et al. (1998) tends to focus on audit-quality differences between Big 6 and non-Big 6 auditors
and implicitly treats the Big 6 auditors as a homogeneous group in terms of audit quality. My
extension treats auditor industry expertise as a dimension of audit quality. Second is the literature on
auditors’ industry specialization. Here I contribute by providing an empirical link between auditors’
industry expertise and audit quality. In their review of audit firm industry expertise literature,
Gramling and Stone (2001) observe that there is limited examination of whether industry specialization is associated with audit quality. They see the dearth of research as surprising, given the importance that client firms and audit standards setters place on industry expertise.
My findings suggest that one reason specialty auditors charge a premium over nonspecialty
auditors is because they are able to constrain accruals-based earnings management better than
nonspecialty auditors and thus add credibility to the quality of reported earnings.
SAMPLE SELECTION AND MEASURES OF AUDITORS’ INDUSTRY EXPERTISE
I searched the 2000 version of Compustat PC Plus to identify the sample firms and their
auditors. My sample period covers a ten-year period from 1989 through 1998. The selection criteria
are as follows. I exclude financial institutions (SICs between 6000 and 6999) because auditor
information for these firms is unavailable on Compustat. I also eliminate firms that changed fiscal
year-ends during the period of analysis. As previously stated, I restrict my sample to firms audited by
the Big 6 auditors.
This sample selection procedure yields 24,114 firm-year observations representing 4,422 firms.
Yearly firm-year observations are 1,481, 1,746, 1,806, 1,857, 2,046, 2,302, 2,624, 3,009, 3,510, and
3,733 for 1989 through 1998, respectively.
Big 6 Auditor Portfolio Shares
Auditor industry expertise is unobservable, so researchers must rely on proxies to estimate it.
Yardley et al. (1992) develop a measure of auditor industry expertise that estimates industry specialization by the proportion of an auditor’s audit fees earned from one industry of all those served. As
audit fee information has been unavailable until recently, researchers have used sales or assets or the
square root of assets as the base to estimate the proportion of audit fees received from a particular
industry.
Similar to Kwon (1996), I estimate auditor portfolio shares as follows (hereafter, Big 6 auditor
portfolio shares, or Big 6 PS):
J ik
Big 6 PSik =
∑ SALES
ijk
j =1
K J ik
∑∑ SALES
(
(1)
ijk
k =1 j =1
where SALES is sales revenue, and the numerator is the sum of the sales of all Jik clients of audit firm
i in industry k. The value of i ranges from 1 to 6, representing the Big 6 auditors. Two-digit SIC
Accounting Horizons, Supplement 2003
4
Krishnan
codes identify industry categories. The denominator in Equation (1) is the sales of all clients of audit
firm i summed over all K industries.
An example will demonstrate the computation of Big 6 PS for Coopers & Lybrand (CL) for the
transportation equipment industry (two-digit SIC code 37). In 1989, CL had seven clients in
the transportation equipment industry. For 1989, the total sales of these seven clients were $97,094.54
million (a large portion generated by a single client, Ford). During 1989, CL served 33 industries and
audited 202 clients. The total sales of these clients amounted to $395,525.09 million. Big 6 PS for
Coopers & Lybrand for the transportation equipment industry for 1989 is thus:
Big 6 PSCL,Transportation equipment,1989 = $97,094.54/$395,525.09 = 0.2455.
This suggests that the transportation equipment industry alone accounted for about a quarter of CL’s
audit fees in 1989. As an auditor’s industry expertise may change over time, I repeat this step for
each year and then aggregate CL’s portfolio shares over the years 1989 through 1998 for each
industry.
During 1989 through 1998, the total sales of all clients of CL in the transportation equipment
industry amounted to $1,094,132.01 million. During the period, CL served 49 industries and audited
2,647 client-years. The combined sales of clients representing those 49 industries amounted to
$6,350,583.13 million.
Big 6 PS for CL using the aggregate data is computed as follows:
Big 6 PSCL,Transportation equipment,1989–1998 = $1,094,132.01/$6,350,583.13 = 0.1723.
The final step involves identifying industries in which CL is considered a specialist auditor. I
code a firm’s top-three portfolio shares as the auditor’s specialty and the remaining industries as
nonspecialty. For CL, industries that represent the top three portfolio shares are communications,
transportation equipment, and food stores. To put it differently, these three industries are the top
three moneymakers for CL during the period 1989 through 1998. Thus, CL is defined as a specialist
auditor only for these three industries over the sample period. I repeat these steps for each Big 6
auditor to identify industry specializations.
Table 1 reports industry specialization and portfolio shares for each Big 6 auditor for the pooled
sample (year-by-year portfolio shares are not reported). With the exception of Arthur Andersen, the
Big 6 firms appear to show an expertise in the transportation equipment industry, although the
portfolio shares range from 29.6 percent for Deloitte & Touche to 12.4 percent for Ernst & Young.
