systematic measurement error in the estimation of discretionary accruals: an evaluation of...

Systematic Measurement Error inthe Estimation of Discretionary

Accruals: An Evaluation ofAlternative Modelling Procedures

Steven Young*

1. INTRODUCTION

A central issue in financial statement analysis is the extent towhich managers manipulate reported earnings. A series ofearnings management instruments are potentially available tomanagers. These range from real operating decisions such asasset sales (Black et al., 1998; and Bartov, 1993) and changes inR&D expenditure (Bushee, 1998; and Bange and De Bondt,1998), to pure financial reporting decisions such as accountingmethod changes (Watts and Zimmerman, 1986) and accrualchoices (McNichols and Wilson, 1988). Following Healy (1985),accrual-based measures are now widely employed in tests of theearnings management hypothesis. From a managerialperspective, accruals are likely to represent a favouredinstrument for manipulating reported numbers because of theirrelative low cost and opaque nature.1 Accrual-based measures are

Journal of Business Finance & Accounting, 26(7) & (8), Sept./Oct. 1999, 0306-686X

ß Blackwell Publishers Ltd. 1999, 108 Cowley Road, Oxford OX4 1JF, UKand 350 Main Street, Malden, MA 02148, USA. 833

* The author is from the Department of Accounting and Finance, Lancaster University.Financial assistance was provided by the International Centre for Research in Accountingand Lancaster University Management School. The author gratefully acknowledgeshelpful comments and suggestions from Mike Adams, Rob Crouchley, Wayne Guay, JohnO'Hanlon, John Pritchard and workshop participants at Lancaster University, Universityof Wales Bangor, the 1995 European Accounting Association Doctoral Colloquium andthe 1996 British Accounting Association Annual Conference. He is especially indebted toKen Peasnell, Peter Pope, and two anonymous referees for their helpful comments onearlier drafts. (Paper received July 1998, revised and accepted January 1999)

Address for correspondence: Steven Young, Department of Accounting & Finance, TheManagement School, Lancaster University, Lancaster LA1 4YX, UK.e-mail [email protected]

also theoretically more appealing because accruals aggregate intoa single number the net effect of numerous accounting policies,thereby capturing the portfolio nature of income determination(Watts and Zimmerman, 1990). However, despite the preferencefor an accrual-based measure of earnings management, studies ofdiscretionary accrual activity have generally failed to documentconsistent evidence of earnings management.2 As McNichols andWilson (1988) demonstrate, measurement error in theaccounting choice proxy is one plausible explanation for theseinconsistent findings. Building on recent US research, this paperevaluates the extent of predictable measurement error inducedby five alternative approaches to the estimation of discretionaryaccruals.

Recognising that not all accrual decisions represent cases ofearnings management activity, researchers have attempted todecompose total accruals into discretionary and non-discretionary elements. However, the unobservable nature ofboth components makes the direct measurement of discretionaryaccruals impossible. Consequently, researchers are forced toestimate the discretionary element by imposing on total accrualsan expectations model of the non-discretionary component. Aseries of expectations models have been proposed in theliterature. The detection of accrual-based earnings managementrelies crucially on the efficiency with which these models isolatethe discretionary element of total accruals. All else equal, thegreater the level of random noise induced by the estimationprocedure, the lower the power of the empirical test. Further, themore systematic error generated by the estimation procedure,the greater the likelihood for bias in the empirical test.3

Recently, researchers have begun to examine the properties ofalternative discretionary accrual modelling procedures. Forexample, Dechow et al. (1995) evaluate five models and concludethat each generates a low power (i.e., noisy) estimate ofdiscretionary accruals. In addition, they present evidence thatall models induce systematic measurement error when applied tofirms with extreme earnings and cash flow performance. Alimitation of the Dechow et al. (1995) methodology, however, isthat it fails to control for differences in the propensity forearnings management. As Guay et al. (1996) discuss, because thepropensity for earnings management is likely to be much greater

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for firms experiencing extreme financial performance, one isunable to ambiguously distinguish measurement error fromactual earnings management activity in the Dechow et al. (1995)study. In an effort to overcome this weakness, Guay et al. (1996)use a market-based procedure to evaluate alternativediscretionary accrual models. Based on predicted relationsbetween stock returns and earnings components they concludethat none of the five models examined generates a reliablemeasure of the discretionary component of total accruals.However, while Guay et al. (1996) present evidence on themagnitude of measurement error in estimated discretionaryaccruals, the nature of their analysis is such that it does notexplicitly identify the source(s) of error. Yet informationregarding the source(s) of predictable measurement error isnecessary for the development and improvement of existingestimation procedures.

This paper evaluates discretionary accruals estimated by fivealternative modelling procedures using a method that (i)provides information on both the level and source of systematicmeasurement error and (ii) controls for potential variation in thepropensity for earnings management activity. Empirical tests focuson the strength of the association between estimated discretionaryaccruals and proxies for the non-discretionary components oftotal accruals. All else equal, a strong (weak) association betweenestimated discretionary accruals and a particular non-discretionary accrual proxy is interpreted as evidence that themodelling procedure generates (does not generate) systematicmeasurement error as a function of the non-discretionary accrualproxy. Empirical findings indicate that all five estimationprocedures yield a measure of discretionary accruals that containsa significant level of predictable measurement error. Of themodels examined, the Jones- and DeAngelo-based proceduresappear to offer the greatest control for non-discretionary accruals.However, even for these models, the extent of the error remainssignificant at the p < 0.001 level. Results indicate that operatingcash flow, sales growth and fixed asset structure representimportant sources of measurement error in all five modelsevaluated, even after controlling for cross-sectional variation inthe propensity for earnings management activity. In addition tocorroborating prior US findings for a different financial reporting

MEASUREMENT ERROR AND DISCRETIONARY ACCRUALS 835


regime using an alternative research design, these resultshighlight a series of empirical associations between estimateddiscretionary accruals and firm-specific characteristics that mayhelp to detect and eliminate potential sources of bias in futureaccrual-based earnings management studies.

The remainder of the paper is organised as follows. Section 2describes five alternative approaches to the estimation ofdiscretionary accruals. Section 3 presents the framework forevaluating the effectiveness of these alternative estimationprocedures. Section 4 identifies four sources of non-discretionaryaccruals. Section 5 describes the data and the empirical model.Results are reported in Section 6 and conclusions are presentedin the final section.

2. MODELLING DISCRETIONARY ACCRUALS

This section describes five procedures used in the extantliterature to estimate the discretionary component of totalaccruals. Each model begins with total accruals defined as thechange in non-cash current assets less the change in currentliabilities (excluding the current portion of long-term debt),minus depreciation and amortisation.4

(i) The Healy Model

The Healy (1985) model defines estimated discretionary accruals(EDA) for firm i in period t as total accruals scaled by lagged totalassets:

EDAit � TAit=Aitÿ1; �1�where TA equals total accruals and A equals total assets. Thismodel represents the simplest and most naõÈve method ofestimating discretionary accruals, effectively assuming thatexpected non-discretionary accruals for the period are zero andhence that any non-zero value for total accruals is attributable tomanagerial discretion. However, as Kaplan (1985) discusses, thisassumption is likely to prove highly restrictive given that the levelof working capital accruals will fluctuate in response to economicconditions.

