The jointness of audit fees and demand for MAS: A … · The jointness of audit fees and demand ......

The jointness of audit fees and demandfor MAS: A self-selection analysis*

A. RASHAD ABDEL-KHALIK University of Florida

Abstract. This paper has two objectives: (1) providing a method to evaluate directly thecosts (benefits) of knowledge spillovers arising from purchasing MAS from the incumbentauditor, and (2) examining the effect of estimates of the cost of auditor change onclients' ability to capture the resulting cost savings. The first is achieved by using self-selection bias parameters estimated from switching regressions. The latter is based ondata generated from clients' assessments of auditor change costs and the related economicconditions. For a sample of 84 companies, the results indicate that purchasing MAS fromthe incumbent auditor does not have a bearing on audit fees. The paper also provides anapplication of the Heckman-Lee method of correcting for self-selection bias.

Resume. L'auteur vise deux objectifs: 1) presenter une methode permettant revaluationdirecte des couts (et des avantages) de la mise a contribution des connaissances ac-quises decoulant du recours aux conseils de gestion du verificateur en titre et 2) analyserl'incidence de l'estimation des couts que suppose un changement de verificateur sur lacapacite du client de realiser les economies qui devraient en resulter. Pour atteindre le pre-mier objectif, l'auteur utilise des parametres de distorsion d'autoselection estimes a partirde la commutation de regressions. Pour atteindre le second, il se fonde sur des donneesproduites a partir de revaluation par les clients des couts que suppose le changement deverificateur et sur la conjoncture economique qui s'y rattache. Pour un echantillon de 84entreprises, les resultats indiquent que le recours aux conseils de gestion du verificateuren titre n'a pas d'incidence sur les honoraires de verification. L'auteur applique egalementla methode Heckman-Lee de correction de la distorsion d'autoselection pour evaluer lesavantages de la mise a contribution des connaissances acquises.

IntroductionThe economics of accounting firms is changing as an increasing number of firmsgenerate larger shares of their revenue from providing management advisoryservices (MAS). Aside from the considerations created by the issue of audit

EDITOR'S NOTE: An earlier draft of this paper and the discussion by Ira Solomon (pp 323—328) werepresented at the 1988 Contemporary Accounting Research Conference on Accounting Informationand its Verification - Implication for the Management of Organizations at McMaster University.* The author wishes to thank Bruce H. Nearon, Carolyn Takeda, and Peter McKay for as-

sistance in data collection and computing. The comments of an anonymous reviewer andparticipants at workshops at San Francisco State University and The Ohio State University areappreciated. Financial support of the Fisher School of Accounting is appreciated.

Contemporary Accounting Research Vol. 6 No. 2 - I pp 295-322

296 A.R. Abdel-khalik

independence, MAS provided by consultants who are familiar with auditingshould assist the auditor because the consultants are able to design or maintainauditable systems that increase the opportunity for information triinsfers betweenMAS and auditing. The synergy arising from providing audits and MAS as jointproducts is a form of extemal economies referred to as knowledge spillovers(Simunic, 1984).

A priori, the presence of knowledge spillovers should not be expected toexert an upward pressure on the cost of audits due to the resultant cost savings.That is, clients should not pay a penalty for acquiring two products from oneinstead of two suppliers. Nevertheless, prior research indicates that the portion ofaudit fees not explainable by certain observable client characteristics (e.g., effortdeterminants) is significantly higher for the clients who purchased MAS fromincumbent auditors (Simunic, 1984). That is, obtaining two products from thesame firm has been found to cost the client more in audit fees than buying thetwo products from two different sources. In addition, Palmrose (1986b) reportedsimilar results for MAS purchasers regardless of whether they are purchasingMAS (or tax advice) from the incumbent audit firm or from others.

One interpretation of this evidence is to suggest the absence of economicsynergy between MAS and audit functions. Simunic (1984) offers another in-terpretation: the increase in audit fee when MAS is also purchased from theincumbent auditor can be viewed as a (composite) quasirent. To the extent thatdemand for auditing is highly inelastic, the excess cost is in effect a holdup cost.However, this interpretation was not convincing to Beck, Frecka, and Solomon,who developed (1988a) and tested (1988b) the conditions necessary for knowl-edge spillovers to occur. Their analytical development led to two different resultsfor the economic bonding between an audit firm and a client based on the fre-quency of recurrence of concurrently providing both MAS and auditing. Theirempirical tests used the length of the auditor's tenure in providing audits to theclient as an indicator of economic bonding (Beck et al., 1988b). However, con-straints related to data availability did not facilitate their dealing directly withthe spillover issues arising from the conclusions of Simunic and Palmrose.

This paper examines the "effect" of knowledge spillovers more directly byestimating the "cost" to the client for self-selecting into a regime of jointlyacquiring MAS and audit from the same firm. In addition, the analysis considersthe relevance of the client's cost of changing auditors, including the effects ofrelated competition in the relevant market segment. From the client's point ofview, the cost of changing auditors includes the cost of search for a new auditorand the intemal cost of adapting operations to auditor change. These costs areinfluenced by the client's own intemal organizational structure and view of theextemal environment in which audit services are acquired. An element of theextemal environment is the degree of monopoly power of the incumbent auditfirm, which varies by locality, depending on the demand and supply conditionsin the region. The effect of audit market segmentation is to generate different

The Jointness of Audit Fees and Demand for MAS 297

degrees of localized monopolistic power for accounting firms. Consequently,the higher the relative monopoly power in the region, the lower the auditors'incentive to pass on to the client the cost savings attributable to knowledgespillovers. Tbe incentive problen arises also in the client's demand function.The greater tbe client's perception of the regional monopoly power of auditfirms, the higher the client's perceived cost of searching for and adapting to anew audit firm. Hence, the cost savings arising from knowledge spillovers maynot be passed on to tbe client if one or both of tbe following situations exist intbe relevant segment of audit markets.

1 Tbe client views tbe cost of cbanging auditors to be bigb.2 Competitive conditions in the relevant segment of audit markets are such that

audit firms are able to keep the cost savings as monopoly rent.

It is not necessary, however, that there would be in reality a greater degree ofmonopoly. Because there is no public disclosure of audit costs of competitors, itis sufficient tbat tbe client believe tbat market conditions are such that acquiringthe audit service from competitive audit firms at lower fees will entail highcosts of search and adjustment. Such belief can be perpetuated by the scarcityof reliable information about audit fees cbarged by competitors.' Whether thecost of changing is real or perceived, tbe outcome is the same: a client acquiringbotb auditing and MAS from tbe same firm is likely to pay a relatively bigbermonopoly rent under those conditions. In this case, the monopoly rent earnedby the firm on the engagement could in effect overcompensate for cost savingsarising from the synergy of providing two product lines—audits and MAS. Thispaper examines these assertions from the demand side by evaluating the relevantcosts in two audit regimes, whicb differ mainly according to the existence orabsence of conditions for knowledge spillover.

Tbe remainder of tbis paper consists of tbe following sections. Specific fea-tures of MAS are discussed in the next section. The following section discussesthe relevance of localized competition in evaluating audit fees. A statement ofresearch objectives follows. The method, data, and test results are in the fifthsection, and the last section consists of discussion and concluding comments.

MAS featuresUnlike audits, acquiring MAS is purely a discretionary activity. The client'schoice is a two-sequence decision:

1 The author is unaware of publicly available information on audit or MAS fees. The Securitiesand Exchange Commission (SEC) has attempted to require the disclosure of such informationfor the purpose of public assessment of independence. The effort culminating with ASR 250in 1978, which required disclosure of the percentage of MAS to audit fees, was short livedand the disclosures were not continued. Audit firms do not release that information. Informalchannels and surveys continue to be the only sources for such information. However, clientsmay test the market by seeking competitive bids, which is a costly process. Or, alternatively,where allowed. CPA firms might msjce unsolicited offers to compete.


