bb sbi FAN-MANAGING EARNING USING CLASIFICATION SHIFTING.pdf

Managing Earnings Using ClassificationShifting: Evidence from Quarterly

Special Items

Yun FanThe University of Oklahoma

Abhijit BaruaFlorida International University

William M. CreadyThe University of Texas at Dallas

Wayne B. ThomasThe University of Oklahoma

ABSTRACT: McVay 2006 concludes that managers opportunistically shift core ex-penses to special items to inflate current core earnings, resulting in a positive relationbetween unexpected core earnings and income-decreasing special items. However,she further notes that this relation disappears when contemporaneous accruals aredropped from the core earnings expectations model. McVay 2006 calls for research toimprove the core earnings expectations model and to provide additional cross-sectionaltests of classification shifting. Using a core earnings expectations model that is notdependent on accrual special items, we show that classification shifting is more likely inthe fourth quarter than in interim quarters. We also find more evidence of classificationshifting when the ability of managers to manipulate accruals appears to be constrainedand in meeting a range of earnings benchmarks. Overall, our evidence provides broadsupport for McVays 2006 conclusion that managers engage in classification shifting.Our study also sheds new understanding of the conditions under which managers aremore likely to employ classification shifting.

Keywords: classification shifting; special items; earnings thresholds; accruals ma-nipulation constraints.

Data Availability: Data are available from public sources identified in the paper.

We are grateful for comments received from two anonymous reviewers, Sarah McVay, Mark Vargas, Han Yi, and workshopparticipants at The University of Oklahoma and The University of Texas at Dallas.

Editors note: Accepted by Sanjay Kallapur.

THE ACCOUNTING REVIEW American Accounting AssociationVol. 85, No. 4 DOI: 10.2308/accr.2010.85.4.13032010pp. 13031323

Submitted: November 2008Accepted: October 2009

Published Online: June 2010

1303

I. INTRODUCTION

McVay 2006 hypothesizes that managers have incentives to report core expenses de-fined as cost of goods sold and selling, general, and administrative expenses as income-decreasing special items in an attempt to inflate core profitability. Managers are moti-vated to manage earnings in this way because market participants may be particularly interested incore earnings or pro forma earnings rather than bottom-line GAAP earnings Bradshaw andSloan 2002; Gu and Chen 2004; Kinney and Trezevant 1997, and core earnings typically receivehigher valuation multiples than non-core earnings Lipe 1986. By shifting core expenses tospecial items, the firm increases core earnings, while bottom-line net income remains unaffected.1

McVay 2006 presents empirical evidence supportive of classification shifting in the form ofa positive relation between income-decreasing special items and unexpected core annual earnings.As income-decreasing special items increase, the firm tends to report higher than expected coreearnings. However, McVay 2006, Table 10 also reports that the positive relation disappears and,in fact, becomes negative when contemporaneous accruals are dropped from the core earningsexpectations model. She further cautions that the initial evidence in favor of classification shiftingmay be attributable to the use of contemporaneous accruals in the core earnings expectationsmodel. More specifically, the core earnings expectations model which includes accruals as acontrol for performance could induce a mechanical relation between unexpected core earningsand special items which are mainly accrual-based. Thus, there is currently some uncertaintyregarding the extent to which income-decreasing special items include shifted core expenses.2

Research in the area of classification shifting is relatively new, and it is essential that subse-quent research provides improvements to the core earnings expectations model. We first extendMcVays 2006 core earnings expectations model and show that as further controls for perfor-mance are included, the evidence in favor of classification shifting increases. We also present aseries of empirical examinations of unexpected core earnings at the quarterly level that are robustto the underlying bias discussed in McVay 2006. As research in this area grows and researchersdevelop fundamental views, it is important that findings from initial studies are validated. Our testsprovide broad support for McVays conclusion of classification shifting by managers.3

A central aspect of our analysis is the use of quarterly rather than annual data. This distinctionallows us to use the differential incentives to manipulate fourth versus interim quarter earnings asa platform for constructing tests of classification shifting. These examinations are inherently testsof a joint hypothesisthat managers both engage in classification shifting and are more prone todo so in fourth quarters. Consequently, our results also contribute to the literature on differentialincentives/constraints facing managers attempting to manipulate fourth quarter earnings. Usingquarterly data, we demonstrate that classification shifting is more pronounced in the fourth quarterthan in interim quarters.

We next consider that classification shifting is one means of earnings manipulation, whileothers exist e.g., accrual manipulation. To the extent that managers are more constrained in theirability to manipulate accruals Brown and Pinello 2007, they may use classification shifting as an

1 Unlike income classification shifting, accrual manipulation and real activities management affect reported bottom-lineGAAP earnings.

2 McVay 2006 provides several approaches to test for classification shifting, some of which are not necessarily expectedto be affected by the mechanical relation between unexpected core earnings and accrual special items. She finds 1subsequent reversal of shifted earnings is more likely when there is no opportunity to shift in the subsequent year, 2shifting is more likely when firms just meet the analyst forecast, especially for high-growth firms, 3 shifting is morelikely when the type of special item is easier to shift, and 4 abnormal returns in the subsequent year tend to benegatively related to shifted earnings as these earnings recur in core earnings, disappointing investors.

3 In this study, we limit our tests to the relation between unexpected core earnings and special items in the current quarter.We do not examine the subsequent reversal of unusually high core earnings associated with special items.

1304 Fan, Barua, Cready, and Thomas

The Accounting Review July 2010American Accounting Association

alternative to inflate core earnings. Finally, we examine classification shifting to meet earningsthresholds such as analysts forecasts, one-year-ago same-quarter earnings, and zero earnings. Wedo so, however, using a core earnings expectations model that does not rely on contemporaneousaccruals.

In the next section, we discuss the background literature and analysis of the classificationshifting issue identified in McVay 2006. In Section III, we develop our hypotheses. Section IVdetails the data, sample, and descriptive statistics. Section V describes the research design andreports tests of hypotheses, while Section VI concludes the study.

II. BACKGROUND LITERATURE AND ANALYSISEarnings management has been the subject of considerable academic research. Evidence of

managers engaging in earnings management through accrual manipulation has been shown inmany different contexts, for many different accruals, and in response to many managerial incen-tives, as reviewed by Healy and Wahlen 1999. A second channel through which earnings couldbe manipulated is real activities management, such as providing temporary price discounts toincrease sales, cutting discretionary expenditures such as research and development and advertis-ing, and overproducing inventory to reduce cost of goods sold e.g., Baber et al. 1991; Bushee1998; Gunny 2005; Roychowdhury 2006; Cohen and Zarowin 2008. Unlike accrual manipula-tion, which involves potential accounting fraud that brings about litigation risk to the firm and/oremployment risk to the manager, real activities manipulation does not violate GAAP but doessacrifice firms future economic benefits.

