Do Individual Auditors Affect Audit Quality? Evidence from Archival Data *

Transcription

1 Do Individual Auditors Affect Audit Quality? Evidence from Archival Data * Ferdinand A. Gul Monash University Sunway campus Donghui Wu The Hong Kong Polytechnic University Zhifeng Yang City University of Hong Kong * Contact: Ferdinand A. Gul (Tel: , [email protected]), Donghui Wu (Tel: , [email protected]), Zhifeng Yang (Tel: , [email protected]). We thank Charles Chen, Shimin Chen, Zhihong Chen, Jong-Hag Choi, John Goodwin, Yuyan Guan, Bingbing Hu, Jong-Bon Kim, Yinghua Li, Nancy Su, Xijia Su, Yong Yu, Yuan Zhang, and the participants in the 2011 Joint Symposium by City University of Hong Kong, National Taiwan University, and Shanghai University of Finance and Economics for their helpful comments, and Joanna Chan, Yanan Wen, and Yuxiao Zhou for their able research assistance.

2 Do Individual Auditors Affect Audit Quality? Evidence from Archival Data Abstract This study examines the importance of individual auditors in determining audit quality using a large set of archival Chinese data. We find that there is a significant variation in audit quality across individual auditors. The effects that individual auditors have on audit quality are both economically and statistically significant, and stronger than the corresponding effects of audit firms. We also find that the individual auditor effects on audit quality can be partially explained by the auditors characteristics such as educational background, Big N audit firm experience, rank in the audit firm, and political background. Our work highlights the importance of analyzing audit quality at the individual auditor level. Keywords: individual auditor; audit quality; auditor characteristics; archival research. Data Availability: data used in this study are publicly available from the sources described in the paper. - i -

3 1. Introduction This study examines whether and how audit quality varies across individual auditors. Our work represents a response to the recent call from academics and policy-makers for more scrutiny and understanding of audit quality at the individual auditor level. The importance of auditor individual differences in the audit process has been articulated by several writers. For example, Nelson and Tan (2005, p.42) make the following point: Auditors need to perform a variety of tasks to form an overall assurance or attestation opinion. To do so, various personal attributes of the auditor (e.g., skills and personality) influence the outcome. As such it is likely that individual characteristics of the auditor could affect the quality of the audit being undertaken. However, prior archival research has largely conducted the audit quality analysis at the audit firm or individual office level (see Francis (2004) for a review). The importance of individual auditors in determining audit quality has received increasing attention from policy-makers and academics in recent years. For example, former SEC commissioner Steven Wallman (1996) suggests that in assessing auditor independence, the focus should be on the individual, office, and other unit of the firm making audit decisions with respect to a particular audit client (p.78, emphasis added). In a recent review paper, DeFond and Francis (2005) suggest that the audit quality analysis be push from the audit firm or office level down to the individual auditor level. Similarly, Church et al. (2008) advocate that researchers investigate whether there is a systematic relationship between individual characteristics and the quality of audit reporting. We analyze the variation in audit quality across individual auditors in the Chinese market, where the engagement auditors are required to sign the audit report. The role of the signing auditors in China is similar to that of engagement partners in other markets, in that the signing auditors lead the audit engagement team and are responsible for decision-making on significant matters in the audit process. Hence the audit reporting outcome and the client firm s financial statements could be significantly influenced by the signing auditors. The names of the signing auditors are disclosed, and their profile data are also publicly available. We therefore can match individual auditors with individual audit engagements, and obtain their personal data. These characteristics make the Chinese market a useful setting in which to investigate the effects of individual auditors on audit - 1 -

4 quality. Although individual auditors may influence the audit outcome with their characteristics, they are constrained by the quality control mechanisms within the audit firm. In the audit process, they must follow auditing standards and highly standardized audit procedures. Key decisions are often centralized at the audit firm level, and their work is subject to internal and external reviews. Thus it is not clear ex ante whether individual auditors can have significant effects on audit quality, and if so, how large their effects would be. The first part of our work attempts to address this question. In our research design, we assign an indicator variable to each individual auditor who meets our requirements, as detailed in Section 3. We then estimate an audit quality model by including these indicators and, also control for client firm, audit firm, and year effects, and time-varying client firm characteristics that could possibly affect audit quality. This research design allows us to separate the effects of individual auditors on audit quality from those of the client firms and the audit firms, and to assess, not only the presence, but also the magnitude and variation of the individual auditors effects on audit quality. By construction, the effects of individual auditors on audit quality estimated here capture their fixed effects. We use multiple audit quality measures, including audit reporting (AR) aggressiveness, the client firm s abnormal accruals and non-core earnings, and the presence of a small profit or not. We find that the effects of individual auditors are significant both statistically and economically for all quality measures. The inclusion of auditor indicators to the base model increases the explanatory power by 4.66% to 8.31%, or by 7.50% to 34.95% relative to the base model s adjusted R-square. The frequency of individual auditors who have significant effects on audit quality is much greater than what would be expected by chance. For example, the percentages of the individual auditors effects for AR aggressiveness that are significant at 5% and 10% levels are 12.9% and 18.0%, respectively. The variation in the effects of individual auditors is also considerable. For example, the abnormal accruals reported by client firms for an auditor at the 75 th percentile of the distribution of individual auditors effects would be 2.7% higher than the one at the 25 th percentile. Thus, individual auditors differ to a great extent in terms of audit quality or audit aggressiveness. Interestingly, the effects of individual auditors on audit quality turn out to be stronger than the effects of audit firms. Adding audit firm indicator variables to the base model increases the adjusted R-squares modestly by 0.63% to 1.77%

5 After showing that audit aggressiveness varies across individual auditors, we next explore whether this variation can be explained by auditors personal characteristics. Research on audit judgment and decision-making (JDM) suggests that audit quality is affected by auditor JDM attributes such as expertise, ability, risk profile, cognitive style, and independence (see Nelson and Tan (2005) and Nelson (2009) for reviews of prior studies). Based on the prior literature, we consider the following personal characteristics: education, gender, birth cohort, Big N experience, rank, and political background, assuming that these characteristics are associated with one or more of the attributes relevant to audit JDM. We find that partners exhibit a relatively conservative style of audit reporting, consistent with prior findings that partners are more conservative and take a tougher stand in requesting accounting adjustments than non-partner auditors (Trotman et al. 2009; Miller 1992). Educational background also makes a difference. Auditors who hold a Master s degree or above tend to be more aggressive, perhaps because they are more confident and optimistic. Auditors who have been exposed to Western accounting systems and modern theories of the corporation in their college education are more conservative. This could be due to their exposure in their early education to the notion that financial statements are designed to solve the information asymmetry between insiders and outside shareholders. We also find that auditors who have worked in international Big N audit firms tend to be more conservative. This is understandable since Big N audit firms are found to be more conservative than other audit firms (Francis 2004). The generally conservative environment in Big N firms may have a significant influence on their auditors judgment and decisions. Alternatively, it is also possible that auditors recruited by Big N audit firms may be inherently more conservative than those recruited by other audit firms. Auditors who have political affiliation, proxied by membership of the Chinese Communist Party, are more likely to be associated with lower quality audits than others. A possible reason for this is that CCP membership may give individual auditors some protection from being penalized for audit failure, which induces them to behave more aggressively. In the final step, we show that the documented effects of individual auditors on audit reporting and clients earnings quality are positively correlated with the likelihood of being sanctioned by the regulatory authority. As regulatory sanction for audit failure is an ex post measure of audit quality, this finding suggests that the documented effects of individual auditors indeed capture the difference in audit quality across individual - 3 -

6 auditors. This study advances our understanding of audit quality in several important ways. First, we show that there is a large variation in audit quality across individual auditors, and thus demonstrate the importance of analyzing audit quality at the individual auditor level (DeFond and Francis 2005). To the best of our knowledge, we are the first to quantify the effects of individual auditors on audit quality. Our results are important, as they reveal that individual auditors play an active role in the financial reporting process. Second, although prior research has examined the influence of auditors personal attributes on the performance of individual audit tasks (Nelson and Tan 2005), we respond to Church et al. (2008) by systematically exploring the associations between a rich set of auditors demographic characteristics and the overall quality of audit engagements. We show that various auditor characteristics can influence audit reporting and client firms earnings quality. Equally important, we find that a large portion of the variation in the effects of individual auditors is not explained, which suggests that more research is required to explore this issue in the future. Third, previous findings on the importance of the effects of auditor characteristics on audit performance are mostly obtained in experimental settings, while our results are based on a large set of archival demographic data. In this sense, our work bridges the archival and behavioral accounting research. This study also links to a growing body of literature on the effects individuals have on business decisions. Recent studies in accounting and finance show that individual managers, such as CEOs and CFOs, matter for a wide range of corporate decisions and policies (Bertrand and Schoar 2003; Ge et al. 2010; Bamber et al. 2010; Dyreng et al. 2010). Although there are significant differences between corporate executives and individual auditors in terms of their influence or power over the underlying decisions, individual auditors still imprint their mark on the audit outcomes. Our evidence is consistent with the notion that people rather than business organizations make decisions and, thus, that the role of individuals in business decisions deserves more attention (Kachelmeier 2010)

