Audit Committee Financial Expertise, Competing Corporate

Governance Mechanisms, and Earnings Management in a Post-SOX World

Joseph V. Carcello University of Tennessee

Carl W. Hollingsworth

Clemson University

April Klein New York University

Terry L. Neal

University of Tennessee

Corresponding Author Professor April Klein Stern School of Business New York University 44 West 4th Street K-MEC 10-93 New York, N.Y. 10012 (212) 998 - 0014 aklein@stern.nyu.edu We would like to thank conference and seminar participants at The Four School Accounting Conference at Rutgers University, University of Delaware, University of Missouri, and NYU for their comments and suggestions. April Klein was a visiting research scholar for the Weinberg Center for Corporate Governance at the University of Delaware for the 2006-2007 academic year.

Audit Committee Financial Expertise, Competing Corporate Governance Mechanisms, and Earnings Management in a Post-SOX World

Abstract

A prime objective of the Sarbanes-Oxley Act and recent changes to stock

exchange listing standards is to improve the quality of financial reporting. We examine

the associations between audit committee financial expertise and alternate corporate

governance mechanisms and earnings management. We find that both accounting and

certain types of non-accounting financial expertise reduce earnings management for firms

with weak alternate corporate governance mechanisms. Importantly, we find that

alternate corporate governance mechanisms are generally an effective substitute for audit

committee financial expertise in constraining earnings management. Moreover, we find

evidence that earnings management declines after an accounting financial expert joins the

audit committee, suggesting that accounting financial experts are effective monitors of

the financial reporting process. Finally, we find no association between financial

expertise and real earnings management. Our results suggest that alternate governance

approaches are equally effective in improving the quality of financial reporting.

Keywords: Corporate Governance; Audit Committees; Sarbanes-Oxley Act of 2002; Earnings management JEL Codes: M4; G3;

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Audit Committee Financial Expertise, Competing Corporate Governance Mechanisms, and Earnings Management in a Post-SOX World

1. Introduction

The U.S. Congress, in enacting the Sarbanes-Oxley Act of 2002 (SOX), and the

New York Stock Exchange (NYSE) and NASDAQ, in modifying their listing

requirements in December 1999, place great reliance on the company’s audit committee

as a means of protecting the integrity of the financial reporting system. However, while

these rules provide clear standards on audit committee independence and number of

committee members, they are ambiguous on the definition and even the need for an audit

committee financial expert. Thus, the desirability and efficacy of how an audit committee

financial expert improves a firm’s corporate governance environment and its financial

reporting process is an open question.

We contribute to this debate by examining the role that an audit committee

financial expert (ACFE) plays in mitigating earnings management for a broad sample of

NYSE and NASDAQ firms in 2003, the first year that public companies are required to

designate whether they have a financial expert on the audit committee and the name of

that financial expert. We choose earnings management as our metric because one of the

main goals of SOX is to limit earnings manipulations (see SEC, 2003b). In support of

this, Li, Pincus, and Rego (2006) find that the positive stock price reactions to events

surrounding the passage of SOX in 2002 are related directly to previous earnings

management. Further, Cohen, Dey and Lys (2008) report a significant drop in earnings

management following SOX, although they also find that managers increased “real”

earnings management (e.g., a reduction in R&D or advertising expenses) during the same

time period.

2

SOX does not require a firm to include a financial expert on its audit committee;

it merely mandates that the firm discloses in its filings whether the committee has such an

expert, and if not to explain why. In addition, SOX assigned the designation of what

constitutes a financial expert to the SEC, who greatly expanded on the limited definition

suggested by SOX to include, in addition to those with direct experience in preparing or

auditing financial statements (e.g., CPAs), individuals with backgrounds in analyzing

financial statements, or actively supervising others who perform any of these functions

(SEC, 2003a). As a result, firms are able to designate an audit committee financial expert

from a large cadre of directors, including top managers from other companies, investment

bankers, venture capitalists, and others.

Our paper examines the role of the audit committee financial expert in mitigating

earnings management within the context of the changed corporate governance

environment that was generated by the ratification of SOX and the changes in corporate

governance listing standards by the stock exchanges in 2003 (see Klein, 2003). To our

knowledge, it is the first paper to examine the ACFE alone as well as its joint role with

competing corporate governance mechanisms in a post-SOX world. As a result, our

findings and inferences are different than previous papers using pre-SOX data in several

important dimensions.

We find negative associations between ACFEs and earnings management for

ACFEs that have explicit accounting backgrounds or careers (e.g., CPAs and CFOs).

This result is consistent with previous studies that examine different types of earnings

management, for example, restatements, fraud, and abnormal accruals for various pre-

SOX time periods (e.g., Agrawal and Chadha, 2005; Bédard, Chtourou, and Courteau,

2004; Dhaliwal, Naiker, and Navissi, 2006). However, unlike previous studies that use

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pre-SOX data (e.g., Dhaliwal, Naiker and Navissi, 2006), we find evidence that ACFEs

who qualify under the SEC’s broader definition of expertise (e.g., those that evaluate

financial statements) also deter earnings management.1 The latter finding is consistent

with the view that the role or expertise of the ACFE may have changed in a post-SOX

world.

To capture the changed dynamics between earnings management and other

financial reporting corporate governance mechanisms, we derive a corporate governance

index using post-SOX data. The index is an additive combination of six governance

factors. These factors are from an original set of 23 governance characteristics and

include board, audit committee, and other factors that might be related to monitoring the

financial reporting process. We document a significantly negative relation between good

governance, as depicted by our overall measure, and earnings management. The factors

in our index are consistent with Larcker, Richardson, and Tuna (2007) who, using a

principle components analysis (PCA) on post-SOX data to derive 14 corporate

governance factors, find similar negative associations between abnormal accruals and

those factors that mirror ours. Thus, our paper adds to the literature on earnings

management and corporate governance by examining which corporate governance

mechanisms outside of the ACFE are most effective in obviating earnings management.

However, because Larcker, Richardson and Tuna (2007) do not include the ACFE

as a potential governance factor, they do not analyze its association with earnings

management. Nor do they examine potential trade-offs between corporate governance

factors and the inclusion of different types of audit committee financial experts. Using

pre-SOX data, DeFond, Hann, and Hu (2005) and Dhaliwal, Naiker, and Navissi (2006)

1 It is also inconsistent with pre-SOX studies that examine the association between “non-accounting” ACFEs and accounting conservatism (Krishnan and Visvanathan, 2006) and the returns-earnings relation (Qin, 2007).

4

find evidence consistent with the hypothesis that ACFEs are complementary to

competing corporate governance mechanisms. In contrast to these papers, we find that

ACFEs act as substitutes for other financial reporting governance mechanisms. We also

find that both the ACFE and the corporate governance index play substantive roles in

reducing earnings management. Our results are robust to using alternative corporate

governance measures, including DeFond, Hann and Hu’s (2005) specification, thereby

implying that the role of the ACFE vis-à-vis other governance measures may have

changed post-SOX.

Our study also examines whether the observed cross-sectional negative relation

between ACFEs and earnings management is due to better monitoring by the ACFE, or if

it is more consistent with a signaling (endogeneity) explanation. Engel (2005) describes

the signaling hypothesis as follows. Suppose financial experts are more likely to sit on

audit committees for firms that have relatively higher quality financial reporting systems,

i.e., lower earnings management, which they discover in the due diligence process. If

this supposition is true, then firms might appoint an ACFE not because they desire better

monitoring of the financial reporting system, but rather to signal that their existing

financial reporting systems pass the due diligence test by the ACFE (p. 199). Thus, ex

post, the observed cross-sectional association between ACFEs and earnings management

may be a continuation of prior-period lower earnings management.

We explore this explanation by conducting three analyses. First, we examine the

relation between lagged abnormal accruals and the appointment of an ACFE. Next, we

calculate changes in abnormal accruals after the appointment of an ACFE. Lastly, we

add lagged stock return performance as an independent variable to our regressions. The

results of these three analyses support the monitoring hypothesis and are inconsistent

5

with the firm signaling hypothesis. Thus, our study adds to the literature on financial

expertise on audit committees by disentangling these competing explanations.

We also examine whether the ACFE is negatively related to real earnings

management. In direct contrast to our findings on accrual-based earnings management,

we find no association between having an ACFE and the amount of real earnings

management. Nor do we find a systematic relation between our corporate governance

index and real earnings management. We interpret the ACFE findings as being

consistent with the role of the audit committee, which is to monitor the financial

reporting system and not the investing decisions of the firm. This finding is potentially

problematic because Graham, Harvey, and Rajgopal (2005) find that firm managers are

willing to manage earnings through operating decisions, even if these operating decisions

reduce firm value.

Finally, we conduct a series of robustness tests to see if our findings are sensitive

to how we define an ACFE, to when the ACFE was appointed, to the measure of

alternative good governance that we use, and to the data used throughout the analyses.

We find that our results are robust to these alternative specifications, thus lending further

support to our main findings.

Section 2 provides further background on the relation between earnings

management and financial expertise and other corporate governance mechanisms, and

discusses the hypotheses between these factors and real earnings management. Section 3

describes our research method and Section 4 presents our results. Section 5 presents other

analyses and sensitivity tests. Section 6 summarizes and recognizes the limitations of our

study.

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2. Background and Hypotheses

2.1 Financial expertise and earnings management

The preamble to SOX is one line, stating that the purpose of the Act is to “protect

investors by improving the accuracy and reliability of corporate disclosures pursuant to

the securities laws, and for other purposes” (SOX, 2002). Although there are several

ways to gauge the accuracy and reliability of corporate disclosures, we examine the

effects of an audit committee financial expert on earnings management.

Section 407 of SOX requires public companies to disclose whether a financial

expert is included on the audit committee. The SEC’s proposed rule implementing

Section 407 defines an audit committee financial expert as having:

1. an understanding of generally accepted accounting principles and financial statements;

2. experience in (a) the preparation or auditing of financial statements of generally comparable issuers; and (b) the application of such principles in connection with the accounting for estimates, accruals, and reserves;

3. experience with internal accounting controls; and 4. an understanding of audit committee functions.

We label a director on the audit committee with these qualifications as an accounting

financial expert since the director is required to have actual experience in accounting.

The business community was adamantly opposed to the proposed definition of a

financial expert. In issuing its final rule (SEC, 2003a) implementing Section 407, the

SEC dramatically expanded the definition of a financial expert to include directors with

experience in analyzing, or evaluating financial statements as well as to those actively

supervising others who perform any of these activities. We refer to these directors as non-

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accounting financial experts because they are not required to directly have accounting

experience.2

Our primary hypothesis throughout the paper is that audit committee financial

expertise mitigates “GAAP” earnings management due to the financial expert having the

training and experience to recognize accounting manipulations.3 The original narrow

definition of an ACFE proposed by the SEC, alongside the SEC’s subsequent broadening

of that definition and the stock exchanges’ expansive delineation of whom an ACFE is,

allows us to present several testable hypotheses on what type of background and

experience is necessary for the financial expert to be an effective monitor of earnings

management.

2.2 Hypotheses

We begin our hypotheses section by proposing a negative relation between

earnings management and having any type of financial expert on the audit committee. In

the alternative form:

H1: There is a significant negative association between earnings management

and the presence of an ACFE (accounting or non-accounting), as disclosed by

firms.

Empirical support for H1 comes from Abbott, Parker and Peters (2004) and

Bédard, Chtourou and Courteau (2004), who report negative relations between earnings

restatements or aggressive earnings management, respectively, and an ACFE, in which

2 For additional information and background on the audit committee financial expert rules under Section 407 of SOX see Carcello, Hollingsworth, and Neal (2006) or DeFond , Hann and Hu (2005). 3 Dionne and Triki (2005) find that the educational background of audit committee members affects their performance. We do not have data on the educational backgrounds of the audit committee members in this study.

8

the ACFE has either accounting or non-accounting expertise.4 Indirectly, Krishnan

(2005) finds that firms with ACFEs are less likely to experience internal control

problems, and Ashbaugh-Skaife et al. (2007, 2008) find that firms with internal control

weaknesses have a higher level of abnormal accruals.

However, prior research provides evidence that accounting and non-accounting

ACFEs may provide different degrees of financial expertise. Davidson, Xie and Xu

(2004) find a positive abnormal stock price reaction to the appointment of a financial

expert to the audit committee, but that this relation is stronger for the appointment of

financial experts with an auditing background. DeFond, Hann and Hu (2005) find a

significantly positive average stock price reaction to the appointment of an accounting

ACFE, but no significant stock price reaction to the appointment of a non-accounting

ACFE. Dhaliwal, Naiker, and Navissi (2006) find that accruals quality is positively

associated with audit committee accounting expertise, but not with financial or

supervisory expertise. These papers and the evolution of the SEC definition of who

qualifies as an ACFE suggest that we split financial experts into two classes based on

whether they are accounting or non-accounting experts. We present the following two

hypotheses:

H2a: There is a significant negative association between the presence of an

accounting ACFE and earnings management.

H2b: There is a significant negative association between the presence of a non-

accounting ACFE and earnings management.

4 Abbott, Parker, and Peters (2004) define a financial expert as a CPA, investment banker, venture capitalist, CFO, controller, or someone who has held a senior management position (CEO, President, EVP, SVP, VP) with financial responsibilities. Bédard, Chtourou, and Courteau (2004) include CPAs, CFAs, and those with “experience in finance or accounting” as financial experts. Other papers that blend the distinctions between accounting and non-accounting financial experts are Felo, Krishnamurthy, and Solieri (2003) and Anderson, Mansi, and Reeb (2004), who examine AIMR financial reporting quality scores and the cost of debt, respectively.

9

2.3 Other corporate governance mechanisms, financial expertise, and earnings

management

DeFond, Hann and Hu (2005) investigate whether the market’s reaction to the

appointment of an accounting ACFE is complementary or a substitute to the firm’s

overall governance environment. They find that the positive market reaction to the

appointment of an accounting ACFE is greater for firms with strong governance

mechanisms than for firms with weak governance mechanisms. DeFond, Hann and Hu

(2005) conclude that audit committee financial expertise and other corporate governance

mechanisms are complements, a result that Engel (2005) argues is counter-intuitive.

Dhaliwal, Naiker and Navissi (2006) come to similar conclusions with respect to earnings

management. However, these papers examine stock price reactions and earnings

management prior to 2002, a regulatory environment that preceded both SOX (July 2002)

and changes by the NYSE and the NASDAQ (2003) in their corporate governance listing

standards (see Klein, 2003) Thus, if the nature of the relation between a firm’s overall

corporate governance environment and financial reporting quality has changed, then we

may observe a different interaction between having an ACFE and the firm’s corporate

governance environment.

Given this possibility, we present the following hypothesis in the null:

H3: The association between abnormal accruals and a financial expert (variously

defined) is not dependent on the overall strength of the firm’s corporate

governance environment.

