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session 3 yong gyu lee - Zicklin School of Business - CUNY

Do Firms Shift within Special Items to Manage Earnings?

An Analysis **of** Restructuring Charges and Other Special Items

Reported under SFAS 146

Divya Anantharaman

Department **of** Accounting & Information Systems

Rutgers University

1 Washington #916

Newark, NJ 07102

divyaa@rutgers.edu

Masako Darrough*

Stan Ross Department **of** Accountancy

Baruch College, Box B 12-225

55 Lexington Ave

New York, NY 10010

Masako.Darrough@baruch.cuny.edu

Yong Gyu Lee

Stan Ross Department **of** Accountancy

Baruch College, Box B 12-225

55 Lexington Ave

New York, NY 10010

Yong**gyu**.Lee@baruch.cuny.edu

May 2011

* Corresponding Author

Do Firms Shift within Special Items to Manage Earnings?

An Analysis **of** Restructuring Charges and Other Special Items

Reported under SFAS 146

Abstract

We examine how managers trade **of**f between various alternative channels **of** earnings

manipulation, using the passage **of** SFAS 146 Accounting for the Costs Associated with

Exit or Disposal Activities as a quasi-experiment. Since SFAS 146 restricted managerial

discretion over restructuring charges, we hypothesize that managers **of** firms with

earnings management incentives shift to non-restructuring special items, where they still

retain some reporting discretion, to manipulate reported earnings post-SFAS 146. We

find, consistent with expectation, that firms with incentives to smooth earnings switched

from restructuring charges to non-restructuring special items (particularly long-lived

asset write-downs and goodwill impairments) to smooth earnings post-SFAS 146. We

also find that asset write-downs taken in such circumstance are valued positively by

investors, probably because the write-downs are likely to mechanically boost future

earnings rather than reflect poor fundamental performance. Furthermore, we find

evidence indicating that the firms we suspect **of** having switched to non-restructuring

special items to smooth earnings in the post-SFAS 146 regime indeed have smoother

earnings when compared to firms with similar smoothing incentives that did not switch.

Overall, our results provide a more complete picture **of** the economic consequences **of**

SFAS 146 and shed insight into how managers with reporting incentives respond to

regulatory changes that tighten some aspect **of** their reporting environment.

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I. INTRODUCTION

Restructuring charges are believed to be a common conduit for earnings

manipulation. Managers have historically had significant discretion over the amount and

timing **of** these charges (Moerhle 2002; Bens and Johnston 2009). The introduction **of**

SFAS 146, Accounting for Costs Associated with Exit or Disposal Activities has,

however, significantly curtailed managerial flexibility in recognizing restructuring

charges (Lee 2011). In this study, we examine how firms with strong incentives to

manage earnings respond to this change in the reporting environment; specifically, we

ask whether such firms “shift” from the use **of** restructuring charges to the use **of** other

special items to manipulate reported earnings following SFAS 146. 1

Managers who wish to manipulate earnings can resort to several alternative

channels for earnings management. When regulatory changes increase the cost **of** one

channel, managers may respond by finding substitute channels that are less costly in the

new environment. Ewert and Wagenh**of**er (2005) propose an analytical model to show

that tighter accounting standards, while decreasing accrual-based earnings manipulation,

may increase earnings manipulation through “real” actions. Cohen et al. (2008) find

empirical support for this idea around the passage **of** the Sarbanes-Oxley Act. They find

that accruals-based earnings management declined but real earnings management

increased after the Act, indicating that the tighter financial reporting environment after

Sarbanes-Oxley simply motivated managers to shift from one mechanism **of**

manipulating earnings to another. Cohen et al. (2008) summarize this notion:

1 Throughout this paper, we characterize “shifting” as the shifting from one earnings component

to another through accrual management, real earnings management (e.g., Roychowdhury 2006),

or by reclassifying revenues or expenses under a different label (e.g., McVay 2006).

1

“Evidence **of** a decline in one type **of** earnings management may lead one to

conclude that such activities have decreased in response to regulators or other events,

when in fact a substitution **of** one earnings management method for another has

occurred.”

This argument motivates our investigation.

We use the passage **of** SFAS 146 as a quasi-experimental setting to study the trade**of**f

between alternative channels for managing earnings. Earnings manipulation through

restructuring charges has received critical scrutiny from accounting regulators over the

past two decades, culminating in SFAS 146 issued in 2002. SFAS 146 was expected to

reduce managerial flexibility as it allowed firms to recognize a restructuring charge only

when related costs were actually incurred. Lee (2011) finds that smoothing behavior

using restructuring accruals declined post-SFAS 146, and concludes that SFAS 146 was

indeed effective at curtailing earnings manipulation using restructuring charges.

This may not, however, provide a complete picture **of** the managerial response to

this sudden loss **of** flexibility in one area **of** reporting. As long as managers function in an

environment with pressure to achieve reporting goals, accounting rules that restrict

manipulation through one channel may simply generate a shift to other channels **of**

manipulation. Restructuring charges are only one component **of** a category **of** accruals

**of**ten referred to as “special items,” which include goodwill and other asset impairment

charges, gains and losses from disposal **of** assets, litigation reserves and other items over

which managers still retain substantial flexibility. Therefore, managers who wish to

manipulate reported earnings in the post-SFAS 146 regime may shift from using

restructuring charges to using other (i.e., non-restructuring) special items. 2

2 We also examine discontinued operations as a potential item to which firms might have

switched from restructuring costs under SFAS 146, as Barua et al. (2010) document that

managers use classification shifting to manage earnings when reporting discontinued operations.

2

We expect a shift to non-restructuring special items for many reasons. First, since

these items are **of**ten “non-recurring” or are at least believed to be such, the valuation

implications **of** these charges are less negative than those **of** more persistent charges (Gu

and Chen 2004; McVay 2006). 3 Second, managerial compensation is **of**ten shielded from

such non-recurring items, even when they are income-decreasing (Dechow et al. 1994;

Gaver and Gaver 1998). Finally, while accounting standards have been issued aiming to

standardize reporting for various special items, managers still retain discretion in the use

**of** these charges (e.g., Francis et al. 1996; Riedl 2004; Ramanna and Watts 2009). In sum,

other special items still represent relatively inexpensive and flexible channels for

manipulation. Therefore, when managers are faced with a tighter reporting standard for

restructuring charges, we expect them first to resort to other special items to manage

earnings, before shifting to other, more persistent accruals or real activities that are

potentially more visible or have more negative operational and valuation implications.

We test our hypotheses on a sample **of** 2,045 firm-years for the pre-SFAS 146

regime (2001-2002) and 3,520 firm-years for the post-SFAS 146 regime (2004-2007).

Preliminary inspection **of** the data shows that long-lived asset write-downs and goodwill

impairments are the largest non-restructuring special items. Between the pre- and post-

SFAS 146 regimes, there is a decrease in restructuring charges as well as in asset writedowns

and goodwill impairments, possibly due to the recession in the early 2000s that

compelled many firms to write down assets. Since our focus is the “managed” or

“discretionary” portion **of** restructuring charges and non-restructuring special items, we

estimate the managed portion **of** restructuring charges (“abnormal restructuring charges”)

3 In recent work, however, Cready et al. (2010) document that as supposedly non-recurring

special items increase in frequency, the market starts valuing them more akin to recurring, or

permanent components **of** earnings.

3

following Bens and Johnston (2009) and Lee (2011), and the managed portion **of** nonrestructuring

special items (“abnormal non-restructuring special items”) by subtracting

the industry average **of** that special item. 4 We observe a significant decrease in abnormal

restructuring charges and a significant increase in abnormal non-restructuring special

items under SFAS 146. Therefore, the shifts over time in the sample are consistent with

switching from restructuring charges to non-restructuring special items.

In our primary tests, we regress abnormal non-restructuring special items on

abnormal restructuring charges, and indicators for earnings management incentives as

well as for the post-SFAS 146 regime. We find that abnormal restructuring charges and

abnormal non-restructuring special items are generally positively associated with each

other - i.e., firms with earnings management incentives tend to use all types **of** special

items. More importantly, this positive association is, however, attenuated significantly in

the post-SFAS 146 period, for firms with smoothing incentives. This implies that firms

switched from restructuring charges to other special items to smooth earnings under

SFAS 146, possibly due to the lower flexibility with restructuring charges imposed by the

new rule. Further examination **of** non-restructuring special items reveals that firms switch

mainly to asset write-downs and goodwill impairments. We do not, however, find a

switch to non-restructuring special items for firms with big bath incentives.

We next examine the valuation implications **of** this shifting behavior. We identify

those firms that are suspected **of** having switched from restructuring charges to long-lived

asset write-downs and goodwill impairments in the post-SFAS 146 regime, and examine

the market valuation **of** such write-downs. Consistent with expectation, we find that the

4 For non-restructuring special items, we capture the abnormal component **of** each special item

with the deviation **of** that item from the industry mean. See Section III for more details.

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market valuation **of** long-lived asset write-downs is significantly more positive for firms

with smoothing incentives (i.e., firms with unusually high earnings that are performing

well fundamentally) that are likely to have switched to write-downs in the post-SFAS 146

regime. For these firms, asset write-downs not only are less likely to indicate poor or

worsening fundamental performance, but also mechanically increase earnings in the

future by reducing depreciation charges. In contrast, we do not find any significant

difference in the market pricing **of** goodwill impairments between firms suspected **of**

switching and other firms, possibly because goodwill impairments do not mechanically

increase earnings in the future, as goodwill is no longer subject to amortization.

In our final analysis, we focus on those firms with ex ante smoothing incentives,

and examine whether the firms we suspect **of** having switched to non-restructuring

special items to smooth earnings in the post-SFAS 146 regime indeed have smoother

earnings when compared to firms with similar smoothing incentives that did not switch.

We find that firms suspected **of** having shifted to non-restructuring special items indeed

have smoother incomes ex post than other firms that also had smoothing incentives but,

for some reason, did not (or were not able to) shift to non-restructuring special items.

This not only corroborates our inference that firms suspected **of** switching to nonrestructuring

special items did so in order to smooth earnings, but also shows that the

switching was economically large enough to generate the desired impact on earnings.

Our study makes several contributions to the literature. First, we provide a more

complete picture **of** the economic consequences **of** SFAS 146. While prior research

documents that earnings manipulation through restructuring charges declines following

SFAS 146, we show that this decline is accompanied by an increase in manipulation

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using other special items over which managers still retain substantial discretion. Our

results are consistent with managers substituting other special items for restructuring

charges while smoothing earnings. Our results also have broader implications for

assessing the effectiveness **of** accounting rules designed to curb reporting discretion in

specific accruals. Examining the effect **of** an accounting rule on earnings manipulation

using that particular accrual alone may give an incomplete picture **of** the overall

consequences **of**, and managerial responses to the rule.

Second, we contribute to the literature on the trade-**of**f between various channels **of**

earnings management. Prior research documents that an overall increase in regulatory

scrutiny on all accounting choices may cause a shift from accruals-based earnings

management to real earnings management (e.g., Ewert and Wagenh**of**er 2005; Cohen et

al. 2008). Another stream **of** research shows that managers opportunistically shift

expenses from “core” expenses to less persistent earnings components, such as special

items (e.g., McVay 2006; Fan et al. 2010) and discontinued operations (e.g., Barua et al.

2010). We complement these streams **of** research by demonstrating that shifting may

occur even within special items, a reporting behavior which potentially represents both

accrual-based and real earnings management when managers are faced with lower

flexibility in accounting choices.

Finally, our results demonstrate a relatively unexamined consequence **of** a “rulesbased”

accounting regime. Researchers and commentators have suggested that highly

detailed accounting rules and guidance may incentivize managers to structure

transactions around the rules (e.g., Nelson et al. 2002). We show that the issuance **of**

highly specific rules that address specific areas **of** accounting may generate the search

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for, and use **of**, other areas **of** accounting that (at least for the time being) **of**fer

comparatively more flexibility and/or suffer from less regulatory scrutiny.

The paper proceeds as follows. Section II provides background and develops the

hypotheses. Section III describes the sample and empirical methodology, and reports

descriptive statistics. Section IV presents the empirical results. Section V concludes.