7 2. Literature Review and Research Questions 2.1. Literature Review The Audit Quality Analysis Audit quality is determined by an auditor s ability to discover breaches of accounting standards and their incentives to report such breaches, i.e., audit quality is a product of auditor competence and independence. DeAngelo (1981) argues that large audit firms are associated with higher audit quality because they are more independent. For large auditors, such as the Big N international audit firms, no single client is of more importance and there is more to lose if they misreport. Furthermore, Big N firms have established brand name reputations and, thus, have incentives to protect their reputation by providing high-quality audits (Simunic and Stein 1987; Francis and Wilson 1988). Motivated by these arguments, early studies use the dichotomy between Big N and non-big N audit firms, and show that Big N audit firms are of higher quality and are more conservative (Becker et al. 1998; Francis and Krishnan 1999; Teoh and Wong 1993). Big audit firms consist of many city-based practice offices. DeAngelo s (1981) argument regarding audit quality and firm size can be applied to the office level. In terms of economic importance, for example, a client that is not big relative to a Big N firm could be very important to one of the firm s offices. Accordingly, more recent studies shift the audit quality analysis from the firm level to the office level (Reynolds and Francis 2000; Krishnan 2005; Francis and Yu 2009). For example, Francis and Yu (2009) show that the bigger offices of Big 4 firms are of higher quality because bigger offices have more in-house expertise. A natural extension of this literature is to move the audit quality analysis further down, from the level of the office to that of the individual auditor, because individual auditors may differ in regard to both dimensions of audit quality, i.e., independence and competence (DeFond and Francis 2005). Accounting scholars have recently begun to investigate the role of individual auditors in determining audit quality. For example, Chen et al. (2010) perform one of the first analyses of how economic dependence affects audit quality at the individual auditor level using Chinese data, and find that the impact of client importance on the independence of individual auditors is conditional on the legal environment

8 2.1.2 How Do Individual Auditors Influence Audit Quality? Individual auditors differ not only in terms of their incentives, but also in regard to attributes such as risk preference, expertise, ability, and cognitive style. Knechel (2000) argues that audit quality is ultimately dependent on an auditor s JDM qualities as auditing is inherently a JDM process. An auditor s JDM qualities are, in turn, determined by the auditor s personal characteristics. Indeed, research on auditors personal attributes has a long tradition in auditing. Prior research (see Nelson and Tan (2005) and Nelson (2009) for reviews of relevant papers) shows that auditor knowledge, expertise, personality, and cognitive style each play a significant role in determining the performance of audit tasks. The extent to which auditor characteristics can influence the audit outcome is likely to be affected by the quality control mechanisms within the audit firm. In fact, audit firms try to maintain consistency in audit quality through control mechanisms, including standardization of work procedures, centralized control with risk and materiality decisions, and socialization precisely because of the idiosyncrasies of individual auditors (Jeppesen 2007) The Managerial Fixed Effect Literature A recent stream of the literature has documented that individual executives exert significant influence over a wide range of firm policies. In a seminal paper, Bertrand and Schoar (2003) show that a significant portion of the heterogeneity in firms investment, financial, and organizational practices can be explained by the presence of executive fixed effects. Following the methodology developed by Bertrand and Schoar (2003), Dyreng et al. (2010) show that top executives have incremental effects on their firms tax avoidance. Ge et al. (2010) find that CFO-specific factors explain a significant portion of the heterogeneity in financial reporting practices. Top executives not only affect corporate strategic decisions, such as M&As, but also those seemingly second-order decisions, such as voluntary disclosures. For example, Bamber et al. (2010) find that top executives exert economically significant individual effects on five aspects of management forecast: forecast frequency, forecast precision, the news conveyed by the forecast, and the bias in, and accuracy of the forecast. Prior literature also examines whether observable executive characteristics, such as gender, education, and experience, can explain manager fixed effects. Overall, the findings suggest that these observable characteristics, at best, partially explain the managerial fixed - 6 -

9 effects on firm decisions. However, the lack of a strong association between observable attributes and manager effects does not lessen the main conclusion that individual managers matter (Dyreng et al. 2010). Instead, it suggests that certain unidentified factors are important in explaining these fixed effects and, thus, highlights the importance of first quantifying the effects of managers characteristics Research Questions Kachelmeier (2010) points out that the managerial effect research shows that people rather than business organizations make decisions and, thus, bridges the archival and behavioral accounting research. Similarly, in the audit process, individual auditors may play an important role in making decisions. The personal attributes of individual auditors, e.g., their risk preferences, experiences, and incentives, may have a significant impact on audit quality (Nelson and Tan 2005). However, the importance of individual auditors in determining audit outcomes has not been widely examined in the existing archival accounting research. This could be due to the lack of data on individual auditors in the United States. DeFond and Francis (2005) thus suggest that scholars analyze audit quality at the individual auditor level in those markets where data are available. We examine the importance of individual auditors in determining audit quality using a large set of archival data related to the audits of public firms in China. We seek to answer two related questions. First, is there a significant variation in audit quality across individual auditors? Second, if so, can observable demographic characteristics of individual auditors, such as educational background, experiences, and gender, explain the variation in audit quality across individual auditors? To answer the first question, we adopt the methodology developed by Bertrand and Schoar (2003). This approach allows us to determine the presence, magnitude, and variation of the individual auditor effects on audit quality. This step is important for several reasons. First, while individual auditors may influence the audit outcome with their characteristics, ex ante it is not clear how large their effects would be. Unlike corporate executives such as CEOs who are very powerful and may dictate corporate decisions, auditors may not have such discretion in their judgment and decision making. Individual auditors must comply with the auditing standards promulgated by the profession body or the regulatory authority, and follow standardized audit procedures to perform their work. The material decisions are also controlled by the audit firm or the - 7 -

10 practice office. Moreover, their work is subject to the internal and external review. Because of these quality control mechanisms, there is a relatively small room for individual auditors to exercise their discretion. Second, we cannot conclude from prior archival auditing research that individual auditors affect audit quality. The archival auditing research has just begun to look at the role of individual auditors. This literature typically examines the association between a single aspect of auditor characteristics such as gender (Hardies et al. 2010) and experience (Chi et al. 2008), and finds no or weak evidence that individual auditor characteristics matter for audit quality. Although prior research controls for time-varying client firm characteristics, the time-invariant client specific factors that could impact audit quality and correlate with the effects of individual auditors are omitted. The research design of our study, outlined in Section 3, allows us to isolate the effects of individual auditors on audit quality after controlling for time-varying client firm determinants, client firm fixed effects, audit firm fixed effects, and year effects. This research design can generate cleaner evidence of the individual auditor effects on audit quality. Third, although the audit JDM research acknowledges the heterogeneity in individual auditors and its impact on audit performance, prior studies in this literature are carried out in the laboratory setting, making it difficult to evaluate the external validity of their findings. Moreover, they typically focus on the effects of auditors attributes on the performance of individual audit tasks instead of the overall quality of an audit engagement, which is the subject studied by us. Fourth, individual auditors differ in numerous aspects and thus focusing solely on a limited set of observable characteristics may seriously underestimate their effects on audit quality. Indeed, the managerial fixed effect literature has shown that unidentified or unobservable factors are much more important than the observable characteristics in explaining the influence of individuals on decisions. This suggests that focusing on the observable characteristics only may lead to incorrect inference that individual auditors have little or no impact on their practice. Hence, to demonstrate the importance of individual auditors on audit quality, it is necessary to estimate the overall effects, which capture the influences of both observable and unobservable or unidentified characteristics, of individual auditors on audit quality. After estimating the effects of individual auditors on audit quality, we then explore - 8 -

11 whether the variation of these effects on audit quality across individual auditors can be explained by their demographic characteristics. Church et al. (2008) suggest that researchers investigate whether there is a systematic relationship between individual characteristics and the overall audit quality. Our second step represents such an attempt. 3. Research Design 3.1. Empirical Models To effectively estimate the effects of individual auditors on audit quality, it is important to control for all other factors that could impact audit quality. We follow the methodology developed by Bertrand and Schoar (2003) to construct the individual auditor sample and estimate the auditors effects on audit quality. For each audit quality measure, we estimate the following ordinary least-square model: y it = βx it + α t YEAR t + γ i CLIENT i + κ j AUDFIRM j + λ k AUDITOR k + ε it, (1) where y it are the audit quality measures, which will be defined below, X it is a vector of time-varying client firm variables that may affect audit quality, YEAR t is a set of year indicators, CLIENT i is a set of client firm indicators, AUDFIRM j is a set of audit firm indicators, AUDITOR k is a set of individual auditor indicator variables, and ε it is the regression error term. 1 The coefficient on the auditor indicator, λ k, captures the fixed effect of individual auditor k on audit quality. For brevity, we use auditor effects or individual auditor effects to refer to the fixed effects of individual auditors on audit quality. Client firm and audit firm effects are included to mitigate the concern that the results are driven by time-invariant client firm and audit firm characteristics. As will be explained later, we define audit quality proxies so that higher values indicate more aggressive audits. A significantly positive value of λ k suggests that individual auditor k is relatively aggressive, 1 We do not control for the fixed effects of audit offices in the model. Many audit firms in China do not have multiple offices. During our sample period, the mean (median) number of city-based offices per audit firm is 1.42 (1.00). The fixed effects of office would be highly correlated with those of audit firm, if both are included in the model and the results could be biased. Moreover, in studying the impacts of client importance on audit quality, Chen et al. (2010) show that results are similar whether the client importance variable is measured at the audit office or firm level. We therefore only include audit firm fixed effects in the model

12 e.g., more tolerant of clients aggressive accounting or maintains higher thresholds for issuing modified audit opinions. 2 We then link the coefficients on the individual auditor indicators obtained from Model (1) to the characteristics of individual auditors that relate to expertise, experience, risk profile, cognitive style, and incentives by the following model: λ k = α + δ k Z k + ε k, (2) where λ k are the estimated fixed effects of individual auditors on audit quality, Z k is a vector of auditor characteristics, and ε is the regression error term. The estimated fixed effects are used as the dependent variables in Model (2). Considering the possible measurement errors in the estimates, we use the Least Trimmed Squares (LTS) method (Rousseeuw 1984) in fitting the regressions. The LTS method generates more stable results in the presence of outliers The Construction of the Individual Auditor Sample Bertrand and Schoar (2003) and other studies in the manager fixed effect literature require that a top manager should work in at least two different firms and the manager should have been in each firm for at least three years. We construct our individual auditor sample in similar fashion. To be assigned an indicator variable, an auditor must meet two conditions: (1) the individual auditor has audited a client for at least three years and there are at least three years in which she/he does not audit this client; and (2) the individual auditor has audited at least two such unique clients. An auditor must audit a client for a few years so that she/he has a chance to imprint her/his mark on the client s earnings quality. We thus require that an auditor has audited a 2 More precisely, a positive value of λ k suggests that the audit outcomes of an individual auditor are relatively aggressive. The aggressive outcome could be due to the auditor being inherently more aggressive, i.e., uses a higher threshold for issuing modified opinions or delineating material and immaterial misstatements. It could also be due to the fact an auditor is unable to detect misstatements because she/he lacks knowledge, ability, and/or expertise and, thus, does not request accounting adjustments, or she/he waives accounting adjustments because she/he is persuaded by invalid evidence presented by client firms or compromises her/his independence in the face of economic incentives. Although the underlying reasons for having aggressive outcomes are different, the results are the same. For convenience, we say an auditor is more aggressive than another if the former s fixed effect on audit quality (λ k ) is larger than the latter s