McVay 2006 investigates a third form of earnings management. She tests whether managersmanipulate core earnings by reclassifiying ordinary operating expenses as special items. To theextent that investors fixate on core earnings instead of bottom-line GAAP earnings, managers mayemploy classification shifting to fool financial statement users by manipulating earnings numberswithin the income statement. This is because financial statement users appear to recognize anddistinguish the closeness to sales of individual line items and weigh them differently e.g., Lipe1986; Fairfield et al. 1996; Francis et al. 1996. Various studies document that analysts andinvestors pay more attention to street or pro forma earnings as defined by managers and viewthem as more value-relevant Bradshaw and Sloan 2002; Bhattacharya et al. 2004; Gu and Chen2004. Thus, it is likely that managers take advantage of the markets focus on core earningsinstead of bottom-line GAAP earnings to misclassify some core expense items in the incomestatement. McVay 2006 finds that managers opportunistically shift core expenses to income-decreasing special items, and investors act as if they are surprised in the following period whenthese expenses shift back into core earnings in the following year.

For empirical tests of classification shifting, firms earnings performance poses a challenge.This occurs because the magnitude and frequency of income-decreasing special items are mark-edly higher among firms experiencing poor performance McVay 2006. Thus, there is a naturalnegative relation between earnings performance and the amount of income-decreasing specialitems. However, classification shifting predicts a positive relation between unexpected core earn-ings and the amount of income-decreasing special items i.e., as core expenses are shifted tospecial items, unexpected core earnings and income-decreasing special items both increase.

The existing body of empirical evidence supportive of classification shifting centers aroundmeasures of expected core earnings, as developed in McVay 2006. A common feature of thesemodels is the inclusion of contemporaneous accruals in the formation of expected core earningsvalues. This inclusion follows from the DeAngelo et al. 1994 linkage of accruals to underlyingeconomic performance. As discussed by McVay 2006, conditioning core earnings expectationson contemporaneous accruals is problematic because this number includes accrual special items.

Managing Earnings Using Classification Shifting: Evidence from Quarterly Special Items 1305


As these same accrual special item amounts also appear within the total special item amount, asituation is created wherein the dependent variable i.e., unexpected core earnings that is partiallydetermined by variable X accrual special items is regressed on a variable income-decreasingspecial items that is also partially determined by variable X. This potentially creates a mechanicalpositive relation between the dependent variable and independent variable.

To illustrate, the basic McVay design employs a two-stage regression procedure. In the firststage, expected firm-year core earnings, ECE, is derived as a function of accruals in that yearmultiplied by an industry-determined multiplier a. The value of this multiplier averages 0.22 seeMcVay 2006, Table 4. Consequently, a dollar of special item accrual expense/loss reduces ECEby $0.22 on average. The difference between actual core earnings and ECE is identified asunexpected core earnings and is regressed on special items SI in a second stage analysis of theform:

Core Earningsi,t ECEi,t = 0 + 1SIi,t + i,t. 1

A dollar of special item accrual expense does not impact core earnings from which specialitems are excluded by construction but, on average, reduces ECEi,t, and therefore increasesCore Earningsi,t ECEi,t, by a SIi,t.4 Definitionally, the special item accrual also increasesSIi,t, forcing 1 0 in cases where, in fact, no relation may exist between SI and true unexpectedcore earnings magnitudes.5,6

As McVay 2006 acknowledges see her Table 10, when accruals are removed from theexpectations model, the positive relation between unexpected core earnings and special itemsbecomes negative. The flipped sign of the relation could be due to either insufficient control forperformance or the possible bias described above. In the tests detailed in the next section, weexclude contemporaneous accruals to avoid introducing the potential bias and attempt to bettercontrol for the performance effect.

III. HYPOTHESESWe rely on comparative analyses of relative shifting activities between firm-groups that are a

priori more versus less likely to engage in classification-shifting activities. Specifically, we usequarterly data, as opposed to annual data in McVay 2006, and partition the sample based onaccrual manipulation constraints and earnings thresholds. To rule out the possibility of spuriousaccrual special item effects driving our results, we eliminate current-period accruals from the coreearnings expectations model.7 It is likely that the overall relation between unexpected core earn-ings and special items includes both classification shifting and a performance-driven effect. How-ever, for certain firms and in certain quarters, classification shifting may be more prevalent be-cause of greater managerial incentives or opportunities. Breaking down the data by specificquarters and by different levels of managerial incentives and opportunities gives us a better

4 This statement holds so long as a is positive. Empirically, this assumption is true most of the time 81.5 percent inMcVay 2006.

5 Gu and Chen 2004, footnote 10 recognize that the interrelationships between non-cash special items and accruals cancreate problems in research designs that include both variables. They show in their Table 6, Panel B that the persis-tence coefficient of special items changes sign after the inclusion of the accruals variable, which they interpret as theeffect of noncash nonrecurring items being included in accruals.

6 It is also straightforward to show that in a changes analysis i.e., unexpected change in core earnings negative accrualspecial items tend to increase expected change in core earnings and, as a consequence, decrease unexpected change incore earnings. This produces a mechanical negative relation between unexpected change in core earnings and income-decreasing special items.

7 In addition, in a sensitivity test reported below, we specifically address whether the relation between our estimate ofunexpected core earnings and income-decreasing special items is related to the amount of contemporaneous accrualspecial items. We find no evidence that it is.



understanding of whether and when classification shifting is more likely to occur.Our motivation for using quarterly data comes from prior literature that suggests that earnings

management activities are likely to differ between the fourth quarter and the interim quarters.Brown and Pinello 2007 specifically examine whether accruals are managed more in the fourthquarter relative to interim quarters. They contend that since the annual reporting process is subjectto a more rigorous audit compared to interim quarters, it is more difficult for managers to ma-nipulate accruals upward at the end of the year. Instead, managers are more likely to resort toexpectations management in the fourth quarter as a way to meet analysts forecasts. Their resultsconfirm these views. However, Brown and Pinello 2007 conclude that the extent of expectationsmanagement in the fourth quarter seems too small to explain the ability to meet fourth quarterearnings expectations, given the decrease in accrual manipulation ability. Our test of classificationshifting in the fourth quarter provides an additional explanation for their puzzling result. Duringthe year, managers are more likely to manipulate accruals to meet analysts expectations, but at theend of the year, managers ability to manipulate accruals is constrained and it is more difficult tomanage expectations. This difficulty leads managers to resort to an alternative earnings manage-ment techniqueclassification shifting.

