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Is the effect of industry expertise on audit pricing an office-level or a partner-level phenomenon?

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Abstract

Several studies report an audit fee premium for auditor industry expertise measured at the office level. We extend this line of research by examining whether there is a fee premium for auditor industry expertise measured at the partner level. Using Australian data, we show that the coefficient for partner-level industry expertise is highly significant and economically important. This is consistent with industry knowledge or expertise residing in the human capital of individual engagement partners. Inconsistent with prior research, we show that there is no auditor industry expertise fee premium at the audit office level when partner-level expertise is controlled for. Consistent with prior research, we find little evidence of a fee premium at the national level. The results suggest that the auditor industry expertise fee premium is mainly a partner-level phenomenon, casting doubt on the belief that industry knowledge or expertise is distributed across engagement partners within an audit office.

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Notes

  1. The male pronoun is used in this study because for our sample, male audit partners signed about 90 % of the audit reports issued by the Big 4 firms.

  2. Section 324 AB(3) of the Corporations Act 2001 requires the audit report to be signed in the names of the audit partner and the audit firm.

  3. The data reveal that industry leadership at the firm/office level is not tantamount to partner-level leadership. Of the 6,617 client-years audited by the Big 4 national or city industry leaders, about 68 % of audits are performed by the engagement partners who are not the industry leaders.

  4. The results are reported in Sect. 5.1.3.

  5. Zerni (2012) controls for the possible firm/office effects on audit pricing by including firm and office fixed effects in regressions. However, adding the firm and office fixed effects to the audit fee regressions does not adequately control for the effects of firm or office industry leadership on audit fees. This is because such fixed effects capture the average fee premium or discount across all the industries audited by the audit firm or office. Given that an audit firm or office typically has clients from multiple industries and could be leaders in some of them, the fee differences attributable to firm/office industry leadership would become the regression residuals or be picked up by the partner leadership indicator to the extent that the indicator is positively correlated with omitted indicators for industry leadership at the firm/office level.

  6. These factors include the inadequacy in audit firms’ information technology to encourage effective knowledge sharing among their personnel, time constraints and workload pressure that reduce knowledge sharing efforts, and exclusive reliance on individually based extrinsic reward systems.

  7. Therefore the audit fee premium can be viewed as the market’s pricing of audit quality. Whether the engagement partner has the autonomy to make the audit pricing decision is irrelevant to the partner-specific versus firm/office-level analysis of the fee premium. Nonetheless, Bedard and Johnstone (2004) suggest that audit pricing decisions are made by individual members of engagement teams.

  8. Before July 1, 2002, the ASX used its own 24 industry sectors to classify listed clients.

  9. The sample size for Australia (US) totals 13,563 (109,753) client-years.

  10. For the years 2003 through 2010 respectively, these year percentages are 89, 89, 88, 89, 89, 88, 86, 86, if, instead, we use audit fees paid to the auditor of the parent entity. See footnote 17.

  11. This percentage is 88 using audit fees paid to the auditor of the parent entity. See footnote 17.

  12. The auditor-general of Australia is a public sector employee from the Australian Government’s National Audit Office (NAO), and only he issued and signed the audit report of Telstra Corporation limited (TLS) for the years before 2007. As the largest telecommunications company in Australia, TLS ranks in the highest percentile of audit fee-paying clients in each of the 2003 to 2010 years. The auditor-general subcontracted audit work to PW/Price Waterhouse before 2000 and to Ernst & Young from 2000 to 2006. In November 2006, the auditor-general resigned as auditor, and Ernst & Young became the auditor, issuing and signing the TLS audit report after 2006. We therefore assign the NAO as the auditor of TLS for the years before 2007 and Ernst & Young as the auditor after 2006. However, in our replication of Ferguson et al. (2003) shown in Sect. 5.1.3, we classify the TLS auditor as PW for consistency with Ferguson et al.’s (2003) apparent classification.

  13. For these 14 industries, the mean of the ratios of partner market share nationally to firm market share nationally is about 39 % and the median is about 34 %.

  14. Note that Ferguson et al. (2003) use audit fees paid to all auditors of the client’s group. If we use their audit fee measure, the percentage is 42 (48 city-industries).