13 client for at least three years. The other criteria are imposed so that we can separate individual auditor effects from client firm fixed effects. The importance of these criteria can be illustrated by the following extreme example. Suppose that an auditor has only one client and she/he has been the only auditor for that client in the firm s history. In this case, the auditor and the client firm indicator variables are perfectly correlated and it is impossible to separate the auditor effect from the client firm fixed effect. We thus require that the auditor must audit at least two clients and there are at least three years in which she/he does not audit each of these two clients. 3 Under this method, we estimate the incremental effect of auditor, k, on audit outcomes from the multiple clients she/he audits over time as the fixed-effect coefficient, λ k. It is worth noting that this method also mitigates the correlated-omitted-variables problem. After controlling for client firm fixed effects and time-varying characteristics in the regressions, the unobservable and thus omitted variables do not bias the auditor fixed-effect coefficients unless such variables change over time and across firms in the same pattern as audits performed by individual auditors over time and across firms Audit Quality Measures Audit reports and audited financial statements are two observable audit outcomes. Accordingly, in prior research, audit quality is measured by determining auditors thresholds for issuing modified audit opinions (MAOs), and the quality of clients reported earnings. The underlying assumption is that high-quality auditors maintain lower thresholds for issuing MAOs and constrain aggressive earnings management. To obtain convincing evidence of auditor effects, we employ four quality proxies, discussed follows. Audit reporting aggressiveness. Following prior studies (e.g., Francis and Krishnan 1999; DeFond et al. 2000), we define an indicator variable MAO, which equals 0 if a client receives a clean audit opinion and 1 otherwise. We then estimate the predicted probability of issuing MAOs by running a logistic model, with MAO as the dependent variable and a set of firm characteristics as explanatory variables. Our audit reporting aggressiveness measure (ARAgg) is the predicted probability minus the actual value of MAO. A higher value in ARAgg means that an auditor s propensity to issue MAOs is lower than what 3 Although these requirements are used in prior research on managerial fixed effects, they are admittedly arbitrary. We alter the number of years/clients in these requirements and examine whether our main findings are sensitive to such requirements in Section

14 would be predicted from the whole sample. The details about how we measure ARAgg are described in Section A.1 of Appendix A. Abnormal accruals. Following Dechow and Dichev (2002) and McNichols (2002), we estimate abnormal accruals (AbAcc) as the difference between working capital accruals and the fitted values from the accrual model. Section A.2 of Appendix A provides the details of the model for estimating abnormal accruals. Consistent with prior studies (Becker et al. 1998; Francis and Krishnan 1999; among others), higher abnormal accruals indicate more aggressive earnings and, thus, lower quality auditing. Below-the-line items. The adoption of below-the-line items or non-core earnings as another proxy for earnings quality is motivated by previous studies which find that Chinese firms tend to inflate earnings by timing the execution of transactions pertaining to below-the-line items (Chen and Yuan 2004; Haw et al. 2005). These transactions are often dubious related-party transactions and attract much attention from regulators and investors. Consistent with these studies, we define variable BL as the sum of investment net income, profits from other operations, and non-operating net income, scaled by year-ending total assets. BL thus measures the effect of these items on pre-tax ROA. Small profits. The presence of a small profit is interpreted as evidence of income-increasing earnings management (Burgstahler and Dichev 1997; Francis and Wang 2008). In this study, a client firm is considered to have a small profit if its ROA is between 0 and 1%, and is indicated by the variable SP. Auditor quality decreases with the likelihood of SP in audited financial reports. 4 Admittedly, earnings management does not necessarily violate Generally Accepted Accounting Principles, and is usually not outright fraud. However, aggressive earnings are often perceived to be of low quality, and can mislead financial statement users. The ambiguous nature of these financial reporting practices provide auditors with considerable 4 Note that SP is a dichotomous variable. While it is theoretically appealing to estimate a logistic model for a dichotomous dependent variable, here, we still apply the OLS method. This is because the complete or quasi-complete separation problem in the logistic fixed effect model occurs in our data, as some auditors clients never take a value of 1 in SP and it is impossible to compute the maximum likelihood values of the fixed effect coefficients for such auditors. Nevertheless, for dichotomous dependent variables, OLS coefficient estimates still remain unbiased, especially in large samples, and can be interpreted as usual (Wooldridge 2005, Ch.7). Bamber et al. (2010) and Ge et al. (2010) also apply the OLS method to models where the dependent variables are discrete

15 latitude to influence the audit outcomes; the extent that auditors may use this latitude could be affected by their individual personal characteristics. Firm characteristics such as profitability, growth, and leverage may affect earnings quality (Dechow et al. 2010) or auditors propensity to issue MAOs (e.g., Francis and Yu 2009). Moreover, prior research using Chinese data shows that the economic importance of client firms to specific auditors, and the ownership of clients may have a bearing on auditors decisions (Chen et al. 2010; Wang et al. 2008; Chan et al. 2006), We therefore control for the following time-varying firm characteristics when estimating the auditor fixed effects on the above audit quality measures: return on assets (ROA), the ratio of sales to assets (Turnover), the presence of loss or not (Loss), the log value of total assets (Size), book-to-market ratio (B/M), leverage ratio (Leverage), firm s listing age (Age), client importance (CI), and a variable to indicate whether a client firm is ultimately controlled by a local government in China (LGOV) Determinants of Auditor Effects The audit JDM literature suggests that audit quality is affected by auditors personal attributes, such as expertise (knowledge), experience, problem-solving ability, risk preference, cognitive style, and incentives (Nelson and Tan 2005; Nelson 2009). Our choice of individual characteristics is based on prior research and the unique characteristics of institutions in China; some of the variables selected are exploratory. We consider the following demographic characteristics of auditors that may relate to the above attributes: educational background, birth cohort, Big N work experience, gender, rank (partner or not), and political affiliation. Education. An auditor s educational background may affect their knowledge, risk preference, and values. The first variable in this category measures whether an auditor obtained a master s degree or above. The holders of master s degrees or above receive more education than others. They also command more job opportunities, higher salaries, and a greater likelihood of being promoted in China. 5 Hence, auditors who hold a 5 For example, a recent survey by MyCOS Inc., a leading education data provider in China, shows that in 2011 the starting salary for bachelor s degree holders is about RMB 2,400 per month, while that for Master s degree holders is about RMB 4,000 per month (an introduction to the report is available on

16 master s degree or above are likely to be more optimistic and aggressive than others. 6 The second variable in the educational category is to indicate an auditor s education cohort. We classify auditors into two groups based on whether they were born in 1971 or later. The idea is that those who were born in 1971 or later are likely to have entered university in 1990 or later, because the typical age for Chinese students to enter university is 19. Thus, this group is likely to have been exposed to Western accounting systems in their college education. In 1990, the first Chinese stock exchange was established in Shenzhen and the Chinese government announced that it would encourage the corporatization of state-owned enterprises. To accommodate these reforms, China decided to abandon the former Soviet accounting system, which was designed to assist planning in a command economy rather than to solve the information asymmetry between managers and outside investors, and stipulated relatively uniform and stringent accounting methods/procedures. Since then, Western accounting systems have been introduced and dominated university accounting education. To determine whether the exposure to the modern principles of financial reporting and concepts of corporate governance through university education influences auditors aggressiveness, we include a variable to indicate when an auditor received his/her college education. Specifically, the education cohort equals 1 if an auditor was born in or after 1971 and 0 otherwise. The third educational variable indicates whether an auditor majored in accounting in her/his college education. It is possible that accounting students behave differently from non-accounting ones in their subsequent careers. Gender. Females and males are arguably different in terms of problem-solving ability, risk preference, and cognitive style (Hardies et al. 2010). For example, Gold et al. (2009) find that female auditors are more influenced by male clients and less influenced by female clients than male auditors. Furthermore, the psychology literature suggests that females are more risk averse and more conservative in finance related matters than males (Fellner and Maciejovsky 2007; Eckel and Grossman 2002; Lundeberg et al. 1994). More recently, Srinidhi et al. (forthcoming), using US data find that firms with female directors 6 A number of extant studies suggest that MBA degree holders could have different styles from other executives. For example, Bertrand and Schoar (2003) show that MBA degree holders are relatively more aggressive than others. Unfortunately, our data only tell us whether the highest degree an auditor obtained is a doctorate, Master s, bachelor s, or below. Hence, in the education category, we consider whether an individual auditor has obtained a Master s degree or above

