Abstract
We examine the heterogeneity of experts in a credence goods setting. In our analytical model, clients are uncertain about how much effort experts need to provide to solve their problem, which is either simple or difficult. Experts have varying degrees of expertise. Less qualified experts are equally effective at solving simple problems but less effective at solving difficult ones. We show that clients pay a fee premium to more qualified experts, even for simple problems. This premium increases with the probability that the client has a difficult problem. We empirically test the model predictions in the context of partner industry expertise for the U.S. operations of the Big Four audit firms. We find, consistent with the model, a positive association between partner industry specialization and audit fees, even for simple audits, and a negative association between partner specialization and the client’s probability of restatements only for difficult audits. The industry specialization premium is higher in industries with higher proportions of difficult audits. Consistent with credence good agency issues, the specialization premium for simple audits is mitigated when information asymmetry between client and auditor is lower.
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Notes
The agency problem that credence goods face is well established in the economics literature and is akin to a doctor who conducts unnecessary procedures or a car mechanic who does unnecessary repairs, to maximize their own profits (Demski and Sap**ton 1987; Patterson 1992; Emons 1997; Iizuka 2007, 2012). Such outcomes are possible because customers cannot assess what services they need.
For example, Francis et al. (2005) find that industry specialist offices command higher fees, but Minutti-Meza (2013) does not find any association between industry specialist offices and audit quality. Using data from Sweden and Australia, where the name of the partner is public, Zerni (2012) and Goodwin and Wu (2014) find positive associations between partner industry specialization and audit fees but remain silent about any association with audit quality. Focusing on partner cognitive abilities (intelligence quotient, IQ), Kallunki et al. (2019) find a positive association between a partner’s IQ and audit fees but very limited evidence of increase in audit quality (Aobdia 2019b).
Consistent with models of credence goods, the auditor typically does not disclose to its client the specific engagement risk rating, which corresponds to the true auditor diagnostic, not the one reported (potentially misreported) to the client.
In all our analyses, we control for office and national levels of industry specialization as well as several proxies for client characteristics and observable risk factors that may influence whether a client requires a simple or difficult audit. Additional analyses, in the online appendix, provide evidence that most of the industry specialization premium in the United States comes from individual audit partner industry specialization, not national level or city level of expertise, a result consistent with the findings of Goodwin and Wu (2014). Therefore we focus on partner-level expertise, instead of more aggregate levels.
Realization rate is equal to total audit fees charged divided by the maximum audit fee the auditor would have charged had all hours been billed at their undiscounted rate, which varies based on each staff member’s seniority and experience. Thus a 100% realization rate corresponds to an audit in which each engagement team member works the planned number of hours and bills these hours at the maximum possible rate. When the realization rate is higher, the contribution margin of the audit and therefore its profitability is likely higher (e.g., Bedard and Johnstone 2010).
The aim of an audit is to obtain reasonable assurance that the financial statements are free of material misstatements (AS 1001 and AS 1015). Further, the higher the restatements probability, the lower the utility provided by the audit. Research finds negative stock returns announcement as well as increased management and board turnover following the announcements of restatements implying a lower utility for many of the client’s stakeholders (Palmrose et al. 2004; Srinivasan 2005; Hennes et al. 2008).
We also conduct several tests to ensure that our results are not too sensitive to the measurement of industry expertise. In particular, to rule out that our results on audit fees are mechanically driven by an audit-fee measurement of industry specialization, we replicate our results using an asset-based measure of industry specialization and find consistent results. See Section 5.6 for more details.
The PCAOB mandated disclosure of the lead partner name on Form AP starting from 2017. We do not believe that whether the name of the partner is public affects the main forces at play in our paper. Audits remain credence goods in both regimes, and pricing structures, in equilibrium, must incorporate the credence nature of audits. However, following public disclosure, partners are likely to change their client acquisition due to increased reputation incentives (Lee and Levine 2016). Further, specific partner characteristics might become priced, due to positive capital market consequences of audit partner quality (Aobdia et al. 2015). For these reasons, we believe that our empirical setting that focuses on a nonpublic regime is cleaner and devoid of such confounding factors.
