Client Industry Competition and Auditor Industry Concentration

Client Industry Competition and Auditor Industry Concentration Jayanthi Krishnan* Temple University

Received February 2004; Accepted April 2005

Abstract Prior studies examining the association between auditor concentration and intra-industry competition in the client industry have yielded opposite results. I attempt to reconcile these findings by using an additional measure of intra-industry competition (the speed of adjustment of abnormal profits) and controlling for industry size. I document a negative association between auditor industry concentration and intra-industry competition, regardless of the measure of auditor concentration used. Thus, although the average level of auditor industry concentration is generally high, there is some evidence that a more competitive industry has lower auditor concentration. Therefore, involuntary audit market changes that reduce the number of audit firms available, may counter the effect of client industry competition in limiting auditor industry concentration.

JEL Classifications: M42, M43, L84 Keywords: auditor industry concentration, intra-industry competition

1. Introduction It is well known that the audit market is characterised by a high degree of supplier concentration. Concerns that increasing concentration may adversely affect competition and auditor independence appear periodically in the popular press and in policy statements.

* I thank an anonymous reviewer, Dana Hermanson (discussant at the Auditing Section Conference), Jagan Krishnan, Eric Press, and participants at the American Accounting Association Auditing Section Mid-Year Conference for their helpful comments. Partial support for this project was provided by Temple University Research and Study Leave.

Jayanthi Krishnan Journal of ContemporaryAccounting & Economics Vol I , No 2 (Dec 2005) 171-192

172

The same concern extends to the concentration of auditors within industries.’ For example, the American Institute of Certified Public Accountants (AICPA) cautions that auditor industry concentration may cause auditors to lose objectivity if they get “so close to the industry that they fail to challenge industry practices that fall short of providing the most relevant and reliable accounting information” (POB, 1994). Similarly, in a recent report on the effects of consolidation of public accounting firms on competition, the General Accounting Office (now Government Accountability Office) concluded that although it “found no evidence of impaired competition to date, the significant changes that have occurred in the profession may have implications for competition and public company choice, especially in certain industries, in the future” (emphasis added) (GAO, 2003a). A number of studies examine the effects of auditor industry concentration on independence and audit quality (e.g., Craswell et al., 1995; Balsam et al., 2003; Krishnan, 2003; Dunn and Mayhew, 2004). In this paper, I focus on the determinants of such concentration. Two factors are generally suggested as determinants of auditor industry concentration: (1) economies of scale enjoyed by auditors as they expand within an industry and (2) demand for industry specialist auditors arising because of auditors’ ability to offer greater assurance through industry specialisation.* An early study in this area (Danos and Eichenseher, 1982) suggested a third factor, client industry competition, which could actually limit the auditor concentration in some ind~stries.~ In a discussion of their finding that nonregulated (and presumably more competitive) industries had lower levels of auditor industry concentration, Danos and Eichenseher (1982, 614) argue that “. .. the shifts to low-involvement CPA firms in ‘nonregulated’ industries might reflect a preference of a significant number of buyers of audit services to use CPA firms not associated with competing (client) firms.” While Danos and Eichenseher (1982) did not explicitly state a hypothesis, the suggestion seems to be that (other things equal) auditor industry concentration is negatively associated with the extent of competition among clients within the client’s industry, because increased competition causes clients to hire auditors that are different from those of their competitors. Perhaps the most cited example of this phenomenon is the resignation of Ernst and Young from PepsiCo following the 1989 merger between Ernst and Whinney and Arthur Young, due to pressure from PepsiCo’s industry rival, Coca-Cola (Berton and Niebuhr, 1990). The implicit argument is that clients in competitive industries are sensitive to the possibility of transfer of sensitive information to competitors via their auditors. For example, Biosepra Inc (a company in the chemical products industry) changed auditors in 1996. The company explained its decision as follows: “The determination to dismiss

’

Auditor industry concentration is defined as the share of the top few auditors in an industry, and is generally measured using some size measure (e.g., square root of client assets). An alternative term, auditor dominance, is sometimes used to signify that one or two auditors have a large share of the industry. Thus dominance indicates extreme levels of concentration. My focus is on auditor concentration, although Kwon (1996). whose measure 1 use later, refers to the measures as auditor dominance measures.

See for example, Eichenseher and Danos (1981) and Craswell et al. (1995). The terms “client industry competition” and “intra-industry competition’’ are used interchangeably throughout the paper.

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

173

Coopers and Lybrand LLP was made because Coopers and Lybrand LLP agreed to serve as independent accountants for PerSeptive Biosystems, Inc, a competitor of the Company” (Source: 10-K, 313 1/97). In this study, I re-examine the association between auditor industry concentration and client industry competition. The association can be important for two reasons. First, if competitive industries are in fact characterised by lower auditor industry concentration, concerns that excessive auditor concentration may affect auditor market competition leading to monopoly power (MacDonald, 1998), and I or affect auditor independence unfavourably, should be less relevant for these competitive industries. Second, if client industry competition causes firms within an industry to seek different auditors, it is important that they have the ability to choose among different available auditors. However, events that reduce the number of auditors available - for example, the demise of Andersen, previous mergers of the former Big Eight auditors, and the bankruptcy of large non-Big Four auditors such as Laventhal and Horwath - can lessen clients’ ability to obtain auditors different from their competitors in some industries. Some limited evidence that this might be a problem is provided in a recent GAO report in which a survey respondent (a large Fortune 1000 firm) noted that because of ... the limited choices we must be willing to choose a firm that audits competitors” (GAO, 2003b, 42).4 The challenge to empirically testing the association between intra-industry competition and auditor industry concentration is the lack of generally accepted measures for both characteristics. Using client industry concentration ratios as the measure of client industry competition, Kwon (1996) finds a negative association, and Hogan and Jeter (1999) a positive association, between client industry concentration and auditor industry concent r a t i ~ n Since . ~ industry competition decreases with increasing client industry concentration ratios, Kwon’s results suggest a positive association, and Hogan and Jeter’s results suggest a negative association, between client industry competition and auditor industry concentration. The two studies differed in focus, in the data used, and most importantly, in the measures of auditor industry concentration and control variables employed, and all of these can explain the difference in their conclusions.6 Thus the prior work, because it yields opposite results, does not indicate whether more competitive industries have lower auditor concentration. I attempt in this paper to reconcile the conflicting findings. I extend the previous work in two ways. First, to measure client industry competition, I use client industry concentration ratios, as in both prior “

A few respondents reported that they would not hire the auditor of a competitor because it might lead to a loss of sensitive information. Yet, the GAO notes that most respondents said they would be willing to hire the auditor of their competitors. However, because some respondents who said they would be willing to hire the auditor of their competitors also indicated a lack of adequate choices, it is not clear what their response would be if in fact there was greater choice.