17 are associated with higher earnings quality. These findings and suggested differences between female and male auditors motivate us to consider gender. Big N experience. An auditor s experience may affect her/his judgment and actions. We use a variable to indicate whether an auditor has worked in one of the international Big N audit firms. Big N international audit firms as a group are more independent and provide higher quality audits than other audit firms. To achieve high and consistent audit quality, Big N firms tend to recruit those who are more sociable and adaptable to bureaucratic systems and the culture, values, and goals of the Big N firms (Jeppeson 2007). The work experience in Big N audit firms thus is likely to mold auditors who end up being different from auditors in a non Big N firm. Alternatively, those who tend to be recruited by Big N audit firms may have relatively more conservative personalities, which also is likely to lead to conservative audit outcomes. Birth cohort. Important events that happen during childhood or youth could have a significant impact on an individual s risk attitude, personality, values, and cognitive base. Hence, auditors of the same birth cohort may have some similarities in their judgment and decision-making because they are likely to be affected by the same important events in their early life. We thus include auditors birth year. 7 Rank. We consider whether a signing auditor is a partner or not. The auditing literature shows that auditors who are partners act differently from other auditors. Audit partners own and manage audit firms, thus the goal congruence of partners and the audit firm should be greater than that of non-partner auditors and the audit firm. From this perspective, Miller (1992) argues that partner auditors should be more conservative than non-partner auditors. Partners also have more authority, both within the audit firm and as perceived by the clients, and can take a harder stand than other auditors in requesting for accounting adjustments. This conjecture is borne out by Trotman et al. (2009) who provide evidence which shows that partners request higher initial proposed write-down than non-partner auditors. Political affiliation. We include a variable to indicate whether an auditor is a member of the Chinese Communist Party (CCP). This is motivated by prior research in accounting and finance which finds that political factors may influence business decisions. 7 Bertrand and Schoar (2003), Bamber et al. (2010), and Dyreng et al. (2010) include birth cohort as a determinant of manager effects for similar reasons

18 Li et al. (2008), for example, suggests that private entrepreneurs in China have incentives to joint CCP. One important benefit brought by political participation is relaxed regulatory oversight (Faccio 2006). It is possible that CCP membership may provide some protection for auditors in case of audit failure, e.g., auditors who are CCP members may receive lighter penalty than others if both are similarly responsible for audit failure. The insurance effect brought by CCP membership may induce auditors with CCP membership to behave relatively aggressively. Hence, we include CCP membership as a proxy for auditors political affiliation and participation. 4. Empirical Results 4.1. Sample and Data Our data are obtained from the following sources. We obtain accounting and stock return data from the China Stock Market and Accounting Research Data Base (CSMAR). Audit opinions and the identities of audit firms and individual signing auditors are manually collected from firms annual reports. We crosscheck the identities of individual signing auditors against the enquiry system complied by the Chinese Institute of Certified Public Accountants (CICPA) at Data for the individual auditors demographic information is also obtained from this database. We manually input each individual auditor s full name into the relevant search fields and match the search results with the audit firm and individual auditor data we collected from companies annual reports. We begin with a population of 15,571 non-financial firm-year observations listed on the Shanghai and Shenzhen stock exchanges between 1998 and We drop 770 observations without the required accounting, stock return, or signatory auditor identity data, resulting in a total of 14,801 firm-year observations in our full sample. Normally, two individual auditors are required to sign an audit report for a client 8 In early years, most audit firms in China were affiliated with the government or with publicly-funded universities. Later, the Chinese government required audit firms to disaffiliate from the government and universities. This disaffiliation program was largely complete by We start our sample period from fiscal 1998 (the actual audits for that year s financial statements were conducted in 1999, immediately after the completion of the disaffiliation program) to mitigate the possible effects of organizational change on audit firms

19 firm. We identify a total of 3,726 unique individual auditors who sign audit reports for client firms. Among them, 878 auditors meet the requirements specified in Section 3. However, when two auditors work as a relatively stable team over time, the client portfolios of these pairs of auditors tend to be almost the same. This leads to high correlations between the two auditor indicator variables, making it difficult to differentiate the effect of one auditor from that of the other. To mitigate this problem, we examine the correlations between the auditor indicator variables. If the correlation coefficients are higher than 0.70, we discard the auditor with the smaller client portfolio. In case two auditors have identically sized client portfolios, we randomly discard one of the two in the pair. After this procedure, we have 861 individual auditors for the fixed effect estimations. 9 Table 1 shows the descriptive statistics of the dependent (Panel A) and independent variables (Panel B) used in the model for estimating auditor fixed effects. To mitigate the undue influence of outliers, we winsorize all of the continuous variables at the bottom and top yearly percentiles throughout the paper. For variables ARAgg and AbAcc, the means are close to zero because both variables are essentially the regression residual values. However, both variables show considerable variation in the data. The mean of BL is 0.012, suggesting that, on average, the use of below-the-line items increases firms pre-tax ROA by 1.2%. Approximately 11.6% of firms report a ROA between 0 and 1% during the sample period. Panel B reports the time-varying firm characteristics. The values of these variables are reasonably distributed with some degrees of variation. The average ROA is 2.9%, and about 13% of our sample firms have losses. The turnover ratio (sales/total assets) is about 62%. The average leverage is 49%, and the average listing age is slightly below 7 years. The client importance measure for each client firm (CI) is determined by the ratio of the log value of the client s assets to the log value of the total assets of all clients audited by its signing auditors. The mean CI is 0.273, with a standard deviation of Finally, about 52% of client firms are ultimately controlled by local governments in China. (Insert Table 1 here) 9 Our main results are not sensitive to the dropping of auditor pairs with high correlations, which is not surprising given the small number of individual auditors dropped from the sample

20 4.2. Individual Auditor Fixed Effects Main Results Table 2 contains the results of the regressions for estimating Model (1), based on the four audit quality measures presented in Columns (1) to (4). In Panel A, we report the coefficients and t-statistics of the control variables. In all regressions, we include year, client firm, audit firm, and individual auditor indicators. The adjusted R 2 s range between 32.08% (ARAgg regression) and 66.80% (AbAcc regression). This suggests that the control variables and the fixed-effect indicators explain a substantial portion of the variation in the dependent variables. In Panels B to D, we assess the significance of client firm, audit firm, and individual auditor fixed effects, respectively. In addition to the F-statistics that evaluate the joint significance of these fixed-effect indicators, we also examine how these indicators improve the models explanatory power. Specifically, following Collins et al. (1997), we calculate the incremental R 2 that can be attributed to each set of fixed-effect indicators as: R 2 CF = R 2 Full R 2 w/o CF, R 2 AF = R 2 Full R 2 w/o AF, R 2 IA = R 2 Full R 2 w/o IA, (3a) (3b) (3c) where R 2 Full is the adjusted R 2 of the full model including all fixed effects, R 2 w/o CF is the adjusted R 2 of the model that excludes client firm fixed effects and, analogously, R 2 w/o AF (R 2 w/o IA) is the adjusted R 2 of the model without the audit firm (individual auditor) fixed effects. R 2 CF thus represents the incremental explanatory power provided by the set of client firm indicators, while R 2 AF and R 2 IA represent the incremental explanatory power contributed by audit firm and individual auditor fixed effects, respectively. We also scale each R 2 statistic by the adjusted R 2 of the base model to determine the relative percentage increase in R 2 : % R 2 CF = (R 2 Full R 2 w/o CF)/R 2 w/o CF, % R 2 AF = (R 2 Full R 2 w/o AF)/R 2 w/o AF, % R 2 IA = (R 2 Full R 2 w/o IA)/R 2 w/o IA. (4a) (4b) (4c) The F-statistics over the panels suggest that all three sets of fixed-effect indicators are highly significant for all regressions across the columns, except the audit firm fixed

21 effects in the small profit regression (Column (4) of Panel C). As for explanatory power, the client firm fixed effects substantially increase the models R 2 s: R 2 CF ranges from 12.50% in the AbAcc regression to 18.46% in the ARAgg regression, which can be translated into % R 2 CF from 23.01% to %. This suggests that audit reporting decisions or earnings quality measures as proxies for audit quality vary considerably across client firms. It is therefore important to control for firm-specific differences in these measures to provide a stronger test of audit firm/auditor effects. 10 In Panel B, we observe that the inclusion of the audit firm indicators modestly improves the explanatory power of the audit quality models. R 2 AF ranges from 0.63% to 1.77%, and % R 2 AF is between 0.96% and 5.83%. As shown in Panel C, adding auditor effects also improves the explanatory power of the model: R 2 IA ranges from 4.56% to 8.31%, translating to 7.50% to 34.95% in % R 2 IA. Taken together, the results suggest that the client firm, audit firm, and individual auditor fixed effects on audit outcomes co-exist. Although individual auditor effects are not as strong as client firm effects, they appear to be stronger than audit firm effects in terms of explanatory power. We next examine the fixed effects of individual auditors, the crux of our analysis, in more detail. (Insert Table 2 here) While the F-statistics suggest that auditor effects are jointly significant, it is possible that the results are driven by a small number of significant coefficients. We therefore examine the frequency of auditor effects that are significant at the conventional levels. The results are reported in Panel A of Table 3. Under the null hypothesis that individual auditors have no effect incremental to the other variables considered in the regressions, one would expect about 5% (10%) of auditors to have coefficients significant at the 5% (10%) level. The results reveal that the actual percentages of auditors with significant coefficients are much greater than the expected ones. For example, in the case of ARAgg, the percentage of auditor effects that are significant at the 5% (10%) level is 12.9% (18.0%) Indeed, in untabulated analysis, we find that the audit firm/auditor effects are seriously overstated if the client firm fixed effects are not controlled for. 11 Dyreng et al. (2010) report that approximately 12% (17%) of top executives fixed effects are significant at the 5% (10%) level in explaining tax avoidance. In Ge et al. (2010), the actual

22 The above analysis suggests that auditor effects are statistically significant. We next examine the economic significance of these effects by analyzing the distribution of the coefficients on auditor indicators in Panel B. The mean values of these fixed effects are close to zero for all four audit quality measures, suggesting that the auditors used to estimate the fixed effects are, on average, not different from others in terms of auditing aggressiveness. However, the inter-quartile range and standard deviation statistics reveal that there are considerable variations in audit quality across these individual auditors. For example, the inter-quartile range of auditor effects on AbAcc is This suggests that the level of abnormal accruals reported by clients of an auditor at the 75 th percentile of the distribution of this variable would be 2.7% higher than that reported by clients of the auditor at the 25 th percentile. To put this difference in perspective, the inter-quartile range of abnormal accruals for the full sample is (see Panel A of Table 1). The variation in individual auditor effects on SP is also prominent. The inter-quartile range is 0.191, suggesting the chance of reporting small profits for clients of an auditor at the 75 th percentile of the distribution of this variable would be 19.1% higher than that for clients of an auditor at the 25 th percentile. Variations for other measures are also economically significant when benchmarked against the distribution of the corresponding variables in Table 1. Taken together, results in Tables 2 and 3 suggest that individual auditor differences do affect audit reporting and audited financial statements in a significant way both statistically and economically. These results also suggest that individual auditors differ systematically in terms of audit quality and audit aggressiveness. (Insert Table 3 here) 4.3. Robustness Tests Alternative Data Requirements So far, we have been focusing on the subset of individual auditors who audit at least percentages of CFO fixed effects that are significant at the 5% level range between 5% and 14.8% across the five financial reporting practice variables. In Bamber et al. (2010), the percentages of significant top manager effects at the 10% level are between 38% and 51% for five voluntary disclosure measures. Although the effects of individual auditors reported in the current study are not as striking as those found in Bamber et al. (2010), they are comparable to the findings in the other two studies

