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 jef.doyle@utah.edu

Weili Ge

Ross School of Business University of Michigan

701 Tappan Street Ann Arbor, MI 48109

geweili@bus.umich.edu

Sarah McVay Stern School of Business

New York University 44 West Fourth Street, Suite 10-94

New York, NY 10012 smcvay@stern.nyu.edu

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

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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.

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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).

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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.

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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.

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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.

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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.

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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).

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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

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“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.

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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.

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#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.

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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.

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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

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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.

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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

+ β4 GROWTH + β5 RESTRUCTURINGS + �Kk 1= 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

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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.

17

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

18

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).

19

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.

20

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

21

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.

22

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.

23

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.

24

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

25

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.

26

“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.

27

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

28

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.

29

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30

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

31

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

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

COMPLEXITY

PROFITABILITY

RESTRUCTURING

INTERNAL CONTROL OVER

FINANCIAL REPORTING

SIZE

EARNINGS QUALITY

POOR EARNINGS QUALITY RESTATEMENTS

DELAYED FILINGS NEGATIVE STOCK REACTIONS

Wea

knes

ses

lead

to

RAPID GROWTH

32

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

Material Weakness Firms 2003 Compustat Firms

(Excluding Material Weakness Firms)

Variable Mean Median Predicted Difference Mean Median Wilcoxon Test Statistic

(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 ASSETS -0.21 0.00 < -0.11 0.03 0.0001

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 SEGMENTS 2.60 2.00 > 2.11 1.00 0.0001

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.

33

Table 2 Spearman\Pearson Correlation Table

MW Log of MV

Log of BV ROA Core

ROA Loss

Indicator Log of OpSegs

Log of GeogSegs

Foreign Currency

Acq. Value

Sales Growth

Special 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)

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

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

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

-0.068 0.477 0.394 0.888 1.000 -0.433 0.059 0.099 0.071 0.030 -0.038 0.025 Core Return on 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)

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

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

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

-0.001 0.288 0.271 0.118 0.138 -0.124 0.061 0.117 0.072 1.000 0.105 -0.036 Acquisition 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.

34

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

Dependent Variable = MW Independent Variables Predicted

Sign Logit Estimate

(Pr > �2) Logit Estimate

(Pr > �2) Logit Estimate

(Pr > �2)

INTERCEPT -3.32 (0.0001)

-3.34 (0.0001)

-3.65 (0.0001)

LOG (MARKETCAP) - -0.198 (0.0001)

-0.194 (0.0001)

-0.148 (0.0001)

RETURN ON ASSETS - 0.044 (0.673)

CORE RETURN ON ASSETS - 0.003 (0.978)

LOSS INDICATOR + 0.582 (0.001)

LOG (OPERATING SEGMENTS) + 0.506

(0.002) 0.504

(0.002) 0.512

(0.001) LOG (GEOGRAPHIC

SEGMENTS) + 0.432 (0.001)

0.436 (0.001)

0.424 (0.001)

FOREIGN CURRENCY + 0.228 (0.224)

0.231 (0.219)

0.263 (0.163)

ACQUISITION VALUE + 1.583 (0.016)

1.595 (0.015)

1.65 (0.013)

SALES GROWTH + 0.070 (0.529)

0.067 (0.545)

0.065 (0.533)

SPECIAL ITEMS - -2.073 (0.015)

-1.955 (0.015)

-1.62 (0.035)

Industry Indicator Variables Included Included Included Number of Material Weakness

Observations

215 215 215

Number of Total Observations

5,594 5,592 5,592

Likelihood Ratio 111.39 (0.0001)

111.21 (0.0001)

123.16 (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.

35

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

Dependent Variable = MW_TYPE_A

Dependent Variable = MW_TYPE_B

Independent Variables Logit Estimate (Pr > �2) Logit Estimate

(Pr > �2)

INTERCEPT -4.28 (0.0001) -4.80

(0.0001)

LOG (MARKETCAP) -0.125 (0.011) -0.141

(0.004)

LOSS INDICATOR 0.572

(0.016) 0.624 (0.009)

LOG (OPERATING SEGMENTS)

0.229 (0.361) 0.680

(0.001) LOG (GEOGRAPHIC

SEGMENTS) 0.219

(0.226) 0.597 (0.001)

FOREIGN CURRENCY 0.516 (0.044) -0.009

(0.973) ACQUISITION VALUE 1.166

(0.240) 2.026 (0.020)

SALES GROWTH -0.101 (0.540) 0.218

(0.099) SPECIAL ITEMS -2.96

(0.004) -0.065 (0.953)

Industry Indicator Variables Included Included Number of Material Weakness

Observations 107 103

Number of Total Observations 5,484 5,480

Likelihood Ratio 72.98 (0.0001) 78.86

(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.