Big 6 Auditor Industry Market Shares
I use an alternative measure of auditors’ industry expertise in order to minimize measurement
error associated with estimation of auditors’ industry specialization and to enhance the reliability of
the findings. Following Gramling and Stone (2001), I calculate an auditor’s industry market share to
proxy for audit fees earned by an auditor in an industry as a proportion of the total audit fees earned
by all auditors that serve that particular industry (hereafter, Big 6 auditor industry market share, or
Big 6 IMS):
J ik
∑ SALES
ijk
Big 6 IMSik =
(2)
j =1
IK J ik
∑∑ SALES
ijk
i =1 j =1
where SALES is sales revenue, and the numerator is the sum of sales of all J ik clients of audit firm i in
industry k. The denominator is the sales of Jik clients in industry k summed over all I k audit firms in
the sample with clients (Jik) in industry k. The denominator does not include the sales of clients of
non-Big 6 auditors as the sample includes only clients of Big 6 auditors. To estimate industry market
Accounting Horizons, Supplement 2003
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
5
TABLE 1
Big 6 Auditor Portfolio Shares for 1989–1998
Auditor
Portfolio Shares
(in %)
Industry (SIC code)
Arthur Andersen
Coopers & Lybrand
Ernst & Young
Deloitte & Touche
KPMG Peat Marwick
Price Waterhouse
Petroleum refining (29)
Chemicals and pharmaceuticals (28)
Air transportation (45)
Communications (48)
Transportation equipment (37)
Food stores (54)
Petroleum refining (29)
General merchandise stores (53)
Transportation equipment (37)
Transportation equipment (37)
Durable goods—wholesale (50)
Chemicals and pharmaceuticals (28)
Electrical and electronic equipment (36)
Machinery and computer equipment (35)
Transportation equipment (37)
Petroleum refining (29)
Machinery and computer equipment (35)
Transportation equipment (37)
12.85
8.05
6.88
17.34
17.23
8.50
16.44
15.55
12.40
29.56
23.89
11.85
20.38
14.07
13.49
25.83
18.83
13.68
Sales is used as the base in calculating portfolio share. The following example illustrates the calculation of portfolio shares
for Coopers & Lybrand (CL). For the period 1989 through 1998, the total sales of all clients of CL in the transportation
equipment industry (two-digit SIC code 37) amounted to $1,094,132.01 million. During the same period, the combined
sales of all clients across all industries served by CL amounted to $6,350,583.13 million. Thus, CL’s portfolio share for
the transportation equipment industry = ($1,094,132.01/$6,350,583.13) × 100 = 17.23%. Portfolio shares for other Big 6
auditors are calculated in a similar manner.
For each auditor, top-three portfolio shares are coded as industries where the auditor is considered a specialist. Total
number of firm-year observations equals 24,114. The sample consists of 2,782 firm-year observations for specialist auditors and 21,332 observations for nonspecialist auditors.
share for each auditor, I require a minimum of ten observations for each pair of two-digit SIC codes
and calendar years.
An example will demonstrate the computation of Big 6 IMS for CL for the transportation
equipment industry. The numerator is the same for both Equations (1) and (2). In 1989, there were 47
firms (including firms audited by other Big 6 auditors) in the transportation equipment industry; their
total sales amounted to $355,634.61 million. Big 6 IMS for CL for 1989 is calculated as follows:
Big 6 IMSCL,Transportation equipment, 1989 = $97,094.54/$355,634.61 = 0.2730.
This indicates that CL’s share of the transportation equipment industry is 27.3 percent. Repeating the steps for each of the other nine years, I aggregate CL’s industry market shares over the years
1989 through 1998. For the period 1989–1998, the total sales of all clients of CL in the transportation equipment industry amounted to $1,094,132.01 million. There were 629 firm-years for the
transportation equipment industry, with combined sales amounting to $5,724,707.32 million. Big 6
IMS for CL for the period is:
Big 6 IMSCL,Transportation equipment,1989–1998 = $1,094,132.01/$5,724,707.32 = 0.1911.
Accounting Horizons, Supplement 2003
6
Krishnan
Shares for other industries served and for each Big 6 auditors are computed the same way to identify
industry specializations.
Table 2 reports industry market shares of Big 6 auditors for the pooled sample for selected industries (year-by-year industry market shares are not reported). A specialty is defined as any industry by
two-digit SIC code where the auditor’s market share exceeds 15 percent. For example, in the period
1989–1998, CL’s market share exceeded 15 percent in the following industries: metal mining, oil and
gas, lumber and wood products, chemicals and pharmaceuticals, primary metal, fabricated metal, transportation equipment, communications, and food stores. According to this alternative measure, CL is
defined as a specialist auditor for these nine industries for the period 1989–1998.