23 two clients and have audited each client for at least three years. We denote the design choice as n t, where n is the number of clients and t is the number of years auditing a client. Accordingly, our results thus far are based on a 2 3 design. Although consistent with the spirit of Bertrand and Schoar (2003) and other studies, we recognize that there is no theoretical basis to this issue. In light of this, we examine whether our results are sensitive to other design choices by varying the values of n and t from 2 to 5 when estimating Model (1). Figure 1 summarizes the results of this exercise. Panel A shows the relative incremental adjusted R-square, % R 2 IA, while Panels B and C plot the percentages of individual auditor effects that are significant at the 5% and 10% levels, respectively. For the sake of parsimony, relevant statistics are averaged across the four audit quality measures. Overall, our results are robust to the choice of the combination of n and t. In all of the design choices, adding auditor indicators increases the explanatory power of the model, and the percentages of significant auditor effects are higher than expected by chance. 12 Panel A also reveals that % R 2 IA decreases when n or t increases. This is not surprising. When n or t increases, the number of individual auditors eligible for estimating the fixed effects decreases monotonically: from 1,315 auditors in a 2 2 design to only 68 in the 5 5 design. Some auditors who have significant fixed effects may not be assigned with indicator variables when we increase n or t because they do not meet the new requirements. The model s explanatory power will be weakened as a result. 13 Another notable pattern is that the percentages of fixed effects that are significant at the 5% level (Panel B) or 10% level (Panel C) for the n 2 designs are consistently lower than other designs with higher t values. This may suggest that auditors are more likely to imprint their own style on their clients financial statements when they audit the same clients for more than two years. (Insert Figure 1 here) 12 F-statistics for the joint significance of auditor fixed effects are not presented in the figure. In fact, they are all significant at the 1% level across the four measures with different designs, with one exception being that the p-value is in the AbAcc regression under the 5 5 design. 13 Take ARAgg as an example. In the 2 2 design, the coefficients on 208 individual auditor indicators are significant at the 10% level. Only 13 of these fixed effect indicators are retained in the 5 5 design (and they are all significant at least at the 10% level). Omitting a substantial number of auditor indicators with significant coefficients dampens the explanatory power of the regression models

24 4.3.2 When will auditor effects be absent? In the foregoing analysis, we examined abnormal accruals and small profits as audit quality measures because client firms may inflate earnings to affect capital market or contracting outcomes. When observing firms reporting choices, auditors will exercise their judgment and decide whether to tolerate a client s earnings management or to make an audit adjustment. Accordingly, we are able to study auditors effects on these measures. It would be interesting to examine the mirrors of these measures: normal accruals (NorAcc) and small loss (SL). NorAcc is the fitted value from the accrual expectation model (see Section A.2 of Appendix A for details of the estimation). As NorAcc represents the portion of accruals that are driven by economic factors, we do not expect that auditors exert their influence over this area. SL is an indicator variable for firms that report a ROA of between 1% and 0. Arguably, auditors are less likely to make audit adjustments when their clients report a small loss, which clearly indicates the absence of earnings management. We thus expect to observe insignificant auditors effects in these two measures. Untabulated results show that the F-statistics for the joint significance of individual auditor effects are not significant for either measure (p = and 0.828, respectively). Moreover, the percentages of individual auditors with fixed-effect coefficients significant at the 5% (10%) level are 4.88% (10.46%) and 5.92% (8.94%) for NorAcc and SL, respectively. These percentages are not much different from what might be expected by chance. Interestingly, we also find that audit firm fixed effects do not exist in either measure, as the audit firm indicators are not jointly significant in the F-statistics. These results suggest that individual auditor and audit firm effects do not exist when there is little room for audit firms or auditors to exercise their judgment. This also indicates that our findings based on the four audit quality measures, for which we expect auditors to play an active role, are not likely to be spurious Bootstrap test It is possible that the standard errors on the F- or t-statistics are downwardly biased for some unknown reasons. Following Dyreng et al. (2010), we bootstrap the data by randomizing the auditor-firm pairings so that each auditor is assigned to a client firm which they do not audit. We then estimate Model (1) using the randomized data. This procedure is repeated 1,000 times. As the randomly matched auditor and firm data are not

25 expected to generate significant results, the empirical distribution of the relevant statistics generated from the randomized data can be used to compare with our actual results and to evaluate whether the F- or t-statistics reported in the prior section are well specified. Table 4 reports the means of the p-values of the F-statistics and the percentages of fixed-effect coefficients on individual auditors from the 1,000 iterations of the randomized data that are significant at the 5% (10%) level. For all four measures, the p-values of the F-statistics are around 0.5, substantially different from the p-values obtained from the actual data (Panel D of Table 2). The percentages of fixed-effect coefficients that are significant at the 5% level are only slightly different from 5%, the value expected under the null hypothesis of no relationship, and are apparently lower than what we observe from the actual data (Panel A of Table 3). The percentages of coefficients that are significant at the 10% level are always lower than the expected 10% threshold for all of four measures. Untabulated results also show that, out of 12,000 statistics (3 statistics*4 measures*1,000 iterations), we never observe that the randomized data yield statistics more significant than those obtained from the actual data. Therefore, we find no evidence of bias that would lead to misspecified statistics in our data. The different results between the randomized and actual data provide more confidence in our results. 14 (Insert Table 4 here) Alternative Specification to Determine the Auditor Effects on Audit Reporting In determining the auditor effects on audit reporting, we do not directly use MAO as the dependent variable. Instead, we use the difference between the predicted probability of MAOs and actual MAOs, ARAgg, as the dependent variable. This is to accommodate the use of a common set of client firm control variables for all audit quality measures, because the auditing literature contains many client firm characteristics that may affect audit reporting decisions but not necessarily affect earnings quality. Here, we directly use MAO as the dependent variable and include the predictor variables in the MAO prediction model (see Table A1 of Appendix A) to estimate auditor effects. Untabulated results show that the coefficients on the predictor variables are generally consistent with those reported in Table 14 We do not tabulate the % R 2 IA results from the bootstrap test because it is difficult to predict its distribution a priori. Nevertheless, for each of the four measures, % R 2 IA estimated from the actual data is larger than any estimate from the 1,000 iterations of the randomized data

26 A1. More importantly, this alternative model specification does not alter our earlier findings on auditor effects. The F-statistic for the joint significance of auditor effects is (p < 0.001). These effects increase the model adjusted R 2 by 10.50% in relative percentage points, and 11.49% and 16.96% of the coefficients on auditor indicator variables are significant at the 5% and 10% levels, respectively Individual Characteristics and Auditor Effects After showing that the effects of individual auditors on audit quality are significant and individual auditors differ systematically in terms of either audit quality or aggressiveness, we next explore whether the documented individual auditor effects can be explained by their demographic characteristics. Sixty-six of the auditors used to estimate fixed effects do not have demographic information in the CICPA database, and thus are dropped from this analysis, leaving a sample of 795 individual auditors. 15 Table 5 reports the descriptive statistics about these auditors. About 52% majored in accounting in their college education and 13.6% hold a master s degree or above. About 27.6% were born in 1971 or later, and thus might have been exposed to Western accounting practices in their college education. The average birth year is 1966 and around one third are female. Only 7.7% have worked in an international Big N audit firm. This is consistent with prior findings that Big N audit firms do not play a dominant role in the Chinese auditing market (Wang et al. 2008). In addition, 39% of individual auditors are partners of the audit firms with which they are affiliated. Finally, slightly more than a quarter are CCP members. 16 We also examine the correlations between these demographic 15 A possible reason for this is that these auditors may not be in the auditing business anymore because of retirement or job changes. The CICPA database only contains information for CPAs who are currently in practice. 16 The statistics described in Table 5 are based on the auditors for whom we estimate fixed effects. We also have information for 2,473 individual auditors for whom we do not estimate fixed effects. As expected, compared with those with fixed effect estimates, these auditors are less likely to be partners or hold a masters degree or above, and are younger and thus more likely to be in the education cohort with exposure to Western accounting in college education. There are more males and CCP members among the auditors with fixed effect estimates. All the differences are significant at least at the 10% level in the t-test (for continuous variables) or Chi-square test (for categorical variables). There is no significant difference between two groups of auditors in the Accountancy major or Big N experience variable

27 variables. Untabulated results show that, except for the relatively high correlation between education cohort and birth cohorts (ρ = 0.598), all other correlation coefficients are below (Insert Table 5 here) Given that an auditor could substitute one dimension of audit quality for another to achieve her/his own optimal level of audit risk, we create an aggregate score, which is the mean value of the four measures. Intuitively, the variable measures the overall aggressiveness associated with an auditor. As the four audit quality measures have different scales of measurement, it is necessary to standardize the fixed-effects estimated from these measures to obtain an aggregate score. We standardize the fixed-effects to have zero mean values and unit variances. The multivariate regression results reported in Table 6 show that the explanatory power of these models are modest (the R 2 s are below 4%). This suggests that observable auditor characteristics can only explain a small portion of the auditor effects on audit outcomes, highlighting the importance of first quantifying such effects. In spite of the modest explanatory power, these results reveal some interesting insights. Table 6 shows that auditors who hold a master s degree or above tend to be more aggressive than others. The coefficients on this variable are positive in all specifications, and significant in three. A possible reason, as indicated earlier, is that individuals with higher degrees tend to be more confident and optimistic, which may cause them to be relatively more aggressive. Another interesting finding is that auditors exposed to Western accounting in their university education appear to be more conservative. An important role of Western financial accounting is to resolve the information asymmetry between corporate insiders and outside shareholders, and thus protect the interests of outside investors. The exposure to Western accounting and management education is likely to inculcate values related to investor protection and thus less tolerance of clients earnings management, compared with auditors who were trained under the Soviet-style accounting system where the investor protection philosophy is less emphasized. Auditors who have work experience in international Big N firms also tend to be more conservative. This variable has negative coefficients across the columns and is significant at the 1% level for the aggregate audit quality measure. This is consistent with our conjecture that auditors from Big N firms may act more conservatively. Table 6 also shows that the auditors who