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(ii) The DeAngelo Model

The DeAngelo (1986) model assumes that non-discretionaryaccruals follow a random walk. Thus, for a steady-state firm, non-discretionary accruals in period t are assumed to equal non-discretionary accruals in period t ÿ 1, with the result that anydifference in total accruals between period t ÿ 1 and t isattributable to accounting discretion. The DeAngelo (1986)procedure, therefore, measures estimated discretionary accrualsas the first difference in total accruals scaled by lagged total assets:

EDAit � �TAit ÿ TAitÿ1�=Aitÿ1: �2�

(iii) The Modified-DeAngelo Model

The steady-state assumption underlying the DeAngelo model isinappropriate for the majority of firms.5 This, coupled with thefact that non-discretionary accruals are expected to vary with thelevel of business activity, led Friedlan (1994) to propose amodified-first difference model as a means of controlling fornon-stationarity in the non-discretionary accrual component.Friedlan's (1994) approach assumes that non-discretionaryaccruals are constantly proportional to operating activity, suchthat increases (decreases) in operating activity are associated withcorresponding increases (decreases) in the level of non-discretionary accruals. Under these conditions, changes in totalaccruals scaled by the change in operating activity provide ameans of controlling for the changes in non-discretionaryaccruals associated with variations in operating performance.Proxying operating activity using sales revenue, denoted S,Friedlan (1994) proposes the following model for discretionaryaccruals:

EDAit � TAit=Sit ÿ TAitÿ1=Sitÿ1: �3�

(iv) The Jones Model

Jones (1991) employs a regression-based expectation model tocontrol for variations in non-discretionary accruals associatedwith the depreciation charge and changes in economic activity.Total accruals are first regressed on proxies for the non-



discretionary component of total accruals using the longestavailable time series of data immediately prior to event period t:

TAip=Aipÿ1 � �i�1=Aipÿ1� � �1i��REVip=Aipÿ1�� 2i�PPEip=Aipÿ1� � "ip ; �4�

where: TAip = total accruals for firm i in period p;�REVip = change in revenue from period p ÿ 1 to p for

firm i;PPEip = gross plant property and equipment for firm i

in period p;Aipÿ1 = beginning of period total assets for firm i;p = 1, . . ., P, estimation years immediately prior to

event year t.

The �REV and PPE terms are designed to control for the non-discretionary component of total accruals associated withchanges in operating activity and level of depreciation,respectively. The parameter estimates ai, b1i, and b2i of �i ; �1i ,and �2i from regression (4) are then combined with data fromevent year t to generate estimated discretionary accruals:

EDAit � TAit=Aitÿ1 ÿ �ai�1=Aitÿ1� � b1i��REVit=Aitÿ1�� b2i�PPEit=Aitÿ1��: �5�

However, the time series data requirements of regression (4)mean that survivorship bias issues naturally arise.6 In an effort tominimise the effects of survivorship bias, DeFond and Jiambalvo(1994) employ a cross-sectional version of the Jones model. Thecross-sectional approach involves first constructing industry-eventperiod matched portfolios for each sample firm. Regression (4) isthen estimated using these matched portfolios to generateindustry/year-specific estimates of �, �1, and �2. These estimatesare then combined with firm-specific data in equation (5) to yieldestimated discretionary accruals for each firm.

(v) The Modified-Jones Model

Since all revenue changes in the Jones and cross-sectional Jonesmodels are assumed to be non-discretionary, the resultingmeasure of discretionary accruals does not reflect the impact ofsales-based manipulation. In an attempt to capture revenue

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manipulation, Dechow et al. (1995) modify the Jones procedureby subtracting the change in receivables (�REC) from �REV foreach sample firm. Effectively, the approach assumes that anychange in the level of credit sales during the period reflectsearnings management activity. Using coefficient estimates fromregression (4) or its cross-sectional equivalent, discretionaryaccruals for the modified-Jones model are computed as:

EDAit �TAit=Aitÿ1ÿ�ai�1=Aitÿ1��b1i��REVit=Aitÿ1ÿ�RECit=Aitÿ1�� b2i�PPEit=Aitÿ1��; �6�

where all variables are as previously defined.

3. TESTING FOR MEASUREMENT ERROR

Following Healy (1985), total accruals (TA) for firm i in time tmay be decomposed into discretionary (DA) and non-discretionary (NDA) components:

TAit � DAit �NDAit ; �7�where DA = ¦(Y1, Y2, . . .,YJ) and NDA = ¦(X1, X2, . .., XJ). The Yj'srepresent the earnings management stimuli while the Xj'srepresent the determinants of the non-discretionary componentof total accruals. In the absence of a direct measure of DA,researchers use the following estimation technique:

EDAit � TAit ÿ E�NDAit�; �8�where: EDA � estimated discretionary accruals;

E(�) � the expectations operator.

Under this approach, the validity of EDA as a measure of the`true' level of discretionary accruals is determined by the modelgenerating E(NDA). More specifically, the source and magnitudeof the measurement error in EDA is governed by the effectivenesswith which the expectations model of NDA controls for thefactors that determine the non-discretionary component of totalaccruals (i.e., the Xj's). Assuming cov�Xj ; Yj� � 0, EDA and Xj willbe orthogonal if the non-discretionary expectations model fullycontrols for the impact of Xj on TA.7 However, if the modelsystematically fails to extract from total accruals the non-discretionary component associated with Xj, then estimated



discretionary accruals will contain measurement error that isdirectly related to the level of Xj. In other words, a modellingprocedure that incorrectly attributes non-discretionary accrualsassociated with Xj to accounting discretion will yield a non-zerocorrelation between EDA and Xj.

This suggests a simple framework for evaluating both theextent and source of systematic measurement error in EDA.Consider the following regression of EDA on the determinants ofnon-discretionary accruals:

EDAi � ��XJ

j�1

�jXji � "i ; �9�

where all variables are as previously defined. Assuming cov(NDA,Yj) = 0, significant �j 's indicate the source of the measurementerror in EDA while the explanatory power of the model (i.e., theR-square) captures the overall magnitude of the error (i.e., thetotal variation in EDA explained by the determinants of NDA).For a given model of discretionary accruals, a large estimatedcoefficient on Xj in regression (9) indicates that the model isineffective at controlling for the non-discretionary component oftotal accruals related to Xj, all else equal. Further, the higher theexplanatory power of regression (9), the greater the overallextent of measurement error in EDA.

4. EMPIRICAL PREDICTIONS

This section presents four possible determinants of the non-discretionary component of total accruals. Empirical predictionsare initially developed assuming either (i) an absence of earningsmanagement activity (i.e., actual discretionary accruals equalzero), or (ii) that the stimuli for earnings management areorthogonal to the determinants of non-discretionary accruals.These conditions are relaxed in subsequent sections.

(i) Cash Flow Performance

Accruals and cash flows sum to produce accounting earnings.Through the matching principle, accruals adjust realised cash

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flows by allocating receipts and outlays to their `appropriate'periods. The inherent smoothing property of accountingaccruals is such that extreme positive (negative) cash flows in aparticular period tend to result in negative (positive) non-discretionary accruals (Samuels et al., 1989, p. 14).8 Failure toadequately control for the association between cash flows andnon-discretionary accruals when estimating discretionary accrualswill cause part of the positive non-discretionary accrualsassociated with extreme negative cash flows to be incorrectlyattributed to income-increasing discretionary accrual activity, andvice versa. In other words, EDA will contain measurement errorthat is negatively correlated with cash flow performance. UsingUS data, Dechow et al. (1995) present evidence of cash flow-related measurement error in each of the five discretionaryaccrual models they examine.

(ii) Growth Rate

Growth (decline) in a firm's operating activities impinges on allaspects of its business, including accounting accruals. All elseequal, growth firms tend to experience large increases in boththeir current asset and current liability accounts as a result ofnon-discretionary working capital requirements, while theopposite is true for firms in decline (Jones, 1991). If the impactof firm growth on non-discretionary current asset and currentliability accruals is symmetric, one might not expect to observe asystematic association between the rate of firm growth and thelevel of non-discretionary accruals. However, Sloan (1996, Table1) provides evidence that a large proportion of the variation inworking capital accruals is attributable to the current assetcomponent.9 This suggests that non-discretionary accruals arelikely to be positively associated with firm growth. All else equal,therefore, failure to control for the relation between firm growthand non-discretionary accruals will cause part of the positive non-discretionary accruals associated with high growth to beincorrectly attributed to income-increasing discretionary accrualactivity, and vice versa. In other words, discretionary accrualmodels that fail to allow for differences in growth rates willinduce predictable measurement error in EDA. The modified-DeAngelo, Jones and modified-Jones models all recognise the



possibility of a link between non-discretionary accruals and therate of firm growth. However, the effectiveness with which thesemodels control for growth remains an empirical question.

