Cost Behavior and Prior Year Earnings:
Evidence for US-listed Firms.
C.H.J. ROODZANT
ANR : 978071
390301 : MSc Thesis Accounting
Supervisor : Dr. B.C.G. DIERYNCK
Second reader : Prof. Dr. E.H.J. VAASSEN
Date of completing thesis: July 3, 2012
Date of defense thesis: July 9, 2012
- 2012 -
1
Cost Behavior and Prior Year Earnings: Evidence for US-listed Firms
Abstract
In this study I examine the behavior of SG&A costs of US-listed firms. I posit that firms that
meet or beat prior year earnings will exhibit a smaller increase in SG&A costs following an
activity increase and a larger decrease in SG&A costs following an activity decrease. This
should show up as more symmetric cost behavior for firms that meet or beat prior year
earnings. Compared to other profitable firms, the behavior of SG&A costs of US-listed firms
is more asymmetric when firms meet or beat prior year earnings. This implies that, US-listed
firms that meet or beat prior year earnings will exhibit a larger increase in SG&A costs
following an activity increase and a smaller decrease in SG&A costs following an activity
decrease.
Keywords: Cost asymmetry; selling, general, and administrative (SG&A) costs; earnings
targets; prior year earnings.
Data availability: Data is available from the sources described in the text.
2
I. INTRODUCTION
In the traditional model of cost behavior, costs are described as fixed or variable with
respect to changes in activity. In this model, variable costs change proportionately with
activity changes. This implies that the magnitude of a change in costs depends only on the
extent of a change in activity. Prior studies, however, provide evidence that costs behave
asymmetrically (Noreen and Soderstrom 1997; Anderson et al. 2003; Dierynck et al. 2012).
Costs increase more rapidly with an activity increase than they decrease with an activity
decrease (Anderson et al. 2003; Balakrishnan et al. 2004). Anderson et al. (2003) show that
an increase of one percent in sales results on average in a 0.55 percent increase of selling,
general and administrative (SG&A) costs, but a decrease of one percent in sales results on
average in a 0.35 percent decrease of SG&A costs. This phenomenon is also labeled as “cost
stickiness”. Dierynck et al. (2012) examine whether and how managerial incentives to meet
or beat the zero earnings benchmark, affect cost behavior in private Belgian firms. The
results show that there is more symmetric labor cost behavior for firms that meet or beat
the zero earnings benchmark. In this study, I will examine whether SG&A costs of US-listed
firms behave more symmetric when they meet or beat last year earnings, which is
mentioned as an important earnings benchmark for US-listed firms (Degeorge et al. 1999;
Philips and Pincus 2003).
Using a sample of 39,738 firm-year observations over the period 1997-2010, I
examine my research question. Controlling for economic determinants of cost asymmetry
identified in prior studies (Anderson et al. 2003; Dierynck et al. 2012) and the extent of
accrual-based earnings management, I find that US-listed firms, on average, exhibit
significant asymmetrical SG&A cost behavior. Specifically, SG&A costs of US-listed firms
increase with 0.46 percent following a one percent increase in activity and decrease with
3
0.32 percent following a one percent decrease in activity. US-listed firms that just not meet
the earnings of prior year exhibit significant asymmetrical behavior. US-listed firms that
meet or beat their earnings of prior year exhibit also significant asymmetrical behavior.
Comparing the just non-meet observations with the meet or beat observations, shows more
asymmetric SG&A cost behavior for the meet and beat observations.
Large profit
observations exhibit symmetrical SG&A cost behavior. In comparison to firms that do not
meet or beat the earnings of prior year, the SG&A costs increase of firms that meet or beat
the earnings of prior year is larger for an increase in sales and the SG&A cost decrease for a
decrease in sales is smaller.
This study complements the asymmetric cost literature in two ways. First, I present
evidence that there is symmetrical SG&A cost behavior for US-listed firms which beat the
earnings of prior year. Second, I add to the accounting literature that there is a difference in
symmetrical behavior of SG&A costs for US-listed firms between small profit, just non-meet,
suspect-increase, and large profit observations.
The remainder of the paper is organized as follows. Section 2 outlines prior studies
and develops the hypotheses. Section 3 describes the data and research methods used.
Section 4 presents the findings and section 5 concludes the paper
II. LITERATURE OVERVIEW AND HYPOTHESES
Asymmetric Cost Behavior
Traditional cost models assume that variable costs change in proportion with certain
changes in the activity of a firm. For example, when the activity of a firm increases with one
percent, the costs will increase with 0.6 percent, and an activity decrease of one percent
results in a decrease of 0.6 percent in costs. Prior studies, however, provided evidence that
4
costs behave asymmetrically in relation to firm activity (Noreen and Soderstrom 1997;
Anderson et al. 2003; Dierynck et al. 2012). Specifically, Anderson et al. (2003) showed that
an increase of one percent in sales results on average in a 0.55 percent increase of selling,
general and administrative (SG&A) costs. In contrast, a decrease of one percent in sales
results on average in a 0.35 percent decrease of SG&A costs. This phenomenon is also
labeled as “cost stickiness”.
Anderson et al. (2003) shows that the degree of cost asymmetry depends on the
difference in adjusting costs for activity increase and decreases, which varies systematically
across firms and over time. They find that sticky costs can be recognized and controlled.
Managers can evaluate their exposure to sticky costs by considering the sensitivity of cost
changes to reductions in volume. This results in a positive relation between the degree of
cost asymmetry and managerial incentives.
The study of Chen et al. (2011) explicitly focuses on managerial intent, because
managerial intent is key to cost asymmetry. They found that the positive correlation
between the agency problem and SG&A cost asymmetry is more pronounced under weak
corporate governance. A study of Dierynck et al. (2012) investigates the influence of
managerial incentives to meet or beat the zero earnings benchmark on labor cost behavior
of private Belgian firms. The authors find that relative to managers of firms reporting
healthy profits, managers meeting or beating the zero earnings benchmark, will increase
labor cost to a smaller extent when activity increases and decreases labor costs to a larger
extent when activity decreases.
The behavior of SG&A costs is the main focus of many studies (Anderson et al. 2003;
Chen et al. 2011; Banker and Chen 2006; Calleja et al. 2006), because SG&A costs capture
most of the overhead cost. Empire building by managers is likely to increase SG&A costs too
5
rapidly when sales go up or to decrease SG&A costs too slow when sales go down. Such
behavior will shift SG&A cost asymmetry away from its optimal level and result in greater
SG&A cost asymmetry than dictated by economic factors (Chen et al. 2011).
Besides SG&A costs, cost stickiness can be related to other costs, like Balakrishnan
and Gruca (2008), and Dierynck et al. (2012) did. First, Balakrishnan and Gruca (2008)
examined the behavior of short-term costs for hospitals in Ontario and they focused on costs
from core versus non-core competencies. The result shows that the extent to which a
function represents the organization’s core competency influences the stickiness of
associated costs. Costs exhibit greater stickiness in functions making greater contributions
to an organization’s core competency. Second, Dierynck et al. (2012) examine whether and
how managerial incentives to meet or beat the zero earnings benchmark, affect labor cost
behavior in private Belgian firms. The results show that there is more symmetric labor cost
behavior for firms that report a small profit compared to firms that report a large profit.
Economic Determinants
Economic determinants have to be identified, because cost stickiness is driven by economic
determinants. These determinants are identified in prior studies (Anderson et al. 2003;
Dierynck et al. 2012). Anderson et al. (2003) show that stickiness is less pronounced in a
second year of revenue decline. Stickiness is also greater in years of macroeconomic growth
and for firms that use relatively more assets to support their sales. Furthermore, stickiness
is greater for firms that employ relatively more people to support their sales. Anderson et al.
(2003) used economic growth and successive activity decreases to capture manager’s
estimation of persistence of the activity change.
6
In addition to the above determinants, employees and assets should also be
considered. Dismissing employees is costly because employers must pay severance costs
(Anderson et al. 2003). With regard to assets, it is relatively easy to scale down purchased
resources when demand drops, but disposing of assets is costly because the company must
pay selling costs and lose firm-specific investments. This results in higher adjustment costs
when SG&A activities rely more on assets owned (Anderson et al. 2003).
Earnings Targets
There are three kinds of earnings targets (Degeorge et al. 1999; Philips and Pincus 2003):
zero earnings benchmark, earnings of prior year, and analysts’ forecast. In my study I will
focus on earnings of prior year in a sample of US-listed firms.
Several studies provide evidence that firms manage their earnings to reach these
targets. Executives manage earnings to influence the perceptions of outsiders and to get
private payoff, because they want a good reputation. Executives focus on thresholds for
earnings because the parties concerned with the firm’s performance attach importance to
these thresholds (Degeorge et al. 1999). Hayn (1995) examined cases with earnings just
above zero, and shows that there is a point of discontinuity around zero. The model of
Degeorge et. al (1999) shows that earnings falling just short of thresholds, like earnings of
prior year, will be managed upward. In contrast, earnings far from thresholds, whether
below or above, will be controlled, making thresholds more attainable in the future.