Consistent with the views expressed by Brown and Pinello 2007, Cornell and Landsman1989 find that both price reactions and analysts revisions are greater around earnings announce-ment in the fourth quarter than in interim quarters. They conclude that audited annual announce-ments are more informative. Prior research also provides evidence that the frequency and magni-tudes of special items are significantly greater in the fourth quarter Burgstahler et al. 2002;Kinney and Trezevant 1997.8 We conjecture that these results indicate that not only do managershave more incentives to employ classification shifting in the fourth quarter, but they also havegreater opportunity to camouflage core expenses with other true special items. This discussionleads to our first hypothesis:

H1: Managers shift core expenses to special items more in the fourth quarter.We next consider that managers propensity to shift core expenses in the current quarter may

be affected by previous accruals manipulation activity. To the extent that managers have alreadymanaged accruals upward in previous quarters, the ability to further manage accruals upward inthe current quarter is constrained. As a result, earnings management through income classificationshifting may provide the better or only alternative. Barton and Simko 2002 measure firmsprevious earnings management using the beginning level of net operating assets scaled by sales.9They find that firms with higher net operating assets i.e., more optimistic reporting in priorperiods are less likely to report earnings that just meet or beat analysts forecasts see Barton andSimko 2002, Table 5, Panel B. Related to our study, to the extent that accruals manipulation isconstrained, managers would be more likely to inflate core earnings through classification shifting.We predict this relation as our second hypothesis:

8 We find similar results for our sample of firms. The average magnitude of income-decreasing special items scaled bysales in the fourth quarter is 5.36 percent, compared to only 1.64 percent in the first three quarters. We also find thatincome-decreasing special items are reported for 31.7 percent of the fourth quarter observations, compared to only 15.6percent of the observations for the first three quarters.

9 The intuition behind their use of net operating assets is that the balance sheet accumulates the effects of previousaccounting choices. Therefore, firms with higher than expected net operating assets are more likely to have reportedoptimistic earnings in the past. To validate their measure, Barton and Simko 2002 report that firms with larger netoperating assets at the beginning of the current year reported more positive abnormal accruals in the previous 20quarters. Using Dechow et al.s 2008 Fraud Score, Omer et al. 2008 also find that the level of net operating assetsprovides a measure of prior earnings management.



H2: Managers shift core expenses to special items more when the ability to manipulateaccruals is constrained.

Finally, we examine the extent to which managers use classification shifting as a way to meetthree earnings benchmarks: analysts forecasts, earnings of the same quarter in the prior year, andprofit. In general, prior studies find evidence consistent with earnings management at each thresh-old e.g., Burgstahler and Dichev 1997a; Degeorge et al. 1999; Payne and Thomas 2003; Brownand Caylor 2005. There are clear market incentives for meeting versus missing the analystforecast threshold e.g., Kasznik and McNichols 2002. Dechow and Skinner 2000 note thatearnings management is greater when such actions allow managers to meet or beat analystsforecasts than when they otherwise would not. On a quarterly basis, Bartov et al. 2002 find thatfirms meeting or beating analysts earnings expectations enjoy a higher return over the quarter thanfirms that fail to meet these expectations. They find that the equity premium to firms meeting orbeating expectations is only marginally affected by whether reported quarterly earnings are genu-ine or are the result of earnings or expectations management. Therefore, the ex post equity pre-mium following quarterly earnings announcements provides an ex ante incentive for managers tomanipulate earnings numbers.

Other studies document that analysts and investors pay more attention to street or pro formaearnings as defined by managers and view them as more value-relevant Bradshaw and Sloan2002; Bhattacharya et al. 2004; Gu and Chen 2004. Bradshaw and Sloan 2002 show that themarket responds to street earnings more than traditional GAAP earnings and that managers havetaken a proactive role in defining street earnings when communicating to investors. Analysts alsotend to exclude special items from their forecasts. Thus, it is likely that managers take advantageof the markets focus on core earnings instead of bottom-line GAAP earnings to shift some coreexpense items in the income statement.

For similar reasons, managers may use classification shifting to report quarterly earnings thatmeet or just beat earnings of the same quarter in the prior year or that just meet or beat zeroearnings. This could occur because managers have incentives e.g., stock premium, bonuses,reputation, job security to avoid quarterly earnings decreases and losses. In summary, we expectthat managers shift core expenses to income-decreasing special items more when quarterly earn-ings of their firms just meet or beat 1 the analysts consensus forecasts, 2 earnings of the samequarter in the prior year, or 3 zero earnings. Our hypothesis related to classification shiftingaround earnings benchmarks follows:

H3: Managers shift core expenses to special items more when quarterly earnings just meet orbeat earnings benchmarks.

Degeorge et al. 1999 suggest that managers first attempt to report a profit, then to reportearnings increases and, last, they are concerned with meeting or beating analysts forecasts. Payneand Thomas 2003 find evidence consistent with earnings management mostly for the zero earn-ings threshold followed by the analyst forecast threshold. In contrast, Graham et al. 2005 reportthat managers most prefer to avoid reporting an earnings decrease. Brown and Caylor 2005 showthat achieving the analyst forecast threshold has become the most important over time. Becauseprior research provides mixed conclusions, we have no a priori expectations on which of the threethresholds should show the most evidence of classification shifting.



IV. DATA, SAMPLE SELECTION, AND DESCRIPTIVE STATISTICSWe collect data for the years 1988 to 2007 from Compustat Industrial Quarterly File. Ana-

lysts forecast data are obtained from the I/B/E/S Detail File. Each firm-quarter observation isrequired to have sufficient data to calculate variables in model 2 and model 3, as discussedbelow. Following McVay 2006, we eliminate firm-quarter observations that have annual sales ofless than $1 million. This allows our sample to be as comparable to McVays 2006 as possible.In addition, eliminating these firms reduces the extreme impact that small firms can have on theresults. We also exclude firms that had a change in fiscal year, to ensure that quarterly data arecomparable across years. Further, requiring a minimum of 15 observations per industry-year-quarter ensures a sufficiently large sample to estimate expected core earnings. Industry classifica-tions are based on Fama and French 1997. The full sample consists of 132,393 firm-quarterobservations. The subsample with available analysts forecasts has 67,980 firm-quarters.

Table 1 lists the definitions of variables used in the analyses, and Table 2 provides descriptivestatistics for these variables. The mean median of core earnings for all firm-quarters, scaled bysales, is 0.038 0.085. The mean median of special items as a percentage of sales is 2.59 0.0percent. Income-increasing special items are set to zero and are not included in the analyses. Themean median of unexpected core earnings deflated by sales is 0.000 0.005. The calculation ofunexpected core earnings is discussed in detail in the following section. Table 3 presents correla-tions among the main variables.

TABLE 1Variable Definitions with Corresponding Compustat Data Item Numbers

Variable Definition

CEq Core Earnings, calculated as sales #2 minus cost of goods sold #30 minus selling,general, and administrative expenses #1 in quarter q.

UE_CEq Unexpected Core Earnings, calculated as the reported core earnings CEq minus theexpected core earnings estimated by industry-year-quarter, excluding firm i:

CEq = 0 + 1CEq4 + 2CEq1 + 3ATOq + 4ACCRUALSq4 + 5ACCRUALSq1+ 6SALESq + 7NEG_SALESq + 8RETURNSq1 + 9RETURNSq + q.

