MANAGERIAL ABILITY AND EARNINGS QUALITY*

Peter Demerjian Emory University

Melissa Lewis

University of Utah

Baruch Lev New York University

Sarah McVay

University of Utah

July 28, 2010

ABSTRACT We examine the relation between managerial ability and earnings quality. We find that earnings quality is positively associated with managerial ability. Specifically, more able managers are associated with fewer subsequent restatements, higher earnings and accruals persistence, lower errors in the bad debt provision, and higher quality accrual estimations. The results are consistent with the premise that managers can and do impact the quality of the judgments and estimates used to form earnings. Keywords: Managerial ability, managerial efficiency, earnings quality, accruals quality. Data Availability: Data is publicly available from the sources identified in the text.

* We would like to thank Brian Cadman, Ilia Dichev, Weili Ge, Phil Shane, and workshop participants at the 2010 Kapnick Accounting Conference at the University of Michigan, the 2010 Western Region Meeting, Emory University, Florida International University, and the University of Utah for their helpful comments. An earlier version of this paper benefited from comments by Carol Anilowski, Asher Curtis, Patty Dechow, Paul Michaud, Venky Nagar, Larry Seiford, Ram Venkataraman, Norman White, and workshop participants at the 2006 AAA Annual Meeting, Harvard University, the University of California–Berkeley, and the University of Indiana.

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MANAGERIAL ABILITY AND EARNINGS QUALITY

I. INTRODUCTION

We examine the relation between managerial ability and earnings quality. We anticipate

that superior managers are more knowledgeable of their business, leading to better judgments

and estimates and thus higher quality earnings.1 Alternatively, the benefit of higher quality

earnings may not be sufficient to warrant the time and attention of skilled management,

especially if the variance of feasible estimates is small, in which case we may not find an

association between managerial ability and earnings quality.2

While the empirical literature in the area of earnings quality has largely focused on firm-

specific characteristics, such as firm size and board independence (Dechow and Dichev 2002;

Klein 2002), we examine the manager-specific aspects of earnings quality. Our study is in the

vein of Bertrand and Schoar (2003), who find that managers have an effect on firm choices such

as acquisitions or research and development expenditures; Aier et al. (2005), who document that

CFOs with more accounting expertise have fewer restatements; and Francis et al. (2008), who

document that earnings quality varies inversely with CEO reputation.3

Our main measure of managerial ability is the score developed in Demerjian et al. (2009),

though we perform robustness checks using historical returns, media citations, and manager

1 Following Schipper and Vincent (2003), we consider high quality earnings to be those that are closest to true economic earnings. As they note, reported earnings will deviate from economic earnings because of recognition and measurement rules in U.S. GAAP as well as preparers’ implementation decisions, including management’s judgments and estimates (see also Section 2 of Dechow et al. 2009). Our focus in this paper is on the quality of management’s judgments and estimates, though we also consider the effect of GAAP rules on earnings quality when examining the Dechow and Dichev (2002) accruals quality measure in Section IV. 2 Costs to poor earnings quality include higher cost of capital (Francis et al. 2004) and economically significant negative price reactions to the announcement of earnings restatements (Palmrose et al. 2004). 3 Francis et al. (2008) measure CEO reputation with the number of articles mentioning the executive. They find that the number of news articles pertaining to the company’s CEO and earnings quality based on the Dechow and Dichev (2002) accruals quality measure are negatively associated. We reconcile the results in this paper with the results in Francis et al. (2008) in Section V. Specifically, when we decompose accruals quality into the “estimation” component and other components related to real activities and other, unexplained, activities, we find that managerial ability is associated with a higher quality estimation component—the component most associated with judgments and estimates made by management.

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fixed effects (e.g., Fee and Hadlock 2003; Milbourn 2003; Francis et al. 2008; Dyreng et al.

2009). Demerjian et al. (2009) first estimate total firm efficiency, where efficient firms are those

that generate more revenues for a given set of inputs. Total firm efficiency is influenced by both

the manager (i.e., managers can, to varying degrees, predict future demand and understand

industry trends) and the firm (i.e., managers in larger firms can negotiate better terms). Thus,

Demerjian et al. (2009) then partition total firm efficiency between the firm and the manager,

and verify that the component attributed to the manager is associated with a variety of

characteristics, including managerial pay and the price reaction to management departures from

the firm.4 Prior research is limited to measures such as media coverage and historical returns,

which are difficult to attribute solely to the manager versus the firm (e.g., Francis et al. 2008), or

manager fixed effects, where there is evidence of a manager-specific effect, but the quantifiable

effect is limited to managers who switch firms (e.g., Bertrand and Schoar 2003; Ge et al. 2008;

DeJong and Ling 2009). The main ability measure we use in this study allows us to better

separate the manager from the firm and retain an ordinal ranking of quality for a large sample of

firms.5

We expect a more able management team to be better able to estimate accruals

accurately. For example, we expect more able managers to have better knowledge of their client

base and macro-economic conditions when estimating bad debt expense, or to better assess the

4 As we will discuss in greater detail in the following sections, Demerjian et al. (2009) estimate total firm efficiency using data envelopment analysis, a type of frontier analysis that measures relative efficiency. They then remove identifiable firm characteristics, such as size, that affect the firm’s relative efficiency but do not depend upon the quality of the management team. They attribute the unexplained portion of total firm efficiency to the management team. 5 In our setting, we would like to determine the quality of the CFO and their delegates, as we focus on the estimation of accruals, whereas CEOs are more focused on the overall strategy of the firm. Though we cannot disentangle the ability score by CEO and CFO, the ability score does encompass CFOs and their delegates, whereas media citations are, by definition, focused on the CEO. We also identify CFOs switching firms within our sample to correlate the CFOs’ scores from their old firms with the accruals quality after their arrival in their new firms (see Section V).

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value of inventory (and thereby correctly identify obsolete inventory) based on their knowledge

of industry trends and how these trends are expected to affect their firm.

We consider four measures of earnings quality: the existence of an earnings restatement

(Anderson and Yohn 2002), the persistence of earnings (Lipe 1990; Penman 2001), errors in the

bad debt provision (McNichols and Wilson 1988), and the extent to which accruals map into

cash flows (Dechow and Dichev 2002; Francis et al. 2008). We include controls for innate

characteristics of firms that make it more or less difficult to estimate accruals (e.g., operating

cycle and sales volatility) as well as structural characteristics shown to affect the quality of

earnings (e.g., board independence and internal control quality). In general, we find that

earnings quality is positively associated with managerial ability. This finding is consistent with

the premise that capable managers are better able to estimate accruals, which results in a more

precise measure of earnings.

We contribute to both the earnings quality literature and the managerial ability literature.

First, establishing a positive and significant relation between managerial ability and earnings

quality suggests a means to improving earnings quality. Many of the factors associated with

earnings quality, such as firm size, industry, or operating cycle, cannot be altered to change

earnings quality. In contrast, attempts are often made to improve managerial quality. This

finding is important for board members considering the costs and benefits of managers;

managerial ability affects not only the operations of the firm, but also the quality of its reported

earnings, and in turn, its share-price attributes and litigation exposure.

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Second, we identify a real activities-related component of the often-used Dechow and

Dichev (2002) accruals quality measure.6 We find evidence consistent with the expectations of

LaFond (2008)—that firms undertaking certain real economic activities, such as R&D

expenditures or M&As, have lower accruals quality because of these activities. This lower

accruals quality is largely a byproduct of the accounting system, and generally does not reflect

estimates or judgments. Therefore, it is difficult for management to mitigate this effect.

Identifying and measuring this component should allow researchers to enhance their research

designs to better identify the desired component of earnings quality (Dechow et al. 2009).

In the next section, we develop our hypotheses with a review of the literature. In Section

III we describe our sample, test variables, and descriptive statistics. In Section IV we present the

main results, and in Section V we consider alternative ability measures and conduct a change

analysis for a subset of managers in our sample who switch firms. In the final section we

conclude the study.

II. PRIOR RESEARCH AND HYPOTHESIS DEVELOPMENT

Earnings quality is an important element of financial reports that affects the efficient

allocation of resources. Earnings are the main input to investors’ and analysts’ valuation models,

and firms with poor earnings quality tend to have higher cost of capital (e.g., Francis et al. 2004),

while those experiencing restatements or SEC enforcement actions tend to experience an

economically significant negative price reaction to the announcement (Feroz et al. 1991;

Palmrose et al. 2004).

6 We use the term earnings quality to capture the general construct of higher quality reported earnings, while we use the term accruals quality to discuss the Dechow and Dichev (2002) estimate of earnings quality (the mapping of accruals to cash flows).

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Following Schipper and Vincent (2003), we consider high quality earnings to be those

that are closest to true economic earnings. Schipper and Vincent (2003) note that reported

earnings deviate from economic earnings because of recognition and measurement rules in U.S.

GAAP as well as preparers’ implementation decisions, including management’s judgments and

estimates.

We expect individual managers to exhibit variation in their abilities to form accurate

judgments and estimates. We expect managers with higher innate abilities to be more

knowledgeable about the firm and the industry, as well as to be better able to synthesize

information into logical forward-looking estimates to report higher quality earnings.

Specifically, we expect accruals estimated by high-ability managers to be more accurate. For

example, consider the allowance for bad debt estimate. A weaker manager might apply the

historical rate of bad debt for the firm, while a stronger manager might adjust the historical rate

by considering the macro-economic and industry trends, as well as changes in the firm’s

customer base. Thus, holding the firm constant, we expect a more able manager to report higher

quality earnings.

H1: Managerial ability is positively associated with earnings quality.

It is possible that the bulk of the variation in earnings quality is driven by the accounting

rules, in which case we will not find an association between the ability of managers and the

quality of earnings. It is also possible that the benefits to the incremental improvement in

earnings quality resulting from the intervention by an able manager (who may, for example,

consider additional information) do not exceed the cost of that manager’s time, in which case,

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again, we will not find an association between the manager’s ability and the firm’s earnings

quality.

To date, the bulk of the literature on earnings quality has examined firm-specific

characteristics. For example, Dechow and Dichev (2002), examining the mapping of accruals

into cash flows, document that earnings quality is poorer for firms that are smaller, are

experiencing losses, have greater sales and cash flow volatility, and have longer operating cycles.

Each of these innate firm characteristics makes accruals more difficult to estimate. In addition to

these innate characteristics, earnings quality has been found to vary with firm infrastructure.

Klein (2002) finds that firms with more independent boards and audit committee members have

higher quality accruals, consistent with stronger governance constraining earnings management.