TABLE 2
Big 6 Auditor Industry Market Shares for Selected Industries for 1989–1998
TwoDigit
SIC
Industry
AA
CL
EY
DT
KPMG
PW
10
13
20
22
23
24
25
26
27
28
29
30
32
33
34
35
36
37
38
45
48
50
53
54
Metal mining
Oil and gas
Food and kindred products
Textile mill products
Apparel and other finished products
Lumber and wood products
Furniture and fixtures
Paper and allied products
Printing and publishing
Chemicals and pharmaceuticals
Petroleum refining
Rubber and plastics
Stone, clay, glass, and concrete
Primary metal
Fabricated metal
Machinery and computer equipment
Electrical and electronic equipment
Transportation equipment
Scientific instruments
Air transportation
Communications
Durable goods—wholesale
General merchandise stores
Food stores
41.35
50.84
14.26
7.11
19.82
61.67
25.93
44.72
16.78
15.05
18.63
9.25
43.95
9.57
8.38
8.43
6.83
4.20
13.96
44.59
7.47
3.48
10.02
2.23
30.38
18.96
14.46
4.37
6.05
17.99
0.46
6.64
6.67
15.28
2.66
4.11
0.52
20.42
23.72
6.26
4.12
19.11
6.59
0.00
50.26
2.37
1.24
45.26
10.24
3.29
18.62
54.83
43.64
1.74
3.77
9.97
22.20
5.55
23.85
21.74
31.05
14.81
19.74
10.92
16.66
13.75
11.56
35.22
14.29
10.02
44.03
2.53
2.39
3.49
12.13
19.06
13.95
5.49
3.79
18.28
21.27
22.16
0.28
1.33
11.43
14.13
5.78
6.27
8.12
32.79
4.57
2.18
10.67
71.73
23.35
37.57
3.98
5.38
19.34
13.14
2.41
0.86
13.31
10.08
10.78
16.88
17.13
7.71
8.28
5.72
20.99
29.14
43.28
14.97
24.66
16.75
4.37
4.62
10.86
5.32
11.66
18.04
21.18
1.49
14.13
12.25
52.74
10.31
22.29
25.08
37.46
55.86
4.77
35.35
21.38
38.98
20.99
15.17
38.66
1.26
12.94
7.77
10.50
7.09
Industry Market Shares (in %)
AA: Arthur Andersen; CL: Coopers & Lybrand; EY: Ernst & Young; DL: Deloitte & Touche; KPMG: KPMG Peat
Marwick; and PW: Price Waterhouse.
Sales is used as the base in calculating industry market share. The following example illustrates the calculation of industry
market shares for Coopers & Lybrand (CL). For the period 1989 through 1998, the total sales of all clients of CL in the
transportation equipment industry (two-digit SIC code 37) amounted to $1,094,132.01 million. During the same period,
the combined sales of all clients in the transportation equipment industry amounted to $5,724,707.32 million. Thus, CL’s
market share in the transportation equipment industry = ($1,094,132.01/$5,724,707.32) × 100 = 19.11%. Industry market
shares for other industries are calculated in a similar manner.
An auditor is coded as a specialist in industries where the auditor’s market share exceeds 15 percent. Bold indicates that
auditor is a specialist. Total number of firm-year observations equals 24,114. The sample consists of 12,221 firm-year
observations for specialist auditors and 11,893 observations for nonspecialist auditors.
Accounting Horizons, Supplement 2003
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
7
Portfolio Share versus Industry Market Share
Both portfolio share and industry market share indicate that CL has a specialty in the following
industries: communications, transportation equipment, and food stores. In the other six industries,
CL has a specialty based on industry market share but not portfolio share. A comparison of Tables 1
and 2 shows that this result is even more marked for the other firms.
The industry market share measure tends to classify more firms as clients of specialist auditors than
the portfolio share measure. About 51 percent of sample firms are classified as clients of specialist
auditors according to industry market share, compared to about 12 percent according to portfolio share.
An examination of year-by-year values of the portfolio shares and the industry market shares indicates
that the top-three portfolios are fairly stable over the ten-year period for each Big 6 auditor (results not
reported). The industry market shares exhibit more variation, suggesting that this may be a noisier
measure of auditors’ industry expertise. This is consistent with Krishnan’s (2001) argument that portfolio share captures the efforts of auditors to differentiate their products better than industry market share,
and may be a better proxy for auditor industry expertise than industry market share.
Finally, the Pearson correlation coefficient (not reported) between portfolio share and industry
market share is 0.45, significant at the 0.01 level. Pearson correlation coefficients between the two
measures for each Big 6 auditor are positive and also significant at the 0.01 level for each auditor
(results not reported).
RESEARCH METHOD
I estimate discretionary accruals using a cross-sectional variation of the Jones (1991) accruals
estimation model also used by DeFond and Jiambalvo (1994).2 I use the absolute value of discretionary accruals (ABDAC) as a proxy for accruals-based earnings management (Becker et al. 1998;
Francis et al. 1999). In other words, all else equal, higher ABDAC is consistent with a conclusion that
auditors allow their clients to exercise greater accounting flexibility.
Several variables have been identified that are correlated with discretionary accruals (Becker et
al. 1998; Bartov et al. 2000): size (SIZE) defined as log of total assets, and leverage (LEV) defined as
long-term debt divided by total assets. Since firms with higher absolute values of total accruals are
likely to have greater discretionary accruals, I include the absolute value of total accruals divided by
total assets at the beginning of the year (ABACCR) as a control variable. DeFond and Subramanyam
(1998) find that discretionary accruals are related to auditor changes. I include a dummy variable
(NEWAUD) equal to 1 if the first sample year is the first year with a new auditor, and 0 otherwise.
Similarly, another dummy variable (OLDAUD) is set equal to 1 if the last sample year is followed by
an auditor change, and 0 otherwise. Further, following Bartov et al. (2000), to control for growth, I
include market-to-book (MKBK) ratio as a control variable.
2
Estimation of discretionary accruals involves two steps. First, nondiscretionary accruals are estimated using the crosssectional version of the Jones (1991) model. This model estimates nondiscretionary accruals as a function of the level
of property, plant, and equipment, and changes in revenue:
ACCR j ,t
TA j ,t −1
= α1
∆REV j ,t
PPE j ,t
1
+ α2
+ α3
+ e j ,t
TAj ,t −1
TA j, t− 1
TA j, t− 1
where ACCRj,t is total accruals for firm j in year t; TA is total assets; ∆REV is change in net revenue; and PPE is property,
plant, and equipment. Total accruals are calculated as the difference between net income before extraordinary items and
discontinued operations and cash flows from operations. Consistent with prior research, this model is estimated separately for each combination of two-digit SIC codes and calendar years. Fitted values are defined as nondiscretionary
(expected) accruals.
