Determinants of weaknesses in internal control over financial reporting and the implications for earnings quality*

Jeffrey Doyle David Eccles School of Business University of Utah 1645 East Campus Center Drive Salt Lake City, UT 84112 [email protected]

Weili Ge Ross School of Business University of Michigan 701 Tappan Street Ann Arbor, MI 48109 [email protected]

Sarah McVay Stern School of Business New York University 44 West Fourth Street, Suite 10-94 New York, NY 10012 [email protected]

March 1, 2005

* We would like to thank the following for their helpful comments and suggestions: Brad Barrick, Eli Bartov, Patty Dechow, Nader Hafzalla, Kalin Kolev, Feng Li, Russ Lundholm, Suzanne Morsfield and Cathy Shakespeare. Determinants of weaknesses in internal control over financial reporting and the implications for earnings quality

Abstract

We examine determinants of internal control deficiencies and their effect on earnings quality using a sample of 261 firms that disclosed material weaknesses from August 2002 to November 2004. We find that material weaknesses in internal control are more likely for firms that are smaller, less profitable, more complex, growing rapidly, or undergoing restructuring. These findings are consistent with firms struggling with their financial reporting controls in the face of a lack of resources, complex accounting issues, or a rapidly changing business environment. Next, we show that firms with material weaknesses in internal control have lower earnings quality, as measured by the extent to which accruals map into cash flows. This relation is robust to the inclusion of discretionary accruals as a proxy for managerial opportunism, as well as proxies for inherent difficulty in accrual estimation (Dechow and Dichev, 2002). Thus, internal control appears to be one of the fundamental drivers of earnings quality. Furthermore, we find that earnings quality is especially poor for those material weaknesses that relate to overall company-level controls, which may be more difficult to “audit around.”

JEL Classification: M41

Keywords: Internal Control; Material Weakness; Earnings Quality; Sarbanes-Oxley

1. Introduction

In this paper we examine the determinants of internal control deficiencies and the effect

that these deficiencies have on earnings quality. We use a sample of companies that disclosed

material weaknesses in internal control over financial reporting under Section 302 of Sarbanes-

Oxley from August 2002 to November 2004.1 As part of the Sarbanes-Oxley Act of 2002, SEC

registrants’ executives are now required to certify that they have evaluated the effectiveness of

their internal controls over financial reporting and indicate in their public filings whether there

are any significant changes in internal control (Section 302).2 If management identifies a material weakness in internal control, they are precluded from reporting that the internal controls are effective and must disclose the identified material weakness.

Although firms were required to maintain an adequate system of internal control before the enactment of Sarbanes-Oxley, they were only required to publicly disclose deficiencies if there was a change in auditor (SEC, 1988). While prior research studies this very limited set of disclosures (Krishnan, 2005), no evidence exists on internal control quality for firms in general.

Furthermore, the literature on earnings quality has largely ignored the fundamental importance of the internal control system, despite the fact that internal control over financial reporting is designed to provide reasonable assurance regarding the reliability of financial reporting for

1 Although internal control is defined in terms of achieving 1) the effectiveness and efficiency of operations, 2) reliability of financial reporting, and 3) compliance with applicable laws and regulations (Statements on Auditing Standards, Section 319), Sarbanes-Oxley only pertains to internal control related to the reliability of financial reporting. For purposes of Sarbanes-Oxley, the SEC defines internal control as “a process, effected by an entity’s board of directors, management and other personnel, designed to provide reasonable assurance regarding the reliability of financial reporting.” 2 In addition, Section 404 requires that each annual report include an assessment by management of the effectiveness of the internal control structure and procedures of the issuer for financial reporting, which is required to be attested to by the firm’s public accountants. Section 302 of the Sarbanes-Oxley Act became effective in August 2002. Section 404 became effective in November 2004.

1 external reporting in accordance with generally accepted accounting principles (PCAOB, 2004).3

We begin our study by hypothesizing that internal control problems result from firm-

specific characteristics that make it more difficult to maintain an effective system of controls. In

our determinants tests, we investigate whether material weaknesses in internal control are

associated with 1) firm size, measured by market value and book value of equity, 2) financial

health, measured by return on assets and a loss indicator variable, 3) financial reporting

complexity, measured by the number of operating segments, the number of geographic segments,

and foreign operations, 4) rapid growth, measured by merger and acquisition activity and annual

sales growth, and 5) restructurings, measured by special items.4 We find that material weaknesses in internal control are more likely for firms that are smaller, less profitable, more complex, growing rapidly, and/or undergoing restructuring. These findings are consistent with firms struggling with their financial reporting controls in the face of a lack of resources, complex accounting issues, or a rapidly changing business environment.

Next, we examine the impact of poor internal control on the earnings quality of the firm.5

Since a material weakness in internal control is defined as “a significant deficiency, or combination of significant deficiencies, that results in more than a remote likelihood that a material misstatement of the annual or interim financial statements will not be prevented or detected” (PCAOB, 2004, emphasis added), we hypothesize that reported material weaknesses will be associated with lower earnings quality. A weak control environment has the potential to allow both 1) intentionally biased accruals through earnings management (e.g., lack of

3 For example, neither of the two recent publications on earnings quality (Schipper and Vincent, 2003, and Dechow and Schrand, 2004) mentions the relation between internal control and earnings quality. 4 The basic relations between internal control determinants, internal control, and earnings quality are presented in Figure 1. Note that Figure 1 is simplified to show the basic relations in this study merely for illustrative purposes. Other, more complex, relations between the variables shown may also exist, but they are not the focus of this study. 5 Other possible secondary consequences of weaknesses in internal control (generally resulting from poor earnings quality) that we do not currently examine include delayed filings, earnings restatements, and negative stock market reactions (see Figure 1).

2 segregation of duties) and 2) unintentional errors in accrual estimation (e.g., lack of expertise in

estimating pension expense). We focus on accrual quality as our measure of earnings quality and

use a metric that gauges the extent to which accruals map into past, present, and future cash

flows (Dechow and Dichev, 2002).6 We use this measure because it is able to effectively capture both intentional and unintentional errors in accruals (i.e., overall accrual quality), which we predict are the result of internal control weaknesses.

We find that weak internal controls are associated with lower quality accruals, as measured by weaker mappings of accruals into cash flows. This relation is robust to the inclusion of discretionary accruals as a proxy for managerial opportunism, as well as proxies for inherent difficulty in accrual estimation (Dechow and Dichev, 2002). Thus, internal control appears to be one of the fundamental drivers of earnings quality. Moreover, this relation holds after controlling for the underlying determinants of material weaknesses, such as firm size, profitability, firm complexity, and restructuring, which may also be correlated with earnings quality. This supports the notion that it is the material weakness per se that results in lower earnings quality.

Finally, we categorize material weakness disclosures into groups related to the type of weakness disclosed. We investigate the determinants of each type of material weakness and also hypothesize that more serious material weaknesses related to company-level controls (e.g., tone at the top, fraud prevention, etc.) will result in the lowest levels of earnings quality since these types may be more difficult to “audit around.”7 We find that these company-level deficiencies in

6 We also use a measure of “discretionary” accruals (Rangan, 1998; Teoh, et al., 1998a, 1998b; Kothari, et al., 2005) as a proxy for earnings quality and find similar results. It is possible that weaknesses in internal control could lead to non-accrual based earnings quality issues, but out focus in this paper is on the quality of accruals. 7 See Doss and Jonas (2004). This classification of different types of material weaknesses is explained further in Section 2.2 and in the appendix.

3 internal control are indeed related to the lowest earnings quality levels, indicating that differentiating the type of material weakness is important for financial statement users.

This paper makes several important contributions to our understanding of internal control over financial reporting and earnings quality in general. First, while prior literature on internal control faced a severe data shortage (Kinney, 2000; Krishnan, 2005), we are able to provide evidence on the determinants of internal control weaknesses for all firms, since all public firms are subject to the new internal control disclosure requirements of Sarbanes-Oxley. Second, we provide evidence that weaknesses in internal control affect earnings quality. While such a relation has been suggested (Kinney, 2000), this fundamental driver of earnings quality has been largely ignored to date. We also show that the determinants of internal control weaknesses and the relation between material weaknesses and earnings quality differ based on the type of material weakness reported. In sum, our findings suggest that the disclosures related to internal control under Sarbanes-Oxley provide information to financial statement users regarding the information reliability of the company.

In the next section we briefly discuss the new requirements on internal control disclosures and our data. In Section 3, we discuss possible determinants of material weaknesses in internal control. We also describe the methodology used to test our hypotheses and discuss the results of this analysis. Section 4 provides a discussion of the potential impact of internal control weaknesses on earnings quality along with the methodology and results. We summarize and conclude in Section 5.

4 2. Internal control over financial reporting 2.1. Background

Internal control over financial reporting has long been recognized as an important feature

of a company and its earnings quality (see Kinney et al., 1990; Kinney, 2000; Kinney, 2001).8

However, prior to Sarbanes-Oxley, actual standards in place were very limited in scope. The sole statutory regulation of internal control was the Foreign Corrupt Practices Act (FCPA) of

1977, while the only required public disclosure of significant internal control deficiencies was in the firm’s 8-K, when disclosing a change in auditors (SEC, 1988; Geiger and Taylor, 2003;

Krishnan, 2005).9

Under Section 404 of Sarbanes-Oxley (effective November 15, 2004), managers must review and provide an annual report on their internal control, assessing the effectiveness of the

internal control structure and procedures. However, even before the implementation of Section

404, firms began disclosing material weaknesses in internal control in response to Section 302 of

Sarbanes-Oxley, where the disclosure requirements related to internal control were strengthened.