II. BACKGROUND AND HYPOTHESES

Earnings Management with Restructuring Charges and SFAS 146

Restructuring costs typically include costs incurred for employee severance and

termination, elimination **of** product lines, consolidation **of** manufacturing facilities, new

systems development and retraining employees to use new systems (Moerhle 2002). Even

though restructuring activities are an integral aspect **of** firm strategy, managers have been

able to exercise discretion over the amount and timing **of** restructuring charges that they

recognize in current period income. For example, managers could overstate restructuring

charges in a particularly bad year, i.e., take a “big bath” (Brickley and Van Drunen 1990;

Elliott and Hanna 1996; Bens and Johnston 2009). Managers may shift future costs into

current restructuring charges and essentially set up a hidden reserve, which they can

reverse (“b**lee**d back”) into income in later periods where the firm would otherwise miss

a crucial earnings benchmark (Moerhle 2002). Since restructuring charges are assumed to

be non-recurring and hence create less negative valuation implications (Gill, Gore and

Rees 1996), restructuring charges may be used both as part **of** a “big bath” strategy or as

a “cookie-jar” reserve to smooth earnings.

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There had been little authoritative guidance on restructuring charges until the

issuance **of** Emerging Issues Task Force Issue No. 94-3 (EITF 94-3), Liability

Recognition for Certain Employee Termination Benefits and Other Costs to Exit an

Activity (including Certain Costs Incurred in a Restructuring). EITF 94-3, issued in

1994, imposed the requirement that a liability for an exit cost (e.g., a restructuring cost)

be recognized at the date **of** an entity’s commitment to an exit plan. Bens and Johnston

(2009) find not only that restructuring charges pre-EITF 94-3 were, on average,

excessive, but also that excess charges were greater for firms that had strong incentives to

take a big bath or to smooth earnings. They find that excess restructuring charges, and the

use **of** these excess charges for big bath and smoothing strategies, declined in the period

following EITF 94-3. They find, however, that the decline was temporary, and restricted

to the period in which the SEC exercised scrutiny over restructurings; once SEC scrutiny

dropped **of**f, the use **of** restructuring charges for earnings manipulation increased again.

The issuance **of** SFAS 146 significantly changed the accounting for restructuring

costs. In contrast to prior guidance, which allowed firms to book restructuring charges

once the plan was committed to, SFAS 146 requires firms to recognize restructuring

charges only in the period in which they are incurred. One **of** the objectives **of** the

standard was to improve the “representational faithfulness” **of** restructuring charges,

reducing the flexibility managers have to book excessive charges as part **of** either a big

bath or a smoothing strategy. Lee (2011) finds the use **of** restructuring charges for

earnings smoothing decreased significantly in the period following SFAS 146, providing

evidence that the standard was indeed effective at curtailing some earnings manipulation.

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Other Special Items

Restructuring charges are only one component **of** a category **of** accruals **of**ten

referred to as “special items”. Special items arise from significant events that are either

unusual in nature or infrequent in occurrence, and must therefore be disclosed as a

separate line item on the income statement or broken out in the footnotes (Revsine et al.

2005). Since special items are **of**ten transitory, they are mostly treated as such by

investors (Lipe 1986; Fairfield et al. 1996; Bradshaw and Sloan 2002; McVay 2006).

Therefore, when faced with reduced flexibility in using restructuring charges for big bath

or smoothing behavior, we expect managers to turn next to other special items to achieve

their reporting goals, before switching to more persistent accruals or real activities.

Appendix A describes the various accruals that are commonly categorized as

special items. While each **of** these items is unusual or infrequent in nature, their

underlying economics and accounting treatment varies widely. Impairments **of** other

long-lived assets were governed by SFAS 121, Accounting for the Impairment **of** Long-

Lived Assets and for Long-Lived Assets to Be Disposed Of, issued in 1995, and then by

SFAS 144, Accounting for the Impairment or Disposal **of** Long-Lived Assets, issued in

2001. Zucca and Campbell (1992) show that asset write-downs were used for income

smoothing. Using data up to 1998, Riedl (2004) documents big bath behavior with longlived

asset write-downs in the period after SFAS 121; however, we are not aware **of**

empirical evidence on earnings manipulation using these charges with more recent data.

Goodwill impairments are governed by SFAS 142, Goodwill and Other Intangible

Assets, issued in 2001. Empirical evidence shows that managers enjoy considerable

discretion over the amount and timing **of** goodwill impairment, since the valuation

9

assumptions and models used are **of**ten difficult to verify and are not mandatory

disclosure (Ramanna and Watts 2009). Therefore, managers may accelerate or postpone

the timing **of** goodwill impairments to a particularly bad year, as part **of** a big bath

strategy, or to a particularly good year as part **of** a smoothing strategy. Such impairments

result in a reduction in the goodwill balance and prevent timely loss recognition when the

goodwill is actually impaired (Ramanna and Watts 2009). However, goodwill is no

longer amortized following SFAS 142; it is instead subject to annual impairment testing.

While this may give managers more discretion with the amount and timing **of** goodwillrelated

charges, it also implies that goodwill impairments no longer reduce future

amortization charges, making goodwill impairments less useful as a tool for smoothing

earnings.

Other components **of** special items include gains or losses from the disposal **of**

long-lived assets, which can be timed in such a way that the recognized gains or losses

from disposals smooth out fluctuations in core earnings (Bartov 1993). Stefanescu (2006)

finds however that this smoothing is reduced considerably following the issuance **of**

SFAS 144 in 2001. In-process R&D write-**of**fs are another special item that was used

extensively by high-technology firms as part **of** big bath strategies in the mid-1990s.

However, intense scrutiny on these charges by the SEC is believed to have eventually

reduced manipulation **of** earnings using these write-**of**fs (AAA FASC 2003; Dowdell and

Press 2006). Litigation reserves are another common component **of** special items,

governed by SFAC 5 that lays out the accounting for loss contingences in general.

Litigation reserves have the potential to be used for big bath or smoothing strategies, as

managers may over-estimate the reserve in one year and then b**lee**d it back into income in

10

a later year; we are not, however, aware **of** direct empirical evidence on the use **of**

litigation reserves for earnings manipulation. Finally, special items also include costs **of**

failed mergers and acquisitions (e.g., fees for lawyers and investment bankers) and gains

and losses on the extinguishment **of** debt.

When faced with lower flexibility with restructuring charges, we believe that

managers are likely to turn to items with similar visibility and similar valuation

implications as tools for managing earnings, making these non-restructuring special items

a natural alternative. 5 However, over the recent past, the FASB has issued a string **of**

guidance that aims to standardize the accounting for many **of** these special items (e.g.,

SFAS 142, 144, 141R). Therefore, whether managers continue to enjoy sufficient

flexibility over these items to make them a feasible alternative for earnings management

is an empirical question. Our first hypothesis follows:

Hypothesis 1: Following the implementation **of** SFAS 146, managers **of** firms with big

bath or smoothing incentives will shift to other special items for managing

reported earnings.

We complete our analyses by examining the valuation implications **of** restructuring

charges and other special items in the post-SFAS 146 regime. If the passage **of** SFAS 146

generates shifts in the extent to which other special items reflect earnings manipulation as

opposed to economic fundamentals **of** the firm, then the valuation implications **of** these

charges may also change systematically. Most other special items, particularly long-lived

5 We do not hypothesize on the specific other special items that firms would switch to as this

choice is not clear ex ante. For example, goodwill impairments do not reduce future amortization

charges post-SFAS 142, making them less attractive as a tool for smoothing earnings. However,

the impairment methodology allows substantial room for managerial discretion, possibly making

it easier to manipulate reporting earnings by managing the timing **of** impairments. Finally, not all

special items are available to every firm; for example goodwill impairments and in-process R&D

write-downs are available only to firms with past or current M&A activity. In our sample, 70.6%

**of** firms have goodwill balances greater than 1% **of** total assets.

11

asset write-downs and goodwill impairments, could reflect poor or worsening

fundamental performance and could therefore have negative valuation implications in the

absence **of** deliberate manipulation. However, if these charges are taken as part **of** big

bath or smoothing strategies, they are less likely to indicate poor fundamental

performance and may thus be viewed less negatively by investors. Moreover, some

special items, such as asset write-downs, generate an almost mechanical increase in

future earnings by reducing future depreciation expenses. As a result, investors’

perception **of** asset write-downs that are likely taken as part **of** big bath or smoothing

strategies could be even less negative, compared to that **of** the managed portion **of** other

special items. Taken together, we expect that the negative market valuation **of** nonrestructuring

special items (especially, asset write-downs) to which firms are likely to

have shifted will be attenuated post-SFAS 146. 6 Our second hypothesis follows:

Hypothesis 2: The market valuation **of** other special items in the post-SFAS 146 regime is

less negative (or more positive) for firms that are likely to have shifted to

these items.

III. SAMPLE AND EMPIRICAL METHODOLOGY

Data

We obtain accounting data from the Compustat Fundamental annual data file and

stock return data from CRSP for fiscal years 2001-2002 (pre-SFAS 146 period) and

2004-2007 (post-SFAS 146 period). CEO turnover is computed based on the ExecuComp

6 Similar reasoning would apply to the managed versus unmanaged components **of** restructuring

charges in the pre-SFAS 146 regime. The differential valuation **of** managed versus unmanaged

special items is, however, not our main research question. We perform this analysis only in the

post-SFAS 146 period for non-restructuring special items since observing differential (less

negative, or more positive) valuation **of** managed non-restructuring special items would

corroborate our inference that firms have shifted to these items to manipulate earnings.

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database. Compustat began providing special items by type in 2000, but we focus on the

2001-2002 and 2004-2007 periods for the following reasons. First, we do not include

observations in fiscal year 2000 because there are much fewer observations than in the

following years. Second, we exclude observations in fiscal year 2003, which can be

viewed as a transition period between the pre- and post-SFAS 146 regimes. Third,

observations in fiscal year 2008 are deleted to minimize the effect **of** a recession on the

results. Our sample selection process is outlined in Panel A **of** Table 1.

The initial sample consists **of** firms that reported nonzero restructuring costs at least

once during our sample period. This sample satisfies the following requirements: no fiscalyear-end

change; sales greater than $5 million; non-financial firms; year-industry

combinations with at least 10 observations; restructuring costs greater than 0.1% **of** sales;

restructurings not recorded after a merger; 7 and non-missing regression variables for the

estimation **of** abnormal restructuring costs. These requirements result in a sample **of**

1,332 firm-years for the pre-SFAS 146 period and 2,029 firm-years for the post-SFAS

146 period, representing 1,781 unique firms.

For these restructuring firms, we add non-restructuring years’ observations with all

available data. We do not however include observations with the sum **of** incomedecreasing

special item components less than 0.1% **of** sales, in order to focus on firms

that report relatively large special items. These procedures result in a sample **of** 2,045

firm-years for the pre-SFAS 146 period and 3,520 firm-years for the post-SFAS 146

period. We use these 5,565 observations in the analysis **of** earnings management. For the

7 SFAS 146 does not apply to restructuring charges recorded after a merger. Such charges are

governed by SFAS 141(R), **Business** Combinations, which requires restructuring costs that the

acquirer expected but was not obligated to incur to be expensed after the merger. Because we

exclude restructurings recorded after a merger, the overall magnitude **of** in-process R&D write**of**fs

is relatively small in our sample, but this does not affect our major findings.

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other analyses, we only use the post-SFAS 146 sample. However, the firm-years used in

the income smoothing analysis are reduced to 3,416 observations, since we require nonmissing

earnings information for the current and previous four years.

Panel B **of** Table 1 details the industry composition **of** the sample. Industries are

classified based on Fama and French (1997) 12 industries. 8 In the pre-SFAS 146 period,

the most common industry is **Business** Equipment (including Computers, S**of**tware, and

Electronic Equipment), followed by manufacturing. A similar industry composition is

observed for the post-SFAS 146 regime, showing that the mix **of** industries in our sample

is relatively constant.

Most industries in our sample experienced a decrease in both restructuring charges

and non-restructuring special items in the post-146 regime. 9 This may be because the pre-

SFAS 146 period overlaps with the recession in early 2000s, which led most firms to

report large special charges, such as restructuring costs, asset write-downs and goodwill

impairments. In the context **of** our study, however, such (unadjusted) special items would

not approximate well the managed portion **of** special items. We address this issue by

estimating the abnormal component **of** restructuring costs and non-restructuring special

items, as we describe next.

Estimation **of** the Managed Portion **of** Special Items

Our analyses require a proxy for the portion **of** special items that managers

overstate due to earnings management incentives, i.e., the abnormal component **of** special

8 The industry classification codes are obtained from Pr**of**essor French’s website:

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

9 Non-restructuring special items are calculated as the sum **of** the following special items (all

after-tax) per Compustat: asset write-downs, goodwill impairments, litigation costs, losses on

asset sales, merger-related costs, in-process R&D, losses on extinguishment **of** debt, and other

special items.