Second, the error term in the model (the difference between total accruals and nondiscretionary accruals) represents the
unexplained or discretionary component of accruals.
The median estimates of α 1, α2, and α3 are –0.100, 0.049, and –0.071, respectively. The percentages of regression
coefficients that are in the predicted direction are 68 percent and 96 percent for α2 and α3, respectively. The median
adjusted R2 is 0.24.
Accounting Horizons, Supplement 2003
8
Krishnan
Finally, I include two performance-related controls. First is earnings persistence (PERSIST).
Following Ali (1994), observations in each year are partitioned into ten groups according to the
absolute value of change in income before extraordinary items. Observations in the four extreme
deciles (top-two deciles and bottom-two deciles) are classified as low-persistence firms, and observations in the middle six deciles are classified as high-persistence firms. Second, observations with
negative earnings (LOSS) are coded as 1; observations of profitable firms are coded as 0.
In all, eight control variables are included:
ABDACt = β0 + β1SIZEt + β 2 LEVt + β3 MKBKt + β4 ABACCRt + β 5NEWAUDt
(3)
+ β 6OLDAUDt + β 7PERSISTt + β 8LOSSt + β9 SPECLST + µ t .
The variable of interest, SPECLST, is defined in four ways. As a continuous measure it equals
the auditor’s portfolio share or its industry market share. As a dichotomous measure, SPECLST
equals 1 for specialist auditors and 0 for nonspecialist auditors, depending on the auditor’s portfolio
share or industry market share. An observation of ß9 < 0 is consistent with the notion that specialist
auditors are able to constrain accruals-based earnings management.
RESULTS
Pearson correlation coefficients for the variables in Equation (3) are reported in Table 3.
Correlations for the portfolio share measure appear above the diagonal, and correlations for the
market share measure below the diagonal. The correlation between SPECLST and ABDAC is negative as
predicted and statistically significant at the 0.01 level for both measures of auditors’ industry expertise.
This suggests that, other things equal, auditor industry expertise is associated with less accruals-based
earnings management.
Discretionary Accruals: Specialist versus Nonspecialist Auditors
Panel A of Table 4 reports descriptive statistics separately for clients of specialist and nonspecialist auditors depending on the portfolio share measure, for income before extraordinary items
over total assets at the beginning of the year, log of total assets, long-term debt over total assets, total
accruals, absolute value of total accruals, absolute value of discretionary accruals, income-increasing discretionary accruals, and income-decreasing discretionary accruals.3
The results indicate that clients of specialist auditors are slightly more profitable, are larger, and
carry less debt than clients of nonspecialist auditors. These differences are significant at the 0.01
level. Differences in mean and median values of measures of accruals-based earnings management
for clients of specialist and nonspecialist auditors are significant at the 0.01 level for all the five
measures. More important, clients of nonspecialist auditors report higher discretionary accruals than
clients of specialist auditors.
Panel B of Table 4 presents statistics for clients of specialist and nonspecialist auditors depending on
industry market share. The differences in mean and median values of earnings management measures for
clients of specialist and nonspecialist auditors are significant at the 0.01 level for four of five measures.
In summary, the level of the absolute value of discretionary accruals is lower for clients of
specialist auditors under both measures of auditor industry expertise. I also calculate mean and
median values of ABDAC by year, and compare the ten annual means and medians of the clients of
specialist auditors to the ten annual means and medians of the clients of nonspecialist auditors. The
differences in mean and median are significant at the 0.01 level (results not reported).
Table 5 highlights the differences in mean value of ABDAC between clients of specialist and
nonspecialist auditors for each Big 6 auditor. The histogram in Panel A is based on portfolio shares.
Clients of specialist auditors have lower discretionary accruals than clients of nonspecialist auditors
3
Mean, median, upper quartile, and lower quartile of discretionary accruals for the pooled sample are –0.006, –0.001,
0.044, and –0.051, respectively.
Accounting Horizons, Supplement 2003
ABDAC
SIZE
LEV
MKBK
ABACCR
NEWAUD
OLDAUD
PERSIST
LOSS
SPECLST
ABDAC
SIZE
LEV
1.000
–0.293*
–0.048*
0.005*
0.661*
0.035*
0.050*
–0.038*
0.238*
–0.088*
–0.293*
–0.048*
1.000
0.247*
0.010*
–0.236*
–0.075*
–0.085*
0.332*
–0.342*
0.193*
0.247*
1.000
0.004
0.007
0.006
–0.001
0.071*
0.050*
0.023*
MKBK
ABACCR
0.005
0.100
0.004
1.000
0.008*
0.002
0.001
0.003
–0.010
–0.005
0.661*
–0.236*
0.007
0.008
1.000
0.035*
0.066*
–0.017*
0.285*
–0.062*
NEWAUD OLDAUD
0.034
–0.075*
0.006
0.002
0.035*
1.000
0.012***
0.001
0.050*
–0.027*
0.050
–0.085*
–0.001
0.001
0.066*
0.012***
1.000
–0.016**
0.086*
0.016**
PERSIST
LOSS
–0.038*
0.238*
0.332*
–0.342*
0.071*
0.003
–0.017*
0.001
–0.016**
1.000
–0.026*
0.081*
0.050*
–0.010
0.285*
0.050*
0.086*
–0.026*
1.000
–0.064*
SPECLST
–0.094*
0.125*
–0.037*
0.001
–0.077*
–0.020*
0.007
0.067*
–0.026*
1.000
9
Accounting Horizons, Supplement 2003
*, **, *** Represent statistical significance at the 0.01, 0.05, and 0.10 levels, respectively, for a two-tailed test
Correlation coefficients for the portfolio share measure appear above the diagonal; correlations for the market share measure appear below the diagonal.