Specifically, under Section 302, the company executives are required to certify in the periodic reports (e.g., the 10-Qs and 10-Ks) filed with the SEC that their system of internal controls are effective and whether or not there have been any significant changes in internal control. Under

Section 302, management is not permitted to conclude that internal control over financial

8 Internal control over financial reporting includes those policies and procedures that: 1) pertain to the maintenance of records that, in reasonable detail, accurately and fairly reflect the transactions and dispositions of the assets of the company; 2) provide reasonable assurance that transactions are recorded as necessary to permit preparation of financial statements in accordance with generally accepted accounting principles, and that receipts and expenditures of the company are being made only in accordance with authorizations of management and directors of the company; and 3) provide reasonable assurance regarding prevention or timely detection of unauthorized acquisition, use or disposition of the company's assets that could have a material effect on the financial statements (PCAOB 2004). 9 See Ge and McVay (2005) for a more detailed discussion of internal control regulation and the specific changes of the Sarbanes-Oxley Act.

5 reporting is effective if any material weaknesses are present.10 Therefore, when management or a firm’s auditors discover a material weakness, it must be disclosed.11 Thus, while our sample predates the effective date of Section 404, the two requirements just listed have resulted in at least 261 firms disclosing a material weakness in internal control from the enactment date of

Sarbanes-Oxley, to November 2004.

Both Sections 302 and 404 use definitions of “effective” internal control similar to those developed in 1992 by the Committee of Sponsoring Organizations (COSO) of the Treadway

Commission. The SEC thus defines internal control as “a process, effected by an entity’s board of directors, management and other personnel, designed to provide reasonable assurance regarding the reliability of financial reporting.”12 Although the COSO framework broadly

defines internal control in terms of achieving 1) the effectiveness and efficiency of operations, 2) reliability of financial reporting, and 3) compliance with applicable laws and regulations

(Statements on Auditing Standards, Section 319), Sarbanes-Oxley only pertains to internal control related to the reliability of financial reporting.

2.2. Data

As mentioned above, material weaknesses in internal control have only been widely disclosed in SEC filings since August of 2002. Since November 2003, Compliance Week (a

10 A material weakness is defined by the Statement on Auditing Standards No. 60 as a “reportable condition in which the design or operation of one or more of the internal control components does not reduce to a relatively low level the risk that misstatements caused by errors or fraud in amounts that would be material in relation to the financial statements being audited may occur and not be detected within a timely period by employees in the normal course of performing their assigned functions.” A “significant deficiency” and a “material weakness” are both deficiencies in the design or operation of internal controls, but significant deficiencies (also known as reportable conditions) are lesser in severity and are not required to be publicly disclosed. 11 Under Sarbanes-Oxley Section 906, criminal penalties may be imposed on managers who knowingly certify a periodic report that does not comport with the requirements; the allowable penalties are a fine of up to $5,000,000 and up to 20 years imprisonment. 12 See www.sarbanes-oxley.com and www.sec.gov for complete final rules and interpretations. Note that Sarbanes- Oxley only pertains to internal control over financial reporting.

6 website dedicated to Sarbanes-Oxley related compliance issues) has been collecting and

publishing monthly reports on firms that disclose internal control deficiencies. We aggregate

these monthly Compliance Week disclosures, and identify the SEC filing in which the firm

disclosed the internal control deficiency. We include only those firms that classify their internal control problem(s) as a material weakness, the most severe internal control deficiency.13 In order to increase our sample size and generalizability, we also search 10-Ks in the EDGAR database beginning in August of 2002 (the enactment date of Sarbanes-Oxley) using the keywords

“material weakness” and “internal control.” If a parent and subsidiary both file with the SEC and report the same material weakness, we include only the parent company; there were no instances where a parent and subsidiary reported different material weaknesses. This procedure identifies 261 firms that disclosed at least one material weakness from August 2002 to November

2004.14 We obtain financial data from the 2003 annual Compustat database, losing 42 firms that have insufficient Compustat data, resulting in a final sample of 219 material weakness firms.

We obtain merger and acquisition data from SDC Platinum.

As a final step, we obtain the related SEC filing in which a material weakness was disclosed, and categorize the types of internal control deficiencies based on the disclosure. We have two classification schemes, described below and in the appendix. In order to minimize the

13 We read through each SEC filing to ensure a material weakness in internal control is disclosed. We focus on material weaknesses for two reasons. First, it is the most severe type of deficiency in internal control and the most likely to affect earnings quality. Second, the disclosure of material weaknesses is mandatory, while the disclosure of “significant deficiencies” is voluntary (see footnotes 10 and 11). Focusing on mandatory disclosures helps avoid self-selection issues associated with voluntary disclosures. 14 This sample should be fairly complete for all firms with material weaknesses, since material weaknesses are required to be disclosed. In addition, we expect that it is unlikely for a manager to withhold the disclosure of an identified material weakness in internal control given the legal liability associated with Sarbanes-Oxley. The knowledge of a material weakness precludes the manager from reporting that internal controls are effective, leading to a discussion of the material weakness; violating Sarbanes-Oxley is a criminal offense (see footnote 11). However, the use of the proxy of a disclosure of a material weakness versus the true underlying existence of a weakness is a limitation of our study. To a great extent, internal control is unobservable and unquantifiable to an outsider of the company (Kinney, 2000).

7 subjectivity of our classification we have each material weakness coded independently by two individuals and then reach consensus after discussing each company in our sample.

The first classification scheme is based on Moody’s, the bond-rating company. Moody’s proposes that material weaknesses fall into one of two categories. Type “A” material weaknesses relate to controls over specific account balances or transaction-level processes.

Moody’s suggests that these types of material weaknesses are “auditable,” and thus do not represent as serious a concern regarding the reliability of the financial statements. Type “B” material weaknesses, however, relate to company-level controls such as the control environment or the overall financial reporting process, which auditors may not be able to effectively “audit around.” Moody’s suggests that Type B material weaknesses call into question not only management’s ability to prepare accurate financial reports but also its ability to control the business (Doss and Jonas, 2004). In some cases, it is straightforward to categorize a company as

Type B; for example, when “ineffective control environment” or “management override” is specifically identified as a material weakness in the disclosure. However, in other cases, it is difficult to distinguish Type B from Type A; management’s discussion of the material weakness may strategically choose to downplay the control problems. In these cases, we decide the type based on how pervasive the material weakness appears.

Our second classification scheme is based on the company’s stated reason for the material weakness. For example, some material weaknesses are related to staffing issues, while others pertain to overseas operations or inter-company transactions. We create three categories of material weaknesses: Staffing, Complexity, and General. The common staffing issues are

8 “inadequate qualified staffing and resources” or “lack of a full time CFO.”15 Complexity issues include, for example, trouble interpreting and applying complex accounting standards, such as those related to hedging and derivatives. “General” issues are usually related to deficient revenue recognition policies or control weaknesses in the period-end reporting process. This final type of weakness allows for greater opportunity to manage earnings. Note that, in the first classification scheme, we classify each material weakness company as either Type A or Type B

(i.e., the categories are mutually exclusive). In the second classification scheme, each company

can have multiple deficiency types. For example, one company can have both staff shortage

deficiency and a material weakness related to applying complex accounting standards of hedge

transactions. Examples of both of our material weakness classification schemes are included in

the appendix.

3. Determinants of material weaknesses in internal control over financial reporting 3.1. Hypotheses and prior research

Internal control is a major focus of recent regulatory changes under Sarbanes-Oxley. At present, however, there is an extremely limited amount of empirical work on internal control quality. The most direct evidence is provided by Krishnan (2005). She examines 128 internal control deficiencies (both significant deficiencies and material weaknesses) reported from 1994-

2000 in the 8-Ks of firms who change auditors. Her focus is on the association between audit committee quality and internal control quality, for which she finds a positive relation. However, her sample is limited to a specific subgroup of firms (those who changed auditors) that may not be generalizable to the entire population of SEC registrants. The use of mandatory internal

15 It is possible that companies might utilize classifications such as staffing to ex post justify the material weakness, when in fact staffing was not the cause. This possibility adds noise to our tests and may reduce the informativeness of our groupings.

9 control disclosures under Section 302 allows us to provide broader evidence on the determinants

of internal control.

Prior research hypothesizes that firm size is a determinant of good internal control (e.g.,

Kinney and McDaniel, 1989; Defond and Jiambalvo, 1991). Intuitively, large firms are likely

more established and have more financial reporting processes and procedures in place. Large

firms also tend to have greater resources to spend on internal auditors or consulting fees, which

may aid in the generation of strong internal control. For example, there is a strong positive

association between non-audit fees and firm size (e.g., DeFond et al., 2002; Frankel et al., 2002).

Although larger firms are clearly more complicated, we explicitly control for complexity in our

tests. Thus we expect to find fewer control weaknesses in larger firms, after controlling for

complexity.16 We use two measures of firm size, market value of equity [data item #25*data

item #199] and book value of equity [data item #60].

A second determinant of strong internal control might be a firm’s financial health.

Poorly performing firms simply may not be able to adequately invest time and/or money in proper controls (Krishnan, 2005). Good internal control requires both financial resources and company time, and this may not be a priority for firms that are concerned about simply staying in business. Consistent with this hypothesis, past research finds that financial reporting errors are negatively associated with performance (DeFond and Jiambalvo, 1991) and that the existence of a loss is positively associated with the reporting of an internal control problem in audit-change firms (Krishnan, 2005). We examine three profitability measures, RETURN ON ASSETS (ROA)

(Earnings before Extraordinary Items [data item #18] scaled by Average Total Assets [data item

16 Interestingly, in her sample of audit change firms, Krishnan (2005) finds that size is positively associated with internal control problems. However, Krishnan (2005) does not control for complexity directly (see Ge and McVay [2005] and herein) as her focus was on monitoring devices; it is possible that her results reflect additional challenges faced by her sample firms related to complex accounting issues.

10 #6]), CORE ROA (Earnings after Depreciation [data item #178] scaled by Average Total Assets

[data item #6]) and LOSS INDICATOR, an indicator variable that is equal to one if Earnings after

Depreciation is less than or equal to zero, and zero otherwise.17 We expect to find fewer internal control weaknesses in more profitable firms.

While we expect the size and profitability of the firm to affect its ability to establish proper internal controls, the need for internal controls is unique to each firm’s particular operating environment.