1 WTiether to develop the service intemally (such as hiring the firm's owncomputer or tax specialists and providing the needed overhead support) or tohire task-oriented consultants; and

2 Whether the outside management consultants should be hired from the in-cumbent auditing firm.

Once a decision is made to hire outside consultants, the client's discretionextends further to the determination of both the scope (effort) and the quality ofservice. In addition, five characteristics of the demand for MAS are of particularrelevance:

1 There is no uniformly known set of MAS activities that a typical client de-mands or a typical accounting firm provides.

2 The product and components of a MAS contract are highly self-tailored tothe client's needs and willingness and ability to pay.

3 There are no specific professional standards with which the supplier of MAScomplies that can be used as a benchmark to evaluate the service.

4 The quality of MAS services is driven by competitive conditions in the localmarket and is free of specific regulatory oversight.

5 The level of MAS services acquired can be indicative of one or both of twophenomena:a complementarity, where the client seeks MAS to complement the existing

systems so as to improve the level of support and operating efficiency, andb substitutability. where the client uses MAS consultants instead of investing

intemally (i.e., have a computer staff, etc.) to develop the required services.

In a sense, MAS consists of many different products (computer service, cashmanagement, attestation on activities other than financial audits, taxes, adviceon utilities rate hearings, consultation on retail management, etc.) that are pro-vided at varying degrees of scope and scale.- In addition, the degree to whichMAS provides complementary or substitutable service is an important factor inevaluating the demand for the particular product line provided by the service,which can be examined only with private knowledge of the extent to which theclient invests intemally in his or her own system. More specifically, the ade-quacy of the client's own subsystem benefiting by the acquired MAS productlines needs to be controlled for if the doUai- amount paid for MAS is to becomemeaningful.^ Without such knowledge, the relevance of MAS in the determina-

Previous researchers have made distinctions among audit, tax, and other MAS. However, therange of products offered under the MAS caption varies considerably and is often related tothe audit firm's specialties. For example, Touche Ross, which audits many large retailers, hasacquired a retail consulting firm based in Atlanta for consulting on retail management issues.Whether MAS is acquired for the purpose of enhancing quality, or for meeting essentialfunctions, is the issue of complementarity versus substitutability. That is, the incrementalbenefits of acquiring MAS depend heavily on the quality of intema) systems and on theintent of purchasing MAS. This is another reason for the difficulty of applying models ofeconomies-of-scope (multiproduct models) to MAS and audit functions. As a proxy for the


tion of audit fees is limited. In particular, dollar amounts of MAS, as well astheir cross-sectional differences, are difficult, if not impossible, to interpret—mere existence of joint purcbase of MAS and audits may be the only reliablemeasure. Acquiring MAS and audits from two different (competitive) firms doesnot give rise to the requisite private information and intrafirm communication.Only in the context of the joint delivery of two products (audit and some kindof MAS) by the incumbent firm can it be presumed tbat knowledge spilloverstake place for which cost savings are expected to occur as a consequence.^ Inaddition. Beck et al. (1988a) argue that benefits of knowledge spillovers accmeto the client with the increase in the frequency of joint demand of MAS andaudits.

Competition and spacial concerns in audit marketsLike other services and products, competition in audit markets is expected toinfluence the client's cost of audits. Because audit markets are segmented, thedegree of competition is expected to differ by locality. Tbe relevance of spacialconsiderations in evaluating competition emanates from tbe nature of tbe auditservice. Because auditing consists of examining documentary evidence and ren-dering an opinion in a manner consistent witb certain professional standards, tbeproduction of an audit is (1) labor intensive and (2) is typically performed attbe client's location—often centered about headquarters' location, where muchof the documentation is made.

Because audit fees are primarily determined on the basis of the hours workedon the job (cost plus overbead), operating economies work against expandingthe proportion of staff time spent on traveling to clients' locations. An optimalmix of travel/work time determines the bounds for the geographic region withinwhicb an audit firm competes for clients. It is thus important to note tbat thespacial location of the auditor and the auditee can be a source of barriers tocompetition among audit firms across separate regions.^ Even for a given client,tbe cost increases witb tbe increase in the number of locations at which a clientoperates. These considerations limit the ability of firms to compete for clientsas well as limiting tbe client's cboice set. In particular, if one of the Big Eightfirms does not have an office in a given city, it would be difficult for that firm tocompete in providing auditing services for business enterprises located in tbat

quality of internal control. Simunic (1984) used the compensation level of the chief internalauditor, but he alluded to the difficulties inherent in this measure (p. 698).It is true that a 1 -0 dichotomous variable can be used to represent this conception of knowl-edge spillovers as in Simunic (1984). However, the analysis presented below carries this issuefurther to evaluate the probability of being in group 1 or group 0.This assertion is based on observation and discussion with managing partners of regional of-fices of Big Eight firms. However, it deserves more rigorous evaluation. Dopuch and Simunic(1980) and Simunic and Stein (1987) proposed and provided evidence suggesting that thewider the geographic dispersion of client's activity, the more likely that the client will choosea Big Eight firm. To expand the geographic service area. Big Eight firms either open newoffices or acquire small local practices.


city. By the same token, it is unlikely that clients will seek the services of a firmthat does not have a local office.''

To alter the degree of monopolistic competition between audit firms in aparticular local market, firms must either enter or exit the service area of thatmarket segment. Entry into a particular segment of the market is accomplishedeither by acquiring smaller local audit firms or by establishing new branchesin the region. In most cases, economic conditions in the region, both presentand expected, are a major determinant of entry (or exit). An economic declinebelow expectations (e.g., regions such as Houston, Texas, or Oklahoma City atthe present time) reduces demand for audit services and intensifies competitionamong audit firms. In contrast, regions experiencing economic growth (e.g.,Atlanta, Georgia, Boston, Massachussetts, and Jacksonville, Florida) create moredemand for the available services and provide opportunities for new entrants intothat segment of audit markets, which contributes to reducing the intensity of auditfirms in competing for clients.

Because the mobility of skilled labor is not instantaneous and is costly, auditfirms would have to adapt slowly to changes such that a decline in local economicconditions might create temporary excess capacity. In such a setting, economicanalysis suggests that firms would react by pricing (at least in the short mn)below their local-office costs in order to contribute to the recovery of theircapacity (personnel) carrying costs. More importantly, if firms were to cut costsin periods of short-term excess capacity, they could begin by passing on totheir clients the cost savings arising from knowledge spillovers. The converse isexpected to hold for the case of local economic growth where local offices offirms would be operating at peak capacity. In this case, audit firms are likely tohave a higher degree of localized monopoly power, and clients may not be ableto negotiate for a lower fee so as to capture the economic benefits of knowledgespillovers.

ObjectivesThe above discussion suggests the following:

1 A necessary condition for knowledge spillovers to occur is for the client toacquire MAS and audits from the same accounting firm.

2 A client's ability to capture the benefits of knowledge spillovers depend onthe client's evaluation of the cost of search and displacement of the incumbentauditor.

3 An audit firm's incentive to pass on to the client the economic benefits ofknowledge spillovers depends on the degree of the audit firm's monopolypower in the relevant segment of audit markets.

6 Until recently, for example, few of the Big Eight firms had offices in Orlando. To enter theOrlando market, some of the Big Eight have acquired local firms. If constraints are not costlyand influence demand. Orlando could have been served from the Tampa offices, which areapproximately 70 miles away.


4 Determinants of both types of incentives include the client's assessment oflocalized economic growth and competitive conditions.

This paper has two objectives. The first considers these assertions from theclients' viewpoint and attempts to evaluate their viability in explaining the previ-ously stated unexpected results reported by Simunic (1984) and Palmrose (1986).The second is to provide a test of the spillover benefits by explicitly estimatingthe cost of self-selection bias arising from the clients' choice of the incumbentinstead of other firms (including the client itself) to provide MAS. The methodused here is the Heckman-Lee method of selectivity, which was first developedin the labor economics area (see Heckman, 1978 and 1979; Lee, 1978, andMaddala, 1983, chapters 8 and 9).