Doyle et al. (2007) find that earnings quality is poorer in firms that have weaker internal controls

over financial reporting, where it is less likely that errors or intentional misstatements will be

discovered and corrected, while Ashbaugh-Skaife et al. (2008) document an improvement in

earnings quality following the remediation of internal control problems.7

With respect to the effects of managers on the firm, Bertrand and Schoar (2003)

document that managers have a real impact on the firms they manage—that firm decision-

making reflects the “style” of different managers. In a similar vein, both Ge et al. (2008) and

DeJong and Ling (2009) examine manager fixed effects on certain financial reporting policies,

and, similar to Bertrand and Schoar, document that individual managers matter: firms’

accounting and disclosure policies vary with manager fixed effects.8 Ge et al. (2008) find that

7 Dechow and Dichev (2002) and Doyle et al. (2007) define higher earnings quality as when more accruals are realized as cash, while other studies, such as Klein (2002) and Ashbaugh-Skaife et al. (2008) assess earnings quality with the absolute value of discretionary accruals, where larger absolute discretionary accruals are deemed low quality. We discuss specific earnings quality measures in detail in Section III. 8 Also using fixed effects, Bamber et al. (2010) find that individual managers appear to have preferred “styles” that are associated with their propensity to issue guidance and the characteristics of the resulting guidance (e.g., the

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CFO-specific factors play a significant role in explaining firms’ discretionary accruals, off-

balance sheet activities, probability of accounting manipulations, financial reporting

conservatism, earnings smoothness, and disclosure accuracy and bias. DeJong and Ling (2009)

examine manager effects on accruals through both investment and accounting choices. As

previously noted, this approach allows researchers to document a manager-specific effect, but it

is constrained to managers who switch employers among the sample firms.

Most closely related to our study, both Aier et al. (2005) and Francis et al. (2008)

examine whether earnings quality varies with managerial characteristics. Aier et al. (2005)

examine 456 firm-year observations over 1997–2002 and document an association between CFO

expertise (e.g., years worked as CFO, experience at another company, advanced degrees and

professional certifications) and restatements; they find that firms with CFOs with greater

expertise experience fewer restatements. Francis et al. (2008) examine the relation between

earnings quality and CEO reputation, measured by the number of business press articles

mentioning each CEO. The authors conduct their analysis for a sample of about 2,000 firm-year

observations from the S&P 500 over 1992–2001 and find a negative relation between CEO

reputation and earnings quality. They conclude that “boards of directors hire specific managers

due to the reputation and expertise these individuals bring to managing the more complex and

volatile operating environments of these firms.” In other words, they suggest that the volatile

operating environments or other innate characteristics of the firm are causing the lower earnings

quality, not managerial actions.

precision of the guidance), and Richardson et al. (2004) examine board member tastes using a sample of 885 firms with common directors in 1999. They find that board member fixed effects are associated with firms’ governance, financial, disclosure, and strategic policy choices. Finally, Dyreng et al. (2009) quantify the economic effect of specific managers on effective tax rates by comparing the relative fixed effects of managers on effective tax rates.

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Finally, in the banking and insurance industry, respectively, Barr and Siems (1997) and

Leverty and Grace (2005) find that managerial ability reduces the likelihood of insurer distress

and the amount of time spent in distress. Both studies use data envelopment analysis (which is

similar to the method we use to measure managerial ability in this paper) to determine the

efficiency of the firm, based on industry-specific inputs and outputs, and they characterize

superior managers as those who use inputs efficiently in the production process.

In sum, there is mixed evidence on the impact of managers on earnings quality. Though

there is some evidence that high quality managers reduce the likelihood of financial distress, and

managers with greater expertise are associated with fewer earnings restatements, Francis et al.

(2008) document that more reputable managers are associated with lower earnings quality. The

latter association is consistent with some firms having low-quality earnings by the nature of their

business (e.g., Dechow and Schrand 2004) and these firms hiring better managers. We posit that,

after we control for the innate challenges affecting the firm, more able managers will be

associated with higher quality earnings, as their judgments and estimations are more informed

and accurate.

We consider four earnings quality measures. The first is earnings restatements, which are

de facto evidence of inaccurate earnings, which offers the least ambiguous proxy for earnings

quality. The second is earnings persistence, a measure of the sustainability of earnings; we

partition earnings into accrual and cash flow components to examine accruals persistence more

directly. Our third earnings quality measure is the accuracy of a specific accrual: the bad debt

provision (McNichols and Wilson 1988). Finally, we examine the mapping of working capital

accruals into cash from operations, based on Dechow and Dichev (2002). Essentially, if an

accrual does not become cash, that accrual is of poor quality. We expand this model by

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partitioning accruals quality into three components (estimation difficulty, real operating

activities, and the unexplained portion).

Each of these measures is affected by both intentional and unintentional errors, and more

able managers may be more likely to introduce intentional errors, either to signal their private

information about the firm or to extract perquisites from the firm and the shareholders. Because

we examine a broad sample of firms across many years, our sample firms are not expected to

have incentives to misreport, on average, and thus our focus is on conscientious judgments and

estimates made by management.9

III. DATA, VARIABLE DEFINITIONS, AND DESCRIPTIVE STATISTICS

We obtain our data from the 2008 Annual Compustat File (to estimate our main measure

of managerial ability and calculate the bulk of our earnings quality variables and controls), CRSP

(to form historical returns, an alternate managerial ability measure), Execucomp (to track CFOs

across firms), IRRC (to obtain board independence data), and Audit Analytics (for recent years

of restatements and internal control opinions). We also obtain several datasets made available by

researchers to obtain media citations (from Baik et al. 2008 and Francis et al. 2008), restatements

(from Hennes et al. 2008) and internal control quality data (from Doyle et al. 2007).

Our main sample includes all firms with available data to calculate managerial ability and

at least one of our earnings quality variables, resulting in a maximum of 86,303 firm-year

observations from 1989–2007. The period begins with 1989 because 1988 is the first year for

which firms widely reported cash flow statements, and the Dechow and Dichev (2002) earnings

9 In the event of earnings management, however, we expect more able managers to be better able to manage earnings successfully, for example, accelerating sales only if they know there will be sufficient sales in the next period to cover the acceleration, thereby avoiding large accruals reversals and restatements. We leave a more direct examination of the interaction between managerial ability and intentional earnings management for future research.

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quality variable requires one year of historical cash flow data. The sample ends in 2007, as our

earnings quality variables (described in the following section) require at least one year of future

realizations.

Variable Definitions Managerial Ability Measure

We estimate managerial ability following Demerjian et al. (2009). Their measure of

managerial ability generates an estimate of how efficiently managers use their firms’ resources.

All firms use common resources—capital, labor, innovative assets—to generate output:

revenues and earnings. High quality managers apply superior business systems and processes

(supply chains, compensation systems) to generate a higher rate of output from given inputs than

lower quality managers.

Following Demerjian et al. (2009), we implement data envelopment analysis (DEA)

within industries, comparing the sales generated by each firm conditional on the inputs used by

the firm (Cost of Goods Sold, Selling and Administrative Expenses, Net PP&E, Net Operating

Leases, Net Research and Development, Purchased Goodwill, and Other Intangible Assets).10

Our measured resources thus reflect assets (tangible and intangible), innovative capital (R&D),

and other inputs that are not reported separately in the financial statement (labor, consulting

services) but whose costs are included in cost of sales and SG&A. We provide the motivation

for, and definition of, each of these variables in the Appendix.

For our implementation of DEA, we solve the following optimization problem:

10 DEA is a form of frontier analysis. DEA calculates efficiency as the ratio of weighted outputs to weighted inputs. Unlike other measures of efficiency (e.g., ROA or ROE), DEA does not require an explicit set of weights. Rather, DEA uses an optimization program to determine the firm-specific optimal weights (termed “implicit weights”) on the inputs and outputs. The implicit weights capture the efficiency of the firm based on the selected inputs and outputs, allowing the optimal mix of inputs and outputs to vary by firm.

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The optimization finds the firm-specific vector of optimal weights on the seven inputs, v, by

comparing each of the input choices of the firm under study to those of the other firms in its

estimation group.11 The efficiency measure that DEA produces, θ, can take a value between zero

and one (due to constraints in the optimization program). Observations with a value of one are

most efficient; the set of firms with efficiency equal to one trace a frontier through the efficient

set of possible input combinations. Observations with efficiency measures less than one fall

below the frontier. Their score indicates the degree to which they are inefficient. For example, a

firm with an efficiency score of 0.85 would need to reduce the costs (in some combination) by

about 15 percent to achieve efficiency.

The efficiency measure generated by the DEA estimation is attributable to both the firm

and the manager, similar to other measures of managerial ability such as historical returns and

media coverage. For example, a more able manager will be better able to predict trends,

regardless of the size of the firm, while a manager in a larger firm will be better able to negotiate

terms with suppliers, regardless of his or her quality. Accordingly, in the second stage of

estimating managerial ability, Demerjian et al. (2009) purge the first-stage estimate of total firm

efficiency from known efficiency drivers unrelated to current managers. This is done in

Equation (1), where the first-stage efficiency estimate is regressed on firm size, market share,

free cash flows, number of segments, and an indicator for global operations, by year and

industry.12

11 Since sales is the only output, its weight is standardized to one across observations. For the general DEA model, please see the Appendix. 12 To the extent that managers also affect some of the independent variables in Equation (1), such as free cash flows, the final managerial ability score is a conservative (understated) measure of managerial efficiency.

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Firm Efficiencyi = β0 + β1Ln(Total Assetsi) + β2Market Sharei + β3Positive Free Cash Flowi + β4Ln(Segmentsi) + β5Foreign Currency Indicatori) + Yeari + εi (1)

The estimation is intended to abstract away from firm-specific benefits or challenges,

where larger firms with more market share and cash are expected to be able to generate more

sales for a given level of inputs, and firms with additional complexities, such as multiple

segments or foreign operations, are expected to generate fewer sales for a given level of inputs.

The residual from the estimation is our main measure of managerial ability.

Demerjian et al. (2009) conduct a series of validity tests on this measure, documenting

that more able managers are paid more and generate higher returns, and that this measure

outperforms alternative ability measures, such as media citations and historical returns, in

explaining stock price reactions to managerial turnovers. In other words, they document that the

departure of a more (less) efficient manager is associated with a negative (positive) price reaction

to the turnover announcement. Nonetheless, we also consider media citations, historical stock

returns, and manager fixed effects in Section V.

We decile rank Managerial Ability by year and industry to make the score more

comparable across time and industries and to mitigate the influence of extreme observations.

Results are similar using a continuous variable (results not tabulated). We have also added firm

fixed effects to Equation (1). Although this reduces the comparability across firms (see

Demerjian et al. 2009), it mitigates the concern that unidentified firm characteristics are driving

the association. Results are similar using this alternate specification of managerial ability (not

tabulated).