%SIq Special Items #32 as a percentage of sales #2. Income-decreasing special items aremultiplied by 1, where special items are income-decreasing, and are set to 0 wherespecial items are income-increasing.

ACCRUALSq Accruals, calculated as net income before extraordinary items #8 minus cash fromoperations #108.

RETURNSq Three-month market-adjusted return corresponding to the fiscal quarter.ATOq Asset Turnover Ratio, defined as SALESq / NOAq + NOAq1 / 2, where NOAq, or net

operating assets, is operating assets minus operating liabilities. Operating assets arecalculated as total assets #44 less cash and short-term investments #36. Operatingliabilities are calculated as total assets #44 less total debt #45 and #51, less bookvalue of common and preferred equity #55 and #56, less minority interest #53.Average NOA is required to be positive.

SALESq Percentage Change in Sales, calculated as SALESq SALESq4 / SALESq4.

NEG_SALESq SALESq if the percentage change in sales is less than 0, and 0 otherwise.



V. RESEARCH DESIGN AND RESULTSMeasuring Unexpected Core Earnings

To measure expected core earnings for firm i, we estimate model 2 within each industry-year-quarter excluding firm i.10

CEq = 0 + 1CEq4 + 2CEq1 + 3ATOq + 4ACCRUALSq4 + 5ACCRUALSq1+ 6SALESq + 7NEG_SALESq + 8RETURNSq1 + 9RETURNSq + q. 2

Model 2 is a variant of the model used by McVay 2006. The intent of the core earningsexpectations model is to capture the natural relation between core earnings CE and firm perfor-mance, leaving the unexplained portion i.e., unexpected core earnings as a measure of abnormalperformance.11 Lagged core earnings are included because core earnings are persistent.12 Weinclude core earnings from the last quarter and four quarters ago. The last quarter is closer to thecurrent quarter, so it better captures firms current economic environments. Thus, it may provide abetter indication of normal core earnings for the current quarter. We also include core earningsfour quarters ago because quarterly earnings exhibit a seasonal pattern for many firms, such thatearnings of the same quarter one year ago may provide a better control for current performance.

10 Variables are defined in Table 1.11 Reported core earnings are calculated as sales minus cost of goods sold and selling, general, and administrative

expenses. As a sensitivity test, we also consider using I/B/E/S actual quarterly earnings in place of Compustat corequarterly earnings. While the use of I/B/E/S data substantially reduces our sample, we continue to find support for ourhypotheses. So that our results are directly comparable to those in McVay 2006, we employ Compustat core earningsas our primary analysis.

12 In Table 3, the Spearman Pearson correlation between CEq and CEq1 is 0.7902 0.7351. The Spearman Pearsoncorrelation between CEq and CEq4 is 0.7357 0.6530.

TABLE 2Descriptive Statistics

Variable Mean MedianStandardDeviation 25% 75%

CEq 0.038 0.085 0.346 0.023 0.154UE_CEq 0.000 0.005 0.189 0.041 0.056%SIq 2.59% 0.00% 0.112 0.00% 0.00%CEq4 0.025 0.086 0.391 0.023 0.154CEq1 0.033 0.084 0.351 0.021 0.155ATOq 2.873 1.010 6.885 0.459 2.259ACCRUALSq4 0.093 0.066 0.541 0.241 0.063ACCRUALSq1 0.111 0.067 0.519 0.248 0.056SALESq 19.26% 8.54% 0.539 4.39% 26.56%RETURNSq1 0.002 0.034 0.331 0.183 0.126RETURNSq 0.001 0.033 0.332 0.184 0.126

See Table 1 for variable definitions. The full sample consists of 132,393 firm-quarter observations. The sample withanalysts forecast data available has 67,980 firm-quarter observations. All variables are winsorized at 1st and 99thpercentile.



Since asset turnover ATO tends to be inversely related to profit margins Nissim and Penman2001 and the definition of core earnings closely parallels profit margins, we include ATO in themodel. Sloan 1996 finds that accrual levels are an explanatory variable for future performance,so we use lagged operating accruals ACCRUALSq1 and ACCRUALSq4 as a control for currentoperating performance. For reasons discussed earlier, we do not include current-period accruals.Also similar to McVay 2006, we include percentage changes in sales SALES and allowdifferent slopes for sales increases and decreases NEG. The reason for this inclusion is that, eventhough core earnings are scaled by sales, as sales grow, fixed costs become smaller per salesdollar. Those costs increase more when activity rises than they decrease when activity falls by anequivalent amount Anderson et al. 2003.

In addition to the variables proposed by McVay 2006, we also add the current quartersreturns RETURNS and the previous quarters returns to the model. The use of current quarters

TABLE 3Spearman/Pearson Correlation Matrix

CEq CEq1 CEq4 ACCRUALSq1 ACCRUALSq4 ATOqCEq 1.0000 0.7351 0.6530 0.2750 0.2806 0.0308CEq1 0.7902 1.0000 0.6446 0.2084 0.3043 0.0295CEq4 0.7357 0.6999 1.0000 0.3019 0.2415 0.0135ACCRUALSq1 0.2947 0.2654 0.3129 1.0000 0.3171 0.0381ACCRUALSq4 0.3055 0.3441 0.2722 0.0342 1.0000 0.0238ATOq 0.0554 0.0558 0.0921 0.1460 0.1039 1.0000

RETURNSq 0.1748 0.1503 0.0675 0.0396 0.0361 0.0789RETURNSq1 0.1627 0.1678 0.0764 0.0166 0.0538 0.0819SALESq 0.2474 0.1928 0.0393 0.0800 0.0094 0.1573NEG_SALESq 0.2621 0.2150 0.0900 0.0486 0.0244 0.1836UE_CEq 0.2392 0.0089 0.0151 0.0121 0.0046 0.0346%SIq 0.0616 0.0327 0.0163 0.0339 0.0685 0.1000

RETURNSq RETURNSq1 SALESq NEG_SALESq UE_CEq %SIqCEq 0.0916 0.0708 0.0847 0.2010 0.4476 0.2293CEq1 0.0678 0.0846 0.0011 0.1468 0.0227 0.1580CEq4 0.0264 0.0274 0.2388 0.0139 0.0099 0.1493ACCRUALSq1 0.0073 0.0033 0.0650 0.0411 0.0020 0.0156ACCRUALSq4 0.0104 0.0232 0.0229 0.0132 0.0068 0.0112ATOq 0.0242 0.0284 0.0488 0.0764 0.0077 0.0434RETURNSq 1.0000 0.0138 0.0688 0.0915 0.0067 0.0755RETURNSq1 0.0147 1.0000 0.0896 0.1000 0.0052 0.0601SALESq 0.1262 0.1421 1.0000 0.4780 0.0130 0.0474NEG_SALESq 0.1277 0.1356 0.8093 1.0000 0.0014 0.1107UE_CEq 0.0277 0.0115 0.0972 0.0936 1.0000 0.0942%SIq 0.0740 0.0559 0.0961 0.0951 0.0119 1.0000

Variable definitions are in Table 1. There are 132,393 firm-quarter observations. Spearman Pearson correlations are belowabove the diagonal. All variables are winsorized at 1st and 99th percentile. Amounts in bold are significant at the 0.01level.



returns controls for current performance. The reason for including prior-period returns is that themarket may detect deteriorating performance and decrease its expectations of core earnings priorto it being reported in the current quarter.