While their names are not publicly disclosed in our setting, industry specialist partners still can demonstrate their expertise through private communication mechanisms. For example, Fiolleau et al. (2013) consider one client that is exploring switching auditors. They find that prospective partners convey their expertise through a combination of biographies, interviews with company management and the audit committee, and, especially, prior references. Fiolleau et al. (2013, p. 881) report that the client’s management contacted all of the partners’ client references and even reached out through informal channels to other industry colleagues not in the partners’ lists.
For example, Emons (1997) mentions that ordinary individuals in Switzerland have 33% more of the seven most important surgeries than physicians and their families. Gruber and Owings (1996) find that doctors in areas with declining birth rates are much more likely to perform caesarean sections than doctors in growing areas. Schneider (2012) in an experimental setting finds that unnecessary auto repairs are recommended in 33% of the test cases.
Causholli and Knechel (2012, p. 632) state: “During the audit process, the auditor is responsible for making decisions concerning risk assessment, total effort, labor allocation, and the timing and extent of audit procedures that will be implemented to reduce the residual risk of material misstatements. As a non-expert, the auditee may not be able to ascertain the extent to which the risk of material misstatement has been reduced even after the audit is completed. Thus, information asymmetry exists between the auditee and the auditor, the benefit of which accrues to the auditor.”
The 2007 bankruptcy of New Century provides a telling case study of a client’s circumstances and therefore risk changing quickly, without the client noticing. New Century was engaged in the origination of subprime mortgages, and its deficiencies in internal controls and financial reporting became material only when the housing market started deteriorating in 2006. The bankruptcy examiner suggested that, had the engagement partner had more industry expertise, the misstatements included in New Century’s financial statements would have been detected long before February 2007 (Missal 2008).
The credence goods nature of auditing might have been exacerbated by the PCAOB oversight regime, which, in some instances, could lead to the need for more auditing and, due to limited interactions between the PCAOB and issuers, an increase in information asymmetry between auditors and their clients (Coates 2007; Lennox and Pittman 2010). Anecdotal evidence suggests that auditors may have exploited this information asymmetry to justify additional and perhaps unnecessary procedures to their clients. In 2016, some companies complained to the PCAOB and the Securities and Exchange Commission that their auditors were requesting immaterial information and conducting unnecessary reviews of internal controls, sometimes justifying this work only by explaining that it was required by the PCAOB (Hanson 2016).
Audit committees do not see any major difference among the Big Four audit firms, do not directly observe the audit process, and typically rely on the expertise of the lead audit partner (Beasley et al. 2009; Fiolleau et al. 2013; Almer et al. 2014). The degree of information asymmetry between the audit committee and audit partner likely varies based on the accounting expertise of the audit committee (Baugh et al. 2019).
Such situations are often modelled with a signaling model, like that of Titman and Trueman (1986).
In practice, this situation resembles that of auditors offering a fee schedule for a particular client that tenders her audit. This situation is common in practice. Consistent with the model, some auditors even offer a different pricing structure depending on whether they assign an industry specialist partner on the engagement (Fiolleau et al. 2013).
Introducing a reservation utility abstracts away from overall market equilibrium issues that derive from multiple clients choosing among multiple partners at the same time. We thank the editor for this suggestion, which greatly simplified the model. Recasting the model such that the partner captures all the rents or the client captures all the rents for partners with the lowest expertise does not change our predictions.
Note that γ also helps modelling higher costs of interaction with the auditor in case the partner is not an expert and the necessary audit is H. Anecdotal evidence suggests that clients care about the costs of interacting with their auditor. Doty (2011), cites two examples from auditor engagement letters: “We will deliver a ‘reduced footprint in the organization, lessening audit fatigue,’ and we will provide you ‘with the best, value-added audit service in the most cost effective and least disruptive manner by eliminating non-value added procedures’” Fiolleau et al. (2013). also mention an auditor bid that explicitly requires more client involvement, in the form of more work from the client’s internal audit department.
For example, a customer having her car serviced by a mechanic could need either an oil change or her entire engine overhauled. Under a credence goods setting, a customer would observe only after the repair whether the problem still existed, that is, whether the car functioned properly. The problem would remain only if the customer needed an overhaul and only obtained an oil change. The car would not work, and the client would obtain, pre-repair costs, zero utility. In all other cases (engine overhaul needed and obtained, oil change needed and either oil change obtained or engine replacement obtained), the client would observe that the car worked and would obtain, before payment of repair costs, the same utility level.