28 are also partners are less aggressive in audit reporting than others ( 0.140, t = 2.329), which is consistent with prior studies that argue that partners are more conservative than non-partner auditors (Miller 1992; Trotman et al. 2009). Auditors who are CCP members are associated with lower quality audits (coeff. on CCP = and t = for ARAgg, and coeff. on CCP = and t = for AbAcc). This could be because CCP membership may give some protection to these auditors, which encourages them to behave more aggressively. Finally, birth cohort, gender and whether an auditor majors in accounting or not appear to have no significant impact on auditor aggressiveness. 17 (Insert Table 6 here) 4.5. An Additional Analysis Audit quality is inherently unobservable. This section examines the association between the above documented auditor effects and the likelihood of being sanctioned by the regulatory authority in China. The aim is to verify whether the above documented auditor effects are related to audit quality using an ex post measure of audit quality. Individual auditors may be sanctioned if material financial misstatements by listed companies are detected and the signing auditors are proven to be negligent in performing the audit. Hence, the likelihood of being sanctioned is another proxy of audit quality. From announcements made by the China Securities Regulatory Commission (CSRC) or relevant news reports about sanctions taken by the Ministry of Finance (MOF), we identify a total of 82 client firm-year observations sanctioned by the regulators for problematic audits during our sample period. 18 After merging these observations with our auditor fixed effect data (n = 861), 76 sanctioned individual auditors remain in the sample, 17 The results in Table 6 are not too sensitive to the use of OLS for data winsorized at the top and bottom percentiles. The only notable change is that birth year becomes significantly positive (t = 1.785) in the aggregate score regression, suggesting that younger generation auditors are more aggressive than those from the older generation. This is consistent with Bertrand and Schoar (2003) who find that CEOs from younger generations are more aggressive. 18 Both the CSRC and the MOF have the authority to sanction auditors. The MOF is in charge of all accounting affairs in China, while the CSRC is empowered to handle accounting and disclosure issues related to firms that have issued securities to the public. While most of the sanction cases used in this study are identified from the CSRC s announcements, there are also cases where the MOF has detected problematic audits after its annual inspection conducted on Chinese firms

29 which means that 8.83% (= 76/861) of the auditors in the original sample received regulatory sanctions. We use an indicator variable, Sanctioned, to indicate these auditors. Our review of sanction announcements made by the CSRC suggests that sanctions are primarily imposed on cases where earnings or equity are overstated. If the individual auditor effects we document above indeed capture audit aggressiveness, we should observe that auditors with larger effects are more likely to be sanctioned by the regulatory authority. We estimate logistic regressions to test this. In fitting the regressions, we apply the GLS method by weighting observations by the inverse of the standard error on the independent variables to mitigate the downward bias in the regression coefficients when measurement errors exist (Bertrand and Schoar 2003). The results are reported in Table 7. Table 7 shows that the auditor fixed effects on every quality measure are positively correlated with the probability of being sanctioned by the CSRC. The aggregate score of aggressiveness is also positively correlated with the probability of being sanctioned, and the coefficient estimate is highly significant (0.356, t = 5.68). In Column (6), we include the auditor effects on all quality measures. The coefficients on the audit quality measures are all significantly positive, except for AbAcc. The associations between the auditor effects on audit quality and the likelihood of regulatory sanction are economically significant. Taking the aggregate score as an example, untabulated odds ratio analysis suggests that a one standard deviation increase in the aggregate score would increase the likelihood of being sanctioned from the sample base rate of 8.83% to 12.60%. These results confirm that the above documented auditor effects indeed reflect audit quality, i.e., larger values in the estimated auditor fixed effects imply more aggressive or lower-quality audits. (Insert Table 7 here) 5. Conclusion This study examines the importance of individual auditors in determining audit quality in a unique setting in which we are able to obtain the identity and profile data of the individual auditors assigned to administer specific audit engagements. We document a significant variation in audit quality across individual auditors. The effects of individual auditors on the quality of audit reporting and clients earnings quality are both statistically

30 and economically significant. The effects of individual auditors on audit quality are incremental to those of client firms and audit firms. We also explore the extent to which the effects of individual auditors can be explained by their demographic characteristics. We find that auditors who are also partners, have been exposed to Western accounting systems in their university education, or have worked in an international Big N audit firm, are more conservative, while auditors who have obtained a master s degree or above or have a political background are more aggressive. These results suggest that auditors individual characteristics, such as their education and experience, can affect their judgment and decisions, which eventually translate to the variation in audit quality across individual auditors. This paper advances our understanding of audit quality in several ways. We present robust evidence that individual auditors matter in the financial reporting process, and thus demonstrate the importance of analyzing audit quality at the individual auditor level. Although we show that some observable demographic characteristics may explain the difference in audit quality across individual auditors to some extent, much of this variation remain unexplained. The auditor characteristics examined in this paper constitute only a small subset of the numerous individual characteristics that may be relevant to auditors judgment and decision-making. Future research can explore whether other characteristics such as appearance, social tie, and family background could contribute to the variation in audit quality across auditors. Our work has many practical implications for audit firms. For example, we find that some auditors are systematically more aggressive than others, and identify some personal characteristics that may be relevant to audit aggressiveness. To mitigate the adverse impact of such individual aggressiveness on audit quality and audit risk, the audit firms may assign auditors with conservative characteristics to teams with aggressive colleagues. Our work may also be of interest to regulators and add to the debates on mandatory auditor rotation regulations (Myers et al. 2003; Chen et al. 2008). One underlying assumption of such regulations is that individual auditors within the same audit firm are heterogeneous, which is supported by our work. Our work also suggests that whether rotations improve audit quality is a complex issue since it may also depend on the differences between the predecessor and successor auditors in their styles. Although this study is based on the Chinese data, its implications may be relevant to

31 other markets. Audit engagements are administered in other markets in a way largely similar to that in China, and the heterogeneity in individual auditors in terms of their judgment and decision-making qualities is universal. Thus, our work could also be interesting to academics, practitioners, and policy makers in other markets. Finally, while overall we shed some light on the issue of the link between individual auditor differences and audit quality, our paper also raises many issues and questions which we leave to future research

32 Appendix A: Estimating audit reporting aggressiveness and abnormal accruals A.1 Audit reporting aggressiveness To measure auditors propensity to issue modified audit opinions (MAOs) to clients, we first estimate a logistic model to predict MAOs. MAOs include unqualified opinions with explanatory notes, and qualified, disclaimed, and adverse opinions. Unqualified opinions with explanatory notes are treated as a form of MAO because Chinese auditors often issue this type of audit report in lieu of a qualified opinion. The dependent variable in the model is MAO, which equals 1 if a client firm received a modified opinion, and 0 otherwise. Following prior auditing research in China (e.g., DeFond et al. 2000, among others), we include a set of variables which may affect the probability of receiving an MAO. In addition, we include a measure of other receivable intensiveness (defined as other receivables divided by total assets). Jiang et al. (2010) find that Chinese auditors are quite sensitive to inter-corporate loans between listed firms and their parent companies and such loans are normally booked as other receivables under Chinese GAAP. The footnotes to Table A1 contain the definitions of the variables used in the prediction model. We estimate the logistic model by year and the results, based on 12 annual logistic regressions, are reported in Table A1. Here, the coefficients are the means of the coefficients from the yearly logistic regressions, and the t-statistics are based on the distribution of these annual regression coefficients. Overall, the results are quite consistent with prior Chinese auditing research. 20 We use the predicted probability of MAOs from the logistic regressions to define variable audit reporting aggressiveness, as follows: ARAgg = Predicted opinion Actual opinion, (A1) where actual opinion equals 1 if the client firm received a modified opinion and 0 otherwise, and predicted opinion is the probability of MAOs derived from the above annual logistic regressions. A higher value of ARAgg suggests that auditors are likely to issue a clean report, although an MAO could be warranted according to the predicted probability. This variable thus represents aggressiveness in audit reporting. 20 Inventory intensiveness is negatively correlated with the probability of MAOs. While unexpected, Wang et al. (2008) also find a negative effect of inventory intensiveness on the probability of MAOs

33 A.2 Abnormal accruals We use the Dechow and Dichev (2002, DD hereafter) model to estimate firms abnormal accruals. The model expresses firms working capital accruals as a function of lagged, current, and future operating cash flows. As suggested by McNichols (2002), it is useful to link the approach by Jones (1991) to that taken by DD to reduce the extent of the omitted-variable problem and thus strengthen the model specification. Therefore, the model we use to estimate abnormal accruals is specified as: ΔWC t = α + β 1 CFO t-1 + β 2 CFO t + β 3 CFO t β 4 ΔSales t + β 5 PPE t + ε, (A2) where ΔWC t is working capital accruals in year t, computed as operating net income plus depreciation, amortization, and financial expenses, minus operating cash flows. 21 CFO t-1, CFO t, and CFO t+1 are operating cash flows in year t-1, t, and t+1, respectively. ΔSales t is sales growth from t-1 to t. PPE t is the gross value of fixed assets. All these variables are scaled by the average of beginning and ending total assets in year t to reduce heteroscedascity. The model is estimated cross-sectionally in each industry-year. We use the 2-digit code for the manufacturing sector and 1-digit for other sectors, following the CSRC industry classification scheme, and require that there should be at least 10 observations in an industry-year combination to estimate the regression. In total, there are 191 industry-year combinations. Table A2 presents the distribution of parameters from the DD model as modified by McNichols (2002), and the t-statistics computed by the distribution of the industry-year regression coefficients. The signs for the coefficients on the explanatory variables are quite consistent with McNichols (2002), except that the mean coefficient on PPE t is significantly positive. 22 We also note that the Chinese data fit the model well, as indicated by a mean adjusted R 2 of Financial expenses are presented before operating net income under the Chinese GAAP, but are not classified as operating activities in the cash flow statement. To maintain consistency between operating net income and cash flow, we undo the effect of financial expenses on operating net income. 22 In the original Jones model, the coefficients on PPE should be negative primarily because of depreciation expenses. However, in the DD model, the dependent variable, working capital accruals, does not include depreciation expenses. It is thus difficult to predict the signs on PPE in DD-type models