Earnings Quality Measures Overview

As noted in the review of earnings quality by Dechow et al. (2009), among others, a

multitude of earnings quality measures are used in the literature, including persistence, abnormal

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accruals, earnings smoothness, asymmetric timeliness and timely loss recognition, benchmarking

(in which just meeting a benchmark is viewed as lower quality), earnings response coefficients,

AAERs, restatements and internal control procedure deficiencies (see their page 3). Our goal in

this study is to examine the impact of managers on accruals, so we select earnings restatements,

earnings persistence, errors in the bad debt provision, and the mapping of accruals into cash

flows as our four measures of earnings quality. We select these measures because increased

correspondence between accruals and the associated economic activity likely reduces earnings

restatements, increases earnings persistence, and lowers the likelihood of errors in accruals. We

expect that better managers are able to report accruals that more closely correspond to the

underlying economic activity; thus we expect the earnings quality metrics that are impacted by

accrual estimation to vary with managerial ability. We discuss each earnings quality metric in

greater detail in Section IV.

We eliminate the absolute value of discretionary accruals, earnings smoothness and

benchmarking, as the relation between improved accruals estimation and these metrics is not

clear.13 We do not consider timely loss recognition, as this is largely an artifact of the

accounting system (Dechow et al. 2009, p. 13), and it is not clear whether more or less timely

loss recognition is more associated with the underlying economics of the firm, the focus of our

analysis. As noted in Dechow et al. (2009), ERCs are a poor measure of earnings quality

because much of the earnings information can be voluntarily disclosed prior to the earnings

announcement. Finally, of the three external indicators of earnings quality—restatements,

AAERs and internal control disclosures—we consider only restatements. We do not consider

13 For example, abnormally high accruals may be high quality accruals that are associated with cash flows, while abnormally low accruals may simply reflect extremely negative performance, which also reflects the underlying economics of the firm. Neither of these “abnormal” accruals provides information on the manager’s ability to appropriately estimate accruals, as the measure does not incorporate ex post realizations.

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AAERs due to data constraints (the data is not readily available in machine readable form) and

because these tend to be more fraudulent than basic errors in estimation (Hennes et al. 2008).

Furthermore, we do not consider internal control deficiencies as an outcome because the

determinants of internal control problems are largely firm-specific, such as having adequate

resources to establish and maintain these controls. The role of an able manager in the

determination of strong internal controls is less clear, and does not speak to management’s

ability to estimate accruals.14

Control Variables

Our main set of control variables is based on the firm-specific determinants of earnings

quality noted in Dechow and Dichev (2002) and Hribar and Nichols (2007): firm size,

proportion of losses, sales volatility, cash flow volatility, and operating cycle.15 Firm Size is the

log of total assets of the firm at the end of year t (XFN = AT). The remainder of the controls are

estimated over at least three out of five years, from year t–4 through year t (except when paired

with Aggregate AQ, when they are estimated over the same estimation period as Aggregate AQ).

Loss Proportion is the ratio of the number of years of losses (IBC < 0); Sales Volatility is the

standard deviation of sales scaled by average assets σ(XFN = SALE / XFN = AT); Cash Flow

Volatility is the standard deviation of cash from operations scaled by average assets σ(XFN =

OANCF / XFN = AT); and Operating Cycle is the log of the average of [(Sales/360) / (Average

14 Future research might consider, however, whether more able managers are better able to sustain high quality earnings in the face of weak internal controls. 15 Dechow and Dichev (2002) also consider earnings volatility and the magnitude of working capital accruals. However, they consider each determinant individually, while our goal is to estimate a multivariate regression. Including earnings volatility along with the other control variables in our regressions introduces a relatively high degree of multicollinearity. Thus, we do not include earnings volatility or the magnitude of working capital accruals in our tabulated results.

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Accounts Receivable) + (Cost of Goods Sold/360) / Average Inventory)], averaged over years t–

4 to t.16

We also consider two infrastructure-related control variables that have been shown to be

associated with earnings quality—governance and internal control quality. With respect to

governance, Klein (2002) finds that audit committee independence and board independence are

negatively associated with the absolute value of abnormal accruals. We consider the percentage

of independent board members, obtained from IRRC from 1996–2007, ranked by year and

industry (Board Independence). With respect to internal control quality, Doyle et al. (2007) find

that earnings quality is relatively poor in firms with internal control problems. We proxy for

internal control quality with the disclosure of material weaknesses in internal control, where

firms reporting material weaknesses are considered to have poor internal control quality. We

obtain internal control data from Doyle et al. (2007) for 2002–2004 and from Audit Analytics

from 2005–2007.17 As we have data for only the last few years of our study for each of these

variables, we do not include the variables in our main analysis, but rather consider them in

supplemental analyses.

Descriptive Statistics

We present descriptive statistics in Table 1; for each of our transformed variables

(MgrlAbility, Historical Ret, Media Count, Firm-Specific Earnings Persistence, Aggregate AQ,

Annual AQ, Firm Size, and Operating Cycle), we present the untransformed variable for ease of

interpretation. Managerial ability has a mean and median close to zero, by construction, as this

is a residual from Equation (1). The five-year historical return has a mean of approximately 15

16 Specifically, operating cycle is the log of the average of (SALE/360) / Average RECT) + (COGS/360) / Average INVT). 17 The Doyle et al. (2007) data is available at http://faculty.washington.edu/geweili/ICdata.html.

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percent, and on average, CEOs are cited by the media approximately 40 times a year (214 times

over five years). Approximately 11 percent of firms experience a restatement in the next three

years, and firm-specific earnings persistence is approximately 0.33, on average. The error in the

provision for bad debt as a percentage of sales (BDE Error) has a mean (median) of 0.014

(0.006). Mean (median) Aggregate AQ is –0.03 (–0.02), similar to that in Francis et al. (2005)

and Dechow and Dichev (2002) (we have multiplied the standard deviation by negative one).

Interestingly, Annual AQ appears to have a very similar distribution to the four-year standard

deviation, with a mean of –0.05 and a similar median and first and third quartile to the

aggregated variables. We compare these two measures further in Section IV. Our control

variables are consistent with those in the literature. For example, the average firm has $1,139

million in assets, and 38 percent of the firm-years examined experience a loss.

In Panel B of Table 1 we partition our main earnings quality measures by managerial

ability, where low-quality (high-quality) managers are those in the bottom (top) quartile of

managerial ability, where quartiles are formed by industry-year. Historical returns are

significantly larger among high quality managers, consistent with Fee and Hadlock (2003) and

Demerjian et al. (2009), though media counts are significantly lower for managers with higher

ability.18 We explore the relations between these ability measures more completely in Section V.

Restatements are more prevalent among low-quality managers, firm-specific earnings

persistence is significantly higher among high-quality managers, and errors in the provision for

bad debt are larger among low-quality managers, providing initial support for our hypothesis.

We do not, however, find consistent evidence when examining earnings quality based on the

Dechow and Dichev (2002) measure; we explore this further in our multivariate analysis.

18 Note that Demerjian et al. (2009) document a “U” shaped relation between their ability score and media citations. Specifically, they note that the best and worst managers have a greater number of media citations.

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We present a correlation matrix in Table 2. In general, the results support our ability

measure: better managers are negatively correlated with losses and positively correlated with

future earnings. Managerial ability is negatively correlated with restatements and errors in the

provision for bad debt, and positively correlated with firm-specific earnings persistence,

consistent with the univariate analysis presented in Table 1, Panel B, though both of the Dechow

and Dichev (2002) accruals quality measures are negatively associated with managerial ability,

consistent with Francis et al. (2008). Because aggregate accruals quality is positively correlated

with restatements, we investigate the attributes of these earnings quality measures when testing

our hypothesis in a multivariate setting.

IV. TEST DESIGN AND RESULTS

Earnings Restatements

The first earnings quality measure we consider is earnings restatements, which are ex

post evidence of erroneous earnings and thus have been used as a signal of poor earnings quality

(e.g., Anderson and Yohn 2002; Aier et al. 2005; Doyle et al. 2007; Dechow et al. 2009).

Though restatements can occur for reasons other than errors in accrual estimation (our focus),

this earnings quality measure is the least reliant on an estimation procedure and thus provides a

relatively unambiguous signal of earnings quality. Moreover, we do expect restatements to be

associated with errors in accrual estimation, as most restatements impact an accrual account

(Palmrose and Shultz 2004).

We use the restatement data from Hennes et al. (2008) for restatements from 1997–2006

and from Audit Analytics from 2007–2009. Hennes et al. (2008) form their restatement sample

18

from the General Accounting Office (GAO) report of restatements.19 Restate is an indicator

variable that is equal to one if the firm announces a restatement in year t, t+1, or t+2, and zero

otherwise; it is available from 1997–2007. Our subsample of restatement firm-years excludes

restatements classified as duplicate restatements per Hennes et al. (2008).

To determine whether managerial ability varies with earnings restatements, we estimate

the following equation using a pooled logistic regression:

Restatementt = β0 + ββββ1 MgrlAbilityt + β2 Firm Sizet + β3 Sales Volatiltyt + β4 Cash Flow Volatilityt + β5 Operating Cyclet + β6 Lossest + β7 Earningst + εt (2) We include each of the control variables discussed above; we also include earnings, as firm

profitability has been shown to be associated with restatements (e.g., Kinney and McDaniel

1989). Because our tests rely on panel data, standard errors may be correlated within years and

across time by firm. Thus, we cluster our standard errors by firm and year following Petersen

(2009).20

Results are presented in Table 3. As in our univariate analysis, we document a negative

relation between managerial ability and restatements, supporting our hypothesis that more able

managers are associated with higher quality earnings. The more efficient the manager, the less

likely the firm is to restate (β1 = –0.32; p < 0.01). The marginal effect is –2.9 percent (not

tabulated). Given that the unconditional likelihood of having a restatement is 11 percent, this

19 We thank the authors for this dataset, which is available at http://sbaleone.bus.miami.edu/. In addition to the restatement indicator variable used in our main tests, we utilize the partitioning of restatements into irregularities (intentional misstatements) and errors (unintentional misstatements) by Hennes et al. (2008) to determine if managerial ability differentially impacts these two types of restatements. We find that ability is associated with both types of restatements (not tabulated). 20We do not tabulate results from the estimation of Equation (2) with firm fixed effects because the sample size is reduced severely for firm fixed effects models with binary dependent variables, as the model can be estimated only for firms with variation in the dependent variable (i.e., firms that have restated at some point during our sample). Nonetheless, when we estimate Equation (2) using firm fixed effects, we continue to observe a significant and negative relation between managerial ability and restatements.