Burgstahler and Dichev (1997) provide evidence that firms manage reported earnings to
avoid earnings decreases and losses. Specifically, in cross-sectional distributions of earnings
7
changes and earnings, they find unusually low frequencies of small decreases in earnings and
small positive income.1 Another motivation for the manipulation of earnings is the desire to
attract external financing at low costs and to avoid debt covenant restrictions (Dechow et al.
1996). Reporting a profit increase conveys an important signal to other stakeholders, such
as employees, customers, and suppliers (Bowen et al. 1995; Burgstahler and Dichev 1997).
Many studies have examined the reasons why firms manage earnings. The evidence
shows that companies will exhibit earnings management to avoid small losses and earnings
decreases, to decrease taxes, to show an almost identical earnings pattern, to avoid breaking
debt covenants, or to reach earnings forecasts of analysts’ (Burgstahler and Dichev 1997;
Degeorge et al. 1999). Subsample analysis of Dierynck et al. (2012) shows that firms that just
meet or beat the zero earnings benchmark actually exhibit cost symmetry. This evidence
shows the importance of meeting or beating earnings targets.
DeAngelo et al. (1996) and Barth et al. (1999) show that a consistent pattern of
earnings increases is important, because when a firm breaks a pattern of consistent earnings
growth, the firm experience an average of 14 percent negative abnormal stock return in the
year the pattern is broken (DeAngelo et al. 1996). Firms with a consistent pattern of
earnings increases, receive a market premium for this performance. Firms with patterns of
increasing earnings have significantly larger earnings multiples2 than other firms.
The
patterns of increasing earnings are positive correlated with proxies for growth and negative
correlated with proxies for risk (Barth et al. 1999). Therefore, firms have incentives to avoid
1
In contrast to prior studies regarding to earnings management, Durtschi and Easton (2005) and Durtschi and
Easton (2010) show that the discontinuities in earnings distributions around zero are influenced by other
factors. Factors like sample selection, scaling, the relation between earnings and profits differ with the
magnitude and the sign of earnings, and distributions that may be used to show evidence of earnings
management are the distributions of net income and earnings per share, which do not exhibit evidence of an
irregularity at zero.
2
The term earnings multiples refers to either the coefficient on earnings in price regressions or the coefficient
on earnings changes in returns regressions.
8
the reporting of earnings decreases, and firms have incentives to avoid reporting losses
(Burgstahler and Dichev 1997). This means that managers will reach the prior year earnings.
Trying to obtain a consistent pattern of earnings increases can cause real earnings
management.
Possible actions to reach earnings targets are: price discount to temporarily increase
sales, overproduction to report lower cost of goods sold, and reduction of discretionary
expenditures to improve reported margins for firms that report small annual profits
(Roychowdhury 2006). Managers also grant sales price reductions in the fourth quarter to
meet annual financial reporting targets (Jackson and Wilcox 2000). Burgstahler and Eames
(2006) provide evidence that to meet or slightly beat analyst forecasts, earnings are
managed upward and forecasts are managed downward.
Executives’ manage earnings through real activities instead of through accruals,
because accrual-based earnings management is more risky (Cohen and Zarowin 2010). A
study by Ewert and Wagenhofer (2005) showed that tighter accounting standards can
increase real earnings management. Zang (2012) observed that managers determine real
manipulation before accrual manipulation. Managers will rely less on cost management
when they use a larger extent of accrual-based earnings management, resulting in a higher
degree of cost asymmetry. The extent of accrual-based earnings management can, as
economic determinants, influence the stickiness of SG&A costs.
Prior literature on earnings management, often makes a distinction between real and
accrual-based earnings management (e.g., Roychowdhury 2006; Cohen and Zarowin 2010).
Real earnings management activities are significantly different than accrual-based ones as
they have direct effects on cash flows. Graham et al. (2005) observed that managers would
rather take economic actions that could have negative long-term consequences than make
9
within-GAAP accounting choices to manage earnings. This might be a consequence of the
disgrace attached to accounting fraud referred to Enron and Sarbanes-Oxley.
Given the importance of prior year earnings as an earnings target for US-listed firms, I
expect that SG&A costs of US-listed firms behave more symmetric when firms meet or beat
the earnings of prior year.
H1: The behavior of SG&A costs of US-listed firms is more symmetric when firms meet or beat
prior year earnings.
III. METHOD
Sample Selection
The sample used in this study was obtained from the Compustat North America database of
Wharton Research Data Services (WRDS). The sample included annual data for US-listed
firms covering the years from 1995 to 2010, and contained 103,300 firm-year observations.
Table 1, Panel A, describes how I compose the final sample. Following Anderson et al. (2003),
Chen et al. (2011), and Dierynck et al. (2012), I require (SG&A) costs and sales to be available
in the current and previous year. Furthermore, SG&A costs have to be less than sales. As in
Chen et al. (2011) and Dierynck et al. (2012), I delete observations for which changes in sales
and (SG&A) costs are in the top and bottom 0.5%. A total of 39,738 firm-year observations
between 1997 and 2010 remained in the sample. Observations in the years 1995 and 1996
are not used, because the analysis required sales data to be available for the prior two years,
because of the variable Successive_Decrease mentioned later.
Based on Roychowdhury (2006), the sample was split in four groups (small profit, just
non-meet, suspect-increase, and large profit). My fifth subsample are all loss making
10
observations. Table 1, Panel B, presents the distribution of the firm-year observations of the
earnings target; earnings of prior year. The so-called suspect-increase sample includes all
firm-year observations for which the change in net income, net income year t minus net
income year t-1, as a percentage of beginning-of-year total assets is larger than or equal to
zero but smaller than one percent.
The large profit sample includes all firm-year
observations that are equal to one percent or higher. Firm-year observations for which the
change in net income as a percentage of beginning-of-year total assets is less than zero but
higher than minus one percent are classified as just non-meet. Firm-year observations for
which the change in net income as a percentage of beginning-of-year total assets is less than
minus one percent are small profit observations.
TABLE 1
Sample Selection
PANEL A: Full Sample
Base sample
-Observations with missing data on either sales
revenue or SG&A costs for the current or preceding year
-Observations for which SG&A costs exceed sales
revenue for the current year
-Observations with missing data on other variables
-Observations for which changes in sales and SG&A costs
are in the top and bottom 0.5%
Finale sample
Number of Firm-Years
103,300
(45,261)
(7,102)
(10,390)
(809)
39,738
PANEL B: Distribution of Firm-Year Observations over Sample-years
Sample-year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total
Loss making
Sample
452
597
649
718
1,016
1,104
958
805
843
843
954
1,303
1,280
999
12,521
Small profit
Sample
260
403
352
352
536
328
268
268
424
434
501
641
638
366
5,771
Just non-meet Suspect-increase
Sample
Sample
107
215
119
197
144
181
150
222
148
180
119
173
159
278
156
238
201
284
205
286
207
319
202
233
182
217
196
274
2,295
3,297
Large profit
Sample
1,034
831
957
928
661
951
1,165
1,500
1,304
1,425
1,305
961
1,107
1,725
15,854
Full Sample
Percent (%)
2,068
2,147
2,283
2,370
2,541
2,675
2,828
2,967
3,056
3,193
3,286
3,340
3,424
3,560
39,738
5.20
5.40
5.75
5.96
6.39
6.73
7.12
7.47
7.69
8.04
8.27
8.41
8.61
8.96
100
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Model Specification and Variable Definitions
Asymmetry of SG&A Costs
To test for SG&A cost asymmetry, I use models similar to those of Anderson et al. (2003) and
Dierynck et al. (2012). The basic model is given by equation (1):
log
SG&A it
SG&A it-1
= β 0 + β 1log
Sales it
Sales it-1
+ β 2Decrease_Dummy it * log
Sales it
Sales it-1
+ Ԑ it , (1)
where SG&Ait denotes SG&A costs of firm i in the year t, Salesit are sales revenues, and
Decrease_Dummyit is an indicator variable set equal to one when sales in year t are smaller
than sales in year t-1, and otherwise 0.
The coefficient, β1, measures the percentage increase in SG&A costs with an one
percent increase in sales; the sum of β1 and β2 measures the percentage decrease in SG&A
costs with an one percent decrease in sales. If SG&A costs are sticky, the variation of SG&A
costs with sales increase should be greater than the variation for sales decreases. In other
words, β1 should be positive, β2 should be negative and the absolute value of β2 should
smaller than the absolute value of β1.
