Are Selling, General, and Administrative Costs “Sticky”?
Mark C. Anderson
Rajiv D. Banker*
Surya Janakiraman
School of Management
The University of Texas at Dallas
October 24, 2000
*Corresponding author.
Rajiv D. Banker
School of Management, JO 43
The University of Texas at Dallas
Richardson, TX 75083-0688
Tel: (972) 883-4185; fax: (972) 883-6811
E-mail address: rbanker@utdallas.edu
Helpful comments from seminar participants at the University of Arizona, the University of
Southern California, the University of Texas at Austin, the University of Texas at Dallas,
and the annual conferences of the European Accounting Association in Munich (2000) and
the American Accounting Association in Philadelphia (2000) are gratefully acknowledged.
Are Selling, General, and Administrative Costs “Sticky”?
Abstract
A fundamental assumption in cost accounting is that the magnitude of a change in
costs is the same for an equivalent magnitude of either an increase or a decrease in activity
volume. In this study, we investigate whether costs are “sticky” – increase more with an
activity increase than they decrease with an equivalent activity decrease. We find, for
7,629 firms over 20 years, that SG&A costs increase on average at a rate of 0.55% per 1%
increase in sales but decrease only 0.35% per 1% decrease in sales. We describe a model
of cost behavior in which sticky costs may occur because managers deliberately adjust the
resources committed to an activity.
1. Introduction
Understanding cost behavior is essential to cost and management accounting.1 In
the classical model of cost behavior that is pervasive in the accounting literature, costs are
described as fixed or variable with respect to changes in activity volume. In this traditional
model, variable costs change proportionately with changes in the activity driver (Noreen
[1991]). This implies that a change in costs depends only on the extent of a change in the
level of activity, but is not influenced by the direction of the change. However, folklore
has it that costs rise more with increases in activity than they fall with decreases in activity
(Cooper and Kaplan [1998, p. 247], Noreen and Soderstrom [1997]). We label this type of
cost behavior “sticky” cost behavior. Specifically, costs are “sticky” if the increase in costs
associated with an increase in the activity driver is greater than the decrease in costs
associated with an equivalent decrease in the driver.
Archival research has provided very little evidence about the behavior of activity
costs in relation to changes in activity levels.2 One reason for this paucity of research may
be the lack of availability of broad-based data that include the costs and relevant drivers.
An exception to this data insufficiency is the availability of data on selling, general and
administrative (SG&A) costs and sales revenue. Information about SG&A costs and sales
revenue is available for a broad set of firms in the Compustat database. The behavior of
SG&A costs can be studied in relation to revenue activity because many of the components
1
See, for example, chapter 4 of Atkinson, Banker, Kaplan and Young [1997], chapter 5 of Garrison
and Noreen [1997], chapter 7 of Hilton [1997], chapter 10 of Horngren, Foster and Datar [1997],
chapter 2 of Horngren, Sundem, and Stratton [1996], chapter 1 of Kaplan and Atkinson [1998]),
and chapter 2 of Zimmerman [1997].
2
To the best of our knowledge, the only exceptions in the accounting literature are the study of
airline costs by Banker and Johnston [1993] and the recent studies of hospital costs by Noreen and
Soderstrom [1994, 1997] and Balakrishnan, Petersen and Soderstrom [1999].
1
of SG&A are driven by sales revenue (Cooper and Kaplan [1998, p. 341]). In its annual
SG&A survey (prepared jointly with Arthur Andersen LLP), CFO Magazine treats sales
revenue as the primary driver of SG&A costs by performing extensive analyses of SG&A
costs in relation to sales revenue (Mintz [1999]). Studying the behavior of SG&A costs is
important because SG&A costs make up about 26.4% of sales revenue for our broad-based
sample.
In our analysis, we refer to activity costs that adjust mechanistically to changes in
activity levels as “engineered” costs and activity costs that change only as a result of
adjustments initiated by managers as “committed” costs.3 Since the actual activity level
cannot exceed the capacity accommodated by the resources committed to an activity, an
increase in demand puts immediate and direct pressure on managers to increase committed
costs. But a decrease in the demand for an activity does not put similar pressure on
managers to decrease committed costs. Sticky costs occur if committed resources are not
reduced to the minimum level necessary to support a reduced activity demand. While the
premise of sticky cost behavior is intuitively striking, no empirical study supports it, and
received wisdom in accounting does not reflect it.
We test for sticky cost behavior by estimating an empirical model that relates
changes in SG&A costs to contemporaneous changes in net sales revenue for a 20 year
panel of data for 7,629 firms. The model includes an interaction dummy variable that
distinguishes between revenue decreasing and revenue increasing periods. We document
that the percentage increase in SG&A costs for an increase in sales revenue is larger than
the percentage decrease in SG&A costs for an equivalent decrease in sales revenue. For
3
Engineered costs are clearly variable costs whereas committed costs may be fixed or variable depending on
managers’ flexibility in adjusting the committed resources provided.
2
our full sample (all firms in the Compustat database for the twenty-year period from 1979
to 1998), SG&A costs increase at a rate of 0.55% per 1% increase in revenue but fall at a
rate of only 0.35% per 1% decrease in revenue.
This basic result is robust to a number of alternative specifications. We include the
value of fixed assets and the number of employees as additional drivers of SG&A costs and
find sticky cost behavior with respect to both of these drivers. We acknowledge that there
may be simultaneous relations between revenue and some types of SG&A costs and
estimate a system of equations that includes both revenue and SG&A costs as endogenous
variables. We find that the main results are not diminished. Since there may be delays in
adjusting costs for changes in activity, we estimate a specification of the model that
includes lagged revenue change and lagged interaction variables for revenue decreasing
periods. Interestingly, the results of this estimation indicate that stickiness partially
reverses over time.
If the committed resources are greater than the minimum level necessary to support
activity demand, the firm bears the cost of slack resources. Managers may permit slack
resource costs in order to avoid economic consequences of reducing and restoring
committed resources, such as severance pay to discharged employees and the costs of
hiring and training employees if demand reverts to higher levels. Managers may also
permit slack resource costs to avoid personal consequences of retrenchment, such as loss of
status or the anguish of dismissing familiar employees.
Expected slack resource costs increase with the permanence of a decline in activity
demand. Managers evaluating the economic consequences would reduce committed
resources more readily when the probability that a decline in activity demand is transitory
3
is lower (probability that the decline is permanent is higher). A decline in activity demand
is less likely to be transitory in periods of economic contraction than in periods of
economic growth. The probability that a decline is transitory is also lower when the
decline occurs in a second consecutive period or when the decline is sustained over a
longer aggregation of periods. Empirical tests based on these observations provide
evidence consistent with sticky cost behavior resulting from deliberate decision-making by
managers who weigh the economic consequences of their actions.
The remainder of this paper is organized as follows. In section 2, the sticky cost
hypothesis and basic empirical model are developed and specified in terms of SG&A costs
and sales revenue. In section 3, the data and sample are described, the basic model is
estimated, and a number of variations of the basic model are described and estimated. In
section 4, a set of empirical hypotheses consistent with managers making deliberate
economic decisions that result in sticky cost behavior is developed and tested. In section 5,
implications of our findings for cost and other accounting research are discussed.