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2.4 Real Earnings Management, Other Corporate Governance Mechanisms, and Audit

Committee Financial Expertise

Real earnings management is when managers take real economic actions to

manage quarterly or annual earnings (or EPS). Graham, Harvey and Rajgopal (2005) find

that 80 percent of surveyed CFOs admit they would be willing to decrease discretionary

spending on R&D, advertising, and maintenance to meet earnings expectations. More

than half of the CFOs (55.3 percent) report they would delay a positive net present value

investment project to meet earnings expectations.5 Empirically, Roychowdhury (2005)

finds evidence that managers use R&D, advertising, SG&A, and production costs to

manage earnings for a sample of firms between 1987-2001. Baber, Fairfield and Haggard

(1991) and Bushee (1998) find similar results for R&D expenditures alone. Cohen, Dey

and Lys (2008), using Roychowdhury’s (2005) methodology, report a rise in real

earnings management after the enactment of SOX.

Real earnings management may diminish firm and shareholder value, but it is not

illegal. Further, it is not a violation of financial reporting rules, and even if discovered

would not result in charges of financial fraud or create cause for an earnings restatement.

Thus, we argue that it may be beyond the scope of the audit committee’s responsibility to

filter out real earnings management. Therefore, we present the following hypothesis in

the null:

H4: There is no association between real earnings management and the presence

of an audit committee financial expert.

5 Graham, Harvey, and Rajgopal (2005; 2006) also present evidence that CFOs are willing to use “GAAP” earnings management to meet earnings targets. Graham, Harvey and Rajgopal (2005) report that 40.4 percent of CFOs are willing to book revenues early, 27.9 percent would draw down on reserves previously set aside, 21.3 percent would postpone taking an accounting charge, and 20.2 percent are willing to sell investments or assets to recognize the gain this quarter.

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3. Research Method

3.1 Data

We use a choice-based sampling technique to derive our final sample. Initially,

we took a random sample of 300 non-financial, domestic firms from Compact D/SEC

with fiscal year-ends between July 15, 2003 and December 31, 20036 that are traded on

the NYSE, Nasdaq’s National Market (NNM), and Nasdaq’s Small Cap Market (SCM).

We select a sample rather than testing the entire population of firms because classifying

audit committee members as accounting or non-accounting ACFEs requires reading each

audit committee member’s biographical sketch in the proxy statement. This process is

labor intensive and non-trivial. We eliminate firms that do not have adequate data on

Compustat to calculate abnormal accruals or the necessary control variables, and firms

that do not have the corporate governance data needed for us to compute our measure of

overall corporate governance quality. We have a useable sample of 238 firms, including

12 firms with no ACFE. To obtain more firms with no ACFE (we want to compare firms

with an accounting financial expert and a non-accounting financial expert against firms

with no financial expert), we took an additional sample of 1,200 firms. We examined

each firm’s proxy and 10-K to identify firms without an ACFE. Forty-five additional

firms without an ACFE were identified.7

6 The SEC’s final rule on the disclosure of whether the firm has an ACFE was effective for fiscal years ending on or after July 15, 2003. Section 407 of SOX requires firms to disclose whether a financial expert is on the audit committee; firms are not required to have an audit committee financial expert. 7 Our sample has a higher than expected proportion of firms with no ACFE. To address any concern regarding selection bias we, following Maddala (1991), run a weighted maximum likelihood model (using probability weights) to address any issue of selection bias given our non-random sample. The results for all five test variables (ACC_ACFE, NONACC_ACFE, STRONGGOV, and the two resultant interaction terms) are qualitatively similar to those reported in Table 5. We note too that previous studies examining ACFEs may not use strictly random samples. For example, Bédard, Chrourou, and Courteau’s (2004)

12

Our final useable sample is 283 firms, 57 without an ACFE8 and 226 with an

ACFE. For the 226 firms with an ACFE, we examine the background of each disclosed

ACFE to determine if he/she had previous experience in accounting. Our sample

indicates that 141 firms have at least one accounting ACFE, and 85 have at least one non-

accounting ACFE but no accounting ACFE.

Table 1 presents details on the job backgrounds of the 160 accounting ACFEs

(ACC_ACFE) and the 157 non-accounting ACFEs (NONACC_ACFE), including

information on when these individuals joined the audit committee. As Panel A shows,

although only 226 firms have an ACFE, there are 317 individual ACFEs in our sample

because some firms disclose more than one ACFE. Of the ACC_ACFEs, 65 percent

(104/160) serve, or have served, as a chief financial officer (CFO), and 25 percent

(40/160) are CPAs. Over 80 percent (130/157) of the NONACC_ACFEs hold (held) non-

financial positions as senior business executives (e.g., CEO, President, Chairman of the

Board, Chief Operating Officer, Vice President). The remaining NONACC_ACFEs

include investment bankers, venture capitalists, consultants, private investors, attorneys,

and academics.9 The percentages on the non-accounting ACFEs are similar to Xie,

Davidson, and DaDalt (2003), who characterize financial experts as corporate (non-

finance) executives, finance executives, lawyers and outside blockholders, and report that

74 percent of their sample consist of corporate executives.

sample is not random, but rather compares the most extreme cases of earnings management with the least extreme cases of earnings management. 8 Based on our reading of the proxy statement for these 57 firms, none of the audit committee members appear to qualify as an accounting ACFE. Although some of the audit committee members at these firms have similar backgrounds to non-accounting ACFEs at other firms, these individuals are not designated as an ACFE by the firms where they sat on the audit committee. 9 No one type of background predominates among non-accounting ACFEs who are not current or retired senior managers. For example, there are five venture capitalists, four consultants, four directors, three private investors, three attorneys, and three principals.

We also gathered data on when the ACFEs joined the audit committee. As Panel

B shows, 46 percent [(44+30)/160] of the ACC_ACFEs joined the audit committee

within the past two years, and 31 percent [(8+6+17+17)/(34+123)] of the

NONACC_ACFEs joined within that time. Accounting experts are approximately 50

percent more likely to have been added to the audit committee within the last two years

than are non-accounting experts, consistent with the intent of SOX. However, it remains

an empirical question as to whether ACC_ACFEs are more effective in monitoring the

financial reporting process than are NONACC_ACFEs, and an empirical question as to

whether the benefits of effective monitoring can be achieved through mechanisms other

than the presence of an ACFE.

3.2 Proxies for Earnings Management

Measures of abnormal accruals (also called discretionary accruals) are commonly

used as proxies for earnings management. To capture different aspects of earnings

management, we use two different cross-sectional models to calculate expected and

abnormal accruals. The first is the Jones Model, which assumes that changes in revenues

and capital intensity drive “normal” accruals.10 One advantage of using this model is that

it is consistent with other post-SOX earnings management studies (e.g., see Cohen, Dey

and Lys, 2008; Li, Pincus, and Rego, 2006), thus allowing us to compare our results to

theirs.

Using OLS, we model expected accruals for each firm i in industry j as:

(1) ijijijijijijijijij PPEREVAssetslagTA εβββα ++Δ++= 321 )1/1(

10 As a sensitivity check, we use the modified Jones model and find that the results are qualitatively the same as those reported in this paper.

13

where TAij is total accruals for firm i in industry j (Compustat item 18 less item 308 less

item 124), ∆REVij is the one year change in net revenues between years t and t-1

(Compustat item 12), and PPEij is gross property, plant, and equipment (Compustat item

7). All variables are scaled by lagged total assets. Industry-specific expected accruals

are calculated using all firms on Compustat with the same three, two, or one-digit SIC

code, conditional on having at least 20 firms with usable data in each SIC group. To

control for the effect of outliers, total accruals are winsorized at the 1st and 99th

percentiles prior to the estimation of normal accruals.

Abnormal accruals (AAC) are the prediction errors from equation (1). More

specifically:

14

).ˆˆ)1/1(ˆˆ( 321 iiiiiiiii PPEREVAssetslagTAAAC βββα +Δ++−=

(2)

Consistent with Kothari, Leone, and Wasley (2005), we performance-adjust the

abnormal accruals based on each sample firm’s prior year return-on-assets (ROA).11

This procedure controls for normal earnings management given the sample firm’s level of

performance. Specifically, we rank firms within each industry group into deciles based

on their prior year’s ROA and calculate the median abnormal accruals for each industry

group ROA decile, where the median ROA value excludes the particular sample firm. We

then calculate the performance-adjusted abnormal accrual (PAC) for each sample firm by

taking the difference between the sample firm’s abnormal accruals and the median

abnormal accruals from the appropriate industry group ROA decile.

11 Kothari, Leone, and Wasley (2005) indicates that performance-adjusted discretionary accrual models are mis-specified when earnings management is expected to vary with performance. However, we find a low correlation between our measures of abnormal accruals and firm performance (measured using ROA) (rho = -0.24, Jones model; rho = -0.21, Ball and Shivakumar model), suggesting that the power of our tests should be adequate.

Our measure of earnings management is the natural log of the absolute value of

the performance-adjusted abnormal accruals (LN_ABS_PAC).12 Our hypotheses, like

those in Warfield, Wild, and Wild (1995) and Francis, Reichelt, and Wang (2006), do not

rely on the direction of abnormal accruals; rather, we examine whether audit committee

financial experts are effective in reducing total abnormal accruals.

Our second accrual estimation model is based on Ball and Shivakumar (2006),

who find that conventional linear models suffer from substantial attenuation bias and are

poorly specified as compared to nonlinear models. We begin by using their estimation

model for expected accruals for each firm i in industry j. Using ordinary least squares,

the model is:

(3) ++++Δ++= ijijijijijijijijijijijij DCFCFPPEREVAssetslagTA 54321 )1/1( βββββα ijijijij DCFCF εβ +)*(6 where CFij is operating cash flow scaled by lagged total assets for firm i in industry j

[Compustat item 308] and DCFij is a dummy variable that is 1 if operating cash flow is

less than 0. All other variables are as previously described.

The Ball and Shivakumar (B&S) model differs from the Jones model in two

important respects, and therefore represents a fundamentally different measure of

abnormal accruals and hence of earnings management. First, the Jones model is a linear

model, whereas the B&S model is a piecewise, non-linear model. A piecewise, non-linear

model is appropriate given the asymmetry in gain and loss recognition (i.e., losses are

recognized more quickly under GAAP than gains). Second, unlike the Jones model, the

B&S model includes a cash flow variable. Prior studies (Dechow and Dichev, 2002;

Leuz, Nanda, and Wysocki, 2003) find cash flow to be an important predictor of

15

12 Our results and inferences are qualitatively the same if we use ABS_PAC as our measure of earnings management.

abnormal accruals. B&S report that simply adding a cash flow variable to the original

Jones model more than doubles the reported R2 of the Jones model (p. 221). And, when

the piecewise, non-linear B&S model is employed, the R2 increases another 20 percent.

Industry-specific expected accruals are calculated using all firms on Compustat

with the same three, two, or one-digit SIC code, conditional on having at least 20 firms

with usable data in each SIC group and at least 5 firms with negative operating cash flow.

Total accruals are winsorized at the 1st and 99th percentiles prior to the estimation of

normal accruals. Abnormal accruals (AAC) are the prediction errors from equation (3).

More specifically,

(4) ++++Δ++−= iiiiiiiiiiiii DCFCFPPEREVAssetslagTAAAC 54321ˆˆˆˆ)1/1(ˆˆ( βββββα

) ii DCFCF )*(ˆ6β

Consistent with our estimation of the Jones model, we performance-adjust the

abnormal accruals calculated using the Ball and Shivakumar model as previously

described. Similarly, we take the natural log of the absolute value of the performance-

adjusted abnormal accruals as our measure of abnormal accruals.

3.2.1 Measurement Errors and Data Used

Since our sample is choice-based and is a relatively small percentage of all public

companies – due to the difficulty in hand collecting proxy data – we examine the

possibility that our sample might not be representative of the entire Compustat population

of firms in terms of firm characteristics or industry clustering.

The five firm characteristics we examine are sales growth, book-to-market ratio

(M/B), firm size, earnings-to-price ratio, and operating cash flow (OCF). Kothari, Leone,

and Wasley (2005) present evidence that the Jones model produces biased measures of

16

17

earnings management if the sample used is stratified in terms of earnings growth, risk, or

size. We partition the Compustat universe into quartiles for each of these five metrics. If

our sample mirrors the Compustat population, we would expect approximately 25 percent

of our sample observations to fall within each quartile for each of these metrics. The

percentage of our sample that falls within each quartile is as follows: sales growth (23%,

22%, 33%, 22%); M/B ratio (22%, 28%, 25%, 25%); firm size (18%, 27%, 25%, 30%);

earnings-to-price ratio (20%, 36%, 25%, 19%); and OCF (22%, 27%, 26%, 25%). These

results suggest that our sample is similar to the overall Compustat population, does not

fall primarily into one quartile, and that our results are not driven by the unique financial

characteristics of our sample. Nevertheless, we include control variables to account for

firm size and risk in our analyses throughout the paper.

In terms of industry clustering, we find that our sample firms are spread across 80

different SIC Code groupings – 52 3-digit level groups, 26 2-digit level groups, and two

1-digit level groups. Of the 283 sample observations, 223 firms are classified at the 3-

digit level, 57 at the 2-digit level, and only 3 at the 1-digit level. We also find that our

three largest industries in terms of being included in the sample are SIC Code 737

(computer programming, data processing, and other computer related services; 12 percent

of the sample), SIC Code 283 (drugs; seven percent of the sample), and SIC code 384

(surgical, medical, and dental instruments and supplies; six percent of the sample).

Although these percentages are consistent with the full Compustat sample for 2003,13 we

13 The percentage of firms on Compustat in 2003 with the data to calculate abnormal accruals (excluding financial services firms) are 11 percent in SIC code 737, seven percent in SIC code 283, and three percent in SIC code 384. Therefore, the proportion of our sample firms in SIC codes 737, 283, and 384 is generally consistent with that of the underlying population.

18

nevertheless examine their influence on our analyses through several avenues.14 First,

we create industry dummy variables for each industry or for all risky industries together

(defined below) and include them in the regressions. Second, as a sensitivity test, we

replicate our analyses by sequentially dropping all firms in the three industries that

represent at least 5 percent of the total sample.

3.3 Regression Model

We initially test the relation between abnormal accruals and the existence of an

ACFE (regardless of type) using the following regression model:

LN_ABS_PAC = b0 + b1ACFE + b2STRONGGOV + b3ACFE*STRONGOV +

b4SIZE + b5M/B + b6DISTRESS + b7OCF + b8LEVERAGE + b9RISKY + b10BIG4 + ε

We define the dependent, test, and control variables as follows: LN_ABS_PAC

=

natural log of the absolute value of performance-adjusted discretionary accruals calculated using either the cross-sectional Jones model or the Ball and Shivakumar model;

ACFE = 1 if firm has at least one ACFE, else 0;

STRONGGOV

=

1 if the firm has strong overall corporate governance with respect to financial accounting oversight based on a derived six-factor measure, else 0 (see definition below);

SIZE = natural log of market value of equity; M/B

=

market value of the total firm divided by the book value of assets, measured at the beginning of the fiscal year;

DISTRESS = Zmijewski’s (1984) financial condition index; OCF

=

cash flow from operations scaled by the beginning of the year total assets;

LEVERAGE = total debt divided by total assets;

14 Cross-sectional variations in normal accruals within industries may result in cross-sectional variations in abnormal accruals. Since industries operate in different economic cycles and we use one year’s data, we are concerned that our metrics for abnormal accruals may reflect cyclical variations in one or more industries and not earnings management.