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items. We estimate the abnormal component separately for restructuring costs and all

other special items. For restructuring costs, we follow the methodology used in recent

studies (e.g., Bens and Johnston 2009; Lee 2011). Specifically, we first identify a control

sample that does not record restructurings. And for both the restructuring and control

samples, we estimate a tobit model where the explanatory variables are proxies for

economic fundamentals. The tobit model is as follows (firm subscripts are omitted for

brevity):

REST t = α 0 + α 1 ΔGDP t + α 2 ΔSALES t + α 3 ΔEBSI t + α 4 ΔOCF t + α 5 PM t-1 + α 6 LOSS3 t-1

+ α 7 AR_TO t-1 + α 8 INV_TO t-1 + α 9 PPE_TO t-1 + α 10 SALE_EMP t-1

+ α 11 RET t-1 + POST*[β 0 + β 1 ΔGDP t + β 2 ΔSALES t + β 3 ΔEBSI t + β 4 ΔOCF t

+ β 5 PM t-1 + β 6 LOSS3 t-1 + β 7 AR_TO t-1 + β 8 INV_TO t-1 + β 9 PPE_TO t-1

+ β 10 SALE_EMP t-1 + β 11 RET t-1 ] + ε t , (1)

where (# refers to Compustat item):

REST t = after-tax restructuring costs (#RCA) less reversal **of** restructuring

costs (#RRA) for period t, multiplied by -1 and deflated by sales

(#SALE) for period t; 10

ΔGDP t = the annual percentage change in real U.S. GDP for period t; 11

ΔSALES t = the percent change in sales from period t-1 to t;

ΔEBSI t = the change in earnings before special items from period t-1 to t,

divided by sales for period t-1; 12

10 When calculating restructuring costs, we subtract reversal **of** restructuring costs because this is

included in the restructuring costs reported in Compustat.

11 Since our sample includes all fiscal-year-ends, we calculate for each fiscal year the annual

percentage change in real U.S. GDP using monthly real GDP data. U.S. GDP data is obtained

from the Federal Reserve Bank **of** St. Louis website: http://research.stlouisfed.org/fred2/.

12 Earnings before special items represents income before extraordinary items (#IB) minus aftertax

special items. After-tax special items are the sum **of** after-tax special item components for

year 2001 and onwards. Prior to 2001, after-tax special items are estimated as pre-tax special

items (#SPI) multiplied by the firm’s effective tax rate, which is measured as total tax expense

(#TXT) divided by pretax income (#PI) as in Cready et al. (2010).

15

ΔOCF t = the change in operating cash flows (#OANCF) from period t-1 to t,

divided by sales for period t-1;

PM t-1 = pr**of**it margin, measured as the ratio **of** income before extraordinary

items to sales for period t-1;

LOSS3 t-1 = an indicator variable equal to 1 if the firm incurred a net loss in

period t-1, t-2, or t-3, and 0 otherwise;

AR_TO t-1 = the ratio **of** sales to trade receivables (#RECTR) for period t-1;

INV_TO t-1 = the ratio **of** cost **of** goods sold (#COGS) to total inventory

(#INVT) plus LIFO reserve (#LIFR), if applicable, for period t-1;

PPE_TO t-1 = the ratio **of** sales to property, plant and equipment (#PPENT) for

period t-1;

SALE_EMP t-1 = the ratio **of** sales (in thousand dollars) to the number **of** employees

(#EMP);

RET t-1 = cumulative monthly stock returns for period t-1 per CRSP; and

POST = an indicator variable equal to 1 for observations occurring in the

post-SFAS 146 regime, and 0 otherwise.

Throughout the study, a positive special item corresponds to an income-decreasing

special item. That is, each special item in Compustat is multiplied by -1, and negative

numbers after this adjustment are set to zero. 13

When fundamentals suggest poor firm performance, we expect firms to be more

likely to undertake a restructuring to address their problems. Hence, normal restructuring

costs are expected to be larger for firms with weaker fundamentals. Therefore, we expect

each **of** the independent variables from ΔGDP t to RET t-1 to be positively correlated with

firm performance and negatively with REST t , with the exception **of** the loss indicator

13 Throughout the paper, we only investigate income-decreasing special items because our focus

is on shifting from restructuring costs to other negative special items. This is consistent with

related studies (e.g., McVay 2006; Fan et al. 2010) that also only examine income-decreasing

special items.

16

(LOSS3 t-1 ), which is expected to have a positive association with REST t . We include

POST and its interaction with other explanatory variables to capture any difference in the

association between restructuring costs and economic fundamentals between the pre- and

post-SFAS 146 regimes. The predicted value from equation (1) serves as our proxy for

normal restructuring costs for period t (NREST t ), and the residual from equation (1) is the

proxy for abnormal restructuring costs for period t (ABREST t ). Therefore, ABREST t

equals ε t from equation (1). Since ABREST t is a residual from a regression, it can take

positive or negative values. ABREST t is set to zero when REST t equals zero.

For all other special items, we assume that the difference between the special item

for each firm and the industry-average **of** that special item proxies for the abnormal

component. 14,15 The abnormal component estimated under this approach may still reflect

firms’ performance, although we want to estimate the portion **of** special items that only

incorporates managers’ earnings management incentives. Therefore, in addition to

filtering out the effects **of** industry-wide shocks, we also control for economic factors in

the regression **of** the abnormal component **of** non-restructuring special items (i.e.,

industry-mean-adjusted non-restructuring special items) on abnormal restructuring costs,

as described in Section IV.

14 To separate out the abnormal component **of** certain other special items (e.g., long-lived asset

write-downs, goodwill impairments), we could alternatively employ models developed in the

literature (e.g., Francis et al. 1996; Riedl 2004; Beatty and Weber 2006). However, we choose not

to do so for two reasons. First, as prior studies focus on identifying the determinants **of** the special

items, rather than the explanatory power **of** the models used in doing so, they show a goodness **of**

fit **of** around 10%. In the context **of** our study, however, employing these models would imply

that about 90% **of** the variation in non-restructuring special items is unexplained. This is likely to

overestimate the managed portion **of** non-restructuring special charges and may erroneously

suggest that the dominant motivation for such charges is the manipulation **of** earnings. Second,

prior research does not identify economic factors that determine each **of** the other special items,

whereas we need to estimate the un-managed component **of** all non-restructuring special items

together.

15 When calculating the industry mean **of** each special item, we winsorize the variable at the top

and bottom 5% within each year-industry combination.

17

Table 2 presents the tobit analysis examining the economic determinants **of**

restructuring costs. The abnormal restructuring cost is the residual from each regression.

We present four different specifications **of** the model.

Model 1 estimates equation (1) excluding interaction terms and industry indicators.

Of the 11 economic fundamentals, eight variables are significant as predicted, but the

other three variables, ΔGDP t , ΔOCF t and SALE_EMP t-1 , are not. This model exhibits a

Dhrymes R 2 **of** 38.3%, which is substantial. 16 Model 2 reflects the accounting regime

shift by including the time indicator variable, POST, and its interactions with other

economic factors. Compared to Model 1, two more variables, ΔGDP t and ΔOCF t , enter

the model significantly, while the sign and significance **of** other variables remain similar.

Concerning the interaction terms, several variables (e.g., ΔGDP t , ΔSALES t , ΔOCF t ,

INV_TO t-1 , and RET t-1 ) are significantly less negative under SFAS 146, while other

variables are insignificant. This result suggests that economic fundamentals have a

weaker association with restructurings post-SFAS 146, primarily because recognition **of**

normal restructuring costs tends to be delayed under the standard. Thus, Model 2 has an

advantage over Model 1 in that the interaction terms capture any difference in the

associations between restructuring costs and economic factors across the two regimes.

Models 3 and 4 extend Models 1 and 2, respectively, by adding industry fixed

effects based on Fama and French (1997) 12 industry classifications to capture different

effects **of** fundamentals on restructurings across industries. 17 The Dhrymes R 2 s **of** Models

16 Dhrymes R 2 is the squared correlation between the predicted and actual values for observations

with nonzero values **of** REST t (e.g., Bens and Johnston 2009).

17 Another way to incorporate industry information would be to estimate the tobit regression by

industry, as in Bens and Johnston (2009). While this method is conceptually appealing, the

advantage **of** using this method is less evident when the mix **of** industries in the sample is

relatively constant, as shown in Panel B **of** Table 1. Moreover, untabulated results indicate that,

18

3 and 4 are 39.8% and 41.2%, respectively, which are higher than in previous models. A

similar relation between restructuring costs and economic fundamentals also emerges.

Overall, using industry fixed effects improves the explanatory power **of** the model

without reducing the significance **of** the variables. Therefore, from this point forward, we

use the residual from Model 4 as our proxy for abnormal restructuring costs.

Descriptive Statistics

Table 3 presents descriptive statistics. Panel A provides descriptive statistics

relating to unadjusted special items for the full sample partitioned into the pre-SFAS 146

and post-SFAS 146 regimes. In both regimes, the three largest special items are

restructuring costs (REST t ), asset write-downs (WD t ), and goodwill impairments (GWD t ),

all **of** which decreased on average under SFAS 146. The relative magnitudes across the

two regimes **of** the other special items are mixed. However, most **of** these items are **of**

small magnitudes in each regime. Specifically, other special items, with the exception **of**

Compustat “other special items (#SPIOA),” have a mean less than 0.1% **of** sales. Since

these other special items are not economically significant, in our analyses we henceforth

combine all non-restructuring special items, except WD t and GWD t , in a new variable

called OSI t (“other special items”). We also consider discontinued operations as a

potential item to which firms might have switched under SFAS 146, as Barua et al.

(2010) document that managers shift to discontinued operations to manage earnings.

Panel B **of** Table 3 presents descriptive statistics on the proxy for the managed

portion **of** special items for the full sample partitioned by SFAS 146. We find that

ABREST t is lower and ABNRSI t is greater for the post-SFAS 146 observations. A

on average, fewer variables are significant in the predicted direction, compared to our model

using industry fixed effects. Therefore, we choose industry fixed-effect models, instead **of**

estimating the regression by industry.

19

decomposition **of** ABNRSI t further shows that the increase in ABNRSI t is driven mainly

by the increase in the abnormal components **of** WD t and GWD t (ABWD t and ABGWD t ,

respectively). This pattern suggests a potential shifting behavior even for the full sample,

although our focus is on the firms with big bath and smoothing incentives.

Panel C **of** Table 3 provides descriptive statistics relating to other variables used in

this study. Most **of** the variables reflecting economic fundamentals and size are greater

for the post-SFAS 146 observations, whereas earnings management variables (e.g.,

LEVERAGE t-1 and ΔCEO t ) generally show no significant difference across the regimes.

Table 4, Panel A provides summary statistics on our proxy for the managed portion

**of** special items for firms with smoothing incentives partitioned by SFAS 146. Firms with

smoothing incentives are likely to decrease earnings when earnings are unexpectedly

high. 18 For these firms, we find that ABREST t is lower and ABNRSI t is greater for the

post-SFAS 146 observations. A decomposition **of** ABNRSI t reveals that the increase in

ABNRSI t is mainly driven by the increase in ABWD t and ABGWD t . This result implies

that firms with smoothing incentives might have switched from restructuring costs to

asset write-downs and goodwill impairments under SFAS 146. Panel B results suggest a

similar shifting behavior for firms with big bath incentives.

Table 5 reports correlations among key variables for the full sample partitioned into

the pre-SFAS 146 and post-SFAS 146 regimes. Focusing on the managed portion **of**

special items, we find that ABREST t is generally not significantly related to other

abnormal special items in both regimes. However, our focus is on how such correlations

18 Firms with big bath or smoothing incentives are defined following the literature. See Section

IV for more details.

20

differ for big bath and smoothing firms across the pre- and post-SFAS 146 regimes. We

investigate this next in a multivariate setting.