We obtain coefficients for model 2 using all observations in a particular industry-year-quarter excluding firm i. Expected core earnings for firm i are measured using these coefficientsmultiplied by the actual values of the variables in the model for firm i. This model controls for themacroeconomic and industry shocks as well as the seasonal effect on expected core earnings. Wethen obtain unexpected core earnings, calculated as the difference between reported and expectedcore earnings:13

UE_CEq = CEq ECEq . 3

We then estimate the relation between UE_CE and %SI:

UE_CEq = 0 + 1%SIq + q, 4

where %SIq is special items as a percentage of sales in the current quarter. Income-decreasingspecial items are special items multiplied by 1, where special items are income-decreasing.Income-increasing special items are set to zero McVay 2006.

If the estimated coefficient on %SIq 1 is positive, then classification shifting has a domi-nating effect i.e., as income-decreasing special items increase, unexpected core earnings in-crease. In other words, managers shift core expenses to special items such that reported coreearnings would be above expectations. If the estimated coefficient is negative, then firm perfor-mance would be the dominating effect. In other words, when income-decreasing special itemsincrease, unexpected core earnings actually decrease. This would be consistent with prior researchfindings that firms incurring large write-offs or corporate restructuring charges tend to be poorperformers Elliott and Shaw 1988; DeAngelo et al. 1994; Carter 2000.

To demonstrate how controlling for performance in model 2 affects the relation betweenUE_CE and %SI, we start with a model that provides no control for performance and thenprogressively add more controls. First, we set UE_CEq equal to CEq. This is the same as settingECE equal to 0 and providing no control for performance. As shown in the first column of resultsin Table 4, the coefficient of %SI is 0.7099 t 85.71; p 0.01. Recall that classificationshifting suggests a positive relation between UE_CE and %SI, whereas a performance effectwould predict that income-decreasing special items are a sign of poor performance i.e., a negativerelation between UE_CE and %SI. Thus, as expected we find strong evidence of a performance-driven relation between core earnings and income-decreasing special items.

In the second column of Table 4, we use a core earnings expectations model that relies on theassumption that expected core earnings in the current quarter are a function of only core earningsin the same quarter one year ago.

CEq = 0 + 1CEq4 + q. 5

Model 5 is estimated within each industry-year-quarter excluding firm i. Coefficient esti-mates, along with lagged core earnings, are used to calculate unexpected core earnings for firm i.We then estimate the relation between this measure of unexpected core earnings and %SI. Thecoefficient on %SI decreases in magnitude to 0.3531 t 60.28; p 0.01.

13 Consistent with McVay 2006, we identify ECEq as expected and UE_CEq as unexpected core earnings. SinceECEq is partially determined by contemporaneous independent variables, these two variables are not, in fact, ex antein nature and so constitute ex post measures of normal and abnormal core earnings.



Next, we use a core earnings expectations model that approximates the one used in McVay2006, except that it excludes current-period accruals and is estimated using quarterly data.

CEq = 0 + 1CEq4 + 2ATOq + 3ACCRUALSq4 + 4SALESq + 5NEG_SALESq + q.6

As shown in the third column of Table 4, the coefficient on %SI decreases in magnitude to0.263 t 48.06; p 0.01.

In the final column, we report results after using the full core earnings expectations model asspecified in model 2. The estimated coefficient on %SIq is 0.1586 t 16.38; p 0.01,indicating a strong performance-dominated relation. As the results in Table 4 clearly demonstrate,

TABLE 4Regression of Unexpected Core Earnings on Special Items as a Percentage of Sales Using

Alternative Measures of Expected Core EarningsDependent Variable = UE_CEq = CEq ECEq

ECEq = 0 ECEq = fCEq4ECEq = fMcVay

model)ECEq = ffull

model)Intercept 0.0564 0.0091 0.0063 0.0041

59.25 13.57 10.82 6.67%SIq 0.7099 0.3531 0.2630 0.1586

85.71 60.28 48.06 16.38R2 5.26% 2.67% 1.71% 0.89%

Variable definitions are in Table 1. The full sample consists of 132,393 firm-quarter observations.In column 1, unexpected core earnings are defined as core earnings in quarter q.In column 2, unexpected core earnings are defined as core earnings minus expected core earnings, where expected coreearnings are calculated using the coefficients from the model shown below, estimated for each industry-year-quarter,excluding firm i:

CEq = 0 + 1CEq4 + q.

In column 3, unexpected core earnings are defined as core earnings minus expected core earnings, where expected coreearnings are calculated using the coefficients from the model shown below, estimated for each industry-year-quarter,excluding firm i:

CEq = 0 + 1CEq4 + 2ATOq + 3ACCRUALSq4 + 4SALESq + 5NEG_SALESq + q.

In column 4, unexpected core earnings are defined as core earnings minus expected core earnings, where expected coreearnings are calculated using the coefficients from the model shown below, estimated for each industry-year-quarter,excluding firm i:

CEq = 0 + 1CEq4 + 2CEq1 + 3ATOq + 4ACCRUALSq4 + 5ACCRUALSq1 + 6SALESq + 7NEG_SALESq+ 8RETURNSq1 + 9RETURNSq + q.

.

All variables are winsorized at the 1st and 99th percentile. Amounts reported are regression coefficients with t-statistics inparentheses.



current performance and special items have a strong negative relation. However, as one providesadditional controls for performance, the performance effect diminishes.14,15

Tests of HypothesesTest of H1

The first hypothesis predicts that shifting is more pervasive in the fourth quarter. As discussedin Section II, prior research provides evidence that accrual manipulation is more constrained in thefourth quarter, leaving managers to resort to alternative earnings management techniques such asincome classification shifting. Thus, we expect that the relation between unexpected core earningsand special items will be more positive or less negative for fourth quarter observations, com-pared to observations for the first three quarters.