Methodology

Self-selection and returns to knowledge spilloversPrior research has utilized both the cost of MAS and a dichotomous variableto examine the effect of knowledge spillovers. As indicated, the dollar cost ofMAS is a noisy measure because it represents payment for different productsand denotes different degrees of substitutability for, or complementarity of, theclient's own system. For this reason, the most relevant indicator of knowledgespillovers is whether the incumbent audit firm also provides MAS to the sameclient. The relevant decision choice is thus a dichotomous one.

The method adopted uses an econometric analysis of the client's cost of se-lecting the incumbent auditor to provide MAS by evaluating the probability thata client will self-select into one of two choices. To estimate the choice (criterion)variable, consider that audit services are offered in two different market regimes:the first ik) is one in which the joint provision of auditing and MAS gives rise toknowledge spillovers; the second(s) is one in which knowledge spillover couldnot exist—the case in which the incumbent auditor provides audit services only.

The effect on audit fees of the client's choice of one of the two regimes canbe examined by using a variable to estimate the self-selectivity bias, which is afunction of the probability that a firm will select one regime or the other. Mad-dala (1983 pp. 257-258) reports that estimating the significance of self-selectionbias was first considered by Roy (1951) in evaluating the returns to changingfrom one career (hunting) to another (fishing). The method has since been exten-sively elaborated upon by numerous labor economists and econometricians (seea detailed survey in Maddala, 1983). Maddala (1977 and 1983, p. 223) notesthat the approach used here has been initiated as switching regressions by Gold-feld and Quandt (1972), Lewis (1974), and Heckman (1974, 1976, and 1979)and further developed by Lee (1978). It is known as the Heckman-Lee method.A variation of the selection model is the mover-stayer model used by Willisand Rosen (1979). The first accounting application of the Heckman-Lee method


was by Shehata (1988) in studying accounting for research and developmentexpenditures.

To show how this method applies, let R represent the two regimes and takeon dichotomous variables k and s, where k = I, and s = 0 such that

Ri = Z,5 - e,

R, = 1 if Z,6 > e,

= 0 otherwise Q )

where Z is an ; x y matrix (of ; firms and j variables) of exogenous variablesfor the determinants of /?, 6 is an 7 x 1 vector of parameters, and £, is an errorterm.

Equation (1) is a probit model, where prob. (k is observed) = prob. (Z,8 >e) = F(Zd)/a) with F being the standard (normal) cumulative distribution func-tion.

Let Xi; and Xg be the matrices of regressors for audit fees of k and s, respec-tively. Without correcting for self-selection bias, audit fee models would be asfollows:

For k : F.^ = X^P^ + M , (2)

F o r i i F , , =:=X,,p, + M,, (3)

where F is audit fees, X is the / x p matrix of / firms (/ = 1,2,... A: for thek-regime; and k + \, k + 2,. ..s, for the i-regime) and p determinants of auditfees, Pjt and % are /? x 1 vectors of parameters, and M^ and «„ are residual terms.Assuming that e, in (1) has a unit variance (probit estimates are normalized tostandard deviations of a unit length) and is correlated with each of Ufa and u ,and that all have a multivariate (standard) normal distribution, then in view of(1), the expected value of each conditional error term M^ and M , is non-zero.That is, the proportions of errors in u^ and Ug that are due to the self-selectivitybias can be estimated using the Heckman-Lee method as follows (see also Lee,1978, and Maddala, 1983, pp. 223-238):

a Self-selecting in k:

Z,5 > ti) = EieiOkc | Z/8 > e,).

= —OjtAfa (4)

b Self-selecting in s:

I Z;8 < e,) = £(e,O,, | Z,8 < e,).

(5)


where <j) is the density function and •!> is the cumulative distribution functionof the standard normal (unit variance) probability distribution. The covariancemeasures Oke and Gsc are supposed to be computed from the variances andcovariances of the trivariate normal distribution as follows:

(6)

(7)

(where o^ is assumed to be unity). However, o^ is not in the likelihood functionand can not be estimated directly (Maddala, 1977). From (2) and (4), we haveek = (Wyt + O/te it), and from (3) and (5), we have e = iUs + OstXs), where ek andes are the error terms after self-selection is corrected. That is, selectivity explainsa systematic portion of the error terms of the audit fee regression equations.

Because (|) and <1> are not known, Heckman (1976 and 1979) and Lee (1978)have suggested (and proved the properties of) estimating 5 using a probit model(maximum likelihood method) to generate estimates for (]), O, X,; /, and X,j,. ThisHeckman-Lee two-stage estimator has been used in several studies, especially inthe labor economics area (for a list of areas of application, see Maddala, 1983,p. 289). Under the Heckman-Lee method, equations (2) and (3) can be restatedas follows^:

inFki = Xki^k - Okthi + eki (8)

lnF,i = Xsi% + OsXi + esi (9)

where eki and esi are residual terms remaining from Uki and Usi error terms in (2)and (3) after taking away the effect of self-selection bias, which is estimated bythe second term on the right-hand side of each equation. Heckman (1976 and1979) and Lee (1978) show that estimating (8) and (9) by ordinary least squares(OLS) results in obtaining consistent estimates of the parameters Oke and Ose asthe estimated coefficients of Xki and Xst, respectively. The significance and thedirection of each of these parameters, as well as the difference between them,indicates whether the client's choice of the incumbent auditor to provide MAS(i.e., self-selecting the k regime) costs more, the same, or less than choosing the^ regime (Maddala, 1983, p. 258).

Variables and dataThe two dependent variables required for implementation of the estimation dis-cussed above are R, indicating whether MAS services are provided by the in-cumbent audit firm (regime k) or by others (regime s), and F for audit fees. R

7 Note that X); and A-j are unsigned and. given their definition, should be positive. Some re-searchers re-sign one of these two variables by multiplying by —1 (e.g., Willis and Rosen,1979). Therefore, care should be exercised in comparing studies because, given the change ofsign of Xt, for example, the coefficient a^e would be expected to have a positive sign insteadof the negative sign shown in equation (8) but would have the same meaning.


is used for estimating the probit model to generate the self-selection variables,and F is used in the second-stage OLS estimates to evaluate the different auditcost functions and significance of clients self-selecting a regime.

Ideally, the probit model should describe the demand for k ox s regime.Identifying proper demand functions is not possible, however, because MASrepresents many different products either as substitutes for, or complements to,the client's own system. Empirically, however, the vector Z, need only consist ofexogenous variables that might include some of X. Consequently, the estimatedprobit model must be viewed as an econometric method to evaluate a criterionfunction for estimating self-selection bias from the known clients' descriptors.

In general, the explanatory variables can be classified in three categories:

1 Client-related determinants of audit effort, whicb include size (S), dispersionof the client's activities as measured by the number of subsidiaries (D), andtype of client (a public or a private company) {T) (Simunic, 1980, 1984;Francis, 1984; Palmrose, 1986a).

2 Demand-related variables, which include the client's evaluation of the cost ofsearch for another good (equally competent) audit firm (C), the internal costof adapting to a new auditor (A), an index of the client's tolerance for auditfee increase (FN), and the client's view of the conditions of local economicgrowth (G).

3 Auditor-related variables, including audit firm quality designation (B) indi-cating whether the audit firm is one of the Big Eight, the degree of localcompetition between audit firms (P), the tenure of the incumbent firm withthe client (F), and the degree of auditor's reliance on the client's internalcontrols (L).