19

indicates that moving from the worst to the best decile of managerial ability reduces the odds of

having a restatement by about one-third.

We re-estimate Equation (2) after adding board independence and internal control

weakness (ICW) to control for the known infrastructure effects of these variables (not tabulated).

We continue to observe a significant and negative coefficient on managerial ability, consistent

with our hypothesis. The coefficient on ICW is significant and positive, consistent with Doyle et

al. (2007), while the coefficient on board independence does not differ significantly from zero,

consistent with Agrawal and Chadha (2005).

Earnings Persistence

Our second measure of earnings quality is earnings persistence, which is frequently

discussed as a measure of earnings quality (e.g., Penman 2001, 623; Revsine et al. 2002, 245).

Though clearly earnings persistence will vary with the underlying economics of the individual

firm, such as earnings volatility, we expect more able managers to report the highest quality

earnings after we control for these innate effects on persistence. We calculate earnings as

earnings before extraordinary items (Xpressfeed variable name (hereafter “XFN”) XFN=IBC)

scaled by average total assets (XFN=AT), and estimate earnings persistence using the following

model:

Earningst+1 = β0 + β1 Earningst

+ ββββ2 Earningst

× MgrlAbilityt + β3Earningst

× NegEarnt

+ ββββ4 Earningst × MgrlAbilityt × NegEarnt + β5 MgrlAbilityt+ β6 NegEarnt

+ ∑ *+,,+-. /0123045+ + ∑ 6*+

,7+-,8 /0123045+ × NegEarnt) +εt

(3)

The coefficient β1 is earnings persistence.21 We allow the coefficient on earnings to vary

with managerial ability and expect earnings persistence to increase with managerial ability for

21 Though we consider firm-specific earnings persistence for descriptive purposes in Tables 1 and 2, we examine earnings persistence in the cross-section to formally test our hypothesis, as we are interested in how earnings persistence varies with managerial ability, which is measured annually.

20

firms with non-negative earnings (β2 > 0). Because earnings persistence is not desirable for loss

firms, we include a negative earnings indicator variable (NegEarn) in Equation (3) and interact

NegEarn with all of the variables in the model. We expect earnings persistence among loss

firms to be lower in the presence of better managers (β4 < 0), as more able managers are

expected to return their firms to profitability more quickly (e.g., Barr and Siems 1997; Leverty

and Grace 2005).

Recent work suggests that performance metrics that are more smooth or persistent are

less value relevant because they conceal underlying changes in cash flows (Barton et al. 2010).

We posit that higher earnings persistence is a sign of better earnings quality when driven by

improved accruals estimation that strengthens the relation between reported earnings and cash

flows. Thus, we next partition Earnings in year t into Accruals and Cash from Operations

(CFO). This allows us to determine if any differential earnings persistence in Equation (3) is, at

least in part, due to more persistent accruals.

Earningst+1 = α0 + α1 Accrualst

+ αααα2 Accrualst × MgrlAbilityt + α3Accrualst

× NegEarnt

+ α4 Accrualst × MgrlAbilityt × NegEarnt +α5 CFOt × MgrlAbilityt + α6CFOt

× NegEarnt +

α7CFO t × MgrlAbilityt × NegEarnt+ α8 MgrlAbilityt+ α9 NegEarnt + ∑ α+

,9+-,: /0123045+

+ ∑ 6α+

8,+-,7 /0123045+ × NegEarnt) +εt

(4)

In Equation (4) we again include NegEarn and interact NegEarn with all of the variables

in the model. After partitioning earnings in year t into the accrual and cash flow components, we

expect more able managers to be better able to estimate accruals, and thus expect these accruals

to be of higher quality (i.e., more persistent). Thus, in Equation (4), we expect to observe a

positive coefficient on Accruals× MgrlAbility (α2 > 0).

We present the estimates from these two estimations in Table 4. Referring to the first

column of estimates, which does not include managerial ability, we see that earnings have a core

21

“persistence” of 0.72, along the lines of prior research. Turning to the second column of

estimates, which includes managerial ability, the base persistence is lower at 0.45, and this

persistence is increasing with managerial ability. Positive earnings persistence is expected to

increase from 0.45 to 0.83 (0.45 + 0.38) when moving from the lowest to the highest decile of

managerial ability. This higher persistence is clearly economically significant and supports our

hypothesis. Also as predicted, negative earnings are less persistent when reported by higher

ability managers (Earnings×MgrlAbility×NegEarn = –0.24, p = 0.001).

When we partition earnings into accruals and cash flows, managerial ability increases the

persistence of both components when firms report positive earnings. The accruals reported by

positive earnings firms have a base persistence of 0.34. The incremental coefficient on accruals

for firms with higher ability managers is 0.26 (p = 0.002), suggesting that the increased earnings

persistence of firms with higher ability managers, in part, is due to more persistent accruals.

These findings support our hypothesis that higher quality managers are better able to estimate

accruals, resulting in higher earnings quality.22 In our second set of estimations, we include firm

fixed effects. Results are similar to those without fixed effects.

McNichols and Wilson (1988) Error in the Provision for Bad Debt

We examine a specific accrual as our third measure of earnings quality: the provision for

bad debt, modeled in McNichols and Wilson (1988), assuming a balance sheet perspective to

estimating bad debt, adherence to GAAP, and perfect foresight of future write-offs, as follows:

Bad Debt Expenset = β0 +β1 Allowance for Doubtful Accountst–1 + β2 Write-offst+ β3 Write-offst+1 + φt (5)

22 That more able managers are also associated with more persistent cash flows is consistent with these managers making more efficient operating decisions.

22

where Bad Debt Expense and Write-offs are hand-collected from the firm’s SEC filings and

Allowance for Doubtful Accounts is available from Xpressfeed (XFN = RECD). All variables

are deflated by sales in year t. The above model expects bad debt expense to increase by the

amount of current period write-offs that exceed the beginning balance in allowance for doubtful

accounts, plus the write-offs anticipated in the coming year. The error (φt) has two components:

a discretionary “earnings management” component, and a forecast error component. McNichols

and Wilson (1988) investigate subsets of firms with specific earnings management incentives

and thus assume that errors in accrual estimates average to zero. In contrast, we assume that, on

average, the error due to earnings management is near zero for the full sample, but we expect

errors in accrual estimates to vary with managerial ability. We expect φt to decrease with

managerial ability.

As illustrated in Equation (5), managers must estimate accounts receivable write-offs

they expect to occur in year t+1. We expect that that more able managers’ judgments and

estimates regarding anticipated write-offs will be more accurate (compared to realized write-offs

in year t+1, which are included in Equation (5)) than those of less able managers.

Because the data for this analysis must be hand-collected from SEC filings, we limit the

analysis to firms with managers in the top or bottom quintiles of managerial ability and in

industries where accounts receivable (relative to assets) and bad debt expense (relative to

earnings) are large. Following McNichols and Wilson (1988), we consider firms from three

industries: (1) printing and publishing, (2) nondurable wholesale goods, and (3) business

services, and we estimate:

BDE Errort = β0 + ββββ1 High Ability Indicatort + β2 Firm Sizet + β3 Lossest + β4 Sales Volatiltyt + β5 Cash Flow Volatilityt + β6 Operating Cyclet + εt (6)

23

where BDE Error is the absolute value of the residual from Equation (5), and High Ability

Indicator is an indicator variable that is equal to one (zero) if the managerial ability score in year

t is in the top (bottom) quintile relative to industry-year peers. A negative coefficient on High

Ability Indicator is consistent with more able managers forming better estimates of bad debt

provisions. Results are presented in Table 5. In support of our hypothesis, β1 is –0.008 (p =

0.05). Managers with higher ability scores produce higher quality bad debt provisions.23

The Dechow and Dichev (2002) Measure of Earnings Quality The Relation between Accruals Quality and Managerial Ability

Our fourth and final measure of earnings quality follows Dechow and Dichev (2002),

who posit that high quality accruals are eventually realized as cash flows. Incorrectly estimated

accruals are less likely to be realized as cash flows. We hypothesize that the better managers

know their business, the less likely they are to have erroneous accruals. We determine how well

a firm’s accruals map into cash flows by estimating the following regression by industry-year.

∆WCt = β0 + β1 CFOt–1 + β2 CFOt + β3 CFOt+1 + β4 ∆REVt + β5 PPEt + εt (7)

The residual from the regression measures the extent to which current accruals (∆WC)

map into past, present, or future cash flows (CFO), with smaller residuals (in absolute terms)

indicating superior mapping.24 Following both McNichols (2002) and Francis et al. (2005), we

include the current year change in sales (∆REV; XFN = SALE) and the current year level of

23 Because our sample selection procedures for this analysis result in a small sample of firm-years that do not necessarily contain the same firm over multiple years (i.e., we do not have panel data), we do not cluster standard errors by firm and year, nor do we estimate a firm fixed-effects specification. In untabulated results, however, we continue to observe the negative relation between managerial ability and BDE Error when we cluster standard errors by year or include year fixed effects. 24 We define the change in working capital from year t–1 to t as ∆WC = ∆Accounts Receivable + ∆Inventory – ∆Accounts Payable – ∆Taxes Payable + ∆Other Assets, or ∆WC = – (RECCH + INVCH + APALCH + TXACH + AOLOCH). CFO is cash flow from operations (OANCF). We use information from the statement of cash flows, rather than the balance sheet, to estimate current accruals because the balance sheet approach can lead to noisy estimates (Hribar and Collins 2002). All variables in Equation (7) are scaled by average total assets (AT) and winsorized at the 1st and 99th percentiles, by year.

24

property, plant, and equipment (PPE; XFN = PPENT) in Equation (7). We estimate the

regressions by year and industry (as do Francis et al. 2005), where industries are defined using

the Fama and French (1997) industry classifications. If an industry group has less than 20

observations in any given year, those observations are deleted.

We generate two earnings quality measures from the residuals of the above regression.

The first follows prior literature by aggregating the residuals over time and considers the

standard deviation of the residuals to provide evidence on the quality of accruals. We aggregate

the residual over a four-year rolling period and multiply the resulting standard deviation by

negative one so that the variable is increasing with earnings quality. Thus, Aggregate AQt = – 1

× Standard Deviation (εt, εt+1, εt+2, εt+3). Because managers may not necessarily be in place for

the full aggregation period, we also introduce a variation on the Dechow and Dichev (2002)

model by considering the residual from Equation (7) directly. The greater the residual, in

absolute terms, the poorer the accruals quality. Thus, Annual AQt = – 1 × |εt|. We decile rank

our earnings quality variables by year and industry to maintain consistency with our managerial

ability measure.25

To examine our hypothesis using this measure of earnings quality, we estimate the

following regression:

AQt = β0 + ββββ1 MgrlAbilityt + β2 Firm Sizet + β3 Lossest + β4 Sales Volatiltyt + β5 Cash Flow Volatilityt + β6 Operating Cyclet + εt (8)

25 When we briefly compare the two Dechow and Dichev (2002) based accruals quality measures, as noted in Section III, the distributions appear similar. Referring to the correlation matrix in Table 2, we see that Aggregate AQ (based on the standard deviation of four years of residuals) and Annual AQ (based on the absolute value of a single year’s residual) are correlated at 0.46. Both measures are increasing in firm size and decreasing in losses, as expected based on prior work (Dechow and Dichev 2002). Thus, Annual AQ seems to be a reasonable variation of the more standard four-year measure.