To control for economic determinants of cost asymmetry identified in prior studies
(Anderson et al. 2003; Dierynck et al. 2012) and the extent of accrual-based earnings
management, I use an extended model (Dierynck et al. 2012), equation (2), based on the
basic model. Successive_Decrease equals one if sales have decreased in two consecutive
years, and zero otherwise. Employee-Intensity is the ratio of total number of employees
over sales. Asset-intensity is the ratio of total assets over sales. Economic_Growth is the
percentage growth in real gross national income (GNI) during year t. Formerly this was
named gross national product (GNP). Loss_Prior_Year indicates whether the firm reported a
loss in the prior year, or not. I include a control for the amount of accrual-based earnings
12
management (Abnor_Accruals) because managers can manipulate accruals to meet or beat
the earnings targets. I include signed abnormal accruals as main term and add two- and
three-way interaction terms. Consistent with Dierynck et al. (2012), I use the DeFond and
Park (2001) model as adapted by Francis and Wang (2008) for accrual-based earnings
management, because the number of industry observations is quite small (Francis and
Wang 2008).
log
SG&A it
SG&A it-1
= β 0 + β 1 log
β 2Decrease_Dummy it * log
β3Abnor_Accruals it * log
β 4Decrease_Dummy it * log
β 5 Decrease_Dummy it * log
β 6 Decrease_Dummy it * log
β 7 Decrease_Dummy it * log
β 8 Decrease_Dummy it * log
β 9 Decrease_Dummy it * log
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
Sales it
Sales it-1
+
+
+
* Successive_Decrease it +
* Employee_Intensity it +
* Asset_Intensity it +
* Economic_Growth t +
* Loss_Prior_Year it +
* Abnor_Accruals it +
β 10Successive_Decrease it + β 11Employee_Intensity it +
β 12Asset_intensity it + β 13Economic_Growth t + β 14 Loss_Prior_Year it +
β 15Abnor_Accruals it + + Ԑ it
, (2).
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The DeFond and Park (2001) model, equation (3), uses a proxy for Abnor_Accrualsit,
which measures the difference between realized working capital, and a proxy for the
market’s expectations of the level of working capital needed to support current sales levels.
Abnor_Accruals it = WC it - [(WC it-1 / Sales it-1 )
* Sales it ]
, (3)
where t is a year, this is different compared with the model used by DeFond and Park (2001),
because they were interested in quarterly data. WCit is non-cash working capital in the
current year, computed as (current assets -/- cash and short-term investments) -/- (current
liabilities -/- short-term debt). Salesit is the sales of current year. Abnormal accruals are
scaled by a firm’s lagged total assets.
Symmetric Cost Behavior when Firms Meet or Beat the Earnings of Prior Year
I use three approaches to investigate whether observations that meet or beat the earnings
of prior year exhibit more symmetric (SG&A) cost behavior. First, I split the sample in five
groups: loss making, small profit, just non-meet, suspect-increase, and large profit. I do
regressions for firms with losses, but do not take the results in account, because such firms
are likely to manage earnings using “big bath” accounting, which make it difficult to compare
their cost behavior with that of other observations. For the other groups I will estimate the
basic and extended model separately. I predict suspect-increase observations will exhibit
more cost symmetry compared to small profit, just non-meet, and large profit observations.
Compared to small profit, just non-meet, and large profit observations, β1 will be smaller and
β2 will be less negative or insignificantly different from zero for suspect-increase
observations. That is to say, firms that attempt to meet or beat the prior year earnings and
that face an increase (decrease) in sales, will increase (decrease) SG&A costs to a smaller
(larger) magnitude than small profit, just non-meet, and large profit firms.
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Second, I use four samples; full sample with loss making observations and controlling
for abnormal accruals, full sample with loss making observations but without controlling for
abnormal accruals, full sample without loss making observations but with controlling for
abnormal accruals, and full sample without loss making observations and controlling for
abnormal accruals. For these samples I will estimate the basic and the extended model
separately. With this approach I show the influence of loss making observations and
controlling for abnormal accruals. I also show the relation between real and accrual-based
earnings management. I predict more symmetrical behavior for the samples with controlling
for abnormal accruals and for the samples without loss making observations. I predict the
size of accrual-based earnings management is a replacement for cost management for both
increases and decreases in sales. I expect to find a positive β3 and a negative β9.
In my third test, I add an indicator variable, Suspect_Increase, which equals one for
firm-year observations that are in the suspect-increase sample and zero otherwise. This
indicator variable include two-way (Suspect_Increase*Log(Salesit/Salesit-1)), three-way
(Suspect_Increase*Decrease_Dummy*Log(Salesit/Salesit-1)),
and
main
term
(Suspect_Increase) interaction terms, which I add to the extended model, equation (2). The
two-way interaction term represents the difference in SG&A cost increase following an
activity increase of suspect-increase observations relative to other firms. The three-way
interaction term represents the difference in SG&A cost decrease following an activity
decrease of suspect-increase observations relative to other firms. I have used the full
sample without loss making observations and a sample with suspect-increase and large
profit observations to do the test. I predict a smaller two-way interaction term, because I
expect that suspect-increase observations have a smaller increase in SG&A costs for an one
percent increase in activity compared to the other firms. I also predict a larger three-way
15
interaction term, because I expect that suspect-increase observations will have a larger
decrease in SG&A costs for one percent decrease in activity. I expect more SG&A cost
symmetry for the suspect-increase and large profit sample compared to the full sample
without loss making observations.
IV. RESULTS
Descriptive Results
The descriptive statistics for the different samples are presented in Table 2. The focus is on
the descriptive statics of the four subsamples: small profit, just non-meet, suspect-increase,
and large profit. And not on the loss making sample, because such firms are likely to
manage earnings using “big bath” accounting, which make it difficult to compare their cost
behavior with that of other observations. On average, the sales revenues of the just nonmeet observations of $4,247,836,953 and the suspect-increase observations of
$4,344,863,022 are high, compared to the small profit, $2,333,922,019 and large profit,
$2,439,191,809 sample.
Also the SG&A costs for the just non-meet observations,
$779,601,430, and suspect-increase observations, $803,575,817, are high compared to the
small profit, $449,466,498, and large profit, $452,075,543, samples. The average ratio of
SG&A costs to sales revenues is for each sample almost equal: 25 percent for small profit, 22
percent for just non-meet, 22 percent for suspect-increase, and 24 percent for large profit.
The percentages of the subsamples are high, indicating that SG&A costs are a major cost
category for these subsamples. The number of employees and beginning total assets give an
indication about the size of the firm in the different groups. Based on these two variables,
the biggest firms are in the just non-meet and suspect-increase sample. These subsamples
have on average the highest number of employees, 16,602 and 19,286 employees. The
16
average beginning total assets of the just non-meet sample are $4,537,095,907 and the
beginning total assets of the suspect-increase sample are $4,310,693,188. The small profit
and large profit sample have on the other hand the smallest firms, based on the average
number of employees, 10,197 and 10,378 , and average beginning total assets,
$2,535,521,858 and $2,245,880,777. The small profit sample has the highest probability of a
decrease in sales, 43 percent, compared to the just non-meet sample, 27 percent, suspectincrease sample, 16 percent, and the large profit sample, 13 percent. The small profit
sample also has a higher probability of a successive decrease, 13 percent, compared to the
just non-meet sample, nine percent, suspect-increase sample, six percent, and the large
profit sample, six percent. The large profit sample has a higher probability of having
recorded a loss in the previous year, 28 percent, compared to the suspect-increase sample,
two percent. The small profit sample and just non-meet sample, do not show any value, this
indicates that there are no observations with a loss in the previous year in these samples.
