Cost Behavior Asymmetry and Business Unit Efficiency
ABSTRACT
This paper investigates asymmetric cost behavior from 2004 to 2011 in an economy lodging
company. Our sample of homogeneous business units allows clean inferences about the
underlying causes of the observed effects. This study makes several contributions to the literature
on asymmetric cost behavior. First, we find that in situations where downward adjustment costs
are low and upward adjustment costs are high, costs are anti-sticky. Second, we provide a more
direct test of hypotheses about the influence of economic conditions and prior level of slack on
cost asymmetry. Third, we document that a change in level of slack resources, and not only prior
level of slack resources, influences observed levels of asymmetric cost behavior. Finally, we
investigate differences in how managers cope with managing different types of cost.
Key Words: cost behavior, sticky costs, cost asymmetry, cost elasticity, hospitality industry,
cost accounting, cost estimation
Data Availability: The confidentiality agreement with the firm that provided data for this study
precludes revealing its identity and disseminating data without its written consent.
JEL Classifications: D24; M41.
1. INTRODUCTION
A good understanding of cost behavior is essential for accurate forecasting (Banker and
Chen 2006; Weiss 2010), detecting earnings management and maintaining budgetary control
(Banker and Byzalov 2014). The cost model most commonly used—a linear model with fixed
and variable costs—is based on three assumptions: (1) That there exists a single cost driver; (2)
with constant returns to scale (Noreen and Soderstrom 1994); and, (3) a volume elasticity of
variable costs that is symmetric. This model neglects managerial discretion in cost management.
Empirical research has come to challenge these assumptions.
About three decades ago researchers began to recommend building more elaborate cost
models with numerous cost pools, activity cost drivers and a cost hierarchy. While such models
eliminated the assumption of a single cost driver, the other assumptions were maintained.
Further, managerial discretion was still neglected.
Extending the work of Noreen and Soderstrom (1994; 1997), Anderson et al. (2003)
observed that, on average, selling, general and administrative (SG&A) costs tend to increase with
activity levels, but do not decrease as much when activity falls in the same proportions. This
asymmetric cost behavior was defined as “cost stickiness.” In other words, this finding meant
that changes in costs depend not only on the extent, but also on the direction of changes in
activity. While the asymmetry in cost behavior has been observed in different countries and with
different cost categories (Banker and Byzalov 2014), the opposite phenomenon—labeled “cost
anti-stickiness”—also has been documented (Anderson and Lanen 2007; Weiss 2010). With antistickiness, costs decrease faster when sales decline than costs increase when sales rise. To
explain these seemingly contradictory results, Banker and Byzalov (2014) developed a
theoretical framework explaining both stickiness and anti-stickiness. However, while the extant
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research has found stickiness in settings where it is expected, we are not aware of research
documenting anti-stickiness in settings were anti-stickiness is expected.
The economic theory of asymmetric cost behavior does not imply that resource
consumption is mechanically linked to activity (Banker and Byzalov 2014). But this does not
imply costs are completely disconnected from activity; resource allocation decisions made by
managers mediate the relation between changes in activity and changes in costs. Cost
adjustments do not happen on their own—they must be both decided upon and implemented
(e.g., acquisition and disposal of assets, or hiring and termination of personnel). In other words,
managers determine whether to reduce committed resources when sales volume decreases, or
increase resources when sales volume increases (Banker and Byzalov 2014).
The principal idea of the theory of asymmetric cost behavior is that resource allocation
decision (hereafter cost adjustments) are themselves costly. Managers, as rational decision
makers, evaluate three kinds of relevant costs: (1) unused capacity costs; (2) opportunity costs;
and, (3) adjustment costs. For example, when sales increase, managers compare upward
adjustment costs (e.g., additional capital investment, or hiring and training new employees) to
the lost contribution (opportunity cost) if an adjustment is not made. Similarly, when sales
decrease, managers compare downward adjustment costs (e.g., disposal of assets, or severance
pay to employees) to the cost of maintaining unused capacity. Managers will adjust capacity only
if the opportunity cost or cost of excess capacity outweighs, in the long run, the adjustment costs.
As adjustment costs rise, the likelihood of adjustment decreases, decreasing the volume elasticity
of cost. In other words, the higher the adjustment cost of a resource, the higher the tolerable
excess capacity (or lost contribution) and the lower the volume elasticity of costs.
2
In the theory of asymmetric cost behavior, variable costs have no adjustment cost and an
elasticity of one, whereas committed fixed costs have prohibitively high adjustment costs and
zero elasticity. These are the extremes of what may be viewed as a continuum (Banker and
Byzalov 2014). However, almost nothing but adjustment costs explains anti-stickiness.
Another important idea underlying the theory of asymmetric cost behavior is that frictions
are asymmetric; that is, reducing and increasing capacity incur different costs. The dynamic
factor demand literature asserts that optimal decision rules are asymmetric, leading to different
slopes for cost increases and decreases (Banker et al. 2013). For example, when a contract is
easier to sign than to break, managers may wait to decrease capacity. Ceteris paribus, with higher
downward adjustment costs, lower upward adjustment costs, lower unused capacity costs, and
higher opportunity costs imply costs decreasing at a slower rate than they increase; that is, cost
stickiness (Anderson et al. 2003). Conversely, cost anti-stickiness occurs with lower downward
adjustment costs, higher upward adjustment costs, higher unused capacity costs and lower
opportunity costs, costs will be decreasing at a faster rate than they increase (Anderson and
Lanen 2007; Weiss 2010). This holds for temporary activity changes; since future adjustment
costs are discounted, the effect of the first adjustment cost should prevail (Banker et al. 2013).
