Research design issues in earnings management studies

Research design issues in earningsmanagement studies

Maureen F. McNichols*

Department of Accounting, Graduate School of Business, Stanford University, Stanford, CA 94305,

USA

Abstract

This paper discusses trade-o�s associated with three research designs commonly used

in the earnings management literature: those based on aggregate accruals, those based

on speci®c accruals and those based on the distribution of earnings after management.

A key theme of the paper is that empirical procedures for aggregate accruals studies lag

both our theories of incentives to manage accruals and our institutional knowledge of

how accruals behave. Empirical ®ndings suggest that aggregate accruals models that do

not consider long-term earnings growth are potentially misspeci®ed and can result in

misleading inferences about earnings management behavior. It is suggested that future

progress in the earnings management literature is more likely to come from application

of speci®c accrual and distribution-based tests than from aggregate accruals

tests. Ó 2000 Elsevier Science Ltd. All rights reserved.

1. Introduction

The earnings management literature attempts to understand why managersmanipulate earnings, how they do so and the consequences of this behavior.These questions are the focus of a signi®cant area of inquiry within ®nancialreporting research. There is broad interest in the ®ndings of this literature, asthe reviews by Schipper (1989), Dechow and Skinner (2000) and Healy andWahlen (1999) indicate. However, as these papers note, interpretations of the

Journal of Accounting and Public Policy 19 (2000) 313±345

* Tel.: +1-650-723-0833; fax: +1-650-725-6152.

E-mail address: [email protected] (M.F. McNichols).

0278-4254/00/$ - see front matter Ó 2000 Elsevier Science Ltd. All rights reserved.

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evidence are controversial. My paper overviews research designs applied in therecent literature and discusses factors that cause it to be di�cult to interpret theevidence.

My paper discusses trade-o�s associated with three research designs com-monly used in the earnings management literature: those based on aggregateaccruals, those based on speci®c accruals and those based on the distribution ofearnings after management.1 A key theme of my review is that much of thecontroversy over interpretation of the literature's ®ndings is due to the ex-tensive use of aggregate accruals models to characterize discretionary behavior.The model of aggregate accruals proposed by Jones (1991), hereafter referredto as the Jones model, is the most commonly used in the literature. Given thelimited theory we have of how accruals behave in the absence of discretion, thetask of identifying and controlling for potentially correlated omitted variablesis daunting indeed. Although aggregate accruals models made an importantcontribution to the literature at the time they were introduced and have hadsubstantial impact, I suggest that further progress in the literature will require adeparture from extensive reliance on aggregate accruals approaches.

An alternative to studying aggregate accruals is to model the behavior ofspeci®c accruals. Studies adopting this approach typically focus on a speci®cindustry, such as banking (e.g., Scholes et al., 1990) or property and casualtyinsurance (e.g., Petroni, 1992), and use knowledge of institutional arrange-ments to characterize the likely nondiscretionary and discretionary behavior ofaccruals. A second alternative to studying aggregate accruals is the method-ology for identifying earnings management developed by Burgstahler andDichev (1997) and Degeorge et al. (1999) based on the distribution of earningsafter management. The latter two studies make strong predictions about thebehavior of earnings in narrow intervals around a target earnings number and®nd compelling evidence that earnings are managed to achieve earnings targets,particularly positive earnings.

In this review, I discuss the characteristics of and trade-o�s associated withthe three most commonly applied designs in the earnings management litera-ture. I ®rst focus on research design issues in aggregate accruals designs, dis-cussing a number of issues associated with modeling aggregate accruals. Next Idiscuss these issues from the perspective of what we know about one compo-nent of aggregate accruals, the change in accounts receivable. An importanttheme of this discussion is that empirical procedures for aggregate accrualsstudies lag both our theories of incentives to manage accruals and our insti-tutional knowledge of how accruals behave. The discussion highlights a

1 This paper focuses on research design issues related to tests of earnings management primarily

through accruals. As such, it does not address issues related to the literature on choice of

accounting principles. See Fields et al. (2000) for a recent discussion of this literature.

314 M.F. McNichols / Journal of Accounting and Public Policy 19 (2000) 313±345

number of issues related to drawing inferences about earnings managementfrom the behavior of aggregate accruals given the behavior of its components.

I then provide evidence concerning the speci®cation of aggregate accrualsmodels. Speci®cally, I conjecture that ®rms with greater expected earningsgrowth are likely to have greater than expected accruals than ®rms with lessexpected earnings growth. The Jones model controls for growth in current yearsales, but ®rms with di�erent expectations for long-term earnings growth likelymake di�erent working capital investment decisions. The empirical ®ndings areconsistent with this conjecture in that estimated discretionary accruals usingthe Jones model or the Dechow et al. (1995) modi®ed Jones model, hereafterreferred to as the modi®ed Jones model, are signi®cantly associated with an-alysts' projections of long-term earnings growth. This ®nding holds aftercontrolling for return on assets, which Dechow et al. (1995) and Kasznik (1999)have shown to be signi®cantly associated with estimated discretionaryaccruals.2 My ®ndings indicate that it is also important to control expectedearnings growth in earnings management studies using an aggregate accrualsapproach.

I then overview research design trade-o�s associated with both speci®c ac-cruals and distribution approaches. An important advantage of the speci®caccrual approach is that an understanding of the nondiscretionary componentis more readily developed, as the researcher can rely on generally acceptedaccounting principles to understand what fundamentals should be re¯ected inthe account in the absence of earnings management. A prime advantage of thedistribution approach is that it allows the researcher to make a strong pre-diction about the frequency of earnings realizations which is unlikely to be dueto the nondiscretionary component of earnings. I suggest that future contri-butions to the literature may result from greater use of these approaches, eitherindividually or in combination.

The layout of the paper is as follows. Section 2 overviews earnings man-agement research designs and provides descriptive evidence on their use in therecent literature. Section 3 discusses research design issues associated withaggregate accruals proxies for earnings management. Section 4 develops thisdiscussion further using the context of a speci®c accrual, accounts receivable.Section 5 presents empirical evidence on the relation between discretionaryaccrual estimates based on aggregate accruals, and earnings performance andearnings growth. Section 6 discusses design issues associated with speci®c ac-cruals studies, Section 7 discusses design issues associated with earnings dis-tribution tests, and Section 8 concludes.

2 See Dechow et al. (1995, pp. 205±211) and Kasznik (1999, pp. 67±69) for further discussion and

evidence.

M.F. McNichols / Journal of Accounting and Public Policy 19 (2000) 313±345 315

2. Earnings management research designs

A fundamental element of any test for earnings management is a measure ofmanagement's discretion over earnings. The literature has followed severalapproaches, with varying characteristics. First, there is a large literature thatattempts to identify discretionary accruals based on the relation between totalaccruals and hypothesized explanatory factors. This literature began withHealy (1985) and DeAngelo (1986), who used total accruals and change in totalaccruals, respectively, as measures of management's discretion over earnings.Jones (1991) introduced a regression approach to control for nondiscretionaryfactors in¯uencing accruals, specifying a linear relation between total accrualsand change in sales and property, plant and equipment. I refer to these ap-proaches as aggregate accruals studies. Panel A of Table 1 describes the mosttypical of the aggregate accruals estimation approaches and indicates the au-thors introducing these approaches.

A second approach in the literature is to model a speci®c accrual, as inMcNichols and Wilson (1988), Moyer (1990), Petroni (1992), Beaver andMcNichols (1998), Penalva (1998), Nelson (2000) and Petroni et al. (2000).These studies often focus on industry settings in which a single accrual is siz-able and requires substantial judgment. Based on these characteristics, as wellas anecdotal evidence, the researchers have priors that management's discre-tion is likely to be re¯ected in a speci®c accrual or set of accruals. As withaggregate accruals studies, a key aspect of the research design task is modelingthe behavior of each speci®c accrual to identify its discretionary and nondis-cretionary components. Panel B of Table 1 provides several examples ofmeasures of speci®c discretionary accruals and indicates the authors intro-ducing these approaches.

A third approach is to examine the statistical properties of earnings toidentify behavior that in¯uences earnings, as developed by Burgstahler andDichev (1997) and Degeorge et al. (1999). These studies focus on the behaviorof earnings around a speci®ed benchmark, such as zero or a prior quarter'searnings, to test whether the incidence of amounts above and below thebenchmark are distributed smoothly, or re¯ect discontinuities due to the ex-ercise of discretion. Panel C of Table 1 describes this approach to testing forearnings management and indicates the authors introducing these approaches.