The data required to generate measures of these variables were collected ina survey conducted in early 1987 from clients located in different audit regionsin five states. After eliminating those responses with incomplete data (mostlyprivate companies) a usable sample of 84 nontinancial companies remained.^Because numbers for sales (not total assets) are publicly available for privatecompanies, sales revenues are used for the size variable {S). The variable FNis obtained as the client's assessment of the tolerable percentage of increase in

8 This represents about a 20 percent response rate to a survey sent to the large companies inMissouri, North Carolina, South Carolina, Florida, and Georgia. The names and addresseswere obtained from the Sibbald guides for these states. The low response rate is attributablemostly to private companies that refused to provide information or provided incomplete re-sponses. It is possible that a response bias exists in this study. However, the 84 companiesare generated from several regions in each of the above states and it is not clear a priori whatwould be the nature of a systematic response bias. However, the degree of involvement washigh. Although the study was made anonymously, most of the respondents requested a copy ofthe results and attached business cards to their completed responses.Financial institutions were excluded from the survey because the measures of revenue wouldnot capture size in the same fashion as sales for other types of industries. This is due to thefact that banks report net interest (interest on loans minus interest on deposits) as revenues.

The Jointness of Audit I^es and Demand for MAS 305

audit fees by the incumbent auditor. This measure could be viewed as anotherindicator of the client's estimate of the extent to which the benefits of retainingthe incumbent auditor exceeds the sacrificed audit cost decrease due to theextemalities of acquiring MAS as well. In a separate question, respondents wereasked to rate on a continuous 10-point scale—from high (10) to low (1)—thevariables related to cost of search for another audit firm (C), the intemal costof adaptation to new auditors (A), the extent of audit competition in the region(P), recent economic growth in the region (G), and the degree to which theybelieve the auditor has relied on intemal controls (L).'

Participants were asked to write the dollar amounts for each of audit fees andMAS paid for 1986, the year preceding the survey date, and to indicate whetherMAS is provided (a) by the incumbent audit firm, (b) by other firms, (c) by bothincumbent and others, or (d) none are provided.'" Finally, a question was askedto indicate whether the auditor is a member of a Big Eight firm (B), and thenumber of years (Y) the incumbent audit firm has been retained by the client."

Some descriptive statistics are presented in Table 1 for all variables for thefull sample (n — 84) and for each of the subsamples of regime k (n = 36)and regime s {n — 48). The statistics on sales size indicate that the sampleconsists of medium-sized to small-sized companies. Average sales for the sampleare $409 million, with slightly larger companies falling in regime k: averagesales are $563 million for clients in regime k, and $293 million for clientsin regime s. Although these numbers indicate that the two regimes have (onaverage) different sized companies, median values are closer ($127 million fork and $110 million for s). The skewness and high variances suggest the needto transform the variable to achieve linearity. A common transformation is thelogarithmic. Francis (1984), Palmrose (1986a), and Abdel-khalik (1986) used logtransformation, and Simunic (1980 and 1984) used square root transformation.The distribution of inS, where In is the natural log, is about the same for bothof the subsamples, k and s, as shown by the normal probabilty plots in Figure1. A similar pattem is observed for audit fees. Mean (median) audit fees are$170 (80) thousand for k, and $134 (79) thousand for s. Also, the logarithmictransformation of fees shows approximately the same distributions for k and sas presented in the normal probability plots in Figure 2.'^

9 A copy of the questionnaire is available from the author upon request.10 A further partitioning of clients in categories b and c (n = 48) would result in samples too

small to yield efficient estimation. For the issue at hand, however, it is irrelevant whetherclients obtain MAS fixim nonincumbent auditors or develop those services internally. In eithercase, the contact required for creating conditions for knowledge spillover does not exist.

11 Although Beck, Frecka, and Solomon (1988b) use auditor tenure as a primary indicator ofbonding, no results concerning this variable have been obtained in this study. In fact, theaverage years of auditor tenure is lower for regime k clients than for regime i clients. Tenuredid not discriminate between the two groups. Nor was it significant in any of the analysesperformed.

12 Mean differences in raw data are influenced more by extreme values, which renders the logtransformation essential for the linearity assumption.


TABLE 1Basic descriptive statistics for the variables used in probit and OLS regressions

Dependent variablesf: audit fees (in $000)

inF.-Ln audit fees (a)

Explanatory variables5: sales (in $ million)

inS: ln sales'D: number of subsidiariesT: ownership type^C: cost of search'A\ cost of adaptation^

FN: tolerance of fee increasesG: local growth conditions'P: competition in local

audit markets^Y: incumbent auditor tenureL: reliance on internal

controls'

Self-selection variables"

K

Total samplein = 84)

Mean

1494.54

409 1,11.323.380.682.764.910.136.60

7.4414.25

7.10

——

SD

2720.85

0811.344.87NM2.553.020.102.49

2.3311.70

2.56

——

Regime k(n = 36)

Mean

1694.50

56311.812.220.753.505.700.116.70

7.3111.30

7.38

0.73—

SD

3910.91

1,5601.492.66NM2.943.170.942.44

2.6113.10

2.61

0.39—

Regime s(n = 48)

Mean

134.404.56

29311.814.250.632.204.300.146.50

7.4014.20

6.89

—0.55

SD

1300.81

4701.245.90NM2.072.800.112.68

2.1310.70

2.53

—0.33

' Not significantly different from normal distribution.^ NM: not meaningful because the mean indicates only the proportion of the sample consisting of

public companies.' Obtained as client estimates on a 10-point scale (10 = high; 1 = low)."* Estimated from probit models in Table 4.In = natural logarithm.

Several other features characterize the subsamples of k and s regimes. First,in general, respondents have reported Felatively low expected levels of cost ofsearching for another auditor (C). On a 10-point (continuous) scale from 1 (low)to 10 (high), average responses for C are 3.5 for regime k and 2.2 for regime s.However, in both regimes, clients estimated the cost of intemal adaptation to newauditors (A) to be higher on average than their estimates of C Mean responsesare 5.7 and 4.3 for regime k and regime s, respectively. Thus, the combined costof auditor changing (CA) is about 40 percent higher for the k regime than fors. Second, clients in regime k believe that auditors rely on their intemal controlsystems slightly more than clients in regime s (an average of 7.38 versus 6.89).Although this is consistent with expectations because the incumbent auditorprovides MAS for k but not for s, the univariate difference is significant only atp < 0.10. Third, clients' views of local competition between auditors (P) andof local economic growth (G) are about the same in both regimes. Finally, theaverage number of years of auditor tenure was shorter (11.3 versus 14.2 years)


Figure 1 Normal Probability Plots—Sales

Cumu1ati

99.9

99

95

80

50

20

0.1

8.5

99.9

cumu1at

99

95

80

V 50e

20

0.18.5

Regime kNormal Probability

. .^.. ^

^ : , ^

y^

11 1 1 1 1 1 1

10.5 12.5Logsales

Regime sNormal Probability Plot

14.5

10.5 12.5Logsales

14.5

16.5

. , .

y ^

A

. . . I

/ \j/'

/

. . ,16.5


Figure 2 Normal Probability Plots—Audit Fees

Regime kNormal Probability Plot

99.9

CumuIatiV

e

Percent

99

95

80

50

20

0.1 , I , , .2.9 3.9 4.9 5.9

Logauf86

Regime sNormal Probability Plot

CumuIati

99.9 ,

99

95

80

50

20

5

1

0.1 1 I I I I I 1 I

t i l l

6.9 7.9

I I I I I I

2.4 3.4 4.4 5.4Logauf86

6.4 7.4


TABLE 2Bivariate correlations between client-related and auditor-related variables

FN: Tolerance for fee "~-increases

C: Cost of searchA: Cost of adaptationL: Relying on internal

controlsP: Auditor competitionG: Economic growth¥: Years of service

FN

0.37"0.08

-0.16-0.07

0.110.14

C

...^0.04

0.33"

-0.21-0 .27 '-0.14

0.03

A

0.26'0.33"