19

RISKY

=

1 if the firm operates in a risky industry, else 0. Risky industries are defined as drugs (2833-2836), computers (3570-3577), electronics (3600-3674), retail (5200-5961), business services (7300-7379), and R&D services (8731-8734) (see Ali and Kallapur, 2001; Hogan and Jeter, 1999; Kasznik and Lev, 1995; Matsumoto, 2002; Raghunandan and Rama, 1999);

BIG4 = 1 if the firm engaged one of the largest four audit firms, else 0.

The primary variables of interest in this analysis are ACFE (H1) and the

interaction between ACFE and STRONGGOV (H3). We expect a negative relation

between an ACFE and earnings management, indicating that firms with at least one

ACFE have performance-adjusted discretionary accruals whose absolute value is less

than firms with no ACFE. We also expect a negative relation between STRONGGOV, a

strong corporate governance environment, and abnormal accruals. The interactive term

ACFE*STRONGGOV tests whether the relation between an ACFE and earnings

management is dependent on the strength of the firm’s overall corporate governance

environment. This interaction variable essentially tests whether ACFEs and other

corporate governance mechanisms are substitutes or complements, as it relates to

earnings management.

We independently develop a composite measure of overall corporate governance

quality as it relates to the financial reporting process. We use stepwise regression

techniques to find a parsimonious combination of a subset of variables that, in a simple

regression, correlate most closely with our abnormal accrual measures. (See Appendix A

for a list of the variables initially included in deriving the index). The final six

20

governance characteristics and their measurements are (one always equals stronger

governance):15

Board size

=

1 if the firm’s board size is between six and 9 members, else 0;

Board expertise

=

1 if the percentage of independent directors who hold seats on other firms’ boards is greater than the sample median, else 0;

Relative audit committee power

=

1 if the proportion of the firm’s audit committee size to its board size is greater than the sample median, else 0;

Audit committee independence

=

1 if the audit committee is 100 percent independent, else 0;

Audit committee meetings

=

1 if the audit committee has five or more meetings, else 0;

Institutional ownership

=

1 if the firm’s percentage of institutional ownership is greater than the sample median, else 0.

We define a firm as having strong corporate governance if the summation of the

individual governance measures for a particular firm equals or exceeds the sample

median for the governance index.16 The following describes how we derive our cutoffs

for each measure:

15 The final index is derived from a possible set of 23 corporate governance variables, including board attributes (e.g., board independence, board meetings, board size, lead director, and split CEO/Chair), audit committee attributes (e.g., audit committee independence, meetings, and size), inside and outside ownership (e.g., institutional ownership, board member shareholdings, and management shareholdings), and other measures (e.g., whether the board has a written corporate governance policy, ISS governance score). See Appendix A. In cases where the cutoffs are not clear cut, we perform sensitivity tests to see if the inferences we make are robust to surrounding cutoff points. In the latter part of the paper, we examine the sensitivity of STRONGGOV to possible alternative definitions. 16 The sample median for the summation of the individual governance measures is three. The sample medians for the underlying factors are: board size (seven), board expertise (0.30), the proportion of audit committee size to board size (0.43), audit committee independence (1.00), audit committee meetings (six), and institutional ownership (0.48). We also note that 28 percent of the firms in the sample had one or more non-independent directors on the audit committee.

21

1. Board Size — The extant literature finds that either large or small boards can be

ineffective. Large boards can suffer from free-rider and coordination problems (Jensen,

1993; Yermack, 1996). Small boards may lack the resources to staff mandated board

committees (Klein, 2002a) and to oversee the operations of larger and more complex

firms (Boone et al., 2007). Although optimal board size varies by context (Raheja, 2005),

we adopt 10 as the optimal ceiling, for example, see Monks and Minow (2008).

2. Board Expertise — Fama and Jensen (1983) argue that directors who sit on multiple

boards have incentives to maintain their reputation as decision experts. Research

suggests that the expertise gained from sitting on other boards improves performance

(e.g., see Gul and Leung, 2004; Kaplan and Reishus, 1990). Zheng (2008) finds a positive

association between audit committee chairs sitting on three or more boards and earnings

quality, the latter measured as the absolute value of Jones’ abnormal accruals.

3. Relative Audit Committee Power — Prior research indicates that audit committee

power is important to its effectiveness (Kalbers and Fogarty, 1993), and the audit

committee needs adequate resources to oversee the financial reporting process (DeZoort

et al., 2002). The size of the audit committee relative to board size measures both the

committee’s (relative) resources and power and has been previously used in the literature

(DeFond, Hann, and Hu, 2005).

4. Audit Committee Independence — A number of prior studies find that better

corporate governance is associated with audit committee independence (e.g., see Carcello

and Neal, 2000; 2003; Klein, 2002b; Abbott, Parkers, and Peters, 2004; Anderson, Mansi,

and Reeb, 2004; and Bédard, Chtourou, and Courteau, 2004). Given that the suggested

standard in SOX and the exchanges is full independence, we use 100 percent as our

cutoff.

22

5. Audit Committee Meetings — Vafeas (2005) and Xie and Xu (2008) find a negative

relation between abnormal accruals and audit committee activity. Farber (2005) finds an

increased number of audit committee meetings in the three years after fraud detection,

suggesting that audit committee activity reduces agency costs arising from the fraudulent

reporting. Indirectly, Raghunandan and Rama (2007) find a positive relation between

accounting financial experts and audit committee meeting frequency, and prior research

finds that more frequent audit committee meetings leads to improved financial reporting

(e.g., see Abbott, Parker, and Peters, 2004; Beasley, Carcello, and Hermanson, 1999;

McMullen and Raghundandan, 1996).

6. Institutional Ownership — Studies find that institutional owners play an important

role in firm governance through their monitoring of management (e.g., see Shleifer and

Vishny, 1986; Coffee, 1991; and Bhojraj and Sengupta, 2003). In addition, institutional

owners monitor the behavior of audit committee members, thereby increasing the

likelihood that audit committee members will effectively discharge their responsibilities.

For example, institutional investors recently have sought the removal of the chair of

Citigroup’s audit and risk management committee because these institutions felt that

Citigroup’s audit committee failed in its risk management oversight (Enrich, 2008).

We also control for several factors suggested by previous literature as being

associated with audit committee characteristics and/or abnormal accruals. These control

variables are the firm’s book-to-market ratio (M/B), firm size (SIZE), operating cash

flows (OCF), debt-to-assets ratio (LEVERAGE), the firm’s financial condition

(DISTRESS), whether a firm operates in an industry characterized by greater business-

and litigation-related risk (RISKY), and whether a firm engages one of the largest four

audit firms (BIG4). We expect positive coefficients on M/B and DISTRESS, and

23

negative coefficients on OCF, LEVERAGE, BIG4 and SIZE. We make no prediction on

RISKY since it is not clear if, post-SOX, managers in risky industries have more or less

incentive to manage earnings (see Warfield, Wild, and Wild, 1995; Dechow, Sloan, and

Sweeney, 1996; Becker et al., 1998; Francis, Maydew, and Sparks, 1999; Bartov, Gul,

and Tsui, 2000; Reynolds and Francis, 2000, Frankel, Johnson, and Nelson, 2002; Klein

2002b).

We also control for whether the abnormal accruals may be related to firm

complexity, firm age, and firm stability by running additional analyses with proxies for

these three constructs. We measure firm complexity as the natural log of the percentage

of foreign sales to total sales (FOR_SALES), FIRM_AGE is the natural log of the

number of years that the firm has been publicly traded, and firm stability is measured as

the natural log of the three-year standard deviation of operating income (StDOI). We

expect positive coefficients on FOR_SALES and StDOI, but a negative coefficient on

FIRM_AGE.

We then extend this initial model to test whether the type of ACFE matters – that

is, we create separate dummy variables for whether an accounting (H2a) or a non-

accounting (H2b) ACFE is on the audit committee. We also disaggregate accounting

financial experts into CPAs, CFOs, and other backgrounds, and non-accounting ACFEs

into senior business executives and other backgrounds.

4. Results

4.1 Descriptive statistics

Table 2 presents descriptive statistics in aggregate and by whether the firm has at

least one accounting ACFE, a non-accounting ACFE but no accounting ACFE, or if the

24

firm discloses not having an ACFE. As expected, firms with at least one accounting

ACFE have lower absolute values of abnormal accruals than firms with no ACFE (p <

0.05, Jones models; p < 0.10, B&S models). However, the absolute value of abnormal

accruals is not significantly different between firms with an accounting ACFE and a non-

accounting ACFE; nor is there a significant difference between firms with at least one

non-accounting ACFE and firms with no ACFE. Firms with a non-accounting ACFE

have stronger governance than firms with no ACFE (p < 0.10). There are some

differences between the three groups on the control variables but no consistent pattern

exists.

Table 3 presents the correlations among the independent variables.17 There are

large positive correlations between strong governance and firm size, use of a Big 4

auditor, and the standard deviation of operating income. In addition, there are large

positive correlations between firm size and use of a Big 4 auditor and the standard

deviation of operating income. Finally, there are large positive correlations between

distress and leverage and between use of a Big 4 auditor and the standard deviation of

operating income, and a large negative correlation between the market-to-book ratio and

operating cash flow. Given the number of high correlations, we calculate variance

inflation factors for all models and find that the highest VIF is 4.72, which is within

acceptable limits.18 Thus, multicollinearity does not appear to be a problem in any of our

models.

17 We present Pearson correlations for continuous variables and Spearman correlations for dichotomous variables. 18 Gujuarati (1995, 339) suggests that as long as the VIF is less than 10.0 multicollinearity is unlikely to be a problem.

25

4.2 Multivariate Results: Earnings Management and Audit Committee Financial

Expertise

4.2.1 Financial Expertise Broadly Defined

Table 4 contains the results of the regression models used to test the relation

between abnormal accruals and the presence of an ACFE. Models (1) and (3) present a

more parsimonious model, and models (2) and (4) include the three additional control

variables discussed in section 3. Models (1) and (2) use abnormal accruals obtained from

the Ball and Shivakumar model, whereas models (3) and (4) use the Jones model.

Overall, the models are highly significant (p < 0.01), and the models’ R2s are between ten

and 18 percent. The adjusted R2 is 80 percent higher for the B&S model than for the

Jones model, suggesting that the B&S model dominates extant models of abnormal

accruals.

Consistent with our expectation in H1, the coefficient on ACFE is significantly

negative in all four models (p < 0.05), indicating that firms with at least one ACFE have

lower levels of abnormal accruals than firms with no ACFE. We also find that firms with

a stronger overall financial reporting corporate governance environment (STRONGGOV)

have a lower level of abnormal accruals (p < 0.05 in the B&S models; p < 0.10 in the

Jones model) after controlling for the ACFE and the control variables in the regression

model. The interactive term ACFE*STRONGGOV tests whether a financial expert and

other financial reporting governance mechanisms are complements or substitutes. We

find a significant positive coefficient on the interactive term (p < 0.10 or better) in the

B&S models, indicating that the relation between abnormal accruals and an ACFE is

conditional on the strength of the firm’s overall financial reporting corporate governance

environment. This result is consistent with a substitution effect between ACFE and

26

STRONGGOV. However, the interaction term is only significant in the Jones model

when foreign sales, firm age, and the standard deviation of operating income are not

included in the model (p < 0.10).

To further analyze the relation between ACFE and STRONGGOV we test

whether (b1 + b3), the sum of the coefficients on ACFE and ACFE*STRONGGOV, is

significantly different from zero. We find that (b1 + b3) is not significantly different from

zero (p > 0.10) in any model, indicating that the negative association between earnings

management and an ACFE only applies for those firms with weak alternate corporate

governance mechanisms. Similarly, the insignificance on the joint test of (b2 + b3), the

sum of STRONGGOV and STRONGGOV*ACFE, indicates that the negative association

between earnings management and strong corporate governance only applies to those

firms without an ACFE. These results suggest that a firm’s overall strong financial

reporting corporate governance environment can substitute for the presence of an ACFE

and vice versa.

The observed substitution between having an ACFE and strong financial reporting

corporate governance is inconsistent with DeFond, Hann, and Hu (2005), who find that

the two variables are complementary to each other. In a later section, we explore if the

difference in inferences between our studies is attributable to the respective metrics used

to represent strong corporate governance or to the fact that DeFond, Hann, and Hu look at

the trade-offs between ACFEs and strong corporate governance within the context of

market returns. As we show, we rule out both explanations.

The coefficients on the control variables are generally consistent with previous

literature. Abnormal accruals are positively related to the market-to-book ratio (p < 0.01;

B&S models; p < 0.05, Jones models) and to financial distress (p < 0.10 B&S models; p

27

< 0.05, Jones models), and negatively related to firm size (p < 0.01, all models), firm

leverage (generally p < 0.05, all models), and Big 4 auditor in the B&S models (p <

0.10). There is a positive relation between abnormal accruals and operating in a risky

industry, but only in the B&S models (p < 0.01). Finally, there are no significant

associations between abnormal accruals and operating cash flows, foreign sales as a

percentage of total sales, firm age, and the standard deviation of operating income.

We next provide evidence on the economic magnitude of the effects of audit

committee financial experts and strong financial reporting corporate governance on

abnormal accruals. Woolridge (2003, p. 226) indicates that the percentage reduction in

abnormal accruals is computed as: 100 * [exp(β) -1]. Using this metric, we find that the

economic effects on abnormal accruals of having a financial expert on the audit

committee and of having strong governance are quite significant. Compared to the base

case (the intercept condition) of no ACFE and weak corporate governance, having an

ACFE leads to a 57 (50) percent decline in the absolute value of performance-adjusted

accruals as a percentage of total assets in the B&S (Jones) model, and STRONGGOV

leads to a 53 (43) percent decline in abnormal accruals in the B&S (Jones) model.19

4.2.2 Accounting and Non-Accounting Financial Experts

We next examine the separate relations between accounting financial experts

(ACC_ACFE) and non-accounting financial experts (NONACC_ACFE) and earnings

management. As Table 5 shows, there is a significantly negative relation between

ACC_ACFE and earnings management (p < 0.05, all models), supporting H2a, which

19 As further evidence of the economic significance of our results, we calculate the potential dollar impact of having an ACFE. For the median firm in our sample (based on the ratio of accruals to assets) the presence of an ACFE is associated with a decline in the absolute value of accruals of approximately $13.8 million ($9.9 million) using the B&S (Jones) model.

28

proposes a negative association between an accounting financial expert and earnings

management. We also find a significantly negative relation between NONACC_ACFE

and earnings management (p < 0.05, all models), supporting H2b, which proposes a

negative association between a non-accounting financial expert and earnings

management.

These findings differ from prior studies that use pre-SOX data in that we find that

both an accounting financial expert and a non-accounting financial expert are effective in

reducing earnings management, whereas other studies primarily find the effect for

ACFEs with accounting backgrounds only. Our results for non-accounting financial

experts may differ from prior studies because post-SOX, the type of non-accounting

expert may differ, or these experts may act differently. Also, in the post-SOX period,

firms specifically designate who is a financial expert, a judgment that is difficult to make

based on proxy disclosures alone for non-accounting financial experts.