* In fact Kwon (1996) hypothesises that clients in concentrated (less competitive) industries are more likely to desire auditors that are different from their competitors. This implies that the danger of transfer of proprietary information is highest in concentrated industries. See Yardley et al. (1992), Gramling and Stone (2001), Krishnan (2001), and Neal and Riley (2004) for a review of the literature on auditor industry concentration.

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 200.5) 171-192

174

studies, but also a different measure, the speed of adjustment of abnormal profits within the industry. Concentration ratios measure the relative shares of large and small firms in an industry but do not capture competition within each of these size groups, or other aspects of competition such as barriers to entry, or fragmentation in the industry. By contrast, the speed of abnormal profit adjustment is a more general measure of client industry competition. Second, unlike prior work, I include a control for industry size, which also can influence the degree of competition (Porter, 1985). The results using both measures of intra-industry competition indicate, consistent with Hogan and Jeter (1999), that relatively more competitive industries have lower levels of auditor concentration. Moreover, this negative association is invariant to the measure of auditor industry concentration used.7 However, it is important to emphasise that the basic level of auditor concentration is high in all industries, suggesting factors that increase auditor shares in industries (e.g., economies of scale) may be more important determinants of auditor concentration than the degree of competition within the client industry. Still, to the extent that client industry competition is important in the equilibrium configuration of the audit market, this study indicates that the exogenous changes in the audit market that reduce the choices available to firms will hinder the maintenance of this equilibrium. The rest of the paper is organised as follows. Section 2 describes the motivation. Section 3 describes the research method. Sections 4 and 5 discuss the data and empirical results and Section 6 contains conclusions.

2. Motivation 2.1 Auditor industry concentration and client industry competition Equilibrium in the audit market is determined by the interaction of supply and demand factors. Supply-side studies have argued that auditor industry concentration arises due to the exploitation by auditors of economies of scale in auditing several clients in one industry (Eichenseher and Danos, 1981). Demand-side studies use product differentiation theory to argue that industry expertise contributes to a higher quality audit (Simunic and Stein, 1987). Demand for differentiated products (and for industry specialists) is determined by clients’ demand for the added assurance provided by the increased quality resulting from industry expertise (Craswell et al., 1995; Balsam et al., 2003; Krishnan, 2003; Dunn and Mayhew, 2004). However, there is another demand-side factor. If a firm fears a potential transfer of information to competitors through its auditor, it is likely to perceive a cost to hiring an industry specialist that may be working for several other competitor firms in the industry.

’

As discussed in detail later, my results are consistent whether I use Hogan and Jeter (1999)’s auditor concentration measure or Kwon (1996)’s“separation indices.” This contrasts with the opposite results reported in these two studies. I attribute my consistent results to my inclusion of industry size as a control, which seems to have been an omitted variable in the previous studies.

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

175

Such transfer need not indicate wrongdoing on the part of the auditor, even though the AICPA Code of Conduct prohibits the auditor from disclosing confidential client information without the client's consent. As Kwon (1996, 5 5 ) notes, (1) auditors could use knowledge acquired during an audit in other engagements in the industry without revealing confidential information, and ( 2 ) an auditor could disclose confidential information to competitors once it ceases to work for a client. Also, during my sample period, auditors provided significant non-audit services in conjunction with audit services. To the extent these non-audit services were associated with knowledge spillovers from audit work, this too could be a potential source of information transfer across clients. In equilibrium, clients will weigh the benefits (enhanced product) against the costs (possible loss of confidential information) and this determines the aggregate degree of industry specialisation in each industry. Other things equal, one would expect a firm to not choose an industry specialist in situations where the costs of loss of information are high. However, we cannot observe the costs of loss of information. Danos and Eichenseher (1982) suggest that clients may be more concerned about hiring their competitors' auditors in more competitive (in their case, non-regulated) industries. That is, auditor concentration in industries would be lower in industries where client industry competition is high relative to industries where client industry competition is low. However, Kwon (1996) suggests an opposite association, by arguing that the demand for auditors that are not associated with competitors is likely to occur in client industries that are concentrated. Since concentrated industries are relatively non-competitive industries, this argument suggests, in contrast to the hypothesis above, that auditor concentration in industries is positively associated with the extent of competition in the industry. Thus, at a theoretical level, the direction of the association between client industry competition and auditor industry concentration is not clear. In a similar vein, a number of recent studies have examined the relation between firms' voluntary disclosure decisions and industry competition (e.g., Gigler, 1994; Darrough, 1993; Verrecchia, 1983, 1990) and predict both increasing and decreasing disclosures with increases in competition.*

2.2 Prior empirical work Two studies, Hogan and Jeter (1999) and Kwon (1996) examine the association between auditor industry concentration and intra-industry competition. Hogan and Jeter (1 999) study the trends in and determinants of, auditor industry concentration using industry level pooled cross-sectional data for the period 1976 to 1993. They regress auditor concentration ratios (a measure of auditor industry concentration) on client industry concentration ratios and other variables. They find a significant positive association between client industry and auditor industry concentration. Recalling that client industry concentration is negatively associated with intra-industry competition, this implies that more competitive industries have lower auditor industry concentration.

* In her discussion of policy changes relating to segment disclosures, Harris (1998) notes that when concerns about competitive harm are expressed, whether the "harm is more likely to occur in more or less competitive industries is not discussed".

