We examine changes over time in Big N auditors’ client selection and retention strategies, from 1970 to 2015, by client size and risk segments. We particularly focus on the period from 1997 to 2001, characterized by numerous tumultuous events that led to the Sarbanes–Oxley Act of 2002 (SOX). We find that Big N auditors shed en masse the smallest and riskiest clients during this tumultuous phase. Our results show that the initial impetus for the change in Big N’s client selection strategies at the dawn of the twenty-first century came from changes in the market conditions, not the demise of Arthur Andersen and the implementation of SOX, as concluded in prior literature. Those changes led to the current divide between the characteristics of Big N and non-Big N client segments that is taken for granted today. Our findings also shed light on the debate about the Big N association with audit quality. While we find existence of a Big N effect, we also find that this effect is highly correlated with, and appears and disappears with, changes in Big N’s client screening criteria.
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All data used in this study are available from public sources.
We used SAS programs.
Shu (2000) shows that Big N’s market concentration increased steadily over a 23-year period from 1974 to 1997, her last study year. Subsequent studies find a decline in Big N’s market concentration after the passage of the Sarbanes–Oxley Act of 2002 (e.g., Landsman et al. 2009) and when the Public Company Accounting Oversight Board (PCAOB) passed Auditing Standard No. 2 (AS2) (Rama and Read 2006; Ettredge et al. 2007),eventually replacing it with Auditing Standard No. 5 (AS5) (Schroeder and Hogan 2013).
This period was examined by Landsman et al. (2009), but with the years between 1993 and 2001 considered a uniform block, that is, a pre-SOX benchmark for post-SOX developments.
See for example, Campbell et al. (2001), Ljungqvist and Wilhelm (2003), and Fama and French (2004). Also see https://site.warrington.ufl.edu/ritter/files/2018/03/Listed1980-2017.pdf for the number of listed companies by year and quarter. The number of restatements went from 92 in 1997 to 225 in 2001, a 145% increase (GAO 2003). The percentage of listed firms restating their financial statements increased from 0.89 to 2.25. Of the 280 stocks in the Bloomberg’s U.S. Internet Index, 79 dropped by more than 90% or more and another 72 dropped 80–89% from their peak values (Kleinbard 2000). These 280 stocks lost $1.755 trillion of market capitalization, an approximately 60% drop, in the dot-com bust.
Client acceptance and retention policies are generally a firm-wide decision, likely dependent on overall reputation and litigation risk considerations (Johnstone and Bedard 2004). The Big N auditors thus could have become more careful in their client selection during this period, even though the engagement-level partners could act less conscientiously. The quality control systems at the firm level aim to reduce these conflicts of interest (e.g., Huddart and Liang 2005). Deficiencies in those systems, allegedly prevalent during the dot-com period, likely contributed to individual audit failures (e.g., Aobdia 2019a). Consistent with the idea that auditors’ client acceptance and retention decisions were reasonably sound prior to SOX, many former Arthur Andersen offices were purchased by the remaining Big 4 audit firms after the collapse, with the majority of the acquired clients being retained subsequent to the purchase (Blouin et al. 2007; Kohlbeck et al. 2008).
One potential explanation is that Big N auditors increased their non-audit services after 1997, which might have caused capacity constraints, assuming that assurance professionals became directly involved in providing non-audit services. We do not have audit and non-audit fees data from the later 1990 s to test this hypothesis, because the Securities and Exchange Commission, as part of its revised auditor independence rules of November 2000, mandated disclosure of audit fees and fees paid for non-audit services beginning from proxy statements filed after February 5, 2001 (see for example Frankel et al. 2002). This hypothesis would be valid if we could show (1) a systematic shift of Big N’s revenues from audit to non-audit services and (2) that this shift was more pronounced for bigger and safe clients than for smaller and riskier clients, making the latter set less profitable. We note that, if valid, this alternative explanation would still support the conclusion that the initial change in Big N’s client selection and retention occurred pre-SOX, not post-SOX.
Admittedly, small auditors cannot cover large clients. Thus, it is expected that Big N would remain dominant in the largest client segment.
Multivariate regression controls for size, profitability, asset turnover, leverage, current ratio and earnings volatility.
Post-2003, the difference declined because a large percentage of the smallest-size non-Big N clients delisted between 1999 and 2003. Post-2003, IPOs of small-size clients never reached the levels of the 1990s (Gao et al. 2013).
The only significant regulation promulgated during dot-com boom and bust phases was Statement on Auditing Standards (SAS) No. 82 (1997). It clarified but did not increase the auditor’s responsibility to detect fraud responsibility (Mancino 1998). That responsibility continued to be framed within the concepts of materiality and reasonable assurance under the overall general standards (auditor identity Section 110 of American Institute of Certified Public Accountants Professional Standards). Zimbelman (1997) shows that auditors who separately assess fraud risk, as required by SAS No. 82, spend more time attending to red-flag cues and significantly increase their budgeted hours but do not increase sensitivity to fraud risk or changed the nature of audit plans. Furthermore, SAS 82 cannot explain the large increase in downgrades of 1999 to 2001 that were far larger than that of 1998.