2. Sticky Cost Behavior
The costs associated with a particular activity driver may be described as either
engineered or committed with respect to the activity driver (Cooper and Kaplan [1992],
Banker and Hughes [1994], and Kaplan [1994]).4 Three key assumptions characterize the
relationship between engineered costs and an activity driver. First, engineered costs equal
price times the engineered resources provided. Second, the engineered resources provided
4
Previous descriptions have used the term flexible or metered costs instead of engineered costs.
The term engineered costs appropriately connotes a tightly coupled direct relationship between the
costs and the activity driver.
4
equal the engineered resources demanded. Third, the engineered resources demanded are
linearly related to the activity driver. Taken together, these three assumptions imply that
engineered costs vary proportionately with changes in the activity driver. By their nature,
engineered costs cannot exhibit sticky behavior, which is characterized by asymmetric
responses to activity driver increases and decreases.
The key distinction between engineered costs and committed costs concerns the
second assumption. Committed resources provided are not necessarily equal to committed
resources demanded. While engineered resources adjust fully in response to activity
demand, managers must determine the level of committed resources provided. Since
managers select the level of committed resources provided, there need not be a proportional
relation between the committed resources provided and the level of activity (although there
may exist a proportional relation between the committed resources demanded and the level
of activity). If the committed resources demanded exceed the committed resources
provided, the available activity resources will be strained (Banker and Hughes [1994]).
This strain puts immediate and direct pressure on managers to increase the committed
resources. If, on the other hand, the committed resources demanded are less than the
committed resources provided, there will be slack. Slack does not put the same type of
pressure on managers to reduce committed resources as strain does to increase committed
resources.5
5
“For committed resources to become variable in the downward direction – after the demand for
the supplied resources has decreased and created unused capacity – the organization must manage
the unused capacity of these resources out of the system… Only at that time will the costs of
resources supplied start to decrease. Thus, what makes a resource cost “variable” in a downward
direction is not inherent in the nature of the resource; it is a function of management decisions”
(Cooper and Kaplan [1998, p. 247]).
5
2.1 Stickiness of SG&A Costs
In this study, we apply these concepts to SG&A costs and revenue activity. An
example of an engineered cost in this context is a sales commission that is equal to a
percentage of the selling price. Sales determine both the amount of the resource demanded
(earned sales commissions) and the amount actually provided. An example of a committed
cost is salaries paid to sales support staff. Mangers determine the amount of committed
sales support resource provided to support the revenue activity. If revenue activity rises to
the point where demand for the sales support resource is greater than the committed
resource, the resource will be strained. Managers will feel pressure to increase the
committed resource to meet customer needs. In the short term, managers may stretch
existing resources by requiring employees to work overtime. In the longer run, managers
may reduce the strain by adding more sales support personnel. If, on the other hand,
revenue activity falls to the point where the demand for sales support is less than the
committed resource, some sales support personnel will be expendable. But, the slack will
not be removed unless managers recognize that slack exists and take steps to reduce or
redeploy the staff not required to support current sales.
Thus, an increase in activity can only take place if committed resources are strained
or additional resources are provided, but a decrease in activity can occur without a
corresponding adjustment to the committed resources. Our objective is to test for sticky
cost behavior by comparing the variation of SG&A costs with sales revenue in periods
when revenue increases with the variation of SG&A costs with sales revenue in periods
when revenue decreases. Our sticky cost hypothesis is formally stated as follows.
6
Hypothesis 1: The proportionate increase in SG&A costs for an increase in sales revenue
is greater than the proportionate decrease in SG&A costs for an equivalent decrease in sales
revenue.
2.2 Empirical Model of SG&A Cost Behavior
An empirical model that enables measurement of the SG&A response to
contemporaneous changes in sales revenue and discriminates between periods when
revenue increases and revenue decreases is presented below. The interaction variable,
Decrease_Dummy, takes the value of one when sales revenue decreases between periods t1 and t and zero otherwise.
Model (I):
 Re venuei,t 
 Re venuei,t 
 SG & A i,t 
log
 + ε i ,t
 + β 2 * Decrease_ Dummyi ,t * log
 = β0 + β1 log
 Revenuei,t -1 
 Revenuei,t -1 
 SG & A i,t -1 
This model provides the basis for our test of stickiness of SG&A costs.6 Since the
estimation is cross-sectional with a wide variety of industries and large variation in sizes of
firms, the ratio form and log specification improves the comparability of the variables
across firms and alleviates potential heteroskedasticity. Empirically, the Davidson and
MacKinnon [1981] test rejects the linear form in favor of this loglinear model.7
Specification of the equation in this manner also accommodates economic interpretation of
the estimated coefficients. Since the value of Decrease_Dummy is zero when revenue
increases, the coefficient β1 measures the percentage increase in SG&A costs with a one
6
If the traditional fixed and variable cost model is valid, upward and downward changes in costs
will be equal and consequently β 2 = 0. Further, if fixed costs are present, then β 1 < 1, signifying
economies of scale.
7
percent increase in sales revenue. Since the value of Decrease_Dummy is one when
revenue decreases, the sum of the coefficients, β1 + β2 measures the percentage increase in
SG&A costs with a one percent decrease in sales revenue. If SG&A costs are sticky, then
the variation of SG&A costs with revenue increases should be greater than the variation for
revenue decreases. Thus, the empirical hypothesis for stickiness, conditional on β1 > 0, is
β2 < 0.8
3. Empirical Tests of the Sticky Cost Hypothesis
The primary variables used in our analysis are SG&A costs (annual Compustat
#189) and net sales revenue (annual Compustat #12). Since these numbers are obtained
from the financial statements, they are accrual-based measurements of SG&A costs and
revenues. The data set includes annual data for industrial firms covering the twenty years
from 1979 to 1998. The data are initially screened for missing observations of either
SG&A costs or sales revenue in the current or preceding year.9 The total number of
observations is 64,663 for 7,629 firms, an average of about 8.5 observations per firm.
Panel A of table 1 provides descriptive information about annual revenues and
SG&A costs for the complete twenty year sample. The mean value of SG&A costs as a
percentage of sales revenue is 26.41% (standard deviation = 17.79%, median = 22.62%),
indicating the importance of SG&A costs. Panel B of table 1 provides information about
7
Results are qualitatively similar for all of the models when they are estimated with a linear
specification.
8
Noreen and Soderstrom [1997] specify a similar model and conduct a test for asymmetric cost
behavior with respect to activity increases and decreases. Using data for hospital overhead costs,
their results provided weak evidence of asymmetric behavior (negative signs on their interaction
term for activity decreases for 12 out of 16 accounts, but overall not significantly different from
zero).
9
Observations where SG&A costs exceeded sales revenue were also deleted.
8
the frequency of firm-periods when revenue fell (relative to the previous period) and firmperiods when SG&A costs fell. Revenue fell in 27.01% of the annual firm-periods in the
sample and SG&A costs fell in 24.98% of the firm-periods. The mean value of revenue
decreases was 17.45% (standard deviation = 18.64%, median value = 10.99%) and the
mean value of decreases in SG&A costs was 15.67% (standard deviation = 16.40%,
median value = 10.07%).