This reasoning shifts researchers’ attention from cost behavior to managerial behavior,
expanding the determinants of cost behaviors. Cost optimization by rational managers under
asymmetric adjustment costs is not the only source of cost stickiness. Economic determinants
affecting adjustment costs (e.g., market conditions and technology) constrain managerial
discretion, but do not eliminate it. As a consequence, economic determinants only partially
explain cost stickiness and anti-stickiness. From behavioral and agency perspectives, asymmetric
cost behavior also may result from managerial biases, skill and opportunism.
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Managerial assessment of unused capacity cost, opportunity cost, downward adjustment
cost and upward adjustment cost is not necessarily accurate. Management accounting systems
and personal biases like managerial reactiveness (Calleja et al. 2006), pessimism, optimism
(Anderson et al. 2007; Banker, Byzalov, Ciftci, et al. 2014; Yasukata and Kajiwara 2011) or
overconfidence (Qin et al. 2015) may, therefore, considerably affect resource allocation
decisions.
Second, adjustment costs should be considered from the agent’s perspective, including
costs and benefits—monetary or otherwise—that enter an agent’s utility function. For example,
social status, reputation, power and wealth relate to the size of the entity they manage (Anderson
et al. 2003; Banker et al. 2013). “Empire-building” incentives (Chen et al. 2012; Dierynck et al.
2012; Banker et al. 2013) and managerial exuberance (Calleja et al. 2006) encourage growth
investments and discourage downsizing. Moreover, as Balakrishnan, Perterson and Soderstrom
(2004) and Anderson et al. (2007) suggest, the disutility of overstaffing may be lower than the
disutility of understaffing because strain exerts a more immediate pressure on managers than
slack. This incentive misalignment results in agency problems that may be mitigated by
supervision mechanisms (Subramaniam and Weidenmier 2003; Calleja et al. 2006; Chen et al.
2012; Chen et al. 2014) and incentives (Kama and Weiss 2013).
Incentives to manage costs efficiently also may backfire. Previous studies document
myopic activities related to earnings management, sacrificing reputation, employee morale and
long-term value creation to meet short-term profit targets (Banker and Byzalov 2014; Dierynck
et al. 2012; Kama and Weiss 2013). The degradation of the SG&A ratio when sales decrease is
therefore an ambiguous signal as it may indicate efficient or inefficient cost control (Anderson et
al. 2007).
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Stickiness is therefore ambiguous. It may reflect rational managerial reaction (reducing
strain, anticipating growth, considering reputation, etc.), bias or opportunism (e.g., empire
building). Anti-stickiness is similarly ambiguous. It may suggest greater cost control (imposed
by competition or good management) or myopic earnings management (incentive misalignment).
For example, Baumgarten et al. (2010) observe that companies with lower SG&A have stickier
costs because of lower slack. Anderson et al. (2007) also show that an increase in SG&A in
sales-decreasing years may be a positive signal, contrary to traditional fundamental analysis
expectations. Further, it may show managers’ expectations about future sales recovery, or the
constitution of intangible assets through advertising or R&D or investments in operating
efficiency (Baumgarten et al. 2010). Since asymmetric cost behavior can be a positive or
negative signal, researchers must not only assess whether costs are sticky or anti-sticky, but also
why they are as such (Banker et al. 2013). Disentangling the effects of multiple factors is nontrivial because of sample heterogeneity and unobserved variables.
This study contributes to the sticky cost literature by investigating cost elasticity and its
asymmetry in the economy lodging industry using the economic theory of asymmetric cost
behavior. We test whether efficient business units are characterized by a lower or greater ability
to manage costs. With disaggregated data we are able to investigate how cost management varies
systematically across multiple cost categories. We investigate simultaneously changes in cost
elasticity and changes in cost behavior asymmetry (as they are interdependent) (Balakrishnan et
al. 2014). This homogeneous setting and micro-level data allows us to control for confounding
effects, drawing clean inferences about deliberate resource commitment decisions made by
managers.
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We also contribute to the literature by providing a direct test of the impact of economic
conditions and slack on cost variability and asymmetric cost behavior. Our setting allows us to
attribute unambiguously such observations to managers’ discretion. The financial crisis of 2007
caused managers to significantly alter the way they managed costs, reducing cost when volumes
increased and cutting cost drastically when volumes decreased. Surprisingly, it appears that
efficient business units have, on average, stickier costs than inefficient business units. This study
refines our understanding of the relationships between cost elasticity, asymmetric cost behavior
and managerial discretion in several other ways. We observe that cost elasticity and asymmetric
cost behavior do not differ significantly between business units remaining efficient or remaining
inefficient. Cost behavior differences are, however, associated with changes in efficiency,
business units becoming efficient showing high cost anti-stickiness and business units becoming
inefficient showing high cost stickiness. These results indicate that researchers should consider
changes in slack resources and not only initial levels of slack resources. Finally, we provide
additional evidence that cost management differs systematically across cost categories.
2. DEVELOPMENT OF HYPOTHESES
2.1 ASYMMETRIC COST BEHAVIOR
Previous studies have provided evidence that, on average, costs appear sticky. This
observation likely results from several mechanisms. First, using broad categories accentuates
stickiness by slowing down cost reductions (e.g., termination-related costs reduce the level of
SG&A cost reduction in the short run) and accelerating costs increases (e.g., costs related to
recruitment increase SG&A costs more than the labor cost) (Banker et al. 2013). Second, cost
reductions do not necessarily follow volume decreases, while cost increases at capacity condition
volume increases. Third, (deflated) sales usually grow, making capacity increases more rational
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than capacity decreases (on average). Finally, determinants of cost stickiness seem, on average,
more likely and stronger than determinants of cost anti-stickiness.