IV. RESULTS

Earnings Management Analysis

Hypothesis 1 predicts that managers **of** firms with big bath or smoothing incentives

under SFAS 146 will shift to other special items for managing reported earnings. To test

this hypothesis, we estimate the following regression:

DepVar t = α 0 + α 1 ABREST t + α 2 BATH t + α 3 SMOOTH t + α 4 ABREST t *BATH t

+ α 5 ABREST t *SMOOTH t + POST*[β 0 + β 1 ABREST t + β 2 BATH t

+ β 3 SMOOTH t + β 4 ABREST t *BATH t + β 5 ABREST t *SMOOTH t

+ β 6 ABREST t *BATH t *LOW t + β 7 ABREST t *SMOOTH t *LOW t ]

+ Controls + µ t (2)

where DepVar t is ABNRSI t , ABWD t , ABGWD t , ABOSI t , or ABDISOP t , as described

earlier. Independent variables are defined as follows (# refers to Compustat item):

BATH t

= an indicator variable equal to 1 if the firm’s change in earnings before

special items from period t-1 to t, divided by sales for period t-1, is below

the median **of** nonzero negative values **of** this variable among all firms in

the same industry, and 0 otherwise;

SMOOTH t = an indicator variable equal to 1 if the firm’s change in earnings before

special items from period t-1 to t, divided by sales for period t-1, is above

the median **of** nonzero positive values **of** this variable among all firms in

the same industry, and 0 otherwise;

LOW t

= an indicator variable equal to 1 if ABREST t in the post-SFAS 146 regime

is (1) negative, (2) zero, or (3) below the median **of** this variable among

all firms in the same industry, and 0 otherwise;

21

Following the literature, we include BATH t to capture managers’ incentives to

reduce earnings when earnings are unexpectedly low, and SMOOTH t to reflect

managers’ incentives to decrease earnings when earnings are unexpectedly high. Thus,

both α 2 and α 3 should be positive to the extent to which firms use special items for big

bath or smoothing purposes.

Of particular interest in this study is whether for firms with big bath or smoothing

incentives, a decrease in abnormal restructuring costs following the implementation **of**

SFAS 146 is accompanied by an increase in the abnormal component **of** other special

items (i.e., WD, GWD, OSI, and DISOP). While negative estimates **of** β 4 and β 5 could

support this prediction, they might also indicate an increase in abnormal restructuring

costs accompanied by a decrease in the abnormal component **of** other special items.

Accordingly, we include the indicator variable LOW t to capture low abnormal

restructuring charges in the post-SFAS 146 period, and interact it with ABREST t *BATH t

and with ABREST t *SMOOTH t for post-SFAS 146 observations. Negative estimates **of**

β 6 (the coefficient on POST*ABREST t *BATH t *LOW t ) and β 7 (the coefficient on

POST*ABREST t *SMOOTH t *LOW t ) would suggest that firms with big bath and

smoothing incentives shifted from restructurings to other special items under SFAS 146.

Our control variables can be categorized into two groups. The first group **of** control

variables is intended to capture economic performance measured at the macro- and firmlevel.

These variables are ΔGDP t , ΔSALES t , ΔEBSI t , ΔOCF t , LOSS3 t-1 , and RET t-1 ,

which we also use in the estimation **of** abnormal restructuring costs (equation (1)). 19 Each

19 Compared to equation (1), however, we do not include asset-specific variables (e.g., AR_TO t-1 ,

INV_TO t-1 , PPE_TO t-1 , etc.) because such variables are generally less likely to explain nonrestructuring

special items.

22

**of** these variables, except for LOSS3 t-1 , is expected to be negatively associated with

special items to the extent that special items reflect poor fundamental performance. These

variables are intended to capture the portion **of** the dependent variable (i.e., the abnormal

component **of** special items) that might be explained by economic factors, so that we can

focus on the association between the managed portion **of** restructuring costs and that **of**

non-restructuring special items.

The second group **of** control variables is intended to capture explicit or implicit

reporting incentives managers may face in recording special items. These variables

include firm size (LOGASSET t-1 ), leverage (LEVERAGE t-1 ), and CEO turnover

(ΔCEO t ). We do not **of**fer unambiguous predictions on the direction **of** these coefficients,

as prior studies provide competing explanations. First, large firms are more visible and,

as a result, may not be able to easily manage earnings. However, if the assets **of** large

firms reflect past levels **of** overinvestment, then these firms are more likely to record

special items such as asset write-downs. Second, while new CEOs have a tendency to

record special items to manage earnings, Smith (1993) notes that new managers may

respond to adverse economic situations that past management either ignored or created.

Third, Watts and Zimmerman’s (1986) debt covenant hypothesis suggests that high

leverage leads to lower excess charges to maintain slack in covenants. However, to the

extent that debt acts as a disciplining device to mitigate overinvestment problems, firms

with higher leverage and overinvestment are more likely to take special charges. Based

on these competing scenarios, we do not make directional predictions on these variables.

23

Table 6 reports the results **of** estimating equation (2), providing a test **of** Hypothesis

1. 20 We first use the abnormal component **of** non-restructuring special items (ABNRSI t )

as the dependent variable. Both BATH t and SMOOTH t enter the regression positively

and significantly, implying that special items are generally used as a tool to reduce

earnings when earnings are unusually low or high. The positive coefficient on ABREST t

indicates that the abnormal components **of** restructuring costs and other special items

generally move in the same direction.

The main variables **of** interest are POST*BATH t *ABREST t *LOW t and

POST*SMOOTH t *ABREST t *LOW t . We find that POST*SMOOTH t *ABREST t *LOW t

enters the regression negatively and significantly (coeff.= -1.962, t-statistic = -1.894).

This negative relation supports Hypothesis 1 only for firms with smoothing incentives.

Therefore, we conclude that managers **of** firms with smoothing incentives under SFAS

146 responded to the reduced flexibility with restructuring costs by shifting to nonrestructuring

special items to smooth earnings. However, we do not find such evidence

for firms with big bath incentives. One explanation for this finding might be that big bath

incentives are less common than smoothing incentives in the post-SFAS 146 period, as

most firms in the pre-SFAS 146 period have already taken a big bath and may not need to

take an additional big bath in the following few years. 21

20 In this table and hereafter, we estimate a pooled regression with t-statistics calculated using

robust standard errors clustered within every year and industry. This adjustment is necessary

because the overall magnitude **of** special items could be affected by these two dimensions.

However, we do not adjust t-statistics for serial correlation because a firm has at most six

observations. In addition, we winsorize continuous variables at the top and bottom 1%.

21 Note that we measure big bath (smoothing) firms roughly as the bottom (top) half **of** negative

(positive) changes in earnings before special items. As a result, when the overall economy is in

slump as in the pre-SFAS 146 period, more firms could be considered to have big bath incentives

compared to subsequent years when the economy picks up.

24

Next, we examine what components **of** non-restructuring special items drive the

shifting behavior. We find that POST*SMOOTH t *ABREST t *LOW t is negatively

associated with ABWD t and ABGWD t , both significant at the 5% level (t-statistics = -

2.181 and -2.284, respectively). This shows that managers **of** firms with smoothing

incentives under SFAS 146 shifted mainly to asset write-downs and goodwill

impairments to achieve income smoothing. We do not find such shifting behavior using

other special items (ABOSI t ) or discontinued operations (DISOP t ), which are smaller in

magnitude than asset write-downs and goodwill impairments. In untabulated tests, we

also run the regression separately for each item that makes up ABOSI t , but do not find

evidence **of** shifting to these items.

Managers might have turned to goodwill impairments and long-lived asset writedowns

because these items are more similar in magnitude to restructurings compared to

other special items. However, they are different in nature. While long-lived asset writedowns

increase future earnings through lower depreciation charges, goodwill

impairments do not, since goodwill is no longer subject to amortization. However,

managers can exercise considerable discretion over the amount and timing **of** goodwill

impairments as the valuation assumptions and models used are **of**ten difficult to verify

(Ramanna and Watts 2009); this could make goodwill impairments an important and

potentially attractive earnings management channel that managers turn to. Overall, Table

6 results are consistent with our hypothesis that managers **of** firms with smoothing

incentives responded to the adoption **of** SFAS 146 by switching to other special items.

25

Valuation Analysis

Hypothesis 2 predicts that the market valuation **of** non-restructuring special items in

the post-SFAS 146 period is less negative for firms with strong earnings management

incentives. Table 6 indicates that only firms with smoothing incentives under SFAS 146

shifted to non-restructuring special items, such as asset write-downs and goodwill

impairments. Accordingly, we examine how these special items that we suspect are used

to smooth earnings under SFAS 146 are associated with stock returns. To perform this

test, we first define “suspect” firms as firms with smoothing incentives that are expected

to have shifted to another special item, and then investigate how the market valuation **of**

that item differs between suspect and non-suspect firms. Specifically, we estimate the

following regression:

CAR t = β 0 + β 1 EBSI t + β 2 LOSS t + β 3 EBSI t *LOSS t + β 4 ΔEBSI t + β 5 REST t + β 6 X t

+ β 7 (NRSI t - X t ) + β 8 SUSPECT_X t + β 9 SUSPECT_X t *REST t

+ β 10 SUSPECT_X t *X t + β 11 SUSPECT_X t *(NRSI t - X t ) + µ t , (3)

where CAR t is 12-month (ending three months after the fiscal year-end) cumulative

market-adjusted returns; EBSI t is earnings before special items for period t; LOSS t is an

indicator variable equal to 1 if EBSI t is negative, and 0 otherwise; ΔEBSI t is the change

in earnings before special items from period t-1 to t; and X t is the non-restructuring

special item to which firms might have switched under SFAS 146 - based on the results

from Table 6, total non-restructuring special items (NRSI t ), asset write-downs (WD t ), or

goodwill impairments (GWD t ). EBSI t , ΔEBSI t , REST t , and X t are all deflated by market

value **of** equity at the beginning **of** period t in the valuation analysis. We also estimate

equation (3) using X t decomposed into the industry-mean and abnormal components.

26

The explanatory variable **of** interest is the interaction between SUSPECT_X t and

X t . SUSPECT_X t is intended to capture firms that might have shifted from restructuring

costs to X t or one **of** the other non-restructuring special items to smooth earnings. For

each X t , SUSPECT_X t takes the value **of** one if the following conditions are satisfied in

the post-SFAS 146 period: (1) SMOOTH t = 1; (2) ABREST t is non-positive or below the

median **of** this variable among all firms in the same industry; and (3) the abnormal

component **of** X t is above the median **of** this variable among all firms in the same year

and industry. The firms with SUSPECT_X t = 1 are likely to have smoothed earnings by

switching from restructuring costs to other special items under SFAS 146. If investor

perceptions **of** the special items to which firms might have shifted under SFAS 146 to

smooth earnings are less negative (or more positive) compared to non-restructuring

special items for other firms, then β 10 will be positive.

Table 7 reports the results **of** estimating equation (3) to test Hypothesis 2. In Panel

A, we examine investor perceptions **of** total non-restructuring special items under SFAS

146. We find that market valuation **of** restructuring costs is on average positive, whereas

that **of** total non-restructuring special items is not significantly different from zero. 22 The

insignificant coefficients on interaction terms suggest that investors perceive special

items similarly for suspect firms.

In Panel B, however, SUSPECT_WD t *WD t is positively and significantly

associated with returns (coeff. = 5.587, t-statistic = 2.079). As shown in the last column, a

decomposition **of** WD t further reveals that such positive association is driven by the

22 Our finding that the market valuation **of** non-restructuring special items is insignificantly

different from zero stands in contrast to the findings **of** Cready et al. (2010), possibly due to the

fact that we do not partition on the frequency **of** these items, and since we examine 12-month

abnormal returns as opposed to a shorter announcement-window return.

27

abnormal component, as evidenced by the significantly positive coefficient on

SUSPECT_WD t *ABWD t (coeff. = 5.574, t-statistic = 2.014). In contrast, both WD t and

ABWD t enter the regression insignificantly. Combined together, these results suggest that

the market valuation **of** asset write-downs in the post-SFAS 146 period is more positive

for smoothing firms that are suspected to have shifted to this item, compared to other

firms. While asset write-downs could reflect poor fundamental performance, they could

mechanically increase future earnings to the extent that they are used to smooth earnings.

If the market anticipates this future earnings effect **of** asset write-downs used to smooth

earnings, it may value these items positively.

In Panel 3, however, the coefficient on SUSPECT_GWD t *GWD t is insignificant,

suggesting that the market does not value goodwill impairments more positively for

suspect firms. Compared to asset write-downs, the effect **of** goodwill impairment on

future earnings is less clear because these charges do not reduce future amortization costs.

Accordingly, investors may not perceive goodwill impairments used to smooth earnings

as positively as asset write-downs.

Overall, Table 7 results indicate that the market valuation **of** asset write-downs in

the post-SFAS 146 period is more positive for firms with smoothing incentives,

consistent with Hypothesis 2. In contrast, we do not find such result for goodwill

impairments. Therefore, market prices appear to incorporate the differential implications

**of** each special item on future earnings.

Income Smoothing Analysis

We have so far examined shifting within special items for firms with ex ante

smoothing incentives. A natural question that follows is whether such firms that are

28

suspected **of** switching to non-restructuring special items indeed have smoother earnings,

compared to other firms that have similar incentives but do not appear to have switched.