176

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

Unlike Hogan and Jeter, Kwon uses “separation indices” intended to measure the propensity of clients in one industry to use different auditors.’ Using industry level data, Kwon regresses measures he calls separation indices on the client industry concentration ratio (to measure competition) and other control variables, and finds unlike Hogan and Jeter (1999) a significant negative association between client industry concentration and auditor industry dominance, and consequently a positive association between intra-industry competition and auditor industry dominance. Hogan and Jeter (1999) note, that, when they use auditor industry dominance measures similar to Kwon’s, their results are similar to Kwon’s results. Thus the test of the effect of intra-industry competition, using client industry concentration ratios as the measure of competition, is sensitive to the measure of auditor industry concentration used.’O

2.3 Measuring auditor industry concentration Most prior work requiring measures of auditor industry concentration has, like Hogan and Jeter (1999), used the auditor concentration ratio, which is the percentage of the industry audited by the top few auditors in the industry, to proxy for such concentration. Typically, these measures are based on some client size measure, such as square root of assets or sales. In general, if clients are likely to avoid auditors of their competitors, one would expect a lower auditor industry concentration ratio. Therefore I use the auditor concentration ratio (defined below) as my first proxy for auditor concentration. However, the propensity to hire different auditors from one’s competitors may not be captured by the concentration ratio in some situations. For example, small industries in which all clients choose different auditors may have high auditor concentration. ‘ I Consequently I also use two “separation indices”, following Kwon (1996), based on the distribution of number of clients across the industry as measures of auditor industry concentration. Definitions of these different measures are presented below. In all the definitions that follow, i denotes the auditor, j denotes the client firm, and k denotes the industry. Let I, denote the number of audit firms in industry k. Further, let [J,,,J,,...... JI& } denote the number of clients served by each auditor (1 through I,) in industry k. The three-audit firm concentration ratio, ACR, in industry k is the sum of the market shares of the top three audit firms in the industry, where the market share for audit

Unlike Hogan and Jeter’s measure which captures auditor concentration, Kwon refers to his measures as “auditor dominance” measures. lo In addition to the difference in their dependent variables, the two studies differ in the data used and sample time period. Kwon uses data for 1989 from the Disclosure database, and Hogan and Jeter use Compustat data for 1976-1993.

” At one extreme, if an industry was comprised of three clients each of which had a different auditor, the concentration ratio for the top three auditors would be loo%, but there is a complete separation of auditors and clients.

Jayanthi Krishnan

177

Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

firm i in industry k is given by

:‘

1,

. A,,, is the total assets for firm j audited by

LC,: JA,, J

auditor i in industry k.I2 Unlike this size- (asset-) based measure, Kwon’s measures of auditor concentration (SZSD, and SZCA) are based on the distribution of number of clients across audit firms. Using the notation above, the average number of clients served per auditor is

Then, the first auditor concentration measure, SISD, which is the dispersion in the number of clients served by auditors in industry k, is given by:

SISD is zero when auditors share the industry equally (i.e., there is no auditor concentration) and increases as auditor concentration in the industry increases. The second auditor concentration measure used by Kwon (SZCA) is the ratio of the number of clients in an industry to the number of auditors in the industry,

This measure is increasing in the degree of auditor concentration in the industry. 2.4 Measuring intra-industry competition Intra-industry competition is proxied, in both studies discussed above, as well as in most other studies, by the client concentration ratio in the industry, which is the proportion of the industry (using square root of assets or sales) represented by the top few firms in the industry. The rationale for the use of the client industry concentration ratio is the empirical finding in industrial organisation studies of a positive association between industry concentration and industry profitability. The level of competition in an industry is of course determined by size distribution and concentration in the industry, but also by the presence or absence of entry barriers, regulation, and product differentiation. While concentration ratios capture the competition for market shares between large and small firms, they do not capture competition for profits within size groups. Thus as Scherer (1980) notes, the

IZ Both Kwon (1996) and Hogan and Jeter (1999) use square root of total assets as the base to measure auditor market share. The use of number of clients instead of square root of assets as a base for the calculation of market share did not change the results of this study.

178

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

use of the concentration ratio to measure monopoly power (or the lack of competition) is subject to several caveats. He summarises his discussion as follows:

“Concentration ratios understate the true quantum of monopoly power when markets are defined to include nonsubstitutes, when meaningful markets are local or regional rather than nationwide, when sellers enjoy strong product differentiation advantages within relevant product lines, and when special institutionalfeatures (. ..) intrude, The degree of monopoly power is over-stated when substitutes are excluded from the industry definition and when import competition is significant. ’’ (Scherel; 1980, 64). In general, it is difficult for the researcher to determine which industries are subject to the above pitfalls.” Therefore, there is no clear way to “correct” for the over- or understatement in the degree of monopoly / competition suggested by this rnea~ure.’~ The long-run effect of the different facets of competition, for example, concentration, barriers to entry, and product differentiation, is reflected in the persistence or dissipation of abnormal profits. Therefore, an alternative approach to measuring competition is to focus directly on the trends in profits within an industry.’* A large literature in industrial organisation has examined the dynamics of profit adjustment. Based on this literature, Harris (1998) develops a measure based on the speed of adjustment of positive abnormal profits to the “normal” industry level to examine the effect of intra-industry competition on managers’ reporting decisions regarding business segment operations. Following Harris, this study uses this alternative measure of intra-industry competition, in addition to the concentration ratio, to test the association between auditor industry concentration and the degree of competition in the client industry.I6 The details about the computation of the concentration ratio and the alternative measure are presented below. The concentration ratio, CCR, is the sum of the four largest shares of client firms in the industry, where f i m j ’ s share in industry k is defined as:

l 3 Anecdotal evidence points to some situations where the concentration ratio does not capture competitive conditions. One example is the PC industry, in which the top 10 firms have a market share of over 65% (Standard and Poor’s, 1998, 8), yet it is known to be a fiercely price-competitive industry. Another example in a service industry are the Big Four accounting firms. Despite the high degree of concentration in the audit industry, no empirical evidence indicates the absence of competition.

“ I n a review of 46 4-digit SIC industries, Scherer (1980, 64) found that the true concentration was understated in 19 industries and overstated in only 8. Is The ideal measure to test the association between intra-industry competition and auditor industry concentration should capture the proprietary costs associated with each client, that is, the costs from possible loss of proprietary information to competitors. Such costs are obviously unobservable. “

Berger and Hann (2003) also use Harris’s (1998) approach to measure the degree of industry competition.