(iii) Fixed Asset Structure

Perry and Williams (1994) and DeAngelo (1988) conjecture thatfor most firms, the primary systematic difference betweenreported earnings and operating cash flow is the periodicdepreciation charge. The relative magnitude of the depreciationcomponent means that, all else equal, a considerable portion ofthe variation in firms' total accruals is likely to result fromdifferences in the level of the depreciation charge. Morespecifically, the level of total accruals will tend to be negativelycorrelated with the magnitude of the depreciation expense sincea high (low) depreciation charge will lead to a more (less)negative value for total accruals.

While the negative association between total accruals and themagnitude of the depreciation charge poses few problems inrelation to the estimation of discretionary accruals ifdepreciation is viewed as primarily discretionary in nature, it isof significant concern if depreciation comprises a large non-discretionary element. This is because failure to adequatelycontrol for differences in the level of firms' depreciationexpense will result in part of the negative non-discretionaryaccruals associated with a large depreciation expense beingincorrectly attributed to income-decreasing discretionary accrualactivity, and vice versa. In other words, even in the absence ofany earnings management activity, firms with a highdepreciation charge may appear as though they are makingincome-decreasing accounting choices.

While acknowledging that in practice, determination of theannual depreciation expense may be subject to considerablemanagerial discretion, this study treats the depreciation expenseas primarily non-discretionary in character.10 This is based on thefollowing observations. First, depreciation is, as a rule, required onassets with finite useful economic lives. Secondly, sincedepreciation-based manipulation is relatively transparent, theassociated earnings effects are comparatively simple to adjust forex post, thereby limiting the potential of depreciation-related

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earnings management strategies.11 Third, it is doubtful whetherthe depreciation accrual can provide management with a sourceof multiperiod manipulation, since consistent changes ofdepreciation method and/or changes in the assumptionsrelating to asset life, residual value, etc., would almost certainlyattract the attention of the incumbent auditor. Finally, viewingdepreciation as primarily non-discretionary in character isconsistent with the discretionary accrual modelling approachadopted by Jones (1991).

Two factors that independently determine the magnitude ofthe depreciation expense are (i) fixed asset intensity and (ii) theuseful economic life of the fixed asset stock. For example, themore a firm's asset base consists of tangible fixed assets, thehigher the firm's depreciation charge, all else equal. Further,holding the level of fixed assets constant, the shorter the time-period over which these assets are depreciated, the higher thedepreciation charge. Accordingly, the magnitude of thedepreciation accrual is expected to be positively associated withfixed asset intensity and negatively association with average fixedasset life. Given that the sign of the depreciation accrual must benegative, discretionary accrual models that fail to control fordifferences in firms' depreciation charge will, therefore, generateEDA that are negatively correlated with fixed asset intensity andpositively correlated with average fixed asset life.

5. MODEL SPECIFICATION AND SAMPLE SELECTION

(i) Model Specification

The four sources of non-discretionary accruals identified in theprevious section suggest the following empirical version ofequation (9):

EDAit � 0 � 1CFOit � 2Growit � 3Intit � 4Lifeit

� 5Levit � 6Ownit � 7Size� 8Smooth� "it �10�where: EDA � estimated discretionary accruals computed

using the Healy, DeAngelo, modifiedDeAngelo, cross-sectional Jones, or modifiedcross-sectional Jones models;



CFO � operating cash flow, measured as operatingincome minus total accruals;

Grow � firm growth, measured as the change in salesscaled by lagged sales;

Int � fixed asset intensity, measured as net fixedassets divided by market capitalisation;

Life � average fixed asset life, measured as the grossvalue of fixed assets divided by thedepreciation expense;

Lev � long-term debt to total shareholders equity;Own � directors' equity ownership as a proportion of

total equity outstanding (collected fromannual reports);

Size � natural log of beginning of period sales;Smooth � 1 if non-discretionary earnings for firm i in

period t exceed median reported earnings forthe industry in period t ÿ 1 and 0 otherwise.

Regression (10) is estimated separately for each of the fivemeasures of discretionary accruals.12 Evidence on the source ofmeasurement error in EDA is provided by the estimatedcoefficients on CFO, Grow, Int and Life, while the degree ofvariation in EDA explained by these four variables providesevidence of the total magnitude of the systematic measurementerror induced by the particular model generating EDA.

In addition to the four proxies for the non-discretionarycomponent of total accruals, regression (10) also includes aseries of variables to control for the variation in EDA due toearnings management activity.13 Prior work suggests several firm-specific factors that may be correlated with managerialaccounting choice. For example, the debt-equity hypothesispredicts a positive association between leverage and income-increasing accounting choices as managers attempt to defer and/or avoid the costs of debt covenant violation (Watts andZimmerman, 1986). Christie (1990) and Holthausen andLeftwich (1983) review the accounting choice literature andreport consistent evidence in support of the debt-equityhypothesis. Accordingly, a measure of leverage (Lev) is includedin regression (10).14 Agency theory also predicts an associationbetween the level of managerial equity ownership and the

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propensity for earnings management as a result of the separationof ownership and control and the resulting demand foraccounting-based contracts to align manager-owner preferences.Consistent with this prediction, Warfield et al. (1995), Dempseyet al. (1993) and Niehaus (1989) present evidence of anassociation between managerial ownership and accountingchoice. Consequently, the level of directors' equity ownership(Own) is also included in regression (10).

Extant research presents consistent evidence of a negativeassociation between firm size and the propensity for income-increasing accounting choices (Watts and Zimmerman, 1990;Christie, 1990; and Holthausen and Leftwich, 1983). Regression(10), therefore, includes a size measure (Size) as a means ofcontrolling for the well-established correlation between firm sizeand accounting choice.15 Finally, the income-smoothinghypothesis predicts that managers use their accountingdiscretion to reduce the volatility of reported earnings (Ronenand Sadan, 1981). Chaney and Lewis (1998), Young (1998),DeFond and Park (1997), Gaver et al. (1995) and Beattie et al.(1994) present evidence consistent with the prediction thatmanagers smooth reported income around a target earningsfigure. To control for this effect, a proxy for income-smoothing(Smooth) is also included in regression (10). Following Chaneyand Lewis (1998), Young (1998) and DeFond and Park (1997),Smooth is defined as pre-managed earnings minus targetearnings, where target earnings are measured as median prior-period reported earnings for the firm's industry and pre-managed earnings are equal to reported earnings minusdiscretionary accruals.

(ii) Sample

The sample is drawn from all UK-incorporated listed non-financial firms in the Datastream Active and Research files for theperiod 30 June, 1993 to 31 May, 1996. The sample window isrestricted to the post-FRS3 period to ensure consistent definitionsof earnings, accruals and cash flows. Financials are excludedfrom the sample because of fundamental differences in thenature of their accruals and cash flows. Since the cross-sectionalversions of the Jones and modified-Jones models require a



sufficient number of observations in the industry/year portfoliosto enable regression (4) to be estimated, all firms in Datastreamlevel-six industry groups with fewer than six firms were alsoexcluded. Finally, firms with insufficient data for the calculationof discretionary accruals by the Healy, DeAngelo, modified-DeAngelo, Jones and modified-Jones models, together with eachof the explanatory variables in regression (10) were excluded.The final sample comprises 1987 firm-year observations spanning46 level-six industry groups. The largest industry group in anyone year is Engineering with 54 firms. Annual sample sizes are645, 653 and 689 for 1993, 1994 and 1995, respectively. Thesample comprises 758 individual firms, with a mean number ofobservations per firm of 2.62.

6. RESULTS

Descriptive statistics for the five measures of discretionaryaccruals are presented in Panel A of Table 1.16 Similar to thefindings reported by Guay et al. (1996), the mean (median) valueof EDA is close to zero for the DeAngelo- and Jones-basedprocedures, while that generated by the Healy model is large andnegative, as reported by Healy (1985) and DeAngelo et al.(1994).