However, we believe there are circumstances where the determinants of anti-stickiness
dominate. Negligible, or even negative adjustment and opportunity costs, imply costs decreasing
at a faster rate than when costs increase. For example, the economy lodging industry employs
cheap, low-skill labor for manual, repetitive tasks. Their workforce is relatively easy to recruit
and replace, and retention is difficult (Hesford, Malina & Pizzini 2015). Moreover, downward
labor adjustment costs are negative. With a turnover rate well beyond one hundred percent
(Hesford et al. 2015; Tracey and Hinkin 2008), business units not only incur no termination costs
when employees quit, but save on annual wage increases and employee benefits as few stay
beyond the qualification period. Labor costs should therefore increase at a slower pace than they
decrease and, since labor represents a large portion of economy lodging costs, we expect antistickiness for controllable costs in the economy lodging industry. Accordingly, we posit our first
hypothesis:
H1:
Costs are anti-sticky in industries with large labor costs and high employee
turnover.
Managers are likely to exert more control over their costs when economic conditions are
difficult, facing urgent pressure to be cost effective. Economic growth provides some leniency
while crises force careful cost control, containing costs when demand increases and cutting costs
drastically when demand decreases. We therefore expect costs to be anti-sticky after a crisis, We
state this formally in H2.
H2:
Costs are anti-sticky after an economic crisis.
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2.2 COST ASYMMETRY AND BUSINESS UNIT EFFICIENCY
When an organization has slack (i.e., inefficiency resulting from maintaining unnecessary
resources), a decrease in activity may lead to unacceptable levels of unused capacity, forcing the
manager to reduce expenses. However with an increase in activity, available slack allows the
manager to absorb the additional activity without increasing the organization’s resources. The
opposite can be expected for high levels of strain (unsustainable efficiency resulting from
managers maintaining a practical capacity below the level of activity). A manager will use
decreased demand to reduce strain and will not proportionally reduce resources. Increases in
demand at full capacity, however, force the manager to increase resources. Accordingly,
previous research has hypothesized that prior slack leads to cost anti-stickiness, while prior strain
results in cost stickiness. Previous tests of this hypothesis has, however, relied on indirect cues
about slack. Baumgarten et al. (2010) used the level of the SG&A ratio compared to the industry
as a proxy since it does not relate inputs to outputs (Janakiraman 2010). Banker, Byzalov, Ciftci,
and Mashruwala (2014) used prior sales decrease as a proxy (prior sales decrease should lead to
unused capacity) and prior sales increase as a proxy for strain (an increase in sales reduces
unused capacity). As indicators of slack and strain, these proxies have two flaws: (1) They are
ambiguous as they affect manager optimism and pessimism; and, (2) they assume capacity is
maintained from the previous period. This leads us to the following hypotheses:
H3a: Prior-year efficiency increases (current year) cost stickiness.
H3b: Prior-year inefficiency increases (current year) cost stickiness.
2.3 COST BEHAVIOR ACROSS COST CATEGORIES
Contractual agreements likely constrain managerial discretion. Contracts, or the absence
of contracts, mean that employees and suppliers are not governed by the same rules.
Accordingly, the way they are managed and the resulting cost behaviors are likely to differ in
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significant ways. Balakrishnan and Gruca (2008) show that core expenses, that is, costs more
closely related to core competencies and customer service, are stickier. Cost behavior will likely
differ based on the nature of the underlying cost function. Accordingly, we hypothesize:
H4:
Different cost categories are not managed the same way and will exhibit different
patterns of cost behavior.
3. DATA AND RESEARCH DESIGN
3.1 RESEARCH SITE
We obtained eight years of financial and operational data for 671 economy lodging
business units (hereafter, properties) owned by the same company and operating in the same
country. Brands in this segment of the industry include, among others, Super 8, Motel 6 and
Days Inn. The properties are dispersed across 46 states in urban, suburban, airport, interstate, and
rural locations. To reduce heterogeneity, we limit our sample to properties between 42 and 164
rooms.1 Removing properties purchased or sold during the sample period, left us with a sample
size of 4,216 property-years.2 The typical property has 103 rooms and is staffed by a general
manager (GM), a head housekeeper, a maintenance worker, multiple front desk attendants and a
staff of housekeepers. Although this setting limits the scope of generalization of our findings, it
offers distinct advantages over the heterogeneous samples used in prior studies. Our setting has a
number of unique attributes: a single output (rooms rented), common cost function (identical
technology and processes), common internal controls and a high degree of managerial discretion
over costs.
1
Large properties often have a second maintenance worker, a second head housekeeper, an assistant general
manager, different physical structure (e.g., a high-rise concrete building vs. the typical two-level wood
construction). Since there were not many large properties in the network, we deemed it better to drop these
observations in the interest of maintaining homogeneity of the data sample.
2
Each year the company reviews its network of properties. Demographic changes, competition and other factors are
considered by management each year and result in a small number of unit sales and unit acquisitions. Although the
number varies by year, the change in number of units is typically 3-5%. Requiring a full sample eliminates any
concern that managers and employees behave differently during a sale period (for those sold) or upon entering the
firm (for those acquired).