TABLE 2
Sample Descriptive Statics
Variable
Sales
(in $000)
SGA
(in $000)
Change_Sales
(in $000)
Change_SGA
(in $000)
Relative_Change_Sales
(in %)
Relative_Change_SGA
(in %)
SGA/Sales
(in %)
Number_of_Employees
Beginning_Total_Assets
(in $000)
Decline_Observations
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Full Sample
2,209,131.49
291,276.50
7,147,762.20
414,761.18
57,390.50
1,402,086.15
163,019.78
13,034.00
772,514.45
27,110.83
2,502.50
117,182.72
2.00
0.08
140.84
0.73
0.01
87.88
0.28
0.23
0.21
9,590.66
1,397
30,238.60
2,275,269.39
279,679.00
8,166,680.67
0.29
0.00
0.46
Loss making Sample
924,259.80
88,545.00
3,506,418.98
182,264.04
26,607.00
734,309.40
5,070.91
256.00
448,125.85
5,570.87
209.00
73,361.74
1.90
0.01
75.68
0.68
0.00
31.97
0.38
0.33
0.25
4,476.04
434
17,573.98
1,241,908.83
102,521.00
5,861,252.41
0.48
0.00
0.50
Small profit Sample
2,333,922.02
406,209.00
6,819,844.73
449,466.50
73,468.00
1,440,036.90
51,592.41
2,609.00
677,355.33
23,622.72
2,421.00
113,444.37
0.05
0.02
0.48
0.03
0.01
0.18
0.25
0.21
0.17
10,197.27
1,842
30,295.63
2,535,521.86
379,927.00
7,996,839.94
0.43
0.00
0.50
Just non-meet Sample Suspect-increase Sample Large profit Sample
4,247,836.95
4,344,863.02
2,439,191.81
896,678.00
960,486.00
389,486.50
11,596,062.68
10,619,601.38
7,456,968.80
779,601.43
803,575.82
452,075.54
143,129.00
155,051.00
70,071.50
2,270,829.93
2,077,999.84
1,416,955.08
210,669.34
309,175.55
291,031.25
26,596.00
52,869.00
44,045.00
878,572.44
916,141.99
915,810.97
39,090.64
50,044.23
38,888.71
5,137.00
8,182.00
5,444.50
142,838.53
151,001.29
130,968.06
0.09
0.13
3.47
0.05
0.07
0.17
0.27
0.36
212.54
0.02
0.02
1.29
0.01
0.01
0.02
0.05
0.05
136.17
0.22
0.22
0.24
0.19
0.19
0.21
0.14
0.14
0.17
16,601.63
19,286.04
10,378.07
4,200
4,443
1,776
38,985.57
50,375.88
30,304.37
4,537,095.91
4,310,693.19
2,245,880.78
761,506.00
887,729.00
311,804.50
15,382,893.66
12,200,714.19
7,007,510.48
0.27
0.16
0.13
0.00
0.00
0.00
0.45
0.37
0.33
17
Abnor_Accruals
Successive_Decrease
Employee_Intensity
Asset_Intensity
Economic_Growth
Loss_Prior_Year
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Mean
Median
Std Dev
Observations
8.39
0.00
1,631.77
0.13
0.00
0.34
0.00
0.00
0.00
1.40
0.98
1.97
0.04
0.04
0.03
0.32
0.00
0.47
39,738
26.26
-0.01
2,913.03
0.24
0.00
0.43
0.00
0.00
0.00
1.60
1.03
2.82
0.04
0.04
0.03
0.65
1.00
0.48
12,521
0.02
0.00
0.39
0.13
0.00
0.33
0.00
0.00
0.00
1.35
1.01
1.30
0.04
0.04
0.03
0.00
0.00
0.00
5,771
0.00
0.00
0.06
0.09
0.00
0.29
0.00
0.00
0.00
1.34
0.98
1.68
0.04
0.04
0.03
0.00
0.00
0.00
2,295
0.00
0.00
0.06
0.06
0.00
0.23
0.00
0.00
0.00
1.38
0.99
1.76
0.05
0.04
0.03
0.02
0.00
0.13
3,297
0.38
0.00
30.09
0.06
0.00
0.24
0.00
0.00
0.00
1.28
0.94
1.42
0.05
0.04
0.03
0.28
0.00
0.45
15,854
Sales
SGA
Change_Sales
Change _SGA
Relative_Change_Sales
= sales revenue;
= SG&A costs;
= change in sales between year t and year t-1;
= change in SGA between year t and year t-1;
= ratio of change in sales between year t and year t-1 to beginning total
assets of year t;
Relative_Change_SGA
= ratio of change in SG&A costs between year t and year t-1 to beginning
total assets of year t;
SGA/Sales
= ratio of SG&A costs to sales revenue;
Number_of_Employees = number of employees in year t;
Beginning_Total_Assets = total assets of year t-1;
Decline_Observations
= percentage of observations with a decrease in sales revenue between year
t and year t-1;
Abnor_Accruals
= signed abnormal accruals following Francis and Wang (2008);
Successive_Decrease
= 1 when salest-2 > salest-1 > salest, and 0 otherwise;
Employee_intensity
= ratio of total number of employees to sales revenue;
Asset_intensity
= ratio of total assets to sales revenue;
Economic_Growth
= growth in real gross national income (GNI);
Loss_Prior_Year
= 1 when the firm reports a loss in the previous year, and 0 otherwise.
Regression Analyses
Asymmetry of SG&A costs (basic model)
Column I of Table 3 presents the regression summary statistics for the basic model, equation
(1), for the full sample, containing 39,738 firm-year observations.
The results show
significant asymmetry of SG&A costs in US-listed firms. β1 is equal to 0.45 (t = 115.473) and
β2 is equal to 0.11 (t = 11.617). These results imply that an increase in sales revenues of one
percent results in an increase in SG&A costs of 0.45 percent and a decrease in sales revenues
of one percent leads to a decrease of 0.56 percent in SG&A costs, this does not result in
sticky costs.
18
The regression summary statistics for the basic model, equation (1), for the loss
making sample, containing 12,521 firm-year observations are reported in column II of Table
3. The results show significant asymmetry of SG&A costs in the loss making sample of USlisted firms, with β1 equal to 0.39 (t = 55.204) and β2 equal to 0.14 (t = 8.443). This implies,
an increase of one percent in sales revenues leads to an increase of 0.39 percent in SG&A
costs, and a decrease in sales revenues of one percent leads to a decrease of 0.53 percent in
SG&A costs, this implies that there is no stickiness of SG&A costs for the loss making sample.
Table 3, column III presents the regression summery statistics for the basic model,
equation (1), for the small profit sample of 5,771 firm-year observations. The results show
significant SG&A cost asymmetry in the small profit sample of US-listed firms. β1 is equal to
0.84 (t = 47.241) and β2 is equal to -0.44 (t = -15.076). These results imply that an increase in
sales revenues of one percent results in an increase in SG&A costs of 0.84 percent and a
decrease in sales revenues of one percent leads to a decrease of 0.40 percent in SG&A costs.
The regression summery statistics for the basic model, equation (1), for the just nonmeet sample, containing 2,295 firm-year observations, is presented in column IV of Table 3.
The results show a significant SG&A costs asymmetry in the just non-meet sample of USlisted firms. With β1 equal to 0.81 (t = 34.564) and β2 equal to -0.34 (t = -8.139). Hence, an
increase in sales revenues of one percent results in an increase of 0.81 percent in SG&A
costs, and a decrease in sales revenues of one percent leads to a decrease of 0.47 percent in
SG&A costs.
Column V of Table 3 presents the regression summery statistics for the basic model,
equation (1), for the suspect-increase sample of 3,297 firm-year observations. The results
show significant asymmetry of SG&A costs in the suspect-increase sample of US-listed firms.
Where β1 is equal to 0.89 (t = 48.842) and β2 is equal to -0.51 (t = -12.391). This implies, an
19
increase in sales revenues of one percent results in an increase in SG&A costs of 0.89
percent, and a decrease in sales revenues of one percent results in a decrease in SG&A costs
of 0.38 percent.
The regression summary statistics for the basic model, equation (1), for the
large profit sample, 15,854 firm-year observations, are presented in column VI of Table 3.
The results show significant asymmetric behavior of SG&A costs in the large profit sample of
US-listed firms. With β1 equal to 0.50 (t = 88.646) and β2 equal to 0.29 (t = 14.560). This
involves that an increase in sales revenue of one percent, results in an increase of 0.50
percent is SG&A costs, and a decrease of one percent in sales revenues leads to a decrease
of 0.79 percent in SG&A costs, this implies that there is no stickiness of SG&A costs for the
loss making sample.
Overall, Table 3 show asymmetric behavior of SG&A costs when the basic model,
equation (1), is used. In this study I will focus on the subsamples; small profit, just non-meet,
suspect-increase, and large profit. The suspect-increase sample shows, compared to the
other subsamples, the most asymmetrical behavior of SG&A costs. In contrast, the large
profit sample shows the most symmetrical behavior of SG&A costs. The large profit sample
shows, compared to the other subsamples, a positive β2, this indicates that the SG&A costs
are not sticky. The large profit sample also differs from the other subsamples based on β 1.
Compared to the other subsamples, 0.84, 0.81, and 0.89, β1 is low, 0.50, for the large profit
sample. This implies that, in comparison with the other subsamples, an increase in activity
results in a lower increase in SG&A costs.
