Managing Budget Emphasis Through the Explicit Design of Conditional Budgetary Slack

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Accounting, Organizations and Society 30 (2005) 587–608

Managing budget emphasis through the explicit designof conditional budgetary slack

Tony Davila a, Marc Wouters b,*

a Graduate School of Business, Stanford University, USAb School of Business, Public Administration, and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, Netherlands

Abstract

Budgetary slack plays an important role in the functioning of budgets in organizations. While theory has found neg-

ative as well as positive elements associated with its presence, the empirical literature has interpreted it as being dysfunc-

tional to organizations. In this paper, we present empirical evidence on how a company purposefully budgeted

additional financial resources with a motivation intention (Lukka. Budgetary biasing in organizations: Theoretical

framework and empirical evidence. Accounting, Organizations and Society 13 (1998) 281–302) to facilitate the manag-

ers� task in achieving the goals of the company. Using quantitative and qualitative data from four logistic sites of a disk

drive manufacturer for 24 months, we examine how the company accepted more slack as the demand on business proc-

esses increased and goals other than budget targets––in particular, service quality––became harder to achieve. By allow-

ing this practice, headquarters made it clear to local managers that product quality and service were at least as

important as meeting budget objectives. We also find that not only was budgetary slack purposefully built during

the budgeting process but also in the budgeting system itself through the underlying cost accounting assumptions.

The results of this paper provide empirical evidence on the positive aspects of budgetary slack and on the role of cost

accounting models used in the budgeting system to facilitate managerial work.

� 2004 Elsevier Ltd. All rights reserved.

Introduction

Budgets are probably the management toolmost widely used in organizations. Their relevance

0361-3682/$ - see front matter � 2004 Elsevier Ltd. All rights reserv

doi:10.1016/j.aos.2004.07.002

* Corresponding author.

E-mail addresses: [email protected] (T. Davila),

[email protected] (M. Wouters).

has nurtured significant research efforts and cre-

ated the ‘‘only organized critical mass of empirical

work in management accounting’’ (Brownell &Dunk, 1991). A key variable that has traditionally

been perceived as limiting the effectiveness of

budgets is budgetary bias (Lukka, 1988) and, in

particular one of its sub-components: budgetary

slack (Dunk & Nouri, 1998; Kamin & Ronen,

1981; Merchant, 1985; Walker & Johnson, 1999;

ed.

mailto:[email protected]

mailto:[email protected]

1 When fitting field observations to theoretical concepts,

some of the richness found in reality is lost. As it will become

clear when we present the evidence, the actual mechanisms used

in our research site are not constrained to the concept of

budgetary slack. Certain mechanisms, like ‘‘padding’’ the

budget numbers when external conditions are more demanding,

or modeling fixed costs as variable (thus increasing the budget

more than proportionally when expected volume is higher), fit

the concept of budgetary slack. Mechanisms such as budgeting

higher costs when volume is higher (simply because certain

costs are variable) are closer to flexible budgeting techniques.

These mechanisms have the common goal of easing budget

pressure when non-financial goals are more demanding and,

conversely, increasing it when these other goals are less taxing.

We label these mechanisms at our research site as budgetary

slack throughout the discussion because (1) their aim is to ease

cost targets when needed, (2) budgetary slack mechanisms

found in the site are more interesting from a theoretical

perspective than the flexible budget mechanisms, and (3) the

evidence in the case speaks directly to the budgetary slack

literature and its bias to interpret it as a negative practice. In

doing so, there is the risk that the negative connotations of the

concept––‘‘wasted resources’’ or ‘‘more than enough

resources’’––will obscure the interpretation of the evidence.

However, we interpret budgetary slack as some definitions in

the literature do; this is as a neutral concept without a positive

or negative tone that only its use determines. The reader should

evaluate the evidence with these qualifications in mind. We

would like to thank one of the reviewers for pointing out this

important clarification.