To characterize the research designs applied in the recent literature, a searchfor the 1993±1999 period was conducted in The Accounting Review, Contem-porary Accounting Research, Journal of Accounting and Economics, Journal ofAccounting, Auditing and Finance, Journal of Accounting and Public Policy,Journal of Accounting Research, Journal of Business Finance and Accounting,and Review of Accounting Studies. This search identi®ed 55 articles testing forearnings management using proxies for discretionary behavior. Appendix Aprovides a reference for each of these articles.


Table 2 presents summary statistics on the publications by year, by journaland by methodology. The data in Panel A document that the earnings man-agement literature is active, with a substantial number of papers published in

Table 1

Discretionary accrual proxies

Panel A: Aggregate accruals models

Authors Discretionary accrual proxy

Healy (1985) Total accruals

DeAngelo (1986) Change in total accruals

Jones (1991) Residual from regression of total accruals on change in sales

and property, plant and equipment

Modi®ed Jones Model from

Dechow et al. (1995)

Residual from regression of total accruals on change in sales

and on property, plant and equipment, where revenue is

adjusted for change in receivables in the event period

Kang and Sivaramakrishnan

(1995)

Residual from a regression of noncash current assets

less liabilities on lagged levels of these balances, adjusted

for increases in revenues, expenses and plant and

equipment

Panel B: Speci®c accrual models

Authors Discretionary accrual proxy

McNichols and Wilson (1988) Residual provision for bad debt, estimated as the residual

from a regression of the provision for bad debts on the

allowance beginning balance, and current and future

write-o�s

Petroni (1992) Claim loss reserve estimation error, measured as the ®ve year

development of loss reserves of property casualty insurers.

Beaver and Engel (1996) Residual allowance for loan losses, estimated as the residual

from a regression of the allowance for loan losses on net

charge-o�s, loan outstanding, nonperforming assets and one

year ahead change in nonperforming assets

Beneish (1997) Days in receivables index, gross margin index, asset quality

index, depreciation index, selling general and administrative

expense index, total accruals to total assets index

Beaver and McNichols (1998) Serial correlation of one year development of loss reserves of

property casualty insurers

Panel C: Frequency distribution approach

Authors Test for earnings management

Burgstahler and Dichev (1997) Test whether the frequency of annual earnings realizations in

the region above (below) zero earnings and last year's earnings

is greater (less) than expected

Degeorge et al. (1999) Test whether the frequency of quarterly earnings realizations

in the region above (below) zero earnings, last quarter's

earnings and analysts' forecasts is greater (less) than expected

Myers and Skinner (1999) Test whether the number of consecutive earnings increase is

greater than expected absent earnings management


recent years. Panel B indicates that papers on earnings management have beenpublished in all the leading accounting journals. Panel C indicates that totalaccruals, speci®c accruals and distribution approaches are being applied and

Table 2

Descriptive data on journal articles on earnings management

Panel A: Frequency distribution of articles published, by publication year

Year of journal issue Number of articles %a

1993 7 12.7

1994 7 12.7

1995 8 14.5

1996 10 18.2

1997 8 14.5

1998 11 20.0

1999b 4 7.3

Total 55 100.0

Panel B: Frequency distribution of articles published, by journal

Journal Number of articles %

The Accounting Review 10 18.2

Contemporary Accounting Research 5 9.1

Journal of Accounting and Economics 18 32.7

Journal of Accounting Research 9 16.4

Journal of Accounting and Public Policy 3 5.5

Journal of Accounting Auditing and Finance 3 5.5

Journal of Business Finance and Accounting 4 7.3

Review of Accounting Studies 3 5.5

Total 55 100.0

Panel C: Frequency distribution of articles published, by primary methodology applied

Measure of manipulation Number of articles %a

Total accruals 4 7.3

Residual from aggregate accruals model 25 45.5

Speci®c accruals 10 18.2

Asset sales and asset write-o� 4 7.3

Accounting changes 5 9.1

Unusual gains and losses 2 3.6

Subject of accounting enforcement by SEC 2 3.6

Distribution of earnings 1 1.8

Change in R&D expenditure from prior year 1 1.8

Other 2 3.6

Total 56c 100.0

a Percentages do not add to 100.0% due to rounding.b Articles were included through September 1999, so 1999 does not include a full year of data.c The total number of approaches exceeds the number of articles because one study applied two

approaches.


accepted.3 However, as Panel C indicates, the greatest number of studies use anaggregate accruals approach based on the Jones model. Although some aca-demics question the validity of this discretionary accruals proxy (e.g., McNicholsand Wilson, 1988, p. 12; Wilson, 1996, pp. 172, 177; Bernard and Skinner, 1996,pp. 315±320), the large number of studies published that use this approachsuggests that it is widely accepted as a proper proxy for earnings management.

3. Research design issues associated with discretionary accrual proxies based on

aggregate accruals

There are several issues that a�ect inferences from aggregate accrualsstudies. As noted by McNichols and Wilson (1988), accruals-based tests ofearnings management require a proxy for management's discretion over ac-cruals. McNichols and Wilson (1988, p. 5) characterize this proxy, DAP, asmeasuring discretionary accruals, DA, with error, g

DAP � DA� g:

The error, g, re¯ects the e�ects of omitted variables in the estimation of DA, aswell as idiosyncratic variation. Jones (1991, pp. 210±212) measures DAP as A,aggregate accruals, less estimated nondiscretionary accruals, NAEST

DAP � AÿNAEST;

where NAEST is characterized as the prediction error from an equation re-gressing total accruals on the change in revenues and level of property, plantand equipment.

McNichols and Wilson (1988, pp. 5±6) characterize a test for earningsmanagement where DA is observed in terms of the following regression:

DA � a� bPART� e;

where PART is an indicator variable partitioning the sample into twogroups, for which di�erences in earnings management behavior are predicted,a is the mean discretionary accrual of observations in the ®rst group anda� b is the mean discretionary accrual of observations in the second group.However, the researcher does not observe DA but rather an estimate, DAP.Tests of earnings management are therefore characterized by the followingregression:

DAP � /� cPART� m;

3 Panel C notes 56 articles because one study applied two methodological approaches.


where

c � b� q�PART; g� � rg

rPART

;

which can be written as

c � b� bias:

The error term g re¯ects the e�ects of omitted variables in the estimation of DAas well as idiosyncratic variation in DAP conditional on DA. As McNicholsand Wilson (1988, p. 6) show, c is a biased estimate of b if the partitioningvariable is correlated with g, the measurement error in the estimate of discre-tionary accruals.

To interpret accruals-based tests as evidence that earnings management didnot occur, one must be con®dent that the discretionary accrual proxy is suf-®ciently sensitive to re¯ect it. To interpret accruals-based tests as evidence thatearnings management occurred, one must be con®dent that measurement errorin the discretionary accrual proxy is not correlated with the partitioningvariable in the study's research design.4

3.1. The behavior of accruals absent earnings management

Given the potential for measurement error in discretionary accrual proxiesto induce bias in earnings management studies, a fundamental question is howaccruals behave in the absence of earnings management.5 To date, the aggre-gate accruals literature has largely taken a black box approach to the factorsthat explain accruals. This causes it to be extremely di�cult to be con®dentthat estimates of discretionary accruals capture discretion by management andposes several di�culties in evaluating research design choices. 6 Accountingresearchers need to understand the factors that cause accruals to ¯uctuate in aworld without incentive problems. Whether accruals are linear in the change insales in the absence of earnings management, as the Jones model speci®es, is anopen question. The existing literature speci®es some parsimonious linear

4 For further discussion of this point, see McNichols and Wilson (1988, pp. 7±13) and Bernard

and Skinner (1996, pp. 315±320).5 This question is fundamental because most studies proceed by modeling nondiscretionary

accruals and inferring discretionary accruals. An alternative approach, followed by Petroni et al.

