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Management of Earnings through the Manipulation of Real Activities That Affect Cash Flow from Operations
Sugata Roychowdhury∗
Sloan School of Management MIT
This Version: October 20, 2003 Abstract Most of the current research on earnings management focuses on the detection of abnormal
accruals. The purpose of this study is to detect manipulation of real activities to meet earnings
targets. I analyze cash flow from operations (CFO), production costs and discretionary expenses.
Using a simple model to determine the normal levels of these variables, I detect abnormally low
CFO and abnormally high production costs for companies that report small positive profits at the
annual level. The evidence is consistent with firms trying to increase reported annual earnings
beyond zero by giving price discounts to boost sales temporarily and by overproduction. I also
find evidence suggesting that some of these firms reduce discretionary expenses to report higher
margins. Further analysis in the paper yields interesting insights into cross-sectional variation in
the nature and extent of real activities management.
∗I am grateful for the guidance and many helpful suggestions I have received from Ross Watts, Jerry Zimmerman and Andy Leone. Ths paper has also benefited from helpful comments and suggestions by Liz Demers, Joanna Wu, Charles Wasley, Jim Brickley, Ludger Hentschel, S.P. Kothari, Joe Weber, Shailendra Pandit , Hema Roychowdhury and Lance Young. All errors in the paper are mine.
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1. Introduction
There is substantial evidence that executives engage in earnings management.1
Incentives to manage earnings arise from contractual agreements, capital market
considerations and regulatory concerns. One way executives can manage earnings is by
manipulation of accruals with no direct cash flow consequences (pure accrual manipulation).
Examples of pure accrual manipulation include under-provisioning for bad debt expenses,
delaying of asset write-offs and opportunistic selection of accounting methods. Pure accrual
manipulation is a convenient form of earnings management because it has no direct cash flow
implications and can be done after the year-end when managers are better informed about
pre-managed earnings. However, managers also have incentives, discussed in detail in the
next section, to manipulate real activities during the year with the specific objective of
meeting certain earnings targets. Real activity manipulation affects both cash flows and
accruals.
Much of the current research on earnings management has focused on the detection
of abnormal levels of accruals, estimated using models that cannot distinguish between the
effects of pure accrual manipulation and real activities manipulation. Studies that directly
examine earnings management through real activities have concentrated mostly on
investment activities.2
My paper contributes to the literature on earnings management by presenting
evidence on the management of operational activities, something that has received scant
attention so far. Instead of examining only accruals, I focus on variables that should be
relatively free of the effects of pure accrual manipulation. Specifically, I concentrate on cash
flow from operations (CFO), production costs and discretionary expenses. Using a simple
model (presented in Dechow, Kothari and Watts 1998) to estimate the normal levels of these
variables, I detect abnormally low CFO and abnormally high production costs for companies
that report small positive profits at the annual level. The evidence is consistent with firms
increasing reported earnings beyond zero by giving price discounts to temporarily boost sales
and by overproduction. I also find evidence suggesting that some of these firms reduce
discretionary expenses to report better margins. The results do not appear to be driven by
1 Healy (1985), Guidry, Leone and Rock (1999). Defond and Jiambalvo (1994), Teo, Welch and Wong (1998) and Kasznik (1999) are a few example of studies that provide evidence on earnings management. Kothari (2001), Fields, Lys and Vincent (2001) and Healy and Wahlen (1999) provide a survey of the literature on earnings management and accrual manipulation. 2 These studies are discussed in greater detail in the next section.
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optimal managerial responses to adverse economic conditions. They are robust to controls for
performance, including non-linear controls.
My empirical results have the following implications. First, evidence that managers
manipulate both accruals and real activities suggests that the degree of manipulation detected
by analyzing accruals only probably understates earnings management. Abnormal real
activities may or may not have accrual effects. Second, the results imply that researchers
should be cautious about using ex-post cash flows as a measure of the true economic
performance of firms. For example, Givoly and Hayn (2000) assert that the ratio of cash flow
from operations to assets is unaffected by accrual accounting and hence is a measure of the
firm’s economic performance. Barth, Cram and Nelson (2001) use the ability of current
earnings, current accruals and current cash flows to predict expected future cash flows as a
way of determining the quality of various components of current earnings. Their proxy for
future expected cash flows is realized cash flows. These studies assume that cash flows are
free from manipulation. But manipulation of real activities affects cash flows and as a result,
realized cash flows are as likely to reflect management incentives as accruals.
My findings offer interesting questions for future research. For example, what is the
relative magnitude of earnings management via accruals alone versus real activities? Does
the manipulation of real activities in any given period give rise to predictable patterns in
accruals and cash flows in subsequent periods?
One caveat is in order at this point. At various places in this paper, I use the words
‘real activities manipulation’ and ‘pure accrual manipulation’ to refer to activities that appear
to be motivated solely by the desire to meet earnings targets and are not a reflection of
normal business practices. However, since I do not attempt to quantify the benefits that arise
to investors from meeting earnings targets, I cannot conclude that these activities are sub-
optimal or firm-value-decreasing.
The rest of the paper is organized as follows. Section 2 contains a discussion on the
incentives of firm managers to manipulate real activities. I also discuss prior academic and
anecdotal evidence on the earnings management through real activities. In Section 3, I
consider three specific kinds of real activities manipulation: management of sales,
overproduction and reduction of discretionary expenses. I develop hypotheses based on the
effects of these activities on cash flow from operations (CFO) and production costs for a
given sales level. Section 4 discusses my data requirements and methods used to estimate
normal levels of CFO, production costs, etc. In Section 4, I also discuss my selection of firms
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reporting small profits and present descriptive statistics for the firms in my sample. I present
the main results of this paper in Section 5. I also develop and test hypotheses on the cross-
sectional variation of real activities manipulation. Section 5 concludes with sensitivity
analyses and robustness checks. Section 6 summarizes the main results of the paper,
discusses their implications, and identifies areas for further research.
2. Motivation behind real activities manipulation and prior evidence
In this section I first discuss why executives have incentives to use methods other than pure
accrual manipulation to manage earnings. Following this discussion is a summary of extant
literature on the management of real activities.
2.1 Motivation behind manipulation of real activities
An important issue in considering the incentives behind manipulation of real
activities is the timing of earnings management. In contrast to accrual management, any
manipulation of real activities has to occur during the course of the year. Managers have the
scope to engage in pure accrual manipulation after the year-end (and before the earnings
announcement), after pre-managed earnings are observed.
Consider the case where firm managers believe that annual earnings will fall below
zero unless they undertake actions that deviate from normal practice. To avoid reporting
losses, one option is to wait until year-end and use pure accrual manipulation to cover the
shortfall between pre-managed earnings and zero.3 This strategy entails the risk that the
realized shortfall at year-end is larger than the discretionary accruals that can be reported
with pure accrual manipulation, as a result of which reported earnings will fall below zero.
Pure accrual manipulation is limited by GAAP and also by any accrual management in prior
years (see Choy 2003).4 Managers can reduce this risk by manipulating real activities during
the year to increase reported earnings.
Another incentive to manipulate real activities arises from the lower likelihood of
auditor or regulator scrutiny for real decisions than for reporting discretionary accruals via
pure accrual manipulation. The costs and benefits of real activities manipulation discussed
above are presented for easy reference in Table 1. The costs of real activities manipulation
3 This is a simplification. Managers probably manipulate accruals during the year to meet quarterly targets. This does not, however, affect my subsequent analysis. 4 Choy (2003) finds that firms with a lower ability to report discretionary accruals because of past accrual manipulation have a lower probability of meeting analyst forecasts.
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include the possibility that cash flows in future periods are affected negatively by the actions
taken this period to increase earnings. For example, price discounts offered in any period to
boost total earnings and meet some short-term target can lead customers to expect such
discounts in future periods as well, leading to lowered cash inflows from sales in the future.
Another cost of real activities manipulation is uncertainty regarding the extent of
manipulation required, as all real activities have to be undertaken prior to year-end, before
managers observe the shortfall between pre-managed earnings and the earnings target. Table
1 also presents the relative costs and benefits of relying solely on pure accrual manipulation.
These have been already discussed above. Managers have incentives to undertake real
activities manipulation when the relative benefits exceed the costs.
A full analysis of how managers choose between managing via accruals or via real
activities is left for future research. My tests on cross-sectional variation in the nature and
extent of real activities manipulation in Section 5.3 provide some initial insights into this
issue.
2.2 Current evidence on manipulation of real activities
The possibility that managers manipulate real activities to meet earnings targets is
discussed in the academic literature. For example, Fudenberg and Tirole (1995) discuss
altering shipment schedules, offering end-of-period sales, and speeding up or deferring
maintenance as possible methods by which firms try to smooth income.
There is also empirical evidence that firms manage earnings using activities with cash
flow consequences. Most of the evidence centers on the opportunistic reduction of R&D
expenses. Bens, Nagar and Wong (2002) find that managers of firms facing EPS dilution
because of exercises of employee stock options (ESOs) repurchase stock. Managers partially
finance these repurchases by reducing R&D. There is similar evidence for firms facing EPS
dilution from outstanding ESOs in Bens, Nagar, Skinner and Wong (2002). The Dechow and
Sloan (1991) find CEOs in their final years reduce spending on R&D to increase short-term
earnings. Bushee (1998) examines firms trying to meet previous year’s earnings and find that
they reduce R&D more if they have lower institutional ownership. He interprets this as
evidence that the R&D reductions by this set of firms are potentially value-destroying and are
prevented by the presence of sophisticated investors.
Anecdotal evidence exists on firms engaging in a whole range of activities in addition
to just R&D expense reduction – for example, providing limited time discounts to increase
sales towards the end of the year, channel stuffing and building up excess inventory to lower
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reported cost of goods sold. Revsine, Collins and Johnson (1998) report that, in 1992-93,
Bausch and Lomb shipped finished products out to their dealers and booked sales. The
dealers were left with large unsold inventories due to declining demand.
Anecdotal evidence also exists on managers taking advantage of the absorption-
costing system required by GAAP to report lower cost of goods sold (COGS). These
managers produce more than the quantity required to meet sales and normal target inventory
levels. As long as average production costs are declining, over-production enables them to
allocate fixed costs to higher-than-normal end-of-period inventories and reduce cost of goods
sold. In 1995, Duracraft reported better-than-expected first quarter earnings, but they suffered
a stock price drop, partially because their closing inventory leapt by over 100%, while the
operating margins did not decline at all compared to previous quarters (Marcial 1995).
Financial analysts, suspecting overproduction, expressed their concern over the
disproportionate increase in inventories.
Systematic evidence on management of real activities to meet earnings thresholds is
lacking. Thomas and Zhang (2002), in their investigation of the accrual anomaly, find
evidence consistent with firms engaging in overproduction to increase reported earnings.5
They classify firm-years into deciles of changes in inventory scaled by total assets. Firm-
years in the top two deciles are defined as the firm-years with extreme changes in inventory.
The authors focus on firms experiencing extreme changes in inventory and call the year in
which the change occurs ‘year 0’. They find that firms with extreme increases in inventory
experience earnings increases in the years leading up to year 0, and earnings decreases in the
subsequent years. These firms also report declining COGS as a percentage of sales in the
years leading up to year 0, and steeply increasing COGS thereafter. The authors argue that
this is consistent with firms engaging in overproduction in year 0 to lower reported cost of
goods sold, in an attempt to report an (unsustainable) earnings increase. One problem in this
study is firm-years are selected on extreme inventory changes. The authors do not explore the
reasons behind these changes and are unable to rule out adverse economic conditions or
changing demand conditions as alternative explanations for their results (see Hribar 2002).
5 Sloan (1996) documents that a zero-investment portfolio long in firms with low accruals and short in firms with high accruals earns positive returns in the future. He argues this is because the market overestimates the persistence of accruals relative to cash flows. Sloan terms this the accrual anomaly. The analysis in Thomas and Zhang (2002) reveals that the accrual anomaly is driven mainly by changes in inventory.
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In addition to the various activities discussed above, there are some other ways of
managing earnings through real activities that have been studied by the literature. Bartov
(1993) examines asset sales and shows that the profit from asset sales is negatively correlated
with earnings changes. He uses this to argue that firms facing earnings declines boost profits
through increased asset sales. Unfortunately, this is not very compelling evidence of earnings
management, because it is possible that firms with declining earnings have more
unproductive assets and choose the abandonment option on these assets. Barton (2001)
provides evidence that firms trying to smooth earnings invest in derivatives to smooth the
underlying cash flows, and this acts as a substitute for smoothing of earnings through accrual
management. Pincus and Rajagopal (2002) find similar evidence for their sample of oil and
gas firms. In this study, I do not examine strategies involving derivatives primarily because
these are longer-term investment decisions. This paper focuses on detecting real activities
manipulation among firms trying to avoid reporting losses. Firms trying to report small
profits instead of small losses in any given year are unlikely to put hedging strategies in place
for this limited short-term goal.