All five models yield relatively large maximum and minimumvalues and the kurtosis values for the DeAngelo and modified-DeAngelo estimates are high. All five models display significantpositive correlations with each other. The DeAngelo andmodified-DeAngelo estimates display the strongest association(Spearman correlation � 0.953) while the Jones and modified-DeAngelo estimates display the weakest association (Spearmancorrelation � 0.528). The Jones and modified-Jones estimates arealso highly correlated, with a Spearman coefficient of 0.910.Interestingly, despite significantly greater computationalcomplexity, both the Jones and modified-Jones estimates displaya strong association with the measure generated by the naõÈveHealy model: the Spearman correlations are 0.749 and 0.763 forthe Jones and modified-Jones models, respectively. In sum, thesefindings suggest that all five models result in broadly similarmeasures of accounting discretion.

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Descriptive statistics for the non-discretionary accrual proxiesand accounting choice variables are presented in Panels B and Cof Table 1, respectively. Operating cash flows are typically around14% of total assets and the median useful economic life of firms'fixed asset stock is approximately 14 years. Both the mean andmedian values for the turnover ratio (Grow) are positive andindicate that firms experienced average sales growth of around

Table 1

Descriptive Statistics for Estimated Discretionary Accruals,Measurement Error Proxies, and Accounting Choice Proxies. TheSample Comprises 1987 Firm-year Observations Pooled Across the

Fiscal Years 1993±1995. Annual Sample Sizes are 645, 653 and 689 for1993, 1994 and 1995, Respectively

Variable Mean Std. Max Q3 Med. Q1 Min Skew. Kurt.

Panel A: Discretionary Total Accruals

Healy ÿ0.036 0.084 0.363 0.004 ÿ0.036 ÿ0.079 ÿ0.420 ÿ0.023 2.500DeAngelo 0.003 0.119 1.728 0.051 0.002 ÿ0.052 ÿ0.861 1.732 27.435Mod-

DeAngelo 0.004 0.144 1.844 0.042 0.003 ÿ0.036 ÿ2.124 ÿ0.790 55.776Jones ÿ0.001 0.067 0.315 0.036 0.000 ÿ0.036 ÿ0.347 ÿ0.212 2.375Mod-Jones ÿ0.001 0.052 0.400 0.037 0.000 ÿ0.034 ÿ0.352 ÿ0.240 2.294

Panel B: Measurement Error Proxies

CFO 0.145 0.135 1.211 0.208 0.138 0.076 ÿ1.125 0.080 11.694Grow 0.149 0.401 11.532 0.201 0.102 0.021 ÿ0.811 15.417 375.10Int 0.654 3.101 93.540 0.612 0.342 0.168 0.004 25.364 707.90Life 17.511 16.717 338.64 18.366 14.447 11.156 1.177 9.043 125.01

Panel C: Control Variables

Own 0.083 0.137 0.737 0.104 0.017 0.002 0.000 2.272 5.018Lev 0.548 0.231 3.516 0.652 0.533 0.415 0.074 3.533 32.128Size 11.797 1.569 16.245 12.715 11.560 10.726 6.404 0.406 0.034

Notes:Estimated discretionary accruals are calculated using the Healy, DeAngelo, modified-DeAngelo, cross-sectional Jones and modified cross-sectional Jones models. Each modelbegins with total accruals, defined as the change in non-cash current assets less the changein current liabilities (exclusive of the current portion of long term debt), minusdepreciation and amortisation.

CFO = operating cash flow, measured as operating earnings minus total accruals;Grow = turnover ratio, measured as change in sales scaled by beginning of period sales;Int = ratio of net fixed assets to total market capitalisation;Life = ratio of gross fixed assets to depreciation expense;

Own = directors' equity ownership / total shares outstanding;Lev = ratio of long-term debt to total shareholders equity;Size = natural log of beginning of period sales (£m).



10% during the sample window. All non-discretionary accrualproxies display high skewness and/or kurtosis values. Mediandirectors' equity ownership for the sample is approximately 2%and the average level of long-term debt relative to totalshareholders' equity is around 54%.

(i) Regression Results

The distributional properties of both the discretionary accrualestimates and the non-discretionary accrual proxies suggest thatestimating equation (10) using a standard OLS regressionapplied to the full sample may yield unstable, sample-dependentresults. Preliminary tests confirmed this was indeed the case.Analysis of OLS residuals indicated a substantial degree of non-normality for all models estimated. In addition, coefficientestimates (particularly those for Grow and Life) were unstableand extremely sensitive to the removal of outlying observations.To address these problems, three alternative estimationprocedures were employed. First, regression (10) was estimatedusing a rank-transform regression since, as Iman and Conover(1979) discuss, estimators from a rank regression are robust tooutliers. The procedure involves ranking all variables (bothdependent and explanatory) within their sample year, beforeestimating an OLS regression on the rank-transformed data.Secondly, a normal transformation procedure (Cooke, 1998) wasapplied to the data. The transformation process effectivelyreplaces the ranks of the data by scores on the normaldistribution. A standard OLS regression is then estimated usingthe transformed data. Finally, all variables were trimmed at their1% and 99% levels. OLS regressions were then estimated usingthe trimmed sample. In addition to controlling for the effect ofextreme values of the dependent and explanatory variables, useof a range of alternative procedures helps to ensure that theresults are not method-driven. All three procedures (the rank-transformed, normal scores and trimmed-OLS models) yieldedquantitatively identical findings. Results using the normaltransformation procedure are reported in the paper.17

Results for regression (10) estimated using the pooled sampleare reported in Table 2.18 Model 1 (Panel A) is restricted toinclude only the four non-discretionary accrual proxies while

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model 2 (Panel B) is restricted to the accounting choice proxies.The full model (model 3), comprising both the non-discretionaryaccrual proxies and the accounting choice variables, is presentedin Panel C.

Findings for model 1 indicate that the non-discretionaryaccrual proxies explain a considerable proportion of thevariation in discretionary accruals estimated by each accrualmodel. All five regressions are significant at the p < 0.001 level. Tothe extent that the size of the adjusted R-squared reflects themagnitude of the measurement error, results for model 1 suggestthat EDA generated by the Healy model contain the greatest levelof predictable measurement error. For example, over 60% of thevariation in the Healy measure of EDA is explained by the fournon-discretionary accrual proxies. Nevertheless, the relative highexplanatory power of model 1 for the DeAngelo- and Jones-basedprocedures suggests that the effectiveness with which thesemodels control for non-discretionary accruals may be limited. Forexample, approximately 20% of the variation in EDA generatedby each of these four models is attributable to predictablemeasurement error related to non-discretionary accruals.Statistical comparisons of the R-squared statistics for the fiveversions of model 1 are presented in Table 3.19 The explanatorypower of the Healy regression is significantly greater than that ofthe remaining four models at the p < 0.001 level. These resultshighlight the primitive nature of the Healy procedure and serveto confirm DeAngelo's (1986) conjecture that total accruals arelikely to provide an extremely inaccurate measure of accountingchoice. No significant differences (p < 0.05) in the explanatorypower of the remaining four regressions are evident from Table3, suggesting that the DeAngelo- and Jones-based modelsgenerate similar levels of predictable measurement error. Thisis particularly interesting given the greater degree ofcomputational complexity associated with the Jones procedure,relative to the simple first difference approach proposed byDeAngelo. Further, the lack of any significant differencesbetween the DeAngelo and modified-DeAngelo models, andthe Jones and modified-Jones models, suggests that therefinements proposed by Friedlan (1994) and Dechow et al.(1995) have little impact on the overall level of misspecificationin the resulting estimate of discretionary accruals.



Table 2

Estimated Coefficients (t-statistics in Parentheses) for Normal Scores Regressions of Estimated Discretionary Accrualson Proxies for Non-discretionary Accruals and Control Variables. The Sample Comprises 1987 Firm-year Observations

Non-discretionary Accrual Proxies Control Variables

Model Intercept CFO Grow Int Life Own Lev Size Smooth Industry1

(?) (ÿ) (+) (ÿ) (+) (?) (?) (?) (ÿ) Adj-R2 F-Stat.