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Homogeneous output. Economy lodging properties have only one output: the provision of
hotel rooms rented. Further, since all rooms are identical, our setting eliminates any impact due
to changes in sales mix. Further, we use sales volume instead of revenue as a measure of activity,
eliminating price effects (Anderson and Lanen 2007; Cannon 2014). The absence of mix and
price effects allows us to draw clean inferences about cost behavior, mitigating concern that cost
stickiness (or anti-stickiness) is an artifact of price changes (Cannon 2014; Anderson and Lanen
2007; Banker and Byzalov 2014). Finally, economy lodging properties have few cost drivers
beyond output, limiting the risks of confounding effects.
Common cost function. Volume elasticity of cost and asymmetry are sensitive to
differences in cost structures resulting from past, rather than current, decisions. Greater asset
intensity, employee intensity (Anderson et al. 2003; Subramaniam and Weidenmier 2003) and
proportion of fixed costs impact in non-trivial ways both the volume elasticity of cost and
asymmetry (Balakrishnan et al. 2014). In our setting, this issue is mitigated because all hotels
have the same processes, same technology and nearly identical cost structure, facilitating the
attribution of observed effects to managerial discretion (Balakrishnan et al. 2014; Cannon 2014).
There was no technological change and no growth (i.e., no hotels were expanded) so we can rule
out differential growth rates (Balakrishnan et al. 2014). Finally, we alleviate attribution concerns
associated with capacity utilization, cost structure (Balakrishnan et al. 2004; Balakrishnan and
Gruca 2008; Balakrishnan et al. 2014) and slack (Weiss 2010; Banker and Byzalov 2014) by
focusing on short-term controllable expense.
Common internal controls and reporting rules. Because all properties are owned the
same company in one country, legal requirements, internal controls, decision rights and
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compensation schemes are essentially the same across all business units.3 Accordingly, we can
control for the effects of legislation and governance mechanisms (Calleja et al. 2006; Anderson
et al. 2007; Janakiraman 2010. Further, inventories are negligible, discretionary accruals
nonexistent, and reporting rules the same for all business units. Accounting conventions and
reporting choices, which may affect costs stickiness, are therefore not concern (Anderson and
Lanen 2007; Banker and Byzalov 2014; Shust and Weiss 2014). Finally, a common system of
detailed accounts eliminates heterogeneous cost categories.
Managerial discretion. Anderson and Lanen (2007) argue that costs subject to managerial
discretion should provide better evidence of overt cost management. In our setting, general
managers have a high degree of autonomy that includes staffing decisions (hiring and firing),
assigning work hours, recommending pricing, ensuring property maintenance, managing the use
of raw materials, contracting with local suppliers (e.g., landscaping and snow removal),
monitoring energy use and recommending capital investments (i.e., renovations). General
managers prepare, or provide input on, annual budgets and are held accountable for business unit
performance. They participate in an incentive compensation plan that rewards financial,
customer satisfaction and internal audit performance. Altogether, this means that a general
manager has a significant degree of influence over property-level expenses and the appropriate
incentives to exercise their influence. This setting is, therefore, particularly suitable to assess the
impact of a general manager’s discretion over cost variability as virtually all other sources of
variance are controlled for or fixed.
Overall, our setting considerably limits the risk of omitted variables and confounding
effects, providing good internal validity at the risk of lower external validity. We can reasonably
3
Differences across states are minor. For example, labor laws vary across states, although not to a great degree
when compared to entities with foreign operations.
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assert that the only meaningful differences between business units are local market conditions
and general managers. Additional proprietary data facilitates separating these effects to allow
good inferences about cost behavior and general managers behavior.
3.2 VARIABLES
Controllable Expenses. Since we are interested in the relationship between cost elasticity,
efficiency and managerial discretion, we focus on costs actually managed by general managers.
These costs are reported in the accounting system as “controllable expenses.” As mentioned in
the preceding section, general managers exert considerable influence on, and are held
accountable for, the amount and timing of controllable expenses. Controllable expenses include
all expenses for labor (front desk, housekeeping, etc.), materials (office supplies, cleaning
materials, detergent, guest amenities, etc.), maintenance (room furnishings, paint, landscaping,
etc.), utilities (gas, water, electric, television, etc.) and so forth.4 Controllable expenses do not
include property taxes, depreciation, interest expense, lease payments and so on. These fixed
expenses are charged to corporate whose strategic, financial and operating decisions resulted in
these costs being incurred. The impact of general managers’ decision is short term, therefore we
do not expect previous years’ decisions to affect current-year decisions. Contrary to the case of
SG&A costs, which may have an impact on sales volume (e.g., advertising), there is no risk of
reverse causality; that is, the costs considered are caused by sales volume, and not vice versa.
Our dependent variable, CEXP, is the log change of inflation-adjusted controllable expenses.
Components of Controllable Expenses. Previous studies have demonstrated that different
cost categories (essentially SG&A and Costs of Goods Sold) have different time-series properties
4
Almost all materials are purchased on a national contract so input prices are fixed across properties. The only
inputs that are negotiated and purchased locally are services not available from national providers (e.g., snow
removal, roofing, parking lot resurfacing, lawn care, etc.). In these cases, the general managers do the negotiations
and select the service providers with the approval of her regional manager. Here, too, managers have discretion
about frequency of service (e.g., when to resurface the parking lot or when to recommend boiler replacement).