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1,No 2 (Dec 2005) 171-192

179

Following Harris (1998, 117), the speed of profit adjustment in each industry is estimated using the following regression model for the behavior of abnormal return on assets: AROAjkl= a,,+ aIk*Dn *AROAJ,,.,+ Ezk*D, *AROAJk1-, + ~jkr

(4)

Denoting firm j ’ s return on assets in industry k in year t by ROAjk!,and the mean industry return on assets in industry k in year t by MROA,, AROAjk,in equation (4) is the abnormal return on assets given by: AROAjkl= ROAJh- MROA,. DRand D, are dummy variables indicating non-positive and positive abnormal return on assets: Dn = 1, if AROAjk,l-, I0,O otherwise D, = 1 , if AROAjk,l-, > 0, 0 otherwise. The coefficient a2k (labelled PROFADJ in the next section) is a proxy for the degree of competition in the industry because it measures the speed with which abnormal positive profits are driven down to a “normal” industry level. Therefore the higher this coefficient, the greater the inability of the firms in the industry to drive down profitability and the lower the degree of competition in the industry.

3. Method The empirical analysis rests on pooled cross-section regressions, of auditor industry concentration measures on intra-industry competition measures and other control variables. Auditor industry concentration is measured in three different ways: the three-audit firm concentration ratio (ACR)based on Hogan and Jeter (1999), and two separation indices (SZSD, SICA) based on Kwon (1996). The independent variables include the two measures of intra-industry competition and other control variables. As discussed, the measures of industry competition are the 4-client firm concentration ratio (CCR),and a proxy for the speed of profit adjustment (Hams, 1998) in each industry (PROFADJ). Control variables included in the analysis are those used in prior work. First, a trend variable (TREND) is included to capture the general upward trend in auditor industry concentration that has occurred over the sample period (Hogan and Jeter, 1999). Second, previous research has documented that auditor concentration is higher in more regulated industries (Danos and Eichenseher, 1982), and relatively fast-growing industries, and lower in industries with high litigation risk (Hogan and Jeter, 1999). Consequently dummy variables are included to identify regulated industries (REG),high growth industries (GROWTH) and high litigation risk industries (LITRISK). Third, economies of scale in audit production may induce auditor concentration in industries with relatively large firms. In addition, large firms generally choose the Big Four auditors, possibly because of their greater need for complex services which the Big

180

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

Four are better able to provide." This may cause greater auditor concentration since there are only four of these firms. A measure of average client size, measured using square root of client assets, in each industry (AVSIZE) is included as a control variable. Finally, I include industry size, INDSIZE measured by the logarithm of the total number of clients in the industry. Generally, one might expect that, in a small industry with relatively few firms, the one-to-one mapping from auditors to clients would create a mechanical positive association between auditor concentration and client concentration. On the other hand, in large industries, this effect may be less strong. In addition, the number of firms in an industry may itself influence the degree of competition (Porter, 1985).

4. Data and Basic Results

4.1 Data The data for this study, covering the period 1988 to 1998, is taken from the annual Cornpustat database.l 8 The variables were constructed for three-digit SIC industrie~.'~ To ensure some variability within industries, only industries with at least 10 observations were retained. The final sample has 1,456 industry-year observations.20Yearly three-audit firm concentration ratios for each industry were constructed using all valid observations on the database. Similarly, average client size in each industry and industry concentration ratios for each year were constructed using all valid observations. The profit adjustment rate measure, PROFADJ, is the coefficient aZk in equation (4). This variable was estimated for each industry, using pooled data for the years 1988-1998. To ensure some precision in the estimates, the regressions were estimated for three-digit industries that had at least 45 observations. This resulted in estimates for 167 out of the 176 industries in the final sample. For the remaining nine industries, the estimates were generated using two-digit classifications. Following Hogan and Jeter (1999), regulated industries (REG)include the following two-digit SIC codes: 10,12,13, 14,20,29,40,41, 42,44,45,46,48,49,60,61,62,63,64, and 67. High litigation risk industries (LITRISK) include the following two-digit SIC codes: 28, 35, 36, 38, 60, 67, and 73. Finally high-growth industries (GROWTH) include the following 2-digit SIC codes: 35, 45, 48, 49, 52, 57, 73, 78, and 80.

I' During the sample period, there were six large audit firms (Big Six). With the merger of Price Waterhouse and Coopers and Lybrand and the demise of Andersen, the number of large firms has been reduced to four (Big Four).

To check for the effect of the 1989 auditor mega-mergers, the equations reported in the next section were re-estimated for the year 1990-1998. The results are similar to those reported. l9 Kwon uses 3 and 4-digit classifications, while Hogan and Jeter use 2-digit classifications and report that their results are unchanged when they employ 3-digit classifications. The use of 3 digits provides an overlap between both Kwon's and Hogan and Jeter's classification.

'"The 1,456 industry-year observations include 176 three-digit industries. Due to the condition that each industry should have at least 10 observations, not all industries are included in every year. Of the 176 industries, 11 appear in 8 out of the 11 sample years, 16 in 9 sample years, 71 in 10 sample years and 26 in 11 sample years.

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

181

Table 1 Auditor Industry ConcentrationMeasures Panel A: Descriptive Statistics

ACR

Mean

Standard Deviation

First Quartile

Median

Third Quartile

0.75

0.11

0.67

0.76

0.83

SISD

2.56

2.85

1.13

1.60

2.71

SICA

3.88

3.37

2.13

2.80

4.13

SISD

SICA

-0.32***

-0.35***

Panel B: Correlations

ACR

0.97***

SISD

Panel C: Correlations Partitioned by Industry Size

Quintile for Industry Size

Correlation (ACR, SISD) 0.46***

1

Correlation (ACR, SICA)

Correlation (SISD, SICA)

0.41***

0.63***

2

0.45** *

0.33***

0.40***

3

0.28***

0.2 I ***

0.46***

4

0.14***

0.14***

0.53***

-0.16***

0.96***

5

-0.12**

***

Significant at the .01 level, two-tailed test Significant at the .05 level, two-tailed test N = 1,456 industry-years. ACR = three-audit firm concentration. SISD = Dispersion of number of clients across auditors in the industry. SICA = Ratio of number of clients to the number of auditors in the industry. *I*

4.2 Descriptive statistics Table 1 presents descriptive statistics for the auditor industry concentration measures. The mean three-audit firm concentration ratio (ACR) is 0.75, indicating that, on average, auditors are quite concentrated in industries. This is comparable with the mean three-audit firm concentration ratio in Hogan and Jeter (1999) of 0.64 for the period 1976-1993. The two other auditor industry concentration measures are Kwon’s separation indices. The mean value of SZSD, the dispersion of number of clients served by each auditor, is 2.56 with considerable variation across the industries. The mean of SICA, the number of clients per auditor, is 3.88, varying between 1 and 28 over the industries in the sample. Both