34 Our abnormal accruals measure is defined as the regression residual estimated from the above regression model. Because lagged and future cash flows are used as independent variables and a few industry-years have less than 10 observations, the number of observations with estimated abnormal accruals is 12,266, which is smaller than the sample size for the other audit quality measures

35 References Bamber, L., J. Jiang, and I. Wang What s my style? The influence of top managers on voluntary corporate financial disclosure. The Accounting Review 85 (4), Becker, C., M. DeFond, J. Jiambalvo, and K. Subramanyam The effect of audit quality on earnings management. Contemporary Accounting Research 15 (1), Bertrand, M., and A. Schoar Managing with style: The effect of managers on firm policies. The Quarterly Journal of Economics 118 (4), Burgstahler, D., and I. Dichev, Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics 24 (1), Chan, K.H., K. Lin, and P. Mo A political-economic analysis of auditor reporting and auditor switches. Review of Accounting Studies 11 (1), Chen, C.-Y., C.-J. Lin, and Y.-C. Lin Audit partner tenure, audit firm tenure, and discretionary accruals: Does long audit tenure impair earnings quality? Contemporary Accounting Research 25 (2), Chen, S., S. Sun, and D. Wu Client importance, institutional improvements, and audit quality in China: An office and individual auditor level analysis. The Accounting Review 85 (1), Chen, K., and H. Yuan Earnings management and capital resources allocation: Evidence from China accounting-based regulations of right issues. The Accounting Review 79 (3), Chi, W., L. Myers, T. Omer, and H. Xie The effects of audit partner pre-client experience on earnings quality and perceptions of earnings quality: Evidence from Taiwan. Working paper, available at SSRN: Church, B., S. Davis, and S. McCraken The auditor s reporting model: A literature overview and research synthesis. Accounting Horizons 22 (1), Collins, D., E. Maydew, and I. Weiss Changes in the value-relevance of earnings and book values over the past forty years. Journal of Accounting and Economics 24 (1), DeAngelo, L Auditor size and auditor quality. Journal of Accounting and Economics 3 (3), Dechow, P., and I. Dichev The quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review 77 (Supplement), Dechow, P., W. Ge, and C. Schrand Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics 50 (2-3), DeFond, M., T.J. Wong, and S. Li The impact of improved auditor independence on audit market concentration in China. Journal of Accounting and Economics 28 (3), DeFond, M., and J. Francis Audit research after Sarbanes-Oxley. Auditing: A Journal of Practice and Theory 24 (Supplement), Dyreng, S., M. Hanlon, and E. Maydew The effects of executives on corporate tax avoidance. The Accounting Review 85 (4),

36 Eckel, C., and P. Grossman Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and Human Behavior 23 (4), Faccio, M Politically connected firms. American Economic Review 96 (1): Fellner, G., and B. Maciejovsky Risk attitude and market behavior: Evidence from experimental asset markets. Journal of Economic Psychology 28, Francis, J What do we know about audit quality? The British Accounting Review 36 (4), Francis, J., and J. Krishnan Accounting accruals and auditor reporting conservatism. Contemporary Accounting Research 16 (1), Francis, J., and D. Wang The joint effect of investor protection and Big 4 audits on earnings quality around the world. Contemporary Accounting Research 25 (1), Francis, J., and E. Wilson Auditor changes: A joint test of theories relating to agency costs and auditor differentiation. The Accounting Review 63 (4), Francis, J., and M. Yu The effects of big four office size on audit quality. The Accounting Review 84 (5), Ge, W., D. Matsumoto, and J. Zhang Do CFOs have styles of their own? An empirical investigation of the effect of individual CFOs on financial reporting practices. Working paper, available at SSRN: Gold, A., J. Hunton, and M. Gomaa The impact of client and auditor gender on auditors judgments. Accounting Horizons 23 (1), Hardies, K., D. Breesch, and J. Branson Are female auditors still women? Analyzing the sex differences affecting audit quality. Working paper, available at SSRN: Haw, I., D. Qi, D. Wu, and W. Wu Market consequences of earnings management in response to security regulations in China. Contemporary Accounting Research 22 (1), Jeppesen, L Organizational risk in large audit firms. Managerial Auditing Journal 22 (6), Jiang, G., C. Lee, and H. Yue Tunneling through intercorporate loans: The China experience. Journal of Financial Economics 98 (1), Jones, J Earnings management during import relief investigations. Journal of Accounting Research 29 (2), Kachelmeier, S Introduction to a forum on individual differences in accounting behavior. The Accounting Review 85 (4), Knechel, W.R Behavioral research in auditing and its impact on audit education. Issues in Accounting Education 15 (4), Krishnan, G Did Houston clients of Arthur Andersen recognize publicly available bad news in a timely manner? Contemporary Accounting Research 22 (1), Li, H., L. Meng, Q. Wang, and L. Zhou Political connections, financing and firm performance: Evidence from Chinese private firms. Journal of Development Economics 87 (2), Lundeberg, M., P. Fox, and J. Punchochar Highly confident, but wrong: Gender

37 differences and similarities in confidence judgment. Journal of Educational Psychology 86, McNichols, M Discussion of the quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review 77 (Supplement), Miller, T Do we need to consider the individual auditor when discussing auditor independence. Accounting, Auditing and Accountability Journal 5 (2), Myers, J., L. Myers, and T. Omer Exploring the term of the auditor-client relationship and the quality of earnings: A case for mandatory auditor rotation? The Accounting Review 78 (3), Nelson, M A model and literature review of professional skepticism in auditing. Auditing: A Journal of Practice & Theory 28 (2), Nelson, M., and H. Tan Judgment and decision making research in auditing: A task, person, and interpersonal interaction perspective. Auditing: A Journal of Practice and Theory 24 (Supplement), Reynolds, J., and J. Francis Does size matter? The influence of large clients on office-level auditor reporting decisions. Journal of Accounting and Economics 30 (3), Rousseeuw, P Least median of squares regression. Journal of the American Statistical Association 79 (388), Simunic, D., and M. Stein Product differentiation in auditing: Auditor choice in the market for unseasoned new issues. Research Monograph 13, The Canadian Certified General Accountants Research Foundation, Vancouver, B.C. Srinidhi, B., F. Gul, and J. Tsui. Female directors and earnings quality. Contemporary Accounting Research, forthcoming. Teoh, S., and T. J. Wong Perceived auditor quality and the earnings response coefficient. The Accounting Review 68 (2), Trotman, K., A. Wright, and S. Wright An examination of the effects of auditor rank on pre-negotiation judgments. Auditing: A Journal of Practice & Theory 28 (1), Wallman, S The future of accounting, part III: Reliability and auditor independence. Accounting Horizons 10 (4), Wang, Q., T.J. Wong, and L. Xia State ownership, the institutional environment, and auditor choice: Evidence from China. Journal of Accounting and Economics 46, Wooldridge, J Introductory Econometrics: A Modern Approach. South-Western College Publishing, M.A

38 Figure 1 Auditor fixed effects with different data requirements Panel A: The relative incremental R 2 IA 25% 20% 15% 10% 5% 0% 2 n t Panel B: The proportion of fixed-effect coefficients that are significant at the 5% level 15% 10% 5% 0% n t Panel C: The proportion of fixed-effect coefficients that are significant at the 10% level 20% 15% 10% 5% 0% n t The number of years an individual auditor audits a client is denoted as t, and the minimum number of unique clients the individual auditor has audited for at least t years is denoted as n. The reported statistics are the averages of the four audit quality measures. In Panel A, the relative incremental R 2 IA is computed as: (R 2 Full R 2 w/o IA)/R 2 w/o IA, where R 2 Full is the adjusted R 2 of the full model including all the fixed effects, and R 2 w/o IA is the adjusted R 2 of the model that excludes the individual auditor fixed effects. Panels B and C present the percentages of fixed-effect coefficients that are significant at the 5% and 10% levels, respectively, in the t-statistics on these coefficients

39 Table 1 Descriptive statistics of variables used in estimating individual auditor effects Variables Mean Q1 Median Q3 Std. Dev. Panel A: Dependent variables ARAgg AbAcc BL SP Panel B: Independent variables ROA Loss Turnover Size B/M Leverage Age CI LGOV The sample size is 14,801 firm-year observations for all variables except abnormal accruals (n = 12,266). Definitions of the dependent variables: ARAgg is the difference between the predicted probability of MAOs and actual MAOs, where the predicted probability of MAOs is derived from annual logistic regression modeling of the probability of MAOs, and actual MAO equals 1 if the firm received a modified opinion, and 0 otherwise. See Section A.1 of Appendix A and Table A1 for details of the logistic model. AbAcc is the regression residuals estimated by the Dechow and Dichev (2002) model. See Section A.2 of Appendix A and Table A2 for details of the estimation. BL is the sum of investment net income (CSMAR data item B ), profits from other operations (B ), and non-operating net income (B minus B ), scaled by the average of beginning and ending total year assets. SP is an indicator variable that equals 1 if the firm has reported a ROA (net income divided by average total assets) of between 0 and 1%, and zero otherwise. Definitions of the independent variables: ROA is net income divided by average total assets. Loss equals 1 if the firm has reported a loss and 0 otherwise. Turnover is total sales divided by average total assets. Size is the natural logarithm of firms total assets, expressed as 1998 constant RMB adjusted by CPI. B/M is book value of equity divided by market value of equity at end of year. Leverage is liabilities divided by total assets at end of year. Age is the number of years a company has been listed. CI is client LnTASTi importance at the individual auditor level, which is computed as m l LnTAST, where k = 1i= 1 LnTAST i is the natural logarithm of the total assets of client i, l is the number of clients audited by auditor k, and m is the number of auditors signing the audit report. LGOV is an indicator variable for firms that are ultimately controlled by local governments. All of the continuous variables have been winsorized at the bottom and top yearly percentiles. i

40 Panel A: Regression results Variables Table 2 Estimating individual auditor fixed effects (1) ARAgg (2) AbAcc (3) BL (4) SP Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. ROA *** *** *** Loss *** *** *** *** Turnover *** *** *** *** Size ** *** *** B/M *** *** *** Leverage *** *** *** Age *** *** *** CI LGOV * *** Year, client firm, audit firm, and individual auditor indicators Included Included Included Included Adj. R % 66.80% 59.25% 40.85% Sample size 14,801 12,266 14,801 14,801 (The table is continued on the next page.)