The coefficient on STRONGGOV is significantly negative (p < 0.05, B&S

models; p < 0.10, Jones models), suggesting that alternative financial reporting corporate

governance mechanisms still play a role in mitigating earnings management after

controlling for the effects of the ACFEs and the control variables. The positive

coefficient on the interactive term ACC_ACFE*STRONGGOV in the B&S models

suggests a trade-off between having an accounting financial expert on the audit

committee and a strong corporate governance environment (p < 0.10; although the

interaction term is not significant in the Jones models). The positive coefficient on the

interaction term NONACC_ACFE*STRONGGOV suggests a tradeoff between a non-

accounting financial expert and a strong financial reporting governance environment

(generally p < 0.10). Since the interaction terms are not significant or more weakly

29

significant in the Jones models, we test both the Jones and B&S models to determine

whether the sum of the coefficients on the ACFE and its interactive term is significantly

different from zero. We find that (b1 + b4), the sum of ACC_ACFE and

ACC_ACFE*STRONGGOV, is not significantly different from zero (p > 0.10 for both

models), indicating that the negative association between earnings management and an

ACC_ACFE only applies for those firms with weak alternate financial reporting

corporate governance mechanisms. Similarly, the insignificant joint test of (b2 + b5), the

sum of NONACC_ACFE and NONACC_ACFE*STRONGGOV for both models, is

consistent with non-accounting financial experts having no effect on earnings

management for those firms with strong overall corporate governance. We note that the

coefficients and the significance levels of the control variables are generally consistent

with Table 4.

We use the same approach as explained previously (Woolridge, 2003) in

determining the economic magnitude of the effects of accounting financial experts, non-

accounting financial experts, and strong financial reporting corporate governance on

abnormal accruals. Compared to the base case (the intercept condition) of no ACFE and

weak corporate governance, having an ACC_ACFE leads to a 57 (48) percent decline in

the absolute value of performance-adjusted accruals as a percentage of total assets in the

B&S (Jones) model. The presence of a NONACC_ACFE leads to a 57 (56) percent

decline in abnormal accruals in the B&S (Jones) model, and STRONGGOV leads to a 53

(42) percent decline in abnormal accruals in the B&S (Jones) model. When we measure

the economic significance in dollar terms, we find results similar to footnote 19.

In summary, our empirical results in Table 5 support the view that financial

experts with both accounting expertise and non-accounting expertise are effective in

30

monitoring earnings management. The results also support the hypothesis that strong

financial reporting governance mechanisms can substitute for either accounting or non-

accounting financial expertise.

4.2.3 Background of Audit Committee Accounting Financial Experts

Accounting financial experts have varied backgrounds. In Table 6, Panel A we

split ACC_ACFE into two groups, those whose background is a CPA or CFO and other

backgrounds (Other_ACC_ACFE). Other backgrounds include treasurer, controller, and

Vice President of Finance (see Table 1, Panel A). In Table 6, Panel B we split

ACC_ACFE into three groups, CPA, CFO, and Other_ACC_ACFE. If an individual is

both a CPA and a CFO, we code that person into the CPA group only.

The results in Panel A indicate a negative relation between having a CFO or CPA

on the audit committee and abnormal accruals for firms with weak governance (p < 0.05,

B&S models; p < 0.10, Jones models). In addition, there is some evidence of a negative

relation between OTHER_ACC_ACFE and abnormal accruals (p < 0.10, Jones model

only). We find a positive coefficient on the interactive term

CFO/CPA_ACFE*STRONGGOV in the B&S models suggesting a trade-off between

having a CFO or CPA on the audit committee and a strong financial reporting governance

environment (p < 0.10; although the interaction term is not significant in the Jones

models). We also find an insignificant coefficient on the interactive term

OTHER_ACC_ACFE*STRONGGOV.20

The results in Panel B indicate a negative relation between having a CPA on the

audit committee and abnormal accruals for firms with weak governance (p < 0.05, B&S

20 We also run the analyses disaggregating ACC_ACFE into CPA_ACFE and Other_ACC_ACFE (CFO is included in this latter group). Both types of accounting financial experts are significantly negatively related to abnormal accruals.

31

models; p < 0.10, Jones models). In addition, there is some evidence of a negative

relation between having a CFO on the audit committee and abnormal accruals for firms

with weak governance (p < 0.05, B&S models only). Finally, there is some evidence of a

negative relation between OTHER_ACC_ACFE and abnormal accruals (p < 0.10, Jones

models only).

In summary, Panels A and B suggest that CPAs placed on the audit committee

play a critical role in mitigating earnings management. We also find that CFOs and audit

committee members with other accounting backgrounds are associated with lower

abnormal accruals, but that the statistical evidence for these groups is weaker overall than

for the CPAs.

4.2.4 Background of Audit Committee Non-Accounting Financial Experts

Just as there are different backgrounds among accounting financial experts, the

backgrounds of the non-accounting financial experts are diverse. In Table 6, Panel C, we

present the results of our analysis where the NONACC_ACFE variable is replaced by

EXEC and OTHER. We define EXEC as an indicator equal to one if the non-accounting

ACFE is a current or retired senior manager in another firm. OTHER is an indicator equal

to one if the non-accounting financial expert is not a current or retired senior manager in

another firm, for example, an investment banker, venture capitalist, consultant, or an

attorney. As Table 1, Panel A showed, senior business executives account for 83 percent

(130/157) of the non-accounting ACFE sample and all other non-accounting ACFEs

account for the remaining 17 percent (27/157).

We continue to find a significant negative relation between ACC_ACFE and

earnings management for firms with weak corporate governance (p < 0.05, all models).

32

The associations between abnormal accruals and EXEC and OTHER differ depending on

the model. We find a significant negative relation between EXEC and abnormal accruals

for firms with weak corporate governance in the Jones model (p < 0.05), and a significant

negative relation between OTHER and abnormal accruals for firms with weak corporate

governance in the B&S model (p < 0.05). We find a positive coefficient on the interactive

term EXEC*STRONGGOV in the Jones models suggesting a trade-off between having

an executive who qualifies as a financial expert on the audit committee and a strong

corporate governance environment (p < 0.10; although the interaction term is not

significant in the B&S models). We also find an insignificant coefficient on the

interactive term OTHER*STRONGGOV.21

In summary, the significant negative association between NONACC_ACFE and

abnormal returns that we report in Table 5 cannot be attributable primarily to business

executives or to other non-accounting financial experts. Instead, the mixed evidence

provided in Table 6, Panel C suggests that non-accounting financial experts of all

backgrounds play similar roles in controlling earnings management.

5. Additional Analyses and Sensitivity Tests

5.1 Endogeneity and Direction of Causality: Monitoring vs. Signaling Hypotheses

Our main results (Table 5) indicate less earnings management when an

accounting or a non-accounting financial expert sits on the audit committee of firms with

weak alternate corporate governance mechanisms. Since our analyses are cross-sectional,

our results could be due to financial experts effectively monitoring the financial reporting

process (i.e., a monitoring hypothesis). However, our results also could be consistent with

21 With two exceptions, none of the joint tests in any of the panels in Table 6 are significantly different from zero (p > 0.10). The negative relation between Other_ACC_ACFE and abnormal accruals in Panels A and B also applies to firms with strong governance (p < 0.10, Jones model only).

33

a signaling hypothesis (Engel, 2005). Under this hypothesis, potential ACFEs are more

likely to join audit committees for firms that have relatively higher quality financial

reporting systems, i.e., lower earnings management, which they discover during a due

diligence process. Thus, the negative cross-sectional association between ACFEs and

earnings that we observe may not be due to better monitoring, but may be a continuation

of prior-period lower earnings management.

We explore these competing hypotheses by conducting three separate empirical

tests. First, we examine the association between one-year lagged abnormal accruals and

the current appointment of a financial expert to the audit committee. An insignificant

association between the two would be consistent with the monitoring hypothesis, whereas

a significant negative coefficient on the financial expert variable would be consistent with

the signaling hypothesis. To implement this analysis, we gather data on the year that

every financial expert in our sample joined the audit committee. In regression results

(not tabled), we find no significant relation between abnormal accruals and ACC_ACFE

(coef. = 0.16 and 0.25 in the B&S and Jones model) or NONACC_ACFE (coef. = -0.10

and 0.02 in the B&S and Jones model) in the year before they joined the audit committee

(p > 0.15 in the B&S and Jones models). Therefore, there is no evidence that financial

experts join the audit committee of those firms with a low level of earnings management.

These results are consistent with the monitoring theory, but are inconsistent with the

signaling hypothesis.

Our second test examines whether there is a change in abnormal accruals in the

year after an accounting or a non-accounting financial expert joins the audit committee. A

significant decline in abnormal accruals after the appointment of a financial expert would

be consistent with a monitoring theory, whereas an insignificant change in abnormal

34

accruals would be consistent with the signaling hypothesis. We find (not tabled) a

significant decline in abnormal accruals in the year after an accounting financial expert

joins the audit committee (coef. = -0.06, t = -1.8 and p < 0.05, for both the B&S and

Jones models). There is also weak evidence of a significant decline in abnormal accruals

in the year after a non-accounting financial expert joins the audit committee (coef. = -

0.05 t = -1.45 and p < 0.10, Jones model only). Thus, our evidence suggests, again, that

financial experts improve the monitoring of the financial reporting process as evidenced

by a decline in earnings management, rather than signaling existing high-quality financial

reporting through their choices of board appointments.

Finally, we follow the approach employed by Ashbaugh-Skaife, Collins, and

LaFond (2006) in addressing endogeneity (pp. 234-236). We examine whether the results

reported in Tables 5 and 6 are affected by the inclusion of an independent variable

representing the firms’ prior years’ stock performances. The purpose of including this

independent variable is to control for endogeneities between current earnings

management and prior firm performance. In results (not tabled), we find qualitatively

similar relations between abnormal accruals and ACC_ACFEs, NONACC_ACFEs, and

STRONGGOV. In addition, we find insignificant coefficients on the one-, three-, and

five-year lagged stock returns, where the stock returns include dividends and are

compounded over the respective time periods. For example, the coefficients on the one-,

three-, and five-year lagged returns when included in model (1) in Table 5 are 0.04 (t =

0.96), 0.06 (t = 0.43), and -0.15 (t = -0.83), respectively. The other models in Tables 5

and 6 yield similar coefficients and t-statistics.

In summary, our tests provide evidence that the cross-sectional results we report

in Section 4 are more consistent with the view that audit committee financial experts

35

effectively monitor earnings management than with the view that an audit committee

financial expert is a signal to the market that the firm engaged a priori in less earnings

management.

5.2 Analyses Surrounding the Appointment of the ACFE

5.2.1 Are There Differences Between More Recently Appointed and Earlier Appointed

Accounting ACFEs?

The descriptive statistics presented in Table 1 indicate that 46 percent

[(44+30)/160] of the accounting ACFEs joined the audit committee within the past two

years, the period encompassing both the Enron scandal and the passage of SOX. In

contrast, only 28 percent [(17+17)/123] of non-accounting ACFEs joined the audit

committee within the past two years. These percentages suggest that the make-up of the

audit committee and hence, the interactions among its committee members, might have

changed post-SOX. One possibility is that the accounting ACFE plays a more significant

role on the committee if appointed more recently. A counter view is that earlier

appointed accounting ACFEs have more experience and/or more ability in monitoring

earnings management. A third view is that there is no discernible difference in earnings

management with respect to when the accounting ACFE was appointed.

We test these propositions by dividing our ACC_ACFE variable into two

variables based on when the accounting financial expert joined the audit committee – one

that measures whether the only ACC_ACFE joined the committee within the past two

years (NEW_ACC_ACFE), the other that measures whether one or more ACC_ACFEs

were on the audit committee pre-2002 (OLD_ACC_ACFE).

36

The results are reported in Table 7. We find significant negative associations

between earnings management and NEW_ACC_ACFE (p < 0.10, B&S models only), as

well as between earnings management and OLD_ACC_ACFE (p < 0.05, B&S models; p

< 0.01, Jones models) for firms with weak corporate governance. Additionally, we find

some indications of differences in coefficients between NEW_ACC_ACFE and

OLD_ACC_ACFE (p < 0.10, Jones models only) These results suggest that the

effectiveness of ACC_ACFEs is not limited to only those experts being added to the audit

committee as a result of SOX. In fact, our results suggest that audit committee members

with greater years of service might be more effective than those placed on the audit

committee more recently.

5.2.2 Stock Price Reaction to the Appointment of Financial Experts to the Board

In this section, we examine the stock price reaction to the appointment of

accounting and non-accounting financial experts to the board, where these financial

experts are appointed post-SOX. Our test complements DeFond, Hann and Hu (2005),

who examine stock price reactions for a sample of director appointments between 1993

and 2002. They find a significant positive stock price reaction to the appointment of an

accounting financial expert to the board, but only when the firm already has a strong

overall corporate governance environment. Since their sample time period precedes the

passage of SOX, it is an unresolved question as to whether their results continue to apply

post-SOX.

For our sample, there are 74 (48) ACC_ACFEs (NONACC_ACFEs) appointed to

the audit committee post-SOX. We lose 97 of these 122 observations for the following

reasons: (1) 26 observations because the firm did not issue a press release when the

37

director was added to the board (i.e., we do not know the date the director joined the

board), (2) 12 observations because a director who already was on the board (pre-SOX)

joined the audit committee (post-SOX), (3) 19 observations because more than one

director was added to the board at the same time, (4) 16 observations because the press

release announcing the addition of a director was concurrent with the issuance of an SEC

filing (e.g., 10-K, 10-Q, proxy), (5) 23 observations because material news releases (e.g.,

earnings releases, supplier agreements, reorganizations, etc.) were concurrent with the

press release announcing the addition of a director, and (6) one observation because the

stock price data were missing from CRSP. We are left with 15 (10) ACC_ACFEs

(NONACC_ACFEs) appointed to the board post-SOX with stock price data on CRSP.

Given our small sample size, our results are preliminary and only suggestive.

To be comparable with DeFond, Hann and Hu (2005), we calculate 3-day size-

adjusted CARs centered around the date when a financial expert is appointed to the

board. For the 15 ACC_ACFEs appointed to the board post-SOX, we find a positive

abnormal stock price return of 2.4% (p < 0.10, one-tailed; results not tabled).22 And,

contrary to the findings in DeFond, Hann, and Hu (2005) for the 10 NONACC_ACFEs

appointed to the board post-SOX, we find a positive abnormal stock price return of 4.1%

(p < 0.10, one-tailed).23 We again caution the reader to interpret these results cautiously

given our small sample sizes. Nevertheless, these market reaction findings are consistent

22 We do not separately examine the stock price reaction for the appointment of ACC_ACFEs based on the overall strength of the firm’s corporate governance environment as all 15 of our ACC_ACFEs are appointed to firms with strong overall corporate governance. In additional analysis, we examine stock returns to the appointment of an ACFE using DeFond et al.’s (2005) governance measure (see Section 5.4.1). 23 We do not separately examine the stock price reaction for the appointment of NONACC_ACFEs based on the overall strength of the firm’s corporate governance environment as there are only three firms with weak overall governance.