20
TABLE 3
Summary Statistics from Regressions of the Basic Modela with Log(SGAit/SGAit-1) as the
Dependent Variable
Variable
Full sample
β0 : Constant
β1: Log(Sales it /Sales it-1 )
β2: Decrease_Dummy*Log(Sales it /Sales it-1 )
Number of observations
Adjusted R-Squared
Loss making
Small profit Just non-meet Suspect-increase
Sample
Sample b
Sample b
Coefficient
Coefficient
Coefficient
Coefficient
(t-statistic)
(t-statistic)
(t-statistic)
(t-statistic)
<significance> <significance> <significance> <significance>
0.05
0.04
0.04
0.02
(32.335)
(11.389)
(14.164)
(4.835)
<.000>
<.000>
<.000>
<.000>
0.45
0.39
0.84
0.81
(115.473)
(55.204)
(47.241)
(34.564)
<.000>
<.000>
<.000>
<.000>
0.11
0.14
-0.44
-0.34
(11.617)
(8.443)
(-15.076)
(-8.139)
<.000>
<.000>
<.000>
<.000>
39,738
12,521
5,771
2,295
36.09%
32.41%
38.05%
43.06%
a
Log(SGAit/SGAit-1 ) = β0 + β1 * Log(Sales it/Sales it-1 ) + β2 *Decrease_Dummy* Log(Sales it/Sales it-1 ) +Ԑit
b
The distribution of the sample is based on Roychowdhury (2006). See Sample Selection for the explanation.
Sample b
Coefficient
(t-statistic)
<significance>
0.01
(0.894)
<.371>
0.89
(48.842)
<.000>
-0.51
(-12.391)
<.000>
3,297
45.56%
Large profit
Sample b
Coefficient
(t-statistic)
<significance>
0.04
(16.756)
<.000>
0.50
(88.646)
<.000>
0.29
(14.560)
<.000>
15,854
41.14%
Asymmetry of SG&A costs (extended model)
My primary prediction is that firms that meet or beat the earnings of prior year exhibit more
symmetric SG&A cost behavior. Specifically, I predict that suspect-increase observations will
exhibit a SG&A increase following an activity increase which is equal to the SG&A decrease
following an activity decrease. To test this, I use the extended model, equation 2.
Table 4 presents the results of the extended model, equation (2), of the full sample
and subsamples. These results are after controlling for determinants of cost asymmetry.
The regression summary statistics for the full sample of 39,738 firm-year observations are
presented in column I. The results of the extended model, equation (2), show significant
SG&A cost asymmetry in US-listed firms. Where β1 is equal to 0.46 (t = 115.003) and β2 is
equal to -0.14 (t = -9.088). This implies, an increase in sales revenues of one percent results
in an increase in SG&A costs of 0.46 percent, and a decrease in sales revenues of one
percent results in a 0.32 percent decrease in SG&A costs. Comparing β1 and β2 of the
extended model, equation (2), with the basic model, equation (1), shows a minimal
21
difference for β1, extended model: 0.46 (t = 115.003), basic model: 0.45 (t = 115.473). The
difference of β2, extended model: -0.14 (t = -9.088), basic model: 0.11 (t = 11.617), is notable.
β2 of the extended model is significantly negative, in contrast, β2 of the basic model is
significantly positive. With regard to the control variables, I find no significant effect of asset
intensity (β6 = 0.00, t = 2.207; β12 = 0.01, t = 10.631) and minimal effect of loss prior year (β8
= 0.08, t = 4.819; β14 = -0.11, t = -39.190). Abnormal accruals do not have significant effect
on cost asymmetry (β3 = 0.00, t = 4.403; β9 = -0.01, t = - 1.460; β15 = 0.00, t = 4.379). USlisted firms with a higher employee intensity exhibit a lower degree of cost asymmetry (β 5 =
1,362.89, t = 3.103; β11 = 802.43, t = 6.901).
The findings for the loss making sample, which is based on the extended model,
equation (2), for 12,521 firm-year observations are in column II of Table 4. The results show
SG&A cost symmetry in US-listed firms for the loss making sample. β1 is equal to 0.37 (t =
51.992) and β2 is equal to -0.04 (t = -1.336).
Column III of Table 4 presents the regression summary statistics for the extended
model, equation (2), for the small profit sample of 5,771 firm-year observations. The results
show significant SG&A cost asymmetry in US-listed firms for the small profit sample. β1 is
equal to 0.83 (t = 45.838) and β2 equal to -0.54 (t = -13.894). An increase in sales revenues
of one percent leads to an increase in SG&A costs of 0.83 percent, while a decrease in sales
revenues of one percent leads to a decrease of 0.29 in SG&A costs. Due to the absence of
loss of prior year observations in the small profit sample, there are no results for β 8 and β14.
The regression summary statistics for the extended model, equation (2), for the just
non-meet sample, containing 2,295 firm-year observations are presented in Table 4 column
IV. The results show significant SG&A cost asymmetry in US-listed firms for the just nonmeet sample. With β1 equal to 0.79 (t = 32.764) and β2 equal to -0.23 (t = -3.666). These
22
results imply, an increase in sales revenues of one percent results in an increase of 0.79
percent in SG&A costs, and a decrease of one percent in sales revenues results in a decrease
in SG&A costs of 0.56 percent. Due to the absence of loss of prior year observations in the
just non-meet sample, there are no results for β8 and β14.
Table 4, column V presents the regression summery statistics for the extended model,
equation (2), for the suspect-increase sample of 3,297 firm-year observations. The results
show significant SG&A cost asymmetry in US-listed firms for the suspect-increase sample.
With β1 equal to 0.89 (t = 47.476) and β2 equal to -0.37 (t = -4.961). These results show, an
increase in sales revenues of one percent results in an increase of 0.89 percent in SG&A
costs, and a decrease of one percent in sales revenues lead to a decrease of 0.52 percent in
SG&A costs.
The findings for the large profit sample, based on the extended model,
equation (2), for 15,854 firm-year observations are in column VI of Table 4. The results show
SG&A cost symmetry in US-listed firms for the loss making sample. β1 is equal to 0.51 (t =
88.412) and β2 is equal to -0.08 (t = -1.791).
Taken together, the results in Table 4 show symmetric behavior of SG&A costs for
loss making and large profit observations and asymmetric SG&A cost behavior for the small
profit, just non-meet, and suspect-increase observations.
23
TABLE 4
Summary Statistics from Regressions of the Extended Modela with Log(SGAit/SGAit-1) as the
Dependent Variable
Variable
β0 : Constant
β1: Log(Sales it /Sales it-1 )
β2: Decrease_Dummy*Log(Sales it /Sales it-1 )
β3: Abnor_Accruals*Log(Sales it /Sales it-1 )
β4: Successive_Decrease*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β5: Employee_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β6: Asset_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β7: Economic_Growth*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β8: Loss_Prior_Year*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β9: Abnor_Accruals*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β10: Successive_Decrease
β11: Employee_Intensity
β12: Asset_Intensity
Full sample
Loss making
Small profit
Just non-meet Suspect-increase
Sample
Sample b
Sample b
Coefficient
Coefficient
Coefficient
Coefficient
(t-statistic)
(t-statistic)
(t-statistic)
(t-statistic)
<significance> <significance> <significance> <significance>
0.05
0.08
0.02
0.02
(18.464)
(10.859)
(3.763)
(2.844)
<.000>
<.000>
<.000>
<.004>
0.46
0.37
0.83
0.79
(115.003)
(51.992)
(45.838)
(32.764)
<.000>
<.000>
<.000>
<.000>
-0.14
-0.04
-0.54
-0.23
(-9.088)
(-1.336)
(-13.894)
(-3.666)
<.000>
<.181>
<.000>
<.000>
0.00
0.00
-0.11
-0.29
(4.403)
(5.502)
(-6.291)
(-1.138)
<.000>
<.000>
<.000>
<.255>
0.17
0.08
0.34
0.18
Large profit
Sample b
Coefficient
(t-statistic)
<significance>
0.01
(2.200)
<.028>
0.89
(47.476)
<.000>
-0.37
(-4.961)
<.000>
0.64
(3.844)
<.000>
-0.07
Sample b
Coefficient
(t-statistic)
<significance>
0.05
(13.642)
<.000>
0.51
(88.412)
<.000>
-0.08
(-1.791)
<.073>
0.00
(8.553)
<.000>
0.22
(9.417)
<.000>
1,362.89
(2.888)
<.004>
1,747.05
(8.022)
<.000>
879.26
(2.014)
<.044>
14,755.93
(-0.624)
<.533>
12,650.36
(5.420)
<.000>
-592.28
(3.103)
<.002>
0.00
(2.719)
<.007>
0.00
(0.425)
<.671>
0.00
(2.925)
<.003>
-0.01
(4.905)
<.000>
-0.02
(-0.412)
<.680>
0.01
(2.207)
<.027>
1.18
(0.172)
<.863>
1.60
(-0.235)
<.814>
0.25
(-6.731)
<.000>
0.69
(-3.765)
<.000>
-0.93
(2.996)
<.003>
2.01
(6.123)
<.000>
0.08
(5.163)
<.000>
0.09
(0.541)
<.588>
-
(0.799)
<.424>
-
(-1.081)
<.280>
-0.38
(4.084)
<.000>
0.00
(4.819)
<.000>
-0.01
(3.510)
<.000>
-0.09
0.15
-1.95
(-3.507)
<.000>
-3.25
(0.047)
<.962>
-0.38
(-1.460)
<.144>
-0.03
(-7.378)
<.000>
802.43
(6.901)
<.000>
0.01
(10.631)
<.000>
(-0.789)
<.430>
-0.06
(-7.358)
<.000>
1,001.98
(4.253)
<.000>
0.01
(7.873)
<.000>
(1.797)
<.072>
0.00
(0.180)
<.857>
303.43
(1.103)
<.270>
0.01
(6.746)
<.000>
(-3.197)
<.001>
0.01
(1.151)
<.250>
402.70
(1.033)