25

where AQt is either Aggregate AQt (the inverse of the standard deviation of four years of

residuals), or Annual AQt (the inverse of the absolute value of the year-specific residual from

Equation (7)).

We present the results in Table 6. The association between earnings quality and our

control variables is consistent with Dechow and Dichev (2002): earnings quality is increasing in

firm size and decreasing in sales volatility, cash flow volatility, operating cycle, and losses.

Inconsistent with our hypothesis, however, we find that earnings quality is decreasing in

managerial ability across both Dechow and Dichev (2002) accruals quality measures. This

counterintuitive result is consistent with Francis et al. (2008), who find that managerial

reputation, measured using media coverage, is negatively associated with earnings quality using

the standard deviation of the residuals from the Dechow and Dichev (2002) accruals quality

model.26

Francis et al. (2008) suggest that better managers are hired by more challenging firms,

which have lower innate accruals quality. In his discussion of Francis et al. (2008), LaFond

(2008) notes that one limitation of their study is that they use only one earnings quality measure.

Our findings suggest that if we use alternative measures (restatements, earnings persistence and

bad debt provision quality), managerial ability is positively associated with earnings quality.

LaFond (2008) also suggests that the underlying economic activities of firms might

unduly affect the Dechow and Dichev (2002) accruals quality measure. For example, it is

possible that firms that undergo mergers and acquisitions or that have large R&D expenditures

have a weaker mapping between working capital accruals and cash from operations because of

their underlying earnings process, and these firms are also more likely to hire better managers

26 In untabulated tests, we replicate the findings of Francis et al. (2008) using their sample of media citations, and we also observe a negative relation between media citations and accruals quality.

26

(LaFond 2008). This logic parallels that of Schipper and Vincent (2003), who note that reported

earnings deviate from economic earnings because of preparers’ implementation decisions,

including management’s judgments and estimates (the focus of this paper), as well as recognition

and measurement rules in U.S. GAAP (e.g., accounting for R&D). To investigate this possibility

further, we attempt to isolate the effect of economic activities on accruals quality in the

following section.

The Relation between Accruals Quality Components and Managerial Ability

In this section we partition total accruals quality into three components: estimation

difficulty, real activities, and residual, denoted ;<=> , ?;@A> , and εB , respectively, and then we

investigate the relation between managerial ability and the three components of accruals quality.

The components are formed by estimating the following regression:

AQ = β0 + β1 Firm Size + β2 Losses + β3 Sales Volatility + β4 Cash Flow Volatility + β5 Operating Cyclet +β6 R&D Volatilityt + β7 Advertising Volatilityt + β8 M&A Volatilityt + β9 M&A Activityt + εt, (9)

which includes the control variables considered in Equation (8) as well as factors related to real

activities that can affect the Dechow and Dichev (2002) estimation of accruals quality: R&D,

advertising, and M&A activities. We add these “real activity” variables to capture mismatches

between working capital accruals and cash from operations that are a byproduct of the firm’s real

economic activities. We include R&D and advertising volatility, as these expenditures must be

immediately expensed, rather than recorded as assets under GAAP, but the expenditures are cash

outflows from operations. We consider volatility rather than the level of expenditures because,

to the extent that expenditures are constant, the standard deviation of the residuals will not be

affected (i.e., cash flows will always be too low). We add M&A volatility and M&A activity

because, at the time of a merger, any cash outflows are via investing, but working capital will

27

increase by the amount of the acquired company. In other words, the above economic activities

might result in seemingly “erroneous” accruals under the Dechow and Dichev (2002) accruals

quality model, but they are not actually errors; rather, they are low-quality accruals that are a

product of the firm’s decision environment (e.g., Schipper and Vincent 2003; LaFond 2008).

The predicted values generated by applying β0–β5 to the firm’s characteristics provide

our estimate of accruals quality related to accrual estimation ( ;<=> ), termed “innate” in Francis

et al. (2005). The predicted values generated by applying β6–β9 to the firm’s characteristics

provide our estimate of accruals quality related to real activities ( ?;@A> ). Finally, the residual is

the unexplained portion of accruals quality (εB).

Managers can affect estimation-related accruals quality by exerting effort and having

firm- and industry-specific knowledge. For example, more able managers can accrue a more

accurate amount of bad debt expense by having better knowledge of their customer base and

macro-economic and industry trends, rather than simply applying the historical bad debt expense

rate. Thus, we expect managerial ability to be associated with the estimation-related portion of

accruals quality.

The component of accruals quality associated with real activities, however, is not a result

of estimation error, but rather is mainly a function of accounting rules. Thus, we expect accrual

mapping errors associated with real economic activities to be largely out of management’s

control.

We do not have a prediction for the association between managerial ability and the

unexplained portion (residual) of accruals quality, because it is not clear whether this component

reflects managerial discretion or other unidentified factors related to firm characteristics or

operational activities. Further, in untabulated tests, we observe that for most firms in our sample,

28

the estimation portion of accruals quality comprises nearly 90 percent of total accruals quality,

while the component related to real activities comprises from 2–4 percent of the total, leaving

less than 10 percent of accruals quality unexplained.

To investigate the association between managerial ability and each of the components of

accruals quality, we estimate the following:

AQ Component = β0 + ββββ1 MgrlAbilityt + β2 SizeDecilet-1 + εt. (10) We exclude our main set of control variables from this table because we use the control

variables in the partitioning of total accruals quality. To control for size, we include the lagged

decile rank of firm size. Results are presented in Table 7. We consider Aggregate AQ (the four

year standard deviation) and Annual AQ (the one year residual) in the first and second set of

columns, respectively. We present the results including firm fixed effects; results are similar if

we exclude firm fixed effects and instead cluster the errors by firm and year.

Turning first to the estimation-related component of accruals quality, we see that

managerial ability is positively associated with higher quality estimation-related accruals quality

for both Aggregate AQ and Annual AQ. Thus, when isolating the identifiable component of

accruals quality that is subject to judgments and estimates, managerial ability results in a higher

quality accrual estimation, consistent with our hypothesis.

Turning next to the real activity-related component of accruals quality, we see that results

are mixed depending on whether accruals quality is the four-year estimation or the one-year

residual. Managerial ability is positively (negatively) associated with the longer-term (shorter-

term) measure. Thus, we do not observe a reliable association.

Finally, managerial ability is negatively associated with the unexplained component of

accruals quality in both estimations, indicating that the negative relation between managerial

29

ability and total accruals quality is driven by the unexplained portion of accruals quality.27 It is

possible that additional real activities are currently allocated to the residual, or that our measures

of real activities are measured with noise.28 It is also possible that more able managers are more

likely to use accruals to manage earnings. Because the unexplained portion of accruals quality

reflects a small percentage of total accruals quality and because our other tests indicate that

better managers report higher quality earnings, we leave the analysis of the residual component

of accruals quality for future research.

We interpret the above findings as supporting our hypothesis, but clearly this is

contingent upon the appropriate partitioning of total accruals quality. Thus, we validate the

above partitioning by investigating the three components’ associations with earnings

restatements. Generally, we expect low estimation-related accruals quality to be associated with

subsequent restatements. We do not, however, expect real activities-related accruals quality to

be associated with subsequent restatements, as this component is largely a byproduct of the

accounting system. Finally, we have no prediction for the association between the unexplained

portion of accruals quality and earnings restatements. Table 8 presents results from these

analyses. The first (second) column reflects accruals quality measured as Aggregate AQ (Annual

AQ).

Consistent with our expectations, the estimation-related portion of accruals quality is

negatively associated with earnings restatements. This finding suggests that the estimation

27 Note that the standard deviation of the unexplained portion of accruals quality is nearly double that of the other two components. 28 Consistent with this, the magnitude on the unexplained portion of accruals quality decreases dramatically when we include the industry-year rank of the magnitude of working capital accruals and the industry-year rank of the firm’s market-to-book ratio. The magnitude of working capital accruals could represent the net impact of economic events that may not be captured by our real activity measure, while the market-to-book ratio (or growth) is an antecedent to many economic events.

30

component of accruals quality, at least in part, reflects errors in accrual estimation that do not

reflect the underlying economics of the firm.

The real activities-related component of accruals quality, however, is not associated with

subsequent restatements, consistent with our conjecture that this component of total accruals

quality is largely a byproduct of the accounting system and does not reflect errors, on average.

Finally, the unexplained portion of accruals quality is also negatively associated with earnings

restatements for the Annual AQ measure. These associations generally support our

decomposition.29

V. ADDITIONAL ANALYSES

Managerial Ability Measures

Our main managerial ability measure is managerial efficiency, following Demerjian et al.

(2009). In this section we first investigate two additional ability measures: historical returns,

following Fee and Hadlock (2003), and media citations, following Francis et al. (2008);

definitions are provided in Panel C of Table 1.30 We first examine the Pearson correlations

among the variables (see Table 2). Managerial ability and historical returns are correlated at

0.25, consistent with these two variables measuring different aspects of “ability,” while there is a

negative correlation between both ability and historical returns and media citations. Consistent

29 We expect better managers to improve the overall quality of the estimation component of accruals quality, as documented in Table 7. Thus, we expect that the negative relation between higher quality accruals estimates (;<=C ) and restatements is driven, at least partially, by high-ability managers. To investigate this, we regress restatements on the interaction of managerial ability and ;<=C , noting a negative and significant coefficient on the interaction term, consistent with our expectations. In further support of the real-activities component of accruals quality reflecting byproducts of accounting rules, the interaction term on managerial ability and ?;@A> is not significant. Note that Ai and Norton (2003) and Powers (2005) observe that standard software inaccurately calculates the marginal effect of interaction terms in logit and probit models. Specifically, they note that the magnitude of the interaction effect does not equal the marginal effect of the interaction term, that it can be of opposite sign, and that its statistical significance is not calculated by standard software. The coefficients and significance levels referred to above adjust for these concerns and, as a result, reflect the accurate marginal effects (and associated statistical significance). 30 Following our examination of these alternate measures, we also examine manager fixed effects, following Bertrand and Schoar (2003).