3.1 Basic Results
The model was estimated using ordinary least squares (OLS). A simple screening
rule was used to eliminate extreme observations from the estimation. Observations with
values of any variable in the top or bottom 0.5% of its distribution were trimmed from the
sample (Chen and Dixon [1972]), resulting in a reduction of 705 observations to 63,958
observations. The OLS significance levels of the coefficients were compared with
significance levels obtained using White’s [1980] heteroskedasticity-corrected statistics
and were found to be nearly identical. The Durbin-Watson [1951] test statistic revealed
significant (at the 5% level) positive autocorrelation for less than 3% of the firms,
indicating that it is not necessary to correct for serial correlation in the data. The Belsley,
Kuh, and Welsch (1980) diagnostic was used to test for multicollinearity. Mulicollinerity
is indicated when the condition index exceeds ten. In no case did the condition index
exceed five.
Table 2 presents the results of estimating model (I) for the pooled sample. The
estimated value of β̂1 of 0.5459 (t-statistic = 164.11) indicates that SG&A costs increase
0.55% per 1% increase in sales revenues. The estimated value of β̂ 2 = -0.1914 (t-statistic
9
= -26.14) provides strong support for the sticky cost hypothesis. The combined value of β̂1
+ β̂ 2 = 0.3545 indicates that SG&A costs decrease only 0.35% per 1% decrease in sales
revenue. The fact that β̂1 and β̂1 + β̂ 2 are both significantly less than one (p-values =
0.001) indicates that SG&A costs are not proportional to changes in revenue, even though
this cost driver is apparently very strong.10 For comparative purposes, we also estimated a
model without the interaction variable for revenue decreasing periods. The coefficient on
β̂1 when this limited model is estimated is 0.4909, representing the variation of SG&A
costs with revenue changes that would be measured if no allowance were made for
asymmetry in the change in costs with revenue increases and revenue decreases.
Since we are using a large panel of data from different industries and over different
time periods, we also estimate a fixed effects model (Greene [1997, pp. 621-623]). In the
“fixed effects estimation” column of table 2, we provide the results from estimating the
model with separate dummy variables for 30 industry groups (as defined in Baber,
Janakiraman and Kang [1996]) and 19 years.11 The estimation results are very similar to
those reported for the pooled estimation with β̂1 = 0.5382 (t-statistic = 158.73), β̂ 2 = 0.1906 (t-statistic = -25.51), and β̂1 + β̂ 2 = 0.3476.
We performed a variety of robustness tests of the Model (I) estimations (not
reported). We estimated the model separately by industry group and by year. The zstatistic for the estimated coefficient of the stickiness variable, β̂ 2 , was -20.31 for an
10
Noreen and Soderstrom [1994] find that overhead costs at hospitals in Washington State were not
proportional to activity. In a related study, Noreen and Soderstrom [1997] find that the average
variation of overhead was about 20% of the variation in the activity driver. They suggest that the
low percentage may reflect maintenance of specific capacities by hospitals.
10
aggregation of 30 industry groups and -15.41 for an aggregation of 19 annual time periods,
providing strong support for the sticky cost hypothesis across industries and over time. To
provide assurance that the results were not systematically affected by inflation, we
converted all SG&A and revenue amounts to equivalent 1984 dollars and reestimated
model (I) with the inflation-adjusted amounts. The results, β̂1 = 0.5466 (t-statistic =
160.92) and β̂ 2 = -0.1721 (t-statistic = -24.18), were very similar to those reported above.
One of the components of SG&A expense is foreign currency translation adjustments
(annual Compustat #150). Since these adjustments introduce noise into the measure of
SG&A, we removed them from the SG&A data and estimated model (I) again. Results of
this estimation, β̂1 = 0.5983 (t-statistic = 85.81) and β̂ 2 = -0.2077 (t-statistic = -13.84),
were also very similar to those reported for the initial estimation.
3.2 Multiple Cost Drivers
There are other drivers of SG&A costs in addition to sales revenue activity. The
G&A components of SG&A costs are likely to vary also with the amount of assets
governed by the firm and with the number of employees whose efforts are coordinated by
the firm. Table 3 presents results of estimating model (I) with additional terms for the
value of total assets (annual Compustat #6) and the number of employees (Compustat #29).
The estimation results confirm that both of these variables are drivers of SG&A costs, with
significantly positive coefficients β̂ 3 of 0.1504 (t-statistic = 35.05) for total assets and β̂ 5
of 0.2026 (t-statistic = 38.11) for the number of employees. Sticky cost behavior with
11
We also estimate a fixed effects model that includes dummy variables for individual firms and
years for a sample of firms that have at least 15 valid observations and at least one revenue decline.
The results are similar to those presented.
11
respect to these two additional drivers is indicated by the significantly negative coefficients
β̂ 4 of -0.0504 (t-statistic = -5.05) and β̂ 6 of -0.0587 (t-statistic = -6.21). Sales revenue
remains a significant driver of SG&A costs in this model ( β̂1 = 0.3769, t-statistic = 86.41)
with significant sticky behavior ( β̂ 2 = -0.1050, t-statistic = -11.67).
3.3 Lagged Adjustments
Unlike engineered resources, committed resources are not tied directly to the
activity driver. Managers must intervene to adjust the level of committed resources,
implying that there may be a delay between the change in the activity driver and the
consequent decision to change the committed resource. There may also be a delay between
the decision and the realization of the change because it takes time to effect changes to
committed resources. For example, it takes time to search for and hire new employees and
it may take time to dismiss employees.
The forces that engender stickiness may cause adjustments to committed costs for
decreases in the activity driver to be delayed more than adjustments to committed costs for
increases in the activity driver. Noreen and Soderstrom [1997] suggested that the pressures
to increase committed resources might be resisted for some time, but that eventually the
strain must be addressed. Salaried employees, for example, may be pushed to handle
greater workloads and work overtime for some period of time, but they will weary of the
extra demands. While slack, on the other hand, does not create immediate strain on
committed resources, it will be reflected in periodic measures of accounting performance.
The more immediate and continuous pressure associated with strain may cause managers to
12
address strain more quickly than they address slack. We extend model (I) by including
terms for one-period lagged changes to sales revenue.
Model (II):
 Re venuei, t 
 SG & A i, t 
 Re venuei, t 
log
+
 + β 2 Decrease _ Dummyi ,t * log
 = β 0 + β1 log
 Revenuei, t -1 
 SG & A i, t -1 
 Revenuei, t -1 
 Re venuei, t -1 
 Re venuei, t -1 
β 3 log
 + β 4 Decrease _ Dummyi ,t −1 * log
 + ε i ,t
 Revenuei, t - 2 
 Revenuei, t - 2 
Results of estimating this empirical specification are presented in table 4. The
significantly positive coefficient β̂1 of 0.5328 (t-statistic = 130.43) is very similar to its
counterpart in the model (I) estimation (table 2), as is the significantly negative coefficient
β̂ 2 of –0.1876 (t-statistic = -23.47), indicating contemporaneous stickiness. The
significantly positive coefficient β̂ 3 of 0.1038 (t-statistic = 29.79) indicates a lagged
adjustment to SG&A for changes in sales revenue. More interesting, the coefficient β̂ 4 is
also a significantly positive 0.1042 (t-statistic = 13.23), indicating a partial reversal of
stickiness in the period subsequent to a revenue decline ( β̂ 4 < βˆ 2 , t-statistic = 9.09).