(2000) is to directly model the discretionary component of accruals based on management's

incentives to exercise discretion. This latter approach allows the researcher to separate the

estimation error in the discretionary accrual proxy from that in the estimate of nondiscretionary

accruals.6 This concern parallels Leamer's (1983) discussion of misspeci®cation in econometric models,

more generally. He quotes Guy Orcutt: ``Doing econometrics is like trying to learn the laws of

electricity by playing the radio'' (see Leamer, 1983, p. 31).


models to explain aggregate accrual behavior, but has presented little theory orevidence for how these accruals behave with or without earnings manage-ment.7

Several empirical studies have examined the validity of alternative earningsmanagement tests. Dechow et al. (1995), hereafter DSS, examine the accrualbehavior of ®rms that are subject to Securities and Exchange Commission(SEC) enforcement actions. Their ®ndings indicate that the accrual proxiesthey examine capture this extreme behavior.8 Bradshaw et al. (1999) extend theDSS sample of ®rms subject to SEC enforcement actions and use accrualmeasures based on the statement of cash from operations rather than thebalance sheet. Their evidence is consistent with DSS, establishing that extremecases of earnings manipulation are re¯ected in aggregate accruals.

Beneish (1997) examines the ability of DSS' modi®ed Jones model toidentify earnings management by ®rms identi®ed as generally accepted ac-counting principles (GAAP) violators, either by the ®nancial press or the SECEnforcement Division. He ®nds that the modi®ed Jones model does not per-form well in detecting GAAP violators, and that a model incorporating ®-nancial statement ratios and other explanatory factors has signi®cantly fewerclassi®cation errors (1997, pp. 292±296). His study suggests that by exploitinginformation about speci®c accruals, potentially more powerful methods fordetecting earnings management can be developed. Kang and Sivaramakrish-nan's (1995) study suggests a similar inference. They develop a model of ag-gregate accruals based on levels of working capital accounts rather thanchanges to test for earnings management and document that their method hasgreater power to identify simulated earnings management.9

The ability to develop more powerful earnings management tests is impor-tant because the DSS simulation results suggest that earnings managementamounts less than 5% of total assets are likely to go undetected by aggregateaccruals tests.10 The median net income before extraordinary items forCOMPUSTAT ®rms for the 1988±1998 period is 3.8% of beginning total as-sets, so average earnings management of even one percent of total assets wouldbe very material to most ®rms' earnings.11 If researchers are to be able tocharacterize the nature and magnitude of more subtle earnings managementactivity, then more powerful methods will be required.

7 A notable exception is Dechow et al. (1998).8 See DSS, pp. 219±223.9 See pp. 356±358 for discussion of their model and pp. 361±365 for their ®ndings.10 The DSS (1995, pp. 204±223) simulation results focus on the frequency of rejection at the 5%

level for speci®c ®rm-year observations, rather than the frequency of rejecting that a sample mean

di�ers from zero. However, the low rates of rejection at low levels of earnings management (pp.

215±218) suggest power can be an issue, even for large sample studies.11 This statistic is presented in Table 3, which will be discussed in Section 5.


This inference is reinforced by the results of Thomas and Zhang (1999).They compare the predictive ability of six models of total accruals and ®nd thatonly the Kang and Sivaramakrishnan (1995) model performs moderately well.Surprisingly, they ®nd that a na�õve model that predicts total accruals equal toÿ5% of total assets outperforms the other ®ve models. They also ®nd that inpredicting current accruals, only the Jones model has predictive ability. Their®ndings indicate that over®tting in estimation periods results in greater per-ceived explanatory power for these models than is warranted.

Finally, DSS (pp. 205±209) and Kasznik (1999, pp. 67±69) show that dis-cretionary accrual estimates are correlated with earnings performance. Firmswith higher (lower) earnings exhibit signi®cantly positive (negative) discre-tionary accruals. Presumably this arises because ®rms with abnormally high(low) earnings have positive (negative) shocks to earnings that include an ac-crual component. A consequence of this is that one is more likely to detectearnings management that increases earnings for the most pro®table ®rms andearnings management that reduces earnings for the least pro®table ®rms.12

One question this raises is whether the ®ndings of some of the studies thatdocument signi®cant discretionary accrual estimates are enhanced by mea-surement error in discretionary accrual proxies that is correlated with thepartitioning variable in the research design. Of the 55 papers reviewed for thispaper, 23 test for hypothesized earnings management behavior using an ag-gregate accruals regression approach and 22 of these conclude that they ®ndevidence of earnings management.13 Even if earnings management is pervasive,these ®ndings are interesting given the DSS conclusion that most of the accrualsmodels in their study would not reject the null hypothesis of no earningsmanagement unless the magnitude of the manipulation were approximately 5%of total assets. One would expect studies testing for earnings management to belikely to ®nd null results except in contexts with incentives to manage earningsby sizable magnitudes. The strength of the ®ndings across studies may indicateaggressive earnings management across the studied contexts, or bias in the testsof earnings management due to measurement error in the discretionary accrualproxy. The strength of the ®ndings across studies may also re¯ect selection inthe editorial process, where papers ®nding no evidence of earnings management

12 Although this ®nding is well documented in the literature, I am aware of only a few studies,

such as Kasznik (1999) that directly control for this e�ect.13 Panel C shows 25 studies using an aggregate accruals regression approach, though two of these

studies, Dechow et al. (1995) and Kang and Sivaramakrishnan (1995) are methodological in

orientation and are therefore excluded from this calculation. The one study of the 23 using the

residuals approach which does not ®nd earnings management as hypothesized is Rees et al. (1996).

Rees et al. ®nd evidence of discretionary accruals, as predicted, but argues that the fact they do not

reverse in subsequent periods suggests they are nondiscretionary rather than discretionary (1996,

pp. 165±168).


are less likely to be published. To the extent this occurs, it is di�cult to assess thestrength of the evidence of earnings management from published studies alone.

3.2. The relation between discretionary and nondiscretionary accruals

Assuming one has a model of how accruals behave absent earnings man-agement, two additional questions must be addressed: What factors causemanagement to exercise discretion? What relation does this imply betweendiscretionary accruals and nondiscretionary accruals? Most of the aggregateaccruals models assume that discretionary accruals are orthogonal to nondis-cretionary accruals.14

The assumption of orthogonality may be plausible in some research designcontexts, but there are plausible scenarios in which nondiscretionary accrualsare correlated with discretionary accruals. Recent papers by Demski and Fri-mor (1999), McCulloch and Black (1999) and Bagnoli and Watts (2000) focuson understanding management's incentives to manage earnings and suggest theassumed orthogonality is too restrictive.

Demski and Frimor (1999) develop a model in which manipulation ofperformance measures is the equilibrium response to nonlinearities in the re-lation between compensation and performance. The intuition is that discrete-ness in the pay-o�s induces the agent to smoothe the report of initial output toincrease the expected pay-o� in the second period. A key feature of their settingis the nonlinearity of incentives, which causes the magnitude of manipulationto be nonlinear in what might be called premanaged earnings.

McCulloch and Black (1999) show that if management alters accruals inresponse to ¯uctuations in nondiscretionary accruals to achieve a smootherincome series, there will be a negative correlation between nondiscretionaryand discretionary accruals. A bonus story as in Healy (1985) suggests nocorrelation between discretionary and nondiscretionary accruals when earningsbefore management are below target and management cannot manipulateupward su�ciently to achieve positive results, but negative correlation betweendiscretionary and nondiscretionary accruals when earnings are above target.Similarly, for ®rms that manage earnings to avoid losses, I expect discretionaryaccruals are more positive when nondiscretionary accruals are more negative.

Bagnoli and Watts (2000) examine how the existence of relative performanceevaluation a�ects earnings management incentives. They ®nd that the tendencyto manage earnings and the amount are increasing in the ®rm's reliance onrelative performance evaluation. Furthermore, ®rms are more likely to manage

14 Note this concern would not apply when discretionary accruals are estimated directly, rather

than being inferred as the residual accrual from a regression of total accruals on factors explaining

nondiscretionary accruals.


earnings if they expect competitor ®rms to manage earnings.15 As I discussfurther below, this poses a challenge for empirical researchers interested inestimating discretionary accruals. Researchers estimating nondiscretionaryaccruals by industry, as the cross-sectional Jones model is often applied, maywell overstate the magnitude of nondiscretionary accruals and understate themagnitude of discretionary accruals, because industry-level controls include theaverage level of discretion exercised by the industry.