3. Development of main hypotheses
Burgstahler and Dichev (1997) report evidence that firms manage earnings
opportunistically to meet certain thresholds, like zero earnings, or last year’s earnings. They
classify firm-years by earnings, and plot the frequency of firm years in each earnings interval.
They find a discontinuity in the distribution of firm-years around zero earnings. Specifically,
they find that the distribution of firm-years shifts sharply upwards immediately to the right of
zero. They interpret this as evidence that firms with slightly less than zero earnings manage
their earnings upwards to exceed the threshold of zero earnings.6 This particular finding was
first documented by Hayn (1995) and has also been corroborated in Degeorge, Patel and
Zeckhauser (1999), Burgstahler and Eames (1999), Dechow, Richardson and Tuna (2003)
and Beaver, McNichols and Nelson (2002 and 2003). Burgstahler and Dichev (1997) find
qualitatively similar results when they categorize firm-years by earnings changes.
6 Beaver, McNichols and Nelson (2003) and Dechow, Richardson and Tuna (2003) have questioned whether the observed discontinuity around zero is earnings management at all, and if so, how much. While the issues raised in Dechow, Richardson and Tuna (2003) are explicitly addressed here, the arguments in Beaver et al (2203) do not explain how the discontinuity changes as new benchmarks emerge over time [see Cao 2003]. Their arguments also cannot be applied to forecast errors where similar discontinuities have been observed.
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Degeorge, Patel and Zeckhauser (1999) find a similar discontinuity around zero for
analyst forecast errors. That is, the distribution of firm-years shifts significantly upwards
immediately right of zero forecast error. Burgstahler and Eames (1999), Dechow, Richardson
and Tuna (2003) and Abarbanell and Lehavy (2002) also present similar evidence with
analyst forecast errors.
Burgstahler and Dichev (1997) present some preliminary analysis on how firms
accomplish this earnings management. They show that if firm-years are grouped by their
earnings intervals, the distribution of accruals shifts upwards in the first interval to the right
of zero. They consider this as evidence that firms who have the flexibility to manage earnings
through accruals do so to report small profits instead of losses. They also find preliminary
evidence that the distribution of cash flow from operations (CFO) appears to shift upwards
going from below zero earnings to above-zero earnings. They present only the 25th, 50th and
75th quartile of accruals and CFO for each earnings interval and do not analyze the
underlying activities behind these patterns. They also do not test whether the shift is
statistically significant, especially after controlling for the level of operations, or for firm
performance.
I use the model presented by Dechow, Kothari and Watts (1998) to estimate the ‘normal’
or expected cash flow from operations given a firm’s sales level.7 Deviations from expected
cash flow from operations (CFO) are termed “abnormal CFO”. I try to distinguish the effects
on abnormal CFO of different kinds of manipulation of real activities. I focus mainly on three
kinds of manipulation:
1. Accelerating the timing of sales or generating additional unsustainable sales through
increased price discounts or more lenient credit terms
2. Decreasing discretionary expenses
3. Reporting lower cost of goods sold by increasing production
The costs of sales management, overproduction and reduction of discretionary expenses,
along with their effects on cash flow from operations, are further discussed below.
Management of sales
Sales manipulation, in this context, refers to managers trying to boost sales during the
year with the sole objective of increasing earnings to meet certain targets. For example, they 7 The model is presented in Appendix 1, and Section 4 describes in greater detail the empirical method used to estimate expected cash flows.
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may try to generate additional sales or accelerate sales from the next fiscal year into the
current year by offering ‘limited-time’ price discounts. The increased sales volumes
generated are likely to disappear when the firm re-establishes the old prices. The total cash
inflow per sale from these additional sales is now lower, though earnings in the current
period increase as the sales are booked, assuming positive margins. For example, assume a
product normally sells for $100. The product costs the firm $70 to make. For simplicity,
assume that both sales price and costs are paid in cash on the day of the sale. If the managers
drop the price to $90 and generate 10 additional sales in 2002 that they would not have had
otherwise, net income and net cash inflow both rise by $200. The net cash inflow, however,
is lower than the normal net inflow of $270 on the additional $900 worth of sales.
A firm may also offer more lenient terms of credit. For example, retailers and automobile
manufacturers often offer lower interest rates (zero-percent financing) towards the end of
their fiscal years. These are all essentially price discounts and lead to lower cash inflow over
the life of the sales, as long as the suppliers do not offer matching discounts. In general, I
expect sales management activities to lead to lower current-period CFO than what is normal
given the sales level.
It is also worthwhile to analyze the effects of these sales manipulation activities on
accruals. If the firm generates additional credit sales with its modified terms and a higher
amount than normal of these credit sales is outstanding at the end of the year, then the firm
should exhibit an abnormal growth in receivables for a given growth in sales.
Reduction of discretionary expenses
Firms can also increase earnings by reducing discretionary expenses. In this paper, I
focus on advertising expenses, research and development expenses (R&D) and selling,
general and administrative expenses (SG&A). The first two are largely discretionary items
and firms trying to boost earnings to meet targets can reduce outlays on advertising and R&D
below what is normal given their sales levels. Some items usually classified as SG&A, for
example, employee training expenses, maintenance and travel, are also likely to be
discretionary. If these outlays are generally in the form of cash, the effect on abnormal
operational cash flows in the current period is positive, possibly at the risk of lower cash
flows in the future as long-term competitiveness and profitability are adversely affected. If
some of these expenses are also incurred on account and are usually outstanding at the end of
the year, then a decrease in these expenses towards the year-end should lower accounts
payable below what is normal and lead to positive abnormal accruals.
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Overproduction
Managers of manufacturing firms might also overproduce (produce more goods than
necessary to meet expected demand) to manage earnings upwards. With higher production
levels, fixed overhead costs are spread over a larger number of units, which reduces the
average unit cost and cost of goods sold (COGS). This improves the operating margins and
reduces cost of goods sold as a percentage of sales in the current period. Nevertheless, the
firm incurs costs on those over-produced items that are not recovered in the same period
through sales. As a result, cash flows from operations are lower than normal given sales
levels.
The firm has to have higher inventories than normal at the year-end for the
overproduction strategy to work. Presumably, managers indulge in overproduction only if the
reduction in reported product costs offsets the inventory holding costs that the firm has to
recognize in the current period. The higher inventories at year-end imply that the partial
effect of overproduction on accruals is positive.
As already discussed, the partial effect on accruals of each real activities
manipulation method is positive. However, positive abnormal accruals are not sufficient
evidence of real activities manipulation, because they are also caused by pure accrual
manipulation. Hence, to concentrate on the effects of real activities, I focus on abnormal
CFO, instead of accruals.8 A potential problem of examining abnormal CFO is that managers
probably undertake more than one kind of manipulation at the same time. Recall that offering
price discounts and overproduction have a negative effect on abnormal CFO, while reduction
of discretionary expenses has a positive effect.9 Consequently, I hypothesize that firms
engaging in real activities manipulation should exhibit at least one of the following –
unusually low CFO or unusually low discretionary expenses.
Relying on Burgstahler and Dichev’s (1997) evidence on the distribution of firms-
years, CFO and accruals, I identify firm-years that report small profits as very likely to have
engaged in earnings management. I call these firm-years the “suspect firm-years”.10
My first hypothesis is formally presented below (in alternate form):
8 Abnormal CFO should be free of the effects of pure accrual manipulation. 9 This differs from the case of accrual manipulation, where the expected sign of abnormal accruals is determined from the hypothesized direction of earnings management. 10 The process of identifying suspect firm-years is discussed in greater detail in subsequent sections.
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H1A. After controlling for sales levels, suspect firm-years exhibit either unusually low cash
flow from operations (CFO) OR unusually low discretionary expenses OR both.
Another way to detect price discounts or overproduction is to focus on production
costs relative to sales. Production costs are defined as (COGS + change in inventory during
the period). Overproduction leads to unusually high production costs for a given level of
sales. If the firm gives discounts to increase sales, this also implies unusually high production
costs for a given sales level, as long as the firm is unable to procure corresponding discounts
from its suppliers. Therefore, my second hypothesis is:
H2A: Suspect firm-years exhibit unusually high production costs, controlling for the level of
sales.
Analyzing production costs relative to sales, instead of COGS, has an additional
benefit. Any pure accrual manipulation to lower reported COGS, for instance, by postponing
write-offs of obsolete inventory, should not affect production costs, because the change in
inventories is correspondingly higher.
4. Data and estimation models
In this section, I first discuss my data requirements (Section 4.1) and subsequently present the
models I use to estimate the normal levels of CFO, accruals, production costs and other
variables (Section 4.2). Section 4.3 discusses the selection of suspect firm-years. In Section
4.4, I present descriptive statistics for the full sample and for the suspect firm-years.
4.1 Data
For my analysis of firms managing earnings to avoid reporting losses, I use all firms
in COMPUSTAT between 1987 and 2001 with sufficient data available to calculate the
variables in Appendix 2 for every firm-year.
Following Burgstahler and Dichev (1997), I eliminate from my sample firms in
regulated industries (SIC codes between 4400 and 5000) and banks and financial institutions
(SIC codes between 6000 and 6500). The models for normal or expected CFO, production
costs, discretionary expenses and accruals are estimated by every year and industry, where
the two-digit SIC code is used to identify an industry. I require at least 15 observations for
each industry-year grouping. Applying all the data requirements leaves 21,758 firm-years
over the period 1987 to 2001, including 36 industries and 4,252 separate firms. This is quite
low compared to the 64,466 firm-years in Burgstahler and Dichev (1997), who had far fewer
data restrictions. It is closer to the sample of around 30,000 firm-years in Dechow,
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Richardson and Tuna (2003), who have similar data requirements. While I require that every
data item in Appendix 2 is available including CFO, cost of goods sold and SG&A, Dechow,
Richardson and Tuna (2003) require data only to calculate abnormal accruals, Second, they
only require ten firms in every industry-year grouping, compared to fifteen in my study.
Further, while I exclude regulated firms and financial institutions, in keeping with
Burgstahler and Dichev (1997), Dechow et al (2003) exclude only financial institutions. This
probably explains the difference in sample size between their study and mine.
4.2 Estimation models
According to the model presented in Dechow, Kothari and Watts 1998, normal cash
flows from operations are given by the following equation (Equation 3 in Appendix 1):
CFOt = (π-δ) St + δSt-1
where CFOt = cash flow from operations in period t
π = the profit margin
St = sales in period t.
and δ is a measure of the operating cash cycle
To estimate the model, I run the following cross-sectional regression for every
industry and year:
CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt (1)
where
At-1 = total assets lagged by one period
For every firm-year, abnormal cash flow from operations is the actual CFO minus the
“normal” CFO calculated using the corresponding industry-year model. Following general
convention in the literature for estimating non-discretionary accruals, the empirical model
includes a scaled intercept, α*(1/At-1), even though the theoretical model in Dechow, Kothari
and Watts (1998) does not.11 Including a scaled intercept in the estimation model allows the
average CFOt/At-1 for a particular industry-year to be non-zero even when the dependent
variables, sales and lagged sales, are zero.
Similarly, to determine normal cost of goods sold and normal discretionary
expenditure, I run the following regressions for every industry and year:
COGSt/At-1 = α *(1/At-1) + β*(St/ At-1) + εt (2) 11 Jones (1991) DeFond and Jiambalvo (1994), Dechow, Sloan and Sweeney (1995), Guidry, Leone and Rock (1999) and Kasznik (1999) estimate models for non-discretionary accruals with scaled intercepts.
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disexpt/At-1 = α *(1/At-1) + β*(St/ At-1) + εt (3)
where
COGSt = cost of goods sold in period t
disexpt = discretionary expenditure in period t = advertising expenses + R&D + SG&A
The above regressions assume a linear relation between COGS and sales as well
between discretionary expenses and sales. While this is the assumption in the model
presented in Dechow, Kothari and Watts (1998), it is not necessarily descriptive of all firms. I
use these linear approximations for simplicity. I define discretionary expenditures as the sum
of advertising expenses, research and development expenses (R&D) and selling, general and
administrative expenses (SG&A). Ideally, I would like to include only advertising and R&D,
but these items are often not disclosed if they are not material.12 This is why I also include
SG&A, under the assumption that if advertising or R&D is not disclosed separately, they are
included in SG&A. Also, many items in SG&A are likely to be discretionary, for example,
employee training expenses, maintenance expenses, etc.
I calculate abnormal accruals using the Jones (1991) model applied to the cross-
section, as in DeFond and Jiambalvo (1994).
Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt (4)
where
∆St is the change in sales over last period’s sales and PPEt denotes property, plant and
equipment. This equation is estimated for every industry and year.13
To test whether growth in net receivables is abnormal, I estimate the following
equation for every industry and year:
∆ARt/At-1 = α*(1/At-1) + β*(∆St/ At-1) + εt (5)
12 Note that the manager’s decision to reduce advertising expenses in order to report better margins will depend on the manager’s assessment of whether advertising affects current period sales or future sales. 13 Unlike DeFond and Jiambalvo (1994), however, I do not use the modified Jones model presented in Dechow, Sloan and Sweeney (1995). The modified Jones model actually estimates the original Jones model, but in the event period that earnings management is being hypothesized, non-discretionary accruals are calculated as α*(1/At-1) + β1*[(∆St - ∆ARt)/At-1] + β2*(PPEt/At-1) where ∆ARt is the change in receivables. Thus, the entire change in receivables is treated as discretionary, to boost power. As discussed earlier, managers of firms may manipulate sales upwards by giving more lenient credit terms or shipping out goods early, both having a positive effect on accounts receivables. Alternatively, managers may just choose to book sales from the next period fraudulently as credit sales in the current period, in contravention of GAAP. In any case, the firms should exhibit abnormal growth in accounts receivable for a given change in sales. Since this is something I test for, I do not use the modified Jones model, which already assumes that the change in accounts receivables is discretionary.
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The above regression reflects the assumption by Dechow, Kothari and Watts (1998)
(see Appendix 1) that ‘normal’ accounts receivables are a constant fraction of sales.
Abnormal change in receivables for a particular firm-year is defined as deviation from the
change in receivables predicted by the corresponding industry-year regression. While
abnormally high changes in net receivables may be the result of real activities manipulation,
they may also reflect earnings management by pure accrual manipulation, for example,
inadequate provisioning of bad debts. To focus on the effect of real activities, I use the above
regression to estimate abnormal changes in gross receivables as well.
Similarly, following the expression Dechow, Kothari and Watts (1998) derive for
‘normal’ level of inventory (see Appendix 1), normal growth in inventory is estimated from
the industry-year regression
∆INVt/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*(∆St-1/ At-1) + εt (6)
Finally, production costs are given by
PRODt = COGSt + ∆INVt
Using the models in regressions (6) and (9), normal production costs are estimated
from the industry-year regression
PRODt/At-1 = α*(1/At-1) + β1*(St/At-1) + β2*(∆St/At-1) + β3*(∆St-1/At-1) +εt (7)
The average adjusted R-squared for the estimation model for normal CFO increases
from around 47% across industries in 1987-1988 to around 70% in 2000-2001. The adjusted
R-squared for the estimation model for normal production costs is above 90% in almost every
single industry-year combination. The adjusted R-squared for the estimation model for
normal discretionary expenses is generally above 60% across all industry-year combinations.
These R-squareds compare favorably to an average R-squared of only around 25% that
Dechow, Richardson and Tuna (2001) report for their estimation model for normal accruals.
4.3 Choice of suspect firms
Figure 1 presents the number of firm-years categorized by net income scaled by total
assets at the beginning of the year. The histogram of scaled earnings is constructed with
widths of 0.005 for the range –0.075 to +0.075. The interval widths are the same as in
Burgstahler and Dichev (1997). The histogram is truncated at the extremes. That is, firm-
years with scaled earnings above 0.075 or below -0.075 are excluded. This is true for all the
figures presented in this paper. In the case of firm-years grouped by scaled earnings, the
intervals presented in the figures include 10,958 firm-years, or just over 50% of my total
sample.
15
The histogram in Figure 1 is very similar to that in Burgstahler and Dichev (1997),
with the prominent upward shift in the frequency of firm-years going from the left of zero to
the right. Burgstahler and Dichev (1997) also find an abnormally low number of firm-years
immediately to the left of zero. This is less pronounced in my sample of firm-years and also
in the sample used by Degeorge, Patel and Zeckhauser (1999). To increase the power of my
tests, I concentrate on firm-years with profits in the interval just to the right of zero. These
firm-years have net income scaled by total assets that is greater than or equal to zero but less
than 0.005 (interval 16 in the figure). Recall that, according to the arguments in Burgstahler
and Dichev (1997), this interval contains firms that have reported earnings marginally above
the zero threshold and it is very likely that these firms have managed their earnings upwards
to avoid reporting losses. I call these firm-years the suspect firm-years. There are five
hundred and three firm-years in this interval with around four hundred and fifty unique firms.
There are two potential problems of concentrating on these suspect firm-years. First,
managers have to pre-commit to real activities manipulation before the end of the fiscal year.
Firms that just meet zero earnings may not be the only ones that manipulate real activities to
try and do so. Focusing on only firm-years in the small interval (interval 16) to the right of
zero restricts the power of my tests. Second, firms whose ‘unmanipulated’ earnings are
substantially above zero may have an incentive to manage earnings downwards to report
profits that are only slightly above zero instead, in order to create reserves for the future. If
this is true, then the interval just right of zero also includes firm-years with downwards
earnings management, and this affects the power of my tests negatively. However, I do not
include other intervals in the suspect category, as these intervals are likely to contain a higher
proportion firm-years that did not manipulate earnings at all or manipulated earnings
downwards.
4.4 Descriptive statistics
Table 2 presents descriptive statistics for the full sample and for the suspect firm-
years. For the full sample, the average market value of equity is $1.4 billion, though the
median market value of equity is much smaller, $137 million. There is a similar skewness in
total assets and sales for the full sample, suggesting the presence of a large number of
relatively small firms. The average market-to-book ratio in this sample is 2.75.
The mean and the median income before extraordinary items for the full sample are
positive, $60 million and $4.46 million respectively. Average net income as a percentage of
total assets is very low, only 0.31%. The median, however, is significantly higher at 4.09%.
16
This is probably because of the presence of a relatively high number of firms with losses that
are large in magnitude relative to their assets.
As expected, mean CFO for the full sample is positive ($127 million) and mean
accruals are negative (-$65 million). As a fraction of total assets, mean CFO and mean
accruals are 6.50% and -6.17% respectively. On average, production costs and discretionary
expenses are respectively, 97% and 44% of total assets.
The descriptive data on the suspect firm-years reveals that they are, on average, much
smaller. Mean market capitalization, at around $746 million, is almost half that of the full
sample, though mean total assets ($1.1 billion) of the suspect firm-years are not significantly
smaller. This suggests that the market values of suspect firm-years are relatively depressed
with respect to book values. Consistent with this, the suspect firm-years have significantly
lower mean market-to-book ratio compared to the overall sample (1.60 versus 2.75). Market-
to-book (MTB) is the ratio of the market value of equity to the book value of equity at the
beginning of the year.
Suspect firm-years are also less profitable compared to the rest of the sample. Mean
net income, or income before extraordinary items, is only $2.81 million, significantly lower
than the mean for the overall sample. This is also true for net income as a percentage of total
assets (0.24% for suspect firm-years). Interestingly, suspect firm years have lower mean CFO
as a percentage of assets – mean scaled CFO is 4.54% for suspect firm-years versus 6.50%
for the whole sample. But their mean scaled accruals (-4.31%) are significantly more
positive, and their mean scaled discretionary expenses (36.63%) are significantly lower,
compared to the full sample.
Table 3 provides descriptive statistics for the abnormal values of various variables
calculated from the estimation models. The means of the abnormal values are generally
different from zero, as the estimation models do not include unscaled intercepts, following
the general convention in models for non-discretionary accruals.14 They appear to be
otherwise well-behaved. The skewness for all the distributions is relatively close to zero,
suggesting the distributions of the abnormal values are symmetric. Also, the kurtosis data
suggests that the distributions are not extremely fat-tailed relative to a normal distribution.
For example, abnormal CFO has a skewness of -0.38 and kurtosis of 2.25. These are roughly
close to the values for a normal distribution (0 and 3, respectively). 14 Estimating the models with a vector of ones does not materially affect the results. This is discussed in detail in Section 5.3.2.
17
Table 4 presents correlations between various variables. In Table 4, all variables are
scaled by total assets at the beginning of the year, except for the market-to-book ratio. Sales
and income before extraordinary items are highly positively correlated, as expected, with a
correlation coefficient of 0.90. Consistent with prior studies, accruals and CFO exhibit a
strong negative correlation, with a correlation coefficient of -78%.
Consistent with Sloan 1996, income before extraordinary items (hereafter referred to
as net income) is correlated positively with accruals (57%) and negatively with CFO (-58%).
Firm with higher net income also have higher production costs and higher discretionary
expenses. The correlation of net income with production costs is 90% and with discretionary
expenses is 49%.
The correlations between the abnormal and total levels of various variables are
usually positive. This correlation is highest between accruals and abnormal accruals (80%),
followed by the correlation between CFO and abnormal CFO (75%). The correlation between
total and abnormal production costs is substantially lower at 38%. Abnormal discretionary
expenses and total discretionary expenses have a correlation coefficient of 59%. Abnormal
CFO and abnormal accruals are positively correlated with net income (45% and 42%
respectively), while abnormal production costs and abnormal discretionary expenses are
negatively correlated with net income (-20% and -19% respectively).
Note the highly negative correlation coefficient (-80%) between abnormal production
costs and abnormal discretionary expenses. One reason could be price discounts leading to
temporarily-increased sales volumes and possibly, a higher dollar value of sales than without
manipulation. As a result, discretionary expenses appear low relative to realized sales even
when they are not reduced as a percentage of unmanaged sales. Since price discounts make
production costs unusually high relative to sales prices, one observes a negative correlation
between abnormal production costs and abnormal discretionary expenses. A second reason
could be managers simultaneously reducing discretionary expenses and undertaking activities
that lead to abnormally high production costs, for example, overproducing or offering price
discounts.
The correlation between abnormal accruals and abnormal CFO is negative (-21%).
One reason for this negative correlation could be firms engaging in pure accrual manipulation
and real activities manipulation at the same time. Another possible reason is that accruals are
abnormally high partially because of the effects of real activities manipulation, which also
have a negative effect on abnormal CFO.
18
5. Results
This section presents my main results and is organized as follows. In Section 5.1, I compare
various earnings manipulation measures of the suspect firm-years to the rest of the sample. In
Section 5.2, I examine whether explanations other than real activities manipulation can
explain patterns in the data. Section 5.3 develops hypotheses on and tests cross-sectional
variation in real activities manipulation. Section 5.4 includes additional sensitivity tests and
robustness checks.
5.1 Earnings management to beat zero earnings – comparison with the rest of the
sample
Section 4.3 identifies the suspect firm-years. In this section, I examine whether
suspect firm-years exhibit abnormal CFO, abnormal production costs and abnormal
discretionary expenses that are consistent with real activities manipulation. I calculate
abnormal CFO for every firm-year using the industry-year model described in Section 4. If
firm-years that report profits just above zero undertake activities that adversely affect their
cash flow from operations, then the abnormal CFO for these firm-years should be negative
compared to the rest of the sample. To test this, I run the following regression:
Xt = α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 + β3*(Net income) t +
β4*(SUSPECT_NI) t + εt (8)
where Xt is the dependent variable. in this case, it is abnormal CFO in period t. Regression
(8) is also estimated with abnormal production costs and abnormal discretionary expenses as
the dependent variables.
SUSPECT_NI is an indicator variable that is set equal to one if firm-years belong to
the earnings category just right of zero, and zero otherwise. The models in Section 4.2 for
normal levels of CFO, production costs, etc., are estimated for every industry-year. This
assumes that all firms within the same industry-year have the same model parameters. This is
a strict assumption. For example, young firms with numerous future growth opportunities are
likely to have higher profit margins than mature firms in the same industry with few growth
opportunities. Hence, it is possible that the former have lower production costs as a
percentage of sales. My estimation models do not explicitly control for growth opportunities,
though the models for CFO, accruals and production costs control for current growth in
19
sales.15 To ensure that the relation between suspect firms and the abnormal levels of various
variables is not driven by variation in growth opportunities, I include a control for growth
opportunities in the above regression. A possible proxy for growth opportunities is the
realized growth rate of sales in the recent past. This imposes a further data availability
requirement that reduces the number of observations and decreases power. Instead, I include
market-to-book ratio (defined earlier) as a control for growth opportunities in the
regressions.16
To control for systematic differences in abnormal CFO, production costs and other
variables with size, I include the logarithm of the market value of equity at the beginning of
the year (Size). Dechow, Sloan and Sweeney (1995) argue that conventional non-
discretionary-accruals models are misspecified for firms with extreme performance. As a
result, abnormal accruals calculated using these models have measurement error positively
correlated with firm performance. This is supported by the strong positive correlation (42%)
between abnormal accruals and net income (see Table 4). Similarly, there is a strong positive
correlation between abnormal CFO and net income (45%) and a strong negative correlation (-
20%) between abnormal production costs and net income. As Guay, Kothari and Watts
(1996) point out, managers’ incentives to manipulate earnings are probably correlated with
firm performance and this can lead to the observed correlations. Nevertheless, I include net
income as a control variable in the regressions to address the possibility that abnormal values
from my estimation models have measurement error correlated with performance. The net
income figure is scaled by lagged total assets, so it is similar to return-on-assets (ROA). To
the extent that the correlations between abnormal CFO and my control variables representing
size, growth and performance are not linear, my controls are imperfect. I later impose non-
linear controls for performance using the performance-matching technique advocated by
Kothari, Leone and Wasley (2001). In the regressions, all the control variables are included
as deviations from the respective industry-year means.