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(Banker and Chen 2006) and may therefore be characterized by different degrees of elasticity
and asymmetry. This is obvious when we consider subdivisions distinguishing between different
functions (e.g., marketing and R&D expense) or cost categories (e.g., labor expense). Our data
set is comprised of more than 150 cost classification and we are able to perform tests on the
principal cost categories of labor (LABOR), supplies (SUPPLIES), maintenance and repair
(MRO), utilities (UTILITIES), and other (MISC). As with controllable expenses, each of these
variables is the log change of inflation-adjusted expense. Disaggregation should provide a better
understanding of managerial discretion.
Rentals. As discussed previously, the number of rooms rented for one night is the
principal cost driver. While all rooms can accommodate more than one guest, the overwhelming
number of rooms are occupied by one person for one night. Furthermore, labor, supplies and
energy expenses should, and do appear to, vary little with respect to the number of guests
occupying a room.5 In many industries sales prices vary considerably across multiple products
and, within product lines, across customers. Price variation is most significant when managers
rely heavily on yield management techniques; that is, actively managing price to establish high
sales volume, as is the case in the air transportation and hospitality industries (e.g., see Cannon
[2014]). With no sales mix, changes in volume are unambiguous. Accordingly, room rented is
the measure of output volume. RENTALS is the log change of the number of rooms rented.
Volume decrease. To test the cost slope difference between periods of increasing activity
and periods of decreasing activity, we follow the procedure of Anderson et al. (2003) and create
the dummy variable DECREASE, coded 1 for decreases in output volume, and 0 otherwise.
5
Labor time taken to change linens, clean the floor and bathroom fixtures, etc. does not change with respect to the
number of persons in a room. Energy use for climate control, lighting and television varies immeasurably with the
number of guests. Water usage will vary, but the effect is very small. Length of stay impacts labor expense as rooms
are not comprehensively cleaned each day when a guest stays more than one day. However, in our setting, 89% of
guests stay just one day and the difference across property locations is minor.
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Economic Conditions. Since we expect general managers to manage differently
controllable expenses before and after the crisis, we create the dummy variable CRISIS, coded 1
for the years 2008 to 2011, and 0 otherwise.
Efficiency. An efficient business unit is one that either (i) uses the minimum amount of
resources for a given output or (ii) produces the maximum output for a given amount of
resources. This notion is complex to operationalize when several partially substitutable resources
and outputs are involved and requires statistical techniques like stochastic frontier estimation (a
parametric technique) or data envelopment analysis (DEA). DEA is a deterministic, nonparametric linear programming technique for measuring the efficiency of a group of comparable
business units (Bogetoft 2013; Cooper et al. 2006). As a benchmarking tool, DEA has been used
extensively in many literatures, including accounting (e.g., Demerjian et al. [2013]) and
hospitality (for a review, see Dittman et al. [2009]). To identify efficient properties, we used a
DEA model incorporating four outputs: room rentals, revenue, customer complaints and guest
satisfaction. Rentals, as previously described, is output volume. Revenue captures the effect of
pricing decisions. Customer complaints is a measure of quality failures (i.e., dissatisfaction) and
guest satisfaction measures the impact of quality performance6. Ten variables are used as DEA
model inputs: rooms available (an uncontrollable input that measures capacity), general manager
salary, labor expense, materials expense and utilities expense.7 The general manager’s salary is
her monthly salary. Labor expense includes wages paid for front desk staff, housekeeping staff,
the maintenance worker and head housekeeper. Materials expense includes expenses incurred for
6
The company tracked complaints registered through multiple channels. Guest satisfaction was also measured and
was done so consistently, within years.
7
Once built, hotels rarely expand or contract. That is, a 130-room hotel usually remains a 130-room hotel over its
useful life. In rare circumstances, available land may permit construction of an additional building, but this did not
occur in our sample. In areas of economic decline, chain hotels usually sell the property rather than close down a
floor or “wing” of a multi-building facility. Accordingly, capacity is fixed.
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supplies (e.g., guest amenities, cleaning chemicals and linens), miscellaneous operating expenses
(e.g., lawn care, local advertising) and repairs (e.g., paint, plumbing and electrical parts, small
fixtures). Utilities expense includes water, gas, electric, trash disposal, telecommunication
services and satellite television. Based on discussions with management and nearly two dozen
site visits, we assume constant returns to scale8. For each year (2003-2011), we used monthly
financial and operational data to estimate each property’s annual efficiency score. Observations
with a score of 100% were deemed efficient, and coded 1, with those below 100% being coded 0.
Prior Efficiency. Since we expect existing slack (or strain) to influence a general
manager’s ability to manage costs, we added a dummy variable PRIOREFF, coded 1 if the
property was efficient the preceding year, and 0 otherwise. We also simplified interaction terms
by introducing 3 dummies: DEGRADE, when a property was coded efficient in the prior year
but inefficient the current year; UPGRADE, when a property was coded inefficient in the prior
year but efficient the current year; and when a property was coded efficient both years,
KEEPEFF.
3.3 TESTING HYPOTHESES
Hypothesis 1. Our first hypothesis predicts anti-sticky costs. Anderson et al. (2003) use a
log-log model to assess whether SG&A costs slopes differ between sales increase and sales
decrease situations. This specification improves the comparability of variables and alleviates
heteroskedasticity concerns. To test our first hypothesis, we use the following model:
CEXP = β0 + β1 RENTALS + β2 * RENTALS * DECREASE+ εi,t
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(1)
Labor for cleaning rooms is a function of rooms rented. Labor for maintenance is a function of property size
(capacity) and, to a lesser degree, rentals. Front desk labor is typically fixed at one person per shift. Accordingly, the
assumption of constant returns to scale is appropriate. The only item we can think of that might exhibit increasing
returns to scale would be laundry equipment.