3.3. Estimation issues

Estimation of discretionary accruals requires speci®cation of an estimationand test period, and speci®cation of ®rm-year observations in which earningswere not actively managed. The maintained assumption is that earningsmanagement occurs in the test period but not in the estimation period. Giventhe rich set of contexts in which management has been hypothesized to manageearnings, this can be a di�cult assumption to maintain in many studies. Atbest, the estimation period includes the e�ects of hypothesized earnings man-agement and the estimate of nondiscretionary accruals in the test period in-cludes a normal level of earnings management, lowering the power of the test.Furthermore, the accruals in the test period will include the reversal of esti-mation period earnings management activities in addition to earnings man-agement induced by current period incentives. To the extent the incentivesdi�er across periods, the benchmark may understate or overstate the nondis-cretionary accruals, leading to an inference of positive or negative discretionaryaccruals in the test period when there were none. Furthermore, these estima-tion issues cause it to be very di�cult to draw conclusions about the frequencyor magnitude of earnings management activity. 16

A second estimation issue is the estimation approach to use. Jones (1991,pp. 210±212) used a ®rm-speci®c model to estimate the relation between totalaccruals and explanatory factors. However, to estimate ®rm-speci®c parameterestimates requires a reasonable time series. Most studies impose the require-ment that sample ®rms have at least 10 years of data, which poses two prob-lems. First, one must exclude ®rms that do not have a su�cient data series in

15 Bagnoli and Watts (2000) focus on relative performance evaluation of managers for

compensation purposes. Although explicit relative performance evaluation is perhaps not very

common, one could envision capital market incentives that might operate in a similar way.

Investors compare performance across similar ®rms, thereby providing similar incentives to those

modeled by Bagnoli and Watts for relative performance evaluation.16 This research design issue is not unique to studies using discretionary accrual proxies based on

an aggregation of accruals. However, our general lack of understanding of incentives to manage

accruals in estimation periods can cause this issue to be more problematic for these designs than for

those based on a speci®c accrual.


COMPUSTAT or other data sources. This leads to potentially smaller sam-ples, and their representativeness is an open question. Second, it is not clearthat sample ®rms have no incentive to manage earnings in the estimation pe-riod, or that data are stationary over such a long period. An alternative is touse a cross-sectional estimation approach, which does not require a time-seriesfor each ®rm. However, then the benchmark for each ®rm's accruals is thebehavior of the other ®rms in the sample. For the reason Bagnoli and Watts(2000) indicate, and for reasons I discuss below, this can result in positive ornegative discretionary accruals that may not re¯ect earnings management.

A third estimation issue is the measure of accruals to use. Many studiesde®ne accruals as the change in current asset and liability accounts less de-preciation. This approach has the advantage of allowing a larger sample sizeand longer time series than studies that require Statement of Financial Ac-counting Standard (SFAS) 95 (FASB, 1987) data, which was not availablebefore 1988. However, as discussed below, the balance sheet approach raisesother concerns. More recent studies, e.g., Collins and Hribar (2000) andBradshaw et al. (1999), measure accruals using the changes in the current ac-counts disclosed on the SFAS 95 (FASB, 1987) statement of cash from oper-ations.

There seem to be two critical issues in choosing a measure of accruals. First,one wants a measure that is sensitive to the hypothesized manipulation. Sec-ond, one wants a measure whose nondiscretionary component is most readilycontrolled. Jones (1999) argues that current accruals provide a more accuratebasis for estimating discretionary behavior than aggregate accruals, becausethe estimated discretionary portion of noncurrent accruals is less likely to re-¯ect year-speci®c discretion. Consistent with this, he ®nds that discretionarycurrent accruals are signi®cantly negatively correlated, consistent with reversalof discretion, whereas discretionary noncurrent accruals are signi®cantly pos-itively correlated.17

4. What we know versus what we do

An important theme of this review is that there is a gap between the em-pirical procedures that are used in the earnings management literature and ourinstitutional knowledge of how accounting accruals behave. Recent papers byCollins and Hribar (2000) and Hansen (1999) provide an example of the gapbetween our empirical procedures and knowledge of the behavior of ®nancialstatement numbers. These papers document that the approach commonlytaken to estimate accruals as the di�erence in succeeding balance sheet

17 See also Beneish (1998) for further discussion on the choice between current and total accruals.


amounts induces substantial measurement error due to the failure to adjust formerger/acquisition and divestiture activity. Further, both papers note that ifpartitioning variables are correlated with variables related to merger/acquisi-tion or divestiture activity, the measurement error in discretionary accrualestimates can lead the researcher to conclude that earnings management existswhen it does not.

To provide additional examples of the gap between our institutionalknowledge of accruals and aggregate accruals models, I consider the researchdesign issues posed by a ®rm whose only accrual is receivables. It is argued thatunusually high receivables relative to sales re¯ect aggressive revenue recogni-tion. The basic rationale for this is that ®rms may exercise judgment regardingwhen revenue is earned but that customers are not likely to pay for the morediscretionary sales, and therefore receivables increase. However, a numberof factors in¯uence the relation between receivables and the change in sales,absent earnings management, which one must control to properly identifydiscretionary revenues.

First, one should consider how receivables are a�ected by credit policy. If a®rm grants more generous credit terms in the test period then, all else equal,days sales outstanding grows and one would observe a greater change in re-ceivables relative to the change in sales, as estimated using the ®rm-speci®cJones model. One could argue that credit policy is a mechanism that managersuse to accelerate revenue. However, credit policy is also a mechanism thatmanagement uses in the absence of earnings management motivations, andtherefore the extent to which its e�ects are discretionary or nondiscretionary isnot readily identi®able.18 For example, consider a ®rm that relaxes its creditterms from net 30 days in the estimation period to net 60 days in the test pe-riod. The estimated relation between change in receivables and change in saleswould be based on the 30 days collection period, resulting in a positive dis-cretionary accrual in the test period.19 The underlying question is whether andto what extent this re¯ects discretion.

Second, one can consider the consequences of credit policy on the Jonesmodel estimates conducted at the industry or cross-sectional level. Variationacross ®rms in credit policy will in¯uence the relation between the change inreceivables and change in sales. Consider estimating the Jones model param-eters for a sample of ®rms, half of which have credit terms of net 30 days and

18 Dechow and Skinner (2000) make a similar argument. Also note that although one cannot

determine whether the change in credit policy is intended to manage earnings in either scenario, the

change accelerates revenues and could be said to reduce earnings quality. In other words, to

forecast the likely level of permanent earnings, one would adjust for such a change regardless of

management's intent.19 This occurs because for a given change in sales, there is a greater increase in receivables,

resulting in a positive discretionary accrual estimate.


half of which have credit terms of net 60 days. The coe�cient from the re-gression of change in receivables on change in revenues is too low for the ®rmswith more generous credit terms and too high for the ®rms with less generouscredit terms. Consequently, in the test period, ®rms with more generous creditterms are considered to have positive discretionary accruals even though theircredit policy has not changed between the estimation and test period.

Next, consider the implications of increases in revenues over time, assumingthat credit policy remains constant. As DSS note, the Jones model provides anestimate of nondiscretionary accruals that is too large (and consequently anestimate of discretionary accruals that is too small) when some revenue ismanipulated because all revenues are treated as nondiscretionary. DSS proposea correction for this, which is to subtract the change in receivables from thechange in revenues. This, however, results in estimates of nondiscretionaryaccruals that are too small for ®rms with growing revenues, because not all ofthe change in receivables is discretionary. 20 This adjustment therefore over-states discretionary accruals for ®rms with growing revenues.21

A number of other factors in¯uence the relation between a ®rm's sales andreceivables in the absence of earnings management. Clearly revenue recogni-tion policy is one of these factors. Additional factors include working capitalmanagement. For example, some ®rms sell o� some of their receivablesthrough factoring or securitization transactions. These transactions result in alower reported level of receivables, and would lead to a lower estimate ofdiscretionary accruals for ®rms engaging in such transactions.

Pressuring distributors to make early purchases, also known as channelstu�ng, is a widely discussed form of manipulation of revenues. Depending onthe nature of the ®rm's relation with its customers, such behavior may or maynot result in a positive estimate of discretionary accruals. If the customers agreeto purchase additional merchandise for resale, then they may well hold theinventory and pay the producer, causing no additional receivables beyondthose predicted by the sales increase. In such cases, the measure of discre-tionary accruals will underestimate the extent of earning manipulation, re-ducing the power of such tests.