The coefficients of regression (8) are estimated in the cross-section every year. Table
5 reports the time-series means of the coefficients from the fifteen annual cross-sectional
regressions over the period 1987 to 2001, along with the corresponding t-statistics (Fama-
15 The models for accruals and production costs (equations 4 & 7) explicitly have growth-in-sales as an explanatory variable. The model for CFO (equation 1) also implicitly controls for growth because it has both current sales and lagged sales as explanatory variables. 16 The ratio is winsorized at the 1% level, to control for the effect of outliers. Using logarithms of the ratio instead of winsorizing does not affect the results materially.
20
Macbeth 1973). The number of cross-sectional observations ranges from around one
thousand firms in 1987 to around two thousand firms every year in the late 1990s. The
coefficient on SUSPECT_NI is negative and significant at the 5% level. Suspect firm-years
have abnormal CFO that is lower on average by 1.44% of assets compared to the rest of the
sample.17 This difference is economically large, given that the median CFO across all firm-
years is 8% of total assets at the beginning of the year (see Table 2). I interpret this as
evidence consistent with firms undertaking activities that lead to lower CFO than what is
normal given sales levels, in order to meet the target of zero earnings. Instead of current
year’s income, if I include net income lagged by one year, or the average performance over
the most recent three years, the coefficient on SUSPECT_NI and its statistical significance
are practically unchanged.
To test my second hypothesis, I calculate normal production costs as the predicted
production costs from running regression (7) of Section 4. Next, I re-estimate regression (8)
using the Fama-Macbeth procedure and setting Xt equal to abnormal production costs in
period t. The results of this regression (the second column of results in Table 5) indicate that
firm-years just right of zero have unusually high production costs as a percentage of sales
levels. The coefficient on the indicator variable is positive and significant at the 5% level.
This is consistent with suspect firms either engaging in overproduction or giving price
discounts, with the result that production costs are abnormally high for a given sales level.
The coefficient indicates that suspect firm-years have abnormal production costs that are 4.9
percent points larger than the mean across the rest of the sample. This is an economically
significant amount, given that the median production costs as a percentage of total assets for
the entire sample is around 78% (Table 2).
I also investigate whether firms reduce discretionary expenses to report profits that
are non-negative. To test this, I first estimate ‘normal’ discretionary expenditure using
regression (3) in Section 4. I then set Xt equal to abnormal discretionary expenses in
regression (8) and estimate the coefficients using the Fama-Macbeth procedure. I find
evidence (the third column of Table 5) that the discretionary expenses of these firm-years are
unusually low given their sales levels. The coefficient on SUSPECT_NI is negative and
significant at the 5% level. This is consistent with suspect firms reducing discretionary
17 Recall that abnormal CFO is defined as deviations from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where St is sales during year t and At is total assets at end of year t.
21
expenses to report better earnings. It indicates that suspect firm-years have discretionary
expenses lower by 2.5 per cent points than the average across the rest of the sample, which
seems economically significant given that the median discretionary expenses for the whole
sample is around 37%.
If firms that report small positive profits engage in pure accrual manipulation or in
the manipulation of real activities with accrual effects, then they should exhibit positive
abnormal accruals. Table 5 reports the results of Fama-Macbeth regressions of abnormal
accruals on size, MTB, net income and SUSPECT_NI [regression (8) with Xt equal to
abnormal accruals in period t]. The coefficient on SUSPECT_NI is positive and statistically
significant at the 5% level, indicating that firm-years that have just met or beat zero earnings
have unusually high abnormal accruals, when compared to the rest of the sample. The
coefficient indicates that suspect firm-years have abnormal accruals that are 1.48 percent
points lower than the mean across the rest of the sample. This is an economically significant
amount, given that the median accruals as a percentage of total assets for the entire sample
are around -5% (Table 2). Of course, it is possible that abnormal accruals are high partially
because of real activities manipulation, such as overproduction. Section 5.5.1 discusses the
relative importance of abnormal production costs and abnormal accruals in explaining firm
presence in the suspect interval.
A further investigation into two important components of accruals reveals that
suspect firm-years experience unusually large growth in inventories. Table 5 reports the
results of a regression of abnormal change in inventory on size, MTB, net income and
SUSPECT_NI. The coefficient on SUSPECT_NI is positive and statistically significant at the
5% level. This suggests that firms trying to meet zero earnings reduce their COGS either by
overproduction or by pure-accrual manipulation of inventories, for example, understatement
of obsolete inventory write-offs.
I fail to find evidence that suspect firm-years experience abnormally high growth in
net accounts receivables. Table 5 indicates that the coefficient on SUSPECT_NI is
insignificant when the dependent variable is abnormal growth in net receivables. Net
accounts receivables reflect the effects of both increased credit sales through more lenient
credit terms and inadequate provisioning for bad debts. Given that both effects tend to
increase net receivables, the lack of evidence on abnormal changes in receivables is
surprising. I also find no evidence (last column of Table 5) of abnormally high growth in
gross accounts receivables for the suspect firm-years. As such, there is no support in my tests
22
for the hypothesis that firms engage in activities to generate additional credit sales that are
outstanding at the end of the year.18
5.2 Are the actions taken by the managers of suspect firms optimal?
An alternate hypothesis for the abnormally high production costs for a given level of
sales is that suspect firm-years face idiosyncratic demand shocks. The optimal sales prices in
these adverse demand conditions may also be lower. Additionally, the firms with inflexible,
pre-determined production plans end up with abnormally high production given the sales
levels, as they take time to adjust to the new reduced demand conditions. This also results in
abnormally high production costs for a given level of sales.
Figure 2 presents the residual production costs for each earnings interval between -
0.075 and +0.075. Recall that these intervals contain around 50% of all firm-years in my
sample. For a large number of intervals in the figure, the average residual production costs
are positive. This suggests residual non-linear variation of production costs with performance
not captured by my estimation models. Nevertheless, the average residual production costs
for the suspect firm-years is sharply higher compared to all the other intervals reported. The
difference between the average residual production costs of the suspect firm-years and the
average production costs across the other intervals presented in the figure is positive and
significant at the 5% level. Under the alternate hypothesis, it would somehow have to be true
that suspect firm-years face unusually adverse economic conditions, even when compared to
firm-years that had worse performance. While this is possible, it is unlikely that firm-years
with worse performance faced better economic circumstances.
Overall, the analysis so far suggests that firm-years in the earnings interval just
above zero have abnormally low CFO and discretionary expenses, along with abnormally
high production costs and accruals, when compared to the rest of the sample. Further
inspection reveals that it is unlikely that the abnormally high production costs reflect adverse
economic conditions faced by the suspect firm-years.
5.3 Cross-sectional variation in real activities manipulation
This section looks at whether predictable cross-sectional variation exists in abnormal
CFO, abnormal production costs, abnormal COGS and abnormal discretionary expenses 18 It is possible that managers do engage in activities that increase credit sales, but the receivables outstanding are factored away. This is probably one reason for the lack of evidence on receivables growth.
23
among suspect firm-years. The sources of cross-sectional variation I focus on are (a) industry
membership, (b) flexibility to engage in pure accrual manipulation and (c) incentives to meet
zero earnings, including the presence of debt and short-term creditors.19 First, in Section
5.3.1, I generate hypotheses on how the above three factors affect the nature and extent of
real activities manipulation. Next, in Section 5.3.2, I develop and implement empirical tests
of the hypotheses.
5.3.1 Hypotheses development on cross-sectional variation
Abnormal production costs ─ manufacturing firms versus non-manufacturing firms
If unusually high production costs for a given level of sales are mainly a result of
overproduction, then suspect firm-years that belong to manufacturing industries should be
primarily responsible for the abnormal production costs. Since overproduction has an adverse
effect on CFO, suspect manufacturing firm-years should also exhibit more negative abnormal
CFO. Recall that abnormally high production costs for a given level of sales may also be the
result of sales discounts. It is possible that non-manufacturing firms are more aggressive at
offering price discounts than manufacturing firms. This reduces the power of my tests to
detect higher abnormal production costs for suspect manufacturing firms. For the purposes of
my empirical tests, I use the classification by the US Department of Labor to identify
manufacturing firms. Industries represented by two-digit SIC codes between 20 and 39 are
classified as manufacturing industries (18 out of the total 36 industries in my sample).
H3A: Suspect firm-years in manufacturing industries will exhibit higher abnormal
production costs and lower abnormal CFO than suspect firm-years in non-manufacturing
industries.
Variation in the components of managed earnings with the level of current assets
Another source of variation in earnings management strategies may be the
asset/liability structure of the firms. Consider firms that have a traditionally low level of
current assets. These firms should have less flexibility to manage earnings through activities
that have a positive effect on accruals. For example, firms that have no credit sales (and
hence, no accounts receivable outstanding at the year-end) cannot increase earnings by 19 This study does not investigate equity-market-related reasons for zero earnings being an important target. Dechow, Richardson and Tuna (2003) find that small-profit firms do not have significantly lower future one-year returns than firms reporting small losses. This suggests that the prospect of higher equity returns may not be the driving motivation behind firms wanting to report above-zero earnings. Dechow, Richardson and Tuna (2003) do not investigate announcement returns or other possible equity-market motivations: eg, it is possible that institutional investors are not allowed to hold stakes in firms with losses. A detailed study of stock-market motivations is beyond the scope of this paper.
24
reducing provisions for bad debts, nor increase total sales by giving more lenient terms of
credit. Similarly, firms that maintain low inventories have less discretion to manipulate
inventory upwards through overproduction without attracting the attention of auditors. If
these firms wish to manage earnings upwards, they can only boost sales by giving price
discounts or they can reduce discretionary expenses. Thus, I expect suspect firm-years with
low current assets to be more aggressive at offering price discounts and reducing
discretionary expenses.
I partition my sample every year into quartiles of current assets at the beginning of
the year scaled by total assets. I classify firm-years in the bottom quartile as the low current
asset group. Table 6 presents data on the low-current-assets group versus the rest of the
sample. Compared to the rest of the sample, these firm-years have much lower accounts
receivables and inventories outstanding at the end of the year. Consistent with my argument
that they have low flexibility to manage earnings upwards with activities that affect accruals
positively, firm-years in the low-current-assets group have generally lower abnormal
accruals.
Abnormal production costs may be relatively low for suspect firm-years with low
current assets because of the inability to indulge in overproduction. Therefore, for these firm-
years, I focus on abnormal cost of goods sold. If suspect firm-years with low current assets
are aggressive at offering price discounts, while their ability to lower reported cost of goods
sold via inventory management is limited, they should have unusually high cost of goods sold
for a given sales level. They should also be more aggressive at reducing discretionary
expenses.
H4A: Suspect firm-years in the lowest quartile of current assets have abnormally high cost of
goods sold (COGS) and abnormally low discretionary expenses, when compared to other
suspect firm-years.
Variation in the degree of earnings management with incentives to meet zero earnings –
debt contracts
In a preliminary investigation of why zero earnings is an important threshold, I
consider the possibility that debt contracts include covenants that become tighter when
earnings fall below zero. As a simple way of testing this, I test whether suspect firm-years
that have debt outstanding engage in real activities management to a greater degree than
suspect firm-years who do not. In this experimental set-up, the existence of debt is used as a
(crude) proxy for the presence of a debt covenant that makes zero earnings an important
25
threshold. Attempts to construct a better proxy by using the SDC database to identify firm-
years that have syndicated loans (which usually carry covenants) outstanding were
unsuccessful.20 A manual examination of SEC 10K filings by randomly chosen suspect firm-
years reveals that SDC fails to identify too many firm-years that, in fact, have syndicated
loans outstanding. Burgstahler and Dichev (1997) mention that there is no evidence on the
prevalence of contractual agreements that explicitly mention zero earnings. While this is true,
debt covenants routinely have minimum tangible net worth requirements that are ratcheted
upwards every year.21 For example, the credit agreement of Atlantic Plastics, a company in
the suspect interval reads “…..the Borrower shall not permit Net Worth as of the last day of
any given Fiscal Quarter in any given Fiscal Year to be less than the sum of (i) eighty five
percent (85%) of Net Worth as of the last day of the first Fiscal Quarter ending after the
Closing Date, plus (ii) seventy-five percent (75%) of Consolidated Net Income (but
excluding net losses) for each Fiscal Quarter following the first Fiscal Quarter ending after
the Closing Date …”. At the very least, losses would make covenants such as these more
binding.
H5A: Suspect firm-years with debt outstanding have abnormally low CFO, abnormally high
production costs and abnormally low discretionary expenses compared to other suspect firm-
years.