These results suggest that stickiness of SG&A costs has a temporal quality, that managers
make upward adjustments to SG&A resources for increases in revenue activity more
quickly than they make downward adjustments for decreases in revenue activity.
3.4 Simultaneous Relations
SG&A costs, as tabulated in the Compustat data, comprise a number of elements
that correspond to a variety of expenditures. These elements include, among other things,
the costs of accounting, advertising, bad debts, foreign currency adjustments, labor and
13
related items not included in cost of goods sold, and marketing. From a cost perspective,
sales revenue activity is a primary driver of SG&A costs. Some SG&A costs, such as sales
commission costs, are engineered costs that move mechanistically with sales revenue.
Other SG&A costs, such as customer service costs, are committed costs that are determined
by managers who assess the anticipated demand for the committed resources. A third
category of costs, “discretionary” costs, are not engineered or committed with respect to
revenue activity.12 This category includes items such as advertising costs that may, in fact,
influence the amount of revenue activity.
To the extent that there are two-way economic relations between SG&A costs and
sales revenue, coefficients in the model (I) or model (II) estimations may be biased. To
address this issue, we estimate a simultaneous system of equations, described below as
model (III), that includes changes in SG&A costs and revenues as endogenous variables.
Model (III):
 Revenuei, t 
 Re venuei, t 
 SGAi, t 
EQUATION 1 : log
+
 + α 2 Decrease _ Dummyi,t * log
 = α 0 + α1 log
 Revenuei, t −1 
 Re venuei, t −1 
 SGAi, t -1 
 Revenuei, t −1 
 Revenuei, t −1 
α3 log
+
 + α 4 Decrease_Dummyi,t −1 * log
 Revenuei, t − 2 
 Revenuei, t − 2 
 Employeesi, t 
 Assetsi, t 
α5 log
 + ϑi, t
 + α6 log
 Employeesi, t −1 
 Assetsi, t −1 
 SGAi,t 
 SGAi,t −1 
 Revenuei,t 
EQUATION 2 : log
 = γ 0 + γ 1 log
 + γ 2 log
 + ζ i,t +1
 Revenuei,t −1 
 SGAi,t −1 
 SGAi,t −2 
The first equation in the system of equations is the model (II) specification extended to
include the additional drivers, value of total assets and number of employees, which also
12
The costs of engineered and committed resources are incurred in order to fill the current period demand.
Discretionary costs are not needed to fill existing demand either in the current period or in future periods, but
rather are incurred to generate current or future demand.
14
serve as instruments for the endogenous variables in the simultaneous estimation. The
second equation relates changes in revenue to changes in SG&A costs in the current and
previous years. The first equation is over-identified and the second equation is exactly
identified, implying that there are no identification problems in estimating the system of
equations (Judge et al. [1985, pp. 576-577]).
Results of estimating the system of equations using two-stage least squares are
presented in table 5. The fact that the coefficients on the endogenous variables, α̂1 =
0.4671 (t-statistic = 11.49) and γ̂ 1 = 1.5285 (t-statistic = 131.53), are significantly positive
in the equations that they appear as independent variables is consistent with two-way
relations between SG&A costs and revenues. Nevertheless, the coefficient α̂ 2 = -0.2207
(t-statistic = -4.93) is significantly negative as predicted by the sticky cost hypothesis.
Also, the coefficients on the lagged revenue terms, α̂ 3 = 0.0602 (t-statistic = 6.09) and
α̂ 4 = 0.0839 (t-statistic = 3.54) are both significantly positive, consistent with a delayed
response to revenue changes and partial reversal of stickiness over time.
Table 6 presents results of estimating an alternative model where SG&A costs are
divided into advertising and non-advertising costs. This model is estimated for the subset
of firms that separately report advertising costs (annual Computstat item #45). For these
firms, advertising costs average about 12% of SG&A costs and about 4% of sales revenue.
In the first equation of the model, non-advertising SG&A costs are related to current and
lagged changes in revenue and, in the second equation, the current change in revenue is
related to current and lagged advertising costs. Advertising, in this model, is regarded as a
discretionary revenue-driving cost. This is a simultaneous system of equations with a
special triangular structure. It is a recursive system of equations but the error terms of the
15
two equations are likely to be correlated. Therefore, consistent and efficient estimators are
obtained by estimating the equations as seemingly unrelated regressions (Greene [1997, p.
737], Lahiri and Schmidt [1978], Zellner [1962]).
The coefficients on the revenue change and lagged revenue change terms in
equation 1, α̂1 = 0.6298 (t-statistic = 68.77) and α̂ 3 = 0.1158 (t-statistic = 15.55), are
significantly positive, consistent with non-advertising SG&A costs being influenced by
both current and lagged revenues. The coefficient on the contemporaneous sticky cost
term, α̂ 2 = -0.1232 (t-statistic = -6.56), is significantly negative, and the coefficient on the
lagged term, α̂ 4 = 0.1142 (t-statistic = 6.11), is significantly positive, consistent with
stickiness and partial reversal of stickiness over time. The significantly positive
coefficients on the contemporaneous and lagged advertising change terms in equation 2,
γ̂ 1 = 0.2214 (t-statistic = 51.69) and γ̂ 2 = 0.1007 (t-statistic = 24.05) are consistent with
advertising positively influencing revenues for multiple periods.
4. Sticky Costs and Adjustment Decisions by Managers
Hypothesis 1 was motivated by the folklore that activity costs rise more with an
increase in activity than they fall with an equivalent decrease in activity (Cooper and
Kaplan [1998, p. 247], Noreen and Soderstrom, [1997]). The asymmetry we document in
favor of hypothesis 1 substantiates the claim that managers do not reduce committed
resources to the minimal level necessary to meet activity demand. While the folklore
provides the insight that slack resources must be managed away, it does not provide a
justification for the failure by managers to do so. In this section, we posit a model of cost
16
behavior in which managers make deliberate adjustments to the level of committed
resources after weighing the possible consequences of their actions.
If a manager were motivated to increase profits and there were no cause for
resistance to reducing the level of committed resources provided, sticky cost behavior
would not be observed. Resistance to reducing committed resources may be due to
consideration of the economic consequences to the firm, such as requirements to provide
severance pay to discharged employees or penalties for terminating lease agreements.
When there is a positive probability that activity demand will increase in the future, the
manager may consider the costs associated with restoring the committed resources, such as
the costs of searching for and training employees or locating and contracting for facilities.
The manager may also consider indirect economic consequences of retrenchment, such as
those caused by loss in employee morale, which would be manifest in subsequent periods.
Alternatively, resistance may be due to personal concerns of a manager who is
reluctant to dismiss familiar employees or feels personally threatened by reductions in
committed resources, a concern echoed by a manager interviewed in the Schrader-Bellows
case: “They have babies to feed and mortgages to pay too. If the logical conclusion of an
analysis would entail liquidation of a person’s position, it is unrealistic to expect much
support from him in implementing the recommendations” (Cooper [1985]).