31

with historical returns and media citations containing a large firm component, they are correlated

with firm size at 0.22 and 0.52, respectively, while managerial ability is correlated with firm size

at only 0.03. Interestingly, both ability and historical returns are negatively correlated with

losses and positively correlated with future earnings; however, media citations are positively

correlated with losses and negatively correlated with future earnings. Generally, the correlations

suggest that historical returns and our main managerial ability measure have the expected

associations with perceptions of managerial ability, while media citations appear, at least in part,

to be correlated with bad news.

To examine the association between earnings quality and managerial ability, we consider

a composite measure of total earnings quality, which is the sum of (1) the rank of the estimation

portion of accruals quality ( ;<=> ), (2) the rank of firm-specific earnings persistence, and (3) –1

× RESTATE. Thus, the variable ranges from a low of negative one to a high of two. In Table 9,

we consider the associations between total earnings quality and each of the three ability

measures.31 Turning first to the Demerjian et al. (2009) measure, we see that more able

managers have higher total earnings quality, as was the case with the individual components.

Turning next to historical returns, we see that the greater the historical returns, the greater total

earnings quality, consistent with our hypothesis. To explore whether either the Demerjian et al.

(2009) ability score or historical returns dominates in explaining earnings quality, we include

both in the same estimation. Both historical returns and managerial ability are positively

associated with total earnings quality, which suggests that they capture different aspects of

“ability.”

31 Results are similar if we consider the individual components of total earnings quality, with the exception that the relation between historical returns and firm-specific earnings persistence is not significant (not tabulated).

32

Media citations, however, are not associated with earnings quality. One difficulty in

comparing results across measures is that media citations are available only for a small subset of

our sample; thus we next consider all three measures simultaneously for the reduced sample.

Media citations continue to be unassociated with total earnings quality, while the coefficients on

historical returns and managerial ability remain positive and significant in the reduced sample.

Generally, our results are consistent when we use historical returns as an alternate ability

measure.

We next consider manager fixed effects for a sample of managers that switch among

Execucomp firms during our sample period. We identify 195 switching CFOs and are able to

estimate manager fixed effects for 88 of these executives across 170 firms. We estimate the

following (not tabulated):

Total EarnQualityi,t = α+β1Firm Sizei,t + β2Lossesi,t + β3Sales Volatilityi,t + β4Cash Flow Volatilityi,t + β5Operating Cyclei,t +∑ *$$ YEARt +∑ *"" FIRMt +∑ *DD MANAGERm +εi,t. (11)

The average manager fixed effect increases by 1.22 when moving from the lowest to the highest

quartile of manager fixed effects.32 For comparison purposes, the average firm fixed effect

increases by 2.00 when moving from the lowest to the highest quartile of firm fixed effects.

Though fixed effects are quantifiable only for CFOs switching firms within our sample, clearly

manager-specific effects are economically significant.

Change Analysis

Though our results are similar using historical returns, both the Demerjian et al. (2009)

ability score and historical returns likely contain a firm-specific element. To better abstract away

from the firm, we consider herein how earnings quality changes across different CFO regimes.

32 Note that the Demerjian et al. (2009) managerial ability score is positively correlated with the manager-specific fixed effects (not tabulated).

33

We expect firms that hire a more efficient CFO to experience an improvement in their earnings

quality, and firms that hire a less efficient CFO to experience a decline in their earnings quality.

Thus, using the sample of the 195 CFOs examined in the prior section who switched across our

sample firms, we identify 116 with sufficient information to estimate the following:

∆AQ = β0 + ββββ1 ∆MgrlAbility + β2 ∆Firm Size + β3 ∆Losses + β4 ∆Sales Volatilty + β5 ∆Cash Flow Volatility + β6 ∆Operating Cycle + ε (12)

where the change in earnings quality, as well as the change in each of the control variables, is

measured from year AQc+1–AQc–1, where c is the year in which the CFO changed. Thus, a

positive value of ∆AQ signifies an improvement in earnings quality following the new CFO

appointment. The change in managerial ability reflects the difference between the newly

appointed CFO’s score from his or her prior firm and the departing CFO’s score from the current

firm (i.e., MgrlAbility j,b,c-1 – MgrlAbility i,a,c-1, where manager b was hired by firm i and was

previously employed by firm j). A positive value of ∆MgrlAbility signifies that the incoming

manager is deemed more efficient than the outgoing manager. Thus, we expect β1 to be positive.

Turning to Table 10, we find a positive and significant coefficient on ∆MgrlAbility, as expected.

That the association between ability and earnings quality spans firms helps to alleviate the

general concern that the associations documented herein are attributable to the firm, rather than

the manager.

VI. CONCLUSION

We examine the relation between managerial ability and earnings quality. While

empirical literature in the area of earnings quality has largely focused on firm-specific

characteristics, such as size and board independence (Dechow and Dichev 2002; Klein 2002), we

examine manager-specific effects by using a measure of managerial ability presented by

34

Demerjian et al. (2009). Our study is in the vein of Bertrand and Schoar (2003), who find that

managers have an effect on firm choices such as acquisitions or research and development

expenditures, and Francis et al. (2008), who find that earnings quality appears to vary with CEO

reputation. Using four alternative earnings quality measures (restatements, earnings persistence,

error in the bad debt provision and the estimation-related portion of accruals quality), we find

that more able managers report higher quality earnings. Our study contributes to the earnings

quality literature in two ways. First, we document a positive association between managerial

ability and earnings quality. Considering the earnings quality metrics that management can

impact (for example, forming the best estimates possible given the operating cycle or cash flow

volatility of the firm), we find that higher quality managers are associated with higher quality

earnings. This finding is consistent with the premise that the more capable the manager, the

better able he or she is to estimate accruals, and it suggests that firms can improve their earnings

quality by employing higher quality managers. Second, we identify a real activities-related

component of the often-used Dechow and Dichev (2002) accruals quality measure. We find

evidence consistent with the expectations of LaFond (2008)—that firms undertaking certain real

economic activities, such as R&D expenditures or mergers and acquisitions, have lower accruals

quality because of these activities. This lower accruals quality is a byproduct of the accounting

system rather than a result of managerial judgments and estimates; thus it is not possible for

managerial ability to mitigate this effect. Identifying and measuring this component should

allow researchers to enhance their research designs to better identify the desired component of

earnings quality (Dechow et al. 2009).

35

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38

APPENDIX

Estimation of Total Firm Efficiency (from Demerjian et al. 2009)

We estimate total firm efficiency using data envelopment analysis (DEA) following

Demerjian et al. (2009). DEA is a non-linear optimization program that calculates unit-specific

relative efficiency. The program is as follows:

max�,I � �∑ IJKJL

MJN

∑ �OPOLQON

(A1)

Subject to: ∑ IJKJL

MJN

∑ �OPOLQON

R 1 (k = 1,…,n) (A2)

T,, T8, … TD V 0 (A3)

X,, X8, … , X� V 0 (A4)

DEA measures the efficiency of a single unit (in our case firm) k relative to a set of comparable

firms. The objective function measures efficiency as the weighted outputs scaled by the weighted

inputs. There are s outputs and m inputs, indexed by i and j respectively. The quantities of each

output i and input j for firm k are yik and xjk. The optimization program maximizes A1 by

selecting the weights on each output (ui) and input (vj). The vectors of weights on the outputs (u)

and inputs (v) are termed implicit weights. Efficiency is based on the level of the weighted

outputs to the level of the weighted inputs. The most efficient firms have the highest level of

outputs for a fixed level of inputs (or equivalently, the lowest level of inputs for a fixed level of

outputs). DEA calculates a unique set of implicit weights for each firm k.

The first constraint, A2, scales the implicit weights so that the most efficient firm (or

firms) has (have) an efficiency value of one. The optimal weights for each firm k are tested for

all the other comparable firms (1,…n; ≠k). This calculates what the efficiency would be for each

comparable firm under the implicit weights calculated in A1 for firm k, allowing for the

determination of relative efficiency. Constraints A3 and A4 require implicit weights to be non-

negative, which prevents solutions calling for negative input levels.

39

Total firm efficiency is estimated using a single output and seven inputs. Total revenue

(“SALE”) is the output, as the principal objective of the firm is to produce sales. The most

successful firms are those that produce the maximum sales at the lowest cost. The cost of

producing the sales is captured by the seven inputs. The first five correspond to assets the

company invests in that are expected to affect their revenue generation. We consider the

beginning of period balance for each of these assets, since managers’ past decisions regarding

these assets are expected to affect current period revenues.

1. Net Property, Plant and Equipment (PP&E; “PPENT”).

2. Capitalized Operating Leases. The discounted present value of the next five years of required operating lease payments (available in the firm’s footnotes to the financial statements and on Compustat).33 The inclusion of Net Operating Leases as an input increases the input comparability among firms that effectively have the same operations, but either lease or buy their revenue-generating equipment.

3. Net Research and Development (R&D). To calculate net R&D, which is not reported as an asset on the balance sheet, we follow Lev and Sougiannis (1996), who use a five-year capitalization period of R&D expense (“XRD”), where the net value (net of amortization) is: ?Y+� � ∑ 61 Z 0.22] ^ ?Y�P�

:$-_` . Thus, for example, R&D expenditures from five years

back receive a weight of .2 (they were already amortized 80%), four years back a weight of .4 (amortized 60%), etc., with the prior year’s R&D (t = –1) receiving full weight.

4. Purchased goodwill, reported on the balance sheet, which is the premium paid over the fair

value of a business acquisition (“GDWL”). Goodwill generally reflects the value of the acquired intangible assets.

5. Other acquired and capitalized intangibles (“INTAN” less “GDWL”), also reported on the

balance sheet, which includes items such as client lists, patent costs and copyrights.

We also include two year t expenses: Cost of Goods Sold and Selling General and

Administrative Expense to account for the cost of inventory (Cost of Goods Sold) and sales

generated from advertising and the quality of the sales force (advertising, training costs and IT

services are included in SG&A).

33 We use a discount rate of 10 percent per year. The data items for the five lease obligations are “MRC1–MRC5.” We would also like to discount the “thereafter” payments; however, this line item was not collected by Compustat for the bulk of the sample period. Note that capital leases are included in Net PP&E.

40

We estimate DEA efficiency by industry (based on Fama and French 1997) to increase

the likelihood that the peer firms have similar business models and cost structures within the

estimations. Consistent with Demerjian et al. (2009), the resulting score ranges from 0.0002 to

one, with one being the optimal output for a given mix of inputs.

