Accounting Standard Precision, Corporate Governance, and Accounting Restatements
Accounting Standard Precision, Corporate Governance, and Accounting Restatements
Li Fang [email protected] Iowa State University
Jeffrey Pittman* [email protected] Memorial University of Newfoundland
Yinqi Zhang [email protected] American University
Yuping Zhao [email protected] University of Houston
July 2018
*Corresponding co-author. We appreciate helpful comments from Matthew Baugh, Jim Cannon, Bong Hwan Kim, Sam Lee, Zhejia Ling, Mark Ma, Gerald S. Martin, Dechun Wang, Qian Wang, Olena Watanabe, and Yijiang Zhao. Our paper has also benefited from constructive comments on an earlier version from conference participants at the 2017 AAA Annual Meeting and the 2018 AAA Audit Midyear Meeting as well as seminar participants at American University and Iowa State University.
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Accounting Standard Precision, Corporate Governance, and Accounting Restatements
Abstract: Prior research documents wide variation in the precision of accounting standards (rules-based standards (RBS) versus principles-based standards (PBS)). We examine whether financial reporting quality evident in restatements is associated with accounting standard precision and whether the role that non-regulatory monitors and the SEC as the regulator play in the financial reporting process varies with accounting standard precision. Our strong, robust evidence implies that the likelihood of a subsequent financial report restatement decreases as the accounting standards applicable to the firm become more principles-based. This inference is robust to examining exogenous shifts in standard precision caused by accounting standard revisions. Additional analyses suggest that our evidence mainly stems from managers’ concerns over second guessing rather than greater litigation risk. We also find that non-regulatory monitors, including independent boards, audit committees, and external auditors, play a more effective role at constraining misreporting when they have the guidance of more detailed rules under RBS, whereas the SEC plays a more prominent role when there is a lack of clear guidance under PBS. Our large-sample empirical evidence suggests a potential trade-off between PBS and RBS: although the average reporting quality measured by misreporting may improve under the more principles-based framework, some of the corporate governance mechanisms carried out by non-regulatory monitors may not function as well under more PBS as under more RBS, potentially reflecting that it is harder for non-regulatory monitors to enforce less precise accounting standards. Our results suggest that more intensive regulatory monitoring would be needed to compensate for the potentially weakened monitoring by non-regulatory monitors should the U.S. financial reporting regime moves to a more principles-based framework.
I. Introduction
Accounting discretion significantly impacts financial reporting quality and the interactions between managers and various monitors (Holthausen 1990; Fields, Lys, and Vincent
2001; Nelson, Elliott, and Tarpley 2002). An important source of accounting discretion arises from the variation in the precision of accounting standards (Nelson 2003). In particular, principles- based accounting standards (PBS) emphasize the importance of reflecting the underlying economic substance of the transaction by allowing greater flexibility in the accounting treatment.
In contrast, rules-based accounting standards (RBS) focus on strict compliance with specific accounting rules by stipulating bright-line tests and detailed implementation guidance. Financial reporting quality is a joint product of accounting standards, management reporting quality, and audit quality (Chen, Jiang, and Zhang 2014). In this study, we examine whether financial reporting quality is affected by the precision of accounting standards, and how accounting standard attributes shape the effectiveness of various forms of monitoring of the financial reporting process.
U.S. accounting standards are predominantly rules-based and have become even more so over time (Donelson, McInnis, and Mergenthaler 2016). RBS have been criticized as a primary driver of restatements due to their excessive details and confusing bright line rules and exceptions. As a result, the Securities and Exchange Commission (SEC) has aimed at moving towards PBS over the past decade (FASB 2002; SEC 2003, 2008, 2015; Plumlee and Yohn 2010).1
Proponents of PBS contend that PBS reduces the complexity of standards (Section 108 of the
1 Some recent accounting standard changes such as the new revenue recognition standard (ASU 2014-09) and lease standard (ASU 2016-02) reflect U.S. regulators’ intention to move towards principles-based standards. In her keynote address at the 2015 AICPA national conference, former SEC Chairwoman Mary Jo White highlighted the possibility of allowing U.S. domestic companies to provide IFRS-based information as a supplement to U.S. GAAP financial statements without requiring reconciliation (SEC 2015).
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Sarbanes-Oxley Act of 2002) and the discretion afforded by PBS allows managers to prepare financial reports that more accurately reflect the underlying economics of the firm (Dye and
Sunder 2001; Folsom, Hribar, Mergenthaler, and Peterson 2017). However, proponents of RBS maintain that detailed guidance and bright-line thresholds increase consistency in the accounting treatment of similar transactions (Schipper 2003; Kothari, Ramanna, and Skinner 2010), relieve preparers of the burden of making sophisticated judgments over complex transactions (SEC
2003), and narrow the scope for managerial opportunism. Since RBS clearly delineate unacceptable behavior and have a lower cost of dispute resolution (Ehrlich and Posner 1974), their compliance can be more amenable to auditing and enforcement (Donelson, McInnis, and
Mergenthaler 2012).
We begin our analysis by examining whether accounting standard attributes shape financial reporting quality measured with financial report restatements. We focus on restatements as they are a readily available signal of misreporting widely accepted by researchers, regulators, and investors (DeFond and Zhang 2014; PCAOB 2015; Christensen, Glover, Omer, and Marjorie 2016; Aobdia 2016; Karpoff, Koester, Lee, and Martin 2017). In analyzing 18,193 firm-year observations during 2000-2009, we find that firms’ principles-based scores, which we operationalize with the PSCORE developed by Folsom et al. (2017), are negatively associated with the likelihood of subsequent financial report restatements, consistent with less financial misreporting committed by firms subject to more PBS.2 Furthermore, PBS is associated with lower frequency of both errors (unintentional mistakes) and irregularities (intentional mistakes).
However, the negative association between PSCORE and restatements may not stem from higher
2 The PSCORE in Folsom et al. (2017) is constructed based on the rules-based score (RBC1) derived in Mergenthaler (2011). Extensive prior research relying on the PSCORE or components of RBC1 includes Donelson et al. (2012; 2016) and Fang, Huang and Wang (2017).
2 reporting quality of firms subject to more PBS since: (i) there may be more reporting of complex transactions at firms subject to more RBS; and (ii) it may be more difficult to detect violations of
PBS in the absence of detailed rules. Importantly, further analyses imply that these competing explanations are not spuriously responsible for our core evidence.3
Since the accounting standards applied by a firm are predominantly determined by its industry and the nature of its transactions, endogenous selection of accounting standards unlikely constitutes a major threat to our conclusions. However, bias due to correlated omitted variables in unobservable firm and industry characteristics may confound our inferences. To address this concern, we exploit two exogenous accounting standard events: the shift from SFAS
123 to the more principles-based SFAS 123r, and the shift from APB 17 to the more rules-based
SFAS 142. Reinforcing our earlier results, we find that the frequency of restatements due to violations of these specific standards by firms affected by the standard shifts decreases (increases) as the accounting standards become more principles-based (rules-based).
Prior research proposes that potential improvements in financial reporting quality under a principles-based regime may reflect higher litigation risk (Donelson, McInnis, and
Mergenthaler 2012) or greater concern over being second guessed when more accounting discretion is permissible (Agoglia, Doupnik, and Tsakumis 2011; Gimbar, Hansen, and Ozlanski
2016).4,5 We find that the main results are more pronounced when there are strong concerns over being second guessed, --- i.e., in the presence of financial distress and high policy uncertainty
3 Later in the paper, we more fully motivate these analyses and outline the results. 4 Consistent prior literature, we define second guessing as the uncertainty surrounding the risk of being perceived as out of compliance (Agoglia, Doupnik, and Tsakumis 2011), or being evaluated more harshly (Kadous and Mercer 2016) by auditors or regulators. 5 Another potential explanation is that the auditor demands more extensive evidence when the firm is more subject to PBS (Cohen, Krishnamoorthy, Peytcheva, and Wright 2013; Peytcheva, Wright, and Majoor 2014). However, Donelson, Folsom, McInnis, Mergenthaler, and Peterson (2017) document lower audit fees for firms more subject to PBS, inconsistent with the lower misreporting of these firms is driven by greater audit effort.
3 when the manager’s decisions are more likely to be challenged; and when the manager exhibits less confidence. However, we find no evidence that financial reporting quality improves for firms more subject to PBS when the litigation risk is greater. Collectively, the evidence implies that concerns over second guessing are primarily responsible for the main results.
Prior research documents that non-regulatory monitors such as independent boards, the audit committee, and external auditors, as well as the regulator – the SEC are important gatekeepers responsible for protecting financial reporting veracity. To the extent that these monitors heavily rely on accounting standards to interpret financial reports and to enforce compliance with GAAP, it is important to analyze whether their roles vary with the precision of accounting standards. We find that board and audit committee independence and the external auditor are more effective in constraining misreporting under the more RBS than under the more
PBS, potentially reflecting that it is harder for these non-regulatory monitors to enforce less precise accounting standards.6 In contrast, the deterrence effect of SEC enforcement is stronger under the more PBS than under the more RBS. The evidence collectively suggests that monitoring mechanisms documented in prior studies do not work equally effectively under PBS and RBS.
Specifically, non-regulatory monitors are potentially more effective at disciplining the manager when they can rely upon clearly articulated detailed rules, whereas the SEC plays a more prominent role when accounting standards are relatively vague, consistent with the SEC’s complete independence from firm managers, its greater focuses on fulfilling the spirit of the
6 Ahmed, Neel and Wang (2013) find similar evidence on the interplay between accounting standards and enforcement in an international setting. They find that accounting quality decreases after the mandatory adoption of IFRS, mainly in strong enforcement countries. They ascribe the finding to the fact that accounting standards are more relevant for strong enforcement countries than for weak enforcement counties. In strong enforcement countries, it is more difficult to enforce principles-based accounting standards under IFRS which is looser than the domestic GAAP. In weak enforcement countries, accounting standards precision is inconsequential.
4 standards, as well as its superior authority to interpret accounting standards when less detailed guidance is available.
We contribute to extant research in several ways. First, accounting standard setting involves a trade-off between the two qualitative characteristics of financial reporting: relevance and faithful representation (FASB 2010).7 This question is important given that the financial reporting quality implications of an accounting standard system is at the center of the debate over a potential shift to a more PBS regime (SEC 2003; SEC 2012). Folsom et al. (2017) primarily examine the impact of PBS on the relevance of financial reports to the decision usefulness of investors.8 We extend Folsom et al. (2017) by studying how PBS shape the representational faithfulness of financial reporting. We focus on reporting aggressiveness that amounts to GAAP violations, which are a major concern to investors and regulators in the potential move to a principles-based regime (SEC 2003; 2012; PCAOB 2015; Christensen et al. 2016). We complement extant research by providing large-sample empirical evidence that, despite their greater latitude and judgment, managers at firms subject to more PBS, on average, are less likely to misreport.
The fact that PBS is associated with both fewer errors and fewer irregularities suggests less noise and bias in financial reporting under PBS. Our results provide support to SEC’s move towards convergence with IFRS, which is considered more principles-based than U.S. GAAP. Further, we
7 Relevance primarily relates to whether financial information is capable of making a difference to user decisions (FASB 2010, QC6), whereas faithful representation focuses on whether financial information is complete, neutral, and free from error (FASB 2010, QC12). 8 Although Folsom et al. (2017, Table 8) find that incentives to manipulate earnings further increase earnings management under more PBS, they do not compare the tendency of earnings management between firms more subject to PBS and firms more subject to RBS in absence of salient earnings management incentives. Such a comparison potentially interests investors and regulators who are concerned about the implications of PBS for the average firms. We extend Folsom et al. (2017) by testing the change in the average restatement frequency and earnings management as firms become more subject to PBS, unconditional on management incentives.
5 find that concerns over being second guessed, rather than litigation risk, is the primary driver behind the higher reporting quality under the more PBS.
Second, despite extensive prior evidence on the role that internal and external governance mechanisms play in constraining managerial reporting opportunism, the channels through which these monitoring mechanisms work remain largely unexplored. Our evidence implies that non- regulatory monitors such as boards and auditors play a more effective role in curbing financial misreporting under RBS. These results suggest that external auditors and board members likely fulfill their monitoring obligations mainly through ensuring better compliance with detailed standards when the applicable standards are more precise, rather than through more narrowly limiting managerial discretion when the standards are less precise. In contrast, the SEC deters more misreporting under PBS. 9 Although it would be difficult to justify proposing policy prescriptions at this early stage, our evidence highlights a potential trade-off between PBS and
RBS: although the average reporting quality measured by misreporting frequency improves under the more principles-based framework, some monitoring structures may not perform as well should the U.S. converge toward a more principles-based regime. The lower effectiveness of the board and external auditors could be a concern for investors and regulators.