“…the size of the company, quality of its staff, risk management processes, the type of information technology systems, complexity of product lines including marketing channels, geographical dispersion, nature of the business (e.g. manufacturing versus service) as well as many other factors can affect the types of internal control necessary. Most often, small businesses will not require the type of complex internal control systems that are required of large international conglomerates engaging in extensive risk management techniques and financial instruments” [Glass Lewis and Company (2004), emphasis added].

As a firm engages in more complex transactions, we expect the need for internal control to be higher, and thus expect the complexity of the firm to be a driver of internal control weaknesses.

Consider the complexity introduced by having multiple geographic or business divisions. These companies face challenges when implementing internal control consistently across different divisions. For each division, different factors might affect the implementation of adequate internal control. For example, for a multinational company, the local institutional and legal environment of each location might differ, and thus affect the effectiveness of internal control.18

One might argue that a complex firm would adopt more sophisticated internal controls.

While this might be true, given the organizational complexity and the existing internal controls,

17 CORE ROA and LOSS INDICATOR allow us to distinguish between profitability and special items (discussed below), as Earnings after Depreciation is before special items. 18 For example, Baxter International Inc., one of our sample companies, disclosed a material weakness in their Brazilian division.

11 the marginal cost of implementing stricter internal control might outweigh the marginal benefit.19

For example, for a U.S. company with a subsidiary in China, it might not be cost effective for the company to send personnel to China to verify every receipt. Therefore, we expect that in equilibrium, given firm size, the greater the organizational complexity, the greater the incidence of material weaknesses in internal control. We examine complexity using three measures: the number of operating segments, the number of geographical segments, and the existence of a foreign currency adjustment (e.g., DeFond et al., 2002; Bushman et al., 2004).

A fourth possible determinant of internal control weaknesses is rapid growth. A quickly

growing firm may outgrow any internal controls it has in place, and may take some time to

establish new procedures (Kinney and McDaniel, 1989; Stice, 1991). Note that new personnel,

processes, and technology are usually needed to match the internal control with the firm’s

growth. For example, in their 2003 10-K, MarkWest Energy disclosed “inadequate

implementation of uniform controls over certain acquired entities” as one aspect of their internal

control deficiencies. We consider two types of growth. The first, ACQUISITION VALUE, is the

aggregate dollar value of acquisitions that result in at least 50% ownership of the acquired

company in 2002 and 2003, scaled by the 2003 market capitalization of the acquiring firm. Since

acquisitions usually involve the integration of multiple organizations / internal control systems, this

type of growth requires relatively more internal control improvements than simple internal sales

growth. Secondly, we measure SALES GROWTH, defined as sales [data item #12] less prior year

sales, scaled by prior year sales. We expect firm growth to be positively associated with internal control weaknesses.

19 In addition, we are not studying the comparative level or size of firms’ internal controls, but rather the ability of each firm to develop its own unique level of effective controls.

12 Finally, we expect firms undergoing substantial restructuring to have relatively more

internal control weaknesses. First, restructurings often result in the downsizing of departments, the loss of experienced employees, and general disarray during and after the re-engineering of the firm. The internal control system must be updated to match the new organizational structure.

Moreover, frequent turnover or reduction of accounting personnel would directly cause internal control problems and poor financial reporting quality thereafter. Second, restructuring typically involves many difficult accrual estimations and adjustments (e.g., impairment of goodwill; see also Dechow and Ge, 2005). Insufficient staff, together with more accounting estimation, likely leads to more internal control deficiencies. For example, Nortel Networks disclosed a lack of compliance with the established procedures for monitoring and adjusting balances relating to restructuring charges as a material weakness in their June 2003 10-Q. We proxy for restructuring charges with Special Items, as reported by Compustat. Compustat defines Special

Items as “unusual or nonrecurring items” including items specifically called restructuring/ reorganization, special, and one-time charges, as well as various types of write-downs and write- offs. Specifically, SPECIAL ITEMS is equal to Special Items [data item #17] scaled by Prior

Year Total Assets [data item #6]. We expect material weaknesses to be more prevalent as firms record larger income-decreasing special items.

3.2. Results 3.2.1. Univariate analysis

Table 1 presents descriptive statistics on the characteristics of material weakness firms and control firms (control firms are all 2003 Compustat firms that are not in our sample of firms that disclosed material weakness in internal control). Although we use logged values for some variables in subsequent analyses (namely Market Capitalization, Book Value of Equity, Number

13 of Operating Segments and Number of Geographic Segments), we report raw values in this table

for ease of interpretation. In general, the mean of each variable is statistically different (using a

Wilcoxon rank-sum test), in the direction predicted, between material weakness and control

firms. First, firm size, measured by both market capitalization and book value of equity, is

significantly smaller for material weakness firms. In general, the material weakness firms appear

to be about half the size of the average Compustat firm. This finding strongly supports the

notion that smaller firms might be less able to establish and maintain strong internal controls.

Our profitability measures, ROA, CORE ROA and LOSS INDICATOR, indicate that firms

disclosing material weaknesses in internal control are significantly less profitable than the

average Compustat firm, as expected. Our three complexity measures: the number of operating segments, the number of geographical segments, and the existence of a foreign currency adjustment are all higher for material weakness firms, providing preliminary support for our hypothesis that accounting complexity creates internal control challenges.

There is little support in the univariate analysis that growth is a determinant of internal control problems. ACQUISITION VALUE is not significantly different between material weakness and control firms, while SALES GROWTH is statistically lower for material weakness firms than the control firms. Finally, we find that our proxy for firm restructuring, SPECIAL

ITEMS, is significantly more negative for material weakness firms (where a negative number represents an expense). Referring to the final row of Table 1, the mean special items of material weakness firms is more than double that of the mean for the control firms.20

20 We have re-estimated our results using prior year variables for special items, size and profitability and our findings are extremely similar.

14 3.2.2. Multivariate logistic analysis

We model the probability of disclosing a material weakness in internal control over

financial reporting as a function of the above-mentioned firm characteristics using a logistic

regression:

Prob (MW) = β0 + β1 SIZE + β2 PROFITABILITY + β3 COMPLEXITY K k =1 + β4 GROWTH + β5 RESTRUCTURINGS +
γ16k INDUSTRY (1)

We present the log of Market Capitalization as our size measure. Market Capitalization and

Book Value of Equity are highly correlated (see Table 2) and taking the log of Book Value

would result in the loss of observations (as Book Value is sometimes negative). We have re-

estimated our analyses in Tables 3-5 using the log of Book Value and results are extremely

similar. We consider all three measures of profitability in Table 2, discussed in greater detail

below. We also include all three complexity measures. Referring to the Spearman correlation

coefficients Table 2, the segment variables, though positively correlated, have a correlation of

only about 0.04. This suggests that these two measures of complexity are likely measuring

different aspects of business complexity.

Geographic Segments and the Foreign Currency Indicator are correlated at about 0.40.

This is reasonable, as many geographical segments may be abroad. We present both measures and re-estimate each of the regressions in Tables 3-5 using OLS to check for multicolinearity by examining the Variance Inflation Factors (VIFs). In no instance does the VIF exceed 2, which is well within the range of acceptability (Kennedy 1998, p. 190). Though not shown, we also

include 16 industry indicator variables. Ge and McVay (2005, Table 1) provide descriptive

15 evidence on material weakness disclosures, and find that these disclosures tend to cluster by

industry.21

Table 3 presents the results of this analysis. The first column of results uses ROA as the profitability metric, the second, CORE ROA (to distinguish between ROA and Special Items), and the third LOSS INDICATOR. Consistent with the univariate results, Market Capitalization is a strong negative predictor of a material weakness disclosure in each of the three regressions.22

Interestingly, after controlling for other possible determinants of material weaknesses, ROA is no longer significantly associated with material weakness disclosures. This insignificance also holds for CORE ROA (second column of results). However, consistent with Krishnan (2005),

the LOSS INDICATOR provides incremental information about the likelihood of disclosing a

material weakness (final column of results). As in the univariate results, loss firms are more likely to disclose a material weakness. This indicates that the relation between material weaknesses and profitability is non-linear. Perhaps a firm must reach a certain level of profitability before investing in internal control, after which, little variation is dependent upon profitability. As such, we include LOSS INDICATOR as our profitability metric in the subsequent determinants analyses (Tables 4 and 5).

21 Industry classifications are compiled using the following SIC codes: Agriculture 100-999; Mining: 1000-1299, 1400-1999; Food: 2000-2199; Textiles: 2200-2799; Drugs: 2830-2839, 3840-3851; Chemicals: 2800-2829, 2840- 2899; Refining: 1300-1399, 2900-2999; Rubber: 3000-3499; Industrial: 3500-3569, 3580-3659; Electrical: 3660- 3669, 3680-3699; Miscellaneous Equipment: 3800-3839, 3852-3999; Computers: 3570-3579, 3670-3679, 7370- 7379; Transportation: 4000-4899; Utilities: 4900-4999; Retail: 5000-5999; Banks: 6000-6999; Services: 7000- 7369, 7380-8999; Miscellaneous: 9000-9999. We also estimate the regression without the industry indicator variables, finding almost identical results. 22 Note that this finding is counter to that found by Krishnan (2005) who examines internal control deficiencies disclosed in 8-Ks at the time of an auditor change. Both our samples and our time periods differ. In addition, it is likely that post-Sarbanes-Oxley, many less egregious weaknesses (e.g., a staffing shortage as compared to an override of financial controls by top management) are disclosed, which are likely more pervasive in smaller firms (Ge and McVay, 2005). Moreover, Krishnan (2005) does not control for complexity directly as her focus was on monitoring devices; it is possible that her results reflect additional challenges faced by her sample firms related to complex accounting issues.

16 Both of the segment complexity measures are positive and significant predictors of

material weakness disclosures; however, FOREIGN CURRENCY is not significant in the

presence of the additional possible determinants of material weaknesses. M&A related growth is

a positive and significant predictor of a material weakness, though SALES GROWTH is

insignificant in all three regressions. As previously discussed, acquisitions usually involve the

integration of multiple organizations / internal control systems, requiring relatively more internal

control improvements than simple internal sales growth. Finally, SPECIAL ITEMS, our proxy for firm restructurings, is a strong predictor of a material weakness disclosure (recall that a negative value is equal to an expense, thus as restructuring charges rise, the incidence of a material weakness disclosure rises).