If we find that firms suspected **of** switching to non-restructuring special items indeed

have smoother earnings than firms that did not switch, that would not only corroborate

our interpretation that these “suspect” firms switched to non-restructuring special items in

order to smooth earnings, but also show that the switching was economically significant

enough to generate the desired effect on earnings.

We address this question by examining the group **of** firms with ex ante smoothing

incentives, and dividing them into firms that are suspected **of** having switched to nonrestructuring

special items in the post-SFAS 146 period (SUSPECT_X = 1), and firms

that do not appear to have switched (SUSPECT_X = 0). We then compare the

smoothness **of** income between these groups.

We measure IS t by the Spearman correlation between the change in earnings before

special items (ΔEBSI t ) and the change in after-tax special items (ΔSI t ), both deflated by

sales for the previous year, using the current year’s and past four years’ observations. 23

This assumes that there is an underlying pre-managed income series and that managers

use special items to make the reported series smooth. In general, ΔEBSI t is expected to be

negatively associated with ΔSI t to the extent that special items reflect the underlying

performance **of** firms’ assets. Such negative association, however, is likely to be reduced

if special items are used to decrease earnings when earnings are unusually high (i.e.,

23 Our income-smoothing measure is calculated similar to Tucker and Zarowin (2006), who

measure income-smoothing by the correlation between the change in discretionary accruals and

the change in pre-discretionary income.

29

when firms have smoothing incentives). Therefore, income smoothing is manifested in a

less negative correlation between ΔEBSI t and ΔSI t , i.e., a higher IS t .

Table 8 presents the results. We first compare IS t between firms with smoothing

incentives (SMOOTH t = 1) and firms without smoothing incentives (SMOOTH t = 0).

Firms with SMOOTH t = 1 exhibit a higher IS t , and the difference is significant at the 5%

level. When we focus on the SMOOTH = 1 group and partition suspect firms based on

NRSI t , we find that, for suspect firms, IS t is higher (i.e., less negative) and significant at

the 1% level. This confirms that firms suspected to have switched to non-restructuring

special items post-SFAS 146 indeed have smoother earnings, compared to firms with

similar smoothing incentives that, for whatsoever reason, did not (or were not able to)

switch. We do not, however, find any significant differences in IS t when we partition

firms based on SUSPECT_WD t or SUSPECT_GWD t separately. We conjecture that

these tests may lack sufficient power since using WD t or GWD t alone may not be

sufficient to achieve income smoothing. Firms might need to take a combination **of** nonrestructuring

special items (especially if they are relatively small in magnitude) to make

up for the reduced flexibility with restructuring costs, which are one **of** the largest special

items. Therefore, shifting from restructuring charges to non-restructuring special items as

a whole appears to have helped firms with smoothing incentives to achieve their goal **of**

smoother earnings.

V. CONCLUSION

SFAS 146 is shown to have curtailed earnings management using restructuring

charges (Lee 2011). In this study, we examine whether managers **of** firms with strong

30

earnings management incentives respond to this change in their reporting environment by

shifting to other special items to manipulate earnings.

In empirical tests, we find that while the managed or “abnormal” components **of**

restructuring charges and **of** non-restructuring special items are generally positively

associated with each other, this positive association is significantly attenuated in the post-

SFAS 146 regime for firms with incentives to smooth earnings. For firms with smoothing

incentives post-SFAS 146, low abnormal restructuring charges are accompanied by

higher abnormal non-restructuring special charges, suggesting that these firms switched

from restructuring charges to other special items for earnings smoothing. Additionally,

we find that firms switch mostly to asset write-downs and goodwill impairments in the

post-SFAS 146 regime. We find also that the market valuation **of** asset write-downs in

the post-SFAS 146 regime is more positive for firms suspected **of** smoothing than for

other firms, consistent with the notion that write-downs for suspect firms indicate a

mechanical increase in future earnings but may not reflect poor prospects for future

performance. In contrast, we do not find such differential valuation **of** goodwill

impairments, probably due to the fact that goodwill impairments are unlikely to

mechanically increase future earnings. Finally, we find evidence implying that firms with

ex ante smoothing incentives under SFAS 146 indeed smoothed earnings by shifting to

non-restructuring special items.

In sum, we show that managers faced with a sudden tightening **of** reporting

flexibility in one area (restructuring charges) respond by switching to other areas (nonrestructuring

special items) to achieve reporting goals. Our study, therefore, not only

provides a more complete picture **of** the economic consequences **of** a particular

31

accounting standard (SFAS 146), but also sheds light on how managers trade **of**f between

various channels **of** earnings management. Future research may examine in greater detail

what determines the particular special items that managers switch to post-SFAS 146, and

more broadly, whether they have a hierarchy **of** preferences across their various

alternative channels for earnings manipulation.

32

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35

APPENDIX A: Special Items, Relevant Accounting Standards and Treatment

Goodwill impairments: SFAS 142 Goodwill and Other Intangible Assets (2001)

• Prior to SFAS 142, goodwill was recognized as an asset and amortized over a

period not to exceed 40 years.

• SFAS 142 eliminated amortization and required goodwill to be tested for

impairment annually.

• Impairment testing to be done using a fair value threshold, and cash flows to be

estimated at the level **of** reporting units.

Long-lived asset impairments, gains or losses on asset disposals, and discontinued

operations: SFAS 144 Accounting for the Impairment or Disposal **of** Long-Lived Assets

(2001)

• Describes a single accounting model to be used for long-lived assets to be

disposed **of** by sale, whether previously held and used or newly acquired.

• Reduced the threshold for discontinued operations treatment by introducing the

“components **of** an entity” concept, potentially allowing for more asset disposals

to be treated as discontinued operations.

In-process research & development: SFAS 141R **Business** Combinations (2008)

• SFAS 141R prohibits immediate write-**of**f **of** in-process research & development,

potentially reducing big bath opportunities at the point **of** acquisition after 2008.

Litigation reserves: SFAC 5 Recognition and Measurement in Financial Statements **of**

**Business** Enterprises

• Firm should recognize losses to the extent to which the losses are probable and

reasonably estimable.

Costs related to mergers and acquisitions: SFAS 141R **Business** Combinations (2008)

• Costs related to mergers and acquisitions (e.g., fees for lawyers, investment

bankers, etc), whether successful or unsuccessful, must be recognized as an

expense in the period incurred.

Gains or losses from extinguishment **of** debt: SFAS 145 Rescission **of** FASB Statements

No 4, 44, and 64, Amendment **of** FASB Statement No 13, and Technical Corrections

• Gains or losses on the extinguishment **of** debt must be recognized as an expense

in the period incurred.

36

TABLE 1

Sample Composition

Panel A: Sample Selection Procedure

Total 2001-2 2004-7

Estimation **of** abnormal restructuring costs

Restructuring sample satisfying the data requirements* 3,361 1,332 2,029

Earnings management analysis

Plus: For the restructuring sample, non-restructuring years’ observations without missing information

on regression variables 4,767

Less: Observations with the sum **of** negative special item components less than 0.1% **of** sales (2,563)

5,565 2,045 3,520

Valuation analysis

Less: Observations in the pre-SFAS 146 period (fiscal years 2001-2002) (2,045) -

3,520 3,520

Income smoothing analysis

Less: Observations without sufficient data to compute income-smoothing measures (104) (104)

3,416 3,416

* This sample satisfies the following requirements: no fiscal-year-end change; sales greater than $5 million; non-financial firms; year-industry

(Fama and French's 12 industries) combinations with at least 10 observations; restructuring costs greater than 0.1% **of** sales; restructurings not

recorded after a merger; and non-missing regression variables for the estimation **of** abnormal restructuring costs.

37

TABLE 1 (Continued)

Panel B: Sample Composition by Industry

Pre-SFAS 146 (n=2,045)

Post-SFAS 146 (n=3,520)

FF12 n REST t NRSI t n REST t NRSI t

1 128 0.0087 0.0095 263 0.0058 0.0092

2 80 0.0070 0.0075 134 0.0051 0.0075

3 342 0.0092 0.0127 551 0.0056 0.0083

4 24 0.0050 0.0507 44 0.0037 0.0103

5 76 0.0089 0.0114 150 0.0052 0.0119

6 774 0.0366 0.0716 1,179 0.0135 0.0217

7 55 0.0196 0.1053 117 0.0060 0.0456

8 42 0.0045 0.0250 48 0.0010 0.0116

9 118 0.0075 0.0083 217 0.0034 0.0071

10 178 0.0169 0.0863 414 0.0169 0.0579

12 228 0.0131 0.0344 403 0.0059 0.0209

Fame-French 12-Industry Classification (FF12) is as follows: 1 = Consumer non-durables; 2 =

Consumer durables; 3 = Manufacturing; 4 = Oil, Gas, and Coal Extraction and Products; 5 =

Chemicals and Allied Products; 6 = **Business** Equipment (Computers, S**of**tware, and Electronic

Equipment); 7 = Telephone and Television Transmission; 8 = Utilities; 9 = Wholesale, Retail,

and Some Services (Laundries, Repair Shops); 10 = Healthcare, Medical Equipment, and Drugs;

11 = Finance (excluded from the sample); and 12 = Others.

The variables are defined as follows: REST t is after-tax restructuring costs less reversal **of**

restructuring costs for period t, multiplied by -1 and deflated by sales for period t. NRSI t is the

sum **of** non-restructuring special items for period t deflated by sales for period t, where nonrestructuring

special items are the sum **of** the following special item components (all after-tax)

per Compustat: asset write-downs, goodwill impairments, litigation costs, losses on asset sales,

merger-related costs, in-process R&D, losses on extinguishment **of** debt, and other special items.

38

TABLE 2

Estimation **of** Abnormal Restructuring Costs

Dependent Variable = REST t

Variable Prediction Model 1 Model 2 Model 3 Model 4

Intercept ? -0.0076 *** 0.0068 * -0.0057 * 0.0090 **

(-3.259) (1.649) (-1.880) (1.992)

ΔGDP t - 0.0002 -0.0086 *** 0.0001 -0.0088 ***

(0.238) (-3.433) (0.074) (-3.563)

ΔSALES t - -0.0572 *** -0.0658 *** -0.0567 *** -0.0647 ***

(-22.339) (-17.760) (-22.149) (-17.503)

ΔEBSI t - -0.0082 ** -0.0075 * -0.0082 ** -0.0071

(-2.438) (-1.708) (-2.438) (-1.632)

ΔOCF t - 0.0040 -0.0090 * 0.0033 -0.0090 *

(1.006) (-1.653) (0.846) (-1.680)

PM t-1 - -0.0203 *** -0.0201 *** -0.0198 *** -0.0192 ***

(-20.118) (-14.517) (-19.325) (-13.882)

LOSS3 t-1 + 0.0101 *** 0.0094 *** 0.0078 *** 0.0073 ***

(7.076) (4.157) (5.319) (3.189)

AR_TO t-1 - -0.0002 *** -0.0002 ** -0.0002 *** -0.0002 ***

(-4.433) (-2.358) (-4.612) (-2.720)

INV_TO t-1 - -0.0002 *** -0.0003 *** -0.0002 *** -0.0003 ***

(-4.540) (-5.373) (-4.785) (-5.577)

PPE_TO t-1 - -0.0006 *** -0.0007 *** -0.0007 *** -0.0008 ***

(-10.745) (-7.155) (-11.977) (-8.163)

SALE_EMP t-1 - 0.0052 * 0.0016 0.0082 ** 0.0058

(1.727) (0.337) (2.437) (1.144)

RET t-1 - -0.0060 *** -0.0101 *** -0.0058 *** -0.0099 ***

(-5.761) (-6.239) (-5.694) (-6.197)

POST - -0.0184 *** -0.0181 ***

(-2.772) (-2.745)

POST*ΔGDP t ? 0.0091 *** 0.0092 ***

(3.012) (3.051)

POST*ΔSALES t ? 0.0160 *** 0.0146 **

(2.776) (2.548)

POST*ΔEBSI t ? 0.0035 0.0033

(0.375) (0.357)

POST*ΔOCF t ? 0.0300 *** 0.0287 ***

(3.176) (3.067)

POST*PM t-1 ? -0.0034 -0.0044

(-1.202) (-1.576)

POST*LOSS3 t-1 ? 0.0000 -0.0001

(-0.013) (-0.035)

POST*AR_TO t-1 ? 0.0000 0.0000

(-0.237) (-0.220)

39

TABLE 2 (Continued)