  15. For these 92 city-industries, the mean of the ratios of partner market share at the city level to firm market share at the city level is about 62 % and the median is about 58 %.

  16. A merged firm is treated as different from the pre-merged firms because a partner’s incentives for audit quality could differ in the merged firm (Chan and Wu 2011).

  17. Note that fees paid to the auditor of the parent entity comprises fees paid for auditing the parent entity and for auditing any subsidiaries of the parent. We acknowledge that fees paid to other auditors within Australia are not allocated to those auditors and that fees paid for auditing unlisted entities are not allocated to auditors. These fees cannot be allocated due to data limitations. These data limitations are present in the other auditor expertise studies referred to in the present paper.

  18. We do, however, expect that using fees paid to the auditor of the parent entity will be relatively more accurate for large clients, because large clients are more likely to have overseas auditors involved in auditing entities in the group, and these overseas auditors can be paid relatively large fees. Consistent with this contention, for the bottom quartile (using total assets to measure size) of Big 4 clients (N = 1,654), the correlation coefficients for the two alternate fee measures are all above 0.96 for each of the three market share measures, but the coefficients range from 0.92 (national market share measures) to 0.97 (partner market share measures) for the first quartile of clients. Although 0.92 is still a strong correlation, these correlations are on the pooled sample across the five cities, meaning that the city-level correlations will likely be weaker, especially when a single client accounts for a large portion of the city-industry fees and that client has a large portion of its fees paid to an overseas auditor.

  19. This suggests that the first-ranked auditors have a more credible reputation for industry expertise, relative to other large auditors in the industry.

  20. We did not impose a condition for the number of clients audited as in Zerni (2012) because, in our cross-sectional sample (N = 6,368), a total of 567 or about 57 % of the 1,001 partner experts audit one client in the industry. Furthermore, a total of 197 of these 567 experts, or about 35 %, audit a client in the top 5 % of listed clients by total assets. This percentage is 19 for the second highest band and below 10 for all other bands. This is not surprising because the Australian listed client population is heavily right skewed. (About 92 % of total assets of all listed clients over the sample period are attributed to the clients in the top 5 % by total assets.) Consequently, imposing a number of clients condition will reclassify more than 56 % of experts as non-experts and relatively more partners who audit the larger clients as non-experts.

  21. Different from prior studies, we include the variable of geographic segments. Our test reveals that this variable captures much of the information in FOREIGN.

  22. As discussed in Sect. 5.1.1, we obtain consistent results with those of Fung et al. (2012) for their office-level industry scale variable (SCALE CIT ) only when both of the partner-level variables (i.e., MSHARE PAR or LEAD PAR and SCALE PAR ) are omitted from our model.

  23. Similar to Fung et al. (2012), we use the proc rank procedure in SAS to rank the clients. See Fung et al. (2012, footnote 15, p.1292) for further information about the calculation of SCALE CIT .

  24. We have partner data only back to fiscal year 1995, and our earliest sample year is 2003, which means the maximum tenure of a partner in 2003 is 9 years in our dataset. We therefore placed a maximum value of 9 on the partner tenure variable, so that tenure is measured on an equal basis over the sample period.

  25. This is estimated by e (Z*σ) − 1, where e () is the exponential function; Z is the regression coefficient on MSHARE PAR ; and σ is the standard deviation of MSHARE PAR . This procedure is described in Simon and Francis (1988).

  26. We used the loneway command in STATA to measure the ICC.

  27. The magnitudes of the regression coefficients on partner market share or leadership variables in the fixed-effect regressions are generally smaller than those in the cross-sectional regressions. This occurs because the coefficients in the fixed-effect regressions represent the within-client and within-partner variations after the absorption of cross-client (partner) variations in audit fees by the client and partner fixed-effect coefficients. In this sense, it is not appropriate to infer the overall magnitude of audit fee premiums for auditor expertise from the estimates of these regressions.

  28. Since these indicator variables have values of zero or one, the fee premium can be estimated by e z − 1, where z is the coefficient estimate for the indicator variable.

  29. Untabulated analysis suggests that the coefficients for these lower-ranked national and city leader variables are insignificant.