Overall, our findings support our hypotheses outlined in Section 3.1. We find that material weaknesses in internal control are more likely for firms that are smaller, less profitable, more complex, growing rapidly, and/or undergoing restructuring. These findings are consistent with firms struggling with their financial reporting controls in the face of a lack of resources, complex accounting issues, and/or a rapidly changing business environment.

3.3. Analysis by deficiency types

The above analysis combines all material weakness disclosure types. However, as discussed in Section 2, not all material weaknesses are equal. Some occur in auditable accounts

(Type A), while others may be more pervasive and difficult to mitigate by additional auditor testing (Type B). Some are associated with staffing concerns (Staffing), and others with complex accounting issues (Complexity). Thus we expect the drivers to differ by material weakness type (outlined in the appendix and discussed in Section 2). In Table 4, we re-estimate

17 the logistic regression equation (1) with the alternate dependent variables (Type A and Type B).

In Table 5, our alternate dependent variables are Staffing, Complexity, and General.

Referring first to Table 4, the first column of results is for the transaction level problems

(Type A). Not surprisingly, firm size is a strong predictor of these types of problems. In

addition, LOSS INDICATOR, FOREIGN CURRENCY and SPECIAL ITEMS are significant in

this estimation. It is very interesting that FOREIGN CURRENCY rather than GEOGRAPHIC

SEGMENTS loads up in this regression. Thus, it is the transactions related to foreign operations,

not simply widespread operations, which determine material weaknesses related to auditable

accounting problems.

Type B material weaknesses have a greater number of determinants, as seen in the final

column in Table 4. Again firm size is significant, suggesting that smaller firms have more

entity-wide control problems, after controlling for organizational complexity.23 LOSS

INDICATOR, OPERATING SEGMENTS and GEOGRAPHIC SEGMENTS are each positive predictors of Type B material weaknesses. Finally, both growth proxies are positive predictors of entity-wide control problems. This finding is interesting, as neither of these measures

predicted transaction-level material weaknesses. This finding suggests that it is not the

transactions that lack control when firms grow rapidly, but rather, that more fundamental issues

arise. We explore this finding further with a second classification scheme below.

In Table 5, our final determinants analysis uses our second classification scheme,

Staffing, Complexity and General. The first column of results in Table 5 has “Staffing” as the independent variable. The size of the firm, as to be expected, is a strong determinant of this type of internal control deficiency. LOSS INDICATOR is also a positive predictor. Interestingly, both

23 It is interesting to note that in a regression that does not control for complexity, size is no longer a significant explanatory variable (not tabulated).

18 OPERATING SEGMENTS and GEOGRAPHIC SEGMENTS predict staffing problems, while

FOREIGN CURRENCY is insignificant. These findings suggest that when the company has

multiple locations or divisions, staffing problems are more of an issue. Finally, ACQUISITION

VALUE is a positive predictor of staffing problems while neither SALES GROWTH, nor

SPECIAL ITEMS are significant. Thus, the immediate growth caused by acquisitions appears to

create a more severe internal control problem related to staffing than (perhaps slower) internal

growth or restructurings.24

Turning to the second column of results, where the independent variable is “Complexity,” for the first time firm size is not statistically significant. Instead, LOSS INDICATOR and

FOREIGN CURRENCY appear to be the main determinants of complexity-related control problems. Under one-sided tests, both GEOGRAPHIC SEGMENTS and ACQUISITION VALUE are also statistically significant. Overall, these findings support the notion that internal control problems are setting-specific, stemming from a variety of sources, and are not limited to small firms.

Finally, after abstracting from Staffing and Complexity, we examine General material weaknesses in our final column of results. Again firm size is insignificant (p = 0.148). LOSS

INDICATOR is positive and significant, as are GEOGRAPHIC SEGMENTS and FOREIGN

CURRENCY. While ACQUISITION VALUE is not statistically significant (recall this variable was associated with staffing problems), both SALES GROWTH and SPECIAL ITEMS predict these more severe weaknesses. Also, to the extent that SPECIAL ITEMS proxies for restructurings, it appears that restructurings result in more severe internal control problems, not simply staffing issues.

24 It could also be that special items is a weak proxy for restructurings.

19 Overall, the determinants of material weaknesses are dependent on the types of material

weaknesses disclosed. For example, a major determinant of Staffing issues is firm size, while

this variable is an insignificant determinant of the other types of material weaknesses

(Complexity and General).

4. Earnings quality and material weaknesses in internal control 4.1. Hypotheses

In this section we examine the impact of internal control deficiencies on earnings quality.

Internal control over financial reporting is defined as “a process…to provide reasonable

assurance regarding the reliability of financial reporting …” (PCAOB, 2004, emphasis added).

By definition, good internal control is supposed to result in higher reliability of financial information. Internal controls might be preventive or detective, the objective being the prevention and/or detection of errors or fraud that could result in a misstatement of the financial statements. However, to our knowledge, there is no direct evidence regarding the relation between internal control and accounting information quality in the existing literature. A major reason is lack of data on internal control; in general, it is difficult to directly observe or verify internal control (Kinney, 2000).

Past literature on earnings quality is generally related to accrual quality (Dechow and

Schrand, 2004), and that is also the focus in this paper. Accruals can be of poor quality for two basic reasons: 1) management may intentionally bias accruals through earnings management and

2) unintentional errors in accrual estimation may occur due to the difficulty of predicting an uncertain future or simply because there are insufficient controls in place to catch errors in estimated amounts. There are two streams of research on accrual quality in the existing literature that relate to these two possibilities. First, it is documented that managers use “discretionary

20 accruals” to manage earnings for various incentives, such as equity offerings (e.g., Rangan,

1998; Teoh, et al., 1998a, 1998b). Second, Dechow and Dichev (2002) point out that that the

quality of accruals and earnings are not limited to managerial opportunism, but related to the

inherent difficulty in estimating certain types of accruals in different situations (e.g., the length

of the operating cycle). They measure the quality of accruals by the extent to which the accruals

map into past, present, and future cash flows.

We argue that internal control quality contributes to the quality of accruals and earnings, both for intentionally biased “discretionary” accruals by limiting management’s ability to manage earnings (e.g., by segregating duties) and by decreasing estimation errors for difficult to

estimate accruals (e.g., by ensuring qualified personnel are doing the pension expense/liability).

Note that we are not arguing that internal control problems and managerial opportunism are

mutually exclusive. Indeed, weak internal control might lead to managerial opportunistic behavior. However, internal control goes beyond managerial opportunism and certain firm characteristics addressed in Dechow and Dichev (2002). We attempt to investigate internal control as one of the fundamental drivers of accruals and earnings quality. If we assume that two companies in the same industry both have managers who do not bias accrual estimates and have similar operating cycle lengths, one company might have higher accrual estimation errors due to internal control deficiencies, such as inadequate qualified accounting staff. For example,

Cardiodynamic International disclosed that they have a material weakness related to the frequency of their analysis of the inventory obsolescence provision. This material weakness is likely to increase the estimation error related to inventory accounts.

21 We use the accrual estimation error measure developed in Dechow and Dichev (2002) as

our comprehensive measure of earnings quality.25 We assume that this measure can capture the

effect of internal control on earnings quality for two reasons. First, from the large number of disclosed material weaknesses that are related to specific accounts (e.g., inventory, see Ge and

McVay [2005]), it appears that these accounts are likely to have estimation errors that will be captured well by this measure.26 Second, compared to other earnings quality measures, the measure in Dechow and Dichev (2002) does not rely solely on earnings management or assumptions related to market efficiency (e.g., value-relevance). This measure can capture both biased “discretionary” accruals and unintentionally poorly estimated accruals, which we predict will be the result of an internal control system with material weaknesses.27

Specifically, the Dechow and Dichev (2002) measure is calculated using the standard deviation of the residuals (SRESID) from the following firm-specific regressions.

WCt = β0 + β1 CFO t-1 + β2 CFOt + β3 CFOt+1 +
q qQTRq + t

To the extent that accruals (WC) map into past, present, or future cash flows (CFO), it is assumed that there is less estimation error in the accruals. Hence, a higher standard deviation of the residuals denotes lower earnings quality. We estimate the above firm-specific regression using quarterly Compustat (Zhang, 2005). We require firms to have at least 12 quarters, but no more than 20 quarters (fiscal 1999 to 2003), of data to run the above regression on a firm-

25 We also use a measure of “discretionary” accruals as a proxy for earnings quality and find similar results to those reported in Section 4.2. 26 For example, if the inventory account is overstated, the obsolete inventory will not result in cash inflows in the next period, resulting in a low correlation between the accrual and cash flows. 27 Roychowdhury (2004), among others, shows that firms appear to manipulate real operating activities in order to “manage” earnings. However, such an action is not a violation of generally accepted accounting principles, and thus we do not expect good internal control to constrain this behavior. Our focus in this paper is on earnings quality as measured by the accrual/cash flow relation.

22 specific basis.28 We follow Das and Zhang (2003) to compute working capital accruals. We define working capital accruals as WC = (∆CA - ∆CASH) - (∆CL - ∆STD), where ∆CA is the

change in current assets (Compustat Quarterly Item #40), ∆CASH is the change in cash and short-term investments (Compustat Quarterly Item #36), ∆CL is the change in current liabilities

(Compustat Quarterly Item #49), and ∆STD is the change in the current portion of long-term debt

(Compustat Quarterly Item #45). CFO is cash from operating activities (Compustat Quarterly

Item #108).29 Following Dechow and Dichev (2002), all variables are scaled by average total assets (Compustat Quarterly Item # 44). QTRq is an quarterly indicator variable indicating quarters q = {1, 2, 3}. Also following Dechow and Dichev (2002), we truncate the most extreme one percent of cash from operations and changes in working capital. The above restrictions yield a sample of 169 material weakness firms and 3,612 non-material weakness Compustat firms in fiscal 2003.