Dependent Variable = REST t

Variable Prediction Model 1 Model 2 Model 3 Model 4

POST*INV_TO t-1 ? 0.0003 *** 0.0003 ***

(3.167) (3.214)

POST*PPE_TO t-1 ? 0.0002 0.0002

(1.219) (1.415)

POST*SALE_EMP t-1 ? 0.0051 0.0029

(0.771) (0.434)

POST*RET t-1 ? 0.0083 *** 0.0083 ***

(3.759) (3.773)

Industry fixed effects No No Yes Yes

Dhrymes R 2 0.383 0.398 0.398 0.412

n 6,722 6,722 6,722 6,722

***, **, * denotes significant at < .01, < .05, and < .10 levels, respectively, for two-tailed tests.

This table presents the results from the following tobit regression:

REST t = α 0 + α 1 ΔGDP t + α 2 ΔSALES t + α 3 ΔEBSI t + α 4 ΔOCF t + α 5 PM t-1 + α 6 LOSS t-1

+ α 7 AR_TO t-1 + α 8 INV_TO t-1 + α 9 PPE_TO t-1 + α 10 SALE_EMP t-1 + α 11 RET t-1

+ POST*[β 0 + β 1 ΔGDP t + β 2 ΔSALES t + β 3 ΔEBSI t + β 4 ΔOCF t + β 5 PM t-1 + β 6 LOSS t-1

+ β 7 AR_TO t-1 + β 8 INV_TO t-1 + β 9 PPE_TO t-1 + β 10 SALE_EMP t-1 + β 11 RET t-1 ] + ε t

The variables are defined as follows: REST t = after-tax restructuring costs less reversal **of**

restructuring costs for period t, multiplied by -1 and deflated by sales for period t; ΔGDP t = the

annual percentage change in real U.S. GDP for period t; ΔSALES t = the percent change in sales

from period t-1 to t; ΔEBSI t = the change in earnings before special items from period t-1 to t,

divided by sales for period t-1; ΔOCF t = the change in operating cash flows from period t-1 to t,

divided by sales for period t-1; PM t-1 = pr**of**it margin, measured as the ratio **of** income before

extraordinary items to sales for period t-1; LOSS3 t-1 = an indicator variable equal to 1 if the firm

incurred a net loss in period t-1, t-2, or t-3, and 0 otherwise; AR_TO t-1 = the ratio **of** sales to trade

receivables for period t-1; INV_TO t-1 = the ratio **of** cost **of** goods sold to total inventory plus

LIFO reserve, if applicable, for period t-1; PPE_TO t-1 = the ratio **of** sales to property, plant and

equipment for period t-1; SALE_EMP t-1 = the ratio **of** sales (in thousand dollars) to the number **of**

employees; and RET t-1 = cumulative monthly stock returns for period t-1 per CRSP; and POST =

an indicator variable equal to 1 for observations occurring in the post-SFAS 146 regime, and 0

otherwise. The sample is from fiscal years 2001-2002 (pre-SFAS 146 period) and 2004-2007

(post-SFAS 146 period). The numbers provided in parentheses are Z-statistics. Dhrymes R 2 is the

squared correlation between the predicted and actual values for non-zero values **of** REST t .

40

TABLE 3

Descriptive Statistics

Panel A: Unadjusted Special Items (and Discontinued Operations)

Pre-SFAS 146 (n=2,045) Post-SFAS 146 (n=3,520) Difference = Post - Pre

Item (scaled by sales) Mean Median

Std

dev

% Nonzero

Mean Median

Std

dev

% Nonzero

Mean Median

Restructuring charges 0.0177 0.0071 0.0238 74.3 0.0087 0.0029 0.0154 67.5 -0.0090 *** -0.0041 ***

Asset write-downs 0.0084 0.0000 0.0157 38.3 0.0031 0.0000 0.0090 23.9 -0.0053 *** 0.0000 ***

Goodwill impairments 0.0060 0.0000 0.0162 16.5 0.0032 0.0000 0.0117 10.6 -0.0028 *** 0.0000 ***

Litigation costs 0.0004 0.0000 0.0014 7.5 0.0006 0.0000 0.0016 12.2 0.0002 *** 0.0000 ***

Merger-related costs 0.0002 0.0000 0.0008 7.1 0.0002 0.0000 0.0007 7.3 0.0000 0.0000

In-process R&D 0.0007 0.0000 0.0023 8.9 0.0007 0.0000 0.0022 9.7 0.0000 0.0000 **

Loss on asset disposal 0.0000 0.0000 0.0000 5.0 0.0000 0.0000 0.0000 2.1 0.0000 0.0000

Loss on debt extinguishment 0.0001 0.0000 0.0006 2.0 0.0005 0.0000 0.0014 14.1 0.0005 *** 0.0000 ***

Other special items 0.0018 0.0000 0.0041 22.9 0.0010 0.0000 0.0029 16.4 -0.0008 *** 0.0000 ***

Discontinued operations 0.0010 0.0000 0.0035 9.1 0.0009 0.0000 0.0031 10.6 -0.0001 0.0000 ***

Panel B: Industry-mean-adjusted Special Items (and Discontinued Operations)

Pre-SFAS 146 (n=2,045) Post-SFAS 146 (n=3,520) Difference = Post - Pre

Variable Mean Median Std dev Mean Median Std dev Mean Median

ABREST t 0.0020 0.0000 0.0154 -0.0011 0.0000 0.0106 -0.0031 *** 0.0000 ***

ABNRSI t -0.0183 -0.0194 0.0722 0.0010 -0.0073 0.0397 0.0193 *** 0.0120 ***

ABWD t -0.0060 -0.0068 0.0181 0.0001 -0.0018 0.0085 0.0061 *** 0.0049 ***

ABGWD t -0.0141 -0.0045 0.0276 0.0001 -0.0018 0.0105 0.0142 *** 0.0027 ***

ABOSI t -0.0019 -0.0048 0.0155 -0.0010 -0.0033 0.0132 0.0008 ** 0.0015 ***

ABDISOP t -0.0006 -0.0009 0.0034 0.0000 -0.0006 0.0030 0.0006 *** 0.0003 ***

41

TABLE 3 (Continued)

Panel C: Other Variables

Pre-SFAS 146 (n=2,045) Post-SFAS 146 (n=3,520) Difference = Post – Pre

Variable Mean Median Std dev Mean Median Std dev Mean Median

ΔGDP t 1.397 1.434 0.429 2.809 2.979 0.519 1.412 *** 1.546 ***

ΔSALES t -0.027 -0.039 0.303 0.109 0.074 0.260 0.136 *** 0.112 ***

ΔEBSI t 0.017 -0.005 0.310 0.020 0.009 0.185 0.003 0.013 ***

ΔOCF t 0.025 0.009 0.204 0.019 0.007 0.157 -0.006 -0.002

PM t-1 -0.235 0.018 0.811 -0.092 0.028 0.538 0.143 *** 0.011 ***

LOSS3 t-1 0.541 1.000 0.498 0.536 1.000 0.499 -0.005 0.000

AR_TO t-1 8.130 5.971 10.599 9.630 6.353 13.770 1.500 *** 0.381 ***

INV_TO t-1 7.327 3.166 16.079 8.487 3.712 17.875 1.161 ** 0.546 ***

PPE_TO t-1 7.721 5.061 8.796 9.154 5.573 10.692 1.433 *** 0.513 ***

SALE_EMP t-1 0.248 0.192 0.213 0.287 0.225 0.228 0.039 *** 0.033 ***

RET t-1 -0.012 -0.153 0.766 0.249 0.114 0.687 0.261 *** 0.267 ***

LOGASSET t-1 6.391 6.238 1.900 6.663 6.586 1.878 0.272 *** 0.348 ***

LEVERAGE t-1 0.401 0.408 0.195 0.398 0.394 0.181 -0.002 -0.014

ΔCEO t 0.163 0.000 0.369 0.152 0.000 0.359 -0.011 0.000

CAR t 0.049 -0.102 0.760 -0.144 -0.182 0.466 -0.192 *** -0.080 ***

Total asset (in $bil) 3.457 0.484 8.843 4.345 0.771 10.131 0.889 *** 0.287 ***

Market value **of** equity

(in $bil) 3.807 0.385 12.417 5.219 0.821 14.022 1.412 *** 0.436 ***

Sales (in $bil) 2.632 0.396 6.418 3.345 0.687 7.317 0.713 *** 0.291 ***

***, **, * denotes significant at < .01, < .05, and < .10 levels, respectively, for two-tailed t-tests (Wilcoxon tests) **of** differences in means

(medians).

42

TABLE 3 (Continued)

The sample is from fiscal years 2001-2002 (pre-SFAS 146 period) and 2004-2007 (post-SFAS 146 period). In Panel B, the variables are defined as

follows: ABREST t = residual from equation (1) as shown in Section III; ABNRSI t = NRSI t minus the industry-mean **of** this variable for period t,

where NRSI t is the sum **of** non-restructuring special items for period t deflated by sales for period t and non-restructuring special items are the sum

**of** the following special item components (all after-tax) per Compustat: asset write-downs, goodwill impairments, litigation costs, losses on asset

sales, merger-related costs, in-process R&D, losses on extinguishment **of** debt, and other special items; ABWD t = WD t minus the industry-mean **of**

this variable for period t, where WD t is after-tax asset write-downs for period t deflated by sales for period t; ABGWD t = GWD t minus the

industry-mean **of** this variable for period t, where GWD t is after-tax goodwill impairments for period t deflated by sales for period t; ABOSI t =

OSI t minus the industry-mean **of** this variable for period t, where OSI t is NRSI t minus the sum **of** WD t and GWD t ; and ABDISOP t = DISOP t minus

the industry-mean **of** this variable for period t, where DISOP t is discontinued operations for period t deflated by sales for period t.

In Panel C, the variables are defined as follows: ΔGDP t = the annual percentage change in real U.S. GDP for period t; ΔSALES t = the percent

change in sales from period t-1 to t; ΔEBSI t = the change in earnings before special items from period t-1 to t, divided by sales for period t-1;

ΔOCF t = the change in operating cash flows from period t-1 to t, divided by sales for period t-1; PM t-1 = pr**of**it margin, measured as the ratio **of**

income before extra-ordinary items to sales for period t-1; LOSS3 t-1 = an indicator variable equal to 1 if the firm incurred a net loss in period t-1, t-

2, or t-3, and 0 otherwise; AR_TO t-1 = the ratio **of** sales to trade receivables for period t-1; INV_TO t-1 = the ratio **of** cost **of** goods sold to total

inventory plus LIFO reserve, if applicable, for period t-1; PPE_TO t-1 = the ratio **of** sales to property, plant and equipment for period t-1;

SALE_EMP t-1 = the ratio **of** sales (in thousand dollars) to the number **of** employees; RET t-1 = cumulative monthly stock returns for period t-1 per

CRSP; LOGASSET t-1 = the log **of** the book value **of** total assets at the end **of** period t-1; LEVERAGE t-1 = the sum **of** long-term debt and debt in

current liabilities for period t-1, divided by this sum plus the book value **of** equity for period t-1; ΔCEO t = an indicator variable equal to 1 if the

firm experiences a change in CEO in period t or t-1, and 0 otherwise; and CAR t = 12-month (ending three months after the fiscal year-end)

cumulative market-adjusted returns.

43

TABLE 4

Comparison **of** Special Items (and Discontinued Operations) for Firms with Smoothing or Bath Incentives

across the Pre- and Post-SFAS 146 Regimes

Panel A: Firms with Smoothing Incentives

Pre-SFAS 146 (n=494) Post-SFAS 146 (n=1,046) Difference = Post - Pre

Variable Mean Median SD Mean Median SD Mean Median

ABREST t 0.0038 0.0000 0.0177 -0.0003 0.0000 0.0117 -0.0041 *** 0.0000 **

ABNRSI t -0.0111 -0.0182 0.0791 -0.0002 -0.0076 0.0384 0.0109 *** 0.0105 ***

ABWD t -0.0049 -0.0068 0.0196 -0.0002 -0.0021 0.0084 0.0047 *** 0.0046 ***

ABGWD t -0.0146 -0.0045 0.0282 -0.0012 -0.0014 0.0078 0.0134 *** 0.0031 ***

ABOSI t -0.0014 -0.0048 0.0163 -0.0002 -0.0031 0.0142 0.0012 0.0017 ***

ABDISOP t -0.0005 -0.0018 0.0038 0.0000 -0.0007 0.0031 0.0005 ** 0.0012 ***

Panel B: Firms with Bath Incentives

Pre-SFAS 146 (n=575) Post-SFAS 146 (n=675) Difference = Post - Pre

Variable Mean Median SD Mean Median SD Mean Median

ABREST t 0.0048 0.0000 0.0178 0.0018 0.0000 0.0137 -0.0029 *** 0.0000 ***

ABNRSI t 0.0077 -0.0102 0.0841 0.0199 -0.0026 0.0548 0.0122 *** 0.0076 ***

ABWD t -0.0013 -0.0054 0.0209 0.0030 -0.0013 0.0113 0.0043 *** 0.0041 ***

ABGWD t -0.0090 -0.0019 0.0302 0.0043 -0.0014 0.0158 0.0133 *** 0.0005 ***

ABOSI t 0.0012 -0.0042 0.0174 0.0017 -0.0031 0.0151 0.0005 0.0011 ***

ABDISOP t -0.0010 -0.0009 0.0028 0.0000 -0.0006 0.0030 0.0010 *** 0.0003 ***

***, **, * denotes significant at < .01, < .05, and < .10 levels, respectively, for two-tailed t-tests (Wilcoxon tests) **of** differences in means

(medians).