<.302>
0.00
(-0.350)
<.727>
(-5.540)
<.000>
-0.01
(-0.562)
<.574>
13.62
(0.054)
<.957>
0.00
(-1.544)
<.123>
(-5.606)
<.000>
-0.03
(-2.990)
<.003>
767.30
(4.529)
<.000>
0.00
(2.457)
<.014>
24
β13: Economic_Growth
β14: Loss_Prior_Year
β15: Abnor_Accruals
Number of observations
Adjusted R-Squared
a
0.33
(8.622)
<.000>
-0.11
(-39.190)
<.000>
0.00
(4.379)
<.000>
39,738
40.44%
0.83
(9.229)
<.000>
-0.13
(-20.595)
<.000>
0.00
(5.486)
<.000>
12,521
38.05%
0.07
(0.910)
<.363>
0.05
(3.654)
<.000>
5,771
40.29%
0.04
(0.387)
<.699>
0.01
(0.122)
<.000>
2,295
45.43%
-0.07
(-0.823)
<.411>
-0.06
(-3.119)
<.002>
0.01
(0.124)
<.902>
3,297
46.81%
0.12
(2.254)
<.024>
-0.12
(-30.995)
<.000>
0.00
(-8.027)
<.000>
15,854
45.74%
Log(SGAit/SGAit-1 ) = β0 + β1 * Log(Sales it/Sales it-1 ) + β2 *Decrease_Dummy* Log(Sales it/Sales it-1 )
+ β3 : Abnor_Accruals*Log(Sales it /Sales it-1 )
+ β4 : Successive_Decrease*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β5 : Employee_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β6 : Asset_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β7 : Economic_Growth*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β8 : Loss_Prior_Year*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β9 : Abnor_Accruals*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β10 : Successive_Decrease + β11 : Employee_Intensity
+ β12 : Asset_Intensity + β13 : Economic_Growth
+ β14 : Loss_Prior_Year + β15 : Abnor_Accruals + Ԑit
b
The distribution of the sample is based on Roychowdhury (2006). See Sample Selection for the explanation.
Asymmetry of SG&A costs (full sample with vs without loss making observations, basic model)
The full sample, used in the regressions before, includes loss making observations. Loss
making observations are likely to manage earnings using “big bath” accounting. This makes
it difficult to compare the cost behavior of loss making observations with that of other
observations. Table 5 presents the results of the basic model, equation (1), for the full
sample with loss making observations compared to the full sample without loss making
observations. The results of the basic model, equation (1), for the full sample with loss
making observations are discussed before, column I of Table 3.
Column II of Table 5 presents the regression summery statistics for the basic model,
equation (1), for the full sample without loss making observations of 27,217 firm-year
observations. The results show significant asymmetry of SG&A costs. Where β 1 is equal to
0.52 (t = 111.704) and β2 is equal to 0.09 (t = 6.691). This implies, an increase in sales
revenues of one percent results in an increase in SG&A costs of 0.52 percent, and a decrease
in sales revenues of one percent results in a decrease in SG&A costs of 0.61 percent.
25
TABLE 5
Summary Statistics from Regressions of the Basic Modela with Log(SGAit/SGAit-1) as the
Dependent Variable
Variable
β0 : Constant
β1: Log(Sales it /Sales it-1 )
β2: Decrease_Dummy*Log(Sales it /Sales it-1 )
Number of observations
Adjusted R-Squared
a
Full sample
Full sample
with loss making without loss making
Coefficient
Coefficient
(t-statistic)
(t-statistic)
<significance>
<significance>
0.05
0.04
(32.335)
(28.825)
<.000>
<.000>
0.45
0.52
(115.473)
(111.704)
<.000>
<.000>
0.11
0.09
(11.617)
(6.691)
<.000>
<.000>
39,738
27,217
36.09%
39.42%
Log(SGAit/SGAit-1 ) = β0 + β1 * Log(Sales it/Sales it-1 ) + β2 *Decrease_Dummy* Log(Sales it/Sales it-1 ) +Ԑit
Asymmetry of SG&A costs (full sample with vs without loss making observations, extended
model)
Table 6 presents the results of the extended model, equation (2), for the full sample with
loss making observations compared to the full sample without loss making observations. I
have also split the two samples in observations with abnormal accruals and without
abnormal accruals.
I expect the size of accrual-based earnings management is a
replacement for cost management for both increases and decreases in sales. Firms with
high abnormal accruals rely less on cost management to attain earnings targets. They will
adjust SG&A costs to a larger magnitude for an increase in sales, and a smaller magnitude for
a decrease. This implies that I expect to find a positive β3 and a negative β9.
I find significant asymmetrical SG&A cost behavior for the full sample with loss
making observations and accruals (β1 = 0.46, t = 115.003; β2 = -0.14, t = -9.088), the full
sample with loss making observations without accruals (β1 = 0.46, t = 116.451; β2 = -0.14, t =
26
-9.283), the full sample without loss making observations with accruals (β1 = 0.55, t =
113.916; β2 = -0.17, t = -8.134), and for the full sample without loss making observations and
accruals (β1 = 0.54, t = 115.678; β2 = -0.18, t = -8.347).
The results of Table 6 shows that controlling for abnormal accruals do not influence
the results to a large extent. There is no notable difference in the behavior of SG&A costs
between the samples when I control for abnormal accruals.
TABLE 6
Summary Statistics from Regressions of the Extended Modela with Log(SGAit/SGAit-1) as the
Dependent Variable
Variable
β0 : Constant
β1: Log(Sales it /Sales it-1 )
β2: Decrease_Dummy*Log(Sales it /Sales it-1 )
β3: Abnor_Accruals*Log(Sales it /Sales it-1 )
β4: Successive_Decrease*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β5: Employee_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β6: Asset_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β7: Economic_Growth*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β8: Loss_Prior_Year*Decrease_Dummy
*Log(Sales it /Sales it-1 )
Full sample
Full sample
Full sample
Full sample
with loss making with loss making without loss making without loss making
with accruals
without accruals
with accruals
without accruals
Coefficient
Coefficient
Coefficient
Coefficient
(t-statistic)
(t-statistic)
(t-statistic)
(t-statistic)
<significance>
<significance>
<significance>
<significance>
0.05
0.05
0.05
0.05
(18.464)
(18.371)
(16.600)
(16.700)
<.000>
<.000>
<.000>
<.000>
0.46
0.46
0.55
0.54
(115.003)
(116.451)
(113.916)
(115.678)
<.000>
<.000>
<.000>
<.000>
-0.14
-0.14
-0.17
-0.18
(-9.088)
(-9.283)
(-8.134)
(-8.347)
<.000>
<.000>
<.000>
<.000>
0.00
0.00
(4.403)
(10.438)
<.000>
<.000>
0.17
0.17
0.27
0.28
(9.417)
<.000>
1,362.89
(9.451)
<.000>
1,361.14
(10.308)
<.000>
2,909.74
(10.596)
<.000>
2,173.66
(3.103)
<.002>
0.00
(3.098)
<.002>
0.00
(3.235)
<.001>
0.00
(2.427)
<.015>
0.00
(2.207)
<.027>
1.18
(2.166)
<.030>
1.18
(-1.360)
<.174>
1.49
(-0.901)
<.368>
1.46
(6.123)
<.000>
0.08
(6.121)
<.000>
0.08
(5.020)
<.000>
0.03
(4.906)
<.000>
0.04
(4.819)
<.000>
(4.900)
<.000>
(1.300)
<.194>
(1.641)
<.101>
27
β9: Abnor_Accruals*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β10: Successive_Decrease
β11: Employee_Intensity
β12: Asset_Intensity
β13: Economic_Growth
β14: Loss_Prior_Year
β15: Abnor_Accruals
Number of observations
Adjusted R-Squared
a
-0.01
-
-0.33
-
(-1.460)
<.144>
-0.03
(-7.378)
<.000>
802.43
(6.901)
<.000>
0.01
(10.631)
<.000>
0.33
(8.622)
<.000>
-0.11
(-39.190)
<.000>
0.00
(4.379)
<.000>
39,738
40.44%
-0.03
(-7.271)
<.000>
801.23
(6.890)
<.000>
0.01
(10.540)
<.000>
0.33
(8.589)
<.000>
-0.11
(-39.235)
<.000>
39,738
40.41%
(-6.788)
<.000>
-0.02
(-3.091)
<.002>
602.69
(4.848)
<.000>
0.01
(5.087)
<.000>
0.10
(2.739)
<.006>
-0.13
(-37.106)
<.000>
-0.01
(-10.010)
<.000>
27,217
43.90%
-0.02
(-3.002)
<.003>
590.27
(4.735)
<.000>
0.01
(5.127)
<.000>
0.10
(2.741)
<.006>
-0.13
(-37.131)
<.000>
27,217
43.57%
Log(SGAit/SGAit-1 ) = β0 + β1 * Log(Sales it/Sales it-1 ) + β2 *Decrease_Dummy* Log(Sales it/Sales it-1 )
+ β3 : Abnor_Accruals*Log(Sales it /Sales it-1 )
+ β4 : Successive_Decrease*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β5 : Employee_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β6 : Asset_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β7 : Economic_Growth*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β8 : Loss_Prior_Year*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β9 : Abnor_Accruals*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β10 : Successive_Decrease + β11 : Employee_Intensity
+ β12 : Asset_Intensity + β13 : Economic_Growth
+ β14 : Loss_Prior_Year + β15 : Abnor_Accruals + Ԑit
Cost Asymmetry and Incentives to Meet and Beat the Earnings Target
My prediction is that suspect-increase observations exhibit more SG&A cost symmetry,
compared to the other subsamples. Specifically, I predict that suspect-increase observations
will exhibit a smaller SG&A cost increase following an activity increase and a larger SG&A
cost decrease following an activity decrease.