Our study is subject to two caveats. First, despite that Folsom et al. (2017) perform extensive tests to validate PSCORE and to confront the concern that it also reflects transaction complexity, as well as our robust evidence from analyzing exogenous shifts in accounting standards, we cannot completely dismiss the possibility that our results are spuriously driven by
9 Our findings extend Agoglia et al. (2011)’s experimental evidence that monitoring by the audit committee is more effective under RBS in at least three ways. First, besides audit committee, we also examine a broader set of monitors with potentially different expertise and incentives, including the external auditor, the board, and the SEC. Second, while Agoglia et al. (2011) operationalize reporting aggressiveness by the within-GAAP treatment of lease classification, we study GAAP violations, which capture more aggressive reporting practices. Third, we contrast how monitoring effectiveness varies with the accounting standard precision for non-regulatory monitors and for the SEC.
6 complexity. Second, as restatements are the result of a complex detection and negotiation process that involves executives, auditors, boards, and sometimes regulators, in the presence of a misstatement, a restatement could be more likely under RBS which involve bright lines and specific guidance. Although our findings hold to discretionary accruals as an alternative measure and we do not find evidence supporting harder detection of misstatements under PBS, we cannot entirely rule out the possibility that our results may reflect detection of misreporting and do not directly speak to occurrence of misreporting in the first place.
The rest of the paper proceeds as follows. Section II reviews prior research. Section III develops the testable predictions. Sections IV and V outline the empirical strategy, data, and results. Section VI concludes.
II. Prior Literature
Although there is a large body of research focusing on how the adoption of more PBS, such as IFRS, impacts the financial reporting properties of international firms (George, Li, and
Shivakumar 2016), prior empirical research seldom analyzes how the relative orientation of principles- versus rules-based accounting standards influences U.S. firms, which operate in the world’s strictest regulatory enforcement and litigation environment. Folsom et al. (2017) find that, earnings informativeness, earnings persistence, and earnings’ predictive ability for future cash flows increase as a firm relies more on PBS, consistent with PBS allowing managers to better communicate the fundamental economics about the firm, although they also document opportunistic managerial behavior in the presence of strong reporting incentives under PBS.
Relatedly, Mergenthaler (2011) finds that firms relying more on RBS engage in a greater magnitude of earnings management, although they are less likely to be penalized by the SEC in the form of enforcement actions. We complement these two studies by examining: (i) whether on average PBS are associated with greater probability of misreporting; (ii) the drivers behind the
7 potentially different frequency of GAAP violations between PBS and RBS; and (iii) how effectively various governance mechanisms function under PBS relative to RBS.
Prior theoretical and experimental research on the impact of accounting standard precision on financial misreporting provides mixed results. In modelling accounting standard tightness and earnings management, Ewert and Wagenhofer (2005) predict that earnings quality increases with tighter standards, although the costs may outweigh the benefits. However, experimental evidence implies that managers report more aggressively under a more rules-based regime (Psaros and Trotman 2004; Agoglia et al. 2011), or that standard precision is irrelevant to manager reporting behavior (Hoffman and Patton 2002).
Prior experimental and survey research on the importance of accounting standards to corporate monitoring activities mainly focuses on the interaction between the manager and auditor. Nelson et al. (2002) find that managers are more likely to attempt earnings management, and auditors are less likely to adjust earnings management attempts, which are structured (not structured) with respect to precise (imprecise) standards. Drawing on the example of lease accounting standards, prior research finds that auditors are more likely to intervene in clients’ off-balance treatment of lease liabilities under PBS than under RBS (Cohen et al. 2013), partially because under PBS jurors perceive that auditors have more control over financial reporting outcomes, and are more likely to hold them liable for audit quality (Gimbar et al. 2016). This greater process accountability under PBS increases the auditor’s demand for audit evidence
(Peytcheva et al. 2014). In experimental research, Agoglia et al. (2011) is the only study to examine the interaction with the manager by monitors other than the auditor. They report that a strong audit committee constrains managers’ aggressive reporting under the more precise accounting standards, although it has no perceptible impact under the less precise accounting standards.
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Although prior research provides useful insights on how accounting standard precision affects manager and auditor behavior, the results are mixed, are generated mostly based on lease accounting standards, and offer little evidence on the net impact on financial reporting quality.
We extend this research by providing large sample archival evidence on how accounting standard attributes affect the role that various internal and external monitors play at constraining financial misreporting.
III. Hypotheses Development
The Association between Accounting Standard Precision and Restatements
A primary distinction between PBS and RBS is the extent of professional judgment or discretion allowed within the system (Schipper 2003; SEC 2003). 10 On one hand, although managers enjoy more discretion under PBS to communicate the economic nature of the transactions, they are held more accountable for the added discretion. Prior studies find jurors may perceive that managers face fewer constraints and have more control over the application of imprecise accounting standards, leading to more negligence verdicts (Gimbar et al. 2016).
Accordingly, concerns surrounding being second guessed or litigation risk exert a chilling effect evident in managers becoming more eager to report truthfully under the more uncertain PBS. On the other hand, since RBS prescribe exact rules to follow and provide clear thresholds for acceptable treatments, they require less expertise of accounting professionals (Schipper 2003) and may constrain some mistakes in the first place. Consistent with this perspective, companies attribute judgment in applying standards as one of the key causes of restatements (Plumlee and
Yohn 2010). Moreover, auditors are in a better position to resist client pressure when proposing
10 RBS “minimize (and indeed, in certain instances, trivialize) the judgmental component of accounting practice through the establishment of complicated, finely articulated rules that attempt to foresee all possible application changes” (SEC 2003: 14).
9 adjustments when they can rely on precise rules (Nelson 2003) and thus more mistakes could be corrected by auditors during annual audits.11 The net effect of the greater discretion under PBS on reporting quality is ambiguous.
A second distinction between PBS and RBS is the complexity of standards. RBS is featured with great volume of implementation guidance and high level of details, which are alleged to add complexity in applying standards (SEC 2003). Peterson (2012) argues that greater complexity causes more mistakes in preparing financial statements, and incentivizes managers to manipulate as complexity increases the costs to detect manipulation. He finds that both intentional and unintentional mistakes increase with the complexity of a company’s revenue recognition policy.
Accordingly, there could be more misreporting under RBS due to its greater complexity.
Taken as a whole, it is unclear ex ante whether more precise accounting standards are associated with a greater likelihood of accounting misstatements, leading to our first prediction
(both hypotheses are stated in null form):
H1: The likelihood of accounting restatements is not associated with the extent to which a firm is subject to PBS.
Does the Monitoring Role of Governance Mechanisms Vary with Accounting Standard Precision?
Shareholders rely on various economic agents to monitor managers to ensure that they behave in the best interests of shareholders, including by truthfully reporting the results of operations (Jensen and Meckling 1976). Prior evidence implies that independent board and audit committee members (Abbott, Parker, and Peters 2004; Zhao and Chen 2008; Beasley, Carcello,
11 In contrast, clients under PBS can argue, “tell me where it says I can’t do what I want” (Niemeier 2008: 5), making it harder for auditors to challenge clients’ preferred reporting treatment or demand corrections of management bias when auditors disagree with their clients. Corroborating the weakened auditor negotiation power under PBS, prior research implies that the auditor is more likely to waive material adjusting journal entries when the issues involve more judgment (Wright and Wright 1997; Braun 2001).
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Hermanson, and Neal 2010; Carcello, Hermanson, and Ye 2011), audit effort (Blankley, Hurtt, and
MacGregor 2012; Lobo and Zhao 2013), industry specialist auditors (Carcello and Nagy 2004;
Jayaramanl and Milbourn 2015), and the SEC (Kedia and Rajgopal 2011) play major roles in protecting financial reporting integrity. However, extant research seldom examines how these monitoring agents constrain managers’ reporting discretion.
We posit that two channels are at work. First, stronger monitoring exerts pressure on managers to more strictly follow specific accounting rules. This channel emphasizes GAAP compliance and is more relevant for the more RBS. Second, tougher monitoring also more narrowly restricts managerial discretion allowed by the standard, minimizes potential bias in management judgment, and enforces faithful representation that reflects not only the legal form, but also the economic substance of the transaction. This channel stresses fair presentation and is more pertinent to the more PBS. Both rule compliance and faithful representation are important characteristics for high quality financial reporting and auditing (DeFond and Zhang 2014). Thus, the precision of accounting standards may not affect the incentives or effectiveness of monitors.
However, for several reasons, monitoring by auditors and the board under the more rules- based regime could be more effective than monitoring under the more principles-based regime.
First, authoritative detailed accounting rules prescribe unacceptable accounting practices and their compliance is more amenable to audit. Detailed rules also strengthen the negotiation power of the auditor and independent board members in requiring managers to book audit adjustments.
Second, in the event of litigation, the auditor and board members may have even a weaker defense against the allegation of financial reporting failure when the firm violates the clear guidance under a rules-based standard (i.e. Donelson et al.’s (2012) “roadmap” theory). As a result, RBS heighten the incentives for the auditor and board members to enforce strict compliance with standards. Third, anecdotal evidence suggest rules-oriented auditors are pretty
11 common in the U.S. (Jamal and Tan 2010) and the Public Company Accounting Oversight Board
(PCAOB) indicated “lack of professional skepticism” is one contributing factor for audit deficiencies in areas that require greater judgment (PCAOB 2012, p. 5). The compliance mentality of auditors could jeopardize audit quality under PBS. In contrast, the SEC always stresses fulfilling the intent of the accounting standard (SEC 2003). 12 Importantly, as the ultimate regulator of financial reporting, the SEC has much stronger negotiation power over the public issuers than auditors do. Moreover, attorneys account for the majority of SEC staff.13 Attorneys are less likely to be subject to the “check-box” mentality than accountants due to their training and legal practice and they may find PSB are easier to understand and work with than RBS. As a result, it is possible that SEC monitoring may more be effective under PBS. Grounded in prior research, our second hypothesis reflects whether accounting standard precision affects the monitoring role of various economic agents.14
H2: The association between the likelihood of accounting restatements and board and audit committee strength, audit quality, and SEC monitoring does not vary with the extent to which a firm is subject to PBS.
12 The SEC has repeatedly stressed the importance of reflecting the substance of transactions to protect investors. In his speech addressed to the American Accounting Association in 1985, SEC past Chief Accountant, Clarence Sampson indicated “the role of judgment in financial accounting must be to determine the real substance of transactions and to choose the appropriate standards to present those transactions in the financial statements in a way that is fair (‘representationally faithful’)”, available at https://www.sec.gov/news/speech/1985/041985sampson.pdf. In her remark to Financial Accounting Foundation Trustees in 2014, the SEC current chairwoman Mary Jo White noted “financial reporting can and should provide investors with a clear picture of a company’s financial condition”, available at https://www.sec.gov/news/speech/2014-spch052014mjw. 13 In 2017, the SEC professional staff comprises of about 1,935 attorneys and 910 accountants (https://www.federalpay.org/employees/securities-and-exchange-commission). 14 In their lease classification experiment, Agoglia et al. (2011) report that a strong audit committee inhibits managers’ aggressive reporting under the more precise accounting standards, but has no effect under the less precise accounting standards. They interpret the results as reflecting managers’ desire to report more truthfully under the less precise accounting standards lessens the burden on the audit committee to curb aggressive reporting. The main distinctions between their strong and weak audit committees are whether independent audit committee members are former employee of the company, proportion of audit committee members with financial expertise, and meeting frequency.
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IV. Research Design and Sample Selection
Principles-based Accounting Standards (PBS) Measure
We closely follow Folsom et al. (2017) in computing a principles-based score (PSCORE) for each firm-year observation during 2000-2009. This involves: (i) downloading 10-Ks for all firms for the 2000-2009 period; (ii) calculating the relative importance score (REL_IMP) of each standard for each firm-year as the mean-adjusted keyword counts of the standard in the firm’s
10-K, divided by the standard deviation of the keyword counts of the standard of all firms; (iii) multiplying the relative importance score by the corresponding standard-year’s rules-based score
(RBC1) from Mergenthaler (2011) to obtain a standardized score that captures the cross-sectional variation in a firm’s reliance on each particular standard; 15 and (iv) summing the standardized scores across all standards mentioned in the 10-K and then multiplying the sum by negative one to derive PSCORE. A higher value of PSCORE reflects increased reliance on PBS. To ensure consistent calculation, we compare our PSCORE with that in Folsom et al. (2017) for 2000-2006.16
Reassuringly, we find that the two sets of scores are positively correlated at the 1% level with a
Pearson correlation coefficient of 0.915. The mean (standard deviation) of PSCORE is -15.96 (8.468) in Folsom et al. (2017) and -16.218 (8.098) in our sample.