4.2. Results 4.2.1. Univariate Analysis

Table 6 presents descriptive statistics on the mean and median SRESID of material weakness and control firms. As hypothesized, the material weakness firms appear to have higher

SRESID, suggesting lower earnings quality. The difference is statistically significant under a

Wilcoxon rank-sum test (p<0.0001). We next investigate whether the relation between material weaknesses and earnings quality varies across different types of internal control deficiencies. As discussed in Section 2 and outlined in the appendix, Type B material weaknesses are related to

28 We do not use annual data as in Dechow and Dichev (2002) because we would lose more than one-third of our material weakness firms if we require firms to have at least eight years of data. Material weakness firms tend to be younger than the average firm in Compustat (Ge and McVay, 2005). 29 Compustat Quarterly reports data for the cumulative interim period year to date for items in a cash flow statement. Therefore, for the second, third, and fourth quarters, we take the difference between the reported amounts in quarter t and quarter t-1 to get the correct cash from operations.

23 company-level controls and are less “auditable” and thus likely have a pervasive effect on the

quality of a company’s financial statements. Therefore, we expect firms with Type B material

weaknesses to exhibit lower earnings quality than Type A material weakness firms. As

evidenced in Table 6, consistent with our prediction, Type B material weakness firms have a

higher SRESID (mean = 0.036) than Type A firms (mean = 0.027).

We also examine earnings quality by deficiency type based on Staffing, Complexity and

General. Interestingly, we find that complexity-related deficiencies show no relation with

earnings quality, while both Staffing and General deficiency weakness firms have lower earnings

quality than the control sample of all 2003 Compustat firms. Finding no statistical difference

between complexity-driven material weaknesses and the Compustat population may be a result

of low power, but also may indicate that complexity-related deficiencies affect accrual accounts

to a lesser degree or auditors were better able to perform substantive audit procedures on the related financial data affected by these weaknesses.

4.2.2. Multivariate regression analysis

In Table 7, we investigate whether the relation between material weaknesses and earnings

quality continues to hold in the presence of other factors that are known to affect SRESID. The regressions are estimated using the decile rank of each of the variables to avoid the influence of outliers and the assumption of a linear relation. Column A in Table 7 indicates that material weakness firms have a higher SRESID (lower earnings quality), after controlling for industry indicator variables. The subsequent two columns incorporate three additional control variables that are expected to be associated with earnings quality: total assets, length of the operating

24 cycle, and the absolute value of discretionary accruals (ABSDACC).30 Consistent with the results

documented in Dechow and Dichev (2002), TOTAL ASSETS is negatively related to SRESID,

while OPERATING CYCLE is positively related to SRESID. ABSDACC is positively related to

SRESID, consistent with the idea that the accrual quality measure captures “discretionary”

accruals. In Column C, We find that MW is positively related to SRESID after controlling for

these three variables. Our findings indicate that internal control quality goes beyond managerial opportunism, firm size, and operating cycle in predicting earnings quality. Internal control

quality appears to be one of the fundamental drivers of accruals and earnings quality.31

Although Columns B and C control for firm size, it is possible that the other determinants of material weaknesses are causing the association between MW and SRESID. Therefore, in

Column D, we control for additional determinants of internal control weaknesses to ascertain the root cause of the lower earnings quality. We find that earnings quality is lower for loss firms, as well as for firms with multiple geographic segments, acquisitions and/or negative special items.

However, we find that material weakness firms continue to have lower earnings quality, after controlling for the main determinants of material weaknesses.32 This finding further supports our

prediction that internal control quality has a positive effect on earnings quality.

4.3. Analysis by deficiency types

Table 8 reports the regression results on the relation between earnings quality and the different types of internal control deficiencies. Similar to the univariate analysis in Table 6,

30 OPERATING CYCLE is calculated as the average of [(Sales (Item #12) /365)/(Average Accounts Receivable (Item #2) +(Cost of Goods Sold (Item #41/365)/(Average Inventory (Item #3)] from fiscal 1999 to 2003. DACC is calculated using performance-adjusted discretionary accruals based on modified Jones model and Kothari et al. (2005). ABSDACC is the average of the absolute value of DACC from fiscal 1999 to 2003. 31 In results not tabulated, we have also performed a matched sample approach. Specifically, we match each material weakness firm based on industry and market value of equity. Results are extremely similar. 32 For brevity we only present the most significant determinants of material weaknesses in Table 7. In results not tabulated, we have also included ROA, FOREIGN CURRENCY and SALES GROWTH. Results are extremely similar.

25 “company-level” (Type B) deficiencies have a stronger relation with SRESID in terms of the

magnitude of the coefficient and the significance level, even after the inclusion of the control

variables (see Columns A and B). This finding further supports our prediction that internal

control weaknesses at the company-level might lead to an increased likelihood of financial

reporting problems. Again, this relation is robust to the inclusion of our earnings management

proxy and expected earnings quality relations based on firm size and operating cycle. In

Columns C and D of Table 8, we report the empirical results based on our second approach to

categorize internal control deficiencies: Staffing, Complexity, and General. Column C reports

that both Staffing and General deficiencies are related to higher SRESID, again consistent with the univariate analysis in Table 6. Interestingly, we find that Staffing deficiencies no longer predict lower earnings quality after the inclusion of the control variables. A closer examination

(not tabulated) reveals that Staffing loses significance after the inclusion of TOTAL ASSETS.

This is not surprising given the strong relation between firm size and the probability of material weaknesses as presented in Table 5. However, this finding yields a very interesting implication.

Dechow and Dichev (2002) document a negative relation between firm size and earnings quality.

Our finding sheds light on the reason for this negative relation by suggesting that smaller firms are more likely to have poor internal control and thus have lower earnings quality.

Overall, our empirical findings support our hypotheses. We find that material weakness firms have lower earnings quality, based upon the measure introduced in Dechow and Dichev

(2002), especially for those firms with less auditable (company-level) control deficiencies. This finding holds even after controlling for managerial opportunism, measured by discretionary accruals, and other firm characteristics that are associated with the inherent difficulty of estimating accruals, such as firm size and the length of the operating cycle.

26

5. Summary and conclusions

The recent passage of the Sarbanes-Oxley Act in 2002 marks the first time that material weaknesses in internal control over financial reporting must be publicly disclosed for all public

firms. Past research on internal control has been limited to deficiency disclosures from firms that

changed their auditors (this was the only prior public disclosure necessary), which created a very

limited sample with self-selection issues (Kinney, 1990; Krishnan, 2005). Using a more

comprehensive sample of mandatory material weakness disclosures made pursuant to Section

302 of Sarbanes-Oxley from August 2002 to November 2004, we examine both the determinants

of material weaknesses and their effect on earnings quality. In our determinants tests, we show that material weaknesses in internal control are more likely for firms that are smaller, less profitable, more complex, growing rapidly, or undergoing restructuring. These findings are

consistent with firms struggling with their financial reporting controls in the face of a lack of resources, complex accounting issues, and a rapidly changing business environment.

Next, we examine the impact of poor internal control on the earnings quality of the firm.

Using a definition of earnings quality that measures the extent to which accruals map into cash flows (Dechow and Dichev, 2002), we find that firms who disclose material weaknesses in internal control are associated with lower earnings quality. This relation is robust to the

inclusion of discretionary accruals as a proxy for managerial opportunism, as well as proxies for inherent difficulty in accrual estimation (Dechow and Dichev, 2002). Thus, internal control appears to be one of the fundamental drivers of earnings quality. Moreover, this relation holds after controlling for the underlying determinants of material weaknesses, such as firm size, profitability, firm complexity, and restructuring, which may also be correlated with earnings quality. We also find that earnings quality is especially poor for firms with material weaknesses

27 that relate to overall company-level controls, which may be more difficult to “audit around.”

These findings suggest that users of financial statements of firms with poor internal control should be wary of the earnings quality of these firms.

A potential limitation of this study is the short time frame over which we gather our results. It is hard to determine how the material weakness disclosures of the first few years of the new Sarbanes-Oxley regime will compare to future periods when both management and auditors are more familiar with the process of implementing, evaluating, and reporting on internal control. The firms with material weakness disclosures could simply be slow adopters of

Sarbanes-Oxley and not necessarily have deficient internal controls relative to non-material weakness firms. In addition, although we attempt to be comprehensive in collecting material weakness disclosures from August 2002 to November 2004, it is still possible that some firms did not properly disclose their material weaknesses, thus causing us to under-identify our true sample. Depending on the systematic characteristics of such firms, this could have an effect on our results. Finally, as pointed out by Dechow and Dichev (2002), our measure of earnings quality merely captures the extent to which current accruals map into cash flows, but it does not capture the proper timing of these cash flows within the time periods of the model.

In sum, the use of internal control disclosures under Sarbanes-Oxley opens up many new research areas related to internal control. Since internal control is such a fundamental aspect of the quality financial statements, these new research opportunities are likely to be of interest to a broad audience of academia, regulators, and practitioners.