An alternative scenario is one where customers anticipate a forthcomingprice increase (decrease) and therefore accelerate (postpone) their purchasingbehavior in the current period. Although it may result in a temporary changein estimated discretionary accruals, and subsequent period's sales, one mightnot typically consider a product price change as an earnings management

20 This is not an issue for ®rms with unchanged revenues because holding their credit policy

constant, all of the change in receivables is discretionary.21 Beneish (1998) shows that the amount of the overstatement is as high as one-third of the change

in receivables, and suggests using the change in cash sales as a potential control variable.


technique. Nevertheless, in this scenario, the behavior of accruals would beinterpreted as consistent with management's manipulation of earnings.

Although a price change can be exogenous to management's reportingconcerns, it can also be endogenous. Firms vary in the timing of their salesacross the ®scal quarter and across the year, resulting in varying levels of re-ceivables at the end of a ®scal period. For example, Crook (1999) indicates that70% of Oracle Corporation's sales are typically concluded in the last month ofthe quarter due to that company's customers historically waiting until then inan e�ort to obtain concessions. More generally, the extent to which customersseek concessions may well be due to the bargaining power between customersand suppliers when suppliers are attempting to meet sales growth targets.Di�erences across ®rms in the timing of sales within ®scal periods will in¯uenceestimates of their discretionary accruals under cross-sectional estimation, evenif they have similar credit terms to other ®rms in their comparison set. 22

One could also examine other components of accruals and the factors thatin¯uence their behavior. For example, inventory levels have changed sub-stantially over time due to just-in-time policies, and accounts payable variesconsiderably across ®rms as a result of di�erences in the nature of their pur-chasing decisions and their bargaining power with suppliers.

5. Correlated omitted variables in aggregate accruals tests

As discussed in Section 3, an unbiased test of earnings management requiresthat measurement error in the discretionary accrual proxy is uncorrelated withthe partitioning variable in the research design. Evidence in DSS and Kasznik(1999) indicates that the Jones model estimates of discretionary accruals arecorrelated with earnings performance. Speci®cally, these studies document thatestimated discretionary accruals are negative for ®rms with low earnings andpositive for ®rms with high earnings. Both studies caution that if pro®tability iscorrelated with the partitioning variable in an earnings management study, the®ndings are biased.

One test of the speci®cation of a model is to include other potential ex-planatory variables. If a model is well-speci®ed, then one would not expect to®nd additional variables with explanatory power. To assess the potential foromitted variables that can potentially be correlated with partitioning variablesin earnings management studies, I examine the relation between accruals andgrowth in earnings, controlling for the change in sales. Because accruals are the

22 To see this, consider the extreme example of a ®rm that conducts all of its sales in the last week

of each quarter and has credit terms of 30 days. The ®rm would recognize 90 days of sales

outstanding, when in fact, its receivables are outstanding for 30 days.


changes in working capital accounts, one would expect rapidly growing ®rmsto experience larger accruals. An open question is whether the change in cur-rent period sales (in the Jones model variants) su�ciently captures the factorscausing growth in accruals.

To test for a relation between discretionary accrual estimates and growth, Iestimate Jones and modi®ed Jones models of accruals from 1988 to 1998, usingcross-sectional estimation at the two-digit industry level, as captured byCOMPUSTAT SIC codes. This sample period allows me to use SFAS 95(FASB, 1987) statement of cash from operations data to estimate accruals,rather than changes in balance sheet accounts. I also exclude companies withmerger and acquisition transactions, following Collins and Hribar (2000) andHansen (1999). Growth in earnings, GROWTH, is measured as the median ofanalysts' long-term earnings growth forecasts reported by I/B/E/S for the lastmonth of the respective ®scal year.

To test whether higher growth ®rms have greater estimates of discretionaryaccruals, I estimate the following equation:

DAi � a0 � a1ROA� a2GROWTH� e1; �1�where DAi is the discretionary accrual estimate from model i, where i � J in-dicates the Jones model and i �MJ indicates the modi®ed Jones model.23 Themodel tests for an association between discretionary accrual estimates andGROWTH after controlling for return on assets, ROA.

Descriptive statistics and data on the correlations among the dependent andexplanatory variables are in Table 3. The data indicate that the mean accrual is1.53% of start-of year total assets. The mean Jones model nondiscretionaryaccrual estimate, NDAJ, is 1.34% of beginning total assets, and the mean Jonesmodel discretionary accrual estimate, DAJ, is 0.19%. The mean modi®ed Jonesmodel nondiscretionary accrual estimate, NDAMJ, is 1.13% of beginning totalassets, and the mean modi®ed Jones model discretionary accrual estimate,DAMJ, is 0.40%. The mean analysts' earnings growth estimate, GROWTH, is17.04% with a median of 15.0%. Average return on assets, ROA, for sample®rms is 1.59% with a median of 3.8%. The average change in sales, DSALES, is10.22% of beginning total assets, and the median is 6.85%.

Panel B of Table 3 presents correlations between accruals variables andexplanatory variables, with Pearson correlations on the upper diagonal andSpearman correlations on the lower diagonal. The panel indicates thatACCRUALS is signi®cantly positively correlated with GROWTH, ROA andDSALES. Furthermore, the estimated nondiscretionary and discretionarycomponents of ACCRUALS are also signi®cantly correlated with GROWTHand ROA. Furthermore, DAMJ, the modi®ed Jones model discretionary

23 Both discretionary accrual estimates are scaled by beginning total assets.


Tab

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sb

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acc

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ean

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27,1

58

0.0

153

0.1

210

0.0

094

ND

AJ

27

,158

0.0

13

40.0

577

0.0

065

DA

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7,1

58

0.0

01

90.1

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0.0

008

ND

AM

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7,1

58

0.0

11

30.0

537

0.0

054

DA

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27

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0.0

04

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121

0.0

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GR

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27

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0.1

70

40.1

047

0.1

500

RO

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0,4

49

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15

90.1

385

0.0

380

DS

AL

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78

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0.1

02

20.2

754

0.0

685

Panel

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Corr

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bet

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0.8

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962

0.3

216

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0.3

229

0.6

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984

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GR

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accrual estimate is signi®cantly positively correlated with DSALES. This arisesbecause the estimation approach assumes all increases in receivables are dis-cretionary, so ®rms with the greatest increase in sales have larger discretionaryaccrual estimates.

The estimation results for Eq. (1) are in Table 4. Panel A presents the®ndings for discretionary accrual estimates from the Jones model and Panel Bpresents the ®ndings for the modi®ed Jones model. The ®ndings indicate,consistent with DSS and Kasznik (1999), that discretionary accrual estimatesare signi®cantly positively associated with return on assets, ROA. The coe�-cient estimate is 0.0817, with a t-statistic of 26.342. The ®ndings also indicatethat discretionary accrual estimates are signi®cantly positively associated withGROWTH, analysts' long-term earnings growth forecasts, with a coe�cient of0.0426 and a t-statistic of 7.942. After controlling for ROA, GROWTH con-tinues to have incremental explanatory power, as evidenced by its coe�cient of0.0473 and t-statistic of 7.837. These ®ndings indicate that growth ®rms arelikely to exhibit positive discretionary accrual estimates, and that a comparisonof their estimated discretionary accruals to those of ®rms with lower growthcan lead to a conclusion of greater earnings management by these ®rms. It ispossible that growth ®rms are more likely to manipulate their earnings.

Table 4

Estimation results from regression of discretionary accrual proxies on return on assets, analysts'

consensus long-term earnings growth forecastsa

DAi � a0 � a1ROA� a2GROWTH� e1: �1�

Panel A: Estimation results for cross-sectional Jones model discretionary accrual proxy

Estimation Intercept ROA GROWTH Adjusted R2 F-valueb n

1 0.0013 0.0817 0.0327 693.894 20,517

(2.998) (26.342)

2 )0.0022 0.0426 0.0065 63.070 9482

()2.028) (7.942)

3 )0.0059 0.0416 0.0473 0.0146 62.718 8335

()5.052) (7.858) (7.837)

Panel B: Estimation results for cross-sectional modi®ed Jones model discretionary accrual proxy

Estimation Intercept ROA GROWTH Adjusted R2 F-valueb n

4 0.0026 0.0980 0.0447 791.550 20,517

(5.229) (28.135)

5 )0.0022 0.0577 0.0109 105.764 9475

()1.911) (10.284)

6 )0.0078 0.0604 0.0668 0.0280 97.097 8347

()6.372) (10.957) (10.151)

a See variable de®nitions in Table 3.b The F-values for models 1±6 are signi®cant with probability values less than 0.0001.