To test the prediction in H1 on the association between accounting restatements and the extent to which a firm is subject to PBS, we follow prior research in selecting the determinants of
15 Specifically, Mergenthaler (2011) constructs a rules-based score (RBC1) for each U.S. GAAP accounting standard from 1953 to 2009 based on four rules-based characteristics: (i) the inclusion of bright-line thresholds; (ii) the presence of scope and legacy exceptions allowed by the standard; (iii) large volumes of implementation guidance; and (iv) high levels of detail. The score ranges from zero to four with zero indicating the standard has no rules-based characteristics and a score of four indicating that standard has all four of the characteristics. Please refer to Appendix B for rules-based scores for the accounting standards during our sample period. We thank Rick Mergenthaler for providing the complete keyword search list. 16 PSCORE for the 2000-2006 period and RBC1 for the 1953-2009 period can be downloaded from Rick Mergenthaler’s website (http://www.biz.uiowa.edu/faculty/rmergenthaler/).
13 restatements and estimate the following logistic regression (Cao et al. 2012; Lobo and Zhao 2013;
Czerney, Schmidt and Thompson 2014; Ettredge, Fuerherm and Li 2014; Lennox and Li 2014):
RESTKit=β0 + β1PSCOREit +β2LNASSETSit +β3SQSEGit+β4 FOROPS it +β5FINit +β6MERGERit+β7ROAit+β8LOSSit +β9LEVit +β10GCit+β11BMit + β12ICMWit +β13DELAYit +β14DECit +β15BIGit +β16SPECIALISTit +β17SHORTTENUREit +β18LNAGEit+β19NAFRATIOit +β20ABFEEit +∑Industryj+∑Yeart+εit (1) where RESTK equals 1 if the annual report for year t is subsequently restated, and 0 otherwise
(all the variables are defined in Appendix A). We focus on annual reports since managers have strong incentives to reach annual performance targets due to their compensation structure and the external auditor audits only annual reports. A negative (positive) coefficient for PSCORE suggests a lower (higher) likelihood of accounting misstatements for firms that rely more on PBS.
We include total assets (LNASSETS) to control for firm size. As the probability of misstatement is likely to rise with the complexity of the business, we include the number of business segments (SQSEG), existence of foreign operations (FOROPS), new financing (FIN) and
M&A activity (MERGER) to control for complexity arising from operation, financing and changes in the business entity. We include return-on-assets (ROA), loss (LOSS), leverage (LEV), and going-concern audit opinions (GC) to control for misstatement risk stemming from client financial distress (Cao et al. 2012), and include the book-to-market ratio (BM) to account for additional risk associated with growth. We control for the presence of internal control material weaknesses
(ICMW) as clients with weak controls are less likely to prevent misstatements in the first place
(Ettredge et al. 2014). We include December fiscal year-end (DEC), Big 4 auditors (BIG), industry specialist auditors (SPECIALIST), and short tenure (SHORTTENURE) to control for the impact of busy season, auditor quality (Czerney et al. 2014), and the auditor’s client-specific knowledge. In addition, the regressions include audit delay (DELAY) to control for the impact of low financial reporting quality on restatement frequency (Czerney et al. 2014). We further include firm age
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(LNAGE) given that older firms may have better established accounting and control procedures to prevent misstatements (Ettredge et al. 2014). Finally, we include the ratio of non-audit fees to audit fees (NAFRATIO) to control for the effect of potential economic bonding due to the provision of non-audit services (Kinney, Palmrose and Scholz 2004; Markelevich and Rosner 2013) and include abnormal audit fees (ABFEE) to control for the impact of audit effort on constraining misstatements (Blankley et al. 2012; Lobo and Zhao 2013).17
To test the prediction in H2, we analyze whether accounting standard precision moderates the effectiveness of corporate governance mechanisms. Specifically, we test whether the impact of board independence (Board_Indep), audit committee independence (Audcom_Indep), auditor effort (ABFEE), auditor industry expertise (SPECIALIST), and SEC monitoring (Proximity100, and
AAER_Intensity) on restatements varies between firms with high and low PSCORE.
Sample Selection
Our sample period begins in 2000, the first year that audit fees and restatement disclosure are comprehensively available from Audit Analytics, and ends in 2009, the last year that RBC1 scores are available. We begin with 101,787 observations with audit fees in Audit Analytics. Since
RBC1 scores reflect U.S. GAAP, we eliminate 33,595 observations that do not follow U.S. GAAP.
We lose 12,729 and 37,270 observations due to missing values of PSCORE and control variables, respectively, leaving 18,193 observations for the misstatement model. For all estimations, we
17 Abnormal audit fees (ABFEE) is computed as the residuals of the regression of audit fees on a comprehensive set determinants following Hay et al. (2006), including total assets, number of business segments, indicators for foreign operations, new financing and M&A, return-on-assets, indicator for loss, financial leverage, going-concern audit opinions, book-to-market ratio, accounts receivables, inventory, indicators for the presence of internal control material weakness, December fiscal year-end, Big 4 auditors, industry specialist auditors, and short tenure. Finally, we include the predicted probability of restatement and lagged value of restatement to control for the impact of ex ante restatement risks on audit effort (Lobo and Zhao 2013) and include litigation risk to control for its impact on audit fee premium (Simunic and Stein 1996).
15 winsorize the continuous variables at the 1st and 99th percentiles, and gauge statistical significance with two-tailed tests based on robust standard errors clustered by firm.
V. Empirical Results
Descriptive Statistics and Univariate Results
Table 1 reports the time-series variation in accounting standard precision score, by year and industry. In Panel A, we find that PSCORE decreased slightly during our sample period, consistent with accounting standards becoming less principles-based (more rules-based) as implementation guidance was added to standards over time (Donelson et al. 2016). The decrease in PSCORE largely stems from the growing scope exceptions and details (PSCORE_EXCEPTION and PSCORE_DETAIL) over our sample period. In Panel B, we observe considerable variation in
PSCORE across industries, with industries with simpler business models (such as Consumer
Non-durables, Wholesale and Retail, and Healthcare) tending to have a higher PSCORE.
------Insert Table 1 here------
In Panel C, we tabulate some descriptive statistics on firm characteristics of the sample.
The firms in our sample are comparable in size and age with those in Folsom et al. (2017), with the median assets of $256 million and the median age of 13 years. On average, 12.8% of the firm- years have subsequent restatements, which is similar to the frequency reported in recent research
(e.g. Lennox and Li 2014; Czerney et al. 2014).
Table 2 reports the correlation matrix. Restatement likelihood is negatively associated with PSCORE. This evidence suggests lower restatement likelihood for firms that are more affected by PBS. However, we naturally exercise caution in drawing inferences from these univariate statistics because the observed correlations may be driven by the underlying firm characteristics, rather than by the precision of accounting standards.
------Insert Table 2 here------
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Multivariate Results
Primary Analyses
In Table 3, we report in Column (1) logistic regression estimates of Model (1). The coefficient on PSCORE is negative and significant at the 1% level, indicating that the likelihood of accounting restatements decreases with the extent that a firm is subject to PBS. Our coefficient estimates imply that a one standard deviation increase in PSCORE decreases the probability of restatement by 0.81%.18 Given that the base rate of restatement in our sample is 12.8%, this impact is economically material.
------Insert Table 3 here------
Turning to the control variables, we find evidence largely consistent with the prior research (Lobo and Zhao 2013; Czerney et al. 2014; Ettredge et al. 2014). The likelihood of restatement increases with firm size (LNASSETS), external financing (FIN), internal control weakness (ICMW), and audit delay (DELAY), and decreases with going concern opinion (GC),
December fiscal year-end (DEC), firm age (LNAGE), industry specialist auditors (SPECIALIST), and abnormal audit fees (ABFEE).
Firms under more RBS may have a higher misstatement probability given thatcomplex rules could be hard to understand or follow, increasing the chance of making innocent mistakes
(Donelson et al. 2012). Innocent mistakes increase the noise of financial reporting, but have much smaller negative consequences than irregularities (Hennes et al. 2008). To test whether the negative association between PSCORE and restatements is driven by innocent mistakes (errors), we further examine misstatement severity. We proxy for restatement severity using four
18 All our absolute change in the likelihood of restatement are based on marginal effects evaluated at sample means. For example, in Column (1) of Table 3, as the marginal effect for PSCORE is -0.11%, for a one standard deviation increase in PSCORE, the unconditional probability of RESTK decreases by (0.11%*8.096) ≈ 0.81%.
17 measures―the issuance of an AAER by the SEC, the direction of restatements, the magnitude of restatements, and the irregularity vs. error classification by Hennes et al. (2008). Consistent with prior research (Lennox and Pittman 2010; Kedia and Rajgopal 2011; Hennes et al. 2014), we consider restatements accompanied by an AAER, income-decreasing restatements, large- magnitude restatements, and irregularities as more severe. In Column (2), we find that PSCORE is negatively associated with the probability of an AAER, albeit at only the 10% significance level.
In Columns (3)-(4), we find that PSCORE is negatively associated with the likelihood of income- decreasing restatements at the 5% level, although it has no discernable impact on non-income- decreasing restatements. In Columns (5) and (6), we find that PSCORE is negatively related to the magnitude of restatements at the 5% and 10% levels, respectively, whether the magnitude is stated as a percentage of total sales or total assets. In Columns (7) and (8), we find lower probability of both accounting irregularities and errors at firms with higher PSCORE. Overall, these results suggest that greater reliance on PBS is associated with a lower incidence of both severe restatements and errors, inconsistent with innocent mistakes solely driving the main results in Column (1).
The precision of accounting standards is shaped by the complexity of the underlying business transactions (Folsom et al. 2017). One alternative explanation suggests that more complicated transactions for firms under more RBS increase the likelihood of misstatements. To address this concern, in addition to variables representing business complexity (SQSEG, FOROPS,
FIN and MERGER) already included in the regressions, we further control for financial reporting complexity by including the natural logarithm of size of the 10-K complete submission text file
(FILESIZE), the natural logarithm of the number of words in the 10-K report after excluding all tables (NUMWORDS), the number of non-missing items on Compustat (NUMITEMS), and the
Bog Index (BOGINDEX). Li (2008) and Fang et al. (2017) argue that firms with more complex
18 financial reports disclose more line items and produce longer annual reports. You and Zhang
(2009), Loughran and McDonald (2014), and Bonsall, Leone, Miller, and Rennekamp (2017) find that 10-K document size and the Bog Index serve as good proxies for financial reporting complexity or readability. In Column (9), the coefficient on PSCORE continues to load highly negatively, inconsistent with financial reporting complexity representing a correlated omitted variable spuriously responsible for the negative association between PSCORE and restatements.19
Since the bright-line tests and more guidance under RBS provide a clear boundary that facilitates defining and detecting violations, another alternative explanation for our results is that the greater difficulty in detecting GAAP violations under PBS leads to lower restatement frequency under PBS. To address this concern, we first use signed abnormal accruals (DACC) computed after Reichelt and Wang (2010), which encompass undetected earnings management, as the proxy for financial reporting quality. In Column (10), we further control for sales growth
(GROWTH) and operating cash flow (OCF) (Menon and Williams 2004; Dechow et al. 1995). The coefficient on PSCORE is negative and highly significant, suggesting that firms relying more on
PBS engage in less upward accrual management, consistent with the findings using restatements.
In Panel B, we further address this alternative view by directly examining the difficulty of misstatement detection. Assuming misstatements that last longer, that take longer to discover, and that take fresh eyes to identify are harder to detect, we conduct the following three tests
19 We cannot reliably estimate a firm fixed effects regression in our setting for two reasons. First, this approach would retain only observations with variation in the dependent variable, i.e., annual report restatements. This sample attrition will restrict the analysis to only low-quality firms with at least one restatement during the sample period, while discarding high-quality firms that did not have a restatement during this timeframe. For our sample, this approach tends to retain firms more subject to RBS and exclude firms more subject to PBS. Second, although PSCORE exhibits good cross-sectional variation, due to the highly stable nature of the types of accounting rules applicable to a firm, it suffers from poor within-firm variation across time. Although firm fixed effects estimation that essentially removes the cross-sectional variation could help alleviate the threat posed by correlated omitted variables (time-invariant characteristics), this approach does not suit the data since the minimal within firm variation would leave the analysis susceptible to failing to identify an impact even when it genuinely exists (e.g., Zhou, 2001).