28 References

Bushman, R., Chen, Q., Engle, E., Smith, A., 2004. Financial accounting information, organizational complexity and corporate governance systems. Journal of Accounting and Economics 37, 167-201. Das, S., Zhang H., 2003. Rounding-up in reported EPS, behavioral thresholds, and earnings management. Journal of Accounting and Economics 35, 31-50. Dechow, P., Dichev, I., 2002. The quality of accruals and earnings. The Accounting Review 77, 35-59. Dechow, P., Ge, W., 2005. The persistence of earnings and cash flows and the role of special items: implications for the accrual anomaly. Working Paper, University of Michigan. Dechow, P., Schrand, C., 2004. Earnings quality. The Research Foundation of CFA Institute. DeFond, M., Jiambalvo, J., 1991. Incidence and circumstances of accounting errors. The Accounting Review 66, 643-655. DeFond, M., Raghunandan, K., Subramanyam, K.R., 2002. Do non-audit service fees impair auditor independence? Evidence from going concern audit opinions. Doss, M., Jonas, G., 2004. Section 404 reports on internal control: Impact on ratings will depend on nature of material weaknesses reported. Moody’s Investors Service, Global Credit Research. October. Frankel, R., Johnson, M., Nelson, K., 2002. The relation between auditors' fees for non-audit services and earnings management. The Accounting Review 77, 71-103. Ge, W., McVay, S., 2005. On the disclosure of material weaknesses in internal control after the Sarbanes-Oxley act. Working Paper, University of Michigan and New York University. Geiger, M., Taylor, P., 2003. CEO and CFO certifications of financial information. Accounting Horizons 17, 357- 368. Glass Lewis and Company Yellow Card Internal Control Compliance Report. September 23, 2004. Kennedy, P., 1998. A guide to econometrics. Cambridge, MA: MIT Press. Kinney, W., 2000. Research opportunities in internal control quality and quality assurance. Auditing 19, 83-90. Kinney, W., 2001. Accounting scholarship: what is uniquely ours? The Accounting Review 76, 275-284. Kinney, W., Maher, M., Wright, D., 1990. Assertions-based standards for integrated internal control. Accounting Horizons 4, 1-8. Kinney, W., McDaniel, L., 1989. Characteristics of firms correcting previously reported quarterly earnings. Journal of Accounting and Economics 11, 71-93. Kothari, S. P., Leone, A., Wasley, C., 2005. Performance matched discretionary accrual measures. Journal of Accounting and Economics 39, 163-197. Krishnan, J., 2005. Audit committee quality and internal control: an empirical analysis. The Accounting Review, forthcoming. Public Company Accounting Oversight Board (PCAOB). 2004. Auditing Standard No. 2 – An audit of internal control over financial reporting performed in conjunction with an audit of financial statements. Rangan, S., 1998. Earnings management and the performance of seasoned equity offerings. Journal of Financial Economics 50, 101-122. Richardson, S., Sloan, R., Soliman, M., Tuna, I., 2005. Accrual reliability, earnings persistence and stock prices. Journal of Accounting and Economics, forthcoming. Roychowdhury, S, 2004. Management of earnings through the manipulation of real activities that affect cash flow from operations. Journal of Accounting and Economics. forthcoming. Schipper, K., Vincent, L., 2003. Earnings quality. Accounting Horizons Supplement, 97-110. SEC, 1988. Disclosure Amendments to Regulation S-K, form 8-K and Schedule 14A Regarding Changes in Accountants and Potential Opinion Shopping Situations, Financial Reporting Release No. 31 (April), SEC Docket 1140-1147. Washington, D.C.: SEC. Stice, J., 1991. Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. The Accounting Review 66, 516-533. Teoh, S., Welch, I., Wong, T., 1998a. Earnings management and the long-run market performance of initial public offerings. Journal of Finance 53, 1935-74. Teoh, S., Welch, I., Wong, T., 1998b. Earnings management and the underperformance of seasoned equity offerings. Journal of Financial Economics 50, 63-99. Zhang, Y., 2005. Revenue recognition timing and attributes of reported revenue: the case of software industry’s adoption of SOP 91-1. Journal of Accounting and Economics, forthcoming.

29 Appendix Material Weakness Classification and Examples

First Classification Scheme

Type A: account specific or transaction level material weaknesses 1. Inadequate internal controls for accounting for loss contingencies, including bad debts 2. Deficiencies in the documentation of a receivables securitization program 3. No adequate internal controls over the application of new accounting principles or the application of existing accounting principles to new transactions

Type B: company-level material weakness 1. Override by senior management 2. Ineffective control environment

Second Classification Scheme

Staffing: 1. Inadequate qualified staffing and resources leading to the untimely identification and resolution of certain accounting and disclosure matters and failure to perform timely and effective reviews 3. The need to increase the training of the financial staff 4. Weak internal controls and procedures relating to separation of duties

Complexity: 1. Inconsistencies in the application of company policies among business units and segments 2. Material weaknesses in the interpretation and application of complex accounting standards, such as standards related to hedge transactions

General: 1. Weak internal controls related to contracting practices 2. Deficiencies related to the design of policies and execution of processes related to accounting for transactions 3. Deficiencies in the period-end reporting process

30

Figure 1 Determinants of Internal Control over Financial Reporting and the Implications for Earnings Quality

SIZE

COMPLEXITY INTERNAL CONTROL OVER EARNINGS FINANCIAL QUALITY

PROFITABILITY REPORTING o t

d a e

l

s e s s e

RAPID GROWTH n k a e W

POOR EARNINGS QUALITY RESTRUCTURING RESTATEMENTS DELAYED FILINGS NEGATIVE STOCK REACTIONS

This figure illustrates the link between determinants of internal control, internal control over financial reporting, and earnings quality.

31 Table 1 Descriptive Statistics of Material Weakness Firms versus 2003 Compustat Firms

Material Weakness Firms 2003 Compustat Firms (Excluding Material Weakness Firms)

Predicted Wilcoxon Test Statistic Variable Mean Median Mean Median Difference (two-tailed p-value)

MARKETCAP (in millions) 1,089.00 123.79 < 2,559.00 235.82 0.0001 BOOKVALUE (in millions) 500.12 43.62 < 972.14 97.24 0.0001

RETURN ON ASSETS -0.27 -0.02 < -0.16 0.01 0.0001 CORE RETURN ON -0.21 0.00 -0.11 0.03 0.0001 ASSETS < LOSS INDICATOR 0.49 0.00 > 0.30 0.00 0.0001

OPERATING SEGMENTS 1.38 1.00 > 1.22 1.00 0.0182 GEOGRAPHIC 2.60 2.00 2.11 1.00 0.0001 SEGMENTS > FOREIGN CURRENCY 0.23 0.00 > 0.16 0.00 0.0064

ACQUISITION VALUE 0.04 0.00 > 0.02 0.00 0.7147 SALES GROWTH 0.16 0.05 > 0.15 0.07 0.0498

SPECIAL ITEMS -0.022 0.00 < -0.008 0.00 0.0001

There are a maximum of 219 material weakness firm observations and 5,576 control firm observations. MARKETCAP is the firm’s Market Capitalization (Compustat #25 * #199). BOOKVALUE is the firm’s Book Value of Equity (Compustat #60). RETURN ON ASSETS is Earnings before Extraordinary Items (Compustat #18) scaled by Average Total Assets (Compustat #6), CORE RETURN ON ASSETS is Earnings after Depreciation (Compustat #178) scaled by Average Total Assets (Compustat #6). LOSS INDICATOR is an indicator variable that is equal to one if Earnings after depreciation (Compustat #178) is less than or equal to zero, and zero otherwise. OPERATING SEGMENTS ranges from one to 11 and is equal to the number of operating segments reported in the firm’s 10-K in fiscal 2003. GEOGRAPHIC SEGMENTS ranges from one to 18 and is equal to the number of geographic segments reported in the firm’s 10-K in fiscal 2003. FOREIGN CURRENCY is an indicator variable that is equal to one if the firm reported a non-zero foreign currency adjustment (Compustat #150) in fiscal 2003, and zero otherwise. ACQUISITION VALUE is the dollar value of the firm’s aggregate acquisitions during 2002 and 2003, scaled by the firm’s Market Capitalization. SALES GROWTH is equal to (Sales – Prior Year Sales) / Prior Year Sales (Compustat #12) and SPECIAL ITEMS is equal to Special Items (Compustat #17, where an expenses is a negative number) scaled by Prior Year Total Assets (Compustat #6). Each of the continuous independent variables is winsorized at 1% and 99% to mitigate outliers.

32 Table 2 Spearman\Pearson Correlation Table

Log of Log of Core Loss Log of Log of Foreign Acq. Sales Special MW ROA MV BV ROA Indicator OpSegs GeogSegs Currency Value Growth Items

1.000 -0.058 -0.039 -0.028 -0.026 0.082 0.033 0.060 0.036 0.031 0.006 -0.036 MW (0.0001) (0.0047) (0.0328) (0.0513) (0.0001) (0.0121) (0.0001) (0.0058) (0.0187) (0.6708) (0.0055)

Log of Market- -0.063 1.000 0.923 0.316 0.339 -0.441 0.200 0.267 0.208 0.104 0.053 -0.019 cap (MV) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.1587)

Log of Book- -0.045 0.934 1.000 0.330 0.373 -0.421 0.218 0.244 0.199 0.115 0.010 -0.014 value (BV) (0.0012) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.4614) (0.3096)

Return on -0.081 0.421 0.331 1.000 0.965 -0.404 0.054 0.090 0.067 0.026 -0.044 0.157 Assets (ROA) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0503) (0.0011) (0.0001)

Core Return on -0.068 0.477 0.394 0.888 1.000 -0.433 0.059 0.099 0.071 0.030 -0.038 0.025 Assets (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0225) (0.0042) (0.0583)

0.082 -0.435 -0.415 -0.705 -0.797 1.000 -0.089 0.002 -0.056 -0.055 -0.009 -0.062 Loss Indicator (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.8697) (0.0001) (0.0001) (0.4855) (0.0001)

Log of Opertg. 0.034 0.188 0.204 0.080 0.101 -0.087 1.000 0.043 0.129 0.029 -0.002 0.013 Segments (0.0097) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0010) (0.0001) (0.0257) (0.8942) (0.3113)

Log of Geog. 0.058 0.257 0.238 0.071 0.101 0.011 0.042 1.000 0.402 0.037 -0.026 -0.055 Segments (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.4222) (0.0013) (0.0001) (0.0049) (0.0524) (0.0001)

Foreign 0.036 0.206 0.200 0.090 0.101 -0.056 0.121 0.394 1.000 0.017 0.016 -0.015 Currency Ind. (0.0058) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.1876) (0.2291) (0.2436)

Acquisition -0.001 0.288 0.271 0.118 0.138 -0.124 0.061 0.117 0.072 1.000 0.105 -0.036 Value (0.9573) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0064)