We could explicitly rule out these multiple equilibria by introducing a small partner disutility ε when he provides the inadequate level of services. Alternatively, the equilibrium discussed above is the most efficient, in the sense that the client always receives on average her reservation utility R, but the partner receives higher utility net of costs when providing the adequate level of services.
We thank an anonymous referee for this suggestion. The inequality stems from two parts. First, for a partner to recommend H to an H client, we need to have \( {A}_{\upgamma}^H-{C}^H\ge {A}_{\upgamma}^L-{C}^L-{L}_{\upgamma} \). Second, for a partner to recommend L to an L client, we need to have \( {A}_{\upgamma}^L-{C}^L\ge {A}_{\upgamma}^H-{C}^H \).
One issue with fees per hour, as a measure of profitability, is that the measure is influenced by the mix of hours between junior and senior team members. The realization rate captures differences in the seniority mix as the maximum hourly rate that can be charged by an engagement team member varies based on seniority. For these reasons, we believe that Realization_Rate is a more accurate measure of audit profitability than Rate_Per_Hour. We nevertheless report the results with Rate_Per_Hour for comprehensiveness, as the definition of realization rates may change across audit firms and over time.
One concern with this definition of Restatement is that some restatements in the dataset might occur because of the audit process. We confirm that the number of such cases is limited and that all results remain qualitatively unchanged if we exclude restatements that occur because of the audit process.
Audit firms have used different client risk scales across firms and time. We recode these variables and consider only the highest risk engagements to construct the High_Risk variable, because it is more difficult to compare moderate risk engagements across different firms and times.
This number is computed as e(2.466 × 0.04)-1.
In terms of control variables, we find a positive association between City_Expertise and Logauditfees, consistent with the findings of Ferguson et al. (2003) and Francis et al. (2005). City expert offices also command higher rates per hour and realization rates on their engagements. We find a positive association between Office_Size and Logauditfees, consistent with the results of Choi et al. (2010).
These numbers are computed as e0.216–1 and e0.153–1, respectively.
We use the issuer’s square roots of assets because a regression of the logarithm of audit fees on the logarithm of the issuer’s assets yields a regression coefficient very close to 0.5, with an R-squared above 60%. Thus audit fees are roughly proportional to the square root of the issuer’s assets, consistent with prior research (Simunic 1980, 1984). For this reason, several papers in auditing assess market share using the square root of the issuers’ assets or use the square root of assets as a deflator (e.g., Hogan and Jeter 1999).
One difference is that the interaction Expertise × High_Risk loads negatively in the replication of Panel A of Table 5.
For ease of comparison of the rating across different audit firms, we use only the riskiest clients in the High_Risk variable. Therefore moderately risky clients are excluded from the variable and are considered to be L clients, which likely explains the association with L clients.
In the same spirit, we conduct an additional analysis with industry specialization measured the prior year, restricting the sample to the two years immediately preceding and following a partner rotation off a client, as the measurement of partner specialization might be more precise during this period. We continue to find qualitatively unchanged results, but the interaction between partner specialization and audit committee expertise, while negative, slightly falls below conventional significance levels.
Given the results of Bell et al. (2015) that show the importance of first-year engagements, we also replicate the analyses replacing Length_Relationship with an indicator variable for first year audits. Our results remain qualitatively unchanged.
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This research paper, formerly titled “Does engagement partner expertise matter? Evidence from the U.S. operations of the Big 4 audit firms,” was prepared while Daniel Aobdia was a Senior Economic Research Fellow in the Center for Economic Analysis at the Public Company Accounting Oversight Board (PCAOB). Saad Siddiqui is an Associate Director of Economic Modeling, and Andres Vinelli a former Chief Economist at the PCAOB. The PCAOB, as a matter of policy disclaims responsibility for any private publication or statement by any of its Economic Research Fellows and employees. The views expressed in this paper are the views of the authors and do not necessarily reflect the views of the board, individual board members, or staff of the PCAOB. We thank Paul Fischer (the editor), two anonymous referees, Mary Barth, Jeremy Bertomeu, Preeti Choudhary, Lauren Cunningham (discussant), Mark DeFond, Ron Dye, George Georgiadis, Michael Gurbutt, Chris Hogan, Robert Knechel, Patricia Ledesma, Robert Magee, Miguel Minutti-Meza, Stephen Taylor (discussant), Luigi Zingales, PCAOB staff, and the seminar participants at the 2017 Financial Accounting and Reporting Section (FARS) Midyear Meeting, the Seventh International conference of the Japanese Accounting Review, Michigan State University, the PCAOB, the 2016 School of Accountancy Research Symposium at Singapore Management University, Université Laval, and the University of Louisville for helpful discussions on earlier versions of this work. Daniel Aobdia acknowledges financial support from the Kellogg School of Management and, in particular, the Lawrence Revsine Fellowship.