However, my ®ndings indicate that without explicitly partitioning on any in-centive variable, ®rms with higher growth in earnings have higher discretionaryaccruals.

The estimation results for the modi®ed Jones model are consistent with andslightly stronger than those for the Jones model. Speci®cally, after controllingfor earnings performance, the coe�cient on GROWTH is 0.0668 with at-statistic of 10.151. These ®ndings indicate that researchers comparing ®rmsthat di�er in earnings performance or growth characteristics may well observe(or not observe) di�erences in estimated discretionary accruals that relate to theperformance characteristics of these ®rms rather than their incentives tomanage earnings. Some of the contexts examined in the prior literature thatmight be correlated with performance, growth or both include asset impair-ments, lawsuits, rising oil prices for oil and gas ®rms, management buyouts,mergers and acquisitions, and initial public and seasoned equity o�erings.

6. Research design issues of speci®c accruals tests for earnings management

An alternative to taking an aggregate accruals approach is to model aspeci®c accrual or a set of speci®c accruals on an individual basis. There areadvantages and disadvantages to this approach relative to the aggregate ac-cruals approach. First, the researcher can develop intuition for the key factorsthat in¯uence the behavior of the accrual, exploiting his or her knowledge ofgenerally accepted accounting principles. Second, a speci®c accrual approachcan be applied in industries whose business practices cause the accrual inquestion to be a material and a likely object of judgment and discretion. Aspeci®c industry setting can also provide insight on variables to control to betteridentify the discretionary component of a given accrual. Third, one can estimatethe relation between the single accrual and explanatory factors directly. If dif-ferent components of aggregate accruals relate di�erently to change in sales, forexample, aggregation can induce estimation error in parameter estimates.

There are three potential disadvantages to using a speci®c accruals ap-proach. First, it is crucial that the speci®c accrual reliably re¯ect the exercise ofdiscretion. If it is not clear which accrual management might use to manipulateearnings, then the power of a speci®c accrual test for earnings management isreduced. Furthermore, if the aim of the research is to identify the magnitude ofmanipulation on earnings, rather than to test whether it is associated withhypothesized factors, then one would require a model for each speci®c accruallikely to be manipulated by management. Second, speci®c accruals approachesgenerally require more institutional knowledge and data than aggregateaccruals approaches. This raises the cost of applying such approaches. Third,the number of ®rms for which a speci®c accrual is managed may be smallrelative to the number of ®rms with aggregate accruals. This may limit the


generalizability of the ®ndings of speci®c accruals studies, and may precludeidenti®cation of earnings management behavior if speci®c accruals are notsu�ciently sensitive.

Given the above trade-o�s, the researcher's goal is to identify contexts wherethe incentives to manage earnings are of interest and reliable and powerfulmeasures of earnings management can be identi®ed. Several studies taking aspeci®c accruals approach have attempted to do so by focusing on a speci®cindustry or set of industries. For example, McNichols and Wilson (1988)choose industries with the highest ratios of receivables to total assets and thelargest number of ®rms in the industry: publishers, business services andnondurable wholesalers. The ®rst criterion serves to identify ®rms for which theallowance for uncollectibles is likely a material account and the second servesto identify industries with a su�ciently large number of ®rms to permit reliablemodel estimation. A number of studies focus on discretion in loan loss pro-visions in the banking industry. These studies include Beaver et al. (1989),Moyer (1990), Scholes et al. (1990), Wahlen (1994), Beatty et al. (1995), Collinset al. (1995), and Beaver and Engel (1996). A distinctive feature of each of theabove studies is the use of generally accepted accounting principles to specifywhat the nondiscretionary component of an accrual should be.

Petroni (1992) identi®ed the loss reserves of property and casualty insurersas an ideal context for application of a speci®c accruals approach. Two factorscontribute to this: First, the claim loss reserve is a very material accrual for thisindustry.24 Second, unique disclosures required by the SEC allow the re-searcher to identify how reserve estimates initially reported correspond to expost outcomes. Petroni's (1992) study, and subsequent studies by Beaver andMcNichols (1998), Penalva (1998), Nelson (2000), Petroni et al. (2000) andBeaver et al. (1999), exploit these unique disclosures to test hypotheses aboutearnings management. Speci®cally, the disclosures allow the researcher toidentify ®rms that ex post under- or over-reserved, and to test hypothesesabout the factors motivating this behavior. Access to this measure greatlymitigates concern about measurement error in the discretionary accrual proxybeing correlated with the partitioning variable because of the transparency ofthe measure.

For example, Petroni (1992, pp. 498±499) documents that ®nancially weakinsurers tend to underestimate loss reserves relative to companies exhibitinggreater ®nancial strength. Gaver and Paterson (2000) build on these ®ndings toexamine the in¯uence of regulation of insurance company ®nancial reportingon ®nancial statement manipulation. Speci®cally, Gaver and Paterson hy-pothesize and ®nd that accreditation of states' ®nancial reporting require-

24 For example, the provision for losses is 72% of premiums earned by ®rms in Beaver and

McNichols' (1998) sample.


ments, which requires an annual audit and establishes minimum standards forloss reserves, is associated with signi®cantly less under-reserving by ®nanciallyweak insurers. Beaver and McNichols (1998, pp. 78±79) propose serial corre-lation in year-by-year loss reserve errors as an indication of loss reserve ma-nipulation, because unbiased reserve estimates by management should result inserially uncorrelated reserve errors. They ®nd strong evidence of manipulationin loss reserves, with the median ®rm exhibiting serial correlation in reserveerrors of 0.59. Petroni et al. (2000) model both the nondiscretionary and dis-cretionary components of loss reserve revisions. They draw on data on thenature and amount of insurance coverage written each year to identify non-discretionary revisions, and incentive variables to identify discretionary revi-sions.

In my view, the studies in the banking and property and casualty insuranceindustries provide strong evidence of earnings management. While the insti-tutional contexts may limit generalizability to other industries, and the issue ofclassifying discretionary and nondiscretionary behavior remains, groundingthe research in a more focused institutional setting can provide greater insightand structure regarding the nature of likely correlated omitted variables. Thestrength of these ®ndings suggests that the speci®c accruals approach may bepro®tably applied to study other industries for which discretion is likely to beconcentrated in a single or small number of accruals.

In contrast to the above studies, Beneish (1997) develops a model based onseveral speci®c accruals, focusing on ®rms from a number of industries. He uses asample of ®rms identi®ed by the SEC as generally accepted accounting principleviolators to calibrate alternative measures of earnings management. Beneish(1997, pp. 282±288) develops a model to identify earnings management which isbased on a number of ®nancial statement ratios, several of which relate to spe-ci®c accruals such as receivables, inventory and accounts payable. What is dis-tinctive about his approach is that a richer information set is utilized to identifyvariation in the levels of these speci®c accruals. Although he focuses on severalaccruals, the modeling of each accrual's behavior is done separately, rather thanfor the aggregate. In addition, the ability of the various accruals to identifyGAAP violations is assessed account by account, allowing for variation in theexercise of discretion across accruals. There seems to be great potential to applythis approach further, with more extensive modeling of the behavior of speci®caccruals. One could also focus on a speci®c accrual or set of accruals by focusingon industry settings where those accruals are most material.

7. Research design issues in distributional tests for earnings management

Recent studies by Burgstahler and Dichev (1997), hereafter BD, and De-george et al. (1999), hereafter DPZ, contribute an innovative approach to


testing for earnings management, by focusing on the density of the distributionof earnings after management. They both suggest that if ®rms have greaterincentives to achieve earnings above a benchmark, then the distribution ofearnings after management will have fewer observations than expected forearnings amounts just below the threshold, and more observations than ex-pected for earnings just above the threshold. Their empirical evidence bearsthis out, in that both studies ®nd signi®cantly more observations than expectedin the range above zero earnings, and in the range above the prior period'searnings.25 The visual representations of the earnings distributions in BD andDPZ suggest earnings are managed to meet earnings targets, particularly toachieve positive earnings.

