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The geography of US auditors: information quality and monitoring costs by local versus non-local auditors

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Abstract

Borrowing from recent finance research that describes geographic distance as a determinant of information asymmetry and performance, we examine the relation between audit quality and auditor–client proximity. After controlling for monitoring costs (audit fees) and client firms’ selection of auditors, we find that accruals quality (a common proxy for audit performance) improves with auditor proximity. The findings indicate that auditors have information advantage that dissipates with geographic distance (i.e., “soft” information in Berger et al. in J Financ Econ 76(2):237–269, 2005; Coval and Moskowitz in J Polit Econ 109(4):811–841, 2001; Petersen and Rajan in J Financ 57(6):2533–2570, 2002), suggesting that that investors, analysts, and regulators should pay additional attention to information risk for firms audited by non-local auditors.

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Notes

  1. Examples include business acquisitions (Kedia et al. 2008; Kang and Kim 2008); bank loan decisions (Peterson and Rajan 2002; Degryse and Ongena 2005) and SEC enforcement (Kedia and Rajgopal 2007; DeFond et al. 2008).

  2. This standard is specified by Generally Accepted Auditing Standards (GAAS), which are determined jointly by the Securities and Exchange Commission (SEC), the Public Company Accounting Oversight Board (PCAOB), and the American Institute of Certified Public Accountants (AICPA).

  3. Coval and Moskowitz (2001) suggest two underlying mechanisms for local investors’ information advantages over remote investors: “This information may be the result of improved monitoring capabilities or access to private information of geographically proximate firms.” (pp. 812–813).

  4. Such costs may include higher transportation costs, higher costs associated with using similar monitoring methods at a greater distance, and costs associated with compensatory monitoring behaviors used to address the increased uncertainty associated with remote audit clients.

  5. See Francis (2004) for a review of this literature.

  6. “Due professional care” is specifically required by Generally Accepted Auditing Standards.

  7. For example, auditors are required to confirm accounts receivable, physically inspect inventories, perform analytical procedures, and communicate with clients’ legal counsel.

  8. For example, the actual monitoring efforts and behaviors of the analysts and investors in the finance literature are not observable. Because of this, it was difficult, if not impossible, for researchers to distinguish whether analysts and investors actually spend more time and resources monitoring local firms versus remote firms (i.e., increased effort). Furthermore, even if we assume they do not, we are still not able to distinguish whether the results are attributable to different monitoring behaviors for local firms (e.g., on-site visits or face-to-face meetings) versus remote firms (e.g., desk analysis or conference calls). Neither can we distinguish the effects of soft information on monitoring quality. Hence, it is difficult to attribute the results of these studies to any particular mechanism. Moreover, although differential monitoring costs are implicated in each of the above studies, actual costs are not observable—either in total or by specific monitoring behavior. Hence, it is not clear to what degree monitoring costs directly affect information asymmetry, or to what degree they moderate monitoring behaviors.

  9. There are some cases where an audit firm has more than one office in a city. In those cases, the current dataset does not allow us to distinguish which branch performed the audit. We do not believe the effects of this are material. Nevertheless we acknowledge this as a limitation for our study.

  10. http://www.census.gov/geo/www/gazetteer/places2k.html.

  11. For example, entries for the city of Los Angeles include, “Los Angelel,” “Log Angeles,” “Los Angles,” and the correct “Los Angeles.” The computer matching may ignore these misspelled cases.

  12. For example, the 2000 Census does not include geographic information of The Woodlands, TX, a new town.

  13. The 2000 Census may not provide the exact one-to-one match for the firms’ city names compiled in the Audit Analytics file. These are cases where clients are located in small satellite cities of metropolitan areas.

  14. Coval and Moskowitz (1999) show that fund managers are, on average, 1,654 km away from their portfolio firms and Kang and Kim (2008) show that block acquirers are 1,603 km from their targets. However, our dataset suggests that the auditors are, on average, only 138 km from their clients. This finding suggests that information quality and verification costs associated with geographical proximity could be more important to the auditor–client relation than the investor/analyst-firm relation. This also suggests that while relatively short distances may have little influence on the investor/analyst-firm relationship, they may have an important influence on the auditor–client relationship. Therefore, we believe that the additional steps taken to locate street address information are critical for this study.

  15. The haversine formula has been extensively used in navigation, giving circle distances between two points on a globe based on longitudes and latitudes information. Note that this formula is only an approximation when applied to the Earth, because the Earth is not a perfect sphere: It is known that the radius of curvature of a north–south line on the earth's surface is 1 % greater at the poles than at the equator. Therefore, the distance approximation generated by the Haversine formula may include errors of 0.5 %.

  16. The “100-mile distance” and “out of state” measures are not primary measures of distance in our analyses. These measures are used for illustrative purposes and as alternative measures in sensitivity tests.

  17. For example, Lerner (1995) attributes increases in post-investment governance activity to lower monitoring costs, and Kedia and Rajgopal (2007) attribute decreases in financial reporting quality to perceptions of higher SEC monitoring costs.