^ ^ - ^ ^

0.02-0.16

0.050.25"

L

0- 0

0

000

.00

.09

.03

"~~-~~..10.25.10

P

0.22-0.36"-0.28"

--..^.04^ ^ \

0.14-0.10

G

0.180.04

-0.10

0.05..^^0.06

0.30

Y

-0.050.120.10

0.05-0.24

0.00

Note: Above diagonal is for regime k. Below diagonal is for regime s.' Significant alp = Q. 10." Significant at p = 0.05.

and had a higher variance (13.1 versus 10.7 years) for regime k than for regime

Respondents' internal consistency can be evaluated by the bivariate corre-lations between their responses. As data in Table 2 indicate, the higher theestimated cost of search (C), the higher the estimated adaptation cost (A); thecorrelation coefficient of A and C in each of the two regimes is 0.33. Similarly,the greater the estimated degree of competition between auditors {P), the lowerthe expected cost of searching to replace the incumbent auditor; the correlationbetween C and P is —0.36 for k, and —0.27 for s. One difference between k and5 is shown in the relationship between the clients' tolerance for fee increasesby incumbent auditors {FN) and other variables. For regime k, FN is positivelycorrelated with C (r = 0.27), but for regime s it is positively correlated with A(r = 0.26). This is not an important difference, however, because both C andA represent two sides of the clients' views of their cost of changing auditors.In brief, the signs of these correlations reveal that responses were internallyconsistent.

Testable hypothesesIn the initial estimation of audit fee functions, inF is used for the dependentvariable in the models of fee with self-selection, and the X vector of regressorsconsists of elements of three sets of explanatory variables, which are listed inTable 3.

Because audit fees are typically billed at cost plus overhead and profit, auditfees are expected to be positively associated with effort-related variables. Auditfees are expected to increase with the increase in client size and in the numberof subsidiaries, and to be higher (ceteris paribus) for public companies because

13 T h i s v a r i a b l e i s o m i t t e d f r o m f u r t h e r u s e f o r t h e r e a s o n s s t a t e d in f o o t n o t e I I .


TABLE 3Explanatory variables and their expected effects

ExpectedRegressor Denoting effect

Effort-related variablesinS Size as measured by sales +D Number of subsidiaries +T Ownership of client as to public (=1), or private (=0) +

Demand-related vadablesCA Cost of search for and adapting to a new auditor +FN Extent of tolerating fee increase +C Level of local economic growth +

Auditor-related vadablesP Degree of competition between audit firms -Y Incumbent auditor's tenure ?L Reliance on intemal control —

Note: CA is the sum of C (cost of search) and adjustment cost (A).

filing reports required by regulatory agencies is expected to increase audit effortover that of private companies. These expectations are consistent with earlierevidence (Simunic, 1980, 1984; Francis, 1984; and Palmrose 1986a). Althoughprior research has not provided evidence on the other sets of variables, a positiveassociation is expected between each of the demand-related variables and auditfees because an increase in the clients' estimates of the total cost of auditorchange amounts to an upward shift in the demand curve. A similar expectationholds for growth and improved local economic conditions. In both cases, theclient has less incentive to negotiate with the incumbent auditor for audit feedecreases (i.e., the client would be willing to pay higher audit fee). Finally, theauditor-related variables are expected to have the opposite effect on audit feesbecause a higher degree of local competitive conditions in audit markets leads tocharging lower audit fees. By the same token, increased reliance on the client'sintemal control system, as well as having a a stronger economic bond (longerworking tenure) with a client, is expected to result in reducing the auditor'sincentive to charge a relatively higher monopoly rent.'"^

As indicated above, estimating two audit fee models for each regime (equa-tions 2 and 3) result in inconsistent estimates due to self-selectivity bias. Toobtain consistent estimates, regressions (8) and (9) should be estimated insteadof (2) and (3) in order to include the self-selectivity bias measures A. andXsi as regressors. These are the variables about which this study tests specifichypotheses.

14 The issue of whether a stronger economic bond leads to a higher or lower audit fee is notclear because the cost savings arising from auditing a client for a long period of time (learn-ing curve) may or may not be passed on to the client. This is likely to depend on the degreeof localized competition and on the client's cost of changing auditors.


In particular, if the differences between audit fees for clients in regimes k ands are in fact explainable by the demand-related and the auditor-related variables,the client's added cost (i.e., being charged either monopoly or quasirent) for thechoice of either k ox s would be insignificant. This is the null hypothesis of nocost to the client for self-selecting either of the two regimes after considering theeffects of the factors determining audit fees (effort, demand, and auditor-relatedvariables). The alternative hypothesis is generated from the results of earlierevidence (Simunic, 1984; Palmrose, 1986b), which suggest that the unexplainedaudit fees in regime k are greater than those of regime s. For this to be true, bothof the estimated coefficients, Ckt for A. , and dje for A. ,, must be significantlynegative (Maddala, 1983, p. 258).

The two hypotheses can then be stated as follows:

The null: HQ : dke = dje = 0

The alternative: Ha : o^t < 0 and dst < 0

Estimation and resultsTo estimate the density, (]), and probability distribution function, O, the probitmodel (1) was estimated with a Z vector consisting of the variables that maxi-mize the likelihood of the probability of selecting among the two regimes. Theestimated probit regression is of the form'^:

Eipik, s)) = 5o + hiP + 82F/V + 53C + 54A + 65G + SeS -1- 87Z) (10)

where E is the expectation operator,

p is the probability of being classified in regime k ox s,

8, is the estimated parameter,

and all other variables are as defined earlier.

The results of these estimations are presented in Table 4, panel A for the wholesample, and in panel B for the sample of Big Eight clients only. This modelcorrectly classifies 68 percent of the observations; the significance of the model(X > 19.5, which is significant at p < 0.01), is attributable to four variables.Three of the significant variables are proxies for the client cost of changingauditors: variables C and FN are significant at p < 0.05, and A is significantat /? < 0.10. The fourth significant variable is the number of subsidiaries thatis significant at p < 0.05.'^ No other effort-related variable contributes to theclassification of companies in the two regimes.

15 Ideally this probit model should represent a theoretically derived demand function for incum-bent auditors' MAS. A reviewer raised a concern that the estimated function is not based on atheory, which is true. The development of such a theory, however, requires greater understand-ing of the nature of the products constituting MAS.

16 The use of this variable is purely descriptive in that it increases the discrimination betweenclients in the two regimes.


TABLE4Probit estimation results with regime as the dependent variable

Panel A: full sample (n = 84)

8o: constant8,828,84858687

for F (competition)for FN (tolerance for fee increase)for C (cost of search)for A (cost of adaptation)for G (growth)for S (sales)*for D (subsidaries)

Coefficient

-1.290.08

"4.090.160.100.052.60

-0.09

t

-1.65I.IO

-2.552.231.740.751.30

-2.10

Probability ofsignificance

0.10NS

<0.05<0.05<0.10

NSNS

<0.05

Chi-squared = 19.54 (significant at p < 0.015 with 7 d.f.)Log likelihood = -46.18Average likelihood = 0.577Correctly classified = 68 percent

Panel B: Big Eight sample (n = 72).