19 within restatement firms. We analyze whether PSCORE is positively associated with misstatement length measured as the duration between the start and end of the misstatement period (Singer and Zhang 2017), and positively associated with the discovery lag between the end of the misstatement period and the disclosure of the misstatement (Schmidt and Wilkins
2013), and whether restatements by firms with a higher PSCORE are more likely to be discovered by the new auditor after an auditor switch. Following Schmidt and Wilkins (2013), we control for the impact of business complexity (FOROPS and SQSEG), fraud (FRAUD), complexity
(MULT_ISSUES), severity (RESTATE_IMPACT), and direction (POS_EARN) of the restatement, and auditor characteristics (BIG, SPECIALIST and AUDCHG, and QTR_1) on the difficulty of detection. In Columns (1) to (3), we find that PSCORE is negatively associated with misstatement length, but fail to find any association between PSCORE and disclosure lag or the likelihood of an auditor switch preceding the restatement announcement, inconsistent with the alternative explanation that it is harder to detect misstatements under PBS.
As each firm is subject to a battery of accountings standards, firms with a higher PSCORE could also be subject to some RBS. Accordingly, the negative association between accounting restatements and PSCORE could stem from fewer violations of RBS rather than PBS by firms with a higher PSCORE, which will not support higher financial reporting quality under more PBS. To test this alternative explanation, we examine the type of accounting standards violated by firms that rely more on PBS compared to those by firms that rely more on RBS. To classify a restatement as a violation of either PBS or RBS, we compute REST_PSCORE based on all restatement disclosures of the final sample using the same algorithm as that for PSCORE of the 10-K disclosure.
We designate a restatement as a restatement due to violation of PBS (RBS) if its REST_PSCORE is above (below) the sample median within firms with restatements, and designate a firm as a PBS
(RBS) firm if its PSCORE is above (below) sample median. After deleting restatements due to
20 violations of RBS by PBS firms and restatements due to violations of PBS by RBS firms, we re- estimate Model (1) and find that RESTK is significantly lower (at the 5% level) for PBS firms than
RBS firms.20 In sum, the above results corroborate that our findings in Table 3 are driven by fewer violations of PBS by firms more subject to PBS and suggest a lower rate of mistakes in the application of PBS than the application of RBS.
Evidence from the exogenous shock of accounting standard shifts
In this section, we rely on exogenous accounting standard shifts in a quasi-experimental framework to examine whether restatement frequency decreases (increases) when the accounting standard becomes more principles-based (rules-based). From a practical econometric standpoint, we focus on accounting standards that involve material changes in the rules-based score (RBC1) and for which the change affects a sufficient number of observations in both the pre- and post- shift period to enable meaningful analysis. During our sample period, two accounting standards are superseded by new standards with material changes in the corresponding RBC1 of the standards. 21 In December 2004, the FASB issued SFAS 123r, “Accounting for Stock Based
Compensation”, which replaced SFAS 123 and became effective beginning June 15, 2005. SFAS
123r eliminates the alternative to use the intrinsic value method and requires expensing of stock
20 In a falsification test, we delete restatements due to violations of PBS by PBS firms and restatements due to violation of RBS by RBS firms and then repeat the procedure. The coefficient on the indicator variable for PBS firms is insignificant at conventional levels, providing further corroboration that the results in Table 3 are not spuriously driven by a lower frequency of restatements related to RBS by firms more subject to PBS. 21 For each standard change, we choose three years before and three years after the change (when available) so that the restatements after the standard change are less likely due to the learning curve effect during the initial application of a new rule. We exclude the replacement of SFAS 121 with SFAS 144, as only 80 firm- year observations refer to SFAS 121 before the replacement. We also exclude the replacement of APB 20 with SFAS 154, as these standards concern accounting changes and error corrections, rather than accounting treatment of business transactions.
21 options via the fair value method with limited exceptions.22 Compared with SFAS 123, which has a RBC1 of 4, SFAS 123r has fewer bright-line thresholds and fewer interpretive pronouncements, leading to a RBC1 of 2. In June 2001, the FASB issued SFAS 142, “Goodwill and Other Intangible
Assets” which superseded APB 17. SFAS 142 requires annual impairment tests of goodwill and intangible assets instead of periodic amortization. Compared with APB 17 which has a RBC1 of
1, SFAS 142 has more detailed guidance and scope or legacy exceptions, translating into a higher
RBC1 of 3.23 In Table 4, we compare in Panel A the restatement frequency surrounding the change for all firms affected by these standards. To examine restatements due to violations of standards under study, we set RESTK_Test_Standard to 1 when RESTK equals 1 and the restatement disclosure mentioned the particular standard under change (i.e., SFAS 123, SFAS 123r, APB 17,
SFAS 142), and 0 otherwise. We find that the mean value of RESTK_Test_Standard decreases significantly from 0.029 during 2002-2004 to 0.005 in 2006-2008 when SFAS 123r superseded SFAS
123, and increases significantly from 0.007 in 2000 to 0.011 in 2002-2004 when SFAS 142 superseded APB 17. This evidence implies that SFAS 123r (APB 17), which is more principles- based, lowers accounting misstatements relative to the more rules-based SFAS 123 (SFAS 142).
Importantly, the reduction in restatement frequency around the change from SFAS 123 to SFAS
123r is unlikely due to the learning curve effect, which would predict an increase in restatement frequency as firms assimilate the new accounting standard.
To further control for the impact of firm characteristics and other concurrent accounting standards surrounding the change, we supplement Model (1) with PRINCIPLE_REGIME, which
22 See the summary of SFAS 123r by the FASB, available at https://www.fasb.org/summary/stsum123r. shtml. 23 To simplify accounting or goodwill impairment, the FASB issued ASC350 in 2017, which removes step 2 of the goodwill impairment test.
22 equals 1 for the year regulated by the more PBS in each pair of accounting standard shifts (i.e.
SFAS 123r and APB 17), and 0 for the year regulated by the more RBS (i.e. SFAS 123 and SFAS
142). We accordingly modify PSCORE by excluding the components corresponding to accounting standards under study to arrive at PSCORE_OTHER.24 In Panel B Column (1), we find that the coefficient on PRINCIPLE_REGIME is negative and statistically significant at the 1% level, suggesting a decrease in restatement probability under the more principles-based SFAS
123r. We continue to find that PRINCIPLE_REGIME enters negatively at the 1% level in Column
(2) after controlling for PSCORE_OTHER. We find highly consistent results in Columns (3)-(4) for the change from APB 17 to SFAS 142. Examining the marginal effect, we find that when these two sets of standards became more principles-based, the frequency of restatement decreases by
2.2% and 1.0%, respectively. Overall, these results offer evidence on the change in restatement likelihood due to plausibly exogenous changes in the precision of applicable accounting standards, helping to further dispel the competing explanation that transaction complexity is spuriously responsible for our main evidence in Table 3.
------Insert Table 4 here------
The Driving Force for the Higher Financial Reporting Quality under PBS
In developing the intuition for our prediction, we stress that managers may provide better financial reporting quality under PBS for at least two non-mutually exclusive reasons: (i) concerns surrounding second guessing; and (ii) higher litigation risk. Distinguishing the underlying reason is important in order to help clarify the conditions under which stakeholders most likely reap the benefits of PBS. If the concern about second guessing (litigation risk) drives the main
24 For example, in the test around the change from SFAS 123 to SFAS 123r, we remove the components related to SFAS 123 or SFAS 123r in calculating PSCORE_OTHER.
23 results, then we would expect to observe more pronounced results in the presence of intensified concerns surrounding second guessing (higher litigation risk).
In Table 5, we initially report results in Panel A on the main effects before including interactions involving each proxy reflecting the second-guessing concern. When a firm’s performance deteriorates, investors and the board of directors are more likely to doubt and challenge the manager’s decisions, thereby intensifying the manager’s concern over being second guessed (Gordon and Poundt 1993; Ertimur, Ferri, and Oesch 2013). Accordingly, we rely on financial distress as the first proxy for second-guessing concern. We set Distress to 1 if the firm’s
Altman Z-score is below 2.99 (i.e., the “Grey” and “Distress” Zones) (Altman 1968), and 0 otherwise. In Column (2), we find the coefficient on PSCORE*Distress enters negatively at the 5% level, reinforcing that more conservative application of PBS ensues when managers are more concerned about second guessing. Similarly, uncertainty routinely motivates people toward
“playing it safe” (Craswell and Calfee 1986). Baker et al. (2016) find that economic policy uncertainty shakes investors’ and employers’ confidence, translating into lower investment and employment. As our second proxy for second-guessing concern, Policy_Uncertainty equals the decile rank of the average economic policy uncertainty in the year.25 In Column (4), we find that the coefficient on PSCORE* Policy_Uncertainty is negative and significant at the 5% level, implying a stronger effect of PBS on curtailing restatements when there is higher uncertainty about economic policy. Prior studies find that overconfident executives rely more on their own knowledge than decision aids (Whitecotton 1996; Nelson et al. 2003). Due to their optimistic bias of their own knowledge, these executives’ firms generally exhibit less conservative accounting and are more likely to start on a path leading to intentional misstatements (Ahmed and Duellman
25 The economic policy uncertainty index is obtained at http://www.policyuncertainty.com/. As the data is on a monthly basis, we take the average of 12 months of the fiscal year to derive the yearly index.
24
2013; Schrand and Zechman 2012). Last, we rely on CEO overconfidence as an inverse measure of the second-guessing concern. We first follow Ahmed and Duellman (2013) in coding
Overconfidence as the sum of four indicators of overconfidence and then code Non_Overconfidence as the inverse of Overconfidence. In Column (6), we find that the coefficient on
PSCORE*Non_Overconfidence is negative and statistically significant at the 1% level, suggesting that CEOs who are less overconfident apply PBS more cautiously (i.e. lower likelihood of restatements), possibly due to the concern about being second guessed. In summary, Panel A of
Table 5 presents robust evidence that firms relying more on PBS are less likely to restate when there is a greater concern over second guessing, consistent with the argument that this concern amounts to an important constraint against managerial opportunism.
------Insert Table 5 here------
The lower restatement frequency for firms that rely more on PBS may also stem from these firms experiencing higher litigation risk (Donelson et al. 2012). To analyze this conjecture, we measure litigation risk with three proxies. We specify an indicator variable, High_Litigation, as 1 if the firm’s litigation score, calculated following Kim and Skinner (2012) and Shu (2000), is above or equal to the sample median for the first two measures, and if the firm belongs to a high litigious industry identified by Francis, Philbrick and Schipper (1994) for the third measure, and 0 otherwise. In Panel B of Table 5, we find that the coefficient on PSCORE*High_Litigation fails to load in Column (2), and is positive and significant at the 1% level in Columns (4) and (6). Tests of the coefficient sum PSCORE+PSCORE*High_Litigation yields insignificant results for Columns
(2), (4) and (6), suggesting that there is no perceptible impact of accounting standard precision on restatement frequency when the litigation risk is high. This evidence implies that it is unlikely that litigation risk explains the lower restatement frequency for firms relying more on PBS; instead, the evidence suggests that higher litigation risk attenuates the greater restatement
25 likelihood of firms relying more on RBS, possibly due to specific rules providing plaintiffs with a
“roadmap” for successful litigation (Donelson et al. 2012), leading to a strong deterrent effect against violations. Overall, our findings in Table 5 support that seconding guessing, rather than litigation risk, as the driving force for the greater reliability of financial statements under PBS.
The Role of Monitoring Mechanisms under PBS vs. RBS
Extensive prior research implies that various internal and external corporate governance mechanisms affect misreporting. Given that internal and external monitors rely heavily on accounting standards to monitor and discipline managers, it is not clear whether prior results vary with accounting standard precision. In Table 6, we focus in Panel A on four sets of governance mechanisms: board independence (Board_Indep), audit committee independence
(Audit_Com_Indep), audit effort (ABFEE), and industry specialist auditor (SPECIALIST) to test whether the effectiveness of these mechanisms is different between firms with below and above sample median value of PBS (i.e., low PBS firms vs. high PBS firms). We define Board_Indep to 1 if super majority (at least two thirds) of the board members are independent, 0 otherwise; and define Audcom_Indep to 1 if the audit committee is fully independent, and 0 otherwise.26 In
Column (1) Board_Indep enters negatively, supporting that for firms that rely less on PBS and more on RBS, more independent boards prevent managerial misconduct (Beasley 1996; Zhao and
Chen 2008). In contrast, Board_Indep fails to load in Column (2) for firms that rely less on RBS and more on PBS. Chow test of coefficient equality indicates that the coefficient of Board-Indep is significantly more negative for the low PBS subsample than for the high PBS subsample. We find nearly identical results in Columns (3) and (4) when we focus on audit committee
26 Many investing advisory agencies and companies regard the board to be independent only if a “substantial majority” of the board’s directors are independent and two-thirds are frequently used as the cutoff for “substantial majority” (e.g. Moody’s Investor Service 2006).