Variation in the degree of earnings management with incentives to meet zero earnings –
short-term suppliers
A second possible reason for zero earnings being an important threshold (discussed
by Burgstahler and Dichev 1997) is that there are stakeholders of the firm who use heuristic
cut-offs at zero to evaluate the performance of a firm. Among the stakeholders that
Burgstahler and Dichev (1997) identify are suppliers, lenders, employees and customers
worried about future services. The authors propose that if the firm’s earnings performance
falls below a certain threshold like zero, the firm’s ability to pay suppliers in time and its
potential as a future buyer are both in doubt. This leads suppliers to tighten terms of credit
and other terms. Managers are more likely to worry about the negative reaction of suppliers if
they have more trade credit and other short-term liabilities outstanding. To test this, I check
whether the degree of earnings management is dependent on the level of current liabilities
(excluding debt classified as current liabilities) at the beginning of the year. 20 SDC Platinum, owned by Thomson Financial. 21 I thank Joe Weber at MIT for pointing this out.
26
H6A: Suspect firm-years with high level of current liabilities have abnormally low CFO,
abnormally high production costs and abnormally low discretionary expenses compared to
other suspect firm-years.
5.3.2 Empirical tests on cross-sectional variation
To test H3A, H4A, H5A and H6A, I estimate the following regression using the
Fama-Macbeth procedure:
Xt = α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 + β3*(Net income) t +
β4*(SUSPECT_NI) t + β5*(MFG) t + β6*(LoCA) t +
β7*(DEBT) t + β8*(CL) t +
β9(MFG*SUSPECT_NI) t + β10(LoCA*SUSPECT_NI) t +
β11(DEBT*SUSPECT_NI) t + β12(CL*SUSPECT_NI) t + εt (9)
where Xt , the dependent variable, is sequentially set equal to abnormal CFO, abnormal
production costs, abnormal COGS and abnormal discretionary expenses.
MFG is an indicator variable that is set equal to one if is a particular firm belongs to a
manufacturing industry and is set equal to zero otherwise. LoCA is an indicator variable that
is set equal to one if is a particular firm belongs to the lowest quartile of scaled current assets
and is set equal to zero otherwise. DEBT is an indicator variable that is set equal to one if the
firm has any long-term or short-term debt outstanding on its balance sheet at the beginning or
at the end of the year. Out of the 21,758 firm-years, 5,552 firm-years have no debt
outstanding. CL is the level of current liabilities at the beginning of the year and is given by
CL= Current liabilities excluding short-term debt / Total assets
Recall that, following conventional models for estimating non-discretionary accruals, the
models for normal CFO, normal production costs, etc., in this paper are also estimated
without an intercept. The indicator variables - MFG, LoCA and DEBT - as well the
continuous variable CL are included in the regression to control for any systematic
differences in the dependent variables across various groups of firms.
The coefficients β9 and β10 on the interacted terms capture the variation in real
activities manipulation by suspect firm-years arising from industry membership and level of
current assets respectively. The coefficients β11 and β12 capture the variation in real activities
management with differing incentives arising from the presence of lenders and short-term
suppliers respectively. For easy reference, Table 7 lists the predictions on these coefficients
for various dependent variables. If manufacturing firms are engaging in overproduction as
well as price discounts, then β9 should be positive when the dependent variable is abnormal
27
production costs. Since overproduction leads to abnormally low CFO, β9 should be negative
when the dependent variable is abnormal CFO. If firms with low current assets are more
aggressive at offering price discounts and reducing discretionary expenses, then β10 should be
positive when the dependent variable is abnormal COGS and negative when the dependent
variable is abnormal discretionary expenses. If a higher level of debt or a higher level of
current liabilities provides greater incentives for firms to manage earnings, then β11 and β12
should be positive when the dependent variable is abnormal production costs and negative
when the dependent variable is either abnormal CFO or abnormal discretionary expenses.
The results of regression (9) are reported in Table 8. As predicted, the difference in
abnormal production costs between suspect firm-years and the rest of the sample is caused
mainly by suspect manufacturing firm-years. When the dependent variable is abnormal
production costs, β9 is positive and significant at the 5% level. Suspect manufacturing firm-
years also exhibit lower abnormal CFO - β9 is significantly negative when the dependent
variable is abnormal CFO. The evidence is consistent with manufacturing firms engaging in
overproduction, in addition to price discounts, with the net effect on their abnormal
production costs and abnormal CFO being more severe than for non-manufacturing firms.
As predicted, suspect firm-years with low current assets exhibit more positive
abnormal COGS for a given sales level as well as more negative abnormal discretionary
expenses. β10 is significantly positive when the dependent variable is abnormal COGS and
significantly negative when the dependent variable is abnormal discretionary expenses. The
evidence indicates that price discounts and reduction of discretionary expenses are the
manipulation methods suspect-firms years are most likely to engage in if they have low levels
of current assets.
Suspect firm-years that have outstanding debt have significantly lower discretionary
expenses. β11 is significantly negative when the dependent variable is abnormal discretionary
expenses. I do not find evidence that firms with outstanding debt engage in activities that lead
to abnormally low CFO or abnormally high production costs. One reason for the failure to
find evidence on abnormally high production costs (or low CFO) is probably that the
presence of debt is a weak proxy for the presence of a covenant that makes zero earnings an
important benchmark. Another reason could be that firms with outstanding debt engage more
in reduction of discretionary expenses than other kinds of real activities management. The
results also show that suspect firm-years with higher current liabilities exhibit lower
abnormal CFO, higher abnormal production costs and lower abnormal discretionary
28
expenses. This is consistent with firms with higher current liabilities managing real activities
to a greater extent to avoid reporting losses. This suggests that zero earnings is an important
threshold for suppliers and other short-term creditors.
5.4 Additional tests and robustness checks
To examine the sensitivity of my results to alternate empirical specifications, I conduct the
following tests and robustness checks:
1. Alternate method of testing whether suspect firms engage in activities that lead to
abnormal CFO, abnormal accruals, etc.
2. Performance-matching of abnormal values of various variables
3. Other robustness checks - inclusion of unscaled intercepts in the estimation models and
augmenting the model for normal change in inventory and normal production costs by a
lead term on sales
The following sub-sections discuss why these tests are important, along with the
empirical designs and results of these tests.
5.4.1 Alternate specification
The tests in Section 5.1 suggest firm-years reporting small profits engage in various
methods of earnings manipulation – both through real activities and accruals. The tests do
not, however, provide evidence on the relative extent to which various manipulation methods
are employed to avoid reporting losses. In this section, I present evidence on the following
question: How important are measures of real activities manipulation relative to measures of
pure accrual manipulation in explaining firm presence in the small-profits interval? Using
pooled cross-sectional time series data, I estimate the following probit regression:
Pr(Suspect_NI =1) =
α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 +
β3*(abnormal CFO) t +
β4*(abnormal production costs) t +
β5*(abnormal accruals) t +
β6*(abnormal discretionary expenses) t +
εt (10)
Recall that methods of real activities manipulation (for example, overproduction and
price discounts) that lead to abnormally low CFO should also lead to abnormally high
abnormal production costs. Because the two should be highly negatively correlated (Table 4
29
indicates that they are), I include only one of them at a time while estimating the regression.
Size and market-to-book (MTB) are included in the regressions as control variables. Smaller
firms with fewer growth opportunities are more likely to have profits close to zero. Table 9
reports the marginal effects of the probit regression for each variable evaluated at the mean of
the dependent variable. The first row presents results of the regression with abnormal CFO,
abnormal discretionary expenses, abnormal accruals, size and MTB as the dependent
variables. The second row presents the results of substituting abnormal CFO with abnormal
production costs.
The marginal effects presented in the first row indicate that, abnormal CFO and
abnormal discretionary expenses have significant negative marginal effects (-0.1487 and -
0.0307 respectively) on the probability of reporting a small profit. Abnormal accruals have a
significantly positive effect (0.0422). Abnormal CFO, measured as a percentage of total
assets, has a standard deviation of 11.82% (see Table 3). A reduction in abnormal CFO by a
single standard deviation raises the probability of being in the suspect interval by 1.76%, that
is, the product of 11.82%*0.1487. Similar analysis shows a single standard deviation
decrease in abnormal discretionary expenses increases the probability of being in the suspect
interval by 1.77%. A single standard deviation increase in abnormal accruals, on the other
hand, affects the probability of being in the suspect interval only by 0.47%.
The second row reveals similar results when abnormal CFO is replaced by abnormal
production costs. Abnormal production costs have a significantly positive marginal effect on
the probability of being in the suspect interval that is much larger than that of abnormal
accruals (0.1507 versus 0.0909). The economic effect of a single-standard-deviation increase
in abnormal production costs is 3.30%, much larger than the 2.16% for abnormal accruals in
this regression. In this specification, the marginal effect of abnormal discretionary expenses
is not significantly different from zero. This is probably because of the high correlation
between abnormal production costs and abnormal discretionary expenses discussed earlier in
Section 4.4. Abnormal discretionary expenses cannot explain presence in the suspect interval
beyond what is already captured by abnormal production costs.
In both tests the marginal effects of size and MTB are significantly negative (-0.008
and -0.003 respectively), consistent with the notion that small firms with few growth
opportunities are more likely to report profits close to zero. The highly significant and
economically large intercepts in both regressions suggest that there may be correlated omitted
variables in these tests. Hence, the results of these tests should be interpreted with caution.
30
5.4.2 Performance matching
Recall that in all my prior analyses, I use Fama-Macbeth regressions in which I
include size, MTB and performance as linear controls. My proxy for performance is
deviation of contemporaneous net income scaled by lagged total assets from the industry-year
mean. This assumes that the relation between the dependent variable (for example, abnormal
CFO) and performance is linear. In this section, I relax that assumption by using a variant of
the performance-matching methodology advocated by Kothari, Leone and Wasley (2001) for
estimating abnormal accruals. One caveat on performance matching has to be kept in mind.
As Guay, Kothari and Watts (1996) argue, firm performance and managerial incentives are
probably correlated. Since my suspect firm-years are selected according to current net
income, matching firm-years on measures of current performance can potentially remove all
power from the tests. At the same time, performance matching addresses the possibility that
my results are driven by measurement error in the abnormal values of various variables that
is correlated with performance. Therefore, I match on lagged performance.
First, I run cross-sectional regressions of the dependent variable, say abnormal CFO,
on size and market-to-book every year. The explanatory variables are expressed as deviations
from the industry-year mean. Next, I match the residual from these regressions on lagged
performance.
In this section, I define lagged performance as net income lagged one year scaled by
total assets lagged by two years. This is expressed for every firm-year as deviation from the
industry-year mean. Note that the correlation between net income and net income lagged one
year is quite high, with a correlation coefficient of around 60%. This will affect the power of
my tests negatively for reasons already discussed. Subsequently, I rank firm-years into
hundred centiles every year based on my performance measure. Each firm’s performance-
matched CFO in a particular year is the residual CFO from the cross-sectional regression
minus the average residual CFO for the performance quantile that includes the firm in that
year. (The performance-matched values of the other variables – production costs,
discretionary expenses, accruals and inventory - are defined in a similar manner.) I then test
whether the performance matched CFO of the suspect firm-years is statistically different
from the rest of the sample using a pooled t-test, allowing for unequal variances. Running a
Fama-Macbeth regression of performance-matched CFO on an indicator variable that
captures whether a particular firm-year has just met zero earnings or just missed analyst
31
forecast yields similar results, which are not reported. I also report the z-statistic from a non-
parametric Wilcoxon test.
Table 10 reports results for suspect firm-years that just beat zero earnings. Most of
my conclusions in Section 5.1 are supported by tests on the performance-matched variable.
The only exception is that according to the non-parametric test, discretionary expenses are
not significantly lower for suspect firm-years than they are for the rest of the sample.
5.4.3 Other robustness checks
In line with the accepted methodology for estimating the Jones (1991) model
for non-discretionary accruals, none of my estimation models described in Section 4.2
includes an intercept (strictly speaking, a vector of ones).22 For example, the regression for
normal CFO is re-produced below:
CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt
Unfortunately, as this regression does not include a vector of ones, the mean
abnormal CFO even within a certain industry-year group is not required to be zero under this
form of estimation. This implies that the empirical evidence on abnormally low CFO for
suspect firm-years can potentially be driven by over-representation of some industries in the
suspect interval, relative to the general population of firm-years. To check whether my results
are sensitive to the inclusion of an intercept in the scaled equation, I re-estimate all the
models in Section 4.2 after including an unscaled intercept. I find that the results presented in
Tables 5 and 8 are robust to this estimation technique.
A second robustness check involves the model for normal changes in inventory. The
target inventory at the end of any fiscal period is determined to a large extent by expected
sales next period. The model proposed by Dechow, Kothari and Watts (1998) uses current
period sales as a proxy for expected sales next period. Another valid proxy is the realized
sales in the next period. I augment the estimation models for change in inventory and for
production costs by a term that captures the change in sales next period. This has no material
effect on any of the results reported.