Panel A: Model 1

Healy 0.0000 ÿ0.7772 0.1708 ÿ0.3469 0.2057 ± ± ± ± No 0.6146 554.278(0.00) (ÿ44.83) (9.71) (ÿ17.05) (10.49)

DeAngelo 0.0000 ÿ0.4722 0.0340 ÿ0.1636 0.0552 ± ± ± ± No 0.2121 94.272(0.00) (ÿ19.04) (1.35) (ÿ5.62) (1.97)

Mod-DeAngelo 0.0000 ÿ0.3846 0.0868 ÿ0.1252 0.0498 ± ± ± ± No 0.1896 75.215(0.00) (ÿ14.84) (3.30) (ÿ4.12) (1.70)

Jones 0.0000 ÿ0.4933 0.0971 ÿ0.1693 0.1917 ± ± ± ± No 0.2470 108.115(0.00) (ÿ20.20) (4.21) (ÿ6.34) (7.45)

Mod-Jones 0.0000 ÿ0.5106 0.0970 ÿ0.1700 0.1909 ± ± ± ± No 0.2419 107.994(0.00) (ÿ20.32) (4.20) (ÿ6.35) (7.31)

Panel B: Model 2

Healy 0.6911 ± ± ± ± 0.0602 ÿ0.0722 ÿ0.0078 ÿ0.9688 Yes 0.3365 15.343(3.59) (2.06) (ÿ2.70) (ÿ0.24) (ÿ21.50)

DeAngelo 0.0882 ± ± ± ± 0.0100 ÿ0.0596 ÿ0.0408 ÿ0.7102 Yes 0.1545 6.659(0.40) (0.30) (ÿ1.93) (ÿ1.10) (ÿ13.64)

Mod-DeAngelo ÿ0.1059 ± ± ± ± ÿ0.0018 ÿ0.0625 ÿ0.0469 ÿ0.6414 Yes 0.1513 6.043(ÿ0.47) (ÿ0.05) (ÿ1.99) (ÿ1.25) (ÿ12.16)

Jones 0.5144 ± ± ± ± 0.0584 ÿ0.1327 ÿ0.0134 ÿ0.9320 Yes 0.2130 10.098(2.81) (2.11) (ÿ5.22) (ÿ0.44) (ÿ20.26)

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Mod-Jones 0.5003 ± ± ± ± 0.0590 ÿ0.1326 ÿ0.0140 ÿ0.9318 Yes 0.2078 10.001(2.75) (2.13) (ÿ5.21) (ÿ0.49) (ÿ20.15)

Panel C: Model 3

Healy 0.0243 ÿ0.7162 0.1626 ÿ0.3516 0.2018 0.0162 ÿ0.0353 0.0227 ÿ0.2383 Yes 0.6538 50.385(0.17) (ÿ30.67) (9.11) (ÿ13.99) (9.12) (0.76) (ÿ1.75) (0.97) (ÿ5.39)

DeAngelo ÿ0.3110 ÿ0.4702 0.0283 ÿ0.2093 0.0716 ÿ0.0080 0.0301 ÿ0.0381 ÿ0.2158 Yes 0.2373 9.138(ÿ1.48) (ÿ13.57) (1.07) (ÿ5.61) (2.18) (ÿ0.25) (ÿ1.01) (ÿ1.09) (ÿ3.29)

Mod-DeAngelo ÿ0.4291 ÿ0.3806 0.0913 ÿ0.1614 0.0766 ÿ0.0251 ÿ0.0498 ÿ0.0312 ÿ0.2511 Yes 0.2143 8.521(ÿ1.96) (ÿ10.55) (3.31) (ÿ4.16) (2.24) (ÿ0.76) (ÿ1.60) (ÿ0.86) (ÿ3.67)

Jones 0.0901 ÿ0.3294 0.1052 ÿ0.2148 0.2328 0.0345 ÿ0.1002 0.0093 ÿ0.7539 Yes 0.2667 13.803(0.53) (ÿ11.70) (4.89) (ÿ7.08) (8.73) (1.34) (ÿ4.13) (0.33) (ÿ14.88)

Mod-Jones 0.0887 ÿ0.3288 0.1051 ÿ0.2204 0.2319 0.0347 ÿ0.0992 0.0101 ÿ0.7521 Yes 0.2599 13.522(0.50) (ÿ11.65) (4.88) (ÿ7.13) (8.69) (1.35) (ÿ4.05) (0.39) (ÿ14.82)

Notes:1 `Industry' indicates the inclusion (Yes) or exclusion (No) of a set of Datastream level-six industry dummy variables.Estimated discretionary accruals are calculated using the Healy, DeAngelo, modified-DeAngelo, cross-sectional Jones and modified-cross-sectionalJones models. Each model begins with total accruals, defined as the change in non-cash current assets less the change in current liabilities (exclusive ofthe current portion of long term debt), minus depreciation and amortisation.


Own = directors' equity ownership / total share outstanding;Lev = ratio of long-term debt to total shareholders equity;Size = natural log of beginning of period sales;Smooth = 1 if non-discretionary earnings exceed expected earnings, where expected earnings are defined as the median earnings before extraordinary

items for each sample firm's industry.

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All coefficient estimates in model 1 have their predicted signsand, with the exception of Grow in the DeAngelo regressionand Life in the modified-DeAngelo regression, all aresignificant at conventional levels. A particularly striking findingis the large magnitude of the estimated coefficient on CFO inall five regressions. These results are consistent with thosepresented by Dechow et al. (1995) and suggest that all fivemodels induce significant misspecification as a function of cashflow performance. The estimated coefficients on CFO, Grow,Int and Life are consistently larger in the Healy regression,confirming the tendency of the Healy model to generate thehighest level of predictable measurement error in EDA.Coefficient estimates are lowest in the DeAngelo andmodified-DeAngelo models. In particular, these two modelsappear to provide the most effective control for non-

Table 3

Z-Statistics1 (p-values in Parentheses) for Comparisons of Adjusted R-Squared's from the Pooled Rank Regressions Presented in Panel A of

Table 2. The Sample Comprises 1987 Firm-year Observations

Model:

EDAit � 0 � 1CFOit � 2Growit � 3Intit � 4Lifeit � "it

Model Generating EDA

Model Generating EDA Healy DeAngelo Mod-DeAngelo Jones

DeAngelo 18.245 ± ± ±(0.001)

Mod-DeAngelo 18.958 1.460 ± ±(0.001) (0.076)

Jones 16.077 1.225 1.702 ±(0.001) (0.123) (0.066)

Mod-Jones 16.101 1.401 1.698 0.098(0.001) (0.112) (0.064) (0.205)

Notes:1 Statistical comparisons of adjusted R-squared's from any two regression models(subscripted 1 and 2) are based on the mean and variance of the R-squared as derivedin Cramer (1987). Z-statistics are computed using:

R21 ÿ R2

2��2�R2

1 � � �2�R22 �

q ;

which is approximately standard normal in large samples.

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discretionary accruals relating to average fixed asset life andgrowth rate. Despite explicit attempts by the Jones andmodified-Jones models to control for non-discretionaryaccruals associated with revenue changes and the depreciationcharge, the estimated coefficients on Grow, Int and Life forboth EDA measures are highly significant (p < 0.01). The Jonesand modified-Jones models also appear to generate EDAmeasures that display a greater association with operating cashflow compared with the DeAngelo and modified-DeAngeloprocedures.

Results presented in Panel B for the accounting choicevariables suggest that managers use discretionary accruals tosmooth reported income. These results are robust to the specificprocedure used to estimate discretionary accruals. Results alsoindicate a significant positive association between EDA and thelevel of managerial equity ownership when discretionary accrualsare estimated using the Healy, Jones, and modified-Jones models.In contrast, no association between EDA and Own is documentedin the DeAngelo and modified-DeAngelo regressions. Finally,evidence of a negative association between EDA and leverage isdocumented in all five regressions, with the association beingparticularly strong in the Jones and modified-Jones regressions.No significant association between EDA and Size is apparent inany of the regressions in Panel B.