  18. A counter argument exists that some company-auditor relationships are determined through the competitive bidding process (see Jensen and Payne 2005). To the extent that some firms select auditors based on the lowest price generated in this process, we are less likely to observe audit fee difference for local versus distant audits.

  19. DIST is designed to reduce the skewness of the DISTANCE measure. All regression results reported in this paper are similar when we replace DIST with DISTANCE, the square root of DISTANCE, the natural logarithm of DISTANCE or a dummy variable cut off at DISTANCE = 100 miles.

  20. For 2000 observations, we treat firms audited by Andersen as Big 4 audits. Dropping year 2000 or coding Andersen audits as non-Big 4 audits does not alter our findings.

  21. We consider audit-firm mergers in calculating tenure. Additionally, replacing TENUREHIGH with a continuous value (i.e., years or the log of years) yields similar results in all our analyses.

  22. These differences are not simply the result of remote audits involving only rural companies, or rural companies driving the results. As Fig. 1 demonstrates, remote audits are not peculiar to rural firms. Additionally, Urcan (2007) shows that rural firms actually have higher levels of reporting quality on average.

  23. E-mail addresses were obtained from Audit Analytics’ Current Auditor File.

  24. A sampling of responses include the following: (a) Proximity to accounting organization which is not located at the corporate office location: “Although Intersil is headquartered in Milpitas, CA, most of our accounting operations are performed in Palm Bay, FL. KPMG is our independent auditor and the KPMG staff comes from the location that is closest to the area being audited.”; (b) Industry expertise of auditors: “Per our Proxy Statement, Aastrom uses PricewaterhouseCoopers LLP as its independent registered accounting firm. Aastrom is located in Ann Arbor, MI, and in the past, our auditing team came from Minneapolis, MN … In the past, the Minneapolis office was the only office that had biotech auditors.”; (c) Past relationship between corporate managers and audit partners: “Universal Detection Technology (Beverly Hills, CA) works with auditors who are in a state other than the one our company is situated in (AJ. Robbins, PC. Denver, CO) … Managers have worked with partners of non-local auditors for some time in the past.”; (d) Audit pricing: “American Ecology Corporation (Boise, ID) has Moss Adams as its independent accountants. Moss Adams does not have a local office and the partner in charge is located in Los Angeles, CA requiring the opinion to be signed in California. Moss Adams was selected by our audit committee back in 2001/2002 as a result of a competitive bid process. At that time, there were three of the Big 5 public accounting firms located locally. As in most companies, pricing played a significant role in the selection of an independent accountant along with other service related and technical competency factors.”; (e) Other firm-specific reasons: “Abraxas wanted a ‘middle tier’ accounting firm, just below the Big4 but still having a national presence. None of those firms had offices in San Antonio so we were forced to look in Dallas or Houston. BDO was the most attractive option we had and I’m very pleased with the service level they provide.”

  25. This could occur for several reasons. For example, Loughran and Schultz (2005) report that rural firms are followed by fewer analysts and financial institutions and have higher trading costs. If these firms have a limited number of quality auditors locally available, they may have incentives to hire distant quality auditors to reduce information asymmetry. Alternatively, although the number of local brand name auditors may be high, firms may desire to employ auditors with industry expertise, because they have high levels of firm-specific assets to be verified.

  26. These metropolitan areas are as follows. New York (NY), Los Angeles (CA), Chicago (IL), Washington (DC), San Francisco (CA), Philadelphia (PA), Boston (MA), Detroit (MI), Dallas (TX), Houston (TX), Atlanta (GA), Miami (FL), Seattle (WA), Phoenix (AZ), Minneapolis (MN), Cleveland (OH), San Diego (CA), St. Louis (MO), Denver (CO), Tampa (FL), Pittsburgh (PA), Portland (OR), Cincinnati (OH), Sacramento (CA), Kansas City (MO), Milwaukee (WI), Orlando (FL), Indianapolis (IN), San Antonio (TX), Norfolk (VA), Las Vegas (NV), Columbus (OH), Charlotte (NC), New Orleans (LA), Salt Lake City (UT), Greensboro (NC), Austin (TX), Nashville (TN), Providence (RI), Raleigh (NC), Hartford (CT), Buffalo (NY), Memphis (TN), Boca Raton (FL), Jacksonville (FL), Rochester (MN), Grand Rapids (MI), Oklahoma City (OK), Louisville (KY).

  27. It has been argued that small firms face greater information asymmetries and have lower quality information environments than large firms. Coval and Moskowitz (1999) find that US fund managers exhibit a strong preference for locally headquartered firms, particularly for small firms. Malloy (2005) shows local analysts covering small stocks issue significantly more accurate earnings forecasts than other analysts. Hong et al. (2000) also argue that information about small firms is revealed more slowly than that of large firms because investors who face fixed cost information acquisition are willing to spend more to learn about a firm in which they can take large positions.

  28. We obtain the percentage of equity held by institutional investors through Thompson Financial’s 13F filing database. We use the fourth quarter’s information. We assign a value of zero for this variable when firm-year observations are not covered by the database.

  29. Industry and year dummies are also included. Industry dummies are particularly important as they are likely to capture the demand for industry specialization.