The eight firms in the 1980 s were Arthur Andersen, Arthur Young, Coopers & Lybrand, Deloitte Haskins & Sells, Ernst & Whinney, Peat Marwick Mitchell, Price Waterhouse, and Touche Ross.
Asthana et al. (2019) find that fee competition among auditors improves audit quality in the highly concentrated U.S. audit market.
Studies offer different criteria for defining a tight oligopoly, all of which are met by the public accounting industry. For example, some define a tight oligopoly as when the top four firms hold more than 60% market share (Shepherd and Shepherd 2004); others, when eight or fewer firms hold more than 50% market share (Kaysen and Turner 1959).
See Ferguson et al. (2016) for the investments by audit firms in exogenous and endogenous sunk costs, creating economies of scale.
Myers et al. (2014) find that SOX changed auditor behavior with respect to going concern reporting. Non-Big N auditors became more conservative while Big N auditors became more accurate.
We estimate the standard deviations of CFO for each company-year using four rolling annual observations (t − 3 through t). We acknowledge that the balance sheet method of estimating cash flows has weaknesses (Hribar and Collins (2002) but use it to maintain feasibility and consistency of measurement of our 1975 onward study period. We conduct an additional analysis with reported cash flows from 1989 and find similar results (not tabulated).
In unreported tests, we find similar results using sales and earnings volatility.
The PCAOB in 2004 released reports for its limited inspections. These reports, restricted to the Big 4 audit firms, corresponded to a test run of the full inspections, which started in 2004.
The PCAOB publicly discloses deficiencies identified in its inspection of individual engagements, as well as quality control deficiencies when these are not addressed to the satisfaction of the board within 12 months following the release of the initial inspection report [Section 104(g)(2) of SOX]. Prior literature finds mixed evidence about the market share impact of the disclosure of such deficiencies, depending on the auditor type and the nature of the deficiency (e.g., Lennox and Pittman 2010; Abbott et al. 2013; Nagy 2014; Aobdia and Shroff 2017; Acito et al. 2018; Aobdia 2018). Dealing with more difficult PCAOB inspections is potentially costly for an audit firm (Aobdia 2018).
We use ordinary least squares instead of Tobit regression because of the ease of interpretation of the coefficients, as each coefficient represents the average percentage during that period.
Audit Analytics provides the client and auditor-office mapping beginning only from 2000. However, the vast majority of audits of publicly traded corporations are conducted locally (Jensen et al. 2015).
We conduct an additional test (not tabulated). We retain clients having a Big N auditor in the last year and match them to CBSAs. We divide all CBSAs by their market size, proxied by the sum of square root of clients’ assets. We then examine their downgrade using Eq. (2). We continue to find a significant increase in downgrades before SOX even for the smallest markets.
In a univariate regression of log audit fees on log assets, the regression coefficient is approximately 0.5 with R-squared exceeding 65%. This is consistent with Simunic (1980).
Datta et al. (2019) also find a positive relation between audit fee and intangible assets.
We find similar trends using accruals measured using the statement of cash flows, but doing so limits our analysis to after 1988. We find that the Big N effect is insignificantly different from zero in years before 1992 and decreases from 1992 to 2006.
We cannot use restatements as a measure of audit quality, despite the advantages (e.g., Aobdia 2019a), because restatements are available in Audit Analytics starting only from 2000.
For example, Huang et al. (2019) find that Big N audit firms reduce managerial expropriation of cash. Kim et al. (2013) find that the loan interest rate is significantly lower for borrowers with Big N auditors than for borrowers with non-Big N auditors. Lobo et al. (2018) find that Big N auditors moderate the negative association between intangibles and financial reporting quality.
Control variables are defined in “Appendix”.
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We thank Ashiq Ali, Bruce Behn, Bugra Ozel, Aaron Crabtree, Keith Czerney, Paul Danos, David Emanuel, Joseph Gerakos, Nathan Goldman, Michael Gurbutt, Sean McCarthy (discussant), Ningzhong Li, Miguel Minutti-Meza, Gary Monroe, Tom Omer, Richard Sansing, Suraj Srinivasan, Nir Yehuda, Chris Yust, Dechun Wang, Chris Wolfe, Jieying Zhang, Yuan Zhang, and workshop participants at Texas A&M University, University of Nebraska (Lincoln), University of Texas (Dallas), the 2015 annual meeting of the American Accounting Association, and the 2016 meeting of the Canadian Academic Accounting Association for helpful comments. Daniel Aobdia gratefully acknowledges financial support from the Northwestern University Kellogg School of Management. He was a Senior Economic Research Fellow in the Center of Economic Analysis at the Public Company Accounting Oversight Board (PCAOB) between September 2014 and September 2016. He co-wrote this paper outside of his affiliation with the PCAOB. 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. Anup Srivastava gratefully acknowledges financial support from the Canada Research Chairs program.