182

Juyanthi Krishnnn Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

means are higher than those reported by Kwon, whose means for SISD is 1.83 and that for SICA is 2.49. However, Kwon includes one year (1989) of data, while my data covers the period 1988 to 1998. The numbers indicate the dispersion of number of clients and the average numbers of clients within industries have increased since Kwon’s sample period. Overall, compared to both studies, auditor concentration and separation indices have gone up in magnitude. Panel B of Table 1 shows the correlations between auditor concentration and the two separation indices. ACR is negatively correlated with SISD and SICA, as in Kwon (1996). Thus industries with greater auditor concentration in terms of client (square root of) assets are characterised by relatively less variability in the distribution of clients across auditors (lower SISD), and lower average number of clients per auditor (lower SICA). One explanation for the negative correlation is that industry size, i.e., the number of firms in the industry, is a confounding factor. As the industry gets larger, one expects lower auditor concentration (ACR) as more clients are spread out over the auditors. However, when the industry gets larger, one also expects greater dispersion of clients across auditors (higher SISD), and greater number of clients per auditor (higher SICA). Thus, the underlying factor, industry size, could engender a negative correlation between ACR and the separation variables. To examine this, I separate the sample into five quintiles according to the number of firms in the industry. The correlations for each subgroup are shown in Table 1, Panel C. The correlations between the ACR and the two separation indices are positive and significant for the first four quintiles of industry size. Only in the fifth quintile, which consists of

Table 2 Descriptive Statistics for the Lntra-industry Competition Measures and Control Variables Variable

Mean

Standard Deviation

First Quartile

Median

Third Quartile

CCR

0.50

0.17

0.37

0.51

0.63

PROFADJ

0.62

0.26

0.43

0.67

0.8 1

22.80

18.73

10.76

16.06

27.94

REG

0.27

0.44

0.00

0.00

1.oo

GROWTH

0.22

0.41

0.00

0.00

0.00

LITRISK

0.21

0.41

0.00

0.00

0.00

INDSIZE

3.18

0.71

2.64

3.00

3.58

AVSIZE

N = 1,456 Industry-years = 4-client firm concentration ratio. CCR PROFADJ = Speed of adjustment of positive abnormal profits (coefficient in the following equation: AROA,,, = aok+ a,, *Dn*AROA,, + azk*D,*AROAII,.,+ &), AVSIZE = average size (in $ millions) of firms in industry, based on square root of assets. = 1, if the industry is regulated, 0 otherwise. REG GROWTH = 1, if the industry is a high-growth industry, 0 otherwise. LITRISK = 1, if the industry is a high litigation-risk industry, 0 otherwise. INDSIZE = Log(Number of clients in industry).

,

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

183

the largest 20% industries, is the correlation negative.*' Thus in the largest industries, a greater dispersion of clients is accompanied by lower concentration for the top three auditors, indicating that industry size is an important determinant of auditor concentration. Table 2 presents information about the two intra-industry competition measures and control variables. The mean four-client firm concentration ratio (CCR) is 0.50. The profit adjustment measure indicates the speed of adjustment of positive abnormal profits to a normal level. Recall that the higher this number, the lower the competition in an industry. The mean PROFADJ is 0.62. Similar to the results reported by Harris (1998), the two competition measures are not correlated (correlation coefficient = 0.0001, p-value = 0.99). Harris notes (pp.119-122) that this could be because they measure different aspects of competition: concentration focuses on competition between large and small firms in the industry, PROFADJfocuses on competition among firms regardless of firm size. The mean for the average size of firms in industries is about US$23 million. On average, 22% of the observations are in high-growth industries, 21 % in high litigation-risk industries, and 27% in regulated industries.22 4.3 Regression results Table 3 reports regression results using the auditor industry concentration measure and the two separation indices as dependent variables. White's (1980) adjusted t-statistics are presented after correcting for heteroskedasticity. I examined the variance inflation factors for the model. The highest number is 3.87, which is below the rule of thumb of 10, indicating multicollinearity is not a concern. Adjusted R-squared for the ACR model is slightly higher than that (0.30) reported in Hogan and Jeter (1999). The adjusted R-squareds for the models for the separation indices are also higher than those in Kwon (1996), who reports adjusted R-squareds ranging between 0.49 and 0.57, and between 0.60 and 0.65 for models with SZSD and SZCA as dependent variables respectively. Recall that both competition measures, CCR and PROFADJ, increase as competition in the industry decreases. Therefore, if increased competition leads to decreased auditor industry concentration, one would expect a positive sign on these variables. Consistent with this expectation, both CCR and PROFADJ have positive signs in all columns. Thus the degree of competition, as measured by the degree of concentration and by how quickly abnormal profits are dissipated in the industry, has a negative effect on the degree of auditor concentration in the industry.

21 The means for the large and small industry groups indicates that all measures of auditor concentration are higher for smaller industries than for larger industries. The mean for ACR for large (small) industries, defined as industries with number of firms greater than (less than or equal to) the median, is 0.70 (0.80), the mean for SZSD for large (small) industries is 3.9 (1.23) and the mean for SICA for large (small) industries is 5.56 (2.2).

22Asnoted earlier, Hogan and Jeter (1999) cover a longer time period, 1976-1993. This would explain the lower mean ($15.5 million) for their average industry size variable, and for their LITRISK (10%)and GROWTH (15.5%) variables.

184

Jayanthi Krishnan Journal of Contemporaly Accounting & Economics Vol 1, No 2 (Dec ZOOS) 171-192

Table 3 Regression Estimates Parameter Estimates (White’s-corrected t-Statistics) Variable ACR (1)

Constant

Dependent Variable SISD (2)

SICA (3)

0.590*** (19.244)

-1 1.227*** (-14.201)

-12.798*** (-14.765)

0.299*** (13.006)

2.255*** (4.975)

2.669*** (5.459)

0.047*** (5.638)

0.345*** (2.603)

0.364** (2.372)

TREND

0.004*** (4.578)

0.022* (1.760)

0.036*** (2.676)

REG

0.019*** (3.140)

0.3 18*** (2.666)

( 1.970)

Measures of Intra-industry Competition

CCR PROFADJ

Control Variables

LITRISK

-0.008 (-1.397)

-0.005 (-0.042)

0.255** 0.077 (0.574)

GROWTH

0.034*** (6.029)

0.229** (2.012)

0.267** (2.135)

AVSIZE

0.001*** (9.837)

0.001 (0.384)

( 1.903)

-0.025*** (-4.331)

3.819*** (21.352)

INDSIZE Adjusted R2 N (Industry Years)

0.005*

4.599*** (22.821)

0.42

0.73

0.77

1,456

1,456

1,456

***

Significant at the .01 level, two-tailed test Significant at the .05 level, two-tailed test * Significant at the .I0 level, two-tailed test See Table 2 for variable definitions.