-0.022 0.234 0.147 0.292 0.318 -0.193 0.039 0.055 0.085 0.184 1.000 0.007 Sales Growth (0.0949) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0033) (0.0001) (0.0001) (0.0001) (0.5813)

-0.063 -0.091 -0.104 0.235 0.035 -0.068 -0.016 -0.175 -0.089 -0.071 0.030 1.000 Special Items (0.0001) (0.0001) (0.0001) (0.0001) (0.0072) (0.0001) (0.2171) (0.0001) (0.0001) (0.0001) (0.0248)

MW is an indicator variable that is equal to one if the firm disclosed a material weakness in internal control from August 2002 to November 2004, and zero otherwise. MARKETCAP is the firm’s Market Capitalization (Compustat #25 * #199). BOOKVALUE is the firm’s Book Value of Equity (Compustat #60). RETURN ON ASSETS is Earnings before Extraordinary Items (Compustat #18) scaled by Average Total Assets (Compustat #6), CORE RETURN ON ASSETS is Earnings after depreciation (Compustat #178) scaled by Average Total Assets (Compustat #6). LOSS INDICATOR is an indicator variable that is equal to one if Earnings after depreciation (Compustat #178) is less than or equal to zero, and zero otherwise. OPERATING SEGMENTS ranges from one to 11 and is equal to the number of operating segments reported in the firm’s 10-K in fiscal 2003. GEOGRAPHIC SEGMENTS ranges from one to 18 and is equal to the number of geographic segments reported in the firm’s 10-K in fiscal 2003. FOREIGN CURRENCY is an indicator variable that is equal to one if the firm reported a non- zero foreign currency adjustment (Compustat #150) in fiscal 2003, and zero otherwise. ACQUISITION VALUE is the dollar value of the firm’s aggregate acquisitions during 2002 and 2003, scaled by the firm’s Market Capitalization. SALES GROWTH is equal to (Sales – Prior Year Sales) / Prior Year Sales (Compustat #12) and SPECIAL ITEMS is equal to Special Items (Compustat #17, where an expenses is a negative number) scaled by Prior Year Total Assets (Compustat #6). Each of the continuous independent variables is winsorized at 1% and 99% to mitigate outliers.

33 Table 3 Logistic Regression of the Probability of Disclosing a Material Weakness

Dependent Variable = MW Predicted Logit Estimate Logit Estimate Logit Estimate Independent Variables Sign (Pr > 2) (Pr > 2) (Pr > 2)

INTERCEPT -3.32 -3.34 -3.65 (0.0001) (0.0001) (0.0001) -0.198 -0.194 -0.148 LOG (MARKETCAP) - (0.0001) (0.0001) (0.0001) 0.044 RETURN ON ASSETS - (0.673) 0.003 CORE RETURN ON ASSETS - (0.978) 0.582 LOSS INDICATOR + (0.001) LOG (OPERATING 0.506 0.504 0.512 + SEGMENTS) (0.002) (0.002) (0.001) LOG (GEOGRAPHIC 0.432 0.436 0.424 + SEGMENTS) (0.001) (0.001) (0.001) 0.228 0.231 0.263 FOREIGN CURRENCY + (0.224) (0.219) (0.163) 1.583 1.595 1.65 ACQUISITION VALUE + (0.016) (0.015) (0.013) 0.070 0.067 0.065 SALES GROWTH + (0.529) (0.545) (0.533) -2.073 -1.955 -1.62 SPECIAL ITEMS - (0.015) (0.015) (0.035)

Industry Indicator Variables Included Included Included

Number of Material Weakness 215 215 215 Observations

Number of Total Observations 5,594 5,592 5,592

111.39 111.21 123.16 Likelihood Ratio (0.0001) (0.0001) (0.0001)

MW is an indicator variable that is equal to one if the firm disclosed a material weakness in internal control from August 2002 to November 2004, and zero otherwise. MARKETCAP is the firm’s Market Capitalization (Compustat #25 * #199). RETURN ON ASSETS is Earnings before Extraordinary Items (Compustat #18) scaled by Average Total Assets (Compustat #6), CORE RETURN ON ASSETS is Earnings after depreciation (Compustat #178) scaled by Average Total Assets (Compustat #6). LOSS is an indicator variable that is equal to one if Earnings after depreciation (Compustat #178) is less than or equal to zero, and zero otherwise. OPERATING SEGMENTS ranges from one to 11 and is equal to the number of operating segments reported in the firm’s 10-K in fiscal 2003. GEOGRAPHIC SEGMENTS ranges from one to 18 and is equal to the number of geographic segments reported in the firm’s 10-K in fiscal 2003. FOREIGN CURRENCY is an indicator variable that is equal to one if the firm reported a non-zero foreign currency adjustment (Compustat #150) in fiscal 2003, and zero otherwise. ACQUISITION VALUE is the dollar value of the firm’s aggregate acquisitions during 2002 and 2003, scaled by the firm’s Market Capitalization. SALES GROWTH is equal to (Sales – Prior Year Sales) / Prior Year Sales (Compustat #12) and SPECIAL ITEMS is equal to Special Items (Compustat #17, where an expenses is a negative number) scaled by Prior Year Total Assets (Compustat #6). Each of the continuous independent variables is winsorized at 1% and 99% to mitigate outliers.

34 Table 4 Logistic Regression of the Probability of Disclosing a Material Weakness by Deficiency Type using Moody’s Descriptors

Dependent Variable = Dependent Variable = MW_TYPE_A MW_TYPE_B Logit Estimate Logit Estimate Independent Variables (Pr > 2) (Pr > 2)

-4.28 -4.80 INTERCEPT (0.0001) (0.0001) -0.125 -0.141 LOG (MARKETCAP) (0.011) (0.004)

LOSS INDICATOR 0.572 0.624 (0.016) (0.009) LOG (OPERATING 0.229 0.680 SEGMENTS) (0.361) (0.001) LOG (GEOGRAPHIC 0.219 0.597 SEGMENTS) (0.226) (0.001) -0.009 FOREIGN CURRENCY 0.516 (0.044) (0.973) 1.166 ACQUISITION VALUE 2.026 (0.240) (0.020) -0.101 SALES GROWTH 0.218 (0.540) (0.099) -0.065 SPECIAL ITEMS -2.96 (0.004) (0.953)

Industry Indicator Variables Included Included

Number of Material Weakness 107 103 Observations

Number of Total Observations 5,484 5,480

72.98 78.86 Likelihood Ratio (0.0001) (0.0001)

MW_TYPE is an indicator variable that is equal to one if the firm disclosed a material weakness (of either Type A or Type B) in internal control from August 2002 to November 2004, and zero otherwise. Following Moody’s, we define Type A material weaknesses as those that are at the transaction level, and Type B material weaknesses as those that are more company-level control problems. MARKETCAP is the firm’s Market Capitalization (Compustat #25 * #199). LOSS INDICATOR is an indicator variable that is equal to one if Earnings after depreciation (Compustat #178) is less than or equal to zero, and zero otherwise. OPERATING SEGMENTS ranges from one to 11 and is equal to the number of operating segments reported in the firm’s 10-K in fiscal 2003. GEOGRAPHIC SEGMENTS ranges from one to 18 and is equal to the number of geographic segments reported in the firm’s 10-K in fiscal 2003. FOREIGN CURRENCY is an indicator variable that is equal to one if the firm reported a non-zero foreign currency adjustment (Compustat #150) in fiscal 2003, and zero otherwise. ACQUISITION VALUE is the dollar value of the firm’s aggregate acquisitions during 2002 and 2003, scaled by the firm’s Market Capitalization. SALES GROWTH is equal to (Sales – Prior Year Sales) / Prior Year Sales (Compustat #12) and SPECIAL ITEMS is equal to Special Items (Compustat #17, where an expenses is a negative number) scaled by Prior Year Total Assets (Compustat #6). Each of the continuous independent variables is winsorized at 1% and 99% to mitigate outliers.

35 Table 5 Logistic Regression of the Probability of Disclosing a Material Weakness by Deficiency Types

Dependent Variable = Dependent Variable = Dependent Variable = MW_STAFFING MW_COMPLEXITY MW_GENERAL Logit Estimate Logit Estimate Logit Estimate Independent Variables (Pr > 2) (Pr > 2) (Pr > 2)

INTERCEPT -3.90 -6.28 -5.45 (0.0001) (0.0001) (0.0001) -0.029 -0.075 LOG (MARKETCAP) -0.217 (0.0001) (0.697) (0.148)

LOSS INDICATOR 0.561 0.997 0.454 (0.019) (0.006) (0.062) LOG (OPERATING 0.455 0.269 0.431 SEGMENTS) (0.090) (0.364) (0.059) LOG (GEOGRAPHIC 0.388 0.383 0.540 SEGMENTS) (0.035) (0.141) (0.003) -0.451 FOREIGN CURRENCY 1.130 0.562 (0.180) (0.002) (0.023) 1.879 0.721 ACQUISITION VALUE 2.00 (0.029) (0.144) (0.493) 0.191 0.034 SALES GROWTH 0.237 (0.120) (0.895) (0.083) -1.28 -0.499 SPECIAL ITEMS -2.388 (0.206) (0.782) (0.042)

Industry Indicator Included Included Included Variables

Number of Material 103 47 99 Weakness Observations Number of Total 5,480 5,424 5,476 Observations

71.33 56.16 94.08 Likelihood Ratio (0.0001) (0.002) (0.0001)

MW is an indicator variable that is equal to one if the firm disclosed a material weakness (coded as STAFFING, COMPLEXITY or GENERAL) in internal control from August 2002 to November 2004, and zero otherwise. MARKETCAP is the firm’s Market Capitalization (Compustat #25 * #199). LOSS INDICATOR is an indicator variable that is equal to one if Earnings after depreciation (Compustat #178) is less than or equal to zero, and zero otherwise. OPERATING SEGMENTS ranges from one to 11 and is equal to the number of operating segments reported in the firm’s 10-K in fiscal 2003. GEOGRAPHIC SEGMENTS ranges from one to 18 and is equal to the number of geographic segments reported in the firm’s 10-K in fiscal 2003. FOREIGN CURRENCY is an indicator variable that is equal to one if the firm reported a non-zero foreign currency adjustment (Compustat #150) in fiscal 2003, and zero otherwise. ACQUISITION VALUE is the dollar value of the firm’s aggregate acquisitions during 2002 and 2003, scaled by the firm’s Market Capitalization. SALES GROWTH is equal to (Sales – Prior Year Sales) / Prior Year Sales (Compustat #12) and SPECIAL ITEMS is equal to Special Items (Compustat #17, where an expenses is a negative number) scaled by Prior Year Total Assets (Compustat #6). Each of the continuous independent variables is winsorized at 1% and 99% to mitigate outliers.