  30. For the fixed-effect market share model (column (3)), among the three market share variables, MSHARE PAR has the highest VIF, which is 11.62. Although the VIF is above 10, the commonly used ceiling for determining whether multicollinearity is causing problems with the estimates, the MSHARE PAR coefficient is highly significant and multicollinearity is not a concern with the estimate of that coefficient. The VIF for MSHARE CIT is 10.15. Prima facie, multicollinearity could be the cause of the insignificance of MSHARE CIT . To alleviate this concern, we estimate the regression using the sample where the partner market share is not equal to the market share of the partner’s office (N = 5,059). The VIFs for the three market share expertise variables are all below 10, and the inferences are the same, i.e., only the partner market share coefficient is significantly positive. Note that, in this sample, the VIFs fall because cases where MSHARE PAR and MSHARE CIT are perfectly correlated are excluded. Among the three market leader variables in the fixed-effect first leader model, LEAD CIT has the highest VIF. However, its VIF is 3.68, well below 10. Therefore multicollinearity is not likely to be the cause of the insignificance of national and city expertise variables. We present additional analysis of the potential multicollinearity problem in Sect. 5.1.2.

  31. Francis et al. (2005) and Basioudis and Francis (2007) use the first-ranked national leader while Ferguson et al. (2003) use the first- and second-ranked national leaders in their models. We do not use Ferguson et al.’s (2003) measures here because we replicate and extend their model below.

  32. The estimates by Francis et al. (2005) and Ferguson et al. (2003) are the premiums for the joint national-city industry leadership, which are higher than premiums for city leadership alone in both studies. In the UK, the premium for city-specific industry leadership averages 19 %, which is higher than the average premium of 12 % for joint national-city industry leadership (Basioudis and Francis 2007).

  33. The exceptions are, for the model excluding the partner leader (column 5), when the PW Sydney office is removed, the ONLY NAT LEADER coefficient is significantly negative (coeff. = −0.05, p = 0.022) and, for the model including the partner leader (column 6), when the PW Brisbane or the PW Sydney offices are removed, the JOINT CITY-NAT LEADER coefficients are 0.022 (p = 0.148) and 0.025 (p = 0.395), respectively.

  34. As noted, from July 1, 2002, the ASX changed the industry classifications.

  35. Ferguson et al. (2003) use “nearly all Australian publicly listed companies” (Ferguson et al. 2003, p. 434). The biggest difference occurs for Sydney, then the largest audit market in Australia. For Sydney, we have a total of 419 clients, and Ferguson et al. (2003) have a total of 397. We conjecture that the difference between their sample size and ours for fiscal 1998 could be because (1) the database provider of Ferguson et al. (2003) did not collect data for all companies, or (2) some companies lodged late and Ferguson et al. (2003) gathered their data shortly after the 1998 fiscal year end prior to those companies lodging.

  36. While we have more observations, we have one fewer city-industry than Ferguson et al. (2003). We have no explanation for this difference.

  37. The auditor general signed the audit report for Telstra Corporation in 1998 because the Australian National Audit Office was Telstra’s external auditor. PriceWaterhouseCoopers was subcontracted audit work in the 1998 year. We assign Telstra’s 1998 audit to PriceWaterhouseCoopers following Ferguson et al. (2003). See footnote 12.

  38. We reproduce only their combined model, namely the model using the joint city and national research framework (their Model 3). P-values in Ferguson et al.’s (2003) table are from one-tailed tests.

  39. The results are available from the authors on request.

  40. We thank the reviewer for this suggestion.

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Acknowledgments

We thank the editor, an anonymous reviewer, Mike Bradbury, Jong-Hag Choi, Bingbing Hu, Jay Lee, Kenny Lin, participants of workshops at Sabancı University, Shanghai University of Finance and Economics, and Xiamen University, and the European Accounting Association 2013 Conference for their comments. We also thank Susanna Andreassen and Henry Yeung for their research assistance.

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  1. School of Management, Sabancı University, Istanbul, Turkey

    John Goodwin

  2. Business School, Center for Institutions and Governance, The Chinese University of Hong Kong, Hong Kong SAR, China

    Donghui Wu

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Goodwin, J., Wu, D. Is the effect of industry expertise on audit pricing an office-level or a partner-level phenomenon?. Rev Account Stud 19, 1532–1578 (2014). https://doi.org/10.1007/s11142-014-9285-8

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