**

Prior work did not examine the effect of PROFADJ. However, Hogan and Jeter (1999) report a positive association between CCR and ACR, while Kwon (1996) reports negative associations between CCR and both SISD and SICA. By contrast, my results indicate consistent results - a positive sign on CCR - for all three dependent variables. It seems that the explanation for the difference between these and Kwon’s results is that I include industry size as a control variable. When I estimate the models excluding industry size, CCR

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

185

continues to be positively associated with ACR, but is negatively associated with SISD and SICA, as in Kwon (1996). Note that INDSIZE has differing effects on ACR (negative) and on SISD and SICA (positive). Thus, in the absence of the industry size control, CCR seems to pick up its effect in columns 2 and 3. Turning to the other variables in Table 3, TREND indicates all measures of auditor industry concentration have an upward time trend over the sample period, 1988-1998. This is consistent with (and extends) Hogan and Jeter’s finding of an upward trend for the time period 1976 to 1993. Among the other control variables, regulated industries have higher levels of auditor industry concentration using all three measures, as noted by Danos and Eichenseher (1982) and Hogan and Jeter (1999). Similarly, AVSIZE is positively associated with auditor industry concentration, consistent with Hogan and Jeter. Both these results could indicate that economies of scale arise in regulated industries and in industries with large firms. They could also reflect the need in these industries for auditor expertise due to the complexities of r e g u l a t i ~ nAs . ~ ~in Hogan and Jeter (1999), high growth industries are positively associated with auditor concentration.In addition, they are also positively associated with the two separation indices, SISD and SICA. LITRISK is not significant.

Table 4 Predicted Levels of Auditor Concentrationat Different Levels of Industry Competition Competition Measured by CCR” Low High Predicted Auditor concentration (ACR)

86.6%

63.7%

Competition Measured by PROFADJb Low High Predicted Auditor concentration (ACR)

76.0%

73.2%

Competition Measured by CCR and PROFADJ’ Low High Predicted Auditor concentration (ACR)

87.4%

61.8%

Note: The predicted values for ACR are generated using the regression in Table 3, column 1. a “Low” and “high” competition are defined as CCR= 0.8 and CCR=0.2 respectively. All other variables (including PROFADJ) are set at their mean values. “Low” and “high” competition are defined as PROFADJ=0.8 and PROFADJ=0.2 respectively. All other variables (including CCR) are set at their mean values. “Low” and “high” competition are defined as (CCRz0.8 and PROFADJ=0.8) and (CCR=O.2 and PROFADJ=0.2) respectively. All other variables are set at their mean values.

23 To the extent regulated industries and industries with large firms are relatively non-competitive, these results are also consistent with the hypothesis that client industry competition is negatively associated with auditor industry concentration.

186

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

4.4 Economic significance of estimates What do these estimates tell us about expected levels of auditor concentration? As noted earlier, auditor concentration is, on average, high. To estimate the impact of intraindustry competition, I present in Table 4, predicted values of ACR for the “average” industry-year in the sample. I present the predicted values for “low” and “high” levels of competition, using the CCR and PROFADJ as the measures of competition. I use the first and third quartiles for CCR and PROFADJ to define high and low competition levels. All control variables are set at their mean values. All numbers indicate that the base level of auditor concentration (at low levels of competition) is high, but there is a decline in auditor concentration as intra-industry competition increases. When competition is measured using CCR, the predicted auditor concentration decreases from 86.6% to 63.7% as competition increases. When competition is measured by PROFADJ, the change is less marked: the predicted auditor concentration decreases from 76.0% to 73.2% as competition increases. Finally when both CCR and PROFADJ are used to measure competition, the predicted auditor concentration decreases from 87.4% to 61.8% as competition increases. Other combinations for CCR and PROFADJ to measure high and low competition yield similar results. An interesting finding is that, in all cases, the predicted level of auditor concentration is above 60% even at high levels of competition. Therefore, causes of auditor concentration other than intra-industry competition, such as economies of scale and product differentiation, are the dominant factors in determining auditor concentration.

5. Further Analyses 5.1 Partitioning into regulated and unregulated industries

Prior work suggests regulated industries are different from non-regulated industries both because they are less competitive, and have more complex accounting issues. The complexity of accounting and compliance may generate economies of scale for auditors. About 27% of the industry-years in the sample comprise regulated industries. To check whether the results vary across regulated and non-regulated industries, I estimated the models separately for the two sub-samples. The results are in panel A of Table 5. Columns 1,3 and 5 present the results for unregulated industries, and columns 2 , 4 and 6 present the results for regulated industries. The results indicate that both intra-industry competition measures are positive and significant (p-value <0.01 in all cases) for the ACR3, SZSD and SlCA models for unregulated industries. Thus the positive association between intra-industry competition and audit market concentration seen in Table 3 holds strongly for unregulated industries. For the regulated industries, the results are different. In column 2, both CCR and PROFADJ are positively associated with ACR, although the latter is weakly significant (p-value = 0.06, two-tailed test). In column 4, CCR is positive and significant but PROFADJ is not statistically significant. Surprisingly, in column 6, CCR is insignificant, and PROFADJ is negative and weakly significant (p-value = 0.06, two-tailed test). Thus, of the six coefficients relating to intra-industry competition (i.e., the coefficients for CCR and PROFADJ for the

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

187

ACR, SZSD and SlCA models), three are significant and positive, two are insignificant, and one is negative and significant. Therefore, the hypothesis that intra-industry competition is positively associated with auditor concentration is strongly supported for unregulated industries but not for regulated industries. Table 5

Further Analyses Panel A: Separate Regressions for Unregulated and Regulated Industries

Parameter Estimates (White’s-corrected t-Statistics) Dependent Variable ACR (1)

Variable

SISD (2)

Unregulated Regulated Industries Industries

SICA

(3) (4) Unregulated Regulated Industries Industries

(6) Unregulated Regulated Industries Industries (5)