26 independence (Audit_Com_Indep). Taken together, these results imply less effective monitoring by independent boards and audit committee when the accounting standards are less precise. In
Columns (5)-(8), we focus on monitoring by the external auditor. For firms that rely less on PBS
(more on RBS) in Columns (5) and (7), the coefficients of ABFEE and SPECIALIST are negative and significant at the 1% level, consistent with higher auditor effort and industry expert auditors helping prevent financial misstatements (Romanus et al. 2008; Chin and Chi 2009; Lobo and Zhao
2013). In contrast, for firms that rely more on PBS (less on RBS), the coefficient of ABFEE is negative, but only significant at the 10% level, and the coefficient on SPECIALIST is insignificant.
Chow tests of coefficient equality suggest that the coefficient for ABFEE and SPECIALIST is significantly more negative at the 10% and 1% levels, respectively, for the low PBS firms than the high PBS firms. These results suggest weaker monitoring benefits stemming from higher auditor effort and industry specialist auditors against financial misreporting at firms more subject to PBS.
One reason could be that board, the audit committee, and the external auditor may need to count on detailed rules in both detecting non-compliance and negotiating with management to correct this non-compliance. The absence of detailed rules weakens their ability to identify violations and undermines their negotiating power against the manager to enforce correction of violations.
------Insert Table 6 here------
In Panel B, we turn to monitoring by the SEC. Keida and Rajgopal (2011) suggest that due to resource constraints, the SEC is more likely to target firms located closer to its offices.
Anticipating this preference by the SEC, firms located closer to the SEC offices or in areas with greater past SEC enforcement activity are less likely to misreport. We follow Kedia and Rajgopal
(2011) and measure SEC enforcement intensity by two variables. Proximity100 is set to 1 if the firm’s headquarters is within 100 kilometers to the closest SEC national or regional office, and 0
27 otherwise.27, 28 AAER_Intensity is the number of AAERs filed for other companies in the same county as the focal company’s headquarters during the past 10 years.29 In contrast to the findings of non-regulatory oversight in Panel A, we find that both Proximity100 and AAER_Intensity load negatively at the 10% level or better in the partition of firms with high PSCORE in Columns (2) and (4), but fail to load in the partition of firms with low PSCORE in Columns (1) and (3). Chow tests suggest that the coefficient of both Proximity100 and AAER_Intensity is significantly more negative at the 5% level for firms relying more on PBS than firm relying less on PBS. The insignificant coefficients for Proximity100 and AAER_Intensity in Columns (1) and (3) support the interpretation that more effective monitoring by independent boards, audit committee, and external auditors at firms relying less on PBS likely lessens the burden of SEC at enforcing compliance by these firms. The stronger deterrence effect of SEC enforcement when accounting standards are less precise are consistent with SEC’s goal on fulfilling the spirit of accounting standards.
Collectively, findings in Table 6 suggest that non-regulatory monitors such as the board, audit committee, and the auditor mainly derive their influence and power over the manager from
27 Following Kedia and Rajgopal (2011), the SEC offices considered are the SEC headquarters in Washington D.C and its regional offices located in New York City, NY; Miami, FL; Chicago, IL; Denver, CO; and Los Angeles, CA. In 2007, the SEC elevated its district offices in six places to regional offices. However, it is not clear when these offices started to fulfill their enforcement responsibilities during the transition period. Our results for the interaction between PSCORE and Proximity100 are robust to including district offices in those six places for 2008-2009. 28 Prior studies argue that 250 miles is a plausible upper bound on the span of being local (Ivkovic and Weisbenner 2005). We find nearly identical evidence (untabulated) to that in Column (2) when we use 250 miles as an alternative cutoff. 29 Given that AAERs are quite scarce, we use AAERs for all firms in the county of the focal firm’s headquarters for the past 10 years to measure AAER intensity. Similarly, Kedia and Rajgopal (2011) use all AAERs filed before 1997 to construct their measure of past SEC enforcement activity. If remote SEC enforcement actions do not affect firms’ perceptions of the likelihood of being targeted by the SEC, this measurement error will bias against finding results supporting our predictions. The sample attrition in Columns (3)-(4) reflects that we only analyze firm-year observations headquartered in counties with nonzero AAERs
28 authoritative detailed rules and thus they work more effectively under RBS. As the ultimate financial reporting regulator, the SEC becomes a more important monitor when there is less detailed guidance to follow and more discretion to exercise. Thus, the two types of oversight mechanism play a substitutive role at safeguarding the integrity of financial reporting.
VI. Conclusions
Accounting standard precision significantly impacts the extent of professional judgment in the financial reporting process as well as interactions between managers and various monitors, and, accordingly, financial reporting quality. We extend prior research by first analyzing whether the likelihood of GAAP violations varies with the precision of accounting standards. We provide strong, robust evidence of a negative association between the extent of reliance on PBS and the incidence of financial report misstatements, including both errors and irregularity. Extensive additional analysis implies that lower detection of violations of PBS or transaction complexity does not explain this core result. Exploring the channels underlying this relationship, we report evidence consistent with the intuition that concerns surrounding second guessing, rather than litigation risk, are behind the main results. Our core evidence is robust to analyzing exogenous changes in accounting standard precision induced by two accounting standard shifts during the sample period.
Next, we examine whether the precision of accounting standards shapes the role of various monitoring mechanisms in constraining restatements. We find that non-regulatory monitors, including the independent boards, the audit committee, and external auditors, are more effective at constraining misreporting at firms relying more on RBS. In contrast, the SEC plays a more prominent role at firms relying more on PBS. Collectively, our analysis suggests that although PBS might be conducive to better financial reporting quality on average, they may compromise the monitoring effectiveness of traditional corporate governance mechanisms
29 carried out by non-regulatory monitors who rely heavily on detailed authoritative rules to negotiate with and discipline the manager. The lax scrutiny of non-regulatory monitors when accounting standards are vague would call for more intensive enforcement by the SEC.
Our study is subject to the following limitation. Although prior research uses multiple methods to extensively substantiate the construct validity of the rules-based score as a measure of the specificity and level of details of accounting standards and to rule out the confounding effects of other firm characteristics such as transaction complexity (Donelson et al. 2012; Folsom et al. 2017), the rules-based score may not perfectly capture the level of discretion and professional judgment in applying a standard (Donelson et al. 2016). Future research could advance our understanding on the relationship between accounting standard precision and manager-monitor interaction by identifying better proxies in this regard.
30
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Table 1 Descriptive Statistics
Panel A: Mean PSCORE and its components by year Year PSCORE PSCORE_BLT PSCORE_EXCEPTION PSCORE_IG PSCORE_DETAIL 2000 -14.089 -3.143 -4.972 -3.304 -2.654 2001 -16.71 -3.157 -6.213 -3.784 -3.549 2002 -15.818 -2.807 -5.573 -3.641 -3.781 2003 -16.028 -2.942 -5.763 -3.572 -3.728 2004 -16.212 -3.004 -5.71 -3.854 -3.629 2005 -17.678 -3.143 -6.346 -3.929 -4.275 2006 -15.368 -2.536 -5.566 -3.511 -3.756 2007 -15.896 -2.597 -5.753 -3.571 -3.958 2008 -16.733 -2.688 -5.959 -3.782 -4.28 2009 -17.061 -2.803 -6.094 -3.762 -4.382
Panel B: Mean PSCORE and its components by industry PSCORE_ PSCORE_ PSCORE_ PSCORE_ Industry PSCORE BLT EXCEPTION IG DETAIL Consumer Non-durables -14.237 -2.817 -4.737 -3.487 -3.207 Consumer Durables -16.184 -3.197 -5.483 -3.921 -3.545 Manufacturing -16.733 -3.331 -5.714 -3.938 -3.712 Energy Oil, Gas, and Coal Extraction and Products -22.388 -4.534 -7.939 -4.057 -5.841 Chemicals and Allied Products -20.949 -4.212 -7.206 -4.953 -4.52 Business Equipment -17.392 -2.71 -6.464 -3.793 -4.418 Telephone and Television Transmission -20.184 -3.449 -7.227 -4.813 -4.688 Utilities -21.689 -3.862 -7.305 -5.068 -5.391 Wholesale, Retail, and Some Services -13.818 -2.536 -4.715 -3.356 -3.228 Healthcare, Medical Equipment, and Drugs -13.824 -2.259 -5.464 -2.871 -3.221 Finance -15.676 -3.018 -5.522 -3.764 -3.35 Other -15.453 -2.828 -5.283 -3.79 -3.537
Panel C: Sample descriptive statistics Variable mean sd p25 p50 p75 N PSCORE -16.218 8.098 -20.319 -14.719 -10.452 18193 RESTK 0.128 0.334 0 0 0 18193 AAER 0.011 0.104 0 0 0 18193 RESTK_NI_Sales 0.03 0.17 0 0 0 18010 RESTK_NI_AT 0.024 0.153 0 0 0 18023 RESTK_IncomeDecrease 0.068 0.253 0 0 0 18193 RESTK_NonIncomeDecrease 0.047 0.211 0 0 0 18193 IRREG 0.016 0.126 0 0 0 10051 ERROR 0.068 0.252 0 0 0 10661 DACC 0.052 0.307 -0.078 0.007 0.126 13020 RESTATE_BEG_END 936.099 666.753 365 730 1095 785 RESTATE_END_DIS 234.949 161.556 124 161 373 785 AUDCHG 0.282 0.45 0 0 1 785
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LNASSETS 5.619 1.826 4.355 5.544 6.796 18193 SQSEG 1.358 0.471 1 1 1.732 18193 FOROPS 0.224 0.417 0 0 0 18193 FIN 0.595 0.491 0 1 1 18193 MERGER 0.135 0.342 0 0 0 18193 ROA -0.063 0.339 -0.089 0.021 0.076 18193 LOSS 0.413 0.492 0 0 1 18193 LEV 0.222 0.279 0.002 0.143 0.339 18193 GC 0.047 0.212 0 0 0 18193 BM 0.472 1.537 0.242 0.459 0.79 18193 ICMW 0.065 0.246 0 0 0 18193 DEC 0.702 0.458 0 1 1 18193 DELAY 4.071 0.402 3.892 4.143 4.304 18193 BIG 0.854 0.353 1 1 1 18193 SPECIALIST 0.401 0.49 0 0 1 18193 SHORTTENURE 0.871 0.335 1 1 1 18193 LNAGE 2.598 0.753 2.079 2.565 3.135 18193 NAFRATIO 0.488 0.731 0.075 0.227 0.559 18193 ABFEE 0.003 0.558 -0.359 0.015 0.373 18193 FILESIZE 6.926 0.836 6.458 7.041 7.469 16035 NUMWORDS 10.337 0.454 10.056 10.347 10.622 16035 BOGINDEX 84.221 6.908 80 84 86 16035 GROWTH 0.002 0.519 -0.049 0.068 0.178 13020 OCF 0.035 0.269 -0.005 0.078 0.145 13020 FRAUD 0.025 0.158 0 0 0 785 MULT_ISSUES 0.595 0.491 0 1 1 785 RESTATE_IMPACT 0.022 0.067 0 0.003 0.013 785 POS_EARN 0.138 0.345 0 0 0 785 QTR_1 0.424 0.495 0 0 1 785 Distress 0.378 0.485 0 0 1 16013 Policy_Uncertainty 5.282 2.971 3 6 8 16858 Non_Overconfidence -1.631 1.016 -1 -2 -2 4362 Board_Indep 0.782 0.413 1 1 1 13227 Audit_Com_Indep 0.817 0.387 1 1 1 13190 Proximity100 0.359 0.480 0 0 1 17888 AAER_Intensity 6.825 8.463 2 4 8 9300 Notes: Panels A and B report the distribution of PSCORE by year and industry, respectively. Panel C reports the descriptive statistics for all variables. See Appendix A for the definitions of variables.