38

with our earnings management regressions, which show that both accounting and non-

accounting ACFEs play a role in mitigating earnings management.

5.3 Financial Expertise, Operating Decisions, and Real Earnings Management

Management can manage earnings either through accounting estimates and

judgments, or through “real” actions that serve to accelerate or delay the recognition of

discretionary expenditures (e.g., advertising, R&D, training, travel, etc.). Management

can also affect earnings through its management of inventory levels. In H4, we posit that

an ACFE has little effect on real earnings management, because the management of

discretionary expenditures and production costs is generally not fraudulent and would be

well within the accepted province of management’s discretion.

Roychowdhury (2005) and Cohen, Dey, and Lys (2008) use a firm’s abnormal

level of discretionary expenditures and abnormal level of production costs (using a

similar technique to the Jones model) as metrics of real earnings management. Following

these papers, we estimate the following cross-sectional regressions for each three, two, or

one-digit SIC code, conditional on having at least 20 firms with usable data in each SIC

group, in order to generate the normal (predicted) levels of discretionary expenses and

production costs:

(5) DiscExpi/Assetsi,t-1 = a1(1/Assetsi, t-1) + a2 Salesit/Assetsi,t-1 + εit

(6) Prodit/Assetsi,t-1 = b1(1/Assetsi,t-1) + b2Salesit/Assetsi,t-1 + b3ΔSalesit/Assetsi,t-1 + b4ΔSalesi,t-1/Assetsi,t-1 + εit We define discretionary expenditures (DiscExp) as the sum of the firm’s advertising

expense (Compustat data item 45), R&D expense (data item 46), and selling, general, and

administrative expense (data item 189). Abnormal discretionary expenditures for firm i is

calculated as the difference between its actual discretionary expenditures and its normal

39

level of discretionary expenditures. We define production costs (Prod) as the sum of the

firm’s cost of goods sold (data item 41) and change in inventories (data item 3).

Abnormal production costs for firm i is the difference between its actual production costs

and normal production costs. Our control variables are drawn from Cohen, Dey and Lys

(2008). We control for the natural log of the market value of equity, cash flow from

operations, audit firm type, and the absolute value of performance-adjusted discretionary

accruals per the Jones model. In addition, we include discretionary expenditures as an

explanatory variable in the production cost regression model, and production cost as an

explanatory variable in the discretionary expenditure model to allow for the trade-offs

between real and accounting earnings management.

Table 8 presents the results of these models. We fail to find significant

associations between either abnormal production costs or abnormal discretionary

expenditures and an ACC_ACFE or NONACC_ACFE. In addition, we fail to find a

significant relation between the strength of the firm’s overall corporate governance

environment and abnormal production costs or discretionary expenditures.

These results, when coupled with our earlier findings, suggest that an ACFE can

mitigate earnings management via discretionary accruals for firms with a weak corporate

governance environment, but that an ACFE has no effect on real earnings management.

In addition, a strong overall corporate governance environment reduces discretionary

accruals but does not appear to have any effect on real earnings management. Although

the focus of SOX and the SEC has been on non-GAAP earnings management, Graham,

Harvey, and Rajgopal (2005) find that CFOs are willing to sacrifice long-term firm value

in order to smooth earnings. As suggested by these authors, boards and audit committees

40

may want to exercise greater vigilance in overseeing wealth destroying actions by senior

management designed to smooth earnings.

5.4 Sensitivity Analyses

5.4.1 Alternate Measures of Good Governance: Comparisons With Larcker,

Richardson, and Tuna (2007) and DeFond, Hann, and Hu (2005)

One contribution of this paper is that we independently derive a financial

reporting corporate governance index from a set of 23 possible corporate governance

attributes. Larcker, Richardson, and Tunas (2007) use factor analysis to combine 37

governance characteristics into 14 corporate governance factors. They then investigate

the association between these 14 factors with various accounting outcomes, including the

absolute value of abnormal accruals. Our original set of possible characteristics is similar

to those used by Larcker, Richardson, and Tuna (2007), with the main exception that we

do not consider components of the CEOs pay package. We also consider attributes that

Larcker, Richardson, and Tuna do not include, for example, the composition of the board

nominating and finance committees (Klein, 1998) and the percent of fees paid by the firm

to its auditor divided by total fees for the audit firm (see Appendix A for a list of the

initial corporate governance variables).

Because Larcker, Richardson, and Tuna (2007) use factor analyses, we are unable

to compare directly the correlations between their measures and ours. Nor are we able to

use their factors as alternative measures of STRONGGOV. Nevertheless, when we

compare their factors to our measure, we find that five of the six characteristics we

include in STRONGGOV are included in the factors that they find are significantly

correlated to the absolute value of abnormal accruals. The lone exception is the number

41

of audit committee meetings, which, as a part of their factor “Meetings” is not

significantly related to these accruals.

We also compare our index to DeFond, Hann, and Hu’s (2005) corporate

governance index. The greatest similarity between the two measures is that both

additively combine six corporate governance attributes. However, our index includes

board expertise and audit committee meetings, whereas they include board independence

and shareholder rights. In addition, there are some differences in how common measures

are defined, including cut-off points.

To assess the sensitivity of our results to the governance measure used, we rerun

the Table 5 analyses replacing our governance measure with DeFond, Hann, and Hu’s.24

In results not tabled, we find that abnormal accruals are significantly negatively related to

ACC_ACFE (p < 0.05, B&S model; p < 0.01, Jones model) for those firms with weak

governance. Contrary to our main result, there is weaker evidence of a relation between

NONACC_ACFE and abnormal accruals (p < 0.10, Jones model only). In addition, we

find a significant negative relation between abnormal accruals and STRONGGOV (p <

0.05, both models). Finally, there is a significant positive interaction between

ACC_ACFE and STRONGGOV (p < 0.10, both models). The results suggest that the

relation between accounting financial expertise and earnings management is invariant to

24 We use all of DeFond, Hann, and Hu’s (2005) factors with one exception, the G index developed by Gompers, Ishii, and Metrick (2003) to measure shareholder rights. They obtain the G index from the Investor Responsibility Research Center (IRRC) database. The G index is skewed toward larger companies; only 135 of the companies in our sample had a G index available on the IRRC database. Therefore, we use Institutional Shareholder Services’ (ISS) Corporate Governance Quotient (CGQ), a comparable index to the G index, to measure shareholder rights. The G index has 24 factors and includes measurements of whether firms have staggered board terms, allow cumulative voting, and have poison pills (DeFond, Hann, and Hu 2005). The CGQ has 61 factors and includes measurements of charter and bylaw provisions, anti-takeover provisions, executive and director compensation, and ownership. A large majority of our original sample has an available CGQ. The correlation between STRONGGOV and the DeFond, Hann, and Hu (2005) governance factor is 0.39, indicating that there is some limited overlap between the two measures.

42

the measure of strong governance, but the relation between non-accounting financial

expertise and earnings management is sensitive to the governance measure used. The

results also suggest the need to derive a new corporate governance index based on post-

SOX data.25

Finally, we test how sensitive our six-characteristic measure is by dropping and/or

adding other factors found to be related to earnings management. We find some

robustness to rearranging our index, but conclude that, overall, the original index most

represents a measure of effective financial reporting corporate governance.

5.4.2 Sensitivity of Results to Certain Industry Groupings

Tables 4 through 7 include an indicator variable, RISKY, to capture the possible

association between earnings management and being a firm in a risky industry. Risky

industries are defined as drugs (SIC codes 2833-2836), computers (SIC codes 3570-

3577), electronics (SIC codes 3600-3674), retail (SIC codes 5200-5961), business

services (SIC codes 7300-7379), and R&D services (SIC codes 8731-8734). To examine

the sensitivity of using these many industries, we redefine RISKY using smaller

groupings, for example drugs and business services only. Our results (not tabled) are

similar to those reported in the paper.

25 We also examine the market return results in section 5.2.2 using the DeFond, Hann, and Hu (2005) corporate governance measure instead of our own metric. With our measure, all 15 of the ACC_ACFEs were appointed to firms with strong overall corporate governance, whereas with DeFond, Hann, and Hu’s (2005) measure, eight of the 15 ACC_ACFEs were appointed to firms with a strong overall corporate governance environment and seven were appointed to firms with weak overall corporate governance. When we use DeFond, Hann, and Hu’s metric, we find that the positive abnormal stock price return only exists when the firm’s overall corporate governance environment is weak. That is, in the presence of a weak overall corporate governance environment, the appointment of an ACC_ACFE to the board is associated with an abnormal positive stock price reaction of 5% (p < 0.10, one-tailed), whereas in the presence of a strong overall corporate governance environment, there is no significant stock price reaction to the appointment of an ACC_ACFE (p > 0.19, one-tailed). These results are in contrast to DeFond, Hann, and Hu, but are consistent with our overall conclusion of a substitution effect between competing corporate governance mechanisms.

43

To assess the possible impact that one industry might have on our results and

inferences, we separately and then sequentially exclude the three industry groups that

represent more than five percent of our sample (SIC codes 737, 283, and 384). Our

results for all five test variables (ACC_ACFE, NONACC_ACFE, STRONGGOV, and

the two resultant interactions) are consistent with the results presented in the paper. We

conclude that our findings and inferences are robust to the sample selection as it relates to

industry clustering.

5.4.3 Analysis of Positive and Negative Abnormal Accruals

We find that financial expertise and other governance characteristics are related to

abnormal accruals on an overall basis. We separately examine these relations for firms

with positive abnormal accruals and those with negative abnormal accruals. In results not

tabled, for firms with positive abnormal accruals, we find significant negative relations

between accruals and ACC_ACFE (p < 0.05, B&S model only), NONACC_ACFE (p <

0.05, both models), and STRONGGOV (p < 0.01, B&S model only). In addition, there is

a significant positive interaction between NONACC_ACFE and STRONGGOV (p <

0.01, B&S model; p < 0.10, Jones model). There are no significant relations between our

test variables and accruals for firms with negative abnormal accruals. Our results are not

surprising given the greater legal risk associated with positive accruals than with negative

accruals.

6. Summary, Limitations, and Conclusions

We examine the association between earnings management and various types of

ACFEs in the first year that firms must publicly disclose the identity of their financial

44

experts. We also examine whether the strength of the firm’s overall corporate governance

environment substitutes for, or complements, the presence of an ACFE. We find that both

an accounting ACFE and a non-accounting ACFE are consistently associated with less

earnings management for firms with weaker overall corporate governance. Moreover, we

find that earnings management is not reduced by the presence of an ACFE if the firm has

a stronger overall corporate governance environment. These results indicate that the

firm’s overall governance environment and audit committee financial experts act as

substitutes for one another. In addition, we find evidence that abnormal accruals decline

after an accounting financial expert joins the audit committee. This result suggests that

the salutary benefits of better monitoring by accounting financial experts may directly

contribute to an improvement in the quality of financial reporting. This result has not

previously been documented in the literature.

In contrast, we fail to find any relation between ACFEs and management of

abnormal production costs and abnormal discretionary expenditures. These findings

suggest that although accounting ACFEs may be effective in constraining the

management of accounting accruals, there is no relation between ACFEs and real

earnings management. The lack of a relation between an ACFE and real earnings

management presumably reflects the fact that the audit committee focuses on the

financial reporting process and cuts management wide swath in making operating

decisions, even if these operating decisions affect reported income.

Our paper is subject to a number of limitations. First, our categorization of an

audit committee member as an ACFE is dependent on firms’ public disclosures.

Although firms are required to identify at least one ACFE if such an individual exists, the

quality and transparency of this disclosure is likely to vary across firms. Second, our

45

measure of earnings management, performance-adjusted abnormal accruals, is only as

good as the underlying measure. However, we have computed this measure in accordance

with the extant literature, and our results are based on two different measures of abnormal

accruals (i.e., the Ball and Shivakumar model and the Jones model). Finally, our results

are based on only one year of data, although we also consider the change in abnormal

accruals in the year after the financial expert joins the audit committee. Our sample is

representative of the overall Compustat population; and given the lack of empirical data

on the relation between various types of ACFEs and earnings management, especially

post-SOX, we provide initial empirical data on an important public policy issue.

Our paper leaves open many questions for future researchers. One question is

what role, beyond the monitoring of the financial reporting process, does an ACFE play?

We find no evidence that an ACFE reduces real earnings management. Dionne and Triki

(2005) find that accounting financial experts play no active role in determining the

corporate hedging activities of a firm. What, if any, other areas are within the purview of

the ACFE? A second question is whether the increased monitoring by the ACFE results

in higher returns, for example higher ROA or stock market returns, for the firm. Another

area of interest may be to examine the costs to a firm of appointing an ACFE to its board.

Kamar, Karac-Mandic, and Talley (2007) present evidence that SOX, as a whole, may

have had a disproportionately negative impact on smaller firms. While SOX does not

require a firm to have an ACFE, its disclosure requirement may place undue pressure on

smaller firms to appoint such a director to its board.

46

APPENDIX A: Derivation of Corporate Governance Index

We derive a financial reporting corporate governance measure using the following variables as possible inputs. DEFINITION Audit Committee Variables AC_IND 1 if the audit committee is 100% independent AC_CHAIR_IND 1 if the audit committee chair is independent AC_SIZE 1 if the audit committee has 4 or more members AC_MEET 1 if the audit committee has 5 or more meetings REL_AC_POWER 1 if the proportion of audit committee size to board size is

greater than the median AVG_OD_AC 1 if the average number of other directorships held by the

members of the audit committee is greater than the median Board of Director Variables AVG_OD_BOARD 1 if the average number of other directorships held by the

members of the board of directors is greater than the median CEOPOWER 1 if the CEO is chairman of the board or a member of the

compensation, audit or nominating committee BOARD_IND 1 if the board has a super-majority (66.7% or more) of

independent directors PCT_CEO_APPOINT 1 if the CEO appointed less than 50% of the board BOARD_SIZE 1 if board size is 6,7,8, or 9 members HAVE_LEAD_DIR 1 if the company has a lead director who is not an insider BOARD_EXPERT 1 if the percentage of independent directors that hold seats on

other firms’ boards is greater than the median BOARD_STOCK_OWN 1 if 100% of the directors own stock (i.e., the median value

for this variable) GOV_POLICY 1 if the firm has a formal governance policy Other Board Committee Variables NOM_IND 1 if the nominating committee is 100% independent COMP_IND 1 if the compensation committee is 100% independent FIN_INSIDE 1 if the percentage of insiders on the finance committee is

greater than the median Other Corporate Governance Variables INSIDE 1 if the percentage of shares held by insiders is greater than

the median ISS_SCORE 1 if the company’s corporate governance quotient is greater

than the median TOTFEES 1 if total fees paid by the company to its auditor divided by

total fees for the firm is less than the median INST 1 if percentage of shares held by institutional investors is

greater than the median BLOCK 1 if the number of blockholders, where blockholders are

defined at the ownership level, is greater than the median

47

REFERENCES

Abbott, L.J., Parker, S., Peters, G.F., 2004. Audit committee characteristics and restatements. Auditing: A Journal of Practice & Theory 23 (1), 69-87.