Table 7, column I and column II, presents findings for the full sample without loss
making observations and suspect-increase observations with large profit observations, based
on the extended model, equation (2), supplemented with the indicator variable,
28
Suspect_Increase.
The suspect-increase and large profit sample, containing 19,151
observations, exhibit symmetrical SG&A cost behavior (β1 = 0.51, t = 93.626; β2 = -0.05, t = 1.233). The full sample without loss making sample, 27,217 observations, shows significant
asymmetrical SG&A cost behavior (β1 = 0.53, t = 109.205; β2 = -0.15, t = -6.850).
Contrary to my predictions, I find a significantly positive coefficient on the two-way
interaction term (β3 = 0.35, t = 14.027) and significantly negative coefficient on the threeway interaction term (β5 = -0.49, t = -8.331). Compared to firms that are not in the suspectincrease observations, the SG&A costs increase of suspect-increase observations is 0.35
percent larger for an one percent increase in sales and the SG&A costs decrease of suspectincrease observations is 0.49 percent smaller for an one percent decrease in sales. I also find
a significantly positive coefficient on the two-way interaction term (β3 = 0.38, t = 14.197) and
significantly negative coefficient on the three-way interaction term (β5 = -0.59, t = -8.996). In
comparison to the large profit observations, the SG&A costs increase of suspect-increase
observations for a one percent increase in sales is 0.38 percent larger and the SG&A cost
decrease of suspect-increase observations for a one percent decrease in sales is 0.59 percent
smaller. These results are in contrast to my predictions, but in line with the other results.
TABLE 7
Summary Statistics from Regressions of the Extended Modela with Log(SGAit/SGAit-1) as the
Dependent Variable
Variable
β0 : Constant
β1: Log(Sales it /Sales it-1 )
β2: Decrease_Dummy*Log(Sales it /Sales it-1 )
Full sample
without
Suspect-increase
and Large profit
Loss making
Coefficient
(t-statistic)
<significance>
0.05
(18.169)
<.000>
0.53
(109.205)
<.000>
-0.15
(-6.850)
<.000>
Sample b
Coefficient
(t-statistic)
<significance>
0.06
(15.782)
<.000>
0.51
(93.626)
<.000>
-0.05
(-1.233)
<.217>
29
β3: Suspect_Increase*Log(Sales it /Sales it-1 )
β4: Abnor_Accruals*Log(Sales it /Sales it-1 )
β5: Suspect_Increase*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β6: Successive_Decrease*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β7: Employee_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β8: Asset_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β9: Economic_Growth*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β10: Loss_Prior_Year*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β11: Abnor_Accruals*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β12: Suspect_Increase
β13: Successive_Decrease
β14: Employee_Intensity
β15: Asset_Intensity
β16: Economic_Growth
β17: Loss_Prior_Year
β18: Abnor_Accruals
Number of observations
Adjusted R-Squared
a
0.35
(14.027)
<.000>
0.00
(10.138)
<.000>
-0.49
0.38
(14.197)
<.000>
0.00
(8.986)
<.000>
-0.59
(-8.331)
<.000>
0.27
(-8.996)
<.000>
0.24
(10.235)
<.000>
3,274.73
(6.542)
<.000>
1,367.62
(3.601)
<.000>
0.00
(1.159)
<.246>
0.00
(-1.561)
<.119>
1.46
(1.461)
<.144>
1.80
(4.929)
<.000>
0.03
(4.158)
<.000>
-0.03
(1.266)
<.206>
-0.33
(-0.812)
<.417>
-0.41
(-6.688)
<.000>
-0.06
(-12.423)
<.000>
-0.02
(-2.906)
<.004>
622.95
(5.030)
<.000>
0.00
(4.929)
<.000>
0.10
(2.706)
<.007>
-0.13
(-37.324)
<.000>
-0.01
(-9.651)
<.000>
27,217
44.34%
(-6.336)
<.000>
-0.06
(-11.898)
<.000>
-0.02
(-2.582)
<.010>
655.84
(4.460)
<.000>
0.00
(2.255)
<.024>
0.10
(2.089)
<.037>
-0.12
(-32.811)
<.000>
0.00
(-8.437)
<.000>
19,151
45.88%
Log(SGAit/SGAit-1 ) = β0 + β1 * Log(Sales it/Sales it-1 ) + β2 *Decrease_Dummy* Log(Sales it/Sales it-1 )
+ β3 : Suspect_Increase*Log(Sales it /Sales it-1 )
+ β4 : Abnor_Accruals*Log(Sales it /Sales it-1 )
+ β5 : Suspect_Increase*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β6 : Successive_Decrease*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β7 : Employee_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β8 : Asset_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β9 : Economic_Growth*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β10 : Loss_Prior_Year*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β11 : Abnor_Accruals*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β12 : Suspect_Increase + β13 : Successive_Decrease + β14 : Employee_Intensity
+ β15 : Asset_Intensity + β16 : Economic_Growth β17 : Loss_Prior_Year
+ β18 : Abnor_Accruals + Ԑit
b
The distribution of the sample is based on Roychowdhury (2006). See Sample Selection for the explanation.
30
Robustness Check
In this section I consider an additional test to determine the sensitivity of my findings
regarding to symmetry of SG&A costs. I will take the financial crisis in account, I have made
a dummy, Financial_Crisis, for the years 2008, 2009, and 2010, which equals one for firmyear observations that are in the years 2008, 2009, and 2010 and zero otherwise. I include a
three-way (Financial_Crisis*Decrease_Dummy*Log(Salesit/Salesit-1)) interaction term and a
main term (Financial_Crisis), which I add to the extended model, equation (2). With this test,
I want to show the influence of the financial crisis on symmetric SG&A cost behavior of the
subsamples: small profit, just non-meet, suspect-increase, and large profit.
Table 8 presents the results of the extended model, equation (2), of my full sample
and subsamples. These results are after controlling for determinants of cost asymmetry and
controlling for the financial crisis. The results shows significant asymmetrical SG&A cost
behavior for the small profit sample (β1 = 0.83, t = 45.908; β2 = -0.45, t = -9.400), and
symmetrical SG&A cost behavior for the full sample (β1 = 0.46, t = 115.007; β2 = -0.05, t = 2.661), loss making sample (β1 = 0.37, t = 51.891; β2 = 0.04, t = 1.211), just non-meet sample
(β1 = 0.79, t = 32.748; β2 = -0.04, t = -0.389), suspect-increase sample (β1 = 0.88, t = 47.390;
β2 = -0.30, t = -2.376), and large profit sample (β1 = 0.51, t = 88.667; β2 = 0.13, t = 2.446).
The main results shows symmetrical SG&A cost behavior for the loss making and
large profit sample. The results of Table 8 shows also symmetrical SG&A cost behavior for
these two samples. For the full sample, just non-meet sample, and suspect-increase sample,
there is also symmetrical SG&A cost behavior when the financial crisis is taken in account.