All firms were audited by Big 6 auditors. Total number of firm-year observations is 24,114. When auditors’ industry expertise is measured based on the portfolio share measure, the
sample consists of 2,782 and 21,332 observations for specialist and nonspecialist auditors, respectively. For the market share measure, the sample consists of 12,221 and 11,893
observations for specialist and nonspecialist auditors, respectively. See Tables 1 and 2 for definitions of auditors’ portfolio shares and industry market shares, respectively.
ABDAC is absolute value of discretionary accruals where discretionary accruals are determined using the cross-sectional version of the Jones (1991) model (see footnote 2). SIZE is
the log of total assets. LEV is leverage calculated as long-term debt divided by total assets. MKBK is market-to-book ratio. ABACCR is absolute value of total accruals divided by
total assets at the beginning of the year. NEWAUD equals 1 if first sample year is the first year with a new auditor, and 0 otherwise. OLDAUD equals 1 if the last sample year is
followed by an auditor change, and 0 otherwise. PERSIST equals 1 for firms with low earnings persistence and 0 for firm with high earnings persistence. Persistence is measured as
follows: observations in each year are partitioned into ten groups based on the absolute value of change in income before extraordinary items. Observations in the four extreme
deciles (top two deciles and bottom two deciles) are classified as low-persistence firms and observations in the middle six deciles are classified as high-persistence firms. LOSS
equals 1 if income before extraordinary items is negative, and 0 otherwise. SPECLST equals 1 for clients of specialist auditors and 0 for clients of nonspecialist auditors.
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
TABLE 3
Pearson Correlation Coefficients for 1989–1998
10
Accounting Horizons, Supplement 2003
TABLE 4
Descriptive Statistics: Clients of Specialist versus Nonspecialist Auditors for 1989–1998
Panel A: Big 6 Auditor Portfolio Shares
Mean
Type of Accrual
PROFITABILITY
SIZE
LEVERAGE
ACCR
ABACCR
ABDAC
Income-increasing DAC
Income-decreasing DAC
Median
Specialist
Nonspecialist
t–statistic
Specialist
Nonspecialist
z–statistic
0.008
5.908
0.163
–0.044
0.073
0.054
0.054
–0.054
–0.009
5.048
0.186
–0.055
0.099
0.080
0.074
–0.085
3.94 *
16.71*
–6.56*
5.71 *
–17.77 *
–22.44 *
–13.27 *
18.14*
0.048
5.538
0.128
–0.046
0.059
0.039
0.039
–0.038
0.040
4.915
0.136
–0.050
0.069
0.049
0.047
–0.052
4.09 *
14.61*
–3.20*
4.27 *
–9.74*
–11.62 *
–6.59*
9.70 *
Nonspecialist
t–statistic
Specialist
Nonspecialist
z–statistic
0.045
5.412
0.151
–0.050
0.064
0.043
0.042
–0.044
0.037
4.611
0.114
–0.048
0.070
0.054
0.051
–0.056
7.23 *
28.00*
8.67 *
–1.09
–7.32*
–13.47 *
–9.00*
9.97 *
Panel B: Big 6 Auditor Industry Market Shares
Mean
Type of Accrual
PROFITABILITY
SIZE
LEVERAGE
ACCR
ABACCR
ABDAC
Income-increasing DAC
Income-decreasing DAC
Specialist
0.003
5.565
0.188
–0.053
0.089
0.069
0.065
–0.073
Median
–0.018
4.717
0.178
–0.054
0.103
0.085
0.079
–0.090
5.33 *
30.67*
3.53 *
0.36
–9.67*
–13.73 *
–9.00*
10.25*
Krishnan
* Represents statistical significance at the 0.01 level.
Auditors are classified into specialists and nonspecialists based on their portfolio shares and industry market shares. See Tables 1 and 2 for definitions of portfolio shares and
industry market shares, respectively.
PROFITABILITY is income before extraordinary items over total assets at the beginning of the year; SIZE is log of total assets; LEVERAGE is long-term debt over total assets.
ACCR is total accruals divided by total assets at the beginning of the year. ABACCR is absolute value of ACCR. ABDAC is absolute value of discretionary accruals. Discretionary accruals (DAC) are computed as the error term from the Jones (1991) model (see footnote 2). Income-increasing discretionary accruals are positive DAC. Income-decreasing
discretionary accruals are negative DAC.
Total number of firm-year observations equals 24,114 representing years 1989 through 1998. When auditors’ industry expertise is measured based on the portfolio share measure, the sample consists of 2,782 and 21,332 observations for specialist and nonspecialist auditors, respectively. For the industry market share measure, the sample consists of
12,221 and 11,893 observations for specialist and nonspecialist auditors, respectively.
Tests are two-tailed. t-statistics are from t-tests of the differences in the means and z-statistics are from Wilcoxon two-sample tests.