22 DeFond and Jiambalvo (1994), Dechow, Sloan and Sweeney (1995), Guidry, Leone and Rock (1999) and Kasznik (1999) are examples of studies that estimate models for non-discretionary accruals without a vector of ones.
32
6. Conclusion
In this paper, I find evidence that is consistent with managers manipulating real
activities to avoid reporting losses. I identify firm-years that report small profits (suspect
firm-years) and find that these firm-years exhibit unusually low cash flow from operations
and unusually high production costs for a given level of sales. The findings are consistent
with managers offering price discounts to boost sales and engaging in overproduction to
reduce reported cost of goods sold. Suspect firm-years exhibit unusually low discretionary
expenses, suggesting managers reduce such expenses to boost earnings above zero. The
results are robust to non-linear controls for performance and alternate methodologies for
estimating abnormal levels of CFO, production costs, discretionary expenses, etc.
The results provide initial insights into how managers choose between different
manipulation methods. Managers of suspect manufacturing firms are more likely to engage in
activities that lead to unusually high production costs relative to sales, where production costs
are defined as the sum of cost of goods sold and change in inventory. This is consistent with
managers of manufacturing firms engaging in overproduction to increase reported earnings.
Suspect firm-years that have low accounting flexibility, that is, a low level of current assets
as a percentage of total assets, are more aggressive at offering price discounts and reducing
discretionary expenses. The cross-sectional analysis of suspect firm-years further reveals that
managers manipulate real activities more when they have higher levels of short-term
liabilities outstanding. This suggests that the target of zero earnings is more important for
firms with short-term creditors
The paper tests the relative ability of different manipulation methods to explain the
probability of a firm-year reporting small profits. The results suggest that firms reporting
small profits engage in real activities manipulation more than pure accrual manipulation.
This paper complements the existing literature on earnings management in several
ways. First, this study is the first to develop empirical methods to detect real activities
manipulation other than reduction of R&D expenses. Second, the focus so far in the earnings
management literature has been on detecting abnormal accruals. My results raise the
possibility that part of these abnormal accruals may not actually be discretionary, but non-
discretionary accruals associated with ‘abnormal’ real activities, like overproduction. Third,
to the extent that some activities such as offering price discounts to accelerate cash sales have
no direct effect on accruals, just focusing on abnormal accruals may understate the degree of
earnings management. Finally, the results in this paper suggest that using ex–post cash flows
33
as true measures of economic performance may be inappropriate. Cash flows from operations
are a part of reported accounting earnings, and are likely to reflect managerial incentives in
the same way as accruals.
This paper also raises several questions for future research. One important issue that
this paper does not investigate is the extent to which earnings is increased by real activities
manipulation versus pure accrual manipulation. Another area for further research is the
timing of real activities manipulation. While they have to occur during the year, their
intensity should increase towards the end of the year, as managers form more reliable
expectations of pre-managed earnings for the year. Patterns in real activity manipulation and
accrual manipulation at the quarterly level may yield some insights into this issue. For
example, managers of firms that have exhausted their accounting flexibility in the first three
quarters of the year via pure accrual manipulation probably manipulate real activities in the
fourth.
Further, it would be interesting to investigate whether firms that engage in
manipulation of real activities habitually engage in such practices. For example, firms that try
to ‘pull in’ sales in a bad year from next year’s sales may be prompted to do the same the
next year. Research on these issues should lead to a more complete understanding of the
importance of meeting earnings targets, the extent of earnings management through real
activities and the long-term effects of real activities manipulation.
34References Abarbanell, J. and R. Lehavy, 2002, Biased forecasts or biased earnings? The role of reported earnings in explaining apparent bias and over/under-reaction in analysts’ earnings forecasts, working paper Barth, M.E., D.P. Cram and K.K. Nelson, 2001, Accruals and the prediction of future cash flows, Accounting Review 76, 27-58 Barton, J. 2001, Does the use of financial derivatives affect earnings management decisions?, Accounting Review 76, 1-26 Barton, J.J., and P.J. Simko, 2002, The balance sheet as an earnings management constraint, Accounting Review 77, 1- 27 Bartov, E., D. Givoly and C. Hayn, 2002, The rewards to meeting or beating earnings expectations, Journal of Accounting and Economics 33, 173-204 Beaver, W.H., M.F. McNichols and K.K. Nelson, 2002, Management of the loss reserve accrual and the distribution of earnings in the property-casualty insurance industry, Journal of Accounting and Economics, forthcoming Beaver, W.H., M.F. McNichols and K.K. Nelson, 2003, An alternative interpretation of the discontinuity in earnings distributions, working paper Bens, D., V. Nagar, and M.H. Franco Wong, 2002, Real investment implications of employee stock option exercises, Journal of Accounting Research 40, 359-393 Bens, D., V. Nagar, D.J. Skinner and M.H. Franco Wong, 2002, Employee stock options, EPS dilution and stock repurchases, working paper. Burgstahler, D. and I. Dichev, 1997, Earnings management to avoid earnings decreases and losses, Journal of Accounting and Economics 24, 99-126 Burgstahler, D. and M. Eames, 1999, Management of earnings and analyst forecasts, working paper Bushee, B., 1998, The influence of institutional investors on myopic R&D investment behavior, Accounting Review 73, 305-333 Cao, Y., 2003, Earnings management motivated by accounting-based regulations: the external evidence from China, working paper , University of Rochester Choy, H., 2003, Impact of earnings management flexibility, working paper, University of Rochester Dechow, P.M., S.P. Kothari and R.L. Watts, 1998, The relation between earnings and cash flows, Journal of Accounting and Economics 25, 133-168 Dechow P.M., S.A. Richardson and I. Tuna, 2003, Why are earnings kinky?, Review of Accounting Studies, forthcoming
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37 Table 1: Costs and benefits to managers of real activities manipulation versus those of relying solely on pure accrual manipulation Manipulate real activities during the year to increase earnings
Do not manipulate real activities – rely solely on pure accrual manipulation to cover shortfall between pre-managed earnings and the target
Costs Costs Cash flow consequences of costly real activities are likely to extend well beyond the current period.
Pure accrual manipulation is limited by GAAP and any accrual management in prior years. If at the end of the year, the shortfall is bigger than the discretionary accruals the managers can report via pure accrual manipulation, managers have to just miss the target. They lose the opportunity to increase reported earnings by manipulating real activities.
The shortfall between pre-managed earnings and the earnings target, that is, the extent of manipulation required is not known with certainty.
Pure accrual manipulation may be detected by auditors, or investors/regulators. This may lead to adverse stock price consequences, sometimes severe, and even bankruptcies. This may affect both the dollar wealth and the human capital of the managers.
Benefits Benefits Makes meeting targets more likely. Managers still retain the opportunity to cover any residual shortfall with pure accrual manipulation.
Managers can undertake pure accrual manipulation at the end of the year, when they have knowledge of pre-managed earnings.
Company is less likely to face auditor or regulator (SEC) scrutiny for real decisions.
Does not affect cash flows – at least not directly (see costs)
Harder to detect
38Table 2: Descriptive statistics This full sample consists of 21,758 firm-years over the period 1987 to 2001. Suspect firm-years are the 503 firm-years with reported income before extraordinary items between 0 and 0.5% of total assets. The numbers in parentheses are t-statistics from t-tests for the differences in means and z-statistics from Wilcoxon tests for the differences in medians. Please see below for variable descriptions. Suspect firm-years Full sample Difference in Mean Median Mean Median Means
(t-stat) Medians
(z-stat) MVE ($ million)
745.82 75.60 1414.43 137.34 **-668.61 (-5.84)
**-61.74 (-5.33)
Market-to-book or MTB
1.60 1.21 2.75 1.93 **-1.15 (-10.64)
**-0.72 (-9.68)
Total assets ($ million)
1180.57 153.17 1124.17 164.54 56.04 (0.34)
-11.37 (-0.61)
Sales ($ million)
1254.14 214.88 1394.35 221.05 -140.21 (-0.88)
-6.17 (-0.14)
IBEI ($ million)
2.81 0.29 61.80 4.46 **-58.99 (-36.29)
**-4.47 (-16.27)
CFO ($ million)
81.06 5.15 126.55 10.76 **-45.49 (-3.45)
**-5.61 (-3.80)
Accruals ($ million)
-78.24 -4.86 -64.67 -5.41 -13.57 (-1.07)
0.55 (0.62)
Sales/TA
1.39 1.25 1.48 1.30 *-0.09 (-1.92)
-0.05 (-1.62)
IBEI/TA (%)
0.24 0.22 0.31 4.09 -0.07 (-0.53)
**-3.87 (-22.09)
CFO/TA (%)
4.54 4.77 6.50 8.25 **-1.96 (-5.11)
**-3.48 (-7.60)
Accruals/TA (%)
-4.31 -4.54 -6.16 -5.20 **1.85 (4.99)
0.66 (1.44)
Production costs/TA (%)
98.99 80.45 97.08 78.79 1.91 (0.08)
1.66 (0.60)
Discretionary expenses/TA (%)
36.63 30.31 44.16 37.44 **-7.53 (-6.41)
**-7.13 (-3.94)
* significant at the 10% level ** significant at the 5% level Variable definitions MVE: The market value of equity Market-to-book (MTB): The ratio of MVE to the book value of equity IBEI: Income before extraordinary items CFO: Cash flow from operations Accruals: IBEI – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses TA: Total assets
39 Table 3: Abnormal CFO, production costs, discretionary expenses and accruals This table reports properties of the abnormal levels of various variables for 21,758 firm-years over the period 1987 to 2001. Reported are the mean, median, standard deviation, the 25th and 75th quintiles, skewness and kurtosis. Please see below for variable descriptions.
Mean Median Standard deviation
25% 75% Skewness Kurtosis
Abnormal CFO
0.0112 0.0144 0.1182 -0.0472 0.0740 -0.38 2.25
Abnormal production costs
-0.0420 -0.0350 0.2193 -0.1615 0.0828 -0.06 1.02
Abnormal discretionary expenses
0.0371 0.0111 0.2371 -0.0918 0.1477 0.56 1.71
Abnormal accruals
-0.0041 0.0012 0.1104 -0.0494 0.0489 -0.56 3.19
Variable definitions
CFO: Cash flow from operations IBEI: Income before extraordinary items Accruals: IBEI – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Abnormal CFO: Measured as deviations from the predicted values from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where At = assets at end of year t, St = sales during yeat t, ∆St = change in sales during year t. Abnormal production costs: Measured as deviations from the predicted values from the corresponding industry-year regression PRODt/At-1 = α *(1/At-1) + β1*(St/ At-1) + β2*(∆St/ At-1) + β3*( ∆St-1/ At-1) + εt, where At = assets at end of year t, St = sales during yeat t, ∆St = change in sales during year t. Abnormal discretionary expenses: Measured as deviations from the predicted values from the corresponding industry-year regression Disexpt/At-1 = α *(1/At-1) + β *(St/ At-1) + εt, where At = assets at end of year t, St = sales during yeat t, ∆St = change in sales during year t. Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t.
40 Table 4: Correlation Table This is the correlation table for scaled variables, the scaling factor being lagged total assets, ‘TA’. The correlations are pooled correlations for the entire sample of 21,758 firm-years over the period 1987 to 2001. Correlations significant at the 5% level are marked in bold. Please see below for variable descriptions.