Table 5 presents the results using subsamples with observations from the fourth quarter andthose from the first three quarters. The results show a less negative coefficient for the fourthquarter subsample compared to the subsample for the first three quarters.16 Recall that a morepositive or less negative relation between unexpected core earnings and special items is evidenceof more classification shifting. Comparing the coefficients of 0.1440 for the fourth quarter and0.1819 for the first three quarters indicates that classification shifting is more pervasive in thefourth quarter. The difference between the two estimated coefficients 0.0379 is statisticallysignificant t 4.05; p 0.01. This result provides support for H1 that classification shifting ismore pervasive in the fourth quarter, even though the performance-driven effect is still the domi-nating factor.17

Test of H2Our next hypothesis H2 suggests that those managers who are more constrained in their

ability to manipulate accruals because of previous accrual management activity are more likely toengage in classification shifting in the current quarter. To the extent that one earnings managementoption is not available, managers are likely to implement the next. Following Barton and Simko2002, we measure managers accrual manipulation constraint using net operating assets at thebeginning of the quarter scaled by sales. They demonstrate that firms that have optimisticallyreported accruals in prior periods tend to have higher net operating assets. We classify firms in thehigh low group when their net operating assets are above or equal to below the median for the

14 To find direct evidence of classification shifting, ideally one would find a positive relation between UE_CE and %SI.Since we do not find this for the full version of the model but our results and anecdotal evidence suggests that it isoccurring, this provides a fruitful avenue for future research. As the literature advances and expectations models bettercontrol for the performance effect, the relation between UE_CE and %SI is expected to become positive.

15 To determine whether the relation between our estimate of unexpected core earnings from Model 2 and income-decreasing special items is mechanically driven by contemporaneous accrual special items, we estimate accrual specialitems as other funds from operations #81 plus gains and losses on the sale of property, plant, and equipment andinvestments #102. We split the sample into those with estimated accrual special items, scaled by sales, greater lessthan 0.5 percent 0.5 percent. We also create a third sample with estimated accrual special items between 0.5 percentand 0.5 percent. We find the relations between UE_CE and %SI across these three groups do not differ significantly.For estimated accrual special items less than 0.5 percent greater than 0.5 percent between 0.5 percent and 0.5percent, the coefficient on %SI i.e., 1 in Model 4 is 0.1542 0.1506 0.1736.16 In Table 5 and all remaining tables, differences in coefficients between Q4 and Q13 are tested using an indicatorvariable for fourth quarter observations. For example, for Table 5 we estimate the following model: UE_CEq = 0+ 1%SIq + 2Q4 + 3%SIq Q4 + q, where the prediction is 3 0. In the tables, we present results separately for Q4and Q13 to make the results for each subsample more evident.

17 The increased evidence of classification shifting in the fourth quarter might be a result of increased opportunity i.e.,firms report more special items in the fourth quarter or a result of increased incentives. However, either explanationsupports H1 that classification shifting is more likely in the fourth quarter.



industry-year-quarter. If classification shifting occurs, then we expect to find more evidence of itfor firms with higher net operating assets relative to the industry-quarter median because thesefirms are more constrained in their ability to manipulate accruals.18

Results are reported in Table 6. We test H2 for the full sample and for subsamples based onQ4 and Q13. The subsample tests also allow us to re-examine H1 within the context of H2. Theinteractive effect provides a stronger test of classification shifting. If classification shifting isoccurring, then we expect it to be more pervasive in fourth-quarter observations with high NOA.The results for the full sample and the subsamples support H2. For the full sample, we find that therelation between unexpected core earnings and special items is significantly less negative for firmswith higher net operating assets. The difference is significant t 9.88; p 0.01. The same isalso true for observations in Q4 t 5.52; p 0.01 and in Q13 t 6.77; p 0.01.Furthermore, fourth quarter observations with high NOA show the most evidence in favor ofclassification shifting relative to the other observations. Supporting H2, we conclude that whenmanagers ability to manipulate accruals is constrained, classification shifting is more likely.19

Test of H3For the next set of tests, we split the sample based on reported earnings relative to earnings

benchmarks. Hypothesis 3 predicts that classification shifting is more prevalent in subsamples thatjust meet or beat the analyst forecast, one-year-ago same-quarter earnings, and zero earnings,respectively. In particular, the coefficients on %SIq are expected to be more positive or lessnegative for subsamples with observations that have these characteristics.

18 Of course, our use of this measure requires the joint assumption that net operating assets provide a reliable measure ofprior upward accrual manipulation and that prior upward accrual manipulation reduces the ability of managers tomanipulate accruals in the current period. To the extent that this joint assumption is false, inferences from our results arelimited. Other studies employing net operating assets find evidence consistent with it being a measure of managersaccrual manipulation constraint e.g., Mosebach and Simko 2005; Ettredge et al. 2007; Omer et al. 2008.

19 While not the intended purpose of our study, these results are also consistent with a portfolio approach to managingearnings e.g., Jiambalvo 1996; Fields et al. 2001; Lin et al. 2006; Cohen and Zarowin 2008; Cohen et al. 2008.

TABLE 5Regression of Unexpected Core Earnings on Special Items as a Percent of Sales:

Fourth Quarter (Q4) versus First Three Quarters (Q13)

Independent Variable Q4 Q13Test (H1): Q4

> Q13Intercept 0.0070 0.0032

5.41** 5.72**%SIq 0.1440 0.1819 0.0379

19.12** 28.54** 4.05**R2 1.08% 0.82%Number of Observations 33,454 98,939

*, ** Indicates significant at the 0.05 and 0.01 levels, respectively, using a two-tailed test.Variable definitions are in Table 1. All variables are winsorized at the 1st and 99th percentile. Amounts reported areregression coefficients with t-statistics in parentheses.



We use the I/B/E/S Detail File to determine whether firms just meet or beat analysts fore-casts. The analyst forecast is defined as the median of the last three individual analysts forecastsafter the previous quarters earnings announcement and before the current quarters earningsannouncement.20 Firms reporting an earnings forecast error equal to $0.00 or $0.01 are consideredas just meeting or beating analysts forecasts. These observations include approximately 26 per-cent of our sample. For the prior period earnings benchmark, we define firms as just meeting orbeating if they report a change in reported operating income per share Compustat data item #177from $0.00 to $0.03, constituting approximately 17 percent of our quarterly observations. For thezero earnings benchmark, firms that report operating income per share from $0.00 to $0.04 aredefined as those just meeting the threshold, which equals approximately nine percent of ourquarterly observations.

Results for testing H3 are presented in Tables 79. In Table 7 for the full sample, the coeffi-cient of %SI is 0.0956 for firms that just meet or beat the analyst forecast threshold JustMBFversus 0.1288 for other firms NotJustMBF. The difference in coefficients of 0.0332 is statis-tically significant t 2.17; p 0.03. The difference increases to 0.0581 t 2.01; p 0.04 forobservations in the fourth quarter and falls to 0.0251 t 1.36; p 0.17 in the interim quarters.