In the face of stochastic future demand, managers who weigh the economic
consequences of their decisions compare the anticipated costs of slack resources with the
expected economic costs of adjusting the level of committed resources. Anticipated slack
resource costs associated with a decline in revenue activity increase with the permanence
of the decline. These observations provide the basis for a set of hypotheses that are
17
consistent with sticky cost behavior resulting from managers weighing the economic
consequences of their decisions.
4.1 Hypotheses
Since the likelihood that a revenue decline is transitory is greater in periods when
the economy is growing, managers who weigh the economic consequences should reduce
committed resources less in periods of macroeconomic growth than in other periods. In
addition, shortages of labor in periods of economic growth increase the cost of replacing
retrenched employees, reinforcing this stickiness. Therefore, we make the following
hypothesis.
Hypothesis 2a: SG&A costs exhibit greater stickiness during periods of macroeconomic
growth.
If revenue declines occur in successive periods, managers’ assessment of the
probability that the declines are permanent will increase. In the second year of decline,
managers who weigh the economic consequences of their actions will be more willing to
reduce the level of committed resources provided.
Hypothesis 2b: Stickiness of SG&A costs is lower in the second successive year of
revenue decline than it is in the first year of revenue decline.
18
As the decision timeframe increases, a manager’s assessment of the permanence of
changes in revenue becomes surer and the costs of adjusting committed SG&A resources
for fluctuations in revenue become relatively lower. Making adjustments for short-term
fluctuations in revenue, for instance, could be very expensive if adjustment costs are high
and the variance of revenue is high. For these reasons, stickiness of activity costs is likely
to be less pronounced over greater aggregations of time periods.
Hypothesis 2c: Stickiness of SG&A costs declines with the aggregation of time periods.
4.2 Empirical Tests
We investigate hypothesis 2a, that stickiness would be more pronounced in growth
periods in the economy, by dividing the data into high growth and low growth periods
based on relative GNP growth in each year.13 Results of separate estimations of model (I)
using observations from high growth periods and low growth periods are presented in the
first two numerical columns of table 7. The values of β̂1 in the “high growth” estimation of
0.5596 (t-statistic = 102.98) and in the “low growth” estimation of 0.5197 (t-statistic =
71.30) are similar to those obtained previously for estimates of model (I). The magnitude
of the stickiness coefficient, β̂ 2 , however, is significantly greater (t-statistic = 5.85) in high
growth periods ( β̂ 2 = -0.2368) than in low growth periods ( β̂ 2 = -0.1471). This indicates
more stickiness in high macroeconomic growth periods, consistent with hypothesis 2a.
The final two columns of table 7 report results of pooled regressions with
interaction terms for economic growth and decreases in sales revenue. In the first of these
13
This is similar to a classification scheme used by Lev and Thiagarajan [1993].
19
columns (second from the right), the interaction term includes a dummy variable that takes
the value of 1 for the higher growth years in the sample. The significantly negative
coefficient on this interaction term, β̂ 3 = -0.0716 (t-statistic = -4.69), supports the notion
that stickiness is greater in periods of macroeconomic growth. In the other column, the
interaction term includes a continuous variable, the percentage growth in real GNP. Again,
the significantly negative coefficient of -0.0182 (t-statistic = -3.80) supports the hypothesis
that stickiness is greater during periods of macroeconomic growth.
Table 8 provides results from estimating model (I) with an additional interaction
term for a second consecutive year of revenue decline. In this specification, stickiness is
measured by β̂ 2 for firms with only one year of revenue decline and by β̂ 2 + β̂ 3 for firms
with at least two consecutive years of revenue decline. The value of β̂ 2 + β̂ 3 = -0.0490 is
significantly less than zero (p-value = 0.001), indicating that there is some stickiness in the
second consecutive year of revenue decline. Since the magnitude of β̂ 2 of -0.3051 is
significantly greater than the magnitude of β̂ 2 + β̂ 3 of -0.0490 (t-statistic of the difference
β̂ 3 is 23.76), stickiness is significantly higher for firms with only one year of revenue
decline when compared to firms with two or more consecutive years of revenue decline.
This is consistent with hypothesis 2b.
The four columns in table 9 present the results of estimating model (I) for one, two,
three and four year aggregation periods. These results show that β̂ 2 decreases as the
aggregation period increases (test of equality of β̂ 2 for each pair of aggregation periods is
rejected at the 5% significance level), indicating that stickiness diminishes with the length
of the aggregation period, consistent with hypothesis 2c.
20
Managers’ weighing the economic consequences of their actions motivates
hypotheses 2a, 2b, and 2c. An alternative explanation for sticky cost behavior is that
managers’ decisions are distorted by personal concerns. While an argument might be made
that these three hypotheses could also be consistent with managers making decisions based
on personal concerns, the argument requires a strong set of assumptions.14
5. Conclusion
The evidence presented in this study documents, in a broad sense, the prevalence of
sticky cost behavior for SG&A costs. In contrast to the commonly received model of fixed
and variable costs, this evidence is consistent with an alternative model of cost behavior
that recognizes managerial discretion in adjusting the level of activity resources provided in
response to changes in the demand for activity resources. These results have important
consequences for research and teaching in cost accounting and in other areas of accounting
where costs are analyzed in relation to revenue changes.
Textbook descriptions of procedures for estimating cost functions recommend
regression analysis that estimates the average amount of the change in costs associated with
14
One might argue that hypotheses 2a, 2b, and 2c are consistent with managers incurring slack
resource costs for personal reasons as follows. Assume that a manager’s information about the
permanence of a revenue decline is private, and not revealed to his supervisor (principal). Then, in
order to justify the incurring of slack resource costs in a period of revenue decline, leading to sticky
costs, the manager may misrepresent a permanent revenue decline as being transitory. The
manager can thus justify his decision as being economically optimal for the firm, and hide his
personal considerations. When revenue declines occur during periods of economic contraction or
in consecutive periods, or when revenue declines over a longer period, a representation that the
revenue decline is transitory is likely to be less credible, and therefore the manager is less likely to
incur slack resource costs. This argument, however, rests on the strong assumptions that the
manager’s incentives to take value-maximizing actions are outweighed by personal concerns, that
information on the permanent or transitory nature of revenue declines is privately available to the
manager and not to his principal, and that the manager cannot be penalized ex post when his
misrepresentation that the revenue decline is transitory is revealed to be false.
21
a unit change in the activity driver (e.g. Hilton [1997, pp. 312-315], Horngren, Foster, and
Datar [1999, pp. 338-339]). Making such estimations without considering sticky cost
behavior leads to underestimation of the responsiveness of costs to increases in activity and
overestimation of the responsiveness of costs to decreases in activity. To arrive at a
benchmark cost for variance analysis, textbook instructions for flexible budgeting indicate
that the budgeted cost should be flexed symmetrically for both positive and negative
differences between the actual and initial budget quantity (Hilton [1997, pp. 526-530],
Horngren, Foster, and Datar [1999, pp. 222-224]). Such methods that ignore the
asymmetry in cost changes with increases and decreases in activity are likely to cause
distortions in the managerial decisions based on cost analysis.
Early proponents of activity-based costing recommended that a product be
considered for elimination if its full costs based on activity analysis exceed its selling price.