Agrawal, A., Chadha, S., 2005. Corporate governance and accounting scandals. Journal

of Law and Economics 43 (2), 371-406. Ali, A., Kallapur, S., 2001. Securities price consequences of the Private Securities

Litigation Reform Act of 1995 and related events. The Accounting Review 76 (3), 431-461.

Anderson, R.C., Mansi, S.A., Reeb, D.M., 2004. Board characteristics, accounting report

integrity, and the cost of debt. Journal of Accounting and Economics 37 (3), 315-342. Ashbaugh-Skaife, H., Collins, D.W., LaFond, R., 2006. The effects of corporate governance on firms’ credit ratings. Journal of Accounting and Economics 42 (1-2): 203-243. _____, _____, Kinney, W.R., 2007. The discovery and consequences of internal control

deficiencies prior to SOX-mandated audits. Journal of Accounting and Economics 44 (1-2), 166-192.

_____, _____, _____, LaFond, R., 2008. The effect of SOX internal control deficiencies

and their remediation on accrual quality. The Accounting Review 83 (1), 217-250. Baber, W.R., Fairfield, P.M., Haggard J.M., 1991. The effect of concern about reported

income on discretionary spending decisions: The case of research and development. The Accounting Review 66 (4), 818-829.

Ball, R., Shivakumar, L., 2006. The role of accruals in asymmetrically timely gain and

loss recognition. Journal of Accounting Research 44 (2), 207-242. Bartov, E., Gul, F.A., Tsui, J.S.L., 2000. Discretionary-accruals models and audit

qualifications. Journal of Accounting and Economics 30 (3), 421–452. Beasley, M.S., Carcello, J.V., Hermanson, D.R., 1999. Fraudulent Financial Reporting:

1987-1997, An Analysis of U.S. Public Companies. New York, NY: COSO. Becker, C.L., DeFond, M.L., Jiambalvo, J., Subramanyam, K.R., 1998. The effect of

audit quality on earnings management. Contemporary Accounting Research 15 (1), 1–24.

Bédard, J., Chtourou, S.M., Courteau, L., 2004. The effect of audit committee expertise,

independence, and activity on aggressive earnings management. Auditing: A Journal of Practice & Theory 23 (2), 13-35.

48

Bhojraj, S., Sengupta, P., 2003. Effect of corporate governance on bond ratings and yields: The role of institutional investors and outside directors. Journal of Business 76 (3), 455-475.

Boone, A.L., Field, L.C., Karpoff, J.M., Raheja, C.G., 2007. The determinants of

corporate board size and composition: An empirical analysis. Journal of Financial Economics 85 (1), 39-65.

Bushee, B., 1998. The influence of institutional investors on myopic R&D investment

behavior. The Accounting Review 73 (3), 305-333. Carcello, J.V., Neal, T.L., 2000. Audit committee composition and auditor reporting. The

Accounting Review 75 (4), 453-467. _____, 2003. Audit committee characteristics and auditor dismissals following “new”

going-concern reports. The Accounting Review 78 (1), 95-117. _____, Hollingsworth, C.W., Neal, T.L., 2006. Audit committee financial experts: A

closer examination using firm designations. Accounting Horizons 20 (4), 351-373. Coffee, J.C., Jr., 1991. Liquidity versus control: The institutional investor as corporate

monitor. Columbia Law Review 91 (6), 1277-1368. Cohen, D.A., Dey, A., Lys, T., 2008. Real and accrual-based earnings management in the

pre- and post-Sarbanes Oxley periods. The Accounting Review, 83 (3): 757-787. Davidson, W., Xie, B., Xu, W., 2004. Market reaction to voluntary announcements of

audit committee appointments: The effect of financial expertise. Journal of Accounting and Public Policy 23 (4), 279-293.

Dechow, P.M., Sloan, R.G., Sweeney, A.P., 1996. Causes and consequences of earnings

manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research 13 (1), 1–36.

_____, Dichev, I., 2002. The quality of accruals and earnings: The role of accrual

estimation errors. The Accounting Review 77 (Supplement), 35-59. DeFond, M.L., Hann, R.N., and Hu, X., 2005. Does the market value financial expertise

on audit committees of boards of directors? Journal of Accounting Research 43 (2), 153-193.

DeZoort, F.T., Hermanson, D.R., Archambeault, D.S., Reed, S.A., 2002. Audit

committee effectiveness: A synthesis of the empirical audit committee literature. Journal of Accounting Literature 21, 38-75.

Dhaliwal, D.S., Naiker, V., Navissi, F., 2006. Audit committee financial expertise,

corporate governance and accruals quality: An empirical analysis. Working Paper, University of Arizona, University of Auckland, and Monash University.

49

Dionne, G., Triki, T., 2005. Risk management and corporate governance: The importance

of independence and financial knowledge for the board and the audit committee. Working paper, HEC Montreal.

Engel, E., 2005. Discussion of does the market value financial expertise on audit

committees of boards of directors? Journal of Accounting Research 43 (2), 195-204. Enrich, D., 2008. Audit panels face reckoning in credit mess. The Wall Street Journal,

April 8. Fama, E.F., Jensen, M.C., 1983. Separation of ownership and control. Journal of Law and

Economics 26 (2), 301-326. Farber, D., 2005, Restoring trust after fraud: Does corporate governance matter? The

Accounting Review 80 (2): 539-561. Felo, A.J., Krishnamurthy, S., Solieri, S.A., 2003. Audit committee characteristics and

the perceived quality of financial reporting: An empirical analysis. Working paper, Penn State Great Valley, Binghamton University, and University of Scranton.

Francis, J.R., Maydew, E., Sparks, H.C., 1999. The role of Big 6 auditors in the credible

reporting of accruals. Auditing: A Journal of Practice and Theory 18 (2), 17–34. _____, Reichelt, K., Wang, D.D., 2006. National versus office-specific measures of

auditor industry expertise and effects on client earnings quality. Working Paper, University of Missouri, Louisiana State University, and University of Nebraska.

Frankel, R.M., Johnson, M.F., Nelson, K.K., 2002. The relationship between audit fees

for nonaudit services and earnings quality. The Accounting Review 77 (Supplement), 71–105.

Gompers, P.A., Ishii, J.L., Metrick, A., 2003. Corporate governance and equity prices.

Quarterly Journal of Economics 118 (1), 107-155. Graham J. R., Harvey, C.R., Rajgopal, S., 2005. The economic implications of corporate

financial reporting. Journal of Accounting and Economics 40 (1-3), 3-73. _____, 2006. Value destruction and financial reporting decisions. Financial Analysts

Journal 62 (6): 27-39. Gujarati, D.N., 1995. Basic Econometrics, 3rd edition. McGraw-Hill, New York, NY. Gul, F.A., Leung, S., 2004. Board leadership, outside directors’ expertise and voluntary

corporate disclosures. Journal of Accounting and Public Policy 23 (5), 351-379. Hogan, C.E., Jeter, D.C., 1999. Industry specialization by auditors. Auditing: A Journal

of Practice & Theory 18 (1), 1-17.

50

Jensen, M., 1993. Presidential address: the modern industrial revolution, exit and the

failure of internal control systems. Journal of Finance 48 (3), 831-880. Kalbers, L.P., Fogarty, T.J., 1993. Audit committee effectiveness: An empirical

investigation of the contribution of power. Auditing: A Journal of Practice & Theory 12 (1), 24-49.

Kamar, E., Karac-Mandic, P., and Talley, E.L., 2007. Sarbanes-Oxley’s effect on small

firms: What is the evidence? In the Name of Entrepreneurship: The Logic and Effects of Special Regulatory Treatment for Small Business. (Susan M. Gates and Kristin Leuschner, eds., RAND).

Kaplan, S.N., Reishus, D., 1990. Outside directorships and corporate performance.

Journal of Financial Economics 27 (2), 389-410. Kasznik, R., and Lev, B., 1995. To warn or not to warn: Management disclosures in the

face of an earnings surprise. The Accounting Review 70 (1), 113-134. Klein, A., 1998, Firm performance and board committee structure. The Journal of Law

and Economics 41(1): 275-303. ______, 2002a. Economic determinants of audit committee independence. The

Accounting Review 77 (2), 435-452. _____, 2002b. Audit committee, board of director characteristics, and earnings

management. Journal of Accounting and Economics 33 (3), 375–400. _____, 2003. Likely effects of stock exchange governance proposals and Sarbanes-Oxley

on corporate boards and financial reporting. Accounting Horizons 17 (4), 343-355. Kothari, S.P., Leone, A.J., Wasley, C.E., 2005. Performance matched discretionary

accrual measures. Journal of Accounting and Economics 39 (1), 163–197. Krishnan, J., 2005. Audit committee quality and internal control: An empirical analysis.

The Accounting Review 80 (2), 649-675. Krishnan, G.V., Visvanathan, G., 2006. Does the SOX definition of an accounting expert

matter? The association between audit committee director's expertise and conservatism. Working Paper, George Mason University.

Larcker, D.F., Richardson, S.A., Tuna, I., 2007. Corporate governance, accounting

outcomes, and organizational performance. The Accounting Review 82 (4), 963-1008.

Leuz, C., Nanda, D.J., Wysocki, P., 2003. Earnings management and investor protection:

An international comparison. Journal of Financial Economics 69 (3), 505-527.

51

Li, H., Pincus, M., Rego, S.O., 2006. Market reaction to events surrounding the Sarbanes-Oxley Act of 2002, Journal of Law and Economics (forthcoming).

Maddala, G.S., 1991. A perspective on the use of limited-dependent and qualitative

variables models in accounting research. The Accounting Review 66 (4), 788-807. Matsumoto, D.A., 2002. Management’s incentives to avoid negative earnings surprises.

The Accounting Review 77 (3), 483-514. McMullen, D.A., Raghunandan, K., 1996. Enhancing audit committee effectiveness.

Journal of Accountancy 182 (2), 79-81. Monks, R.A.G., Minow, N., 2008. Corporate Governance, 4th edition. John Wiley, West

Sussex, UK. Qin, B., 2007. The influence of audit committee financial expertise on earnings quality:

US evidence. The Icfai Journal of Audit Practice 4(3): 7-28. Raghunandan, K., and Rama, D.V., 1999. Auditor resignations and the market for audit

services. Auditing: A Journal of Practice & Theory 18 (1), 124-134. _____, 2007. Determinants of audit committee diligence. Accounting Horizons 21 (3),

265-279. Raheja, C.G., 2005. Determinants of board size and composition: A theory of corporate

boards. Journal of Financial and Quantitative Analysis 40 (2), 283-306. Reynolds, J.K., Francis, J.R., 2000. Does size matter? The influence of large clients on

office-level auditor-reporting decisions. Journal of Accounting and Economics 30 (3), 375–400.

Roychowdhury, S., 2005. Earnings management through real activities manipulation.

Journal of Accounting and Economics 42 (3), 335-370. Sarbanes-Oxley Act of 2002 (SOX), Public Law 107-204, 107th Congress, 2d Session,

July 24, 2002. Available on Congress.gov. SEC, 2003a. Disclosure Required by Sections 406 and 407 of the Sarbanes-Oxley Act of

2002, Release Nos. 33-8177; 34-47235, January 23, 2003. Available on SEC.gov. _____, 2003b. Standards Relating to Listed Company Audit Committees, Release Nos.

33-8220, 34-47654, April 9, 2003. Available on SEC.gov. Shleifer, A., Vishny, R., 1986. Large shareholders and corporate control. Journal of

Political Economy 94 (3), 461-489. Vafeas, N., 2005. Board meeting frequency and firm performance. Journal of Financial

Economics 53(1): 1093-1122.

52

Warfield, T.D., Wild, J.J., Wild, K.L., 1995. Managerial ownership, accounting choices,

and informativeness of earnings. Journal of Accounting and Economics 20 (1), 61–92.

Woolridge, J.M., 2003. Introductory Econometrics, 2nd edition. Thomson South-Western,

Mason, OH. Xie, B., Davidson, W.N., DaDalt, P.J., 2003. Earnings management and corporate

governance: The role of the board and the audit committee. Journal of Corporate Finance 9 (3), 295-316.

Xie, H., and Xu W., 2008. Accruals quality and audit committee activity, Working paper,

University of Iowa and University of Buffalo Yermack, D., 1996. Higher market valuation of companies with a small board of

directors. Journal of Financial Economics 40 (2), 185-211. Zheng, X., 2008. An empirical analysis of the relationship between audit committee

multiple directorships and financial reporting quality, Dissertation paper, Drexel University.

Zmijewski, M.E., 1984. Methodological issues related to the estimation of financial

distress prediction models. Journal of Accounting Research 22 (Supplement), 59-82.

53

Table 1

Job Backgrounds and Tenure of Audit Committee Financial Experts

Panel A - ACFE Background a

Accounting Expertise

Non-Accounting Expertise

(ACC_ACFE) (NONACC_ACFE)

(n=160) (n=157)

CFO Other Senior Exchange b and CPA CFO CPA Financial c Executives Other d Total

SCM (n = 63) 6 20 10 8 26 7 77NNM (n = 83) 5 42 9 9 37 8 110NYSE (n = 80) 3 28 7 13 67 12 130 Total (n = 226) 14 90 26 30 130 27 317

Panel B – Tenure on the Audit Committee Less than 2 years but Less than greater than Greater than Firms with 1 year 1 year 2 years Total

At least one ACC_ACFE (n = 141): ACC_ACFE tenure 44 30 86 160 NONACC_ACFE tenure 8 6 20 34 At least one NONACC_ACFE but no ACC_ACFE (n = 85) 17 17 89 123 Total 69 53 195 317 ___________________________ a Current and previous work experience are coded based on the highest position held. Several

individuals held multiple positions such as CFO and VP-Finance. These individuals were coded as CFO.

b SCM stands for Nasdaq Small Cap Market, NNM stands for Nasdaq National Market, and NYSE stands for New York Stock Exchange.

c Indicates individuals who held the position of VP-Finance, treasurer, controller, etc. d Indicates individuals who held the position of venture capitalist or investment banker,

consultant, private investor, academic, attorney, etc.