A noteworthy feature of the BD and DPZ designs is that the power of theirapproach comes from the speci®city of their predictions regarding which groupof ®rms will manage earnings, rather than from a better measure of discretionover earnings. These studies measure discretion over earnings as the behaviorof earnings after management, which no doubt includes discretionary andnondiscretionary components. However, it seems implausible that the behaviorof the nondiscretionary component of earnings could explain such large dif-ferences in the narrow intervals around their hypothesized earnings targets.Stated di�erently, measurement error in their proxy for discretionary behaviorseems unlikely to be correlated with their partitioning variable.

BD and DPZ parallel DSS and Bradshaw et al. (1999), which focus on®rms subject to SEC enforcement actions, by identifying ®rms manipulatingearnings and then examining hypotheses about management's incentives andmethods of managing earnings. In contrast, the designs of many prior studiesrest on the joint hypotheses that ®rms manage earnings in response tospeci®ed factors and that the proxy for discretion over earnings is su�cientlysensitive to detect it. BD and DPZ provide a powerful tool to the earningsmanagement arsenal in that they identify contexts in which large number of®rms appear to manage earnings. The approach also provides an indicationof the frequency of manipulation, though this rests on an assumption aboutthe distribution of earnings absent manipulation. As BD indicate, the as-sumption that the expected frequency in a given region is the average of theobserved frequencies in the adjacent regions of the earnings distribution isnot valid for the distribution of earnings after manipulation. This occursbecause manipulation may have a�ected the adjacent regions around thehypothesized benchmark, and because manipulation may be re¯ected in otherregions of the distribution.

25 Burgstahler and Dichev (1997) examine annual earnings and Degeorge et al. (1999) examine

quarterly earnings.


The distribution approach per se is silent on the approach applied tomanipulate earnings. However, hypotheses about the form of manipulationcan be tested for sample ®rms identi®ed as earnings manipulators using thedistribution approach. Myers and Skinner (1999), in the spirit of theearnings distribution approach, test whether the frequency of consecutivequarterly earnings increases is greater than would be expected by chance,and ®nd that it is. Furthermore, they examine the correlation between cash¯ows and accruals for these ®rms and their use of special items, to provideevidence on the ways these ®rms manage earnings. Beatty et al. (1999) ex-amine di�erences in the incentives of private and public banks to manageearnings around zero and ®nd evidence supporting their hypothesis thatpublic banks have greater incentives to manage earnings. Beaver et al.(2000) examine the earnings management incentives of public and privateproperty and casualty insurance ®rms, and ®nd that they both avoid losses.Furthermore, Beaver et al. (2000) document that loss reserves are under-stated for ®rms just achieving positive pro®ts, and overstated for ®rms withgreater pro®tability.

The distribution approach is also silent on the incentives for managementto achieve speci®c benchmarks. How these incentives vary across ®rms, andwhat targets might be appropriate in di�erent contexts are importantquestions for future research. A better understanding of why managersmanipulate earnings will allow researchers to assess the power of alternativeearnings management tests, and ultimately strengthen our understanding ofthe implications of earnings management for investors and other contractingparties.

8. Where to go from here

Although the trade-o�s an earnings management researcher faces depend onthe question addressed, the objective of the research often focuses on under-standing whether earnings are being managed in a given context, how they aremanaged and the incentives that shape the environment for discretionary be-havior. As such, the researcher aims to identify contexts where the incentives tomanage earnings are of interest and reliable and powerful measures of earningsmanagement can be applied.

This review argues that earnings management measures based on the Jonesand modi®ed Jones model approach are not su�ciently powerful or reliable toassess earnings management behavior in many contexts likely to be of interestto accounting researchers, standard letters and analysts. I provide evidence ofpossible misspeci®cation of these models, which have been used extensively inthe literature to identify discretionary behavior. Speci®cally, I conjecture that®rms with greater expected earnings growth are likely to have greater accruals


than ®rms with less expected earnings growth. Furthermore, this e�ect is notcontrolled by the current year change in sales and therefore estimated discre-tionary accruals using the Jones and modi®ed Jones models are signi®cantlyassociated with analysts' projections of long-term earnings growth. This ®nd-ing holds after controlling for return on assets, which has been shown byDechow et al. (1995) and Kasznik (1999) to be signi®cantly associated withestimated discretionary accruals. My ®ndings raise questions about the ®ndingsof prior studies whose partitioning variables are correlated with earningsgrowth.

Further progress in this literature depends on our ability to understandthe set of actions managers take to achieve a given earnings objective. Thepractical relevance of such an understanding is important both to regulatorsand securities analysts. In this vein, I believe that future contributions to theearnings management literature will come from papers that model the be-havior of speci®c accruals with and without manipulation. I think this ap-proach has greater potential to develop measures of earnings managementthat are both responsive to earnings management and whose measurementerror is uncorrelated with partitioning variables of interest. This approachalso allows the development of tests that are well grounded in the institu-tional speci®cs of those accruals and can provide greater insight into howaccruals are managed. This may ultimately lead to alternative reportingstandards and analysis techniques that better permit investors to evaluate®rm performance. It seems more likely that such measures can be developedin industry-speci®c contexts or for speci®c accruals. However, given thestrong interest in aggregate accruals, there is certainly a high pay-o� tomore meaningful characterization of the behavior of aggregate accruals aswell. At either the aggregate or speci®c accrual level, our comparative ad-vantage as accounting academics is to understand how accounting numbersre¯ect underlying transactions and to interpret ®nancial statement relation-ships.

I believe that future contributions to the earnings management literaturewill also come from papers that exploit the distributional properties ofearnings and other components to identify behavior consistent with earningsmanagement. A signi®cant element of these designs is a strong predictionabout the behavior of earnings around the target which is not plausibly dueto nondiscretionary forces. This approach also has the potential of allowingthe researcher to study how earnings are managed for samples of ®rmsidenti®ed as likely to increase or decrease earnings based on their ex postearnings after management.

There is also a need for further research on the factors that motivatemanagers to manipulate earnings, and for this understanding to be betterre¯ected in our empirical methods. Although the literature generally as-sumes that discretion is orthogonal to nondiscretionary accruals, this is


counter to many models of management incentives to exercise discretion.Estimation techniques that estimate discretion relative to a ®rm's pastbehavior or in comparison to other ®rms may also be problematic if theearnings management behavior in the comparison group is not well un-derstood.

Finally, there is a gap between our institutional knowledge and our em-pirical procedures. Even with a general characterization of the behavior ofaccruals absent discretion, there is far more richness in the behavior of accrualsthan simple models allow. My paper overviewed several issues in the context ofaccounts receivable that can a�ect the identi®cation of discretionary behaviorfrom a regression of the change in receivables on change in sales. There is thepotential to reduce this gap through the use of richer research designs andmeasures of discretionary behavior which condition on more information thanthe prior literature has attempted.

Acknowledgements

I thank Bill Beaver, Daniel Beneish, Ron Kasznik, and Steve Loeb for theirhelpful comments on an earlier version of this paper, and Gilad Livne andPeter Wilson for their helpful discussions. I thank Yulin Long for her helpfuldiscussion and excellent research assistance. I appreciate the support of theStanford Business School Financial Research Initiative. I thank I/B/E/S forproviding long-term growth data, provided as part of a broad academic pro-gram to encourage expectations research.

Appendix A. Papers published on earnings management from 1993 to 1998

Aharony, J., Lin, C.-J., Loeb, M.P., 1993. Initial public o�erings, accountingchoices, and earnings management. Contemporary Accounting Research 10(1), 61±81.Ali, A., Kumar, K.R., 1993. Earnings management under pension account-ing standards: SFAS 87 versus APB 8. Journal of Accounting, Auditing andFinance 8 (4), 427±446.Arya, A., Glover, J., Sunder, S., 1998. Earnings management and the reve-lation principle. Review of Accounting Studies 3 (1±2), 7±34.Balsam, S., 1998. Discretionary accounting choices and CEO compensation.Comtemporary Accounting Research 15 (3), 229±252.Balsam, S., Haw, I., Lilien, S., 1995. Mandated accounting changesand managerial discretion. Journal of Accounting and Economics 20 (1),3±29.