20 We use the I/B/E/S data unadjusted for stock splits for reasons discussed in Payne and Thomas 2003.


High Net Operating Assets (HighNOA) versus Low NOA (LowNOA)

Independent Variable Full Sample Q4 Q13Test (H1): Q4

> Q13HighNOA 0.0130 0.0218 0.0103

17.41** 12.05** 12.97**LowNOA 0.0051 0.0084 0.0040

6.80** 4.62** 5.04**%SIq HighNOA A 0.1153 0.1019 0.1441 0.0422

17.83** 9.44** 16.55** 3.22**%SIq LowNOA B 0.2063 0.1848 0.2305 0.0457

31.49** 17.70** 24.67** 3.42**Test H2: A B 0.0910 0.0829 0.0864

9.88** 5.52** 6.77**R2 1.25% 1.77% 1.07%Number of Observations 132,393 33,454 98,939

*, ** Indicates significant at the 0.05 and 0.01 levels, respectively, using a two-tailed test.Variable definitions are in Table 1. HighNOA LowNOA is an indicator variable that equals 1 if a firm-quarter observationhas net operating assets at the beginning of the quarter above or equal to below the median for the industry-year-quarter,and 0 otherwise. All variables are winsorized at 1st and 99th percentile. Amounts reported are regression coefficients witht-statistics in parentheses.



We also note that the differences in Q4 versus Q13 are significant for both JustMBF and Not-JustMBF firms. Overall, we conclude that finding the most evidence of classification shifting inthe fourth quarter for firms that just meet or beat the analyst forecast supports H3.21

We then split the full sample into the subsample with firm-quarter earnings that just meet orbeat prior period earnings JustMBP and the subsample with firm-quarter earnings that do not justmeet or beat prior period earnings NotJustMBP. Managers of firms with earnings that ex post justmeet or beat earnings in the same quarter of previous year are expected to have had strongerincentives ex ante to shift core expenses to special items, especially in the fourth quarter. Theresults in Table 8 show that for the full sample and for each of the subsamples, the relationbetween unexpected core earnings and special items is less negative for JustMBP firms. Theseresults are consistent with H3. We also find stronger evidence of classification shifting to justmeeting or beating prior period earnings in the fourth quarter.

21 As an additional test using analyst data, we consider the extent to which analysts exclude income-decreasing specialitems. We do this by comparing I/B/E/S actual earnings to reported earnings per share excluding extraordinary itemsfrom Compustat. To the extent that analysts do not incorporate income-decreasing special items in their forecasts, weexpect I/B/E/S actual earnings to be greater than reported earnings. For our sample of observations with I/B/E/S data n 67,980, we find that approximately 23 percent have I/B/E/S earnings greater than reported earnings per shareexcluding extraordinary items. Furthermore, if classification shifting is meant to mislead financial statement users, thenwe expect to find more evidence of classification shifting when I/B/E/S earnings exclude expenses. This is what we find.When I/B/E/S earnings are greater than reported earnings, we find that the relation between unexpected core earningsand special items is less negative for the full sample t 4.12; p 0.01, in Q4 t 2.33; p 0.02, and in Q13 t 3.63; p 0.01.

TABLE 7Regression of Unexpected Core Earnings on Special Items as a Percent of Sales:Just Meet or Beat Forecasts (JustMBF) versus Not Just Meet or Beat Forecasts

(NotJustMBF)


> Q13JustMBF 0.0083 0.0133 0.0069

7.68** 4.98** 5.98**NotJustMBF 0.0061 0.0118 0.0042

9.33** 7.75** 5.93**%SIq JustMBF A 0.0956 0.0533 0.1270 0.0737

6.79** 1.98* 7.62** 2.54**%SIq NotJustMBF B 0.1288 0.1114 0.1521 0.0407

21.57** 10.78** 19.84** 3.38**Test H2: A B 0.0332 0.0581 0.0251

2.17* 2.01* 1.36R2 0.84% 0.92% 0.94%Number of Observations 67,980 17,563 50,417

*, ** Indicates significant at the 0.05 and 0.01 levels, respectively, using a two-tailed test.Variable definitions are in Table 1. JustMBF NotJustMBF is an indicator variable that equals 1 0 if a firm-quarterobservation has an analyst forecast error of $0.00 or $0.01, and 0 1 otherwise. Analyst forecast error is defined as actualearnings per share as reported by I/B/E/S less the median of the last three individual analyst forecast after the previousquarters earnings announcement and before the current quarters earnings announcement. All variables are winsorized at1st and 99th percentile. Amounts reported are regression coefficients with t-statistics in parentheses.



Table 9 reports results for firms that just meet or beat the zero earnings threshold JustMBZversus other firms NotJustMBZ. For the full sample, the coefficient on %SI for JustMBZ firms ispositive 0.0304 and significant t 1.96; p 0.05, while the coefficient on %SI for NotJust-MBZ firms is significantly negative 0.1762; t 36.60; p 0.01. The difference is significantt 12.71; p 0.01. We also find significant differences in Q4 and in Q13. For JustMBZ firms,the coefficient on %SI is positive in Q4 0.0400 and negative in Q13 0.0039, but thedifference is not significant t 1.39; p 0.16, probably due to lower power tests resulting fromthe small number of observations that just meet or beat the zero earnings threshold. The differencebetween Q4 and Q13 for NotJustMBZ is significantly positive t 3.73; p 0.01. Overall, theevidence supports classification shifting for firms that just meet or beat the zero earnings bench-mark, again, supporting H3.

As a final test, we consider the three earnings thresholds jointly. That is, we test for incomeclassification shifting when firm-quarter observations just meet or beat any of the three earningsthresholds. This joint test presumably contains less measurement error because evidence in favorof classification shifting is found for each of the three earnings thresholds in Tables 79. Forexample, in our test of the zero earnings threshold in Table 9, the explicit assumption is that firmsthat are classified as not just meeting or beating the benchmark NotJustMBZ are less likely tohave shifted core expenses. However, these firms may just meet or beat the analyst forecast priorperiod earnings threshold. These firms may shift in response to these alternative earnings bench-marks, but our test in Table 9 would not properly test this.

Because we require information for all earnings thresholds jointly, the sample size reduces to66,034 firm-quarter observations. We first note that for this smaller sample, approximately 12.6


Just Meet or Beat Prior Earnings (JustMBP) versus Not Just Meet or Beat Prior Earnings(NotJustMBP)


> Q13JustMBP 0.0120 0.0156 0.0113

9.15** 4.21** 8.41**NotJustMBP 0.0027 0.0057 0.0017

4.52** 4.04** 2.72**%SIq JustMBP A 0.0995 0.0741 0.1373 0.0632

6.70** 3.06** 6.69** 2.10*%SIq NotJustMBP B 0.1630 0.1484 0.1860 0.0376

32.74** 18.23** 27.09** 3.73**Test H3: A B 0.0635 0.0743 0.0487


*, ** Indicates significant at the 0.05 and 0.01 levels, respectively, using a two-tailed test.Variable definitions are in Table 1. JustMBP NotJustMBP is an indicator variable that equals 1 0 if a firm-quarterobservation has a reported change in operating income per share Compustat data item #177 in the current quartercompared to the same quarter in the prior year between $0.00 and $0.03, and 0 1 otherwise. All variables are winsorizedat 1st and 99th percentile. Amounts reported are regression coefficients with t-statistics in parentheses.