Using DEA instead of traditional ratio analysis has several advantages. First, DEA

allows the weightings on each of the inputs to vary, whereas traditional efficiency ratios restrict

all weightings to be equal to one. For example, within DEA, a dollar at historical cost (i.e., PPE)

can count differently than a dollar at or near replacement cost (i.e., COGS), but both are

weighted identically in a traditional efficiency ratio. Second, DEA compares each firm within an

industry to the most efficient firm, whereas traditional efficiency analysis compares each firm to

the mean or median firm. See Demerjian et al. (2009) for additional information, explicit

comparisons of this score with a residual from an OLS regression, and a comparison of variable

ratios, such as return on assets.

41

Table 1 Descriptive Statistics

Panel A – Descriptive Statistics for the Full Sample (1989–2007)

Variable N Mean Median Standard

Dev. 25% 75%

MgrlAbility 86,303 0.0003 –0.009 0.15 –0.09 0.08 Historical Ret 44,639 0.15 –0.30 2.31 –1.01 0.61 Media Count 13,169 214.75 92.00 742.79 46.00 177.0 Restate 49,263 0.11 0.00 0.32 0.00 0.00 F.S. EarnPer 20,009 0.33 0.39 0.39 0.09 0.63 BDE Error 1,124 0.014 0.006 0.05 0.002 0.01 Aggregate AQ 55,550 –0.03 –0.02 0.03 –0.04 –0.01 Annual AQ 86,303 –0.05 –0.03 0.06 –0.07 –0.01 Total EarnQuality 19,785 1.003 1.11 0.55 0.66 1.44 WC 86,303 0.07 0.04 0.11 0.01 0.08 CFO 86,303 –0.001 0.06 0.30 –0.03 0.13 Firm Size 86,303 1139.14 95.41 4,446.78 20.31 469.2 Losses 80,235 0.38 0.20 0.37 0.00 0.66 SalesVolatility 74,173 0.24 0.16 0.23 0.09 0.29 CashFlowVolatility 71,331 0.10 0.06 0.13 0.04 0.11 OperCycle 85,237 154.50 111.28 267.16 68.41 170.9 FutureEarnings 86,303 –0.11 0.02 0.69 –0.10 0.08 PctInd 12,226 0.65 0.66 0.18 0.54 0.80 ICW 54,881 0.15 0.00 0.35 0.00 0.00

Panel B – Accruals Quality Variables by Managerial Ability

Lowest Quartile of MgrlAbility

Highest Quartile of MgrlAbility Diff.

Mean Diff. Med.

Variable Mean Median Mean Median MgrlAbility –0.17 -0.15 0.19 0.16 * * Historical Ret –0.35 -0.64 0.85 0.11 * * Media Count 250.15 98.00 211.56 84.00 * Restate 0.13 0.00 0.10 0.00 * F.S. EarnPer 0.29 0.35 0.35 0.42 * * BDE Error 0.02 0.008 0.007 0.005 * * Aggregate AQ –0.036 –0.026 –0.035 –0.026 * Annual AQ –0.056 –0.033 –0.062 –0.037 * * Total EarnQuality 0.93 1.00 1.06 1.11 * * WC 0.07 0.04 0.09 0.05 * * CFO –0.08 0.03 0.04 0.09 * * Losses 0.53 0.60 0.28 0.20 * *

Notes: Variable definitions are provided in Panel C. Differences in means (medians) in Panel B are using a t-test (Chi-Square test) and signify a p-value of less than 0.01. In Panel B, we examine the highest and lowest quintile (rather than quartile) for BDE Error due to data constraints.

42

Table 1, Continued

Panel C: Variable Definitions Variable Name Description Definition

MgrlAbility Managerial Ability

The decile rank (by industry and year) of managerial efficiency from Demerjian et al. (2009); the residual from Equation (1); see also the Appendix.

Historical Ret Historical Return

The decile rank (by industry and year) of the 5-year past value-weighted industry-adjusted return (year t–5 to t–1) using monthly CRSP data.

Media Count MediaCount The decile rank (by industry and year) of the number of articles mentioning the CEO over the preceding five year period.

Restate Restatement An indicator variable that is equal to one if the firm announced a restatement in years t, t+1, or t+2, and zero otherwise (available from 1997–2007).

F.S. EarnPer Firm-Specific

Earnings Persistence

The firm-specific Pearson correlation coefficient between current and past earnings (Earningst and Earningst-1). We calculate the firm-specific measure using data from the current year and prior nine years (i.e., from a total of 10 years). We exclude firm years with negative net income (Earnings < 0), and we require a minimum of five years of data.

BDE Error Unexplained

Bad Debt Expense

The absolute value of the residual (φt) from Equation (5), estimated by industry, where three industries are considered: printing and publishing, nondurable wholesale goods, and business services.

Aggregate AQ Aggregate Std

Dev of Accruals Quality

The decile rank (by industry and year) of – 1 × Standard Deviation (εt, εt+1, εt+2, εt+3), where εt is the residual from Equation (7) estimated by industry-year, where industries are defined per Fama and French (1997).

Annual AQ Annual Accruals Quality

The decile rank (by industry and year) of – 1 × |εt|, where εt is the residual from Equation (7) estimated by industry-year, where industries are defined per Fama and French (1997).

Total EarnQuality

Earnings Quality Summation Variable

The sum of three earnings quality variables: (1) the rank of the estimation portion of accruals quality ( ;<=> ), (2) the rank of firm-specific earnings persistence, and (3) –1 × RESTATE. Thus, the variable ranges from a low of negative one to a high of two.

∆WC Working Capital Accruals

The change in working capital scaled by average total assets, where working capital is defined as follows: [– (RECCH + INVCH + APALCH + TXACH + AOLOCH)].

CFO Cash From Operations Cash from operations (OANCF) scaled by average total assets (AT).

MB Market to Book Ratio

The firm’s market capitalization (PRCC_F × CSHO) divided by stockholder’s equity (SEQ) at the end of year t.

Earnings Net Income Earnings (IBC) scaled by average total assets (AT). Accruals Accruals Accruals scaled by average total assets (AT), where ACC = EARN – CFO. Firm Size Firm Size The natural log of the firm’s assets (AT) reported at the end of year t.

Losses Loss History The percentage of years reporting losses in net income (IBC) over at least three of the last five years (t–4, t).*

SalesVolatility Sales Volatility The standard deviation of [sales (SALE) / average assets (AT)] over at least three of the last five years (t–4, t).*

CashFlow Volatility

Cash Flow Volatility

The standard deviation of [cash from operations (OANCF) / average assets (AT)] over at least three of the last five years (t–4, t).*

43

Table 1, Panel C, Continued

Variable Name Description Definition

OperCycle Operating Cycle

The natural log of the length of the firm’s operating cycle, defined as sales turnover plus days in inventory [(sales/360)/(average accounts receivable) + (cost of goods sold/360)/(average inventory)] and is averaged over at least three of the last five years (t–4, t).*

R&D Volatility R&D Volatility The standard deviation of R&D (XRD) over the last five years (t–4, t).* If XRD is missing, then we set XRD equal to zero.

Advertising Volatility

Advertising Volatility

The standard deviation of advertising expense (XAD) over the last five years (t–4, t).* If XAD is missing, then we set XAD equal to zero.

M&A Volatility Merger and Acq. Volatility

The standard deviation of merger and acquisition activity (AQC) over the last five years (t–4, t).* If AQC is missing, then we set AQC equal to zero.

M&A Activity Merger and Acquisition

Activity

The sum of merger and acquisition activity (AQC) over the last five years (t–4, t), scaled by current period assets.* If AQC is missing, then we set AQC equal to zero.

FutureEarnings Future Earnings One-year ahead earnings (IBC) scaled by average total assets (AT).

PctInd Board Independence

The percentage of board members classified as independent based on IRRC’s classification (available from 1996–2007).

ICW Internal Control Weakness

An indicator variable for firms reporting material weaknesses in internal control (available from 2002–2007).

*The accumulation period is t to t+3 when the dependant variable is Aggregate Accruals Quality.

44

Table 2 Univariate Correlations

Mgrl Ability

Hist Ret

Media Count

Rest F.S. Per

BDE Error

Agg AQ

Ann AQ

Total EQ

Firm Size

Losses Fut

Earn

MgrlAbility 0.25 –0.07 –0.03 0.05 –0.22 –0.02 –0.06 0.10 0.03 –0.24 0.28

Historical Ret 0.25 –0.02 –0.04 0.00 –0.06 0.15 0.07 0.12 0.22 –0.44 0.41

Media Count –0.07 –0.02 0.02 -0.09 –0.03 0.08 0.05 0.07 0.52 0.02 –0.10

Restate –0.03 –0.04 0.02 -0.01 –0.00 0.01 –0.01 -0.52 0.08 0.02 –0.03

F.S. EarnPer 0.05 0.00 -0.09 -0.01 0.04 0.03 0.01 0.63 0.02 -0.09 0.08

BDE Error –0.16 –0.14 0.11 –0.00 0.04 –0.02 –0.06 0.00 –0.18 0.31 –0.17

Aggregate AQ –0.02 0.15 0.08 0.01 0.03 –0.03 0.46 0.25 0.40 –0.31 0.21

Annual AQ –0.06 0.07 0.06 –0.01 0.01 –0.03 0.46 0.17 0.31 –0.22 0.14

Total EarnQuality 0.09 0.12 0.06 -0.62 0.61 0.02 0.25 0.17 0.39 -0.29 0.18

Firm Size 0.01 0.06 0.38 0.02 0.00 –0.03 0.16 0.12 0.19 –0.44 0.28

Losses –0.23 –0.42 0.01 0.02 -0.08 0.18 –0.31 –0.22 -0.27 –0.15 –0.56

FutureEarnings 0.08 0.26 –0.02 0.00 0.06 –0.13 0.18 0.16 0.13 0.06 –0.33

Notes: This table reports Pearson correlation coefficients below the diagonal and Spearman correlation coefficients above the diagonal. See Table 1, Panel C for variable definitions. We decile rank MgrlAbility, Historical Ret, Media Count, F.S.EarnPer, Aggregate AQ, and Annual AQ by industry-year.

45

Table 3 Restatements and Managerial Ability Dependent Variable = Restate Intercept –2.76

0.001

MgrlAbility –0.32

0.001

Firm Size 0.18

0.001

SalesVolatility 0.22

0.04

CashFlowVolatility 0.94

0.001

OperCycle –0.06

0.15

Losses 0.42

0.001

Earnings –0.06

0.18

N 40,157

Pseudo R2 2.04%

Notes: This table reports the results from the logistic regression of earnings restatements on managerial ability and controls for innate firm characteristics. p-values are based on standard errors that are clustered by firm and year (Petersen 2009). We decile rank MgrlAbility by industry-year. See Table 1, Panel C for variable definitions.