36

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

Dependent Variable = MW_STAFFING

Dependent Variable = MW_COMPLEXITY

Dependent Variable = MW_GENERAL

Independent Variables Logit Estimate (Pr > �2)

Logit Estimate (Pr > �2)

Logit Estimate (Pr > �2)

INTERCEPT -3.90 (0.0001)

-6.28 (0.0001)

-5.45 (0.0001)

LOG (MARKETCAP) -0.217 (0.0001)

-0.029 (0.697)

-0.075 (0.148)

LOSS INDICATOR 0.561

(0.019) 0.997

(0.006) 0.454 (0.062)

LOG (OPERATING SEGMENTS)

0.455 (0.090)

0.269 (0.364)

0.431 (0.059)

LOG (GEOGRAPHIC SEGMENTS)

0.388 (0.035)

0.383 (0.141)

0.540 (0.003)

FOREIGN CURRENCY -0.451 (0.180)

1.130 (0.002)

0.562 (0.023)

ACQUISITION VALUE 2.00 (0.029)

1.879 (0.144)

0.721 (0.493)

SALES GROWTH 0.191 (0.120)

0.034 (0.895)

0.237 (0.083)

SPECIAL ITEMS -1.28 (0.206)

-0.499 (0.782)

-2.388 (0.042)

Industry Indicator Variables Included Included Included

Number of Material

Weakness Observations 103 47 99 Number of Total

Observations 5,480 5,424 5,476

Likelihood Ratio 71.33 (0.0001)

56.16 (0.002)

94.08 (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.

37

Table 6 Earnings Quality and Internal Control Deficiencies: Descriptive Analysis

Earnings Quality (SRESID)

Material Weakness Firms 2003 Compustat Firms

(Excluding Material Weakness Firms)

Classification Variable Mean Median Mean Median Wilcoxon Test

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.

38

Table 7 Earnings Quality and Internal Control Deficiencies: Regression Analysis

Dependent Variable = Earnings Quality (SRESID)

Independent Variables

Predicted Sign

Coefficient Estimate

(P-Value)

Coefficient Estimate

(P-Value)

Coefficient Estimate

(P-Value)

Coefficient Estimate

(P-Value) (A) (B) (C) (D) INTERCEPT 5.01

(0.0001) 6.52

(0.0001) 4.72

(0.0001) 6.55

(0.0001) MW + 0.712

(0.001) 0.454

(0.016) 0.417

(0.029) 0.375

(0.050)

TOTAL ASSETS - -0.441 (0.0001)

-0.379 (0.0001)

-0.433 (0.0001)

OPERATING CYCLE + 0.131

(0.0001) 0.120

(0.0001)

ABS(DACC) + 0.178 (0.0001)

LOSS INDICATOR + 0.892 (0.0001)

OPERATING SEGMENTS + -0.008

(0.818)

GEOGRAPHIC SEGMENTS + 0.041

(0.006)

ACQUISITION VALUE + 0.048 (0.045)

SPECIAL ITEMS - -0.057 (0.0001)

Industry Indicator Variables

Included Included Included Included Number of Material

Weakness Observations

169 169 159 163 Number of Total

Observations 3,781 3,718 3,409 3,631

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).

39

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.

40

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

Dependent Variable = Earnings Quality (SRESID)

Independent Variables Coefficient

Estimate (P-value)

Coefficient Estimate (P-value)

Coefficient Estimate (P-value)

Coefficient Estimate (P-value)

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

INTERCEPT 5.01 (0.0001)

4.72 (0.0001)

5.01 (0.0001)

4.74 (0.0001)

MW_TYPE_A 0.503 (0.085)

0.384 (0.136)

MW_TYPE_B 1.034

(0.001) 0.508

(0.074)

MW_STAFFING 0.669

(0.032) -0.074 (0.794)

MW_COMPLEXITY 0.415 (0.389)

0.676 (0.119)

MW_GENERAL 0.620

(0.052) 0.605

(0.029)

TOTAL ASSETS -0.378 (0.0001) -0.381

(0.0001) OPERATING CYCLE 0.120

(0.0001) 0.120 (0.0001)

ABS(DACC) 0.178 (0.0001) 0.178

(0.0001)

Industry Indicator Variables Included Included Included Included

Number of Material Weakness Observations 169 159 169 159

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.