This prescription relies on the belief that when the sales volume goes down so will the
costs. But costs may not fall automatically as a consequence of reduction in volume. A
marketing manager in the Schrader-Bellows case (Cooper [1985]) suggests a similar idea:
“There is a natural resistance to dropping products, and some of that resistance is justified.
It was my experience as director of marketing that often when products are dropped, the
sales go away but the costs do not and there is no improvement in profitability. It is
difficult to eliminate overhead and very hard to believe that costs will go away with
selective rationalization.” By demonstrating that cost reductions in response to reductions
in activity volume require deliberate managerial decisions, our study lends credence to this
argument.
22
Assumptions about SG&A costs being proportional to sales revenue are prevalent
also in other branches of accounting, such as financial statement analysis and auditing. A
common procedure in financial statement analysis involves comparison of SG&A expense
items as a percentage of net sales across firms within an industry or over time for a specific
firm (White, Sondhi, and Fried [1997, p. 148]). Analysts interpret a disproportionate
increase in selling expenses as a negative signal because it may represent a loss of
managerial control or an unusual sales effort (Bernstein and Wild [1998, p. 583], Mintz
[1999]). This analysis may be misleading because the underlying assumption that selling
expenses move proportionately with sales is not empirically valid when the data include
both sales increases and decreases. Similarly, auditors implicitly assume that costs should
move proportionately with sales when performing analytical review procedures (Messier
[1997, p. 563]). These procedures may be improved by a better understanding of how
SG&A costs change with revenues, leading to greater efficiency in identifying when
additional analysis may be required in auditing a firm.
The empirical models employed in this study provide a platform for further research
on the causes and consequences of sticky cost behavior. While the use of Compustat data
enabled documentation of the prevalence of sticky cost behavior for a large cross-section of
firms, it did not permit finer disaggregation of the SG&A costs. Future research using finer
data may provide information on cost behavior for different components of SG&A costs as
well as other types of costs. Evidence was also provided that sticky cost behavior is
consistent with deliberate decision-making by managers who weigh the economic
consequences of their actions. Developing a greater understanding of the managerial
23
decision-making processes and the forces that lead to sticky cost behavior is vitally
important to improving cost analysis.
24
References
Atkinson, A., R. Banker, R. Kaplan, and M. Young. Management Accounting. Upper
Saddle River, NJ: Prentice Hall, 1997.
Baber, W., S. Janakiraman, and S. Kang, 1996. “Investment Opportunities and the
Structure of Executive Compensation.” Journal of Accounting and Economics 21 (1996):
297-318.
Balakrishnan, R., M. Petersen, and N. Soderstrom. “On the Behavior of Labor Costs in
Therapy Clinics.” Working paper, The University of Iowa, 1999.
Banker, R., and J. Hughes. “Product Costing and Pricing.” Accounting Review 69 (1994):
479-494.
Banker, R., and H. Johnston. “An Empirical Study of Cost Drivers in the U.S. Airline
Industry.” Accounting Review 68 (1993): 576-601.
Belsley, D., E. Kuh, and R. Welsch. Regression Diagnostics: Identifying Influential Data
and Collinearity. New York: Wiley, 1980.
Bernstein, L., and J. Wild. Financial Statement Analysis. New York: Irwin McGraw-Hill,
1998.
Chen, E., and W. Dixon. “Estimates of Parameters of a Censored Regression Sample.”
Journal of the American Statistical Association 67 (1972): 664-671.
Colvin, G. “The Most Valuable Quality in a Manager.” Fortune (December 29, 1997): 279280.
Cooper, R. “Schrader Bellows (F), (G).” Harvard Business School Cases 1-186-055 and 1186-056 (1985).
Cooper, R., and R. Kaplan. “Activity-based Systems: Measuring the Costs of Resource
Usage.” Accounting Horizons 6 (1992): 1-13.
Cooper, R., and R. Kaplan. The Design of Cost Management Systems: Text, Cases, and
Readings. Upper Saddle River, NJ: Prentice Hall, 1998.
Davidson, R., and J. MacKinnon. “Several Tests for Model Specification in the Presence of
Alternative Hypotheses.” Econometrica 49 (1981): 781-793.
Durbin, J., and G. Watson. “Testing for Serial Correlation in Least-Squares Regression.”
Biometrika 38 (1951): 159-177.
25
Garrison, R., and E. Noreen. Managerial Accounting. New York: Irwin McGraw-Hill,
1997.
Greene, W. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall, 1997.
Hilton, R. Managerial Accounting. New York: McGraw-Hill, 1997.
Horngren, C., G. Foster, and S. Datar. Cost Accounting: A Managerial Emphasis. Upper
Saddle River, NJ: Prentice Hall, 1997.
Horngren, C., G. Sundem, and W. Stratton. Introduction to Management Accounting.
Upper Saddle River, NJ: Prentice Hall, 1996.
Jensen, M., “Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers.” The
American Economic Review 76 (1986): 323-329.
Judge, G., W. Griffiths, R. Hill, H. Lütkepohl, and T. Lee. The Theory and Practice of
Econometrics. New York: John Wiley and Sons, 1985.
Kaplan, R. “Flexible Budgeting in an Activity-based Costing Framework.” Accounting
Horizons 8 (1994): 104-109.
Kaplan, R., and A. Atkinson. Advanced Management Accounting. Upper Saddle River, NJ:
Prentice Hall, 1998.
Lahiri, K., and P. Schmidt. “On the Estimation of Triangular Structural Systems.”
Econometrica 46 (1978): 1217-1221.
Lev, B., and R. Thiagarajan. “Fundamental Information Analysis.” Journal of Accounting
Research 31 (1993): 190-215.
Messier, W. Auditing: A Systematic Approach. New York: Irwin McGraw- Hill, 1997.
Miller, W. “How CEOs Make Decisions.” Industry Week (February 23, 1987): 41.
Mintz, S. “Unsnarling SG&A Costs Requires Constant Vigilance and a Grip on
Complexity.” CFO Magazine 15 (December 1999): 44-53.
Noreen, E. “Conditions under which Activity-based Cost Systems Provide Relevant
Costs.” Journal of Management Accounting Research 3 (1991): 159-168.
Noreen, E., and N. Soderstrom. “Are Overhead Costs Strictly Proportional to Activity?
Evidence from Hospital Service Departments.” Journal of Accounting and Economics 17
(1994): 255-278.
26
Noreen, E., and N. Soderstrom. “The Accuracy of Proportional Cost Models: Evidence
from Hospital Service Departments.” Review of Accounting Studies 2 (1997): 89-114.
White, G., A. Sondhi, and D. Fried. The Analysis and Use of Financial Statements. New
York: John Wiley and Sons, 1997.
White, H. “A Heteroskedasticity-consistent Covariance Matrix Estimator and a Direct Test
for Heteroskedasticity.” Econometrica 48 (1980): 817-838.
Zellner, A. “An Efficient Method of Estimating Seemingly Unrelated Regressions and
Tests for Aggregation Bias.” Journal of the American Statistical Association 57 (1962):
348-368.
Zimmerman, J. Accounting for Decision Making and Control. New York: Irwin McGrawHill, 1997.