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Table 2 Pearson Correlation Matrix
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) RESTK (1) 1.00 PSCORE (2) -0.06* 1.00 LNASSETS (3) 0.09* -0.30* 1.00 SQSEG (4) 0.03* -0.21* 0.31* 1.00 FOROPS (5) 0.01 -0.16* 0.16* 0.08* 1.00 FIN (6) 0.05* -0.02* 0.13* 0.02* -0.01 1.00 MERGER (7) 0.03* -0.10* 0.14* 0.08* 0.04* 0.14* 1.00 ROA (8) 0.04* -0.13* 0.32* 0.15* 0.08* -0.09* 0.03* 1.00 LOSS (9) -0.03* 0.06* -0.30* -0.14* -0.04* 0.00 -0.07* -0.53* 1.00 LEV (10) 0.01 -0.07* 0.15* 0.08* -0.03* 0.18* 0.00 -0.12* 0.11* 1.00 GC (11) -0.06* 0.04* -0.21* -0.05* -0.03* 0.00 -0.06* -0.30* 0.23* 0.22* 1.00 BM (12) 0.02* 0.01 -0.01 0.01* -0.01 -0.05* 0.02* 0.09* -0.05* -0.30* -0.30* 1.00 ICMW (13) 0.06* -0.02* -0.02* 0.02* 0.07* -0.01 0.01 -0.02* 0.07* 0.02* 0.06* -0.05* 1.00 DEC (14) -0.06* -0.01* 0.06* 0.02* -0.02* 0.06* 0.03* -0.08* 0.07* 0.11* 0.04* -0.06* -0.01 1.00 DELAY (15) -0.01 0.12* -0.15* 0.01 0.04* -0.01 -0.01 -0.09* 0.12* 0.12* 0.17* -0.06* 0.27* 0.02* 1.00 BIG (16) 0.05* -0.13* 0.31* 0.05* 0.03* 0.06* 0.05* 0.05* -0.05* -0.01 -0.07* -0.01* -0.08* 0.08* -0.15* 1.00 SPECIALIST (17) 0.02* -0.04* 0.24* 0.11* -0.05* 0.04* -0.01 0.09* -0.10* 0.08* -0.03* 0.00 0.00 0.00 -0.05* 0.20* 1.00 SHORTTENURE (18) 0.00 0.07* -0.16* -0.18* -0.04* 0.04* 0.03* -0.09* 0.12* -0.01 0.02* -0.01 0.02* 0.10* 0.04* 0.01 -0.07* 1.00 LNAGE (19) 0.00 -0.12* 0.25* 0.30* 0.10* -0.07* -0.05* 0.20* -0.20* 0.05* -0.01 0.00 0.00 -0.17* 0.02* -0.06* 0.12* -0.49* 1.00 NAFRATIO (20) 0.07* -0.16* 0.10* 0.02* -0.04* 0.08* 0.09* -0.03* -0.01 0.00 -0.04* 0.02* -0.09* -0.01 -0.29* 0.12* 0.02* -0.02* -0.09* 1.00 ABFEE (21) -0.03* -0.12* 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12* -0.01 0.00 0.00 -0.03* -0.14* 1.00
Note: The table reports Pearson correlations for the variables in model (1). * indicates correlations which are significant at or below the 0.05 level. See Appendix A for the definitions of variables.
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Table 3 Principles-based-scores and Accounting Restatements Panel A: Severity of Restatements Annual Nature Direction Magnitude Irregularity vs. Error Further Abnormal Report Control for Accruals Restatements Complexity Dependent Var. RESTK AAER RESTK_ RESTK_ RESTK_NI RESTK_ IRREG ERROR RESTK DACC Income NonIncome _Sales NI_AT Decrease Decrease (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Constant -4.310*** -5.885*** -5.014*** -5.350*** -6.366*** -5.581*** -10.274*** -6.118*** -6.017*** 0.351*** (-12.212) (-5.803) (-10.765) (-9.722) (-8.531) (-7.000) (-8.208) (-9.579) (-6.179) (9.370) PSCORE -0.011*** -0.018* -0.011** -0.006 -0.017** -0.014* -0.033*** -0.017*** -0.012*** -0.003*** (-3.023) (-1.731) (-2.466) (-1.114) (-2.354) (-1.681) (-3.573) (-2.660) (-2.805) (-8.338) LNASSETS 0.149*** 0.386*** 0.162*** 0.057** 0.086** -0.050 0.329*** 0.062* 0.122*** -0.025*** (7.989) (6.516) (6.694) (1.961) (2.248) (-1.113) (5.286) (1.932) (5.716) (-11.991) SQSEG 0.046 0.179 -0.056 0.218*** -0.165 0.013 -0.170 0.095 0.042 -0.009* (0.869) (1.007) (-0.776) (2.766) (-1.466) (0.099) (-1.026) (1.001) (0.729) (-1.940) FOROPS 0.092 -0.194 0.109 0.174* 0.087 0.078 0.694*** -0.173 0.103* -0.010** (1.568) (-1.020) (1.416) (1.937) (0.771) (0.598) (3.503) (-1.362) (1.667) (-2.026) FIN 0.282*** 0.332** 0.292*** 0.220*** 0.336*** 0.616*** 0.320* 0.190** 0.262*** 0.029*** (5.517) (1.961) (4.255) (2.816) (3.294) (5.241) (1.647) (2.128) (4.793) (6.474) MERGER 0.009 0.455*** 0.041 -0.036 0.158 0.209 0.228 -0.237* 0.042 -0.035*** (0.141) (2.751) (0.484) (-0.347) (1.337) (1.588) (1.109) (-1.815) (0.617) (-5.119) ROA 0.013 -0.105 0.150 -0.137 -0.173 -0.257** 0.309 0.129 -0.027 0.658*** (0.130) (-0.438) (1.068) (-0.867) (-1.397) (-2.025) (1.283) (0.501) (-0.249) (13.307) LOSS -0.064 -0.299 -0.124 0.053 0.242** 0.157 -0.097 -0.241** -0.127* 0.018 (-1.072) (-1.525) (-1.524) (0.590) (2.217) (1.307) (-0.459) (-2.053) (-1.942) (1.557) LEV 0.005 -0.712** -0.122 0.308** -0.081 -0.398* 0.287 -0.218 0.063 0.053*** (0.044) (-1.975) (-0.822) (2.088) (-0.417) (-1.646) (0.775) (-1.106) (0.548) (3.320) GC -1.028*** -1.405 -1.063*** -0.797*** -0.848*** -1.021*** -2.358** -1.480*** -1.083*** 0.077*** (-5.539) (-1.334) (-3.778) (-3.136) (-2.940) (-3.181) (-2.174) (-4.097) (-5.154) (3.592) BM 0.013 0.032 -0.001 0.045* -0.015 -0.071*** 0.043 -0.083*** 0.011 -0.003 (0.816) (0.521) (-0.050) (1.679) (-0.577) (-2.686) (1.024) (-3.127) (0.572) (-1.479) ICMW 0.670*** 1.350*** 0.617*** 0.673*** 0.782*** 0.772*** 2.206*** 1.639*** 0.744*** -0.010 (7.557) (5.021) (5.168) (5.221) (4.751) (4.236) (7.828) (11.090) (7.764) (-0.899) DEC -0.333*** -0.775*** -0.445*** -0.077 -0.231** -0.272** -0.222 -0.590*** -0.354*** 0.012** (-6.351) (-5.067) (-6.568) (-0.915) (-2.220) (-2.460) (-1.110) (-6.295) (-6.326) (2.443) DELAY 0.349*** 0.121 0.314*** 0.279*** 0.250* 0.257* 0.928*** 0.596*** 0.281*** -0.035*** (5.284) (0.612) (3.675) (2.713) (1.936) (1.831) (4.352) (5.362) (3.805) (-4.726) BIG 0.068 0.177 0.093 0.392*** -0.196 -0.335** -0.370 0.374** 0.062 0.024***
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(0.833) (0.513) (0.815) (2.984) (-1.290) (-2.189) (-1.039) (2.173) (0.703) (3.114) SPECIALIST -0.104** -0.222 0.043 -0.215*** -0.054 -0.107 0.131 -0.054 -0.138** -0.006 (-2.051) (-1.363) (0.646) (-2.764) (-0.528) (-0.899) (0.709) (-0.605) (-2.569) (-1.261) SHORTTENURE 0.094 0.009 0.223** -0.090 0.642*** 0.480** 0.252 0.029 0.171** -0.006 (1.185) (0.036) (2.049) (-0.761) (2.954) (2.018) (0.832) (0.201) (1.987) (-0.978) LNAGE -0.075** -0.414*** -0.134*** 0.008 -0.234*** -0.280*** -0.153 -0.152** -0.066 -0.013*** (-1.991) (-3.525) (-2.855) (0.134) (-3.376) (-3.616) (-1.236) (-2.161) (-1.587) (-3.029) NAFRATIO 0.037 0.210*** 0.067 -0.046 0.173*** 0.219*** 0.150* 0.078 0.036 -0.012** (1.073) (2.768) (1.557) (-0.788) (2.922) (3.357) (1.693) (1.437) (0.960) (-2.519) ABFEE -0.228*** -0.383*** -0.138** -0.217*** -0.343*** -0.126 -0.125 -0.244*** -0.264*** 0.010* (-5.328) (-2.614) (-2.452) (-3.343) (-4.042) (-1.276) (-0.803) (-3.133) (-5.588) (1.938) FILESIZE 0.028 (0.700) NUMWORDS 0.041 (0.529) NUMITEMS 0.008*** (3.596) BOGINDEX -0.005 (-1.014) GROWTH 0.039*** (4.119) OCF -0.953*** (-18.298) [Marginal effect%] [-0.11%] [-0.01%] [-0.06%] [-0.02%] [-0.03%] [-0.02%] [-0.02%] [-0.06%] [-0.12%] [-0.31%] Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes pseudo/adjusted R2 0.064 0.162 0.079 0.038 0.085 0.083 0.205 0.157 0.065 0.291 N 18193 18103 18193 18193 18010 18023 10051 10661 16035 13020
Panel B: Detection of Misstatements Detection Difficulty Measure Misstatement Length Disclosure Lag Auditor Change Dep. Var.= RESTATE_BEG_END RESTATE _END_DIS AUDCHG (1) (2) (3) Constant 5.828*** 5.380*** 0.114 (29.239) (16.937) (0.111) PSCORE -0.006** -0.000 0.005 (-2.200) (-0.102) (0.432) LNASSETS 0.066*** -0.046** -0.042 (3.686) (-2.459) (-0.605) LEV -0.153* 0.085 -0.065 (-1.692) (0.843) (-0.175)
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ROA 0.146*** 0.038 0.082 (3.187) (0.664) (0.320) FOROPS 0.067 -0.074 0.177 (1.179) (-1.253) (0.779) SQSEG -0.039 0.099** 0.142 (-0.799) (2.138) (0.769) FRAUD 0.182 -0.035 -2.735*** (1.549) (-0.181) (-3.269) MULT_ISSUES 0.182*** -0.094* 0.274 (3.542) (-1.894) (1.417) RESTATE_IMPACT 1.254** -1.080*** 5.178** (2.555) (-2.635) (2.320) POS_EARN 0.096 -0.188*** 0.128 (1.417) (-2.975) (0.493) BIG 0.162** 0.002 -1.360*** (2.420) (0.024) (-5.642) SPECIALIST -0.001 0.016 0.199 (-0.023) (0.329) (1.056) QTR_1 0.039 0.376*** 0.284 (0.839) (8.256) (1.605) AUDCHG 0.220*** 0.168*** (4.426) (3.171) Year fixed effect Yes Yes Yes Industry fixed effect Yes Yes Yes chi2 272.975 197.385 (pseudo) R2 0.107 N 785 785 785
Notes: Panel A reports the results for variations of Model (1) based on the full sample, except Column (10) which uses DACC as the dependent variable. In columns (7) and (8), we include observations up to 2006 as that is the last year that Hennes et al. (2008) provide the classification of errors vs. irregularities. We delete firm-years with errors when estimating the regression of IRREG and delete firm-years with irregularities when estimating the regression of ERROR. We also report the marginal effect for the test variable in bracket in Panel A. All columns in Panel A are estimated using logistic regressions, except Column (10) which is estimated using OLS. Panel B reports the association between PSCORE and the detection difficulty of misstatements based on the subsample of restatement firms. Columns (1) and (2) of Panel B are estimated using the negative binomial regression, and Column (3) is estimated with logistic regression. For each variable, we report the regression coefficient and z-statistics (in parentheses) calculated based on robust standard errors clustered by firm. **, **, * represent 1%, 5%, 10% significance levels, respectively, two-tailed tests. The variable definitions are presented in Appendix A.