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Appendices
Appendix 1. Proof of Proposition 1
We solve the game using backward induction. We know from the proof of Lemma 2 that the partner’s optimal service strategy is based on given audit fees \( {A}_{\gamma}^H \)and \( {A}_{\gamma}^L \). The risk-neutral client infers the partner’s action based on the posted prices and chooses to use the partner’s services only if at least provided her reservation utility R on average. The partner chooses prices \( {A}_{\gamma}^H \)and \( {A}_{\gamma}^L \) to maximize his average surplus while providing to the client on average her reservation utility R. Overall, in equilibrium, the partner is either always truthful; provides only H services, that is overtreats all L audits; or provides only L services, that is, undertreats all H audits. Three possibilities emerge.
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In the truthful structure, we must have \( {A}_{\upgamma}^H-{C}^H={A}_{\upgamma}^L-{C}^L={M}_{\gamma } \) (Lemma 2), and the average client utility is \( p\left(\gamma u-{A}_{\upgamma}^H\right)+\left(1-p\right)\left(u-{A}_{\upgamma}^L\right) \). Setting this average utility to the client reservation value R, we find that \( {A}_{\upgamma}^H={C}^H+{M}_{\gamma } \) and \( {A}_{\upgamma}^L={C}^L+{M}_{\gamma } \), where Mγ is the auditor profit, equal to Mγ = p[γu − CH] + (1 − p)[u − CL] − R.
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In the always H possibility, the partner posts an out-of-equilibrium \( {A}_{\upgamma}^L \), always obtaining \( {A}_{\upgamma}^H-{C}^H \), and the average client utility is \( p\left(\gamma u-{A}_{\upgamma}^H\right)+\left(1-p\right)\left(u-{A}_{\upgamma}^H\right) \). Setting this average utility to the client reservation value R, we find that \( {A}_{\upgamma}^H= p\gamma u+\left(1-p\right)u-R \), and the auditor profit MH is equal to MH = pγu + (1 − p)u − R − CH.
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In the always L possibility, the partner posts an out-of-equilibrium \( {A}_{\upgamma}^H \), always obtaining \( {A}_{\upgamma}^L-{C}^L \), and the average client utility is \( \left(1-p\right)u-{A}_{\upgamma}^L \). Setting this average utility to the client reservation R, we find that \( {A}_{\upgamma}^L=\left(1-p\right)u-R \), and the auditor profit ML is equal to ML = (1 − p)u − R − CL.
The second possibility, when the partner provides only H services, is always trumped by the constant markup structure, because Mγ > MH, from the assumption that CH > CL. The intuition is that the partner must provide unnecessary services with probability 1-p and incurs the extra-costs for such services, without additional average revenues. Thus, when setting prices, the partner prefers a constant markup structure over a higher markup for H services.
The third possibility, when the partner provides only L services, is trumped by the constant markup structure when γu ≥ ΔC, that is, when the benefits of providing an H audit are higher than the extra costs incurred by the auditor. The inequality can be easily determined from Mγ ≥ ML, which is the necessary condition for a partner to provide the constant markup structure. Thus, when γu ≥ ΔC, the partner chooses the constant markup structure, and, when γu < ΔC, the partner chooses a higher markup for L audits than for H audits, such that the client infers that the partner will always provide L and undertreatment will occur for H audits.
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Aobdia, D., Siddiqui, S. & Vinelli, A. Heterogeneity in expertise in a credence goods setting: evidence from audit partners. Rev Account Stud 26, 693–729 (2021). https://doi.org/10.1007/s11142-020-09569-2
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DOI: https://doi.org/10.1007/s11142-020-09569-2