27
Table 1
Summary statistics
Panel A: Distribution of annual revenue and SG&A costs
during the period 1979 – 1998a
Mean
Sales revenue
$1277.09
Standard
deviation
$5983.43
Median
$87.53
Lower
quartile
$17.51
Upper
quartile
$447.75
Selling, general and
administrative (SG&A) costs
$229.45
$1042.49
$17.49
$4.56
$79.12
SG&A costs as a
percentage of revenue
26.41%
17.79%
22.62%
13.66%
34.31%
Panel B: Periodic fluctuations in revenue and SG&A costs
during the period 1979 – 1998b
Sales
revenue
SG&A
costs
10.99%
Upper
quartile of
percentage
decreases
across
periods
23.76%
Lower
quartile of
percentage
decreases
across
periods
4.38%
10.07%
21.63%
3.94%
Median
percentage
decrease
across
periods
17.45%
Standard
deviation of
percentage
decreases
across
periods
18.64%
15.67%
16.40%
Percentage of
firm-years with
negative
percentage
change from
previous period
27.01%
Mean
percentage
decrease
across
periods
24.98%
a
All the reported numbers are in millions of dollars. The distribution of sales revenue and SG&A
costs is for a population of 64,663 firm-year observations from 7,629 firms in the 1999 Compustat
data set that satisfy the following selection criteria: no missing observations for the current and
preceding year sales revenue (Annual Compustat item # 12), no missing observations for the
current and preceding year SG&A costs (Annual Compustat item # 189), and no firm years in
which SG&A costs exceeded sales revenue.
b
Observations with a negative change in sales revenue form the basis for the reported numbers in
the first row. Observations with a negative change in SG&A costs form the basis for the reported
numbers in the second row.
28
Table 2
Results of regressing annual changes in SG&A on annual changes in sales revenue
for the twenty year period 1979 – 1998a
Coefficient estimates (t-statistics)
a
Pooled estimation
Fixed effects estimationb
β̂ 0
0.0481
(39.88)
0.0736
(4.22)
β̂1
0.5459
(164.11)
0.5382
(158.73)
β̂ 2
-0.1914
(-26.14)
-0.1906
(-25.51)
Adjusted R2
0.3663
0.3730
Number of observations
63,958
63,958
Regression specification:
 SG & A i, t 
 Re venue i, t 
 Re venue i, t 
log 
 = β 0 + β 1 log 
 + β 2 * Decrease _ Dummy i ,t * log 
 + ε i ,t
 SG & A i, t -1 
 Revenue i, t -1 
 Revenue i, t -1 
Decrease_Dummy takes the value of 1 when revenue in period t is less than revenue in
t-1, zero otherwise. To eliminate the influence of extreme observations, 705 observations with
values of any variable in the top or bottom 0.5% of its distribution were trimmed from the sample
of 64,663 firm-year observations, resulting in 63,958 observations.
b
The regression specification is the same as the basic model with dummy variables for industries
and years. The estimated coefficients for the industry and year dummies are not reported.
29
Table 3
Results of estimating the model relating SG&A costs to sales revenue for the twenty year period
1979-1998 with assets and number of employees as additional driversa
Coefficient estimates (t-statistics)
a
β̂ 0
Pooled estimation
0.0333
(26.12)
Fixed effects estimation
0.0769
(4.73)
β̂1
0.3769
(86.41)
0.3760
(85.82)
β̂ 2
-0.1050
(-11.67)
-0.1093
(-12.07)
β̂ 3
0.1504
(35.05)
0.1469
(34.20)
β̂ 4
-0.0504
(-5.05)
-0.0466
(-4.65)
β̂ 5
0.2026
(38.11)
0.2027
(38.06)
β̂ 6
-0.0587
(-6.21)
-0.0577
(-6.11)
Adjusted R2
0.4346
0.4393
Number of observations
54,810
54,810
Regression specification:
 Re venue i, t 
 Revenue i,t 
 SG & A i, t 
log 
+
 + β 2 Decrease _ Dummy i ,t * log 
 = β 0 + β1 log 
 Revenue i,t -1 
 Revenue i, t -1 
 SG & A i,t -1 
 Re venue i, t -1 
 Revenue i,t -1 
β 3 log 

 + β 4 Decrease _ Dummy i ,t −1 * log 
 Revenue i,t -2 
 Revenue i, t -2 
 Assets i,t 
 Assets i, t 
β 5 log 
+
 + β 6 Decrease _ A _ Dummy i ,t * log 
 Assets i, t -1 
 Assets i,t -1 
 Employees i, t 
 Employees i,t 
β 7 log 
 + ε i ,t
 + β 8 Decrease _ E _ Dummy i ,t * log 
 Employees i, t-1 
 Employees i, t -1 
Decrease_Dummy takes value of 1 when the revenue in period t is less than that in t-1,
Decrease_A_Dummy takes the value of 1 when the total assets in period t are less than in t-1, and
Decrease_E_Dummy takes the value of 1 when the number of employees in period t is less than in
t-1.
30
Table 4
Results of regressing annual changes in SG&A on annual changes and lagged annual changes in
sales revenue for the twenty year period 1979 – 1998a
Coefficient estimates (t-statistics)
a
Pooled estimation
Fixed effects estimationb
β̂ 0
0.0333
(25.90)
0.0668
(3.91)
β̂1
0.5328
(130.43)
0.5295
(128.67)
β̂ 2
-0.1876
(-23.47)
-0.1879
(-23.26)
β̂ 3
0.1038
(29.79)
0.1017
(28.92)
β̂ 4
0.1042
(13.23)
0.0995
(12.53)
Adjusted R2
0.3893
0.3936
Number of observations
56,420
56,420
Regression specification
 SG & A i, t 
 Re venue i,t 
 Re venue i, t 
log 
 = β 0 + β1 log 
 + β 2 Decrease _ Dummy i ,t * log 
+
Revenue
SG
&
A


i, t -1 
i, t -1 


 Revenue i, t-1 
 Revenue i, t -1 
 Revenue i, t -1 
β 3 log 
 + β 4 Decrease _ Dummy i ,t −1 * log 
 + ε i ,t
 Revenue i,t -2 
 Revenue i,t -2 
Decrease_Dummyi,t takes a value of 1 when revenue of the ith firm for period t is less than that in
the preceding period.
b
The regression specification is the same as the above specification with dummy variables for
industries and years. The estimated coefficients for the industry and year dummies are not reported.