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
11
TABLE 5
Mean Values of Absolute Discretionary Accruals between Clients of Specialist
and Nonspecialist Big 6 Auditors for 1989–1998
Panel A: Auditor Industry Expertise Based on Portfolio Shares
0 .0 9
0 .0 8
Mean Absolute Disc Accruals
0 .0 7
0 .0 6
0 .0 5
S p ecialist
N o n -sp ecialist
0 .0 4
0 .0 3
0 .0 2
0 .0 1
0
AA
CL
EY
DT
KPMG
PW
B ig 6 A u d ito rs
Panel B: Auditor Industry Expertise Based on Industry Market Shares
0 .1
0.09
0.08
e a n A b s Disc
o l u t eAccruals
D isc A
MeanMAbsolute
0.07
0.06
S p e c i a l is t
0.05
N o n -s p e c i a l i s t
0.04
0.03
0.02
0.01
0
AA
C L
EY
DT
KPMG
PW
B ig 6 A u d ito r s
Auditors are classified into specialists and nonspecialists based on portfolio share and industry market share. AA is Arthur
Andersen; CL is Coopers & Lybrand; EY is Ernst & Young; DL is Deloitte & Touche; KPMG is KPMG Peat Marwick;
and PW is Price Waterhouse. See Tables 1 and 2 for definitions of portfolio shares and industry market shares, respectively.
Discretionary accruals are computed as the error term from the Jones (1991) model (see footnote 2). Total number of firmyear observations equals 24,114 representing years 1989 through 1998. When auditors’ industry expertise is measured
based on the portfolio share measure, the sample consists of 2,782 and 21,332 observations for specialist and nonspecialist auditors, respectively. For the industry market share measure, the sample consists of 12,221 and 11,893 observations
for specialist and nonspecialist auditors, respectively.
Accounting Horizons, Supplement 2003
12
Krishnan
in every case. The differences in mean value of discretionary accruals between clients of specialist
and nonspecialist auditors are significant at the 0.01 level in a two-tailed test (results not reported).
The histogram in Panel B is based on auditor industry market shares. Once again, clients of
specialist auditors have lower discretionary accruals than clients of nonspecialist auditors, except in
the case of Price Waterhouse. Overall, these findings are consistent with the notion that specialist
auditors mitigate accruals-based earnings management more than nonspecialist auditors.
Multivariate Analysis
Results in Tables 4 and 5 do not control for research confounds that might be associated with
discretionary accruals. Estimation of Equation (3) controls for variables correlated with discretionary accruals. I estimate Equation (3) year-by-year as well as for the pooled sample. Pooling data
allows for more powerful tests because of the larger sample size. An upward bias in t-statistics due to
cross-sectional correlation in regression residuals is, however, a concern with models using annual
data (Bernard 1987).
I address cross-sectional correlation in two ways. In the pooled model, I include nine yeardummy variables DY to indicate fiscal years 1989 through 1997 and 14 industry-dummy variables D I
representing two-digit SIC code numbers 13, 20, 28, 33, 34, 35, 36, 37, 38, 48, 49, 50, 58, and 73.
Each two-digit SIC code represents at least 2 percent of the total sample. The objective is to capture
time- and industry-specific commonalities in the dummy variable coefficients and thus reduce correlations among regression residuals.
The second procedure involves estimating cross-sectional regressions for each year. Following
Ali (1994), I test significance of parameter estimates using t-statistics for the cross-temporal distributions of the year-by-year estimates. I also examine intertemporal independence by analyzing
correlations between residuals across years.
Table 6 presents descriptive statistics for the pooled and year-by-year samples for variables in
Equation (3) based on portfolio share. Results using both the dichotomous variable specification and the
continuous variable specification are reported. SPECLST is negative and statistically significant at the
0.01 level for the pooled sample. Results for the year-by-year samples indicate that SPECLST is negative
as expected in each of the ten years, and the mean coefficient is significant at the 0.01 level. The year-byyear results also indicate that the pooled results are not just due to use of a large sample.
Table 7 reports the same descriptive statistics for the pooled and year-by-year samples for
variables in Equation (3) based on industry market share. SPECLST is negative and significant in this
case at the 0.05 level. Overall, the results indicate strongly that the level of absolute discretionary
accruals is negatively associated with auditors’ industry expertise.
In summary, the results hold under both measures of auditors’ industry expertise for both dichotomous and continuous variable specifications. Results are consistent with the notion that specialist auditors serve to mitigate accruals-based earnings management more than nonspecialist auditors.
Additional Tests for Robustness of Findings
I apply some additional tests to examine the sensitivity of my results to alternative variable
definitions and model specifications. I use the cross-sectional variation of the modified Jones (1991)
model to estimate discretionary accruals. The mean values of absolute discretionary accruals under
the modified Jones model for clients of specialist and nonspecialist auditors are 0.053 and 0.076,
respectively. The median values are 0.037 and 0.047, respectively. The differences in mean and
median values are statistically significant at the 0.01 level. I re-estimate Equation (3) using discretionary accruals obtained from the modified Jones model, and the results are consistent with those
already reported. For the market share measure, I increase the cutoff rate to 25 percent to identify
specialist auditors and re-estimate Equation (3). The results are comparable to results reported in
Table 7; SPECLST is negative and significant at the 0.01 level.