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where the variables have been defined above. In this model, β1 is interpreted as the
increasing volume elasticity (upward elasticity; less rigid cost structure) (Banker, Byzalov, and
Plehn-Dujowich 2014) of controllable expenses (percentage of controllable expenses increase for
a contemporaneous 1% increase in rentals), and β1 + β2 represents decreasing volume elasticity
(downward elasticity) of controllable expenses. β2 measures the difference in slopes between
upward elasticity and downward elasticity and, therefore, the level of asymmetry in cost changes.
If β2 is negative, controllable expenses are less responsive to sales decreases than to sales
increases. Costs are then deemed “sticky.” If β2 is positive, controllable expenses are more
responsive to sales decreases than to sales increases. Such costs have been defined as “antisticky” (Weiss 2010). H1 is supported if β2 is significantly positive.
Hypothesis 2. To test H2, which predicts anti-sticky cost behavior after an economic
crisis, we add a two-way interaction between RENTALS and CRISIS and a three-way interaction
between RENTALS, CRISIS and DECREASE, as shown below:
CEXP = β0 + β1 RENTALS + β2 * RENTALS * DECREASE
+ β3 * RENTALS * CRISIS + β4 * RENTALS * CRISIS * DECREASE
+ εi,t
(2)
Here, we are interested in β3 and β4 being positive. A negative coefficient for β3 means
that, after the crisis, general managers increase costs at a lesser rate when volumes increase
relative to before the crisis. If β4 is positive, general managers cut more costs when volumes are
decreasing, after the crisis, relative to before the crisis (the β2 coefficient). To support H2, (β1 +
β3) must be less than (β2 + β4). That is, the combined slope of post-crisis sales increases must be
less than the combined slope of the post-crisis sales decreases.
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Hypothesis 3. The third hypothesis predicts the impact of prior-period slack or strain. To
test this, we had to (2) a two-way interaction between RENTALS and PRIOREFF and a threeway interaction between RENTALS, PRIOREFF, and DECREASE as follows:
CEXP = β0 + β1 RENTALS + β2 * RENTALS * DECREASE
+ β3 * RENTALS * CRISIS + β4 * RENTALS * PRIOREFF
+ β5 * DECREASE * RENTALS * CRISIS
+ β6 * RENTALS * DECREASE * PRIOREFF + εi,t
(3)
H3 is supported if β4 is significantly positive (managers of previously efficient properties
increase more costs when volumes increase) or if β6 is significantly negative (managers of
previously efficient properties decrease less costs when volumes decrease).
In subsequent analysis, we also assess cost elasticity and its asymmetry for currently
inefficient and efficient properties (model 4) compared to the cost behavior of properties
remaining inefficient, becoming efficient, becoming inefficient, and remaining efficient. This is
done using model 5. These models are as given below:
CEXP = β0 + β1 RENTALS + β2 * RENTALS * DECREASE
+ β3 * RENTALS * CRISIS + β4 * RENTALS * CRISIS * DECREASE
+ β7 * RENTALS * EFFICEINT
+ β8 * RENTALS * EFFICIENT * DECREASE + εi,t
(4)
CEXP = β0 + β1 RENTALS + β2 * RENTALS * DECREASE
+ β3 * RENTALS * CRISIS + β4 * RENTALS * CRISIS * DECREASE
+ β5 * RENTALS * PRIOREFF + β6 * RENTALS * PRIOREFF * DECREASE
+ β7 * RENTALS * EFFICIENT + β8 * RENTALS * EFFICIENT * DECREASE
17
+ β9 * RENTALS * PRIOREFF * EFFICIENT
+ β10 * RENTALS * PRIOREFF * EFFICIENT * DECREASE + εi,t
(5)
Hypothesis 4 predicts that different cost categories will be managed differently. To
investigate differences in the way various cost categories are managed, we use a simplified
version of equation 5 and using the variables LABOR, SUPPLIES, MRO, UTILITIES, and
MISC as dependent variables in separate models. The equations for these separate models are not
shown.
4. RESULTS
4.1 DESCRIPTIVE STATISTICS
Descriptive statistics are provided in Table 1 and table 2 provides the correlation matrix
for the variables used in our study.
- Insert Tables 1 and 2 about here 4.2 RESULTS FOR HYPOTHESES
Model 1 in panel A of table 3 provides the results for H1. As expected, controllable
expenses in the economy lodging industry appear anti-sticky on average: b2 is positive and
highly significant (b2 = 0.214, p < 0.01). The economic theory of asymmetric cost behavior is
therefore supported in the sense that in situations where downward adjustment costs are low but
upward adjustment costs are high, costs decrease faster than they increase.
- Insert Table 3 about here Model 2 in table 3 provides the results for the test of H2. As expected, controllable
expenses are significantly more anti-sticky after the crisis than before, because of a double effect.
First, when volumes increase, general managers appear to contain their costs more after the crisis
than before as indicated by a significant reduction of upward elasticity (b3 = -0.251, p < 0.01).
18
Second, when volumes decrease, they appear to cut more drastically their costs than before the
crisis, as indicated by a significant increase of the downward elasticity (b4 = 0.571, p < 0.01). By
comparing model 1 and model 2, we can also see that before the crisis, controllable costs were
actually sticky; that is, increasing faster with growing volumes than they decrease with falling
volumes. The crisis appears to have caused a dramatic change in cost management, from a lack
of cost control to a very aggressive policy of cost containment and cost cutting.