31
Table 5
Results from estimating regression of change in SGA on changes in revenue during the current and
the preceding periods, change in number of employers and change in assets jointly with regression
of change in revenue on change in SGAduring the current and the preceding periodsa
EQUATION 1
EQUATION 2
Coefficient estimates
(t-statistics)
a
Coefficient estimates
(t-statistics)
α̂ 0
0.0144
(3.49)
γ̂ 0
-0.0488
(-20.01)
α̂1
0.4671
(11.49)
γ̂ 1
1.5285
(131.53)
α̂ 2
-0.2207
(-4.93)
γ̂ 2
0.2284
(18.27)
α̂ 3
0.0602
(6.09)
α̂ 4
0.0839
(3.54)
α̂ 5
0.2350
(63.05)
α̂ 6
0.0489
(3.93)
Adjusted R2
0.3864
0.3236
Number of
observations
50,974
50,974
Regression specification:
 SGA i,t 
 Re venuei, t 
 Revenuei, t 
EQUATION 1 : log
 = α 0 + α 1 log
 + α 2 Decrease _ Dummyi,t * log
 +
 SGA i, t -1 
 Re venuei, t −1 
 Revenuei, t −1 
 Revenuei, t −1 
 Revenuei, t −1 
α 3 log
 + α 4 Decrease_Dummyi,t −1 * log
+
 Revenuei, t −2 
 Revenuei, t − 2 
 Assetsi, t 
 Employeesi, t 
α 5 log
 + α 6 log
 + ϑ i, t
 Assetsi, t −1 
 Employeesi, t −1 
 SGAi, t −1 
 SGAi, t 
 Revenuei, t 
EQUATION 2 : log
 + ζi, t +1
 + γ 2 log
 = γ 0 + γ1 log
 SGAi, t −2 
 SGAi, t −1 
 Revenuei, t −1 
Decrease_Dummyi,t takes the value of 1 when revenue in period t is less than revenue in t-1; zero
otherwise.
32
Table 6
Results from estimating regression of change in non-advertising SGA on changes in revenue during
the current and the preceding periods jointly with regression of change in revenue on change in
advertising cost in the current and the preceding periodsa
EQUATION 1
EQUATION 2
Coefficient estimates
(t-statistics)
Coefficient estimates
(t-statistics)
α̂ 0
0.0268
(12.37)
γ̂ 0
0.0751
(38.92)
α̂1
0.6298
(68.77)
γ̂ 1
0.2214
(51.69)
α̂ 2
-0.1232
(-6.56)
γ̂ 2
0.1007
(24.05)
α̂ 3
0.1158
(15.55)
α̂ 4
0.1142
(6.11)
System weighted R2
0.3629
Number of observations
13,955
a
Regression specification:
 nonAdvSGAi, t 
 Re venuei, t 
 Revenuei, t 
EQUATION 1 : log
 = α 0 + α 1 log
 + α 2 Decrease _ Dummyi,t * log
 +
 nonAdvSGAi, t -1 
 Re venuei, t −1 
 Revenuei, t −1 
 Revenuei, t −1 
 Revenuei, t −1 
α 3 log
 + α 4 Decrease_Dummyi,t −1 * log
 + ϑ i, t
 Revenuei, t − 2 
 Revenuei, t − 2 
 Advi, t −1 
 Advi, t 
 Revenuei, t 
EQUATION 2 : log
 + ζi, t +1
 + γ 2 log
 = γ 0 + γ1 log
 Advi, t − 2 
 Advi,t −1 
 Revenuei, t −1 
Decrease_Dummyi,t takes the value of one when revenue in period t is less than revenue in t-1; zero
otherwise.
33
Table 7
Effect of macroeconomic growth on the stickiness of SG&A costs
Coefficient estimates (t-statistics)
High growth
periodsa,b
0.0526
(23.46)
Low growth
periodsa,c
0.0448
(18.34)
High and low
growth periodsd,e
0.0493
(29.80)
High and low
growth periodsd,f
0.0493
(29.81)
β̂1
0.5596
(102.98)
0.5197
(71.30)
0.5466
(126.46)
0.5465
(126.43)
β̂ 2
-0.2368
(-17.32)
-0.1471
(-9.76)
-0.1585
(-12.03)
-0.1473
(-8.76)
-0.0716
(-4.69)
-0.0182
(-3.80)
β̂ 0
β̂ 3
Adjusted R2
0.3974
0.3323
0.3757
0.3756
Number of
observations
20,045
15,055
35,104
35,104
a
Regression specification for columns1 and 2:
 SG & A i, t 
 Re venue i, t 
 Re venue i, t 
log 
 = β 0 + β1 log 
 + β 2 * Decrease _ Dummy i ,t * log 
 + ε i ,t
 SG & A i, t -1 
 Revenue i, t -1 
 Revenue i, t -1 
Decrease_Dummy takes the value of 1 when revenue in period t is less than revenue in
t-1, zero otherwise.
b
The regression is based on annual observations for Compustat years 1983, 1984, 1985, 1988,
1989, 1994, 1996, 1997 and 1998.
c
The regression is based on annual observations for Compustat years 1980, 1981, 1982, 1986, 1987,
1990, 1991, 1992, 1993 and 1995.
d
Regression specification for columns 3 and 4:
 SGA i, t 
 Re venuei, t 
 Re venuei, t 
log 
 = β 0 + β1 log
 + β 2 * Decrease _ Dummyi ,t * log
 +
 SGA i, t -1 
 Revenuei, t -1 
 Revenuei, t -1 
 Re venuei, t 
β 3 Decrease _ Dummyi ,t * Growth * log
 + ε i ,t
 Revenuei, t -1 
Decrease_Dummyi,t takes the value of 1 when revenue in period t is less than revenue in t-1, zero
otherwise.
e
Growth takes the value of one in periods of high GNP growth and the value of zero in periods of
low GNP growth.
f
Growth takes the value of the percentage growth in real GNP for the period.
34
Table 8
Results of estimating the model relating SG&A costs to sales revenue for the twenty year period
1979-1998 with an additional dummy variable for consecutive loss yearsa
Coefficient estimates
(t-statistics)
β̂ 0
0.0478
(39.84)
β̂1
0.5463
(164.95)
β̂ 2
-0.3051
(-34.98)
β̂ 3
0.2561
(23.76)
Adjusted R2
0.3718
Number of observations
63,958
a
Regression specification:
 Re venuei, t 
 Re venuei,t 
 SG & A i,t 
log
 + β 2 * Decrease _ Dummyi ,t * log
+
 = β 0 + β1 log
 Revenuei,t -1 
 Revenuei,t -1 
 SG & A i, t -1 
 Re venuei, t 
β 3 * Successive _ Decreasei ,t * log
 + ε i ,t
 Revenuei, t -1 
Decrease_Dummy takes the value of 1 when revenue in period t is less than revenue in
t-1, zero otherwise.
Successive_Decrease dummy equals 1 if the revenue in period t is less than that in period t-1 which
is less than that in period t-2, and zero otherwise.
35
Table 9
Results of regressing SG&A on changes in sales revenue for different aggregation periodsa
Aggregation period
One yearc
Two yearsc
Three yearsd
Four yearse
β̂ 0
0.0481
(39.88)
0.0574
(25.12)
0.0603
(16.31)
0.0783
(16.67)
β̂1
0.5459
(164.11)
0.6816
(141.91)
0.7148
(104.71)
0.7427
(97.00)
β̂ 2
-0.1914
(-26.14)
-0.1569
(-13.40)
-0.0919
(-5.56)
-0.0343
(-1.76)
Adjusted R2
0.3663
0.5349
0.5933
0.6513
Number of
observations
63,958
26,052
12,398
8,565
a
Regression specification:
 SG & A i, t 
 Re venue i, t 
 Re venue i, t 
log 
 = β 0 + β1 log 
 + β 2 * Decrease _ Dummy i ,t * log 
 + ε i ,t
 SG & A i, t -1 
 Revenue i, t -1 
 Revenue i, t -1 
Decrease_Dummy takes the value of 1 when revenue in period t is less than revenue in
t-1, zero otherwise.
36
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