Panel C presents the results for the full model. Estimatedcoefficients on CFO, Grow, Int and Life are similar to the levelsreported in Panel A, suggesting that prior findings are not beingdriven by earnings management activity. In contrast, coefficientestimates decline markedly for several of the earningsmanagement variables. For example, the Own variable ceases tobe significant at conventional levels in the Healy, Jones andmodified-Jones regressions. Further, while Smooth remainssignificant in all five models, its effect is considerably reducedfrom that reported in Panel B. The results for Smooth and Ownclearly demonstrate the possibility for erroneous conclusionsregarding the existence of earnings management activity whenthe accounting choice proxy contains predictable measurementerror. At a minimum, these findings indicate the need to controlfor potential sources of measurement error when using accrual-based measure of accounting choice.



Table 4

Estimated Coefficients (t-statistics in Parentheses) for Normal Scores Regressions of Estimated Discretionary WorkingCapital Accruals on Proxies for Non-discretionary Accruals and Control Variables. The Sample Comprises 1987 Firm-

year Observations

Non-discretionary Accrual Proxies Control Variables

Model Intercept CFO Grow Int Life Own Lev Size Smooth Industry1

(?) (ÿ) (+) (ÿ) (+) (?) (?) (?) (ÿ) Adj-R2 F-Stat.

Panel A: Model 1

Healy 0.0000 ÿ0.6793 0.2156 ÿ0.1294 ÿ0.0272 ± ± ± ± No 0.3412 174.581(0.00) (ÿ32.53) (10.17) (ÿ5.50) (ÿ1.15)

DeAngelo 0.0000 ÿ0.4553 0.0813 ÿ0.0881 0.0177 ± ± ± ± No 0.1924 83.571(0.00) (ÿ18.14) (3.19) (ÿ2.40) (0.62)

Mod-DeAngelo 0.0000 ÿ0.3882 0.0596 ÿ0.0827 0.0314 ± ± ± ± No 0.1798 77.334(0.00) (ÿ14.98) (2.27) (ÿ2.27) (1.07)

Jones 0.0000 ÿ0.5905 0.0982 ÿ0.0901 0.0021 ± ± ± ± No 0.2023 82.814(0.00) (ÿ25.68) (4.21) (ÿ2.45) (0.08)

Mod-Jones 0.0000 ÿ0.5901 0.0990 ÿ0.0950 0.0032 ± ± ± ± No 0.2011 82.021(0.00) (ÿ25.64) (4.29) (ÿ2.61) (0.12)

Panel B: Model 2

Healy 0.3619 ± ± ± ± 0.0704 ÿ0.0932 ÿ0.0281 ÿ0.9487 Yes 0.2870 12.387(1.81) (2.33) (ÿ3.36) (ÿ0.84) (ÿ20.31)

DeAngelo 0.0703 ± ± ± ± 0.0131 ÿ0.0507 ÿ0.0577 ÿ0.6924 Yes 0.1269 6.386(0.31) (0.39) (ÿ1.63) (ÿ1.55) (ÿ13.25)

Mod-DeAngelo ÿ0.1051 ± ± ± ± ÿ0.0132 ÿ0.0617 ÿ0.0588 ÿ0.6539 Yes 0.1196 5.984(ÿ0.47) (ÿ0.39) (ÿ1.97) (ÿ1.56) (ÿ12.39)

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Jones 0.4521 ± ± ± ± 0.0744 ÿ0.1049 ÿ0.0011 ÿ0.9908 Yes 0.2064 9.980(2.33) (2.53) (ÿ3.89) (ÿ0.03) (ÿ21.57)

Mod-Jones 0.4601 ± ± ± ± 0.0742 ÿ0.1112 ÿ0.0013 ÿ0.9899 Yes 0.2120 10.094(2.41) (2.50) (ÿ3.92) (ÿ0.04) (ÿ21.28)

Panel C: Model 3

Healy 0.0119 ÿ0.6073 0.2165 ÿ0.1552 ÿ0.0396 0.0254 ÿ0.1143 0.0062 ÿ0.3402 Yes 0.5052 27.703(0.07) (ÿ21.76) (10.15) (ÿ5.16) (ÿ1.50) (0.99) (ÿ4.75) (0.22) (ÿ6.43)

DeAngelo ÿ0.2671 ÿ0.4506 0.0776 ÿ0.0921 0.0312 ÿ0.0108 ÿ0.0384 ÿ0.0460 ÿ0.3300 Yes 0.2183 8.305(ÿ1.26) (ÿ12.84) (2.90) (ÿ2.64) (0.94) (ÿ0.34) (ÿ1.27) (ÿ1.30) (ÿ4.46)

Mod-DeAngelo ÿ0.4154 ÿ0.3817 0.0630 ÿ0.1023 0.0555 ÿ0.0324 ÿ0.0472 ÿ0.0496 ÿ0.3561 Yes 0.2086 6.304(ÿ1.89) (ÿ10.55) (2.28) (ÿ2.71) (1.62) (ÿ0.98) (ÿ1.51) (ÿ1.36) (ÿ4.73)

Jones 0.1752 ÿ0.4145 0.1053 ÿ0.1120 ÿ0.0209 0.0418 ÿ0.1001 0.0214 ÿ0.6779 Yes 0.2492 15.480(0.97) (ÿ13.95) (4.64) (ÿ2.62) (ÿ0.74) (1.54) (ÿ3.91) (0.72) (ÿ11.81)

Mod-Jones 0.1805 ÿ0.4144 0.1062 ÿ0.1124 ÿ0.0199 0.0520 ÿ0.1015 0.0224 ÿ0.6624 Yes 0.2501 16.014(1.01) (ÿ13.95) (4.68) (ÿ2.63) (ÿ0.69) (1.59) (ÿ3.94) (0.80) (ÿ11.05)

Notes:1 `Industry' indicates the inclusion (Yes) or exclusion (No) of a set of Datastream level-six industry dummy variables.Estimated discretionary accruals are calculated using the Healy, DeAngelo, modified-DeAngelo, cross-sectional Jones and modified-cross-sectionalJones models. Each model begins with working capital accruals, defined as the change in non-cash current assets less the change in current liabilities(exclusive of the current portion of long term debt).


Own = directors' equity ownership / total share outstanding;Lev = ratio of long-term debt to total shareholders equity;Size = natural log of beginning of period sales;Smooth = 1 if non-discretionary earnings exceed expected earnings, where expected earnings are defined as the median earnings before extraordinary

items for each sample firm's industry.

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In sum, these results provide evidence of a significant degree ofpredictable measurement error in discretionary accrualsestimated using all five modelling procedures. While the Healymodel displays the greatest incidence of measurement error, theDeAngelo- and Jones-based procedures also generate non-trivialamounts of error. All models induce systematic measurementerror as a function of cash flow performance, growth rate andasset structure.

(ii) Additional Tests of the Depreciation Effect

Significant associations between EDA and Int and EDA and Lifein Table 2 are attributed to the inability of discretionary accrualmodels to effectively extract the non-discretionary component oftotal accruals associated with the depreciation charge. However,the possibility remains that these associations could be the resultof non-depreciation-related factors. To address this concern,discretionary accruals are computed using a working capitalmeasure of accruals, defined as the change in non-cash currentassets less the change in current liabilities.20 Regression (10) isthen re-estimated using measures discretionary working capitalaccruals. If the depreciation accrual is driving the results for Intand Life in Table 2, then one would expect to observeinsignificant coefficient estimates on these variables whenestimated discretionary accruals are measured exclusive of thedepreciation expense.

Results using estimated discretionary working capital accrualsare presented in Table 4. Life is no longer significant atconventional levels in any of the five regressions. Further, thecoefficient estimates and significance levels on Int aredramatically reduced in all regressions. These findings suggestthat the significant coefficient estimates on Life and Int reportedin Table 2 are indeed an artefact of the depreciation componentof total accruals. However, since the fixed asset intensity variableremains significant after the depreciation charge is excludedfrom the measure of total accruals, findings suggest that Int isalso capturing some unspecified effect. The extent to which thiseffect represents measurement error in EDA, as opposed to afundamental determinant of discretionary accrual activity,remains unclear.