TABLE 2

Descriptive Statistics for Earnings Management and Control Variables Mean (Median) [Standard Deviation]

Accounting Non-Accounting No Entire ACFE Firms ACFE Firms ACFE Firms Sample (ACC_ACFE) (NONACC_ACFE) (NOACFE) (ACC_ACFE - (ACC_ACFE - (NONACC_ACFE - Variablea (n = 283) (n = 141) (n = 85) (n = 57) NONACC_ACFE)b NOACFE)b NOACFE)b

JONES 0.110 0.088 0.120 0.149 -0.032 -0.061** -0.029 (0.056) (0.050) (0.064) (0.066) (-0.014) (-0.016)** (-0.002) [0.184] [0.128] [0.199] [0.260]

B&S 0.130 0.113 0.127 0.173 -0.014 -0.060* -0.046 (0.061) (0.059) (0.065) (0.056) (-0.006) (0.009) (0.003) [0.216] [0.155] [0.196] [0.339]

STRONGGOV 0.770 0.750 0.835 0.719 -0.085 0.031 0.116* (1.000) (1.000) (1.000) (1.000) (0.000) (0.000) (0.000)* [0.421] [0.434] [0.373] [0.453]

MV_EQUITY 3,510.884 2,347.849 6,900.863 1,332.631 -4,553.014* 1,015.218 5,568.232 (225.051) (237.931) (357.527) (160.147) (-119.596) (77.784) (197.380)* [18,140.260] [8,240.821] [30,761.640] [6,497.939]

SIZE 5.542 5.559 5.853 5.034 -0.294 0.525* 0.819** (5.416) (5.472) (5.879 (5.076) (-0.407) (0.396) (0.803)* [2.183] [2.103] [2.458] [1.863]

M/B 1.464 1.420 1.267 1.868 0.152 -0.448 -0.600 (0.943) (1.005) (0.791) (0.968) (0.214) (0.036) (-0.178) [2.281] [1.301] [1.193] [4.427]

DISTRESS -3.891 -3.945 -3.766 -3.945 -0.179 -0.000 0.179 (-4.046) (-4.208) (-3.892) (-3.919) (-0.316)* (-0.289) (0.027) [1.471] [1.604] [1.210] [1.494]

54

TABLE 2 (continued)

Accounting Non-Accounting No Entire ACFE Firms ACFE Firms ACFE Firms Sample (ACC_ACFE) (NONACC_ACFE) (NOACFE) (ACC_ACFE - (ACC_ACFE - (NONACC_ACFE - Variable (n = 283) (n = 141) (n = 85) (n = 57) NONACC_ACFE)b NOACFE)b NOACFE)b

OCF 0.020 0.049 0.012 -0.038 0.037 0.086 0.049 (0.073) (0.071) (0.074) (0.079) (-0.004) (-0.008) (-0.005) [0.339] [0.239] [0.275] [0.565]

LEVERAGE 0.188 0.193 0.193 0.167 -0.000 0.026 0.027 (0.147) (0.139) (0.173) (0.138) (-0.035) (0.000) (0.035) [0.205] [0.230] [0.187] [0.159]

RISKY 0.378 0.376 0.400 0.351 -0.024 0.025 0.049 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) [0.486] [0.486] [0.493] [0.481]

BIG4 0.799 0.801 0.871 0.684 -0.069 0.117 * 0.186*** (1.000) (1.000) (1.000) (1.000) (0.000) (0.000)* (0.000)*** [0.402] [0.400] [0.338] [0.469]

FOR_SALES -2.326 -2.421 -2.745 -1.457 0.324 -0.964 -1.288 (0.693) (0.662) (0.703) (0.818) (-0.041) (-0.156) (-0.115) [5.978] [6.003] [6.558] [4.926]

FIRM_AGE 2.663 2.534 2.807 2.766 -0.273*** -0.232** 0.041 (2.485) (2.303) (2.565) (2.639) (-0.262)*** (-0.336)** (-0.074) [0.7184] [0.674] [0.781] [0.683]

StDOI 2.136 2.005 2.550 1.845 -0.545** 0.160 0.705** (2.044) (2.000) (2.323) (1.628) (-0.323) (0.372) (0.695)* [1.901] [1.800] [2.101] [1.760]

________________________________ *, ** and *** indicate significance at p ≤ 0.10, ≤ 0.05 and p ≤ 0.01, respectively, based on two-tailed tests.

55

56

TABLE 2 (continued)

a Variable definitions:

JONES = absolute value of the discretionary accrual measure adjusted for performance calculated using the Jones model; B&S

=

absolute value of the discretionary accrual measure adjusted for performance calculated using the Ball & Shivakumar model;

ACC_ACFE = 1 if firm has at least one accounting audit committee financial expert (ACFE), else 0; NONACC_ACFE = 1 if firm has at least one non-accounting ACFE, but no accounting ACFE, else 0;

STRONGGOV = 1 if the summary measure of governance is greater than or equal to the sample median, else 0; MV_EQUITY = market value of equity (in millions);

SIZE = natural log of the market value of equity; M/B = market value of the total firm divided by the book value of assets, measured at the beginning of the fiscal year;

DISTRESS = Zmijewski’s (1984) financial condition index; OCF = cash flow from operations scaled by the beginning of the year total assets;

LEVERAGE = total debt divided by total assets; RISKY

=

1 if the firm is in SIC codes 2833 – 2836, 3570 – 3577, 3600 – 3674, 5200 – 5961, 7300 – 7379, and 8731 – 8734, else 0;

BIG4 = 1 if the firm engaged one of the largest four audit firms, else 0; FOR_SALES = natural log of the percentage of foreign sales to total sales;

FIRM_AGE = natural log of the number of years publicly traded; and StDOI = natural log of the three year standard deviation of operating income.

b Test for differences in the means are based on t-statistics (Z-statistics) for continuous variables (proportions). Tests in differences in the median are based on the Wilcoxon rank sum test. The Wilcoxon rank sum test tests whether the observations in the two groups are from populations with different medians. Thus, the test can indicate a significant difference even though the medians for the two groups are the same.

57

TABLE 3

Correlation Matrix a

Variable b ACC_ACFE NONACC_ACFE STRONGGOV SIZE M/B DISTRESS OCF LEVERAGE RISKY BIG4 FOR_SALES FIRM_AGE

NONACC_ACFE -0.6529 STRONGGOV -0.0439 0.1012 SIZE 0.0240 0.0708 0.4753 M/B 0.0694 -0.0876 0.1314 0.0799 DISTRESS -0.0884 0.0922 0.0305 -0.0358 0.1033 OCF 0.0472 -0.0119 0.0135 0.1586 -0.6446 -0.3221 LEVERAGE -0.0268 0.0453 0.0401 0.1154 -0.0837 0.7628 0.1111 RISKY -0.0045 0.0296 -0.0766 -0.1021 -0.0199 -0.0596 -0.2302 -0.2236 BIG4 0.0070 0.1176 0.3751 0.5056 0.0112 -0.0221 0.0505 0.0756 0.1009 FOR_SALES -0.0195 -0.0289 -0.0791 0.0493 0.0436 -0.1196 0.0859 -0.0108 -0.0720 -0.1596 FIRM_AGE -0.1776 0.1323 0.1301 0.3499 -0.0374 0.0517 0.1274 0.1800 -0.2849 0.0192 0.0119 StDOI -0.0484 0.1162 0.4203 0.8216 -0.0888 0.0859 0.1677 0.1894 -0.0665 0.4612 -0.0223 0.3096

________________________________ a We report Spearman rank correlation coefficients for ACC_ACFE, NONACC_ACFE, STRONGGOV, RISKY, and BIG4 and Pearson correlations otherwise. All bolded amounts are significant at the p < 0.10. Additionally, correlations with an absolute value of greater than 0.117 (0.15) are significant at the p < 0.05 (0.01). b Variables are defined in Table 2.

TABLE 4

Regression of Earnings Management on Audit Committee Financial Expertise and Control Variables

LN_ABS_PAC = b0 + b1ACFE + + b2STRONGGOV + b3ACFE*STRONGGOV + b4SIZE + b5M/B + b6DISTRESS + b7OCF + b8LEVERAGE + b9RISKY + b10BIG4 + b11FOR_SALES + b12FIRM_AGE + b13StDOI + ε

Ball and Shivakumar (B&S) Jones Exp. Estimated Estimated Estimated Estimated

Variable a Sign Coefficients

(1) t-statistic

Coefficients

(2) t-statistic

Coefficients

(3) t-statistic

Coefficients

(4) t-statistic

INTERCEPT -0.7530 -1.20 -0.3519 -0.50 -0.6451 -1.06 -0.4725 -0.69 ACFE - -0.8420 -2.22** -0.8251 -2.15** -0.7009 -1.90** -0.6855 -1.83** STRONGGOV - -0.7640 -1.94** -0.6990 -1.77** -0.5544 -1.45* -0.4999 -1.29* ACFE*STRONGGOV ? 0.9476 2.14** 0.8558 1.92* 0.5536 1.28* 0.4880 1.12 SIZE - -0.1581 -3.44*** -0.2062 -2.78*** -0.1180 -2.64*** -0.1600 -2.20***M/B + 0.1498 3.14*** 0.1608 3.21*** 0.0751 1.62** 0.0855 1.75** DISTRESS + 0.1422 1.30* 0.1572 1.38* 0.2260 2.13** 0.2467 2.22** OCF - 0.5951 1.51 0.6254 1.56 0.1865 0.49 0.2438 0.62 LEVERAGE - -1.1932 -1.60* -1.3574 -1.76** -1.1659 -1.61** -1.3617 -1.81** RISKY ? 0.6406 3.80*** 0.6182 3.55*** 0.1568 0.95 0.1696 1.00 BIG4 - -0.3029 -1.28* -0.3336 -1.39* -0.1555 -0.68 -0.1542 -0.66 FOR_SALES + -0.0076 -0.57 -0.0037 -0.28 FIRM_AGE - -0.0881 -0.72 0.0021 0.02 StDOI + 0.0896 1.16 0.0625 0.82 N 283 281 283 281 F-statistic 6.85 5.58 4.11 3.28 p-value 0.0000 0.0000 0.0000 0.0001 Adjusted R2 0.1719 0.1753 0.0994 0.0959

58

TABLE 4 (continued) ________________________________ *, ** and *** indicate significance at p ≤ 0.10, p ≤ 0.05 and p ≤ 0.01, respectively, based on one-tailed (two-tailed) tests for variables whose relation to the dependent variable is (is not) predicted.

a Variable definitions:

LN_ABS_PAC

=

natural log of the absolute value of performance adjusted discretionary accruals calculated using either the Jones model or the Ball and Shivakumar model;

ACFE = 1 if the firm names at least one audit committee financial expert (ACFE), else 0;

Other variables are defined in Table 2.

59

TABLE 5

Regression of Earnings Management on Accounting and Non-Accounting Financial Expertise and Control Variables

LN_ABS_PAC = b0 + b1ACC_ACFE + b2NONACC_ACFE + b3STRONGGOV + b4ACC_ACFE*STRONGGOV + b5NONACC_ACFE*STRONGGOV + b6SIZE + b7M/B + b8DISTRESS + b9OCF + b10LEVERAGE + b11RISKY + b12BIG4 + b13FOR_SALES + b14FIRM_AGE + b15StDOI + ε

Ball and Shivakumar (B&S) Jones Exp. Estimated Estimated Estimated Estimated

Variable a SignCoefficients

(1) t-statistic

Coefficients

(2) t-statistic

Coefficients

(3) t-statistic

Coefficients

(4) t-statistic

INTERCEPT -0.7670 -1.22 -0.3021 -0.43 -0.6514 -1.07 -0.3909 -0.57 ACC_ACFE - -0.8453 -2.14** -0.8387 -2.11 ** -0.6615 -1.72** -0.6492 -1.67** NONACC_ACFE - -0.8466 -1.73** -0.8422 -1.67 ** -0.8288 -1.75** -0.8712 -1.77** STRONGGOV - -0.7606 -1.93** -0.6991 -1.77 ** -0.5507 -1.44* -0.5022 -1.30* ACC_ACFE*STRONGGOV ? 0.8519 1.84* 0.7613 1.64 * 0.3809 0.85 0.3140 0.69 NONACC_ACFE*STRONGGOV ? 1.1125 1.99** 1.0335 1.80 * 0.9008 1.66* 0.8833 1.57 SIZE - -0.1649 -3.51*** -0.2030 -2.69 *** -0.1304 -2.86*** -0.1626 -2.21***M/B + 0.1579 3.26*** 0.1669 3.28 *** 0.0879 1.87** 0.0970 1.96** DISTRESS + 0.1365 1.25 0.1504 1.32 * 0.2199 2.07** 0.2391 2.15** OCF - 0.6463 1.62 0.6725 1.65 0.2713 0.70 0.3350 0.84 LEVERAGE - -1.1427 -1.53* -1.2789 -1.65 ** -1.0976 -1.51* -1.2551 -1.66** RISKY ? 0.6408 3.79*** 0.6081 3.48 *** 0.1579 0.96 0.1601 0.94 BIG4 - -0.3003 -1.26* -0.3383 -1.40 * -0.1418 -0.61 -0.1460 -0.62 FOR_SALES + -0.0075 -0.57 -0.0034 -0.26 FIRM_AGE - -0.1223 -0.97 -0.0423 -0.34 StDOI + 0.0814 1.04 0.0555 0.73 N 283 281 283 281 F-statistic 5.84 4.94 3.72 3.07 p-value 0.0000 0.0000 0.0000 0.0001 Adjusted R2 0.1708 0.1742 0.1038 0.0997

60

TABLE 5 (continued) ________________________________ *, ** and *** indicate significance at p ≤ 0.10, p ≤ 0.05 and p ≤ 0.01, respectively, based on one-tailed (two-tailed) tests for variables whose relation to the dependent variable is (is not) predicted. a Variables are defined in Tables 2 and 4.

61

TABLE 6

Panel A: Regression of Earnings Management on CFO/CPA Accounting Financial Experts, Other Accounting Financial Experts, Non-Accounting Financial Expertise and Control Variables

LN_ABS_PAC = b0 + b1CFO/CPA_ACFE + b2OTHER_ACC_ACFE + b3NONACC_ACFE + b4STRONGGOV +

b5CFO/CPA_ACFE*STRONGGOV + b6OTHER_ACC_ACFE *STRONGGOV + b7NONACC_ACFE *STRONGGOV + b8SIZE + b9M/B + b10DISTRESS + b11OCF + b12LEVERAGE + b13RISKY + b14BIG4 + b15FOR_SALES + b16FIRM_AGE + b17StDOI + ε

Ball and Shivakumar (B&S) Jones Exp. Estimated Estimated Estimated Estimated

Variable a Sign

Coefficients

(1) t-statistic

Coefficients

(2) t-statistic

Coefficient

(3) t-statistic

Coefficients

(4) t-statistic

INTERCEPT -0.7649 -1.21   -0.2793   -0.39   -0.7007 -1.15 -0.5083 -0.74 CFO/CPA_ACFE - -0.8744 -2.12 ** -0.8735   -2.11** -0.6095 -1.53* -0.5939 -1.47* OTHER_ACC_ACFE - -0.7276 -1.22   -0.7051   -1.18   -0.8612 -1.50* -0.8414 -1.45* NONACC_ACFE - -0.8465 -1.73 ** -0.8435   -1.67** -0.8262 -1.74** -0.8650 -1.76** STRONGGOV - -0.7600 -1.92 ** -0.6987   -1.76** -0.5501 -1.44* -0.5016 -1.30* CFO/CPA_ACFE *STRONGGOV ? 0.8795 1.83 * 0.7847   1.63*  0.3858 0.83 0.3238 0.69 OTHER_ACC_ACFE *STRONGGOV ? 0.7435 1.03   0.6911   0.95   0.1813 0.26 0.1203 0.17 NONACC_ACFE*STRONGGOV ? 1.1124 1.98 ** 1.0354   1.79* 0.8958 1.65* 0.8735 1.56 SIZE - -0.1649 -3.49 *** -0.2023   -2.67*** -0.1276 -2.79*** -0.1639 -2.22*** M/B + 0.1583 3.26 *** 0.1670   3.27*** 0.0879 1.87** 0.0980 1.97** DISTRESS + 0.1372 1.25   0.1518   1.33* 0.2142 2.02** 0.2334 2.10** OCF - 0.6498 1.63   0.6763   1.65   0.2615 0.68 0.3299 0.83 LEVERAGE - -1.1483 -1.52 * -1.2966   -1.66** -0.9910 -1.36* -1.1615 -1.53* RISKY ? 0.6435 3.78 *** 0.6072   3.44*** 0.1685 1.02 0.1793 1.04 BIG4 - -0.3014 -1.26