5o8,8283848586

87

-1.500.04

-4.460.170.133.000.07

-0.94

-1.710.53

-2.432.111.891.401.02

-2.01

0.09NS0.010.040.06NSNS0.04

Chi-squared = 23.357 (significant at p < 0.01 with 7 d.f.).Log likelihood = 36.8Correctly classified = 70 percent* Sales scaled to millions

The results of the probit regression were used to generate the self-selectionestimates measuring A,; , and A. ,. To test the hypothesis regarding the effect ofknowledge spillovers on audit fees, these two self-selection variables were thenused as explanatory variables in the audit fee models to estimate the regres-sion functions (8 and 9). To maintain a similar audit quality, the analysis wasconducted for the entire sample (n = 84) and for the sample consisting of BigEight clients only in = 72). However, as shown in Table 5, neither self-selectioncoefficient, dke for Xk, or a^e for X^, was found significant for the full sample,and only cSse was significantly negative for the Big Eight sample, suggesting (1)a failure to reject the null hypothesis of the absence of self-selection bias for thefull sample emd (2) that Big Eight clients of regimes are charged audit fees lessthan is expected for all clients in that regime. Because multicollinearity betweenvariables increases the estimated standard errors of regressions, steps were takento evaluate the extent to which the lack of significance might be due to the effectof collinearity on estimated variances. Several regressions were estimated with


TABLE5Regression estimates for Ln audit fees for each regime for the full sample and Big Eight clients' sampleusing Heckman's method

Po: Constant(0

PI: Log sales(0

^2. CA (Switching cost)«)

P3: £> (Subsidiaries)(0

P4: G (Economic growth)(')

PJ: T (Type of ownership)(')

ai^: Xt (Selectivity bias)(0

CTj^: \.5 (Selectivity bias)

(f)

Adjusted R^

F-Statistics(d.f.)

X'(6d.f.)

Total sample(« = 84)

Regime k(n = 84)

-0.97(-0.74)

0.37(4.99)°

-0.016(-0.53)

0.02(0.38)

0.08(1.81)'

0.81(3.33)'

0.21(0.05)

—

0.50

6.90'(6;29)

39.70'

Regime s(n = 48)

0.25(0.27)

0.36(4.67)'

0.03(1.1)0.02

(0.84)

-0.025(-0.72)

0.26(1.22)

—

-0.31(-0.80)

0.35

5.20'(6;41)

34.60'

Big Eight(n = 72)

Regime k(n = 29)

-0.57(-0.35)

0.36(4.02)'

-0.02(0.47)

0.03(0.61)

0.05(1.00)

0.86(2.78)'

-0.16(-0.34)

—

0.49

5.50'(6;22)

34.60'

Regime s(n = 43)

0.19(0.20)

0.36(4.73)'

0.05(1.75)'

0.01(0.01)

0.01(0.26)

0.17(0.84)

—

-0.68(-1.78) '

0.36

(4.96)'(6;36)

33.50'

• statistically significant at p < 0.05 (one tail)

smaller subsets of the explanatory variables presented in Table 3. All showedconsistent results.'^ Estimates of one of those regressions using a subset of theexplanatory variables are presented in Table 5 for regime k and regime s, theentire sample, and the sample Big Eight clients.

These results can be summarized as follows.

1 Audit effort-related variables are the major explanatory variables of inF (asshown by the size of t statistics and as will be shown next by the size ofexplained variation). inS is significant (at p < 0.01) in both regimes for

17 First regressions used all independent variables, but only effort-related variables were signifi-cant. One important reason for estimating regressions with less than the full set of explanatoryvariables is that the matrices X for regressions and Z for probit may overlap but should notbe identical. That is, Z must have some variables in estimating the probit function that are notused as regressors in the audit fee function. This is also one of the reasons that the search cost(C) and adaptation cost (A) were added into one variable CA.


both types of samples. However, type of ownership (public or private) isstatistically significant only for firms in regime k. The lack of significances ofthe coefficient for subsample s might be due to the fact that only 25 percentof firms in that regime are private companies. A positive coefficient indicatesthat audit fees paid by publicly owned companies are higher than those paid byprivate companies after considering the effect of size. This is consistent withexpectations because auditors file reports with regulators and are exposedto different types of risks when dealing with public companies than whenauditing private companies. The significance and direction of this coefficientis consistent with the evidence reported by Palmrose (1986a, 1986b).

2 Of the client-related and auditor-related variables, only the clients' views oflocal economic growth (G) are moderately significant (at p < 0.05, one tail)for the k regime.

3 The evidence fails to reject the null hypothesis of no impact of MAS se-lectivity on audit fees because the coefficients of self-selectivity variablesestimating Okt and CTjf are not statistically significant in the direction positedby the altemative hypothesis. That is, neither type of client pays significantlyhigher audit fees than is expected by other fee determinants. The selectiv-ity coefficient for regime s of the Big Eight clients is significantly negativeip < 0.05, one tail). Because the comparable coefficient of regime k is notsignificant, the only interpretation of the regime s coefficient is that Big Eightfirms charge clients in regime s slightly more than average charges for allclients in regime s. No comparison of selectivity is thus possible betweenclients in regime k and regime s.

To validate that neither of the self-selectivity variables is significant for thefull sample, a step-wise regression was applied to each of the three sets ofthe full sample: pooled sample, regime k, and regime s. Table 6 presents asummary of the results. In all cases, the client size ilnS) entered the regressionfirst and explained about 30 percent (for s) and 38 percent (for k) of the totalvariation in InF. Type of ownership (public or private) provided incrementalexplanations of about 16 percent for the k regime, but only about 3 percentfor the s regime. For the latter, the number of subsidiaries added an incre-mental explanation of 8 percent, and clients' views about local growth (G)added only 4 percent for regime k. None of the other variables, particularlythe self-selectivity variables, entered the regressions (using an alpha level of0.15).

4 The estimated regression functions (8) and (9) reported in Table 5 have rea-sonably good fit as shown by adjusted R^ values of about 0.50 for regime k,and about 0.36 for regime s, for the full sample and the sample consisting onlyof Big Eight clients, respectively. For all estimated functions, the F-statisticsare statistically significant (at p < 0.01). As indicated earlier, regardless offit, the relevant coefficients for testing the stated hypotheses are Gkt arid c^tfor the self-selection bias, which are discussed above.


TABLE6Increments of /J^ for significant explanatory variables selected in step-wise analysis

Variable selection Total sample Regime k Regime jorder (n = 48) (n = 36) (n = 48)

Ln sales (LnS)Ownership type (DGrowth (G)Subsidiaries (D)

0.3370.103NSNS

0.3820.1550.040NS

0.2970.029NS0.080

Total 0.440 0.577 0.406

Selection criterion was p = 0.15 to enter the step-wise regression.NS = not selected.

Although the estimated functions for regime k and regime s have significantlydifferent explanatory power,'^ it is important to determine which variablescontribute to these differences. Both regimes use the same set of explanatoryvariables; thus, as Maddala (1983, p. 227) shows, the differences between thecoefficients can be tested by estimating the following equation:

(11)

where X, stands for Xki and X^i,

O is the estimated cumulative distribution function,

^ is the estimated density function from (1) and PJ should correspond tothe estimated coefficients of regime s regression (equation 8).

The differences between estimated coefficients (P/i — Ps) of the variables X,,weighted by the probability distribution function $,• represent the difference inthe coefficients on X, between regime k and regime i. The coefficient of thedensity function represents the differences between the self-selection coefficientsof both regimes. Thus, if in fact a cost difference exists between the two regimes,then Ad = (a« — 6ke) should be statistically significant.

Equation (11) was estimated for the entire sample and for the sample of BigEight clients. The results are reported in Table 7. As shown, adjusted R^ valuesare 0.41 and 0.43 (which are effectively the averages of adjusted R^ values forthe two regressions estimated separately), and the corresponding F-statistics arestatistically significant at p < 0.01.

The following observations can be made about these results:

1 In general, the magnitudes and directions of PJ coefficients estimated by thedifference regression equation (11), and reported in Table 7, are about the

18 The F-statistic of the difference between adjusted R^ is significant at p < 0.05.


TABLE 7Test for differences between parameters of regime k and regime

Variable

Oo) Constant(P^i) Ln Sales(ps2) D- Subsidiaries(PJ3) T. Type of ownershipOrf) CA\ Cost of changing(PJ5) G: Local Economic growth<iPi = Oti - PJI) Ln*Sales <I>d^2 = (P*2 - P.2) 0 * $rfP3 = (P*3 - P.3) T * ^dP4 = 0*4 - P»4) CA**d?>5 = 0*5 - P.5) G*$Sel. Coef. Aa(CT,j - CT^JAdj«^F-Statisticd.f. (n;dn)

Full sample (« = 84)

Coef.