The sample is from fiscal years 2001-2002 (pre-SFAS 146 period) and 2004-2007 (post-SFAS 146 period). See Table 3 for variable definitions.

44

TABLE 5

Correlations

Panel A: Pre-SFAS 146 Period (n=2,045)

Variable

ABREST t

ABWDt

ABGWD t

ABOSI t

ABDISOP t

ΔGDP t

ΔSALES t

ΔEBSI t

ΔOCFt

LOSS3 t-1

RET t-1

LOGASSET t-1

LEVERAGE t-1

ΔCEO t

ABREST t 1.000 -0.002 *** -0.024 -0.014 -0.007 0.011 0.044 -0.054 *** 0.002 *** 0.065 *** 0.023 -0.103 *** -0.184 *** -0.044 *

ABWD t 0.081 *** 1.000 0.417 *** 0.244 *** 0.048 -0.034 ** -0.109 -0.047 -0.050 -0.049 * -0.043 *** 0.104 0.110 0.033

ABGWD t 0.023 0.261 *** 1.000 0.196 0.218 ** -0.050 ** 0.045 -0.023 -0.029 -0.095 *** 0.081 *** 0.195 *** 0.164 *** 0.027

ABOSI t 0.004 0.101 *** 0.030 1.000 -0.105 ** 0.007 -0.005 -0.058 *** -0.046 *** -0.041 *** 0.034 0.114 0.075 ** 0.006

ABDISOP t -0.005 0.020 0.054 ** -0.051 ** 1.000 -0.264 *** 0.072 0.009 0.007 -0.083 0.092 0.051 *** 0.077 *** 0.044

ΔGDP t 0.022 -0.056 ** -0.049 ** -0.022 -0.063 *** 1.000 0.020 ** 0.221 ** 0.029 0.085 *** 0.102 *** -0.052 * -0.060 *** -0.015

ΔSALES t 0.009 -0.022 0.033 0.011 -0.021 0.045 ** 1.000 0.225 *** 0.216 *** -0.138 0.363 *** 0.122 0.070 ** -0.014

ΔEBSI t 0.065 *** 0.034 0.006 -0.083 *** 0.012 0.049 ** 0.428 *** 1.000 0.410 *** 0.148 * -0.019 *** -0.030 ** 0.148 -0.015

ΔOCF t 0.068 *** -0.003 -0.008 -0.062 *** 0.006 0.012 0.391 *** 0.746 *** 1.000 0.057 ** -0.055 *** -0.076 *** 0.043 -0.022

LOSS3 t-1 0.161 *** 0.039 * -0.060 *** 0.069 *** 0.003 0.073 *** -0.003 0.041 * 0.055 ** 1.000 -0.281 *** -0.343 *** -0.097 *** -0.115 ***

RET t-1 -0.032 -0.080 *** -0.063 *** 0.021 -0.016 0.143 *** 0.290 *** -0.103 *** -0.110 *** -0.103 *** 1.000 0.199 0.087 * 0.034

LOGASSET t-1 -0.132 *** 0.034 0.184 *** 0.006 0.070 *** -0.041 * 0.001 -0.056 ** -0.072 *** -0.334 *** 0.018 1.000 0.285 *** 0.273 ***

LEVERAGE t-1 -0.194 *** -0.003 0.117 *** -0.055 ** 0.113 *** -0.068 *** -0.047 ** 0.023 -0.006 -0.078 *** -0.042 * 0.239 *** 1.000 0.038

ΔCEO t -0.043 * -0.005 0.012 -0.016 0.023 -0.017 -0.031 -0.024 -0.033 -0.115 *** 0.011 0.272 *** 0.036 1.000

45

TABLE 5 (Continued)

Panel B: Post-SFAS 146 Period (n=3,520)

Variable

ABREST t

ABWDt

ABGWD t

ABOSI t

ABDISOP t

ΔGDP t

ΔSALES t

ABREST t 1.000 0.013 *** 0.016 0.061 -0.057 ** 0.040 *** 0.154 *** -0.023 -0.012 0.010 *** 0.096 *** -0.124 *** -0.099 *** -0.064 ***

ABWD t 0.086 *** 1.000 0.119 *** 0.172 -0.043 *** -0.047 -0.062 *** -0.105 *** -0.063 ** -0.058 *** -0.072 ** 0.048 *** 0.073 ** 0.030

ABGWD t 0.026 0.170 *** 1.000 -0.038 ** -0.041 ** 0.326 ** -0.025 *** -0.034 *** -0.023 0.079 *** 0.054 *** -0.050 *** 0.003 ** 0.023

ABOSI t -0.007 0.015 -0.034 ** 1.000 -0.034 -0.081 0.055 *** -0.043 *** -0.021 *** -0.016 *** 0.037 *** 0.102 0.147 *** 0.035

ABDISOP t -0.038 ** 0.046 *** 0.039 ** -0.002 1.000 -0.076 -0.094 *** -0.008 0.002 ** 0.081 *** -0.124 -0.073 * -0.074 *** 0.039 *

ΔGDP t 0.045 *** -0.027 0.040 ** -0.015 -0.009 1.000 0.064 *** 0.099 0.016 0.133 *** 0.241 *** -0.087 *** -0.016 -0.015

ΔSALES t 0.055 *** -0.062 *** -0.056 *** 0.047 *** -0.091 *** 0.055 *** 1.000 0.374 *** 0.319 *** -0.030 ** 0.239 *** 0.036 *** -0.058 *** -0.047 **

ΔEBSI t -0.013 -0.054 *** -0.050 *** -0.099 *** -0.016 0.020 0.179 *** 1.000 0.452 *** 0.152 *** 0.091 ** 0.016 0.008 0.012

ΔOCF t 0.004 -0.042 ** -0.026 -0.046 *** -0.038 ** -0.016 0.098 *** 0.597 *** 1.000 0.062 ** 0.002 *** 0.022 -0.004 ** 0.000

LOSS3 t-1 0.078 *** 0.067 *** 0.068 *** 0.085 *** 0.059 *** 0.139 *** 0.035 ** 0.061 *** 0.038 ** 1.000 -0.038 *** -0.380 *** 0.005 -0.019

RET t-1 0.062 *** -0.035 ** -0.053 *** 0.073 *** -0.027 0.197 *** 0.098 *** -0.040 ** -0.048 *** 0.081 *** 1.000 0.076 *** 0.031 -0.020 **

LOGASSET t-1 -0.163 *** -0.088 *** -0.060 *** 0.004 -0.031 * -0.095 *** -0.046 *** -0.022 -0.019 -0.373 *** -0.084 *** 1.000 0.283 *** 0.196 ***

LEVERAGE t-1 -0.099 *** -0.041 ** -0.035 ** 0.059 *** 0.048 *** -0.017 -0.067 *** -0.003 0.034 ** 0.025 0.009 0.228 *** 1.000 0.072 ***

ΔCEO t -0.063 *** 0.016 0.005 -0.007 0.033 * -0.017 -0.042 ** 0.010 -0.007 -0.019 -0.036 ** 0.188 *** 0.061 *** 1.000

***, **, * denotes significance at the 1%, 5%, and 10% levels, respectively.

Pearson (Spearman) correlations are presented below (above) diagonal. The sample is from fiscal years 2001-2002 (pre-SFAS 146 period) and

2004-2007 (post-SFAS 146 period). See Table 3 for variable definitions.

ΔEBSI t

ΔOCFt

LOSS3 t-1

RET t-1

LOGASSET t-1

LEVERAGE t-1

ΔCEO t

46

TABLE 6

Shifting within Special Items

Dependent Variable = ABNRSI t ABWD t ABGWD t ABOSI t ABDISOP t

Variable Prediction Estimate Estimate Estimate Estimate Estimate

Intercept ? -0.092 * -0.013 * -0.052 -0.005 -0.003

(-1.734) (-1.909) (-1.543) (-0.925) (-0.686)

BATH t + 0.069 *** 0.019 *** 0.024 *** 0.010 *** 0.000

(5.734) (6.260) (5.541) (3.114) (-0.275)

SMOOTH t + 0.052 *** 0.010 *** 0.016 ** 0.009 *** 0.002 *

(4.207) (2.646) (2.174) (3.824) (1.781)

ABREST t ? 0.542 ** 0.229 ** 0.110 0.035 0.046

(2.271) (2.201) (0.641) (1.527) (0.699)

BATH t *ABREST t ? 0.785 ** -0.119 0.844 *** -0.007 -0.029

(2.088) (-1.061) (2.633) (-0.123) (-0.525)

SMOOTH t *ABREST t ? 0.766 -0.011 0.489 -0.009 -0.062 **

(1.387) (-0.136) (1.054) (-0.275) (-2.172)

ΔGDP t - -0.005 -0.002 -0.001 0.000 -0.001

(-0.399) (-1.004) (-0.168) (-0.305) (-0.886)

ΔSALES t - -0.008 -0.014 0.001 0.011 -0.006 ***

(-0.185) (-1.633) (0.056) (1.569) (-2.986)

ΔEBSI t - -0.025 0.001 -0.002 -0.015 *** 0.002

(-0.558) (0.206) (-0.063) (-3.388) (1.463)

ΔOCF t - 0.004 0.011 -0.018 0.006 0.001

(0.102) (0.905) (-0.568) (0.664) (0.379)

LOSS3 t-1 + 0.055 *** 0.009 *** 0.027 ** 0.006 *** 0.001 *

(3.504) (3.874) (2.520) (3.232) (1.767)

RET t-1 - -0.022 *** -0.004 *** -0.010 *** -0.001 -0.001 *

(-3.176) (-2.626) (-2.622) (-0.913) (-1.773)

LOGASSET t-1 ? 0.016 *** 0.002 *** 0.009 *** 0.000 0.001 *

(2.961) (3.763) (2.648) (0.375) (1.808)

LEVERAGE t-1 ? -0.183 *** -0.020 *** -0.082 *** -0.016 *** 0.006 ***

(-4.359) (-4.365) (-3.175) (-3.069) (2.935)

ΔCEO t ? -0.021 *** -0.004 * -0.009 *** -0.002 -0.001

(-3.383) (-1.896) (-3.660) (-1.024) (-0.953)

POST ? 0.070 0.009 0.050 -0.008 0.007 *

(1.368) (1.345) (1.556) (-1.235) (1.900)

LOW t ? 0.021 *** 0.003 0.006 0.007 *** -0.002 **

(3.230) (1.394) (1.276) (5.190) (-1.973)

POST*BATH t ? -0.034 ** -0.016 *** -0.005 -0.003 0.000

(-2.491) (-4.523) (-0.797) (-0.674) (-0.121)

47

TABLE 6 (Continued)

Dependent Variable = ABNRSI t ABWD t ABGWD t ABOSI t ABDISOP t

Variable Prediction Estimate Estimate Estimate Estimate Estimate

POST*SMOOTH t ? -0.045 *** -0.009 ** -0.020 *** -0.003 -0.001

(-3.363) (-2.365) (-2.629) (-1.145) (-0.558)

POST*ABREST t ? 0.247 0.007 -0.104 0.147 -0.074

(0.795) (0.050) (-0.514) (1.268) (-1.041)

POST*BATH t

*ABREST t ? -0.814 0.071 -0.797 ** -0.118 0.122

(-1.289) (0.358) (-2.254) (-0.825) (1.487)

POST*SMOOTH t

*ABREST t ? -0.708 -0.037 -0.219 -0.191 0.053

(-1.150) (-0.262) (-0.437) (-1.377) (0.865)