46

Table 4 Earnings Persistence and Managerial Ability Dependent Variable = Future Earnings Intercept 0.02 0.03 –0.03 –0.01 –0.00 0.03 0.001 0.001 0.005 0.69 0.96 0.38 Earnings 0.72 0.45 0.43 0.24 0.001 0.001 0.001 0.001 Earnings × MgrlAbility 0.38 0.28 0.001 0.006 Earnings× NegEarn 0.14 0.33 –0.07 0.21 0.15 0.002 0.03 0.004 Earnings × MgrlAbility×NegEarn –0.24 –0.17 0.001 0.10 Accruals 0.34 0.18 0.001 0.01 Accruals × MgrlAbility 0.26 0.19 0.002 0.06 Accruals× NegEarn 0.26 0.05 0.03 0.50 Accruals × MgrlAbility× NegEarn –0.12 –0.06 0.19 0.54 CFO 0.64 0.48 0.001 0.001 CFO× MgrlAbility 0.27 0.15 0.002 0.17 CFO× NegEarn 0.47 0.46 0.001 0.001 CFO× MgrlAbility× NegEarn 0.003 0.04 0.98 0.68 MgrlAbility –0.01 0.00 –0.00 0.00 0.18 0.84 0.85 0.41 MgrlAbility× NegEarn 0.04 0.03 0.00 –0.003 0.14 0.28 0.52 0.83 NegEarn 0.09 0.08 0.04 –0.00 –0.00 –0.05 0.008 0.02 0.14 0.70 0.88 0.06 N 70,002 70,002 R2 58.56% 58.71% 60.55% 57.96% 58.04% 58.30% Controls Included Included Firm Fixed Effects Excluded Included

Notes: This table presents the OLS regression results investigating the relation between managerial ability and earnings persistence. p-values are based on standard errors that are clustered by firm and year (Petersen 2009) in specifications excluding firm fixed effects. We decile rank MgrlAbility by industry-year. Our main set of control variables (Firm Size, Sales Volatility, Cash Flow Volatility, OperCycle, and Losses) are included in the model, as are interaction terms between the control variables and NegEarn. For succinctness, however, results for the control variables and interaction terms are not tabulated. See Table 1, Panel C for variable definitions.

47

Table 5 Errors in the Allowance for Bad Debt and Managerial Ability Dependent Variable = BDE Error Intercept 0.004

0.77

High Ability Indicator –0.008

0.05

Firm Size –0.002

0.03

SalesVolatility –0.008

0.25

CashFlowVolatility 0.01

0.52

OperCycle 0.004

0.10

Losses 0.02

0.00

N 963

R2 5.17%

Notes: This table presents the OLS regression results investigating the relation between managerial ability and errors in the bad debt provision. BDE Error is the absolute value of the residual from Equation (5), and High Ability Indicator is an indicator variable that is equal to one (zero) if the managerial ability score in year t is in the top (bottom) quintile relative to industry-year peers. Requisite information for this test requires hand collection from SEC filings. Thus, we limit the analysis to firms in three industries (following McNichols and Wilson 1988) where accounts receivable (relative to assets) and bad debt expense (relative to earnings) are large: (1) printing and publishing, (2) nondurable wholesale goods, and (3) business services. We consider only those firm-year observations where managerial ability falls among the highest and lowest quintile relative to industry-year peers. See Table 1, Panel C for variable definitions.

48

Table 6 Accruals Quality and Managerial Ability Dependent Variable = Aggregate AQ Annual AQ Aggregate AQ Annual AQ Intercept 0.58 0.56 0.58 0.44

0.001 0.001 0.001 0.001

MgrlAbility –0.04 –0.06 –0.006 –0.05 0.001 0.001 0.20 0.001

Firm Size 0.02 0.03 0.03 0.04

0.001 0.001 0.001 0.001

SalesVolatility –0.22 –0.12 –0.15 –0.04

0.001 0.001 0.001 0.001

CashFlowVolatility –0.81 –0.26 –0.66 –0.20

0.001 0.001 0.001 0.001

OperCycle –0.006 –0.01 –0.02 –0.003

0.06 0.001 0.001 0.38

Losses –0.11 –0.08 –0.09 –0.06

0.001 0.001 0.001 0.001

N 55,125 70,002 55,125 70,002

R2 26.26% 13.12% 25.68% 12.49%

Firm Fixed Effects Excluded Excluded Included Included

Notes: This table reports the results from the OLS regression of accruals quality on managerial ability and controls for innate firm characteristics. p-values are based on standard errors that are clustered by firm and year (Petersen 2009) in specifications excluding firm fixed effects. We decile rank MgrlAbility, Aggregate AQ, and Annual AQ by industry-year. See Table 1, Panel C for variable definitions.

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Table 7 Accruals Quality Components and Managerial Ability Dependent Variable =

;<=C ?;@A> εB ;<=C ?;@A> εB

Intercept 0.28 0.40 0.51 0.21 0.47 0.51

0.001 0.001 0.001 0.001 0.001 0.001

MgrlAbility 0.04 0.05 –0.01 0.04 –0.04 –0.05

0.001 0.001 0.02 0.001 0.001 0.001

Lagged Size Decile 0.39 0.15 –0.02 0.53 0.09 0.01

0.001 0.001 0.16 0.001 0.001 0.41

N 55,125 55,125 55,125 66,820 66,820 66,820

R2 55.14% 3.46% 1.08% 69.13% 0.00% 0.10%

Firm Fixed Effects Included Included Included Included Included Included

AQ = Aggregate AQ Annual AQ

Notes: This table presents the results of the OLS regression investigating the relation between accruals quality components and managerial ability. We partition the two Dechow and Dichev (2002) accruals quality metrics into “estimation difficulty,” “real activities,” and the “unexplained” portion. Specifically, we estimate the following: AQ = β0 + β1 Firm Size + β2 Losses + β3 Sales Volatility + β4 Cash Flow Volatility + β5 Operating Cyclet +β6 R&D Volatilityt + β7 Advertising Volatilityt + β8 M&A Volatilityt + β9 M&A Activityt + εt. The predicted values generated by applying β0 –β5 to the firm’s characteristics provide our estimate of accruals quality related to estimation constraints ( ;<=> ). The predicted values generated by applying β6 –β9 to the firm’s characteristics provide our estimate of accruals quality related to real activities ( ?;@A> ). Finally, the residual is the “unexplained” portion of accruals quality (εB). We exclude the controls for innate firm characteristics that make it difficult to estimate accruals from this table because we use the control variables in the partitioning of total accruals. Thus to control for size, we included the lagged decile rank of firm size. We decile rank MgrlAbility and each of the AQ components by industry-year. See Panel C of Table 1 for additional variable definitions.

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Table 8 Restatements, Managerial Ability and Accruals Quality Components

Dependent Variable =

Restate Intercept –2.68 –2.08

0.01 0.01

;<=> –0.79 –0.54

0.01 0.01

?;@A> –0.17 –0.06

0.30 0.32

εB –0.12 –0.22

0.20 0.01

Earnings –0.02 –0.10

0.85 0.11

Lagged Size Decile 1.57 0.91

0.01 0.01

N 26,986 41,041

Pseudo R2 1.60% 0.55%

AQ = Aggregate AQ Annual AQ

Notes: This table presents the results of the logistic regression investigating the relation between managerial ability and subsequent earnings restatements. p-values are based on standard errors that are clustered by firm and year (Petersen 2009). We partition the two Dechow and Dichev (2002) accruals quality metrics into “estimation difficulty,” “real activities,” and the “unexplained” portion, ;<=> , ?;@A> and εB, respectively (see Table 7). We exclude the controls for innate firm characteristics that make it difficult to estimate accruals from this table because we use the control variables in the partitioning of total accruals. Thus to control for size, we included the lagged decile rank of firm size. We decile rank MgrlAbility and each of the AQ components by industry-year. See Panel C of Table 1 for additional variable definitions.

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Table 9 Earnings Quality and Alternative Proxies for Managerial Ability Dependent Variable = Total EarnQuality

MgrlAbility 0.47 0.43 0.58 0.00 0.00 0.00 Historical Ret 0.26 0.21 0.29 0.00 0.00 0.00

Media Count –0.03 –0.05

0.70 0.58

Firm Size 0.25 0.24 0.24 0.17 0.18

0.00 0.00 0.00 0.00 0.00

SalesVolatility –1.20 –1.10 –1.17 –1.07 –1.24

0.00 0.00 0.00 0.00 0.00

CashFlowVolatility –5.22 –5.57 –5.96 –5.94 –6.41

0.00 0.00 0.00 0.00 0.00

OperCycle 0.07 0.07 0.08 –0.02 0.003

0.00 0.00 0.00 0.52 0.93

Losses –1.07 –0.98 –0.93 –1.24 –0.93

0.00 0.00 0.00 0.00 0.00

Earnings 0.12 0.58 0.32 0.84 0.16

0.27 0.00 0.02 0.00 0.58

N 19,785 17,016 17,016 6,149 5,958

Pseudo R2 3.97% 3.34% 3.42% 1.86% 2.03%

Notes This table reports the results from the ordered logistic regression of total earnings quality (Total EarnQuality) on managerial ability and controls for innate firm characteristics. We decile rank MgrlAbility, Historical Returns, and Media Counts by industry-year. The sample size is reduced for specifications including Media Count, as this variable spans only Execucomp firms from 1995–2005. See Table 1, Panel C for variable definitions.

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Table 10 Change in Accruals Quality and Change in Managerial Ability Dependent Variable =

Annual ∆AQ Intercept 0.01 0.095

∆MgrlAbility 0.04 0.082

∆Firm Size 0.01 0.660

∆SalesVolatility 0.01 0.790

∆CashFlowVolatility –0.12 0.467

∆OperCycle –0.01 0.765

∆Losses 0.02

0.366

N 116 R2 3.67%

Notes: This table presents the OLS regression of changes in accruals quality on changes in managerial ability and changes in control variables, where the change in earnings quality, as well as the change in each of the control variables, is measured from year AQc+1–AQc–1, where c is the year in which the CFO changed. Thus, a positive value of ∆AQ signifies an improvement in earnings quality following the new CFO appointment. The change in managerial ability reflects the difference between the newly appointed CFO’s score from his or her prior firm and the departing CFO’s score from the current firm (i.e., MgrlAbilityj,b,c-1 – MgrlAbility i,a,c-1, where manager b was hired by firm i and was previously employed by firm j). A positive value of ∆MgrlAbility signifies that the incoming manager is deemed more efficient than the outgoing manager. See Table 1, Panel C for variable definitions.