Constant

0.553*** (15.989)

0.636*** (9.921)

-11.252*** (-11.168)

CCR

0.355*** (13.511)

0.256*** (5.804)

3.372*** (6.079)

1.914** (2.470)

3.937*** (6.773)

0.923 (1.113)

0.050*** (5.234)

0.030* (1.878)

0.444*** (3.324)

0.402 ( 1.498)

0.731*** (4.900)

(- 1.878)

TREND

0.005*** (5.825)

0.001 (0.787)

0.025** (1.998)

0.046 (1.578)

0.030** (2.142)

LITRISK

0.004 (0.646)

GROWTH

0.003 (0.426)

AVSIZE

0.001*** (5.397)

PROFADJ

INDSIZE

-12.325*** -13.378*** (-12.488) (-9.166)

-11.407*** (-7.502)

-0.577* 0.083** (2.544)

-0.0 15 (-0.160)

3.831 *** (2.973)

-0.018 (-0.170)

3.558** (2.270)

0.084*** (8.833)

-0.133 (-1.076)

1.098*** (6.041)

0.005 (0.036)

0.95 1*** (4.618)

0.001*** (6.274)

-0.001 (-0.329)

0.002 (0.656)

0.010*** (3.323)

4.1 14*** (14.172)

4.513*** (18.519)

-0.041 (-1.556)

-0.024*** -0.02 1* (-1.924) (-3.586)

3.652*** (16.186)

-0.003 (-0.716) 4.569*** (12.840)

Adjusted RZ

0.45

0.42

0.73

0.79

0.77

0.80

N (Industry Years)

1,065

391

1,065

39 1

1,065

391

Jayantki Krishnan Journal of Contemporary Accounting & Economics Vol 1, No 2 (Dec 2005) 171-192

I88

Table 5 (cont.) Further Analyses ~

Panel B: Alternative Measures of Auditor Concentration

-

Variable Constant

Parameter Estimates (White’s-corrected t-Statistics) ~~

Dependent ACR2 0.452*** ( 11.922)

~~

Variable ACRl 0.175*** (5.323)

CCR

0.21 8*** (7.169)

0.234*** (8.550)

PROFADJ

0.026** (2.473)

0.018* (1.885)

TREND

0.007*** (7.603)

0.004*** (4.789)

REG

0.028*** (4.006)

0.013** (2.007)

LITRISK

0.003 (0.416)

GROWTH

0.032*** (4.938)

0.019*** (3.189)

AVSIZE

0.001*** (6.384)

0.001*** (5.433)

INDSIZE

-0.027*** (-3.742)

-0.001 (-0.206)

-0.004 (-0.551)

Adjusted R2

0.26

0.18

N (Industry Years)

1,456

1,456

ACR 1 = share of the top auditor in the industry ACR2 = two-firm auditor concentration See Table 2 for other variable definitions. *** Significant at the .01 level, two-tailed test ** Significant at the .05 level, two-tailed test

5.2 Alternative auditor concentration measures The auditor concentration measure and the two separation indices are conceptually different because ACR is based on client size, while the separation indices are based on client numbers, and measure dominance rather than concentration. Two dominance measures that are based on client size are the two-firm auditor concentration (ACR2), and the share of the top auditor (ACRI)in the industry. In Panel B, Table 5, I present results using these two measures as dependent variables. In column 1, both CCR and PROFADJ are significant and positive, with p-value <0.05. In column 2, both are significant and

Jayanthi Krishnan Journal of Contemporay Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

189

positive, with p-values 0.00 and 0.06 respectively. Thus the dominance of one or two auditors in an industry is greater for less competitive industries.

6. Discussion and Conclusions Previous work has documented some determinants of auditor industry concentration. Two factors consistently recognised in the literature are the existence of economies of scale in auditing some industries, and the differences in the demand for the added assurance arising from auditor industry specialisation. A third factor, less often recognised, is client industry competition. It has been suggested that auditor concentration in some industries may be restricted because firms may prefer to hire auditors that are not associated with their competitors. This preference is conjectured to arise due to concerns that competing firms may acquire sensitive information via their auditors. Hogan and Jeter (1999) and Kwon (1996), using different measures of auditor industry concentration, reach opposite conclusions about its association with intra-industry client competition. I seek to reconcile these findings. I examine the association between auditor industry concentration and intra-industry competition for the period 1988 to 1998. I measure auditor concentration by a three-firm auditor concentration ratio (as in Hogan and Jeter, 1999) and two separation indices (as in Kwon, 1996). To measure intra-industry competition, I use the client industry concentration ratio as in prior work, and the speed of adjustment of abnormal profits within the industry (not used in prior work). In addition to the controls used in the prior studies, I include a control for industry size. I find that both measures of intra-industry competition indicate - consistent with Hogan and Jeter (1 999) but not with Kwon (1996) - that more competitive industries have lower levels of auditor concentration. The results are strongest for non-regulated industries which comprise 73% of the sample of industry-years. The results hold regardless of whether auditor industry concentration is,measured by the auditor concentration ratio, or the separation indices. Thus, there is no longer a conflict between the findings in Hogan and Jeter (1999) and Kwon (1996). I attribute this to the additional control for industry size that was not included in the prior work. Thus industries with greater intra-industry client competition are associated with lower auditor concentration. What does this tell us about audit market structure? In theory, this means auditor concentration cannot increase indefinitely in some industries because clients will want different auditors. Therefore, concerns that increasing auditor concentration will adversely affect audit market competition and audit quality should be alleviated in these industries because intra-industry competition would prevent individual auditors from dominating the However, it is important to note that, while I find a statistically significant negative association between intra-industry competition and auditor industry concentration, the economic significance of this association may be limited. All measures of auditor industry concentration indicate - as has prior work - that the degree of auditor

24 However, there is little evidence to date of reduced competition (or the adverse use of monopoly power), or reduction in audit quality (Beattie et al., 2003).