41 Table 2 (Continued) Variables (1) (2) (3) (4) ARAgg AbAcc BL SP Panel B: Testing the significance of client firm fixed effects F-statistics (p-value) (0.000) (0.000) (0.000) (0.000) R 2 AF 18.46% 12.50% 17.04% 16.16% % R 2 AF % 23.01% 40.38% 65.44% Panel C: Testing the significance of audit firm fixed effects F-statistics (p-value) (0.000) (0.000) (0.000) (0.448) R 2 AF 1.77% 0.63% 0.78% 0.74% % R 2 AF 5.83% 0.96% 1.33% 1.84% Panel D: Testing the significance of individual auditor fixed effects F-statistics (p-value) (0.000) (0.000) (0.000) (0.000) R 2 IA 8.31% 4.66% 4.56% 6.63% % R 2 IA 34.95% 7.50% 8.34% 19.37% See Table 1 for the definitions of the dependent and independent variables. In Panel B, R 2 CF of client firm fixed effects is defined as: R 2 Full R 2 w/o CF, where R 2 Full is the adjusted R 2 of the full model including all the fixed effects, and R 2 w/o CF is the adjusted R 2 of the model that excludes the client firm fixed effects. % R 2 CF is defined as: (R 2 Full R 2 w/o CF)/R 2 w/o CF. In Panel C, R 2 AF of audit firm fixed effects is defined as: R 2 Full R 2 w/o AF, where R 2 Full is the adjusted R 2 of the full model including all the fixed effects, and R 2 w/o AF is the adjusted R 2 of the model that excludes the audit firm fixed effects. % R 2 AF is defined as: (R 2 Full R 2 w/o AF)/R 2 w/o AF. In Panel D, R 2 IA of individual auditor fixed effects is defined as: R 2 Full R 2 w/o IA, where R 2 Full is the adjusted R 2 of the full model including all the fixed effects, and R 2 w/o IA is the adjusted R 2 of the model that excludes the individual auditor fixed effects. % R 2 IA is defined as: (R 2 Full R 2 w/o IA)/R 2 w/o IA. *, **, and *** denote two-tailed significance at the 0.10, 0.05, and 0.01 levels, respectively

42 Table 3 Testing the significance of individual auditor fixed effects Variables (1) (2) (3) (4) ARAgg AbAcc BL SP Panel A: The percentages of significant estimated individual auditor fixed effects % significant at the 5% level 12.9% 11.4% 11.0% 10.9% % significant at the 10% level 18.0% 17.8% 16.1% 17.1% Panel B: Distribution of estimated individual auditor fixed effects Mean value Inter-quartile range Standard deviation Panels A and B present the properties of 861 estimated individual auditor fixed-effect coefficients. The percentages of fixed effects that are significant at the specified levels are based on the t-statistics on the fixed-effect coefficients

43 Table 4 Testing the significance of individual auditors by pseudo data (1) (2) (3) (4) Variables ARAgg AbAcc BL SP Mean p-value of F-statistics Mean % significant at the 5% level 5.31% 5.14% 5.39% 4.96% Mean % significant at the 10% level 9.78% 9.77% 9.48% 9.75% This table presents the results obtained from randomized data. Specifically, the data are randomized by shuffling the auditor-firm parings so that each auditor is assigned to a client firm which is actually audited by another auditor. The reported results are the mean values from 1,000 iterations of the randomized data. The percentages of fixed effects that are significant at the specified levels are based on the t-statistics on the fixed-effect coefficients

44 Table 5 Descriptive statistics for individual auditor characteristics variables Variables Mean Q1 Median Q3 Std. Dev. Master s degree or above Education cohort Accountancy major Female Big N experience Birth year Partner CCP membership The sample consists of 795 individual auditor observations. Definitions of variables: Master s degree or above equals 1 if the auditor has obtained a Master s degree or above, and 0 otherwise. Education cohort equals 1 if the auditor was born in or after 1971, and 0 otherwise. Accountancy major equals 1 if the auditor majored in accountancy in her/his college education. Female is an indicator variable for female auditors. Big N experience indicates that the auditor has work experience in an international Big N audit firm (Deloitte Touche Tohmatsu, Ernst & Young, KPMG, PwC or their predecessors). Birth year is the year in which the auditor was born. Partner equals 1 if the auditor is a partner and 0 otherwise. CCP membership equals 1 if the auditor is a member of the Chinese Communist Party, and 0 otherwise

45 Table 6 The association between individual auditor fixed effects and demographic characteristics (1) (2) (3) (4) (5) Independent variables ARAgg AbAcc BL SP Aggregate score Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Intercept Master s degree or above * * ** Education cohort ** ** Accountancy major Female Big N experience *** *** Birth year Partner ** CCP membership ** * LTS R % 2.39% 1.18% 3.37% 2.10% The sample consists of 795 individual auditor observations. The dependent variables are the estimated individual auditor fixed-effect coefficients. All the dependent variables are standardized to have zero mean values and unit variances. The variable aggregate score is the mean value of the four standardized measures. The independent variables are defined in Table 5. The regressions are estimated by the Least Trimmed Squares (LTS) method (Rousseeuw 1984) to generate more stable results in the presence of outliers. *, **, and *** denote two-tailed significance at the 0.10, 0.05, and 0.01 levels, respectively. Slope coefficients that are significant at the 10% level are highlighted in bold

46 Variables Table 7 Individual auditor effects on audit quality and the likelihood of regulatory sanction (1) (2) (3) (4) (5) (6) Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Intercept *** *** *** *** *** *** ARAgg *** *** AbAcc * BL *** *** SP ** Aggregate score *** Pseudo R % 0.44% 8.31% 0.30% 3.66% 4.52% In this table, the dependent variable is Sanctioned, which indicates whether individual auditors have been sanctioned by the regulators. In estimating the logistic regressions, observations are weighted by the inverse of the standard error on the independent variables. The sample consists of 861 individual auditor observations. All the independent variables are standardized to have zero mean values and unit variances. The variable aggregate score is the mean value of the four standardized measures. *, **, and *** denote two-tailed significance at the 0.10, 0.05, and 0.01 levels, respectively. Slope coefficients that are significant at the 10% level are highlighted in bold

47 Table A1 Estimating the propensity of issuing MAOs Variables Coefficients t-statistics Intercept Quick ratio Accounts receivable intensiveness Other receivable intensiveness *** Inventory intensiveness *** ROA *** Loss indicator *** Leverage ratio *** Firm size ** Listing age *** Industry indicators Included Mean pseudo R % Sample size 12 years, n = 1, per year. This table reports the Fama-MacBeth (1973) regression results. The coefficients are the means of the coefficients from the yearly logistic regressions, and the t-statistics are based on the distribution of these yearly regression coefficients. The dependent variable is MAO, which equals 1 if the firm received a modified opinion and 0 otherwise. MAOs include unqualified opinions with explanatory notes, and qualified, disclaimed, and adverse opinions. Quick ratio is quick assets (sum of cash, short-term investments, notes receivables, and accounts receivables) divided by current liabilities. Accounts receivable, other receivables, and inventory intensiveness are ending accounts receivable, other receivables, and inventory, respectively, divided by total assets. ROA is earnings divided by total assets. Loss indicator equals 1 if the firm has reported a loss and 0 otherwise. Leverage ratio is liabilities divided by total assets. Firm size is the natural logarithm of firms total assets. Listing age is the number of years a company has been listed. Industry indicators are a set of indicator variables for industry membership (2-digit for the manufacturing sector and 1-digit for other sectors), following the CSRC industry classification scheme. *, **, and *** denote two-tailed significance at the 0.10, 0.05, and 0.01 levels, respectively

48 Table A2 Distribution of modified Dechow and Dichev (2002) model parameters Variables Mean t-statistics Q1 Median Q3 Std. Dev. Intercept *** CFO t *** CFO t *** CFO t *** ΔSales t *** PPE t *** Adj. R Sample size This table presents the distribution of the cross-sectional regression parameters of the Dechow and Dichev (2002) model. There should be at least 10 observations in an industry-year combination to estimate the regression. The sample period is from 1998 to The CSRC industry classification scheme (2-digit for the manufacturing sector and 1-digit for other sectors) is used to define industries. In total, there are 191 industry-year combinations. The dependent variable is working capital accruals in year t, computed as: operating net income plus deprecation, amortization, and financial expenses, minus operating cash flows. Note that financial expenses are presented before operating net income under the Chinese GAAP, but are not classified as operating activities in cash flow statements. CFO t-1, CFO t, and CFO t+1 are operating cash flows in year t-1, t, and t+1, respectively. ΔSales t is sales growth from t-1 to t. PPE t is the gross value of fixed assets. All these variables are scaled by the average of beginning and ending total assets in year t. *** denotes two-tailed significance at the 0.01 level