0.200.350.010.040.07

-0.020.04

-0.010.04

-0.150.08

-1.110.416.35

(13;71)

(0

0.204.34»0.201.211.85"

-0.410.43

-0.010.05

-2 .12 '0.62

-0.95———

White's t

0.245.08'0.250.061.98'

-0.520.360.060.06

- 1 . 9 1 '0.70

-0.91———

Big Eight clients (n

Coef.

0.040.37

-0.010.180.090.010.020.020.40

-0.150.03

-1.420.435.88'

(13;59)

t

0.044.47'

-0.220.472.23'0.100.180.210.51

-2 .06 '0.25

-1.33———

= 72)

White's (

0.045.40'

-0.310.432.32'0.150.140.210.49

-1 .76 '0.29

-1.18———

White's t is based on standard errors generated from White [1980] heteroskedastic consistent estimator.' Significantly different at p < 0.05 (one tail).

same as those estimated for the regression of regime s alone (Table 5). But,due to the (combined) larger sample size, the estimation is more efficient(smaller standard errors of estimates) as shown by the size of the t-statistics.

2 The coefficients of size, the most explanatory variable, are not significantlydifferent between the two regimes because the difference between the esti-mated coefficients, d^p — {^kp — Psp) is not significantly different from zero.

3 The effect of cost of changing auditors {^SA) is significantly positive {p <0.05) for regime s but is significantly lower for regime k than for regime s,as shown by the significance (atp <0.05) of cfp4 = (pw — P.s4)-

4 The differential self-selection coefficient. Ad = (djt —djte), is not significantlydifferent from zero at conventional levels for either the full sample or thesample of Big Eight clients. This result suggests that, on average, neithergroup of client pays more audit fees than the other group.

Response bias and validationBecause the data were collected from clients by means of a questionnaire, theanalysis could be subject to nonresponse bias. One way to test the seriousnessof the problem is to compare the analysis of early and late responses. However,the arrival dates of responses were not noted. As an altemative, the analysiswas performed on a subset of the sample after random deletion of some cases:74 cases were used after deleting 10 cases selected at random." Five iterations

19 This approach is similar to the one used in Abdel-khalik (1986). See also footnote 8.


(with replacement) were performed. The probit function (1) and the regressionmodels (8) and (9) were estimated for each of the five iterations.

Panel A of Table 8 includes the results of estimating the probit functions.As shown, there is a general consistency of significance levels and signs ofcoefficients among estimates of the five iterations. The percentage of correctlyclassified cases ranges between 68 percent and 76 percent.

For each of the five iterations, three regressions were estimated, one for thetotal sample and one for each regime with the self-selection bias variables Xkand Xs reestimated in each iteration. Denote those regressions for each iterationas follows:

E(y)T = E{lnF)T = a, +X,Pr (12)

E(y)irk = E(lnF)irk = a^ +Xik^k - OkX (13)

)irs = E{lnF)irs = a, +X,,p, + OsX (14)

where E is an expectation operation,

T is for total sample,

r denotes the iteration of random deletion of 10 cases at a time (r = 1 to 5),

and all other terms are as before.

The regression results of (12—14) do not alter any of the results reported for the84 observations. Because it would not be instructive to present the detailed resultsof all estimated functions, only summary statistics are presented in panel B ofTable 8. As shown, the pattern of adjusted R^ values is consistent throughout:adjusted R^ ranges from 0.39 to 0.43 for the total sample, from 0.44 to 0.56 forthe subsample of regime k, and from 0.32 to 0.38 for the subsample of regimes.

The functions estimated for (12-14) were then used for cross-sectional vali-dation (prediction) by calculating the following prediction errors for each of the10 randomly omitted cases:

)] (15)

E(yirk)] (16)

£(y,>,)] (17)

where PE is prediction error, and all other terms are as before. Descriptivestatistics for PE for each of the five iterations are reported in Table 9 for thetotal sample (T). The prediction errors for each of the k and s subsamplesare reported for all five iterations combined.^" In all cases, mean and median

20 Because each iteration of cross-validation (prediction) has only 10 observations, statistical testscan be performed only on the combined set of predictions.


TABLE 8Results for reduced sample size by random deletion of ten cases

Panel A: t-statistics for probit results

80: Constant8,: f (Competition)82: FW (Tolerance for fee inc.)83: C (Cost of search)84: A (Cost of adaption)8,: G (Growth)8 : 5 (Sales)87: D (Subsidiaries)

Number of observationsLog likelihoodPercentage of correct classification

Panel BSummary Statistics of Regressions

Adj R : For total sample

Adj R ; For regime k(n) = number of obs.

Adj R ; For regime i(«) = number of obs.

Iteration

1

-2.16*1.75t

-1.76t2.28*2.13*0.981.04

-2.32*

74-38.3

72

0.42

0.53(33)

0.38(41)

2

-1.93*1.43

-2.44*2.09*I.75t0.721.31

-1.73t

74-39.98

68

0.42

0.59(31)

0.32(43)

3

-1.421.10

-2.60*2.42*1.470.600.93

-2.03*

74-39.47

72

0.39

0.44(33)

0.36(41)

4

-1.85t1.46

-2.03*2.30*1.93*0.131.23

-2.09*

74-38.70

76

0.43

0.56(30)

0.36(43)

5

-0.900.42

-2.38*1.90*0.930.721.37

-2.04*

74-41.15

68

0.40

0.48(30)

0.33(43)

* Significant at p < 0.05.t Significant at /J < 0.10.

prediction errors are small, and the means are not significantly different fromzero (at conventional levels).

Discussion and conclusionThis study was motivated by the need to explain the findings in the extant liter-ature indicating that purchasing MAS from incumbent auditors appears to costclients higher audit fees than can be explained by audit effort-related variables.Simunic was careful in pointing out the existence of a positive association ratherthan inferring causal links (1984, p. 698), but his conclusion was supported byPalmrose (1986b) using a different sample. Although it might be a valid assertionthat such behavior describes audit firms' charging quasirent, it is difficult to thinkof economic incentives that could exist a priori for clients to pay more for thejoint acquisition of two products than for the sum of acquiring them separately.In that respect, the Simunic-Palmrose conclusion is unexpected. Nevertheless,Simunic explains that, from the auditor's viewpoint, excess audit fee might bea quasirent for investing in MAS resources.

Another explanation was also suggested by Simunic (1984, p. 698):


TABLE 9Errors of prediction and cross-validation

Panel A: for total sample (PETT

MeanS.D.Median(-Statistic (above mean)(n) = number of observations

Panel B: for PEr, PE* and PE,"

MeanS.D.Mediant-statistic (about mean)Prob. of (t)(n) = number of observations

Iteration

1

-0.020.650.02

-0.1110

2

-0.040.70

-0.13-0.1610

3

0.400.880.231.44

10

4

0.200.880.100.60

10

5

-0.120.48

-0.00-0.9510

By regime and combined iterations

T

0.080.670.000.820.41

40

k

0.170.88

-0.020.920.38

17

s

-0.030.710.07

-0.230.82

23

Statistics per regime are not meaningful for each iteration due to small sample size.Mean and median value of k and s are good descriptions of each individual iteration.

If this [fee] model is not well specified, then an alternative explanation for the resultscould simply be that companies which purchased MAS from their auditors were firms ex-periencing unusual problems, motivating the ptirchase of MAS along with an abnormallylarge amount of auditing.