Sales / TA
IBEI / TA
CFO / TA
Accruals /TA
PROD / TA
Disexp / TA
Abnormal CFO
Abnormal PROD
Abnormal disexp
Abnormal accruals
CA / TA
IBEI/TA
0.90
CFO/TA
-0.81 -0.58
Accruals / TA
0.96 0.57 -0.78
PROD / TA
0.95 0.90 -0.82 0.96
Disexp / TA
-0.74 0.49 -0.78 0.63 0.73
Abnormal CFO
-0.05 0.45 0.75 -0.25 -0.14 -0.10
Abnormal PROD
0.15 -0.20 -0.26 0.02 0.38 -0.42 -0.38
Abnormal disexp
-0.15 -0.19 -0.13 -0.12 -0.32 0.59 -0.10 -0.80
Abnormal accruals
0.06 0.42 -0.18 0.80 0.06 -0.06 -0.21 0.03 -0.16
CA/TA
0.47 0.05 -0.09 0.17 0.42 0.33 -0.13 0.12 -0.05 0.06
MTB
0.00 0.01 0.01 -0.01 -0.06 0.18 0.05 -0.18 0.15 -0.02 -0.08
Variable definitions IBEI: Income before extraordinary items CFO: Cash flow from operations Accruals: IBEI – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Abnormal CFO: Measured as deviations from the predicted values from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal production costs: Measured as deviations from the predicted values from the corresponding industry-year regression PRODt/At-1 = α *(1/At-1) + β1*(St/ At-1) + β2*(∆St/ At-1) + β3*( ∆St-1/ At-1) + εt, where At = total assets at end of year t, St = sales during yeat t and ∆St = change in sales during year t. Abnormal discretionary expenses: Measured as deviations from the predicted values from the corresponding industry-year regression Disexpt/At-1 = α *(1/At-1) + β *(St/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t. CA: Current assets at the beginning of the year TA: Total assets at the beginning of the year Market-to-book (MTB): The ratio of MVE to the book value of equity
41 Table 5: Earnings management to meet zero earnings This table reports the results of Fama-Macbeth regressions, over a period of fifteen years from 1987 to 2001. The total sample includes 21,758 observations. The number of firms increases from around 1,000 in the early 1980s to 2,000 every year in the late 1990s. The regressions being estimated are of the form Xt = α + β1*(Size)t-1 + β2*(Market-to-book ratio)t-1 + β3*(Net income)t + β4*(SUSPECT_NI)t + εt . Each column presents the results of the above regression for a different dependent variable, whose name appears at the top of the respective column. T-statistics are calculated using standard errors corrected for autocorrelation using the Newey-West procedure. They are reported in parentheses. Please see end Table 5 for variable descriptions. Abnormal CFO Abnormal production
costs Abnormal discretionary
expenditure
α **0.0062 (6.11)
**-0.0386 (-8.08)
**0.0451 (7.83)
β1 0.0011
(0.19) **-0.0100
(-5.31) **0.0158
(8.66)
β2 **0.0010 (3.45)
**-0.0047 (-7.06)
**0.0037 (2.95)
β3 **0.2101
(5.76) **-0.1731
(-4.13) **-0.2116
(-5.99)
β4 **-0.0144 (-2.15)
**0.0493 (5.08)
**-0.0250 (-2.87)
* Significant at the 10% level ** Significant at the 5% level
Abnormal
accruals Abnormal change
in inventory Abnormal change in
net accounts receivables
Abnormal change in gross accounts
receivables
α **-0.0101 (-5.50)
*-0.0015 (-0.65)
-0.0008 (-0.59)
-0.0004 (-0.29)
β1 **-0.0059 (-4.78)
0.0001 (1.39)
*0.0007 (1.79)
*0.0009 (1.92)
β2 0.0004 (0.95)
-0.0001 (-0.11)
-0.0002 (-0.70)
-0.0002 (-0.68)
β3 **0.2176 (5.37)
**0.0203 (2.44)
0.0012 (0.16)
-0.0012 (-0.17)
β4 **0.0148 (2.99)
**0.0112 (4.39)
0.024 (0.72)
0.0013 (0.40)
* Significant at the 10% level ** Significant at the 5% level
42Variable definitions CFO: Cash flow from operations Accruals: Income before extraordinary items – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Net accounts receivable: Accounts receivables net of doubtful accounts Gross accounts receivables: Net accounts receivables + receivables estimated doubtful Abnormal CFO: Measured as deviations from the predicted values from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal production costs: Measured as deviations from the predicted values from the corresponding industry-year regression PRODt/At-1 = α *(1/At-1) + β1*(St/ At-1) + β2*(∆St/ At-1) + β3*( ∆St-1/ At-1) + εt, where At = total assets at end of year t, St = sales during yeat t and ∆St = change in sales during year t. Abnormal discretionary expenses: Measured as deviations from the predicted values from the corresponding industry-year regression Disexpt/At-1 = α *(1/At-1) + β *(St/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t. Abnormal change in inventory: Measured as deviation from the predicted values from the corresponding industry-year regression ∆INVt/At-1 = α *(1/At-1) + β1*(∆St/ At-1) + β2*( ∆St-1/ At-1) + εt. ∆INVt=change in inventory during year t, At = total assets at end of year t, St = sales during yeat t and ∆St = change in sales during year t. Abnormal change in net receivables: Measured as deviation from the predicted values from the corresponding industry-year regression ∆NetRect/At-1 = α *(1/At-1) + β*(∆St/ At-1) + εt, where ∆NetRect=change in net recivables during year t, At = total assets at end of year t and ∆St = change in sales during year t. Abnormal change in gross receivables: Measured as deviation from the predicted values from the corresponding industry-year regression ∆GrossRect/At-1 = α *(1/At-1) + β*(∆St/ At-1) + εt, where ∆NetRect=change in net recivables during year t, At = total assets at end of year t and ∆St = change in sales during year t. Net income: Income before extraordinary items scaled by lagged total assets (TA), expressed as deviation from the corresponding industry-year mean.. Size: Logarithm of the market value of equity, expressed as deviation from the corresponding industry-year mean. Market-to-book (MTB): The ratio of market value of equity to the book value of equity, expressed as deviation from the corresponding industry-year mean. SUSPECT_NI: An indicator variable that is set equal to one if income before extraordinary items scaled by lagged total assets is between 0 and 0.005, and is set equal to zero otherwise.
43
Table 6: Firms with low current assets vs. the rest of the sample – descriptive statistics The total sample of 21,758 firm-years over the period 1987 to 2001 is split into quartiles every year based on current assets a percentage of total assets, lagged one year. Firm-years in the bottom quartile are classified as firms with low current assets. All reported variables are scaled by lagged total assets. Please see below for variable descriptions.
Firms with low current assets
(N=5,430)
Rest of the sample
(N=16,318)
Difference
(t-stat of difference)
Lagged current assets
0.1224 0.4866 **-0.3642
(-215.98)
Lagged accounts receivables
0.0696 0.2264 **-0.1568
(-128.88)
Lagged inventory 0.0336 0.2215 **-0.1879
(-142.23)
Current abnormal accruals
-0.0100 -0.0030 **-0.0070
(-4.00)
* Significant at the 10% level
** Significant at the 5% level
Variable definitions CFO: Cash flow from operations Accruals: Income before extraordinary items – CFO Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t.
44
Table 7: Predicted signs of coefficients β9, β10, β11, and β12 in regression (9) for various dependent variables. Regression (9): Xt = α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 + β3*(Net income) t + β4*(SUSPECT_NI) t +
β5*(MFG) t + β6*(LoCA) t + β7*(DEBT) t + β8(CL) t-1 + β9(MFG*SUSPECT_NI) t + β10(LoCA*SUSPECT_NI) t + β11(DEBT*SUSPECT_NI) t + β12(CL t-1*SUSPECT_NI t) + εt
Each column presents the predictions on various coefficients for a different dependent variable, whose name appears at the top of the respective column. Please see below for variable descriptions. Abnormal CFO Abnormal
production costs Abnormal COGS Abnormal
discretionary expenses
β 9 Negative: overproduction by manufacturing firms
Positive: overproduction by manufacturing firms
Ambiguous: manufacturing firms can lower COGS by overproduction, but non-manufacturing firms can also lower COGS by pure accrual manipulation.
Ambiguous: Unclear why manufacturing firms would be more or less aggressive at reducing discretionary expenses
β 10 Ambiguous: abnormal CFO is affected negatively by price discounts but positively by the reduction of discretionary expenses
Ambiguous: while firms with low current assets can offer price discounts, they are less likely to indulge in overproduction to lower reported COGS
Positive: more aggressive price discounts and less downward manipulation of COGS by firms with low current assets
Negative: more aggressive reduction of discretionary expenses by firms with low current assets
β 11 Negative: firms with debt covenants that make zero an important target should manage real activities more aggressively
Positive: firms with debt covenants that make zero an important target should manage real activities more aggressively
Ambiguous: More aggressive price discounting and more aggressive lowering of reported COGS have opposite effects
Negative: firms with debt covenants that make zero an important target should manage real activities more aggressively
β 12 Negative: firms concerned about short term trade creditors should manage real activities more aggressively
Positive: firms concerned about short term trade creditors should manage real activities more aggressively
Ambiguous: More aggressive price discounting and more aggressive lowering of reported COGS have opposite effects
Negative: firms concerned about short term trade creditors should manage real activities more aggressively
Variable definitions CFO: Cash flow from operations Accruals: Income before extraordinary items – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Abnormal CFO: Measured as deviations from the predicted values from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal production costs: Measured as deviations from the predicted values from the corresponding industry-year regression PRODt/At-1 = α *(1/At-1) + β1*(St/ At-1) + β2*(∆St/ At-1) + β3*( ∆St-1/ At-1) + εt, where At = total assets at end of year t, St = sales during yeat t and ∆St = change in sales during year t. Abnormal discretionary expenses: Measured as deviations from the predicted values from the corresponding industry-year regression Disexpt/At-1 = α *(1/At-1) + β *(St/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t.
Contd.
45Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t. Net income: Income before extraordinary items scaled by lagged total assets (TA), expressed as deviation from the corresponding industry-year mean. Size: Logarithm of the market value of equity, expressed as deviation from the corresponding industry-year mean. Market-to-book (MTB): The ratio of MVE to the book value of equity, expressed as deviation from the corresponding industry-year mean. SUSPECT_NI: An indicator variable that is set equal to one if income before extraordinary items scaled by lagged total assets is between 0 and 0.005, and is set equal to zero otherwise. MFG: An indicator variable set equal to one if the firm belongs to a manufacturing industry and is set equal to zero otherwise. LoCA: Firms are divided every year into quartiles based on the level of lagged current assets (CA) as a percentage of total assets (TA). LoCA is an indicator variable that is set equal to one if the firm belongs to the lowest quartile of CA/TA and is set equal to zero otherwise. DEBT: An indicator variable set equal to one if there is long-term or short-term debt outstanding at the beginning of the year or at the end of the year CL: Current liabilities excluding short-term debt, scaled by total assets
46 Table 8: Cross-sectional variation in real activities manipulation This table reports the results of Fama-Macbeth regressions, over a period of fifteen years from 1987 to 2001. The total sample includes 21,758 observations. The number of firms increases from around 1,000 in the early 1980s to 2,000 every year in the late 1990s. The regressions being estimated are of the form Xt = α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 + β3*(Net income) t + β4*(SUSPECT_NI) t +
β5*(MFG) t + β6*(LoCA) t + β7*(DEBT) t + β8(CL) t-1 +
β9(MFG*SUSPECT_NI) t + β10(LoCA*SUSPECT_NI) t +
β11(DEBT*SUSPECT_NI) t + β12(CL t-1*SUSPECT_NI t) + εt
Each column presents the results of the above regression for a different dependent variable, whose name appears at the top of the respective column. T-statistics are calculated using standard errors corrected for autocorrelation using the Newey-West procedure. They are reported in parentheses. Please see end of Table 8 for variable descriptions. Abnormal CFO Abnormal production costs
Predicted sign Coefficient Predicted sign Coefficient α **0.0263
(2.28) **-0.1252
(-8.06)
β1 (Size) -0.0005 (-0.31)
**-0.0129 (-7.87)
β2 (MTB) **0.0009
(2.36) **-0.0041
(-6.13)
β3 (Net income) **0.2121 (7.40)
**-0.1334 (-5.72)
β4 (SUSPECT_NI) 0.0457
(0.22)
-0.1286 (-1.43)
β5 (MFG) **-0.0079 (-2.48)
**0.0529 (4.76)
β6 (LoCA) **0.0213 (2.72)
-0.0067 (-0.91)
β7 (DEBT) **-0.0114 (-2.70)
**0.0440 (5.02)
β8 (CL) -0.0537 (-1.25)
0.0780 (1.36)
β9 (MFG*SUSPECT_NI)) _ **-0.0245 (-3.65)
+ **0.0642 (2.34)
β10 (LoCA*SUSPECT_NI) ? *-0.0283 (-1.97)
? **0.0943 (2.21)
β11 (DEBT*SUSPECT_NI) _ 0.0057 (0.51)
+ 0.0207 (0.70)
β12 (CL*SUSPECT_NI) _ **-0.2108 (-3.42)
+ **0.4898 (2.73)
* significant at the 10% level ** significant at the 5% level
47 Table 8: (contd.) Cross-sectional variation in real activities manipulation This table reports the results of Fama-Macbeth regressions, over a period of fifteen years from 1987 to 2001. The total sample includes 21,758 observations. The number of firms increases from around 1,000 in the early 1980s to 2,000 every year in the late 1990s. The regressions being estimated are of the form Xt = α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 + β3*(Net income) t + β4*(SUSPECT_NI) t +
β5*(MFG) t + β6*(LoCA) t + β7*(DEBT) t + β8(CL) t-1 + β9(MFG*SUSPECT_NI) t + β10(LoCA*SUSPECT_NI) t + β11(Leverage*SUSPECT_NI) t + β12(CL t-1*SUSPECT_NI t) + εt
Each column presents the results of the above regression for a different dependent variable, whose name appears at the top of the respective column. T-statistics are calculated using standard errors corrected for autocorrelation using the Newey-West procedure. They are reported in parentheses. Please see end of Table 8 for variable descriptions. Abnormal COGS Abnormal discretionary expenses
Predicted sign Coefficient Predicted sign Coefficient α **-0.1264
(-8.58) **0.1251
(4.59)
β1 (Size) **-0.0132 (-7.74)
**0.0231 (9.29)
β2 (MTB) **-0.0044
(-6.17) **0.0030
(2.66)
β3 (Net income) **-0.1331 (-5.29)
**-0.3013 (-6.31)
β4 (SUSPECT_NI) -0.1132
(-1.34) 0.1285
(1.60)
β5 (MFG) **0.0537 (4.86)
**-0.0445 (-2.97)
β6 (LoCA) -0.0057
(-0.71) *-0.0244
(-1.96)
β7 (DEBT) **0.0436 (5.05)
**-0.0507 (-4.12)
β8 (CL) 0.0856
(1.67) -0.0094
(-0.17)
β9 (MFG*SUSPECT_NI)) ? **0.0583 (2.11)
? -0.04187 (-1.63)
β10 (LoCA*SUSPECT_NI) + **0.0866
(2.34) _ **-0.0941
(-2.21)
β11 (DEBT*SUSPECT_NI) ? 0.0190 (0.72)
_ **-0.0631 (-2.10)
β12 (CL*SUSPECT_NI) ? **0.4020
(2.36) _ *-0.2863
(-1.76)
* significant at the 10% level ** significant at the 5% level
48Variable definitions CFO: Cash flow from operations Accruals: Income before extraordinary items – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Abnormal CFO: Measured as deviations from the predicted values from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal production costs: Measured as deviations from the predicted values from the corresponding industry-year regression PRODt/At-1 = α *(1/At-1) + β1*(St/ At-1) + β2*(∆St/ At-1) + β3*( ∆St-1/ At-1) + εt, where At = total assets at end of year t, St = sales during yeat t and ∆St = change in sales during year t. Abnormal discretionary expenses: Measured as deviations from the predicted values from the corresponding industry-year regression Disexpt/At-1 = α *(1/At-1) + β *(St/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t. Net income: Income before extraordinary items scaled by lagged total assets (TA), expressed as deviation from the corresponding industry-year mean.. Size: Logarithm of the market value of equity, expressed as deviation from the corresponding industry-year mean. Market-to-book (MTB): The ratio of MVE to the book value of equity, expressed as deviation from the corresponding industry-year mean. SUSPECT_NI: An indicator variable that is set equal to one if income before extraordinary items scaled by lagged total assets is between 0 and 0.005, and is set equal to zero otherwise. MFG: An indicator variable set equal to one if the firm belongs to a manufacturing industry and is set equal to zero otherwise. LoCA: Firms are divided every year into quartiles based on the level of lagged current assets (CA) as a percentage of total assets (TA). LoCA is an indicator variable that is set equal to one if the firm belongs to the lowest quartile of CA/TA and is set equal to zero otherwise. DEBT: An indicator variable set equal to one if there is long-term or short-term debt outstanding at the beginning of the year or at the end of the year CL: Current liabilities excluding short-term debt, scaled by total assets
49
Table 9: Earnings management to meet zero earnings – alternate specification
Probit regressions: This table reports the results of a pooled probit regression. The total sample includes 21,758 firm years over the period 1987-2001. The regressions being estimated are of the form
Pr(Suspect_NI =1) = α + β1*(Size) t-1 + β2*(Market-to-book ratio) t-1 + β3*(abnormal CFO) t + β4*(abnormal production costs) t + β5*(abnormal discretionary expenses) t + β6*( abnormal accruals) t + εt
Marginal effects are reported, along with z-statistics calculated using Huber-White standard errors. They are reported in parentheses. Please see end of Table 9 for variable descriptions.