36 Table 6 Earnings Quality and Internal Control Deficiencies: Descriptive Analysis

Earnings Quality (SRESID) 2003 Compustat Firms Material Weakness Firms (Excluding Material Weakness Firms) Wilcoxon Test Classification Variable Mean Median Mean Median Statistic (two-tailed p-value)

MATERIAL WEAKNESS 0.031 0.022 0.027 0.017 <0.0001

MW_TYPE_ A 0.027 0.022 0.027 0.017 0.0415

MW_TYPE_ B 0.036 0.025 0.027 0.017 0.0005

MW_STAFFING 0.034 0.022 0.027 0.017 <0.0001

MW_COMPLEXITY 0.025 0.020 0.027 0.017 0.232

MW_GENERAL 0.030 0.024 0.027 0.017 0.001

There are 169 material weakness firm observations with available data to calculate SRESID and 3,612 control firm observations with SRESID data. We also provide two schemes of classification of material weaknesses. In the first scheme, we classify a material weakness firm into STAFFING, COMPLEXITY or GENERAL). In the second classification scheme, following Moody’s, we define Type A material weaknesses as those that are at the transaction level, and Type B material weaknesses as those that are more company-level control problems. Earnings quality is measured using the standard deviation of the residuals (SRESID) based on the residuals from the following firm-specific regressions: WCt = β0 + β1 CFO t-1 + β2 CFOt + β3 CFOt+1 +
q qQTRq + t. A higher standard deviation denotes lower earnings quality. We estimate the above firm-specific regression using Compustat Quarterly. We require firms to have at least 12 quarters, but no more than 20 quarters (fiscal 1999 to 2003). We define working capital accruals as WC = (CA - CASH) - (CL - STD), where CA is the change in current assets (Compustat Quarterly Item #40), CASH is the change in cash and short-term investments (Compustat Quarterly Item #36), CL is the change in current liabilities (Compustat Quarterly Item #49), and STD is the change in the current portion of long-term debt (Compustat Quarterly Item #45). CFO is cash from operating activities (Compustat Quarterly Item #108). All variables are scaled by average total assets (Compustat Quarterly Item # 44). QTRqs are indicator variables for quarters 1, 2, and 3.

37 Table 7 Earnings Quality and Internal Control Deficiencies: Regression Analysis

Dependent Variable = Earnings Quality (SRESID)

Predicted Coefficient Coefficient Coefficient Coefficient Independent Variables Sign Estimate Estimate Estimate Estimate (P-Value) (P-Value) (P-Value) (P-Value) (A) (B) (C) (D)

INTERCEPT 5.01 6.52 4.72 6.55 (0.0001) (0.0001) (0.0001) (0.0001)

0.712 0.454 0.417 0.375 MW + (0.001) (0.016) (0.029) (0.050)

-0.441 -0.379 -0.433 TOTAL ASSETS - (0.0001) (0.0001) (0.0001)

OPERATING CYCLE + 0.131 0.120 (0.0001) (0.0001)

0.178 ABS(DACC) + (0.0001)

LOSS INDICATOR + 0.892

(0.0001)

OPERATING -0.008 + SEGMENTS (0.818)

GEOGRAPHIC + 0.041 SEGMENTS (0.006)

ACQUISITION VALUE + 0.048

(0.045)

SPECIAL ITEMS - -0.057

(0.0001)

Industry Indicator Included Included Included Included Variables

Number of Material 169 169 159 163 Weakness Observations

Number of Total 3,781 3,718 3,409 3,631 Observations

Adjusted R2 10.28% 30.20% 31.62% 31.53%

MW is an indicator variable that is equal to one if the firm disclosed a material weakness in internal control in our sample period (August 2002 to November 2004), and zero otherwise. There are 169 firm observations with available data to calculate SRESID and 3612 control firm observations with SRESID data. Earnings quality is measured using the standard deviation of the residuals (SRESID) based on the residuals from the following firm- specific regressions: WCt = β0 + β1 CFO t-1 + β2 CFOt + β3 CFOt+1 +
q qQTRq + t. A higher standard deviation denotes lower earnings quality. We estimate the above firm-specific regression using Compustat Quarterly. We require firms to have at least 12 quarters, but no more than 20 quarters (fiscal 1999 to 2003).

38 Table 7, Continued

We define working capital accruals as WC = (CA - CASH) - (CL - STD), where CA is the change in current assets (Compustat Quarterly Item #40), CASH is the change in cash and short-term investments (Compustat Quarterly Item #36), CL is the change in current liabilities (Compustat Quarterly Item #49), and STD is the change in the current portion of long-term debt (Compustat Quarterly Item #45). CFO is cash from operating activities (Compustat Quarterly Item #108). All the above variables are scaled by average total assets (Compustat Quarterly Item # 44). QTRqs are indicator variables for quarters 1, 2, and 3. TOTAL ASSETS is from Compustat Annual Item #6. OPERATING CYCLE is calculated as the average of [(Sales (Item #12) /365)/(Average Accounts Receivable (Item #2) +(Cost of Goods Sold (Item #41/365)/(Average Inventory (Item #3)] from fiscal 1999 to 2003. DACC is calculated using performance-adjusted discretionary accruals based on modified Jones model and Kothari et al. (2005). ABS(DACC) is the average of the absolute value of DACC from fiscal 1999 to 2003. LOSS is an indicator variable that is equal to one if Earnings after depreciation (Compustat #178) is less than or equal to zero, and zero otherwise. OPERATING SEGMENTS ranges from one to 11 and is equal to the number of operating segments reported in the firm’s 10-K in fiscal 2003. GEOGRAPHIC SEGMENTS ranges from one to 18 and is equal to the number of geographic segments reported in the firm’s 10-K in fiscal 2003. ACQUISITION VALUE is the dollar value of the firm’s aggregate acquisitions during 2002 and 2003, scaled by the firm’s Market Capitalization. SPECIAL ITEMS is equal to Special Items (Compustat #17, where an expenses is a negative number) scaled by Prior Year Total Assets (Compustat #6). The regressions are estimated using the decile rank of each of the variables.

39 TABLE 8 Earnings Quality and Internal Control Deficiencies Types: Regression Analysis

Dependent Variable = Earnings Quality (SRESID) Coefficient Coefficient Coefficient Coefficient Independent Variables Estimate Estimate Estimate Estimate (P-value) (P-value) (P-value) (P-value)

(A) (B) (C) (D)

INTERCEPT 5.01 4.72 5.01 4.74 (0.0001) (0.0001) (0.0001) (0.0001)

0.503 0.384 MW_TYPE_A (0.085) (0.136)

MW_TYPE_B 1.034 0.508 (0.001) (0.074)

-0.074 MW_STAFFING 0.669 (0.032) (0.794) 0.415 0.676 MW_COMPLEXITY (0.389) (0.119)

MW_GENERAL 0.620 0.605 (0.052) (0.029)

TOTAL ASSETS -0.378 -0.381 (0.0001) (0.0001)

OPERATING CYCLE 0.120 0.120 (0.0001) (0.0001)

ABS(DACC) 0.178 0.178 (0.0001) (0.0001)

Industry Indicator Variables Included Included Included Included

Number of Material 169 159 169 159 Weakness Observations

Number of Total Obs 3,781 3,409 3,781 3,409

Adjusted R2 10.31% 31.61% 10.24% 31.66%

MW is an indicator variable that is equal to one if the firm disclosed a material weakness in internal control in our sample period (August 2002 to November 2004), and zero otherwise. There are 169 firm observations with available data to calculate SRESID and 3612 control firm observations with SRESID data. We also provide two schemes of classification of material weaknesses. In the first scheme, we classify a material weakness firm into STAFFING, COMPLEXITY or GENERAL). In the second classification scheme, following Moody’s, we define Type A material weaknesses as those that are at the transaction level, and Type B material weaknesses as those that are more company-level control problems. Earnings quality is measured using the standard deviation of the residuals (SRESID) based on the residuals from the following firm-specific regressions: WCt = β0 + β1 CFO t-1 + β2 CFOt + β3 CFOt+1 +
q qQTRq + t. A higher standard deviation denotes lower earnings quality. We estimate the above firm-specific regression using Compustat Quarterly. We require firms to have at least 12 quarters, but no more than 20 quarters (fiscal 1999 to 2003). We define working capital accruals as WC = (CA - CASH) - (CL - STD), where CA is the change in current assets (Compustat Quarterly Item #40), CASH is the change in cash and short-term investments (Compustat Quarterly Item #36), CL is the change in current liabilities (Compustat Quarterly Item #49), and STD is the change in the current portion of long-term debt (Compustat Quarterly Item #45). CFO is cash from operating activities (Compustat Quarterly Item #108). All the above variables are scaled by average total assets (Compustat Quarterly Item # 44). QTRqs are indicator variables for quarters 1, 2, and 3. TOTAL ASSETS is from Compustat Annual Item #6. OPERATING CYCLE is calculated as the average of [(Sales (Item #12) /365)/(Average Accounts Receivable (Item #2) +(Cost of Goods Sold (Item #41/365)/(Average Inventory (Item #3)] from fiscal 1999 to 2003. DACC is calculated using performance-adjusted discretionary accruals based on modified Jones model and Kothari et al. (2005). ABS(DACC) is the average of the absolute value of DACC from fiscal 1999 to 2003. The regressions are estimated using the decile rank of each of the variables.

40