31
TABLE 8
Summary Statistics from Regressions of the Extended Modela with Log(SGAit/SGAit-1) as the
Dependent Variable
Variable
β0 : Constant
β1: Log(Sales it /Sales it-1 )
β2: Decrease_Dummy*Log(Sales it /Sales it-1 )
β3: Abnor_Accruals*Log(Sales it /Sales it-1 )
β4: Financial_Crisis*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β5: Successive_Decrease*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β6: Employee_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β7: Asset_Intensity*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β8: Economic_Growth*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β9: Loss_Prior_Year*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β10: Abnor_Accruals*Decrease_Dummy
*Log(Sales it /Sales it-1 )
β11: Financial_Crisis
β12: Successive_Decrease
β13: Employee_Intensity
β14: Asset_Intensity
Full sample
Loss making
Small profit
Sample
Sample b
Just non-meet Suspect-increase
Sample b
Coefficient
Coefficient
Coefficient
Coefficient
(t-statistic)
(t-statistic)
(t-statistic)
(t-statistic)
<significance> <significance> <significance> <significance>
0.06
0.09
0.04
0.03
(18.840)
(10.642)
(5.178)
(3.737)
<.000>
<.000>
<.000>
<.000>
0.46
0.37
0.83
0.79
(115.007)
(51.891)
(45.908)
(32.748)
<.000>
<.000>
<.000>
<.000>
-0.05
0.04
-0.45
-0.04
(-2.661)
(1.211)
(-9.400)
(-0.389)
<.008>
<.226>
<.000>
<.697>
0.00
0.00
-0.11
-0.28
(4.393)
(5.506)
(-6.342)
(-1.108)
<.000>
<.000>
<.000>
<.268>
-0.16
-0.15
-0.18
-0.28
Large profit
Sample b
Sample b
Coefficient
(t-statistic)
<significance>
0.02
(3.123)
<.002>
0.88
(47.390)
<.000>
-0.30
(-2.376)
<.018>
0.64
(3.845)
<.000>
-0.10
Coefficient
(t-statistic)
<significance>
0.07
(14.084)
<.000>
0.51
(88.667)
<.000>
0.13
(2.446)
<.014>
0.00
(8.604)
<.000>
-0.44
(-7.076)
<.000>
0.17
(-4.166)
<.000>
0.08
(-3.217)
<.001>
0.36
(-2.954)
<.003>
0.20
(-0.638)
<.524>
-0.06
(-7.612)
<.000>
0.21
(9.606)
<.000>
1,195.56
(2.870)
<.004>
1,576.38
(8.368)
<.000>
784.77
(2.307)
<.021>
11,075.07
(-0.544)
<.587>
11,966.31
(5.069)
<.000>
-1,416.25
(2.720)
<.007>
0.01
(2.450)
<.014>
0.00
(0.380)
<.704>
0.00
(2.137)
<.033>
-0.01
(4.362)
<.000>
-0.02
(-0.985)
<.325>
0.02
(3.028)
<.002>
-0.05
(0.532)
<.595>
0.48
(0.469)
<.639>
-1.22
(-6.599)
<.000>
-1.51
(-3.688)
<.000>
-1.66
(5.456)
<.000>
-1.13
(-0.203)
<.839>
0.08
(1.150)
<.250>
0.10
(-1.839)
<.066>
-
(-1.312)
<.190>
-
(-1.084)
<.278>
-0.41
(-1.747)
<.081>
0.00
(4.827)
<.000>
-0.01
(3.651)
<.000>
-0.01
0.15
-1.98
(-3.432)
<.001>
-3.20
(0.046)
<.964>
-0.41
(-1.347)
<.178>
-0.02
(-6.421)
<.000>
-0.03
(-7.405)
<.000>
767.59
(6.599)
<.000>
0.01
(10.912)
<.000>
(-0.681)
<.496>
-0.02
(-2.734)
<.006>
-0.06
(-7.432)
<.000>
971.04
(4.120)
<.000>
0.01
(7.977)
<.000>
(1.725)
<.085>
-0.03
(-3.834)
<.000>
0.00
(0.415)
<.678>
262.81
(0.956)
<.339>
0.01
(6.972)
<.000>
(-3.251)
<.001>
-0.02
(-2.260)
<.024>
0.02
(1.344)
<.179>
296.86
(0.761)
<.447>
0.00
(-0.256)
<.798>
(-5.437)
<.000>
-0.02
(-2.354)
<.019>
-0.01
(-0.482)
<.630>
-21.52
(-0.085)
<.932>
0.00
(-1.386)
<.166>
(-5.967)
<.000>
-0.02
(-5.040)
<.000>
-0.03
(-3.579)
<.000>
730.49
(4.316)
<.000>
0.00
(2.768)
<.006>
32
β15: Economic_Growth
0.19
(4.206)
<.000>
-0.11
(-38.977)
<.000>
0.00
(4.370)
<.000>
39,738
40.54%
β16: Loss_Prior_Year
β17: Abnor_Accruals
Number of observations
Adjusted R-Squared
a
0.69
(6.589)
<.000>
-0.13
(-20.530)
<.000>
0.00
(5.489)
<.000>
12,521
38.13%
-0.11
(-1.185)
<.236>
0.05
(3.695)
<.000>
5,771
40.46%
-0.09
(-0.860)
<.390>
0.01
(0.080)
<.936>
2,295
45.65%
-0.17
(-1.889)
<.059>
-0.06
(-3.116)
<.002>
0.01
(0.247)
<.805>
3,297
46.87%
-0.02
(-0.390)
<.696>
-0.12
(-30.854)
<.000>
0.00
(-8.078)
<.000>
15,854
45.98%
Log(SGAit/SGAit-1 ) = β0 + β1 * Log(Sales it/Sales it-1 ) + β2 *Decrease_Dummy* Log(Sales it/Sales it-1 )
+ β3 : Abnor_Accruals*Log(Sales it /Sales it-1 )
+ β4 : Financial_Crisis*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β5 : Successive_Decrease*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β6 : Employee_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β7 : Asset_Intensity*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β8 : Economic_Growth*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β9 : Loss_Prior_Year*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β10 : Abnor_Accruals*Decrease_Dummy*Log(Sales it /Sales it-1 )
+ β11 : Financial_Crisis + β12 : Successive_Decrease + β13 : Employee_Intensity
+ β14 : Asset_Intensity + β15 : Economic_Growth β16 : Loss_Prior_Year
+ β17 : Abnor_Accruals + Ԑit
b
The distribution of the sample is based on Roychowdhury (2006). See Sample Selection for the explanation.
V. CONCLUSION
In this study I examine whether SG&A costs of US-listed firms behave more symmetric when
they meet or beat last year earnings. My results clearly demonstrate that firms that meet or
beat the earnings of prior year exhibit significant asymmetrical SG&A cost behavior, this is in
contrast with my hypothesis.
Meet and beat observations along with large profit
observations show, compared to the full sample without loss making observations, more
symmetrical SG&A cost behavior. Controlling for the financial crisis shows symmetrical
SG&A cost behavior when firms meet or beat their earnings of prior year.
Separate regressions for the suspect-increase sample do not show more symmetrical
SG&A cost behavior than the just non-meet observations. Compared to other firms, firms
that meet or beat the earnings of prior year have a larger increase in SG&A costs for an
increase in sales, and a smaller decrease in SG&A costs for a decrease in sales. These results
are in contrast with my predictions.
33
Comparing the full sample, with loss making observations, with the full sample,
without loss making observations, and controlling for abnormal accruals, do not show
notable differences in the symmetrical behavior of SG&A costs.
The observed results do not meet my prediction, which was that meet and beat firms
exhibit more symmetric SG&A cost behavior. A possible reason for this discrepancy is the
importance of the earnings of prior year as earnings target. The importance of the prior year
earnings as target differ between the firms. The importance of the target is driven by other
variables than the control variables I used in this study. This results in asymmetrical SG&A
cost behavior for firms that meet or beat the earnings of prior year.
This study complements the asymmetric cost literature in two ways. First, I present
evidence that there is symmetrical SG&A cost behavior for US-listed firms which beat the
earnings of prior year. Second, I add to the accounting literature that there is a difference in
symmetrical behavior of SG&A costs for US-listed firms between small profit, just non-meet,
suspect-increase, and large profit observations.
This study is subject to some limitations. First, in this study, my focus is on SG&A
costs, it is difficult to generalize the results of these costs to other types of costs. Second, the
distribution of my sample based on Roychowdhury (2006) can divide firm-year observations
in different groups, where it does not belong. This can be interpreted like a random error.
A recommendation for future studies is an equivalent study with US-listed firms for
the zero earnings benchmark and analysts’ forecast. And subsequently, compare the results
of the different earnings targets: earnings prior year, zero earnings benchmark, and analysts’
forecast, for each subsample separately.
34
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