Accounting Horizons, Supplement 2003
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
13
TABLE 6
Regression of Absolute Discretionary Accruals on Control Variables
and Big 6 Auditor Portfolio Shares for 1989–1998
ABDAC t = β 0 + β1 SIZEt + β 2 LEV t + β3 MKBK t + β 4 ABACCR t + β5 NEWAUDt + β 6 OLDAUDt
+ β7 PERSISTt + β 8 LOSS t + β9 SPECLST + µ t
Pooled Sample
Dichotomous Variable
Independent
Variables
Coefficients
Intercept
SIZE
LEV
MKBK
ABACCR
NEWAUD
OLDAUD
PERSIST
LOSS
SPECLST
Adjusted R2
0.058
–0.005
–0.001
0.000
0.495
–0.001
–0.002
0.004
–0.000
–0.012
Year-by-Year Samples
Continuous Variable
t-statistic
Coefficients
t-statistic
30.97*
–23.06 *
–0.48
0.42
122.97 *
–0.72
–1.28
3.77 *
–0.38
–8.47*
0.479
0.058
–0.005
–0.003
0.000
0.537
0.002
0.000
0.004
–0.003
–0.042
31.49*
–24.04 *
–1.33
0.18
131.28 *
1.37
0.15
4.07 *
–2.46**
–5.01*
0.501
Mean
Coefficients
0.058
–0.006
–0.012
0.000
0.497
0.000
–0.004
0.003
0.002
–0.007
t-statistic
13.57*
–13.70 *
–3.83*
1.00
17.89*
0.16
–2.59**
2.97 **
0.97
–9.66*
0.467
Number
Positive
10/10
0/10
2/10
6/10
10/10
4/10
2/10
9/10
7/10
0/10
*, ** Indicate two-tailed significance at the 0.01and 0.05 levels, respectively.
Big 6 auditors are classified into specialists and nonspecialists based on their portfolio shares. Sales is used as the base in
calculating the portfolio share (see Table 1 for more information on the calculation of portfolio share).
ABDAC is absolute value of discretionary accruals where discretionary accruals are determined using the cross-sectional
version of the Jones (1991) model (see footnote 2). SIZE is the log of total assets. LEV is long-term debt divided by total
assets. MKBK is market-to-book ratio. ABACCR is absolute value of total accruals divided by total assets at the beginning
of the year. NEWAUD equals 1 if first sample year is the first year with a new auditor, and 0 otherwise. OLDAUD equals 1
if the last sample year is followed by an auditor change, and 0 otherwise. PERSIST equals 1 for firms with low earnings
persistence, and 0 for firm with high earnings persistence. Persistence is measured as follows: observations in each year
are partitioned into ten groups based on the absolute value of change in income before extraordinary items. Observations
in the four extreme deciles (top two deciles and bottom two deciles) are classified as low-persistence firms and observations in the middle six deciles are classified as high-persistence firms. LOSS equals 1 if income before extraordinary items
is negative, and 0 otherwise.
SPECLST equals portfolio shares for the continuous variable specification. For the dichotomous variable specification,
SPECLST equals 1 for industries representing the top three portfolio shares and 0 for the remaining industries. Year-byyear results are based on the dichotomous specification.
The specification for the pooled sample include nine year-dummy variables DY for 1989 through 1997, and 14 industrydummy variables DI representing two-digit SIC code numbers 13, 20, 28, 33, 34, 35, 36, 37, 38, 48, 49, 50, 58, and 73.
Total number of observations equals 24,114 consisting of 2,782 and 21,332 observations audited by specialist and nonspecialist auditors, respectively. Yearly firm-year observations are 1,481, 1,746, 1,806, 1,857, 2,046, 2,302, 2,624, 3,009,
3,510, and 3,733 for 1989 through 1998, respectively.
The pooled sample aggregates the individual year samples. Means of individual-year parameter estimates are reported for
the year-by-year models with t-values (with 9 degrees of freedom) estimated from the cross-temporal distribution of these
estimates. Reported adjusted R2s for the year-by-year samples are cross-temporal means.
I re-estimate Equation (3) using an alternate measure of auditors’ industry expertise—industry
market share calculated using square root of total assets as the base instead of sales (Krishnan 2001).
Once again, SPECLST is negative and significant at the 0.05 level. Next, I exclude two-digit SIC
categories not represented by specialist auditors and estimate the equation using the remaining observations. The results indicate that absolute discretionary accruals are lower for clients of specialist
auditors, and SPECLST is negative and significant at the 0.01 level. My results are not sensitive to the
inclusion of utilities (SICs from 4000 and 4999).
Accounting Horizons, Supplement 2003
14
Krishnan
TABLE 7
Regression of Absolute Discretionary Accruals on Control Variables
and Big 6 Auditor Industry Market Shares for 1989–1998
ABDAC t = β 0 + β1 SIZEt + β 2 LEV t + β3 MKBK t + β 4 ABACCR t + β5 NEWAUDt + β 6 OLDAUDt
+ β7 PERSISTt + β 8 LOSS t + β9 SPECLST + µ t
Pooled Sample
Dichotomous Variable
Independent
Variables
Coefficients
Intercept
SIZE
LEV
MKBK
ABACCR
NEWAUD
OLDAUD
PERSIST
LOSS
SPECLST
Adjusted R2
0.058
–0.005
–0.001
0.000
0.513
–0.001
–0.003
0.003
–0.001
–0.002
t-statistic
30.41*
–23.08*
–0.49
0.38
126.48*
–0.69
–1.76***
3.44*
–1.05
–2.45**
0.488
Year-by-Year Samples
Continuous Variable
Coefficients
t-statistic
0.058
–0.005
–0.001
0.000
0.513
–0.001
–0.003
0.003
–0.001
–0.005
30.08*
–23.16*
–0.45
0.38
126.49*
–0.69
–1.76***
3.45*
–1.05
–2.08**
0.488
Mean
Coefficients
0.057
–0.005
–0.012
0.000
0.517
0.001
–0.005
0.002
0.001
–0.005
t-statistic
11.81*
–11.77*
–3.84*
0.64
23.24*
0.27
–3.18**
2.50**
0.46
–2.34**
0.483
Number
Positive
10/10
0/10
2/10
8/10
10/10
4/10
1/10
8/10
5/10
1/10
*, **, *** Indicate two-tailed significance at the 0.01, 0.05, and 0.10 levels, respectively.