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7. SUMMARY AND CONCLUSIONS

Discretionary accruals have emerged as the preferred measure ofaccounting choice in contemporaneous tests of the earningsmanagement hypothesis. This paper reports the results of testsdesigned to evaluate the extent to which five alternativeapproaches to the estimation of discretionary accruals controlfor the non-discretionary component of total accruals. Resultssuggest that all five models induce systematic measurement erroras a function of operating cash flow performance, sales growthand asset structure. Findings for the Jones and modified-Jonesprocedures are particularly striking given the explicit attemptmade by these models to control for non-discretionary accrualsrelated to sales growth and the level of depreciable assets. Resultsindicate that discretionary accruals generated by the Healy modelare associated with the highest level of predictable measurementerror. While the remaining four models generate significantlylower error levels, the magnitude of error remains significant atthe p < 0.001 level. Overall, these findings reinforce the evidencepresented by Dechow et al. (1995) and Guay et al. (1996) thatexisting models of discretionary accruals generate relatively poormeasures of managerial accounting choice.

In addition to corroborating the results of prior research for adifferent financial reporting regime using an alternative researchdesign, these findings suggest several practical opportunities forimproving the specification of empirical tests of the earningsmanagement hypothesis. For example, to the extent thatoperating cash flow, growth, fixed asset intensity and averagefixed asset life represent valid sources of non-discretionaryaccruals, one possible modelling approach would be to includethese variables as additional regressors in a Jones-style estimationprocedure. Alternatively, given the mounting evidencechallenging the reliability of accrual-based measures ofaccounting choice, the examination of alternative means ofearnings management might be more appropriate.

An important caveat regarding the interpretation of the resultspresented in this paper relates to the extent to which thevariables used to proxy for the non-discretionary accrualcomponent are associated with the stimuli for accounting choice.While explicit attempts are made to control for sources of



earnings management activity, the possibility remains that some(all) of the documented associations may result from someunspecified source of earnings management activity rather thanfrom measurement error in the accounting choice proxy. Thisconcern is particularly relevant in the case of the fixed assetintensity variable, whose significance persists (albeit at a reducedlevel) even when depreciation is excluded from the calculation oftotal accruals.

NOTES

1 For example, accrual choices represent a less costly mechanism by whichmanagers can affect reported numbers than, for example, changes in R&Dexpenditure or asset disposals. Further, because they are often difficult toobserve directly, the effect of accrual decisions are more difficult forexternal parties to adjust for ex-post, compared with the effect of asset salesand changes in accounting methods. A disadvantage of using the accrualmechanism to manipulate reported numbers is that under- (over-)statements made in the current period must reverse in the future.

2 Examples of inconsistent findings include Healy (1985), Gaver et al. (1995)and Holthausen et al. (1995) for management compensation contracts,DeAngelo et al. (1994) and DeFond and Jiambalvo (1994) for debtcovenant violations, and DeAngelo (1986) and Perry and Williams (1994)for management buy-outs. Young (1998) documents inconsistent findingsacross time in relation to the leverage hypothesis for a sample of UK firms.While each of these studies tests for evidence of earnings managementusing discretionary accruals, it is important to note that accruals do notrepresent the only means by which management can influence reportedincome. Alternative methods of earnings management include acceleratingand delaying the recognition of sales, asset disposals, changes in the level ofadvertising and R&D expenditure, etc. Failure to examine these alternativesources of earnings management reduces the power of the empirical test.

3 The term `systematic error' refers to instances where the sign and/ormagnitude of the measurement error in estimated discretionary accruals isdirectly and predictably related to the sign and/or magnitude of a variableorthogonal to actual discretionary accrual activity.

4 Datastream items [�(376±375)±�(389±387)] ÿ (402 + 562), where �signifies the change from period t ÿ 1 to t. Consistent with much of theextant literature, this definition of accruals is restricted to operatingaccruals. Long-term accruals such as those relating to financing andinvestment decisions are, therefore, largely excluded from this measure.

5 The assumption that non-discretionary accruals follow a random walk is alsoinconsistent with the self-reversing property of accruals (Dechow, 1994).

6 An additional limitation of the time-series approach proposed by Jones isthat it assumes the non-discretionary accrual coefficient estimates arestationary through time.

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7 The assumption that the determinants of non-discretionary accruals areorthogonal to the stimuli for earnings management is adopted to simplifythe discussion. The assumption is relaxed in the empirical tests.

8 Note that the predicted association between cash flow and non-discretionary accruals is expected to hold even in the absence of explicitattempts by management to smooth reported income. The impact ofexplicit attempts by management to smooth reported income is addressedin the following section.

9 Consistent with Sloan's (1996) findings, the standard deviation of non-cashcurrent asset accruals for the current sample is 0.1283, compared with0.1016 for current liability accruals.

10 For example, in relation to the determination of residual values and usefuleconomic lives. In addition, managers can exercise some discretion overtheir choice of depreciation method. However, in the context of the presentstudy, the important point is that for firms with large stocks of depreciableassets, the magnitude of the depreciation expense will still represent asignificant portion of the period's operating accruals, irrespective of theextent of discretion exercised by management.

11 The transparency argument against the use of depreciation as an effectivemechanism for earnings management implicitly assumes that accrualmanipulation is opportunistically motivated. Consistent with thetransparency view, Hunt et al. (1996) present evidence that managers donot use the depreciation accrual as a means of smoothing earnings orlowering debt-related costs.

12 The Jones and modified-Jones models are operationalised using the cross-sectional estimation approach detailed in the second section.

13 Failure to control for possible earnings management activity would lead to apotentially serious correlated omitted variables problem in regression (10).Specifically, if management manipulate reported earnings and the stimulusfor this activity (Yj) is correlated with the source of non-discretionaryaccruals (Xj) then, in the absence of any control for Xj, �j would represent abiased estimate of the true parameter. The direction of this bias isdetermined by the sign of the correlation between Yj and Xj.

14 In additional (unreported) tests, a dummy variable for leverage was employedinstead of a continuous measure. This dummy variable was defined as one forfirms whose leverage was in the top quartile of the distribution in a given fiscalyear, and zero otherwise. Results using this leverage dummy werequantitatively identical to those reported in the body of the paper.

15 The extent to which firm size captures political cost considerations (Wattsand Zimmerman, 1986), as opposed to some other (unspecified) factor, isnot clear. As a result, the inclusion of Size in regression (10) is motivated onthe grounds that it controls for the well-established correlation betweenfirm size and accounting choice, rather than as a direct test of the politicalcost hypothesis.

16 The industry-time matched portfolios used to estimate the Jones andmodified-Jones models were constructed using Datastream level-six industrygroups. All industry groups containing fewer than six member firms wereexcluded from the sample. The mean (median) number of firms in theindustry-year portfolios was 16.44 (16.38). Consistent with prior research,the average estimated coefficient on �REV from regression (4) was positiveand insignificant while that on PPE was negative and significant at the p <0.01 level.



17 Results for the rank-transform and trimmed-OLS models are available fromthe author on request. The normal scores model is preferred to the rankregression model for reporting purposes because (i) coefficient estimatesfrom a rank regression are often difficult to interpret, (ii) conventionalsignificance tests using F- and t-tests are not appropriate since ranks aredistribution-free and (iii) the ordinal nature of ranks means that tests areeffectively non-parametric and as such are weaker than parametric tests(Cooke, 1988). Normal scores models are preferred over the trimmed-OLSmodels because they use all the available data.

18 In addition to estimating regression (10) using pooled data, the model wasalso estimated separately for each year. Annual regressions, which areavailable from the author on request, give quantitatively identical results tothose based on the pooled sample.

19 Statistical comparisons of the R-squared from any two models (subscripted 1and 2) possessing the same set of explanatory variables are based on themean and variance of the R2 statistic, as derived in Cramer (1987). Z-statistics are computed as:

R21 ÿ R2

2��2�R2

1 � � �2�R22 �

qwhich is approximately standard normal in large samples.