Model 3 provides results for H3. As expected, prior year efficiency is associated with
greater stickiness: upward elasticity is higher (b4 = 0.096, p < 0.01) and downward elasticity is
lower (b6 = -0.239, p < 0.01). In other words, efficient properties need to increase committed
resources to sustain volume growth, but do not have to reduce their costs as much when volumes
fall.
These results, however, do not take into account changes in efficiency. Properties
remaining inefficient, becoming efficient, becoming inefficient, or remaining efficient may have
very different cost management patterns. Models 4 and 5 in table 4 investigate this issue. Model
4 shows that currently efficient properties have, on average, anti-sticky costs: upward elasticity
of efficient properties is lower (b7 = -0.117, p < 0.01) and downward elasticity of efficient
properties greater (b8 = 0.231, p < 0.01). Model 5 explains this seemingly contradictory result:
Properties becoming efficient (previously inefficient but currently efficient) are characterized by
anti-stickiness (i.e., aggressive cost management) while properties becoming inefficient are
characterized by cost stickiness (i.e., lack of cost control). Perhaps more interestingly, the cost
behavior of properties remaining efficient is not significantly different from the cost behavior of
properties remaining inefficient.
- Insert Table 4 about here -
19
Finally, panel B of table 3 explores the differences in cost management across
controllable costs categories. The previous conclusion remains essentially valid once we
decompose the dependent variable. Some sharp differences across cost categories can however
be observed. The most noteworthy differences are about supplies, maintenance and repair, and
utilities.
First, contrary to most of the other cost categories, supplies upward elasticity increased
and supplies downward elasticity decreased after the crisis, making these costs stickier. The
pattern is amplified for business units becoming inefficient and attenuated for business units
becoming efficient. Although we do not have a clear explanation for this observation, it may be
related to the fact that supplies is the only expense associated to an inventory and as such the
most amenable to theft (i.e., from customers or disgruntled employees).
Second, it appears the main difference between properties becoming inefficient and
properties becoming efficient is the way a general manager controls maintenance and repair
expenses. Upgrading properties drastically cut maintenance and repair costs while downgrading
properties increase these costs when sales volume increases, and maintain them when volumes
drop. This may indicate that a general manager postpones these expenses, temporarily increasing
efficiency owing to a lag in customer satisfaction response, and incurring expenses when
customer satisfaction drops sharply.
Finally, utilities are managed the same way by all properties, going from highly sticky
before the crisis to highly anti-sticky after the crisis.
5. CONCLUSION
We contribute to the literature first by providing a direct test of the impact of economic
conditions (crisis) and slack (efficiency) on cost elasticity (i.e., cost variability) and asymmetric
20
cost behavior (i.e., cost stickiness or anti-stickiness). Our setting allows us to attribute
unambiguously such observations to managers’ discretion. The financial crisis in 2008 caused
managers to significantly alter the way they managed costs, limiting growth in costs when sales
volume increases, and cutting costs drastically when volume decreases. It appears that efficient
properties have, on average, stickier costs than inefficient properties. This paper refines our
understanding of the relationships between cost elasticity, asymmetric cost behavior and
managerial discretion in several other ways. We observe that cost elasticity and asymmetric cost
behavior do not differ significantly between business units remaining efficient or remaining
inefficient. Cost behavior differences are, however, associated with changes in efficiency, that is,
business units becoming efficient (inefficient) show high cost anti-stickiness (stickiness).
Further, our results indicate that researchers should consider changes in slack resources, in
addition to levels of slack resources. Finally, we provide additional evidence that cost
management differs systematically across cost categories and functions.
21
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24
Table 1
Descriptive statistics
Mean
Standard
Deviation
Median
Lower
Quartile
Upper
Quartile
1. Rooms Available
103.0
21.7
103.0
90.0
120.0
2. Rooms Rented
24,088.1
5,453.7
23,887.0
20,332.0
27,410.0
3. Sales Revenue
1,035,776.0
306,865.8
982,606.0
815,243.0
1,204,010.0
4. Controllable Expense
533,298.9
102,607.9
524,765.0
459,527.0
597,677.0
5. Front Desk Expense
81,002.8
12,071.6
80,467.0
72,585.0
88,599.0
6. Housekeeping Expense
86,320.6
21,929.6
84,523.0
70,756.0
100,063.0
7. Supplies Expense
35,082.5
9,986.4
33,847.0
27,862.0
41,186.0
8. Other Operating Expense 168,693.5
39,458.5
163,707.0
141,277.0
191,478.0
9. Repairs Expense
54,314.0
19,586.7
51,612.0
40,478.0
65,550.0
10. Utilities Expense
107,885.5
30,907.9
103,267.0
86,775.0
124,411.0
Notes. With the exception of rooms available, all other measures are annual figures. Rooms available is
the size of the business unit and is a per-night figure. N = 3,135. Controllable Expense includes the
amounts of all other expenses listed. Financial amounts are in USD.
25
Table 2
Correlation Matrices
Panel A: Pearson Correlations for continuous variables.
1
2
3
1. Controllable Expenses
2. Labor
0.634
3. Supplies
0.483
0.452
4. Repairs
0.574
0.272
-0.320
5. Utilities
0.490
0.207
0.100
6. Miscellaneous
0.730
0.181
0.143
7. Rentals
0.475
0.534
0.450
Panel B: Polychoric correlations between dichotomous variables.