Big 6 auditors are classified into specialists and nonspecialists based on their industry market shares. Sales is used as the
base in calculating the industry market share (see Table 2 for more information on the calculation of industry market
share).
ABDAC is absolute value of discretionary accruals where discretionary accruals are determined using the cross-sectional
version of the Jones (1991) model (see footnote 2). SIZE is the log of total assets. LEV is long-term debt divided by total
assets. MKBK is market-to-book ratio. ABACCR is absolute value of total accruals divided by total assets at the beginning
of the year. NEWAUD equals 1 if first sample year is the first year with a new auditor, and 0 otherwise. OLDAUD equals 1
if the last sample year is followed by an auditor change, and 0 otherwise. PERSIST equals 1 for firms with low earnings
persistence and 0 for firm with high earnings persistence. Persistence is measured as follows: observations in each year are
partitioned into ten groups based on the absolute value of change in income before extraordinary items. Observations in
the four extreme deciles (top two deciles and bottom two deciles) are classified as low-persistence firms and observations
in the middle six deciles are classified as high-persistence firms. LOSS equals 1 if income before extraordinary items is
negative, and 0 otherwise. SPECLST equals industry market shares for the continuous variable specification. For the
dichotomous variable specification, SPECLST equals 1 for industries where the auditor’s market share exceeds 15 percent, and 0 for the remaining industries. Year-by-year results are based on the dichotomous specification.
The specification for the pooled sample include nine year-dummy variables DY for 1989 through 1997, and 14 industrydummy variables DI representing two-digit SIC code numbers 13, 20, 28, 33, 34, 35, 36, 37, 38, 48, 49, 50, 58, and 73.
Total number of observations equals 24,114 consisting of 12,221 and 11,893 observations audited by specialist and
nonspecialist auditors, respectively. Yearly firm-year observations are 1,481, 1,746, 1,806, 1,857, 2,046, 2,302, 2,624,
3,009, 3,510, and 3,733 for 1989 through 1998, respectively.
The pooled sample aggregates the individual year samples. Means of individual-year parameter estimates are reported for
the year-by-year models with t-values (with 9 degrees of freedom) estimated from the cross-temporal distribution of these
estimates. Reported adjusted R2s for the year-by-year samples are cross-temporal means.
As an additional diagnostic check, I compare the nondiscretionary accruals of clients of specialist
auditors and nonspecialist auditors. The ability of the specialist auditors in constraining earnings management should be more evident in the level of discretionary rather than nondiscretionary accruals.
Consistent with this expectation, I find that the differences in mean and median values of nondiscretionary
accruals between the clients of specialist and nonspecialist auditors are not significant at the 0.10 level.
Finally, I examine in a two-stage analysis whether self-selection of clients of specialist and
nonspecialist auditors is driving the observed differences in discretionary accruals. In the first stage,
Accounting Horizons, Supplement 2003
Does Big 6 Auditor Industry Expertise Constrain Earnings Management?
15
I run a logistic model of auditor choice similar to the one used by Francis et al. (1999) and compute
the inverse Mills ratio (IMR) (see Berndt 1991, Chapter 11). In the second stage, I estimate Equation
(3) after including the IMR as an additional independent variable. The results indicate that SPECLST
is negative and significant at the 0.01 level. This finding mitigates concern that sample self-selection
is driving the reported results.
The results of all these additional tests confirm the basic finding that clients of nonspecialist
auditors consistently exhibit higher levels of absolute discretionary accruals than clients of specialist
auditors.
CONCLUDING REMARKS
The rise of accruals-based earnings management in recent years has prompted calls for reforms
to restore confidence in reported accounting information. Specialist auditors have the expertise, the
resources, and the incentive to constrain opportunistic reporting of accruals and thereby enhance the
quality of earnings. This study examines whether auditors’ industry expertise mitigates the tendency
of managers to engage in accruals-based earnings management.
When Big 6 auditors are partitioned into specialists and nonspecialists, I find that clients of
nonspecialist auditors exhibit higher levels of discretionary accruals than clients of specialist auditors. This finding persists after controlling for firm size, industry effects, and other factors that are
known to affect discretionary accruals. In summary, the finding is consistent with the notion that
auditors’ industry expertise moderates accruals-based earnings management.
One implication is employment of a Big 6 auditor that is also an industry specialist can further
enhance the credibility of accounting information. While audit firms have undertaken significant
restructuring efforts along industry lines, empirical evidence on the payoff from these efforts is
limited. The findings of this research support the notion that there are likely returns to investing in
specialization in the form of increased audit effectiveness and associated credibility.
I should note one caveat. One cannot rule out the possibility that audit clients with lower
discretionary accruals tend to self-select specialist auditors, even though a sensitivity test suggests
that the results are not driven by sample self-selection.
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