20 The procedures for calculating discretionary working capital accruals forthe Healy, DeAngelo and modified-DeAngelo models are equivalent tothose described in the second section, with the exception that in each case,a measure of working capital accruals (WC) is substituted for total accrualsin the relevant equation. Working capital accruals are defined as the changein non-cash current assets minus the change in current liabilities (excludingthe current portion of long-term debt). Since PPE is employed in the cross-sectional and modified-cross-sectional Jones models to control for non-discretionary depreciation-related accruals, it is obsolete for a workingcapital measure of accruals. Consequently, industry-year non-discretionaryaccrual parameter estimates for both cross-sectional models are determinedusing the following regression:

WCit=Aitÿ1 � �i�1=Aitÿ1� � �1i��REVit=Aitÿ1� � "it :

Using the estimated coefficients from this regression, discretionary workingcapital accruals (EDWCA) for the cross-sectional Jones model are thencomputed as:

EDWCAit � WCit=Aitÿ1 ÿ �ai�1=Aitÿ1� � b1i��REVit=Aitÿ1��:

A similar procedure is used for the modified-cross-sectional Jones model,with the exception that �REV is deducted from �REV in the aboveequation.

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REFERENCES

Bange, M.M and W.F.M. De Bondt (1998), `R&D Budgets and CorporateEarnings Targets', Journal of Corporate Finance, Vol. 4, pp. 153±84.

Bartov, E. (1993), `The Timing of Asset Sales and Earnings Manipulation', TheAccounting Review, Vol. 68, pp. 840±55.

Beattie, V., S. Brown, D. Ewers, B. John, S. Manson, D. Thomas and M. Turner(1994), Èxtraordinary Items and Income Smoothing: A PositiveAccounting Approach', Journal of Business, Finance & Accounting, Vol. 21,No. 6 (September), pp. 791±811.

Black, E.L., K.F. Sellers and T.S. Manley (1998), Èarnings Management UsingAsset Sales: An International Study of Countries Allowing Noncurrent AssetRevaluation', Journal of Business, Finance & Accounting, Vol. 25, Nos 9 & 10(November/December), pp. 1287±317.

Bushee, B. (1998), `The Influence of Institutional Investors on Myopic R&DInvestment Behavior', The Accounting Review, Vol. 73, pp. 305±33.

Chaney, P.K and C.M. Lewis (1998), Ìncome Smoothing andUnderperformance in Initial Public Offerings', Journal of Corporate Finance,Vol. 4, pp. 1±29.

Christie, A.A. (1990), Àggregation of Test Statistics: An Evaluation of theEvidence on the Contracting and Size Hypotheses', Journal of Accountingand Economics, Vol. 12, pp. 15±36.

Cooke, T.E. (1998), `Regression Analysis in Accounting Disclosure Studies',Accounting and Business Research, Vol. 28, pp. 209±24.

Cramer, J.S. (1987), `Mean and Variance of R2 in Small and Moderate Samples',Journal of Econometrics, Vol. 35, pp. 253±66.

DeAngelo, H., L.E. DeAngelo and D.J. Skinner (1994), Àccounting Choice inTroubled Companies', Journal of Accounting and Economics, Vol. 17, pp. 113±43.

DeAngelo, L.E. (1986), Àccounting Numbers as Market Valuation Substitutes:A Study of Management Buyouts of Public Stockholders', The AccountingReview, Vol. 61, pp. 400±20.

_____ (1988), `Managerial Competition, Information Costs and CorporateGovernance: The Use of Accounting Performance Measures in ProxyContests', Journal of Accounting and Economics, Vol. 10, pp. 3±36.

Dechow, P. (1994), Àccounting Earnings and Cash Flows as Measures of FirmPerformance: The Role of Accounting Accruals', Journal of Accounting andEconomics, Vol. 18, pp. 3±42.

_____ R. Sloan and A. Sweeney (1995), `Detecting Earnings Management', TheAccounting Review, Vol. 70, pp. 193±226.

DeFond, M. and J. Jiambalvo (1994), `Debt Covenant Violation andManipulation of Accruals', Journal of Accounting and Economics, Vol. 17,pp. 145±76.

_____ and C. W. Park (1997), `Do Managers Smooth Income in Anticipation ofFuture Earnings?' Journal of Accounting and Economics, Vol. 23, pp. 145±76.

Dempsey, S.J., H.G. Hunt and N.W. Schroeder (1993), Èarnings Managementand Corporate Ownership Structure: An Examination of ExtraordinaryItem Reporting', Journal of Business, Finance & Accounting, Vol. 20, No. 4(June), pp. 479±500.

Friedlan, J.M. (1994), Àccounting Choices of Issuers of Initial PublicOfferings', Contemporary Accounting Research, Vol. 11, pp. 1±32.

Gaver, J., K. Gaver and J. Austin (1995), Àdditional Evidence on Bonus Plans



and Income Management', Journal of Accounting and Economics, Vol. 19, pp.3±28.

Guay, W.R., S.P. Kothari and R.L. Watts (1996), À Market-Based Evaluation ofDiscretionary Accrual Models', Journal of Accounting Research, Vol. 34, pp.83±105.

Harris, T.S., M. Lang and H.P. MoÈller (1994), `The Value Relevance of GermanAccounting Measures: An Empirical Analysis', Journal of Accounting Research,Vol. 32, pp. 187±209.

Healy, P. (1985), `The Effect of Bonus Schemes of Accounting Decisions',Journal of Accounting and Economics, Vol. 7, pp. 85±107.

Holthausen, R.W., D. Larcker and R. Sloan (1995), Ànnual Bonus Schemes andthe Manipulation of Earnings', Journal of Accounting and Economics, Vol. 19,pp. 29±74.

_____ and R. Leftwich (1983), `The Economic Consequences of AccountingChoice: Implications of Costly Contracting and Monitoring', Journal ofAccounting and Economics, Vol. 5, pp. 77±117.

Hunt, A., S.E. Moyer and T. Shevlin (1996), `Managing Interacting AccountingMeasures to Meet Multiple Objectives: A Study of LIFO Firms', Journal ofAccounting and Economics, Vol. 21, pp. 339±74.

Iman, R.L., and J.W. Conover (1979), `The Use of Rank Transformation inRegression', Technometrics, Vol. 21, pp. 499±509.

Jones, J. (1991), Èarnings Management During Import Relief Investigations',Journal of Accounting Research, Vol. 29, pp. 193±228.

Kaplan, R.S. (1985), `Comments on Paul Healy: Evidence of the Effect of BonusSchemes on Accounting Procedures and Accrual Decisions', Journal ofAccounting and Economics, Vol. 7, pp. 109±13.

McNichols, M. and G.P. Wilson (1988), Èarnings Management From theProvision of Bad Debts', Journal of Accounting Research, Vol. 26, pp. 1±31.

Niehaus, G.R. (1989), Òwnership Structure and Inventory Method Choice', TheAccounting Review, Vol. 64, pp. 269±84.

Perry, S. and T. Williams (1994), Èarnings Management PrecedingManagement Buyout Offers', Journal of Accounting and Economics, Vol. 18,pp. 157±81.

Ronen, J. and S. Sadan (1981), Smoothing Income Numbers: Objectives, Means andImplications (Addison-Wesley Publishing Co., Reading, Mass.).

Samuels, J.M., C. Rickwood and A.G. Piper (1989), Advanced FinancialAccounting (McGraw-Hill, London).

Sloan, R. (1996), `Do Stock Prices Fully Reflect Information in Accruals andCash Flows About Future Earnings?', The Accounting Review, Vol. 71, pp.289±315.

Warfield, T.D., J.J. Wild and K.L. Wild (1995), `Managerial Ownership,Accounting Choices, and Informativeness of Earnings', Journal ofAccounting and Economics, Vol. 20, pp. 61±91.

Watts, R.L. and J. Zimmerman (1990), `Positive Accounting Theory: A Ten YearPerspective', The Accounting Review, Vol. 65, pp. 131±56.

_____ _____ (1986), Positive Accounting Theory (Prentice Hall, New Jersey).Young, S. (1998), `The Determinants of Managerial Accounting Policy Choice:

Further Evidence for the U.K.', Accounting and Business Research, Vol. 28, pp.131±43.

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