α β1 β2 β3 β4 β5 β6
Pr(Suspect_NI =1)
**-0.7980 (-10.43)
**-0.0081 (-2.33)
**-0.0030 (-3.27)
**-0.1487 (-4.69)
**-0.0746 (-3.58)
*0.0422 (1.65)
Pr(Suspect_NI =1)
**-0.7982 (-10.31)
**-0.0077 (-2.24)
**-0.0031 (-3.30)
**0.1507 (5.96)
0.0165 (1.50)
**0.0909 (2.80)
* Significant at the 10% level ** Significant at the 5% level Variable definitions SUSPECT_NI: An indicator variable that is set equal to one if income before extraordinary items scaled by lagged total assets is between 0 and 0.005, and is set equal to zero otherwise. CFO: Cash flow from operations Accruals: Income before extraordinary items – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Abnormal CFO: Measured as deviations from the predicted values from the corresponding industry-year regression CFOt/At-1 = α*(1/At-1) + β1*(St/ At-1) + β2*( St-1/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal production costs: Measured as deviations from the predicted values from the corresponding industry-year regression PRODt/At-1 = α *(1/At-1) + β1*(St/ At-1) + β2*(∆St/ At-1) + β3*( ∆St-1/ At-1) + εt, where At = total assets at end of year t, St = sales during yeat t and ∆St = change in sales during year t. Abnormal discretionary expenses: Measured as deviations from the predicted values from the corresponding industry-year regression Disexpt/At-1 = α *(1/At-1) + β *(St/ At-1) + εt, where At = total assets at end of year t and St = sales during yeat t. Abnormal accruals: Measured as deviations from the predicted values from the corresponding industry-year regression , Accrualst/At-1 = α*(1/At-1) + β1*(∆St/ At-1) + β2*( PPEt/ At-1) + εt, where At = total assets at end of year t, ∆St = change in sales during year t and PPEt=property, plant and equipment at end of year t. Net income: Income before extraordinary items scaled by lagged total assets (TA), expressed as deviation from the corresponding industry-year mean.. Size: Logarithm of the market value of equity, expressed as deviation from the corresponding industry-year mean. Market-to-book (MTB): The ratio of MVE to the book value of equity, expressed as deviation from the corresponding industry-year mean.
50
Table 10: Results on earnings management to meet zero earnings, using performance-matched variables
The table reports results of difference-of-means tests between performance-matched residuals of suspect firm-years and those of the rest of the sample. Suspect firm-years are firm-years that belong to the earnings category just right of zero (income before extraordinary items/total assets between 0 and 0.005). The t-stat reported is from a standard t-test allowing for unequal variances. The z-stat is from a non-parametric Wilcoxon test. Please below for variable descriptions
Performance-matched value of :
Suspect firms
(N=503)
Rest of the sample
(N=21,255)
Difference T-stat Z-stat
Abnormal CFO -0.0140
0.0006
-0.0146
-3.31
-3.94
Abnormal production costs 0.0340
-0.0020
0.0360
3.90
3.53
Abnormal discretionary expenses
-0.0200
-0.0010
-0.0190
-2.17
-1.49
Abnormal accruals 0.0163
0.0002
0.0161
3.75
3.33
Abnormal change in Inventory
0.0100
-0.0006
0.0106
3.34
3.30
Variable definitions CFO: Cash flow from operations Accruals: Income before extraordinary items – CFO Production costs (PROD): Cost of goods sold + Change in inventory Discretionary expenses (Disexp): R&D + Advertising + Selling, General and Administrative expenses Performance-matched abnormal CFO: An annual regression is run of abnormal CFO on size and MTB. Firms are classified annually into centiles based on the deviation of their net income in the previous year. The residual from the above regression minus the average residual of the corresponding net income centile is the performance-matched CFO. Performance-matched abnormal production costs, performance-matched abnormal discretionary expenses, performance-matched abnormal accruals and performance-matched abnormal change in inventory are defined similarly as performance-matched abnormal CFO above. Abnormal CFO, abnormal production costs, abnormal discretionary expenses, abnormal accruals and abnormal change in inventory are as defined in Table 5. Net income: Income before extraordinary items scaled by lagged total assets (TA), expressed as deviation from the corresponding industry-year mean.. Size: Logarithm of the market value of equity, expressed as deviation from the corresponding industry-year mean. Market-to-book (MTB): The ratio of MVE to the book value of equity, expressed as deviation from the corresponding industry-year mean.
51 (Figures 1&2: 21,758 firm-years over the period 1987 to 2001 are classified into earnings intervals over the range –0.075 to +0.075, where earnings is defined as income before extraordinary items scaled by total assets Each interval is of width 0.005, with category 16 including firm-years with scaled earnings greater than or equal to zero and less than 0.005. Each figure is truncated at the two ends.)
Figure 1: number of firm years by earnings category where earnings is scaled by total assets
0
100
200
300
400
500
600
700
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
earnings category
num
ber o
f firm
yea
rs
Fig 2: Residual production costs by earnings category
-0.0300
-0.0200
-0.0100
0.0000
0.0100
0.0200
0.0300
0.0400
0.0500
0.0600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
earnings category
Res
idua
l pro
dn. c
osts
52
Appendix 1: The model for ‘normal’ accruals and cash flows
Dechow, Kothari and Watts (1998) present a model that relates the earnings of a
company to its cash flows and accruals. They make some simplifying assumptions: absent
manipulation, sales follow a random walk, accounts receivables at the end of the year are a
constant fraction of current year’s sales, target inventories at the end of the year are a constant
fraction of next period forecasted cost of sales, accounts payable are a constant percentage of
the firm’s purchases during the year and there are no fixed costs23. Note that these are the same
assumptions underlying the Jones (1991) model of non-discretionary accruals. Earnings can be
represented as:
Eqn.1. Et = πSt
where π is the profit margin, Et is earnings for period t and St is sales for period t.
Dechow, Kothari and Watts (1998) presume the following about current asset items.
Accounts receivables, ARt, are given by a constant fraction α of sales in period t.
ARt = α*St
Target inventory is a constant fraction, γ1 , of next period’s forecasted cost of sales.
Under the assumptions that sales follows a random walk, target inventory at end of period t is
γ1(1- π)St, γ1>0. Actual inventory deviates from target inventory because of sales realizations
in period t different from what was expected for period t, and it can be shown that the deviation
is given by γ2 γ1 *(1- π)(St - St-1), where γ2 is a constant that captures the speed with which a
firm adjusts its inventory to its target level. So, actual inventory at the end of period t is given
by
INVt = γ1(1- π)St - γ2 γ1 *(1- π)(St - St-1)
Purchases are calculated as [cost of goods sold + closing inventory - opening
inventory]. Accounts payable at the end of period t are a constant fraction β of that amount.
Working capital is defined as [accounts receivable + inventory – accounts payable]. The
change in working capital in period t gives the accruals for period t, At.
Eqn.2. At = [α + (1- π) γ1 – (1- π)β] εt - (1- π) γ1[β + γ2(1- β)] ∆εt + (1- π) γ1γ2 β ∆εt-1
where
α = the constant percentage of accounts receivables to sales
β = the constant percentage of accounts payable to purchases
23 The assumption of zero fixed costs is not very descriptive of real-world firms. However, it is also probably not very costly while estimating abnormal accruals or cash flows. Please see discussion at the end of this Appendix.
53γ1 = the constant percentage of target inventory to expected cost of sales next period
γ2 = a constant that represents speed at which firm adjusts inventory
εt = St - St-1
∆ is the first difference operator.
Dechow, Kothari and Watts (1998) further simplify this expression by noting that the
second and the third terms are likely to be negligible in practice and denoting [α + (1- π) γ1 –
(1- π)β] by δ.
Essentially, δ is a measure of the operating cash cycle and accruals in this model would
be the operating cash cycle times the change in sales, or the sales shock, given last period’s
expectation.
After this simplification, accruals are given by
At = δ*εt
This is the basic underlying equation for the Jones (1991) model for determining
normal working capital accruals. To estimate normal depreciation accruals, Jones (1991) also
includes property, plant and equipment as an explanatory variable.
Cash flows from operations, CFOt , is then given by
Eqn.3. CFOt = Et - At = πSt - δεt = (π-δ) St + δSt-1
The above equation expresses cash flows as a function of current-period sales and last-
period sales. This is the equation I use in my subsequent regressions.
The estimation equation does not change much in the presence of fixed costs. Equation
3 is augmented by another term, the change in outflow on fixed costs, assuming that fixed
expenses are paid in cash. Incorporating this in the equation would make the model for normal
cash flows more powerful, but I omit this term for the sake of simplicity. Besides, in my
estimation of abnormal cash flow from operations, I include industry membership, size and the
market-to-book ratio. To the extent that operating leverage is likely to be correlated with these
variables, I do control for the effect of fixed costs.
54
Appendix 2: Variables required for my analysis and corresponding COMPUSTAT data
items
Sales = COMPUSTAT data item # 12
Cost of goods sold = data #41
Research and development expenses (R&D) = data #46
Advertising expenses = data #45
Selling, general and administrative expenses (SG&A) = data #189
(As long as SG&A is available, advertising and R&D are set to zero if they are missing)
Earnings / income before extra-ordinary items = COMPUSTAT data item #18
Accruals = Earnings – data #308
Cash flow from operations = Data #308
Working capital excluding cash = data#4 – data#5 – data#1 + data#34
(where data #4 = current assets, data#5 = current liabilities, data#1 = cash and short term
investments and data#34 = debt in current liabilities)
Long-term debt = data #9
Short-term debt = data #34
Market value of equity = data #199 * data #25 (year-end price times common shares
outstanding)
Total assets = data #6