  30. Some international studies (e.g., Chan et al. 2006) suggest that geographically proximate auditors may provide lower quality audits due to the bond or collusion between auditors and local clients. However, we do not believe that this scenario is likely in the US setting.

  31. Archival auditing research employs three traditional measures of audit quality. The most common measure is the magnitude of abnormal accruals (e.g., Becker et al. 1998; Reynolds and Francis 2000; Antle et al. 2006), followed by auditors’ issuing modified going-concern reports (e.g., DeFond et al. 2002) and earnings distribution tests (e.g., Frankel et al. 2002). The first and third measures are based on the idea that earnings management is more likely to be detected if audit quality is good. We do not use the distribution analysis due to the asymmetry in the number of local versus remote audits. While we have hundreds of observations from local auditor firm-year observations, we only have tens of observations from non-local auditor firm-years. This violates the underlying assumptions of the distributional approach. The second methodology is based on the idea that independent auditors are more likely to be indifferent in releasing a modified going-concern opinion. The auditors’ independence is not conceptually identical to what we want to capture—the difference in audit quality due to the information asymmetry. Thus, we do not use modified going-concern reports test. Descriptive statistics in Table 4 clearly suggest that the portion of modified going-concern opinion is more pronounced in remote audits. This is likely because financially distressed firms are clustered in remote-auditor groups. In untabulated reports, we regress the probability of receiving a modified going-concern report on two distance measures and other control variables for out-of-state auditor firms with Altman Z score below 3, matched by firm-years with the size, loss, and the closest DE ratio. We do not find any evidence that distance explains the probability of auditors issuing a modified going-concern report.

  32. In untabulated reports, we use a cash flow-accrual matching measure used in Dechow and Dichev (2002) as an alternative measure for audit quality and find similar results. We do not choose this as the primary measure, because the measurement requirement in Dechow and Dichev (2002) significantly reduces the sample firm-year observations.

  33. In estimating Eqs. (1), (2), (4), (5a), (5b), and (5c), and tabulating Tables 5, 6, 7, and 8, we have not used a recent panel data estimation technology developed by Petersen (2009). The main reason is there is no known way to handle three stage OLS estimation by two-way clustering. However, when we independently run CLUSTER2 command by GVKEY and FYEAR in STATA for Eqs. (1), (2), and (4), all results [except BIG4RATIO in Eq. (2)] remain statistically significant at p values of 0.05.

  34. To explore whether investors are aware of the distance-quality relation (see Ghosh and Moon 2005), we regress short and long window size adjusted returns on earnings change deflated by market value, with control variables such as growth, earnings persistency, loss, DIST, and interaction terms of earnings change and these variables. Untabulated results offer no evidence that they are aware of this relation.

  35. We have performed a few diagnostic tests including VIF and Granger causality tests to see whether there are special correlation effects. We conclude that our results are not likely to be affected by either multicollinearity or other spurious factors (untabulated).

  36. Prior literature shows that states can serve as important constraints to information flows. For example, local media such as newspapers, radio, and television regularly provide coverage of events within the state. In addition, in-state economic agents can access information about state regulations that influence other economic agents’ corporate policy, performance, and even governance activities more easily and accurately than out-of-state economic agents. Consistent with this view, Audretsch and Stephan (1996) emphasize that the most relevant unit of policy-making is at the state level. Also, Coffee argues that in the U.S., state regulatory agencies play a more important role in investor protections than the Securities and Exchange Commission (Federal Securities Report Letter, February 4, 2004). Similarly, local auditors may have better access to information about local firms than non-local auditors.

  37. This suggests that while new local auditors can enjoy the monitoring efficiency gain from the firm previously audited by non-local auditors (due to existing low quality earnings), new non-local auditors may not have the same privilege from the client previously audited by local auditors (due to existing high quality earnings).

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Acknowledgments

We thank Tom Hardy of Audit Analytics™ for helpful discussions relating to the data we used in this study. We thank Raquel Meyer Alexander, Scott Dawson, Stephen Fafatas, David Hay, Robert Knechel, Carol Knapp, Marlys Lipe, Robert Lipe, Sandeep Nabar, Wayne Thomas and workshop participants at the University of Florida, the University of Oklahoma, Oklahoma State University and 2007 American Accounting Association Annual Meeting. We also acknowledge the able research assistance of Brandon Bradford, Jeung-Yoon Chang, and Eunsun Yang.

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Authors and Affiliations

  1. University of Oklahoma, Norman, OK, USA

    Kevan Jensen

  2. Rutgers Business School, Newark, NJ, USA

    Jin-Mo Kim

  3. Korea University Business School, Seoul, South Korea

    Han Yi

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  1. Kevan Jensen
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Correspondence to Jin-Mo Kim.

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Data Availability: All data used in this study are publicly available from the sources identified in the text.

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Jensen, K., Kim, JM. & Yi, H. The geography of US auditors: information quality and monitoring costs by local versus non-local auditors. Rev Quant Finan Acc 44, 513–549 (2015). https://doi.org/10.1007/s11156-013-0416-2

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