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Table 4 Accounting Standard Change and Restatement
Panel A: Univariate analysis Mean value for RESTK_test_standard Difference in Accounting Sample RBC1 N PRINCIPLE_REGIME=1 PRINCIPLE_REGIME=0 Mean Standard Period (i.e. lower RBC1) (i.e. higher RBC1) RESTK_test_standard 2002- SFAS 123 4 13,107 0.029 2004 -15.033*** 2006- SFAS 123r 2 13,948 0.005 2008 APB 17 1 2000 2,675 0.007 2002- -2.015** SFAS 142 3 7,966 0.011 2004
Panel B: Regression of RESTK_test_standard Dependent Var. RESTK_test_standard SFAS 123 vs. SFAS 123r APB 17 vs. SFAS 142 (1) (2) (3) (4) Constant -5.738*** -5.837*** -5.187*** -5.150*** (-5.198) (-5.233) (-2.827) (-2.790) PRINCIPLE_REGIME -2.348*** -2.353*** -1.129* -1.126* (-5.303) (-5.323) (-1.664) (-1.661) PSCORE_OTHER 0.010 -0.003 (0.887) (-0.160) LNASSETS 0.361*** 0.380*** 0.122 0.115 (7.859) (7.479) (1.328) (1.133) SQSEG -0.003 0.023 0.459 0.451 (-0.018) (0.136) (1.379) (1.364) FOROPS -0.048 -0.020 0.113 0.104 (-0.297) (-0.121) (0.293) (0.265) FIN 0.575*** 0.581*** 0.240 0.240 (3.703) (3.746) (0.652) (0.650) MERGER 0.127 0.142 0.792** 0.792** (0.778) (0.862) (2.340) (2.340) ROA -0.084 -0.093 0.443 0.452 (-0.239) (-0.267) (0.536) (0.552) LOSS 0.140 0.159 0.155 0.152 (0.791) (0.884) (0.374) (0.356) LEV -0.993** -0.961** -0.016 -0.023 (-2.237) (-2.176) (-0.026) (-0.037) GC -2.218** -2.216** (-2.203) (-2.202) BM -0.029 -0.031 0.074 0.075 (-0.830) (-0.882) (0.833) (0.837) ICMW 0.832*** 0.858*** 0.940* 0.934* (3.283) (3.408) (1.706) (1.696) DEC -0.374** -0.376** -0.054 -0.056 (-2.494) (-2.507) (-0.139) (-0.142) DELAY -0.102 -0.087 0.012 0.006 (-0.549) (-0.467) (0.037) (0.016)
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BIG 0.147 0.147 0.229 0.231 (0.538) (0.541) (0.369) (0.371) SPECIALIST -0.681*** -0.678*** 0.237 0.236 (-4.122) (-4.091) (0.706) (0.703) SHORTTENURE 0.973*** 0.987*** -0.685 -0.689 (2.583) (2.618) (-1.340) (-1.347) LNAGE -0.337*** -0.343*** -0.379* -0.377 (-3.171) (-3.232) (-1.657) (-1.645) NAFRATIO -0.143 -0.140 -0.026 -0.026 (-1.370) (-1.336) (-0.109) (-0.108) ABFEE -0.170 -0.143 -0.473* -0.480* (-1.442) (-1.161) (-1.695) (-1.692) [Marginal effect] [-02.18%] [-2.18%] [-1.00%] [-1.00%] Year fixed effects Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes pseudo R2 0.172 0.172 0.077 0.077 N 10215 10215 3534 3534
Notes: Panel A represents the univariate difference for the restatement likelihood between two regimes for two pairs of accounting standards. Within each pair, one standard is superseded by the other. RBC1 is the rules-based score for each standard. PRINIPLE_REGIME is 1 for the years under the more principles-based standard in each pair of accounting standard change (i.e. SFAS 123r and APB 17), and 0 for the years under the more rules-based standard in each pair (i.e. SFAS 123 and SFAS 142). RESTK_test_standard equals 1 when RESTK is 1 and the restatement disclosure mentions the specific accounting standard subject to the change, and 0 otherwise.
Panel B represents the multivariate analysis for the two pairs of standards. PSCORE_Other represents the portion of PSCORE based on standards other than the standards under test. For example, PSCORE_Other in column (2) represents PSCORE value for 87 out of 89 standards in Appendix B (excluding SFAS 123 and SFAS 123r). For each variable, we report the regression coefficient and z-statistics (in parentheses) calculated based on robust standard errors clustered by firm. ***, **, * represent 1%, 5%, 10% significance levels, respectively, two-tailed tests. The variable definitions for other variables are presented in Appendix A.
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Table 5 The Impact of Second Guessing and Litigation Risk on the Association between Principles-based-score and Restatements
Panel A: The impact of second guessing on the Association between Principles-based-score and Restatements Proxies for Second_Guessing Distress Policy_Uncertainty Non_Overconfidence
(1) (2) (3) (4) (5) (6) PSCORE -0.011*** -0.003 -0.013*** -0.002 -0.017** -0.037*** (-2.868) (-0.681) (-3.470) (-0.377) (-2.515) (-3.842) Second_Guessing 0.066 -0.195 -0.026 -0.057* -0.001 -0.249** (1.005) (-1.564) (-0.895) (-1.732) (-0.016) (-2.424) PSCORE×Second_Guessing -0.014** -0.002** -0.013*** (-2.493) (-2.013) (-2.689) Control variables Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Yes Yes pseudo R2 0.061 0.062 0.063 0.063 0.086 0.088 N 16013 16013 16856 16856 4362 4362 Test of coefficient sum PSCORE+PSCORE* Second_Guessing χ2 13.99*** 0.60 11.53***
Panel B: The impact of litigation risk on the Association between Principles-based-score and Restatements Proxies for High_Litigation Litigation score by Litigation score by High litigation based Kim and Skinner (cutoff: Shu (cutoff: on industry median) median) classification (1) (2) (3) (4) (5) (6) PSCORE -0.011*** -0.015*** -0.011*** -0.024*** -0.011** -0.017*** (-2.913) (-2.773) (-2.841) (-3.968) (-2.575) (-3.657) High_Litigation 0.017 0.119 0.036 0.338*** 0.184** 0.506*** (0.289) (1.025) (0.579) (2.752) (2.334) (3.706) PSCORE×High_Litigation 0.006 0.019*** 0.019*** (1.012) (2.839) (2.897) Control variables Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Yes Yes pseudo R2 0.064 0.064 0.062 0.062 0.058 0.059 N 15701 15701 15551 15551 13707 13707 Test of coefficient sum PSCORE+PSCORE*High_Litigation χ2 2.62 1.39 2.32
Notes: This table presents the logistic regression results for the moderating effects of second guessing and litigation risk. In Panel A, Second_guessing is measured by three different proxies. In Panel B, High_Litigation is 1 if a firm’s litigation score is above or equal to the sample median for the litigation measure following Kim and Skinner (2012) in Columns (1) and (2); or Shu (2000) in Columns (3) and (4), or if the firm belongs to a high litigious industry defined in Francis, Philbrick and Schipper (1994) in Columns (5) and (6). For each variable, we report the regression coefficient and z-statistics (in parentheses) calculated based on robust standard errors clustered by firm. ***, **, * represent 1%, 5%, 10% significance levels, respectively, two-tailed tests. The variable definitions are presented in Appendix A.
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Table 6 The Effectiveness of Monitors under Principles-based-standards vs. Rules-based-standards
Panel A: The effectiveness of monitoring from board or auditors under principles-based-standards vs. rules-based-standards Proxies for board Board Independence Audit Committee Abnormal Audit Fees Industry Specialist or auditor (Board_Indep) Independence (ABFEE) Auditor (Audcom_Indep) (SPECIALIST) Subsamples Low High Low High Low High Low High PSCORE PSCORE PSCORE PSCORE PSCORE PSCORE PSCORE PSCORE (1) (2) (3) (4) (5) (6) (7) (8) Proxies for board -0.424*** -0.116 -0.151* 0.116 -0.297*** -0.114* -0.196*** 0.019 or auditor (-6.114) (-1.444) (-1.678) (1.051) (-5.146) (-1.759) (-2.881) (0.245) PSCORE -0.010* -0.006 -0.009 -0.015 -0.009* -0.006 -0.009* -0.006 (-1.923) (-0.501) (-1.559) (-0.957) (-1.858) (-0.462) (-1.858) (-0.462) Control variables Yes Yes Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Industry fixed Yes Yes Yes Yes Yes Yes Yes Yes effects pseudo R2 0.059 0.085 0.067 0.098 0.054 0.085 0.054 0.085 N 9072 9121 6940 6250 9072 9121 9072 9121 Test of coefficient Board_IndephighPSCORE Audcom_IndephighPSCORE ABFEEhighPSCORE SPECIALISThighPSCORE equality = Board_IndeplowPSCORE = Audcom = ABFEElowPSCORE = SPECIALISTlowPSCORE _IndeplowPSCORE χ2 4.62 5.20 3.64 12.65 p-value 0.032 0.023 0.056 0.000
Panel B: The effectiveness of monitoring from the SEC under principles-based-standards vs. rules-based- standards Proxies for SEC monitoring Within 100 Kilometers of Number of AAERs filed SEC office around the firm’s headquarter (Proximity100) (AAER_Intensity) Subsamples Low PSCORE High PSCORE Low PSCORE High PSCORE (1) (2) (3) (4) Proxies for SEC 0.006 -0.145* 0.003 -0.017** monitoring (0.090) (-1.793) (0.502) (-2.267) PSCORE -0.007 -0.005 0.002 -0.006 (-1.406) (-0.381) (0.279) (-0.368) Control variables Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes pseudo R2 0.053 0.087 0.053 0.102 N 8864 9024 4762 4538 Test of coefficient equality Proximity100highPSCORE AAER_IntensityhighPSCORE = Proximity100lowPSCORE = AAER_IntensitylowPSCORE χ2 6.10 5.46 p-value 0.014 0.020 Notes: This table compares the monitoring effectiveness of the board, the auditor, and the SEC for firms relying more and less on PBS. In panel A, monitoring by board or auditor is proxied by Board_Indep, Audcom_Indep, ABFEE, and SPECIALIST. In panel B, monitoring by the SEC is proxied by Proximity100 and AAER_intensity. In each panel, we partition the sample at the median value of PSCORE of the full sample and compare the coefficient of the proxy for board/auditor/SEC monitoring between firms with high and low PSCORE. For each variable, we report the regression coefficient and z-statistics (in parentheses) calculated based on robust standard errors clustered by firm. ***, **, * represent 1%, 5%, 10% significance levels, respectively, two-tailed tests. The variable definitions are presented in Appendix A.