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Appendix: Sample selection and definition of variables
Appendix: Sample selection and definition of variables
This Appendix describes the measurement or definition of variables used in this study. The corresponding data items in the Compustat annual database are in capital letters. We use three sets of samples from 1975 through 2015. The first set requires non-missing observations on the auditor identity (AU) and assets (AT) variables in Compustat. We retain firms headquartered in the United States (Compustat LOC of “USA”), leaving 274,273 company-year observations from 1975 to 2015. The second set requires additional data for calculation of stock prices and shares outstanding to determine market value of equity, leaving 221,721 observations. The third set requires data for calculation of cash flow volatility, where cash flows are determined using the balance sheet method. This leaves a sample of 194,073 company-year observations.
|TotalAssets||AT. For certain tests, assets are inflation-adjusted to 1997 values based on the Consumer Price Index for All Urban Consumers (CPI-U), provided by the U.S. Department of Labor Bureau of Labor Statistics (Fig. 3 and Table 1). In other tests, assets are inflation-adjusted to 1975 values (Tables 4, 6). LogAsset is the natural log of total assets. LogInflationAdjustedAssets is the natural log of inflation-adjusted assets. SquareRootInflationAdjustedAssets is the square root of inflation-adjusted assets|
|Earnings||IB, scaled by average TotalAssets for the year|
|AssetTurnover||Asset turnover. Revenues (SALE), scaled by average TotalAssets for the year|
|Leverage||Total debt (DLC + DLTT)/TotalAssets|
|CurrentRatio||Current assets (ACT)/current liabilities (LCT)|
|R&D||Research and Development Expenditures (XRD)/TotalAssets|
|Accruals||Change in Current Assets (ACT) − Change in Cash (CHE) − Change in Current Liabilities (LCT) + Change in Debt in Current Liabilities (DLC) − Depreciation and Amortization (DP), scaled by beginning TotalAssets|
|CFO||IB, scaled by beginning TotalAssets − Accruals|
|AuditQuality||− 1 × absolute value of discretionary accruals. Discretionary accruals is the residual of a regression of Accruals, defined using the balance sheet method, on gross property, plant and equipment (PP&E, deflated by beginning assets), the year-on-year change in revenues (deflated by beginning assets), one over beginning assets and prior year return on assets (IB deflated by average assets) using the cross-sectional modified Jones model (Dechow et al. 1995; Kothari et al. 2005; Reichelt and Wang 2010)|
|EarningsVolatility||Uncertainty of firm performance. Standard deviations of earnings (IB, scaled by total assets) using four rolling annual observations (t − 3 through t)|
|CashFlowVolatility||Uncertainty of firm performance. Standard deviations of CFO using four rolling annual observations (t − 3 through t)|
|BookToMarket||Book-to-market ratio of company’s shareholder equity|
|BigN||Dummy variable that takes a value of one for Big N firms and zero otherwise. Arthur Andersen, Arthur Young, Coopers & Lybrand, Deloitte Haskins & Sells, Ernst & Whinney, Peat Marwick Mitchell, Price Waterhouse, or Touche Ross, and their successors are Big N firms. BigN%, also called Big N’s market concentration, is percentage of sample companies that have a Big N auditor. Following Shu (2000), we identify these firms with an AU variable between one and eight|
|Downgrade||Dummy variable that takes a value of one if the company changes from a Big N auditor in the previous year to a non-Big N auditor in the current year and zero otherwise|
|Upgrade||Dummy variable that takes a value of one if the company changes from a non-Big N auditor in the previous year to a Big N auditor in the current year and zero otherwise|
|Listing year||First year in which the company has valid share price (PRCC_F), asset (AT), and auditor (AU) data in Compustat|
|Pre-1970s||Companies whose listing year is before 1970|
|New-lists||Companies that are not pre-1970s|
|Listing cohorts||All of the cohorts listed in a common decade are referred to as a cohort of new companies. Companies are divided into the pre-1970s cohort or a cohort from the 1970s, 1980s, 1990s, 2000s, or 2010s|
|Pre1997||Dummy variable that takes a value of one for observations in years before 1997 and zero otherwise|
|Year1997||Dummy variable that takes a value of one for observations in year 1997 and zero otherwise|
|Pre-SOX (PeakDotcomBoom)||Dummy variable that takes a value of one for observations from years 1998 and 1999 and zero otherwise|
|Pre-SOX (DotComCrash)||Dummy variable that takes a value of one for observations from years 2000 and 2001 and zero otherwise|
|PostSOX||Dummy variable that takes a value of one for observations from years 2002 (when the Sarbanes–Oxley Act is enacted), 2003, and 2004 and zero otherwise|
|PostPCAOB||Dummy variable that takes a value of one for years 2005 (when PCAOB started releasing the first inspection reports), 2006, and 2007 and zero otherwise|
|PostAS5||Dummy variable that takes a value of one for years after 2008 (when PCAOB Auditing Standard No. 5 became effective) and zero otherwise|
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Aobdia, D., Enache, L. & Srivastava, A. Changes in Big N auditors’ client selection and retention strategies over time. Rev Quant Finan Acc 56, 715–754 (2021). https://doi.org/10.1007/s11156-020-00907-8
- Competitive structure
- Public companies
- Big N audit firms
- Audit risks
- Audit quality
- Dot-com boom and bust