1
2
3
1. Crisis
2. Decrease
0.146
3. Efficient
0.046
-0.109
4. Prior Efficient
0.094
0.064
0.683
26
4
5
6
0.114
0.196
0.189
0.183
0.268
0.216
Table 3
Regression Models
Panel A: Asymmetric cost behavior for controllable expenses.
Dependent variable:
CEXP
(1)
(2)
(3)
(4)
(5)
RENTALS
0.277***
(0.025)
0.437***
(0.033)
0.416***
(0.034)
0.481***
(0.036)
0.460***
(0.036)
RENTALS:DECREASE
0.214
(0.037)
***
-0.201
(0.050)
***
-0.139
(0.051)
***
-0.268
(0.053)
***
-0.203
(0.053)
***
-0.255
(0.038)
***
-0.253
(0.038)
***
-0.262
(0.038)
RENTALS:CRISIS
-0.251
(0.038)
**
RENTALS:PRIOREFF
0.229
(0.076)
-0.117***
(0.040)
RENTALS:DECREASE:CRISIS
0.571
***
(0.050)
RENTALS:DECREASE:PRIOREFF
0.577
***
(0.050)
-0.239
0.569
***
(0.050)
***
-0.157***
(0.050)
0.580
***
(0.050)
-0.446
(0.055)
RENTALS:DECREASE:EFFICIENT
***
***
0.096
(0.045)
RENTALS:EFFICIENT
***
***
(0.086)
0.231
***
(0.052)
RENTALS:PRIOREFF:EFFICIENT
0.335
***
(0.070)
(0.089)
(0.100)
RENTALS:DECREASE:PRIOREFF:EFFICIENT
0.124
(0.123)
Constant
Observations
R2
Adjusted R2
Residual Std. Error
F Statistic
0.038***
(0.002)
0.035***
(0.002)
0.035***
(0.002)
0.035***
(0.002)
0.035***
(0.002)
4216.000
0.232
0.231
0.070 (df =
4213)
635.802***
(df = 2; 4213)
4216.000
0.257
0.257
0.069 (df =
4211)
364.915***
(df = 4; 4211)
4216.000
0.262
0.261
0.069 (df =
4209)
249.149***
(df = 6; 4209)
4216.000
0.261
0.260
0.069 (df =
4209)
247.720***
(df = 6; 4209)
4216.000
0.270
0.269
0.068 (df =
4205)
155.901***
(df = 10; 4205)
Note: p<0.1; p<0.05; p<0.01
27
Table 3 (continued)
Panel B: Cost Categories
Dependent variable:
CEXP
(1)
***
RENTALS
0.460
(0.036)
RENTALS:DECREASE
-0.203
(0.053)
RENTALS:CRISIS
LABOR
(2)
***
SUPPLIES
(3)
MRO
(4)
UTILITIES
(5)
0.571
(0.072)
***
0.347
(0.124)
***
0.515
(0.052)
***
0.148
(0.107)
0.240
(0.184)
-0.445
(0.076)
***
0.330
(0.077)
***
(0.024)
***
0.800
(0.263)
0.596
(0.039)
***
MISC
(6)
***
0.324
(0.067)
***
-0.189
(0.058)
-0.262
(0.038)
***
-0.211
(0.041)
***
0.158
(0.083)
0.546
(0.153)
-0.157
(0.050)
(0.058)
(0.054)
-0.281
(0.101)
-0.656
(0.173)
(0.072)
-0.273
(0.092)
RENTALS:KEEPEFF
(0.017)
(0.054)
(0.000)
(0.058)
(0.024)
(0.107)
(0.100)
(0.184)
(0.033)
(0.077)
(0.002)
(0.099)
RENTALS:DECREASE:CRISIS
0.580
(0.050)
***
0.432
(0.054)
***
-0.229
(0.099)
**
0.156
(0.170)
1.023
(0.071)
***
0.686
(0.091)
RENTALS:DECREASE:DEGRADE
-0.446
(0.086)
***
(0.073)
-0.506
(0.157)
RENTALS:DEGRADE
RENTALS:UPGRADE
RENTALS:DECREASE:UPGRADE
0.229
(0.076)
***
***
***
*
***
***
(0.131)
***
***
***
***
-0.220
(0.098)
-0.622
(0.055)
***
-0.279
(0.070)
(0.014)
0.296
(0.141)
(0.109)
0.014
**
***
**
***
***
***
-0.359
(0.093)
-0.956
(0.172)
-1.418
(0.294)
0.020
0.627
(0.141)
***
1.358
(0.241)
(0.100)
0.534
(0.129)
***
(0.122)
0.128
***
0.335
(0.070)
(0.076)
RENTALS:DECREASE:KEEPEFF
0.013
(0.068)
-0.127
(0.073)
*
(0.028)
(0.136)
0.050
(0.233)
0.128
(0.097)
0.073
(0.125)
Constant
0.035
(0.002)
***
0.027
(0.002)
***
0.027
(0.003)
***
0.040
(0.006)
***
0.046
(0.002)
***
0.040
(0.003)
4,216
4,216
4,216
4,216
4,216
4,216
0.27
0.302
0.22
0.055
0.123
0.074
0.269
0.3
0.219
0.053
0.121
0.072
0.068
0.074
0.137
0.235
0.098
0.126
Observations
2
R
2
Adjusted R
Residual Std. Error (df = 4205)
F Statistic (df = 10; 4205)
155.901
***
181.780
***
Note: p<0.1; p<0.05; p<0.01
28
118.887
***
24.663
***
59.235
***
***
33.816
***