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Appendix A
Variable Definitions
Variables Definitions Test Variables PSCORE principles-based score of the 10-K report following Folsom et al. (2016)’s methodology; PRINCIPLE_REGIME 1 for observations under more principles-based regime in each pair of accounting standard change, and 0 otherwise. HPSCORE 1 if PSCORE is above or equal to the sample median, and 0 otherwise. Dependent variables RESTK 1 if the current year annual report is subsequently restated, 0 otherwise; AAER 1 if the current year annual report is subsequently restated and receives an Accounting Auditing Enforcement Release from the SEC, 0 otherwise; RESTK_IncomeDecrease 1 if the annual report is subsequently restated and the restatement decreases the original net income, 0 otherwise; RESTK_NonIncomeDecrease 1 if the annual report is subsequently restated and the restatement does not decrease the original net income, 0 otherwise; RESTK_NI_Sales 1 if the absolute value of change in net income after the restatement deflated by net sales of the current year exceeds 1%, 0 otherwise; RESTK_NI_AT 1 if the absolute value of change in net income after the restatement deflated by lagged total assets exceeds 1%, 0 otherwise; IRREG 1 if the current year annual report is subsequently restated and classified as an irregularity by Hennes et al. (2008), 0 otherwise; ERROR 1 if the current year annual report is subsequently restated and classified as an error by Hennes et al. (2008), 0 otherwise; DACC Abnormal accruals, computed following equation (3) in Reichelt and Wang (2010); RESTATE_BEG_END number of days between the beginning and the end of the misstatement period; RESTATE_END_DISCLOSURE number of days between the end of the misstatement period and disclosure of restatement details; AUDCHG 1 if the client changed auditors from misstated period to the disclosure of restatement details, 0 otherwise; Variables for moderating effects Distress 1 if the firm’s Altman Z-score is below 2.99, 0 otherwise;
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Policy_Uncertainty the decile rank of the average economic policy uncertainty in the year; Non_Overconfidence the sum of the four indicators of CEO overconfidence (i.e. HOLDER67, CAPEX, OVERINVEST, and PURCHASE) following Ahmed and Duellman (2013), multiplied by (-1); High_Litigation 1 if the firm’s litigation score is above or equal to the sample median for the litigation measure following Table 7 of Kim and Skinner (2012) or Table 3 of Shu (2000) or if the firm belongs to a high litigious industry defined in Francis, Philbrick and Schipper (1994), 0 otherwise; Variables for monitors Board_Indep 1 if the percentage of independent board members is larger than or equal to two thirds, 0 otherwise; Audcom_Indep 1 if the audit committee is fully independent, 0 otherwise; ABFEE abnormal audit fee (the difference between the actual and the fitted values of audit fees estimated based on the model in Table 10); SPECIALIST 1 if the company is audited by an industry specialist auditor, 0 otherwise; Proximity100 1 if the firm’s headquarters is within 100 km to SEC headquarters or the closest regional office, 0 otherwise; AAER_Intensity the number of AAERs filed for the companies in the same county as the firm’s headquarters for the past 10 years; Control variables LNASSETS natural logarithm of total assets at the end of the current year; SQSEG squared root of the number of business segments; FOROPS 1 if the company is incorporated outside the United States, 0 otherwise; 1 if the sum of new long-term debt plus new equity exceeds 2 FIN percent of lagged total assets, 0 otherwise; MERGER 1 if the company engaged in a merger or acquisition, 0 otherwise; ROA return on assets; LOSS 1 if net income is negative, 0 otherwise; LEV long-term debt scaled by total assets; GC 1 if the company receives a going concern modified opinion in the current year, 0 otherwise; BM book-to-market value of stockholders’ equity at the end of the current year; ICMW 1 if the audit report identifies material weaknesses in internal controls over financial reporting, 0 otherwise; DEC 1 if company i’s fiscal year ends on December 31, 0 otherwise;
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DELAY natural logarithm of the number of days between the balance sheet date and audit report filing date; BIG 1 if the company is audited by one of the Big 5 (4) accounting firms, 0 otherwise; SHORTTENURE 1 if audit tenure is below 3 years, 0 otherwise; LNAGE natural logarithm of the number of years the company is listed on Compustat; NAFRATIO ratio of nonaudit fees to total fees paid to the auditor; NUMITEMS number of non-missing items on Compustat; NUMWORDS the natural logarithm of number of words in the 10-K report after excluding all tables; FILESIZE the natural logarithm of file size of the 10-K complete submission text file; BOGINDEX a measure of readability following Bonsall, Leone, Miller and Rennekamp (2017); GROWTH one-year growth rate of a firm’s sales revenue; OCF operating cash flows deflated by lagged total assets; QTR_1 an indicator variable that equals 1 if the restatement was disclosed in the first quarter, and 0 otherwise; RESTATE_IMPACT the absolute value of the cumulative impact of the restatement on income, scaled by net income at the end of the year prior to the restatement announcement; FRAUD 1 if the misstatement is associated with allegations of accounting fraud, and 0 otherwise; MULT_ISSUES 1 if the restatement involved more than one accounting rule (GAAP/FASB) application failure, 0 otherwise; POS_EARN 1 if the restatement increased earnings, 0 otherwise.
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Appendix B Accounting Standards Classification Standard # Year Standard Title RBC1 Category APB 2 1962-2009 Accounting for the "Investment Credit" 1 tax APB 4 Amend APB2 Accounting for the "Investment Credit" N/A N/A Reporting the Results of Operations I—Net Income and the Treatment of Extraordinary Items APB 9 1966-2009 and Prior Period Adjustments II—Computation and Reporting of Earnings per Share 1 other Accounting for Convertible Debt and Debt Issued APB 14 1966-2009 debt with Stock Purchase Warrants 0
APB 16 1970-2001 Business Combinations N/A (3 in 00-01) N/A APB 17 1970-2001 Intangible Assets N/A (1 in 00-01) N/A The Equity Method of Accounting for Investments APB 18 1971-2009 in Common Stock 3 other APB 20 1971-2005 Accounting Changes 1 (N/A in 06-09) other APB 21 1971-2009 Interest on Receivables and Payables 1 debt APB 23 1971-2009 Accounting for Income Taxes—Special Areas 1 tax APB 25 1972-1994 Accounting for Stock Issued to Employees N/A N/A APB 26 1973-2009 Early Extinguishment of Debt 1 Debt APB 29 1973-2009 Accounting for Nonmonetary Transactions 2 (1 in 00-03) other Reporting the Results of Operations—Reporting the Effects of Disposal of a Segment of a Business, APB 30 1973-2009 and Extraordinary, Unusual and Infrequently Occurring Events and Transactions 1 other ARB 43 Ch. 3a 1953-2009 Current Assets and Current Liabilities 0 other ARB 43 Ch. 3b 1953-2009 Offsetting Securities Against Taxes Payable 0 other ARB 43 Ch. 4 1953-2009 Inventory Pricing 0 other mergers, 0 acquisitions ARB 43 Ch. 7a 1953-2009 Quasi-Reorganization or Corporate Readjustment and reorganizations ARB 43 Ch. 7b 1953-2009 Stock Dividends and Stock Split-ups 0 other 0 Long-term ARB 43 Ch. 9a 1953-2009 Depreciation and High Costs assets 0 Long-term ARB 43 Ch. 9b 1953-2009 Depreciation on Appreciation assets ARB 43 Ch. 0 1953-2009 Real and Personal Property Taxes 10a tax ARB 43 Ch. 0 revenue 1953-2009 Cost-Plus-Fixed-Fee Contracts 11a recognition ARB 43 Ch. 0 1953-2009 Renegotiation 11b other ARB 43 Ch. 0 revenue 1953-2009 Terminated War and Defense Contracts 11c recognition foreign ARB 43 Ch. 12 1953-2009 Foreign Operations and Foreign Exchange 0 operations and
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related party issues 0 revenue ARB 45 1955-2009 Long-Term Construction-Type Contracts recognition ARB 51 1959-2009 Consolidated Financial Statements 3 (4 in 08-09) other Cons 5: Recognition and Measurement in Conceptual Financial Statement 5 & 1985-2009 Statements of Business Enterprises; Cons 6: 1 6 Elements of Financial Statements other 1 Long-term SFAS 2 1975-2009 Accounting for Research and Development Costs assets liabilities other 2 than debt and SFAS 5 1975-2009 Accounting for Contingencies reserve estimates Accounting and Reporting by Development SFAS 7 1976-2009 Stage Enterprises 0 other SFAS 13 1977-2009 Accounting for Leases 4 lease Accounting by Debtors and Creditors for SFAS 15 1977-2009 debt Troubled Debt Restructurings 2 SFAS 16 1977-2009 Prior Period Adjustments 0 other Financial Accounting and Reporting by Oil and SFAS 19 1978-2009 Gas Producing Companies 1 other SFAS 34 1979-2009 Capitalization of Interest Cost 0 other Accounting and Reporting by Defined Benefit SFAS 35 1980-2009 Pension Plans 1 (0 in 06-09) other liabilities other 1 than debt and SFAS 43 1980-2009 Accounting for Compensated Absences reserve estimates 0 revenue SFAS 45 1981-2009 Accounting for Franchise Fee Revenue recognition liabilities other 1 than debt and SFAS 47 1981-2009 Disclosure of Long-Term Obligations reserve estimates 1 revenue SFAS 48 1981-2009 Revenue Recognition When Right of Return Exists recognition liabilities other 1 than debt and SFAS 49 1981-2009 Accounting for Product Financing Arrangements reserve estimates Financial Reporting in the Record and Music 0 SFAS 50 1981-2009 Industry other Financial Reporting by Cable Television 0 SFAS 51 1981-2009 Companies other Foreign Currency Translation foreign SFAS 52 1982-2009 2 operations and
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related party issues Financial Reporting by Producers and SFAS 53 1981-2000 Distributors of Motion Picture Films N/A (0 in 2000) other foreign 1 operations and SFAS 57 1982-2009 Related Party Disclosures related party issues Accounting and Reporting by Insurance 1 (2 in 08-09) SFAS 60 1982-2009 Enterprises other SFAS 61 1982-2009 Accounting for Title Plant 0 other SFAS 63 1982-2009 Financial Reporting by Broadcasters 0 other Accounting for Certain Mortgage Banking 1 SFAS 65 1982-2009 Activities other SFAS 66 1982-2009 Accounting for Sales of Real Estate 3 other Accounting for Costs and Initial Rental SFAS 67 1982-2009 Operations of Real Estate Projects 1 other liabilities other 1 than debt and SFAS 68 1982-2009 Research and Development Arrangements reserve estimates Accounting for the Effects of Certain Types of SFAS 71 1983-2009 Regulation 3 other Reporting by Transferors for Transfers of SFAS 77 1983-1996 Receivables with Recourse N/A other SFAS 80 1984-1999 Accounting for Futures Contracts N/A N/A Accounting for the Costs of Computer Software to SFAS 86 1986-2009 Be Sold, Leased, or Otherwise Marketed 1 other SFAS 87 1986-2009 Employers’ Accounting for Pensions 3 (4 in 06-09) other Employers’ Accounting for Settlements and SFAS 88 1985-2009 Curtailments of Defined Benefit Pension Plans and for Termination Benefits 0 (1 in 08-09) other Accounting and Reporting by Insurance Enterprises for Certain Long-Duration Contracts SFAS 97 1988-2009 and for Realized Gains and Losses from the Sale 1 of Investments other Regulated Enterprises—Accounting for the SFAS 101 1988-2009 Discontinuation of Application of FASB Statement No. 71 0 other Disclosure of Information about Financial Instruments with Off-Balance-Sheet Risk and SFAS 105 1990-2000 Financial Instruments with Concentrations of N/A (1 in 2000) Credit Risks other Employers’ Accounting for Postretirement SFAS 106 1992-2009 Benefits Other Than Pensions 4 (3 in 00-03) other Disclosures about Fair Value of Financial 1 financial SFAS 107 1992-2009 Instruments instruments SFAS 109 1992-2009 Accounting for Income Taxes 4 tax
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Accounting and Reporting for Reinsurance of SFAS 113 1992-2009 Short-Duration and Long-Duration Contracts 0 other Accounting for Certain Investments in Debt and financial SFAS 115 1993-2009 Equity Securities 3 instruments Accounting for Contributions Received and SFAS 116 1994-2009 Contributions Made 1 other Disclosure about Derivative Financial Instruments SFAS 119 1994-2000 and Fair Value of Financial Instruments N/A (0 in 2000) N/A Accounting for the Impairment of Long-Lived SFAS 121 1995-2001 Assets and for Long-Lived Assets to Be Disposed Of N/A (1 in 00-01) N/A 4 (N/A in 06-09) stock-based SFAS 123 1995-2005 Accounting for Stock-Based Compensation compensation 2 (N/A in 00-04) stock-based SFAS 123r 2005-2009 Share-Based Payment compensation Accounting for Transfers and Servicing of SFAS 125 1996-2001 Financial Assets and Extinguishments of N/A Liabilities other SFAS 130 1997-2009 Reporting Comprehensive Income 1 other Accounting for Derivative Instruments and financial SFAS 133 2000-2009 Hedging Activities 3 instruments Accounting for Transfers and Servicing of SFAS 140 2001-2009 Financial Assets and Extinguishments of 4 Liabilities other mergers, 3 (N/A in acquisitions SFAS 141 2001-2008 Business Combinations 00&09) and reorganizations 3 (N/A in 00, Long-term SFAS 142 2001-2009 Goodwill and Other Intangible Assets 2 in 01) assets 2 (N/A in 00-01, liabilities other 1 in 08-09) than debt and SFAS 143 2002-2009 Accounting for Asset Retirement Obligations reserve estimates Accounting for the Impairment or Disposal of 3 (N/A in 00, Long-term SFAS 144 2001-2009 Long-Lived Assets 2 in 01) assets liabilities other Accounting for Costs Associated with Exit or 0 (N/A in 00-01) than debt and SFAS 146 2002-2009 Disposal Activities reserve estimates Accounting for Certain Financial Instruments SFAS 150 2003-2009 with Characteristics of both debt Liabilities and Equity 1 (N/A in 00-02) SFAS 154 2005-2009 Accounting Changes and Error Corrections 0 (N/A in 00-04) other Liability Recognition for Certain Employee Termination Benefits and Other Costs to Exit an EITF 94-03 None Activity (including Certain Costs Incurred in a Restructuring)-Nullified by FAS 146 N/A N/A
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Revenue Arrangements with Multiple 2 revenue EITF 00-21 2000-2009 Deliverables recognition 2 revenue SOP 97-2 1997-2009 Software Revenue Recognition recognition 1 revenue SAB 101 1999-2009 Revenue Recognition in Financial Statements recognition
55