26.8 33.3 percent of the observations that are classified as not just meeting or beating theanalyst forecast prior period earnings zero earnings threshold actually just meet or beat one ofthe other two thresholds. Thus, we know that our tests in Tables 79 contain some measurementerror and inferences could be affected. The results of our joint tests are reported in Table 10. Wefind for the full sample, for Q4, and for Q13 that the coefficient on %SI is significantly lessnegative for firms that just meet or beat any earnings threshold JustMB than for other firmsNotJustMB. We also note that the coefficient on %SI is significantly less negative in Q4 than inQ1Q3 for JustMB firms and for NotJustMB firms. The results in Table 10 provide broad supportfor H3.

VI. CONCLUSIONThis study addresses the issue of whether and when managers use classification shifting to

manage core earnings. Prior research provides evidence that investors and analysts tend to fixateon core earnings Bradshaw and Sloan 2002; Gu and Chen 2004; Kinney and Trezevant 1997 andthat earnings reported higher in the income statement tend to have higher valuation multiples Lipe1986. Thus, the extent to which managers shift core expenses to special items is important forunderstanding how managers may take advantage of the markets fixation on core earnings num-bers.

McVay 2006 documents a positive relation between unexpected core earnings and income-decreasing special items, concluding that managers shift core expenses to special items to improvecore earnings. However, McVay 2006 notes that this finding does not hold and in fact becomesnegative when contemporaneous total accruals which include accrual special items are excludedfrom core earnings expectations model. Thus, there is some debate of the extent to which man-


Just Meet or Beat Zero Earnings (JustMBZ) versus Not Just Meet or Beat Zero Earnings(NotJustMBZ)


> Q13JustMBZ 0.0388 0.0514 0.0355

22.46** 11.41** 19.76**NotJustMBZ 0.0005 0.0031 0.0003

0.90 2.30* 0.47%SIq JustMBZ A 0.0304 0.0400 0.0039 0.0439

1.96* 1.57 0.18 1.39%SIq NotJustMBZ B 0.1762 0.1622 0.1980 0.0358

36.60** 20.65** 29.76** 3.67**Test H3: A B 0.2066 0.2022 0.1941


*, ** Indicates significant at the 0.05 and 0.01 levels, respectively, using a two-tailed test.Variable definitions are in Table 1. JustMBZ NotJustMBZ is an indicator variable that equals 1 0 if a firm-quarterobservation has a reported operating income per share in the current quarter between $0.00 and $0.04, and 0 1 otherwise.All variables are winsorized at 1st and 99th percentile. Amounts reported are regression coefficients with t-statistics inparentheses.



agers shift core expenses to special items. McVay 2006 calls for research to improve the coreearnings expectations model and to provide additional cross-sectional tests of classification shift-ing.

Using quarterly data, we first extend McVays 2006 core earnings expectations model bydropping current-period accruals and including additional controls for performance. We developalternative measures for unexpected core earnings based on different models for expected coreearnings. We then examine the relation between unexpected core earnings and income-decreasingspecial items measured as a positive number. Without sufficient control for performance, we firstobserve a strong negative relation, as opposed to a positive relation that indicates classificationshifting. However, this negative relation significantly diminishes with additional performancecontrols.

Next, we provide additional cross-sectional tests to evaluate increased classification shiftingin specific settings. First, we show that classification shifting is more prevalent in the fourthquarter than in interim quarters. This finding is consistent with managers adopting alternativeearnings management techniques in the fourth quarter Cohen et al. 2009 and that it is moredifficult to achieve earnings thresholds using accrual manipulation in the fourth quarter Brownand Pinello 2007. Second, we document that classification shifting is more prominent whenmanagers appear to be constrained in their ability to manipulate current-period accruals because ofprior upward accrual manipulation Barton and Simko 2002. This result is consistent with thereasoning that managers have a portfolio of choices for manipulating reported performance. To theextent that one option for manipulating earnings is limited, managers will turn to the next. While


Just Meet or Beat Any of the Three Earnings Benchmarks (JustMB) versus Not Just Meetor Beat Any of the Three Earnings Benchmarks (NotJustMB)


> Q13JustMB 0.0108 0.0196 0.0083

11.84** 8.56** 8.63**NotJustMB 0.0040 0.0078 0.0027

5.56** 4.64** 3.49**%SIq JustMB A 0.0812 0.0445 0.1140 0.0695

6.96** 2.02* 8.19** 2.90**%SIq NotJustMB B 0.1355 0.1185 0.1569 0.0384

21.42** 10.90** 19.15** 3.01**Test H3: A B 0.0543 0.0740 0.0429


*, ** Indicates significant at the 0.05 and 0.01 levels, respectively, using a two-tailed test.Variable definitions are in Table 1. JustMB NotJustMB is an indicator variable that equals 1 0 if a firm-quarterobservation has any of the following: an analyst forecast error of $0.00 or $0.01, a reported change in operating income pershare Compustat data item #177 in the current quarter compared to the same quarter in the prior year between $0.00 and$0.03, or a reported operating income per share in the current quarter between $0.00 and $0.04, and 0 1 otherwise. Allvariables are winsorized at 1st and 99th percentile. Amounts reported are regression coefficients with t-statistics inparentheses.



accrual manipulation and real activities management have been extensively investigated in theliterature, income classification shifting has received relatively little attention and represents aviable opportunity for managers to manipulate reported earnings.

In addition, we find greater evidence of classification shifting for samples of firms that justmeet or beat analysts forecasts, one-year-ago same-quarter earnings, and zero earnings. Theseresults are consistent with managers attempting to avoid market penalties Barth et al. 1999;Bartov et al. 2002; Kasznik and McNichols 2002 and other negative consequences associatedwith missing earnings targets. These results are also consistent with managers having more incen-tive to shift income when investors are likely giving more weight to earnings performance e.g.,Hayn 1995; Burgstahler and Dichev 1997b; Barth et al. 1998; Collins et al. 1999; Barua et al.2006.

Clearly, the extent, timing, and incentives under which managers engage in classificationshifting is an important area of research. To the extent that managers attempt to fool investors byshifting core expenses to special items, it is important for researchers to better understand theeffects on investors, auditors, regulators, and other market participants. Because this area ofresearch is relatively new, there is ample opportunity for future research to explore the settingsunder which classification shifting occurs and to improve upon models to measure expected coreearnings or core expenses.22 Although we improve upon the model in McVay 2006, we have notbeen able to fully control for performance. Future research may consider further advancements ofthe model to facilitate research on classification shifting.

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