190

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

concentration is high, even in competitive industries. Using the regression estimates to predict auditor concentration at various levels of intra-industry competition, I find that while predicted auditor concentration decreases as client intra-industry competition increases, the predicted level of auditor concentration is over 60% even at high levels of competition. This could be because other factors impacting auditor industry concentration - average firm size and growth in the industry, both of which reflect economies of scale are more dominant than intra-industry client competition. However, to the extent intra-industry competition is important in determining audit market structure, my results have another implication. Involuntary changes in the audit market may adversely affect clients’ ability to choose auditors. For example, with the recent collapse of Andersen, companies must choose among four large auditing firms particularly if they wish to hire a “brand-name” auditor. Policy changes that require separation of audit and certain non-audit services further restrict companies’ choice of auditors (Beattie et al., 2003; GAO, 2003b). Thus it is likely that the demand for separation of auditors arising out of competition in the client industry will be more difficult to a~hieve.’~ An interesting extension of this study would be to examine the association between intraindustry competition and auditor concentration in the period following the Sarbanes-Oxley Act of 2002. As Beattie et al. (2003, 262) point out, the dramatic change in the environment in recent years means “reduced choice and conflicts of interest may be growing”. In addition, future studies should also examine this issue at the micro (firm or engagement) level. In particular, since the relevant element of competition is the existence of proprietary costs, future work should examine possible measures of such costs at the micro level in relation to the auditor choice decision.

References Balsam, S., J. Krishnan, and J. S. Yang, 2003, “Auditor industry specialization and earnings quality”, Auditing: A Journal of Practice & Theory 22 (September), 71-97. Beattie, V., A. Goodacre, and S. Fearnley, 2003, “And then there were four: A study of UK audit market concentration -causes, consequences and the scope for market adjustment”, Journal of Financial Regulation and Compliance 11 (3), 250-265. Berger, P.G. and R. Hann, 2003, “The impact of SFAS No. 131 on information and monitoring”, Journal of Accounting Research 41 (May), 163-223. Berton, L. and R.G. Niebuhr, 1990, “Emst & Young is forced to drop PepsiCo as client”, The Wall Street Journal (February 26), B8. Craswell, A.T, J. R. Francis, and S. L. Taylor, 1995, “Auditor brand name reputations and industry specializations” Journal of Accounting and Economics 20 (December), 297-322.

2s Beattie et al. (2003) suggest some ways in which the reduced choice of auditors may be alleviated: a possible expansion of the non-Big Four into the audits of public companies, possible mergers of non-Big Four firms to be able to compete with the Big Four, and demergers of the Big Four.

Jayanthi Krishnan Journal of ContemporaryAccounting & Economics Vol I , No 2 (Dec 200.5) 171-192

191

Danos, P. and J.W. Eichenseher, 1982, “Audit industry dynamics: Factors affecting changes in client-industry market shares”, Journal of Accounting Research 20 (Autumn), 604-6 16. Darrough, M. N., 1993, “Disclosure policy and competition: Cournot vs. Bertrand”, The Accounting Review 68 (July), 534-56 1. Dunn, K. A. and B. W. Mayhew, 2004, “Audit firm industry specialization and client disclosure quality”, Review of Accounting Studies 9 (March), 35-58. Eichenseher, J.W. and P. Danos, 1981, “The analysis of industry-specific auditor concentration: Towards an explanatory model”, The Accounting Review 61 (July), 479-492. General Accounting Office (GAO), 2003a, Public Accounting Firms: Mandated Study on Consolidation and Competition, Report to the Senate Committee on Banking, Housing, and Urban Affairs and the House Committee on Financial Services. GAO-03-864. GAO, 2003b, Accounting Firm Consolidation: Selected Large Public Company Views on Audit Fees, Quality, Independence, and Choice. Report to the Senate Committee on Banking, Housing, and Urban Affairs and the House Committee on Financial Services. GAO-03-1158. Gigler, F., 1994, “Self-enforcing voluntary disclosures”, Journal of Accounting Research 32 (Autumn), 224-240. Gramling, A. A. and D. N. Stone, 2001, “Audit firm industry expertise: A review and synthesis of the archival literature”, Journal of Accounting Literature (20), 1-29. Harris, M. S., 1998, “The association between competition and managers’ business segment reporting decisions”, Journal ofAccounting Research 36 (Spring), 111-1 28. Hogan, C. E. and D. C. Jeter, 1999, “Industry specialization by auditors”, Auditing: A Journal of Practice & Theory 18 (Spring), 1-17. Krishnan, J., 2001, “A Comparison of auditor’s self-reported industry expertise and alternative measures of industry specialization”, Asia-Pacific Journal of Accounting and Economics 8 (December), 127-142. Krishnan, G.V., 2003, “Does Big 6 auditor industry expertise constrain earnings management?’, Accounting Horizons 17 (Supplement), 1-16. Kwon, S.Y., 1996, “The impact of competition within the client’s industry on the auditor selection decision”, Auditing: A Journal of Practice & Theory 15 (Spring), 53-70. MacDonald, E., 1998, “Ernst blamed for collapse of merger”, The Wall Street Journal (February 17), A3. Neal, T. L. and R. R. Riley, Jr., 2004, “Auditor industry specialist research design”, Auditing: A Journal of Practice & Theory 23 (September), 169-177. Porter, M. E., 1985, Competitive Advantage: Creating and Sustaining Superior Pei$omzance (The Free Press: New York). Public Oversight Board (POB), 1994, Strengthening the Professionalism of the IndependentAuditol: Report to the Public Oversight Board of the SEC Practice Section, AICPA from the Advisory Panel on Auditor Independence (POB: Stamford, CT). Scherer, F. M., 1980, Industrial Market Structure and Economic Performance (Rand McNally College Publishing Company: Chicago). Simunic, D. A, and M. T. Stein, 1987, Product Differentiation in Auditing: Auditor Choice in the Market for Unseasoned New Issues. Canadian Certified General Accountants’ Research Foundation. Research Monograph.

192

Jayanthi Krishnan Journal of Contemporary Accounting & Economics Vol I , No 2 (Dec 2005) 171-192

Standard and Poor’s, 1998, Computers: Hardware Zndustry Survey (19 November 1998), 1-33. Verrecchia, R. E., 1983, “Discretionary disclosure”, Journal of Accounting and Economics 5 (December), 179-194. Verrecchia, R. E., 1990, “Endogenous proprietary costs through firm interdependence”, Journal of Accounting and Economics 12 (Januaryhlarch), 245-250. White, H., 1980, “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity”, Econometricu 48 (May), 8 17-838. Yardley, J. A., N. L. Kauffman, T. D. Cairney, and W. D. Albrecht, 1992, “Supplier behaviour in the US audit market”, Journal ofAccounting Literature 11, 151-184.