POST*BATH t

*ABREST t *LOW t - 0.482 -0.171 -0.045 0.399 -0.152

(0.361) (-0.645) (-0.040) (1.040) (-0.977)

POST*SMOOTH t

*ABREST t *LOW t - -1.962 * -0.285 ** -1.541 ** 0.264 * -0.125

(-1.894) (-2.181) (-2.284) (1.736) (-0.957)

POST*ΔGDP t ? 0.002 0.003 0.000 -0.001 0.001

(0.173) (1.213) (0.050) (-0.510) (0.602)

POST*ΔSALES t ? 0.011 0.013 -0.001 -0.002 -0.001

(0.237) (1.504) (-0.052) (-0.282) (-0.466)

POST*ΔEBSI t ? -0.046 -0.010 -0.010 -0.002 0.002

(-0.874) (-1.185) (-0.331) (-0.181) (0.912)

POST*ΔOCF t ? 0.002 -0.018 0.019 -0.006 -0.007 *

(0.028) (-1.243) (0.564) (-0.441) (-1.700)

POST*LOSS3 t-1 ? -0.034 ** -0.007 *** -0.017 0.000 0.000

(-2.126) (-2.802) (-1.607) (-0.154) (-0.413)

POST*RET t-1 ? 0.019 *** 0.004 ** 0.005 0.004 * 0.001

(2.638) (2.063) (1.342) (1.779) (1.565)

POST*LOGASSET t-1 ? -0.015 *** -0.002 *** -0.008 ** 0.000 -0.001 **

(-2.623) (-3.945) (-2.229) (0.425) (-2.303)

POST*LEVERAGE t-1 ? 0.163 *** 0.021 *** 0.056 ** 0.022 *** -0.004 *

(3.706) (4.236) (2.114) (2.996) (-1.837)

POST*ΔCEO t ? 0.014 ** 0.004 * 0.006 * 0.000 0.001

(2.057) (1.830) (1.871) (-0.077) (1.002)

Adjusted R 2 0.133 0.074 0.096 0.049 0.028

n 5,565 5,565 5,565 5,565 5,565

***, **, * denotes significant at < .01, < .05, and < .10 levels, respectively, for two-tailed tests.

48

TABLE 6 (Continued)

This table presents the results from the following regression:

DepVar t = α 0 + α 1 ABREST t + α 2 BATH t + α 3 SMOOTH t + α 4 ABREST t *BATH t

+ α 5 ABREST t *SMOOTH t + POST*[β 0 + β 1 ABREST t + β 2 BATH t

+ β 3 SMOOTH t + β 4 ABREST t *BATH t + β 5 ABREST t *SMOOTH t

+ β 6 ABREST t *BATH t *LOW t + β 7 ABREST t *SMOOTH t *LOW t ] + Controls + µ t,

where DepVar t is ABNRSI t , ABWD t , ABGWD t , ABOSI t , or ABDISOP t . See Table 3 for the

definition **of** these variables. The main explanatory variables are defined as follows: ABREST t =

residual from equation (1) as shown in Section III; BATH t = an indicator variable equal to 1 if the

firm’s change in earnings before special items from period t-1 to t, divided by sales for period t-1,

is below the median **of** nonzero negative values **of** this variable among all firms in the same

industry, and 0 otherwise; SMOOTH t = an indicator variable equal to 1 if the firm’s change in

earnings before special items from period t-1 to t, divided by sales for period t-1, is above the

median **of** nonzero positive values **of** this variable among all firms in the same industry, and 0

otherwise; POST = an indicator variable equal to 1 for observations occurring in the post-SFAS

146 regime, and 0 otherwise; and LOW t = an indicator variable equal to 1 if ABREST t in the

post-SFAS 146 regime is (1) negative, (2) zero, or (3) below the median **of** this variable among

all firms in the same industry, and 0 otherwise. The control variables are defined as in Table 3.

The sample is from fiscal years 2001-2002 (pre-SFAS 146 period) and 2004-2007 (post-SFAS

146 period). The numbers provided in parentheses are t-statistics, which are calculated using

robust standard errors clustered within every year and industry (Fame-French 12-Industry).

49

TABLE 7

Market Valuation **of** Shifting within Special Items

Panel A: Market Valuation **of** Shifting to Non-restructuring Special Items

Dependent Variable = CAR t

Variable Prediction Estimate Estimate

Intercept ? -0.254 *** -0.279 ***

(-7.161) (-5.389)

EBSI t + 2.178 *** 2.142 ***

(5.163) (5.437)

LOSS t - -0.067 ** -0.064 **

(-2.454) (-2.353)

EBSI t *LOSS t - -2.393 *** -2.341 ***

(-5.028) (-5.338)

ΔEBSI t + 0.969 *** 0.978 ***

(6.692) (6.957)

REST t ? 1.215 ** 1.245 **

(2.432) (2.539)

NRSI t ? -0.088

(-0.492)

INDNRSI t ? 1.585

(0.986)

ABNRSI t ? -0.154

(-0.807)

SUSPECT_NRSI t ? 0.019 0.015

(0.602) (0.294)

SUSPECT_NRSI t *REST t ? 3.304 3.283

(1.405) (1.401)

SUSPECT_NRSI t *NRSI t + -0.456

(-1.061)

SUSPECT_NRSI t *INDNRSI t ? -0.070

(-0.029)

SUSPECT_NRSI t *ABNRSI t + -0.461

(-1.049)

Adjusted R 2 0.246 0.154

n 3,520 3,520

50

TABLE 7 (Continued)

Panel B: Market Valuation **of** Shifting to Long-lived Asset Write-downs

Dependent Variable = CAR t

Variable Prediction Estimate Estimate

Intercept ? -0.248 *** -0.244 ***

(-6.763) (-6.447)

EBSI t + 2.073 *** 2.074 ***

(5.259) (5.252)

LOSS t - -0.068 ** -0.070 **

(-2.498) (-2.552)

EBSI t *LOSS t - -2.270 *** -2.275 ***

(-4.959) (-4.979)

ΔEBSI t + 0.999 *** 0.999 ***

(7.283) (7.272)

REST t ? 1.311 *** 1.310 ***

(2.796) (2.829)

WD t ? 1.417

(1.248)

INDWD t ? -0.323

(-0.402)

ABWD t ? 1.681

(1.492)

NRSI t - WD t ? -0.325 ** -0.325 **

(-2.172) (-2.165)

SUSPECT_WD t ? 0.007 0.030

(0.255) (1.093)

SUSPECT_WD t *REST t ? -5.473 -5.521

(-1.135) (-1.162)

SUSPECT_WD t *WD t + 5.587 **

(2.079)

SUSPECT_WD t *INDWD t ? -4.196 ***

(-2.762)

SUSPECT_WD t *ABWD t + 5.574 **

(2.014)

SUSPECT_WD t *(NRSI t - WD t ) ? -1.081 -1.107 *

(-1.634) (-1.668)

Adjusted R 2 0.158 0.159

n 3,520 3,520

51

TABLE 7 (Continued)

Panel C: Market Valuation **of** Shifting to Goodwill Impairments

Dependent Variable = CAR t

Variable Prediction Estimate Estimate

Intercept ? -0.250 *** -0.275 ***

(-6.868) (-6.051)

EBSI t + 2.146 *** 2.061 ***

(5.330) (5.768)

LOSS t - -0.067 *** -0.064 **

(-2.602) (-2.534)

EBSI t *LOSS t - -2.412 *** -2.305 ***

(-5.285) (-5.743)

ΔEBSI t + 1.003 *** 1.039 ***

(7.319) (8.128)

REST t ? 1.276 *** 1.246 ***

(2.685) (2.652)

GWD t ? -0.380 *

(-1.748)

INDGWD t ? 7.617

(1.519)

ABGWD t ? -0.453 **

(-2.058)

NRSI t - GWD t ? -0.097 -0.083

(-0.288) (-0.260)

SUSPECT_GWD t ? 0.015 0.032

(0.238) (0.463)

SUSPECT_GWD t *REST t ? 7.338 6.629

(0.715) (0.649)

SUSPECT_GWD t *GWD t + -0.389

(-0.510)

SUSPECT_GWD t *INDGWD t ? -3.733

(-0.141)

SUSPECT_GWD t *ABGWD t + -0.384

(-0.429)

SUSPECT_GWD t *(NRSI t - GWD t ) ? -4.326 -4.249

(-1.279) (-1.189)

Adjusted R 2 0.153 0.158

n 3,520 3,520

***, **, * denotes significant at < .01, < .05, and < .10 levels, respectively, for two-tailed tests.

52

TABLE 7 (Continued)

This table presents the results from the following regression:

CAR t = β 0 + β 1 EBSI t + β 2 LOSS t + β 3 EBSI t *LOSS t + β 4 ΔEBSI t + β 5 REST t + β 6 X t + β 7 (NRSI t - X t )

+ β 8 SUSPECT_X t + β 9 SUSPECT_X t *REST t + β 10 SUSPECT_X t *X t

+ β 11 SUSPECT_X t *(NRSI t - X t ) + µ t ,

The dependent variable is CAR t , computed as 12-month (ending three months after the fiscal

year-end) cumulative market-adjusted returns. The explanatory variables are defined as follows:

EBSI t is earnings before special items for period t, deflated by market value **of** equity at the

beginning **of** period t; LOSS t is an indicator variable equal to 1 if EBSI t is negative, and 0

otherwise; ΔEBSI t is the change in earnings before special items from period t-1 to t, deflated by

market value **of** equity at the beginning **of** period t; and REST t is after-tax restructuring costs less

reversal **of** restructuring costs for period t, multiplied by -1 and deflated by market value **of** equity

at the beginning **of** period t. SUSPECT_X t takes the value **of** 1 (and 0 otherwise) if the following

conditions are satisfied in the post-SFAS 146 period: (1) SMOOTH t = 1; (2) ABREST t is nonpositive

or below the median **of** this variable among all firms in the same industry; and (3) the

abnormal component **of** X for period t is above the median **of** this variable among all firms in the

same industry, where X is NRSI (non-restructuring special items), WD (asset write-downs), or

GWD (goodwill impairments), all after-tax and scaled by market value **of** equity at the beginning

**of** the period. In the last column **of** each Panel, X is decomposed into the industry-mean (INDX)

and abnormal components (ABX). The sample is from fiscal years 2004-2007 (post-SFAS 146

period). The numbers provided in parentheses are t-statistics, which are calculated using robust

standard errors clustered within every year and industry (Fame-French 12-Industry).

53

TABLE 8

Comparison **of** Income Smoothing Measures between Suspect and

Non-suspect Firms

Panel A: Post-SFAS 146 Period – All Firms (n=3,416)

SMOOTH t = 1 SMOOTH t = 0 Difference t-statistic

Mean **of** IS t -0.170 -0.213 0.043 (2.098)**

n 1,013 2,403

Panel B: Post-SFAS 146 Period – Firms with Smoothing Incentives (n=1,013)

SUSPECT_NRSI t = 1 SUSPECT_NRSI t = 0

Mean **of** IS t -0.104 -0.224 0.120 (3.422)***

n 455 558

SUSPECT_WD t = 1 SUSPECT_WD t = 0

Mean **of** IS t -0.184 -0.166 -0.018 (-0.438)

n 235 778

SUSPECT_GWD t = 1 SUSPECT_GWD t = 0

Mean **of** IS t -0.154 -0.171 0.017 (0.276)

n 76 937

This table presents the mean **of** our income smoothing measure (IS t ) for the sample partitioned by

SMOOTH t , SUSPECT_NRSI t , SUSPECT_WD t , or SUSPECT_GWD t . IS t is defined as the

Spearman correlation between the change in earnings before special items and the change in

after-tax special items (both deflated by sales for the previous year), and calculated using the

current year’s and past four years’ observations. SMOOTH t is an indicator variable equal to 1 if

the firm’s change in earnings before special items from period t-1 to t, divided by sales for period

t-1, is above the median **of** nonzero positive values **of** this variable among all firms in the same

industry, and 0 otherwise. SUSPECT_X t takes the value **of** 1 (and 0 otherwise) if the following

conditions are satisfied in the post-SFAS 146 period: (1) SMOOTH t = 1; (2) ABREST t is nonpositive

or below the median **of** this variable among all firms in the same industry; and (3) the

abnormal component **of** X for period t is above the median **of** this variable among all firms in the

same industry, where X is NRSI (non-restructuring special items), WD (asset write-downs), or

GWD (goodwill impairments), all after-tax and scaled by sales for period t. The sample consists

**of** 3,416 observations from fiscal years 2004-2007 (post-SFAS 146 period).

54