 *  -0.3388   -1.40* -0.1558 -0.67 -0.1520 -0.65

FOR_SALES +          

            

-0.0074   -0.55   -0.0043 -0.33 FIRM_AGE -   -0.1282   -1.00   -0.0140 -0.11 StDOI +   0.0811   1.03   0.0560 0.73 N 283 281       283 281 F-statistic 4.97 4.33       3.31 2.80 p-value 0.0000 0.0000       0.0001 0.0002 Adjusted R2 0.1648 0.1684       0.1031 0.0985

62

TABLE 6 (continued)

Panel B: Regression of Earnings Management on CFO and CPA Accounting Financial Experts, Other Accounting Financial Experts, Non-Accounting Financial Expertise and Control Variables

LN_ABS_PAC = b0 + b1CPA_ACFE + b2CFO_ACFE + b3OTHER_ACC_ACFE + b4NONACC_ACFE + b5STRONGGOV +

b6CPA_ACFE*STRONGGOV + b7CFO_ACFE*STRONGGOV + b8OTHER_ACC_ACFE*STRONGGOV + b9NONACC_ACFE*STRONGGOV + b10SIZE + b11M/B + b12DISTRESS + b13OCF + b14LEVERAGE + b15RISKY + b16BIG4 + b17FOR_SALES + b18FIRM_AGE + b19StDOI + ε

Ball and Shivakumar (B&S) Jones Exp. Estimated Estimated Estimated Estimated

Variable a Sign

Coefficients

(1) t-statistic

Coefficients

(2) t-statistic

Coefficients

(3) t-statistic

Coefficients

(4) t-statistic

INTERCEPT -0.7639 -1.20 -0.2726 -0.38 -0.7297 -1.19 -0.5192 -0.75 CPA_ACFE - -1.0365 -1.96 ** -1.0206 -1.92** -0.8054 -1.58* -0.7745 -1.50* CFO_ACFE - -0.7803 -1.71 ** -0.7898 -1.72** -0.4958 -1.13 -0.4930 -1.11 OTHER_ACC_ACFE - -0.7248 -1.21 -0.7029 -1.18 -0.8587 -1.49* -0.8398 -1.45* NONACC_ACFE - -0.8417 -1.71 ** -0.8409 -1.66** -0.8209 -1.73** -0.8642 -1.75** STRONGGOV - -0.7544 -1.90 ** -0.6931 -1.74** -0.5387 -1.41* -0.4918 -1.27* CPA_ACFE *STRONGGOV ? 0.9775 1.57 0.8376 1.34 0.3646 0.61 0.2737 0.45 CFO_ACFE *STRONGGOV ? 0.8110 1.54 0.7367 1.39 0.3607 0.71 0.3128 0.61 OTHER_ACC_ACFE *STRONGGOV ? 0.7405 1.02 0.6899 0.95 0.1772 0.25 0.1192 0.17 NONACC_ACFE*STRONGGOV ? 1.1090 1.97 ** 1.0355 1.79* 0.8952 1.65* 0.8789 1.56 SIZE - -0.1642 -3.46 *** -0.2018 -2.64*** -0.1281 -2.80*** -0.1654 -2.23*** M/B + 0.1588 3.25 *** 0.1672 3.27*** 0.0874 1.85** 0.0978 1.97** DISTRESS + 0.1364 1.23 0.1485 1.28* 0.2053 1.92** 0.2201 1.96** OCF - 0.6597 1.63 0.6805 1.65 0.2566 0.66 0.3220 0.80 LEVERAGE - -1.1488 -1.50 * -1.2749 -1.60* -0.9257 -1.25 -1.0705 -1.39* RISKY ? 0.6417 3.75 *** 0.6035 3.40*** 0.1666 1.01 0.1732 1.01 BIG4 - -0.3179 -1.32 * -0.3581 -1.46* -0.1866 -0.80 -0.1860 -0.78 FOR_SALES + -0.0074 -0.54 -0.0048 -0.37 FIRM_AGE - -0.1344 -1.04 -0.0265 -0.21 StDOI + 0.0817 1.04 0.0590 0.77 N 283 281 283 281 F-statistic 4.35 3.87 2.98 2.58 p-value 0.0000 0.0000 0.0001 0.0004 Adjusted R2 0.2073 0.2200 0.1522 0.158

63

TABLE 6 (continued)

Panel C: Regression of Earnings Management on Accounting Financial Experts, Senior Business Executives, Other Non-Accounting Financial Expertise and Control Variables

LN_ABS_PAC = b0 + b1ACC_ACFE + b2EXEC + b3 OTHER + b4STRONGGOV + b5ACC_ACFE*STRONGGOV + b6EXEC*STRONGGOV

+ b7OTHER*STRONGGOV + b8SIZE + b9M/B + b10DISTRESS + b11OCF + b12LEVERAGE + b13RISKY + b14BIG4 + b15FOR_SALES + b16FIRM_AGE + b17StDOI + ε

Ball and Shivakumar (B&S) Jones Exp. Estimated Estimated Estimated Estimated

Variable a Sign

Coefficients

(1) t-statistic

Coefficients

(2) t-statistic

Coefficients

(3) t-statistic

Coefficients

(4) t-statistic

INTERCEPT -0.7182 -1.14 -0.2076 -0.30 -0.6194 -1.01 -0.3358 -0.49 ACC_ACFE - -0.8356 -2.11** -0.8328 -2.09 ** -0.6669 -1.73** -0.6556 -1.69** EXEC - -0.6085 -1.10 -0.6400 -1.16 -0.9495 -1.76** -0.9618 -1.78** OTHER - -1.3780 -1.87** -1.4573 -1.76 ** -0.5942 -0.83 -0.6425 -0.79 STRONGGOV - -0.7370 -1.87** -0.6772 -1.71 ** -0.5559 -1.45* -0.5049 -1.31* ACC_ACFE*STRONGGOV ? 0.8523 1.84* 0.7590 1.63 * 0.3882 0.86 0.3198 0.70 EXEC*STRONGGOV ? 0.9506 1.54 0.9144 1.47 1.0841 1.80* 1.0371 1.71* OTHER*STRONGGOV ? 1.1107 1.24 1.0903 1.12 0.2187 0.25 0.1969 0.21 SIZE - -0.1669 -3.52*** -0.2032 -2.69 *** -0.1375 -2.98*** -0.1658 -2.24***M/B + 0.1546 3.17*** 0.1647 3.23 *** 0.0928 1.95** 0.0998 2.00** DISTRESS + 0.1385 1.27* 0.1530 1.34 * 0.2251 2.12** 0.2450 2.20***OCF - 0.5947 1.48 0.6475 1.58 0.3032 0.77 0.3485 0.87 LEVERAGE - -1.1879 -1.59* -1.3110 -1.69 ** -1.1433 -1.57* -1.3048 -1.72** RISKY ? 0.6211 3.64*** 0.5899 3.36 *** 0.1707 1.03 0.1660 0.97 BIG4 - -0.3435 -1.40* -0.3812 -1.54 * -0.1079 -0.45 -0.1196 -0.49 FOR_SALES + -0.0061 -0.45 -0.0033 -0.26 FIRM_AGE - -0.1401 -1.11 -0.0511 -0.42 StDOI + 0.0800 1.02 0.0528 0.69 N 283 281 283 281 F-statistic 5.19 4.52 3.28 2.78 p-value 0.0000 0.0000 0.0001 0.0003 Adjusted R2 0.1721 0.1759 0.1016 0.0973

64

65

TABLE 6 (continued)

________________________________ *, ** and *** indicate significance at p ≤ 0.10, p ≤ 0.05 and p ≤ 0.01, respectively, based on one-tailed (two-tailed) tests for variables whose relation to the dependent variable is (is not) predicted. a Variable definitions:

CFO/CPA_ACFE = 1 if firm has at least one accounting ACFE with current or previous experience as a CFO or CPA, else 0; CPA_ACFE = 1 if firm has at least one accounting ACFE with current or previous experience as a CPA, else 0; CFO_ACFE = 1 if firm has at least one accounting ACFE with current or previous experience as a CFO, but no CPA_ACFE, else 0;

OTHER_ACC_ACFE = 1 if the firm has at least one accounting ACFE, but no CFO/CPA_ACFE, else 0; NONACC_ACFE = 1 if firm has at least one non-accounting ACFE, but no accounting ACFE, else 0;

EXEC

=

1 if firm has at least one non-accounting ACFE with current or previous experience as a CEO, President, Vice-President, or Chairman of the Board but no ACC_ACFE, else 0;

OTHER = 1 if the firm has at least one non-accounting ACFE, but no ACC_ACFE or EXEC, else 0. Other variables are defined in Tables 2 and 4.

TABLE 7

Regression of Earnings Management on Accounting Financial Experts Appointed Post-SOX, Accounting Financial Experts Appointed Pre-SOX, Non-Accounting Financial Expertise and Control Variables

LN_ABS_PAC = b0 + b1NEW_ACC_ACFE + b2OLD_ACC_ACFE + b3NONACC_ACFE + b4STRONGGOV + b5NEW_ACC_ACFE*STRONGGOV +

b6OLD_ACC_ACFE*STRONGGOV + b7NONACC_ACFE*STRONGGOV + b8SIZE + b9M/B + b10DISTRESS + b11OCF + b12LEVERAGE + b13RISKY + b14BIG4 + b15FOR_SALES + b16FIRM_AGE + b17StDOI + ε

Ball and Shivakumar (B&S) Jones Exp. Estimated Estimated Estimated Estimated

Variable a Sign

Coefficients

(1) t-statistic

Coefficients

(2) t-statistic

Coefficients

(3) t-statistic

Coefficients

(4) t-statistic

INTERCEPT -0.7436 -1.17 -0.0078 -0.58 -0.5744 -0.94 -0.0046 -0.36 NEW_ACC_ACFE - -0.7206 -1.46* -0.6996 -1.41 * -0.1776 -0.37 -0.1485 -0.31 OLD_ACC_ACFE - -0.9180 -2.12** -0.9200 -2.12 ** -0.9440 -2.26*** -0.9410 -2.23***NONACC_ACFE - -0.8505 -1.73** -0.8488 -1.68 ** -0.8425 -1.78** -0.8943 -1.82** STRONGGOV - -0.7578 -1.92** -0.6962 -1.76 ** -0.5397 -1.41* -0.4914 -1.28* NEW_ACC_ACFE*STRONGGOV ? 0.7458 1.32 0.6324 1.11 -0.0892 -0.16 -0.1787 -0.32 OLD_ACC_ACFE*STRONGGOV ? 0.9072 1.78* 0.8327 1.63 * 0.6476 1.32 0.5969 1.20 NONACC_ACFE*STRONGGOV ? 1.1181 1.99** 1.0418 1.80 * 0.9208 1.70* 0.9120 1.63 SIZE - -0.1665 -3.52*** -0.2056 -2.70 *** -0.1360 -2.98*** -0.1726 -2.34***M/B + 0.1599 3.28*** 0.1695 3.31 *** 0.0952 2.02** 0.1062 2.13** DISTRESS + 0.1408 1.28* 0.1542 1.34 * 0.2341 2.20*** 0.2510 2.25***OCF - 0.6745 1.67 0.7048 1.70 0.3730 0.96 0.4479 1.11 LEVERAGE - -1.1544 -1.54* -1.2868 -1.66 ** -1.1288 -1.56* -1.2753 -1.69** RISKY ? 0.6430 3.79*** 0.6112 3.49 *** 0.1675 1.02 0.1718 1.01 BIG4 - -0.3029 -1.27* -0.3414 -1.41 * -0.1550 -0.67 -0.1586 -0.67 FOR_SALES + -0.0078 -0.58 -0.0046 -0.36 FIRM_AGE - -0.1219 -0.97 -0.0399 -0.33 StDOI + 0.0825 1.05 0.0606 0.80 N 283 281 283 281 F-statistic 4.99 4.34 3.41 2.90 p-value 0.0000 0.0000 0.0000 0.0001 Adjusted R2 0.1652 0.1687 0.1070 0.1034

66

67

TABLE 7 (continued) ________________________________ *, ** and *** indicate significance at p ≤ 0.10, p ≤ 0.05 and p ≤ 0.01, respectively, based on one-tailed (two-tailed) tests for variables whose relation to the dependent variable is (is not) predicted. a Variable definitions: NEW_ACC_ACFE = 1 if firm added all their accounting ACFEs since the enactment of SOX, else 0; OLD_ACC_ACFE = 1 if firm had at least one accounting ACFE who was appointed prior to SOX, else 0;

Other variables are defined in Tables 2 and 4.

68

TABLE 8

Regressions of Real Earnings Management on Audit Committee Financial Experts and Control Variables

PRODCOST (DISCEXP) = b0 + b1ACC_ACFE + b2NONACC_ACFE + b3STRONGGOV + b4ACC_ACFE*STRONGGOV + b5NONACC_ACFE*STRONGGOV + b6SIZE + b7OCF + b8BIG4 + b9ABSPAC + b10DISCEXP (PRODCOST) + ε

    Production Costs Discretionary Expenditures Estimated   Estimated Variable a Coefficients t-statistic   Coefficients t-statistic   INTERCEPT 0.0362 0.22   -1.0943 -1.22 ACC_ACFE 0.1774 1.03   0.9393 1.25 NONACC_ACFE -0.2189 -1.05   0.5991 0.61 STRONGGOV 0.1794 1.04   0.7425 0.93 ACC_ACFE*STRONGGOV -0.0697 -0.40   -0.7179 -0.90 NONACC_ACFE*STRONGGOV 0.3702 1.69 *   -0.1409 -0.13 SIZE -0.0148 -0.90   -0.0183 -0.30 OCF -0.6198 -3.17 ***   -1.7093 -4.22 *** BIG4 -0.2190 -2.18 **   0.1217 0.29 ABSPAC -1.1303 -2.44 **   0.3279 0.51 DISCEXP -0.0889 -1.99 **              PRODCOST     -0.7337 -3.14 ***           N   280   280 F-statistic   3.04   5.83 p-value   0.0012   0.0000 Adjusted R2     0.2859           0.1817

________________________________ *, **, and *** indicate significance at p ≤ 0.10, p ≤ 0.05 and p ≤ 0.01, respectively, based on two-tailed tests.

a Variable definitions: DISCEXP

=

the level of abnormal discretionary expenditures, where discretionary expenses are the sum of advertising expenses, R&D expenses and SG&A expenses;

PRODCOST

=

the level of abnormal production costs, where production costs are defined as the sum of cost of goods sold and the change in inventories;

Other variables are defined in Tables 2 and 4.