Bartov, E., 1993. The timing of asset sales and earnings manipulation. TheAccounting Review 68 (4), 840±855.Beatty, A., Chamberlain, S., Magliolo, J., 1995. Managing ®nancial reportsof commercial banks: the in¯uence of taxes, regulatory capital and earnings.Journal of Accounting Research 33 (2), 195±212.Beaver, W.H., Engel, E.E., 1996. Discretionary behavior with respect to al-lowances for loan losses and the behavior of security prices. Journal of Ac-counting and Economics 22 (1±3), 177±206.Beaver, W.H., McNichols, M.F., 1998. The characteristics and valuation ofloss reserves of property casualty insurers. Review of Accounting Studies 3(1±2), 73±95.Becker, C.L., DeFond, M.L., Jiambalvo, J.J., Subramanyam, K.R., 1998.The e�ect of audit quality on earnings management. Contemporary Ac-counting Research 15 (1), 1±24.Beneish, M.D., 1997. Detecting GAAP violation: implications for assessingearnings management among ®rms with extreme ®nancial performance.Journal of Accounting and Public Policy 16 (3), 271±309.Black, E.L., Sellers, K.F., Manly, T.S., 1998. Earnings management usingasset sales: an international study of countries allowing noncurrent asset re-valuation. Journal of Business Finance and Accounting 25 (9±10), 1287±1317.Burgstahler, D., Dichev, I., 1997. Earnings management to avoid earningsdecreases and losses. Journal of Accounting and Economics 24 (1), 99±126.Bushee, B.J., 1998. The in¯uence of institutional investors on myopic R & Dinvestment behavior. The Accounting Review 73 (3), 305±333.Cahan, S.F., Chavis, B.M., Elmendorf, R.G., 1997. Earnings managementof chemical ®rms in response to political costs from environmental legisla-tion. Journal of Accounting, Auditing and Finance 12 (1), 37±65.Collins, J., Shackelford, D., Wahlen, J., 1995. Bank di�erences in the coor-dination of regulatory capital, earnings and taxes. Journal of AccountingResearch 33 (2), 263±291.DeAngelo, H., DeAngelo, L., Skinner, D.J., 1994. Accounting choice introubled companies. Journal of Accounting and Economics 17 (1/2), 113±143.Dechow, P.M., Sloan, R.G., Sweeney, A.P., 1995. Detecting earnings man-agement. The Accounting Review 70 (2), 193±225.Dechow, P.M., Sloan, R.G., Sweeney, A.P., 1996. Causes and consequencesof earnings manipulation: an analysis of ®rms subject to enforcement actionsby the SEC. Contemporary Accounting Research 13 (1), 1±36.DeFond, M.L., Jiambalvo, J., 1994. Debt covenant violation and manipula-tion of accruals. Journal of Accounting and Economics 17 (1±2), 145±176.DeFond, M.L., Park, C.W., 1997. Smoothing income in anticipation of fu-ture earnings. Journal of Accounting and Economics 23 (2), 115±140.


DeFond, M.L., Subramanyam, K.R., 1998. Auditor changes and discretion-ary accruals. Journal of Accounting and Economics 25 (1), 35±68.Dempsey, S.J., Hunt III, H.G., Schroeder, N.W., 1993. Earnings mana-gement and corporate ownership structure: an examination of extraordi-nary item reporting. Journal of Business Finance & Accounting 20 (4),479±500.Demski, J.S., 1998. Performance measure manipulation. Contemporary Ac-counting Research 15 (3), 261±285.Eldenburg, L., Soderstrom, N., 1996. Accounting system management byhospital operating in a changing regulatory environment. The AccountingReview 71 (1), 23±42.Elliott, J.A., Hanna, J.D., 1996. Repeated accounting write-o�s and the in-formation content of earnings. Journal of Accounting Research 34 (Supple-ment), 135±156.Erickson, M., Wang, S.-W., 1999. Earnings management by acquiring ®rmsin stock for stock mergers. Journal of Accounting and Economics 27 (2),149±176.Evans III, J.H., Sridhar, S.S., 1996. Multiple control systems, accrual ac-counting, and earnings management. Journal of Accounting Research 34(1), 45±65.Francis, J., Hanna, J.D., Vincent, L., 1996. Causes and e�ects of discretion-ary asset write-o�s. Journal of Accounting Research 34 (Supplement), 117±134.Gaver, J.J., Gaver, K.M., Austin, J.R., 1995. Additional evidence on bonusplans and income management. Journal of Accounting and Economics 19(1), 3±28.Guay, W.R., Kothari, S.F., Watts, R.L., 1996. A market-based evaluationof discretionary accrual models. Journal of Accounting Research 34 (Sup-plement), 83±105.Guenther, D.A., 1993. Earnings management in response to corporate taxrate changes: evidence from the 1986 tax reform act. The Accounting Re-view 69 (1), 230±243.Guidry, F., Leone, A.J., Rock, S., 1999. Earnings-based bonus plan andearnings management by business-unit managers. Journal of Accountingand Economics 26 (1/3), 113±142.Hall, S.C., 1993. Political scrutiny and earnings management in the oilre®ning industry. Journal of Accounting and Public Policy 12 (4), 325±351.Hall, S.C., Stammerjohan, W.W., 1997. Damage awards and earnings man-agement in the oil industry. The Accounting Review 72 (1), 47±65.Han, J.C.Y., Wang, S.-W., 1998. Political costs and earnings management ofoil companies during the 1990 Persian gulf crisis. The Accounting Review 73(1), 103±117.


Holthausen, R.W., Larcker, D.F., Sloan, R.G., 1995. Annual bonusschemes and the manipulation of earnings. Journal of Accounting and Eco-nomics 19 (1), 29±74.Kang, S.-H., Sivaramakrishnan, K., 1995. Issues in testing earnings manage-ment and an instrumental variable approach. Journal of Accounting Re-search 33 (2), 355±367.Kasanen, E., Kinnunen, J., Niskanen, J., 1996. Dividend-based earningsmanagement: empirical evidence from Finland. Journal of Accounting andEconomics 22 (1±3), 283±312.Kasznik, R., 1999. On the association between voluntary disclosure andearnings management. Journal of Accounting Research 37 (1), 57±81.Key, K.G., 1997. Political cost incentives for earnings management in thecable television industry. Journal of Accounting and Economics 23 (3), 309±337.Kinnunen, J., Kasanen, E., Niskanen, J., 1995. Earnings management andthe economy sector hypothesis: Empirical evidence on a converse relation-ship in the Finnish case. Journal of Business Finance and Accounting 22(4), 497±520.LaSalle, R., Jones, S.K., Jain, R., 1993. The association between executivesuccession and discretionary accounting changes: earnings management ordi�erent perspectives? Journal of Business Finance & Accounting 20 (5),653±671.Maydew, E.L., 1997. Tax-induced earnings management by ®rms with netoperating losses. Journal of Accounting Research 35 (1), 83±96.Mensah, Y.M., Considine, J.M., Oakes, L., 1994. Statutory insolvency reg-ulations and earnings management in the prepaid health-care industry. TheAccounting Review 69 (1), 70±95.Miller, G.S., Skinner, D.J., 1998. Determinants of the valuation allowancefor deferred tax assets under SFAS No. 109. The Accounting Review 73(2), 213±233.Perry, S.E., Williams, T.H., 1994. Earnings management precedingmanagement buyout o�ers. Journal of Accounting and Economics 18 (2),157±179.Pourciau, S., 1993. Earnings management and nonroutine executive chang-es. Journal of Accounting and Economics 16 (1/3), 317±336.Rees, L., Gill, S., Gore, R., 1996. An investigation of asset write-downs andconcurrent abnormal accruals. Journal of Accounting Research 34 (Supple-ment), 157±170.Soo, B.S., 1999. Accrual response to mandated accounting principles: thecase of SFAS Nos. 2 and 34. Journal of Accounting and Public Policy 18(1), 59±84.Subramanyam, K.R., 1996. The pricing of dicretionary accruals. Journal ofAccounting and Economics 22 (1/3), 249±281.


Sweeney, P.A., 1994. Debt-covenant violatins and managers' accounting re-sponses. Journal of Accounting and Economics 17 (3), 281±308.Teoh, S.H., Wong, T.J., Rao, G.R., 1998. Are accruals during initial publico�erings opportunistic? Review of Accounting Studies 3 (1/2), 175±208.Wahlen, J.M., 1994. The nature of information in commercial bank loanloss disclosures. The Accounting Review 69 (3), 455±478.Wu, Y.W., 1997. Management buyouts and earnings management. Journalof Accounting Auditing and Finance 12 (4), 373±390.

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