Beck et al. (1988a) consider this point and argue for the need to examine whetherpurchasing MAS from incumbent auditors is a recurrent activity. A third expla-nation concerns the degree to which purchased MAS complements or substitutesfor the client's own investment in internal auditing, internal control, and relatedsystems. Simunic (1984) attempted to evaluate this issue but recognized theattendant measurement problems by assuming that "all systematic differencesbetween MAS purchasers and nonpurchasers which affect the demand for bothMAS and auditing" are controlled for (p. 698).

This paper considered alternative explanations for having obtained the resultsstated above and provided a more refined test of knowledge spillover using theHeckman-Lee method of self-selectivity. Because the degree of substitutabilityand complementarity of pui"chased MAS in relationship to the clients' own inter-nal systems cannot be evaluated without obtaining inside information, the use ofMAS fees as an indicator for the level or quality of service entails some risks. Apossible problem is obtaining spurious correlation between audit and MAS feescaused by client size: larger firms will pay more for both products. Hence, the


size of MAS fees cannot determine different degrees of information synergy.Absent knowledge of detailed information about the client's intemal auditingand control systems, analysis of knowledge spillovers arising from providingMAS can be made only as a dichotomous case: knowledge spillover can existonly if the incumbent auditor also provides MAS. In this case, a direct way totest the cost (or benefits) of knowledge transfers from MAS to audits is to eval-uate the cost of the client's self-selecting into one regime or the other. Measuresof self-selection bias can be estimated using Heckman-Lee two-stage method ofswitching regressions, which is discussed in Heckman (1976 and 1979) and Lee(1978) and was first applied to accounting by Shehata (1988).

The second approach used in examining this problem is to consider theclient's view of the regional monopoly of local auditing firms. The clients'willingness to forego the possible savings attributable to information synergyand auditor knowledge spillovers depends on the degree to which they appraisethe local market for auditors. In particular, clients may not be able to bargain fora better deal in a market characterized by high demand for audit services (i.e.,good conditions of economic growth and low competition for clients amongauditors). In such a setting, the cost of search and adjustment to another auditorwould be high, and clients would be willing to forego capturing the cost savingsarising from the synergy of purchasing audits and MAS from the same firm.An effort is made here to evaluate, through using imprecise measures collectedfrom clients, the clients' assessments of these factors.

The results obtained for a sample of 84 companies, 72 of which were servedby Big Eight firms, differ from the earlier findings of Simunic (1984) and Palm-rose (1986b). Using the Heckman-Lee method to test self-selecting into eitherregime (acquiring MAS from the incumbent auditor, or making other choices)does not show that audit fees systematically differ by the choice of sourcingMAS. The lack of significance for the parameters for clients' self-selection (orthe difference between them) in the stipulated directions signifies the absenceof cost (or benefit) accruing to clients for selecting the incumbent auditors tosupply MAS. Furthemiore, the clients' views of local audit market conditionsdo not explain variation in audit fees. In general, audit fees for this sample aremostly determined by the known effort-related variables (size, subsidiaries, andtype of ownership).

These results are consistent with expectations in that it would not be rationalfor clients to pay higher audit fees simply because they also pay the firms of theirauditors additional sums of money for MAS. Although additional replicationsare needed, these findings suggest that there is a least one sample in which theearlier conclusions by Simunic and Palmrose do not hold. One weakness of thepresent study is the unavailability of more diagnostic measures of effort-relatedinformation (i.e., total assets, the size of accounts receivable, etc.) for privatecompanies. Although the use of sales as a measure of size is acceptable, priorevidence has shown that a suitable transformation of total assets explains more


of the variation in audit fees than sales. Similarly, the present study was unableto obtain reliable infonnation on the size of receivables or current assets frommost of the responding private companies or from data banks operated by DowJones or Dun and Bradstreet. Consequently, not all effort-related variables havebeen used or accounted for. Finally, a future study can make use of real, hardevidence on the effect of different comf»etitive conditions in different segmentsof the audit market.

ReferencesAbdel-khalik, A.R., "Client Size, Loss-of-Control, and Discretionary Demand for

Auditing," Working paper. University of Florida, Accounting Research Center (July1986).

Beck, P., T. Frecka, and I. Solomon, "A Model of the Market for MAS and Audit Ser-vices: Knowledge Spillovers and Auditor-Auditee Bonding," Journal of AccountingLiterature, vol. 7 (1988a) pp. 50-64.

, "An Empirical Analysis between MAS Involvement and Auditor Tenure:Implications for Auditor Independence," Journal of Accounting Literature, vol. 7(1988b) pp. 65-84.

Dopuch, N., and D. Simunic, "The Nature of Competition in the Auditing Profes-sion: A Descriptive and Normative View," in J. Buckley and F. Weston (eds.).Regulation and the Accounting Profession (Belmont, Calif.: Lifetime LearningPublications, 1980) pp. 77-94.

Francis, J., "The Effect of Audit Firm Size on Audit Prices: A Study of the AustralianMarket," Journal of Accounting and Economics (August 1984) pp. 133-151.

Goldfeld, S. M., and R. E. Quandt, Nonlinear Methods in Econometrics (Amsterdam:North-Holland, 1972).

Heckman, J., "Shadow Prices, Market Wages, and Labor Supply," Econometrica (July1974) pp. 679-694.

, "The Common Structure of Statistical Models of Truncation, Sample Selec-tion, and Limited Dependent Variables and a Simple Estimator for Such Models,"Annals of Economic and Social Measurement (Fall 1976) pp. 475—492.

-, "Sample, Selection Biases as a Specification Error," Econometrica (January1979) pp. 153-161.

Lee, L.F., "Unionism and Wage Rates: A Simultaneous Equation Model with Qualita-tive and Limited Dependent Variables," International Economic Review (June 1978)pp. 415--433.

Lewis, H.G.. "Comments on Selectivity Biases in Wage Comparisons," Journal ofPolitical Economy (November/December 1974) pp. 1145-1155.

Maddala, G.S., "Self-Selectivity Problems in Econometric Models," in P. Krishniah(ed.). Applications of Statistics (Amsterdam: North-Holland, 1977) pp. 351-66.

, Limited Dependent and Qualitative Variables in Econometrics (Cambridge,England: Cambridge University Press, 1983).

Palmrose, Z.-V., "Auditor Fees and Auditor Size: Further Evidence," Journal of Ac-counting Research (Spring 1986a) pp. 97-110.

, "The Effect of Nonaudit Services on the Pricing of Audit Services: FurtherEvidence," Journal of Accounting Research (Autumn 1986h) pp. 405-411.

Roy, A.D., "Some Thoughts on the Distribution of Earnings," Oxford Economic Papers(January 1951) pp. 135-146.

Securities and Exchange Commission, ASR 250 (Washington, D.C.: U.S. GovernmentPrinting Oftice, June 1978).


Shehata, M,, "The Effect of SFAS No, 2, and the Economic Recovery Tax Act of 1981on Firm Expenditures on R & D," Ph,D, dissertation, University of Florida (1988),

Simunic, D,, "The Pricing of Audit Services: Theory and Evidence," Journal of Ac-counting Research (Spring 1980) pp, 161-190.

, "Auditing, Consulting, and Auditor Independence," Journal of AccountingResearch (Autumn 1984) pp, 679-702,

and M, Stein, Product Differentiation in Auditing: Auditor Choice in theMarket for Unseasoned New Issues. Research Monograph Number 13 (Vancouver,Canada: The Canadian Certified General Accountants' Research Foundation, 1987).

White, H., "Heteroskedasticity Consistent Covariance Matrix Estimator and a DirectTest for Heteroskedasticity," Econometrica (May 1980) pp, 817-838.

Willis, R., and S. Rosen, "Education and Self-Selection," Journal of Political Economy(October 1979) pp. S507-S536,

The jointness of audit fees and demand for MAS: A … · The jointness of audit fees and demand ......

Documents

Transcript of The jointness of audit fees and demand for MAS: A … · The jointness of audit fees and demand ......