588 T. Davila, M. Wouters / Accounting, Organizations and Society 30 (2005) 587–608

Webb, 2002). The presence of budgetary slack

makes budgets easier to attain and empirical work

has assumed that its presence is dysfunctional––or

even unethical (Douglas & Wier, 2000)––and

should be limited (Dunk, 1995; Fisher, Frederick-son, & Peffer, 2000, 2002a; Nouri, 1994; Young,

1985). Budgetary slack isolates organizational

members from the motivational effects of budgets,

limits the effort that these people exert, and leads

to inefficient use of the resources that the organiza-

tion controls.

However, the theoretical literature indicates

that the presence of budgetary slack may also havepositive consequences (Lukka, 1988). For in-

stance, companies following strategies that require

innovation and experimentation can benefit from

budgetary slack because it allows managers to fo-

cus on relevant long-term and short-term objec-

tives other than meeting budgets such as quality

or customer service (Van der Stede, 2000). In these

settings, budgetary slack provides operating flexi-bility to increase the predictability of earnings, re-

duce the time spent on control tasks, reduce risks

of dysfunctional behavior, and give managers dis-

cretion to pursue multiple goals while dealing with

adverse exogenous factors (Lillis, 2002; Merchant

& Manzoni, 1989).

In this paper we present evidence consistent

with the purposeful use of budgetary slack to facil-itate managers� work when they are responsible for

pursuing multiple goals beyond achieving cost

budgets. In particular, we argue that even if budg-

etary slack has been presented as dysfunctional in

most of the empirical literature, in certain set-

tings––characterized by uncertainty and multiple

goals––budgetary slack can be beneficial to moti-

vate the appropriate behavior.Using a case study research design with both

quantitative and qualitative data, we present evi-

dence on how a company uses budgetary slack to

enhance managers� motivation (motivation inten-

tion) (Lukka, 1988) in the presence of multiple

goals. Specifically, budgetary slack is generated

when meeting the demands on key non-financial

performance dimensions––product quality andservice––becomes more difficult. When achieving

multiple goals (including budget goals) is relevant

to success, budgetary slack allows the company

to balance these different goals as external condi-

tions change. 1

The paper is among the first to present archival

empirical evidence on the purposeful use of budg-

etary slack to facilitate managerial work. Thedata includes budget and actual financial per-

formance for four logistic sites of a computer disk

drive manufacturer for 24 months and service

performance for 18 months. We also conducted

more than 70 hours of interviews with managers

in the financial office, quality control, supply

chain management and logistic centers to fully

understand the budgeting process as well as thedistribution of knowledge in the organization.

The case study methodology sacrifices external

validity in an effort to sharpen the results and

facilitate ‘‘analytical generalization’’ (Yin, 1989,

p. 38) through the confirmation of an untested

theory. Also in contrast to previous budgeting

studies that rely on perceptual data collected

T. Davila, M. Wouters / Accounting, Organizations and Society 30 (2005) 587–608 589

through survey methods, we use archival data

from a company supplemented with qualitative

information to better understand the implications

of the findings.

Our findings indicate that budgetary slack(measured as favorable cost efficiency variance) is

larger when the expected demand on the business

process (as measured by the budgeted volume of

product shipped) is larger, and when the unex-

pected demand (as measured by unexpected vol-

ume) is larger. Interestingly, the budgeting

model––through its assumptions about cost

behavior––creates (removes) this budgetary slackwhenever volume is above (below) expectations.

When actual volume is higher than budgeted vol-

ume, budgeted costs increase faster than actual

costs and create budgetary slack. Thus, the mech-

anisms imbedded in cost accounting are not neu-

tral to budgeting but affect how budgetary slack

is generated.

The rest of the paper is structured as follows. Inthe next section we review the theory and existing

empirical evidence that lead to our propositions.

In Section ‘‘Qualitative evidence: Company PCC

and its budgeting process’’, we describe the com-

pany, its budgeting process, and the qualitative

evidence. Section ‘‘Quantitative evidence: Re-

search design’’ describes the research design for

the quantitative data and Section ‘‘Results’’ theassociated results. Section ‘‘Discussion and con-

clusions’’ discusses the findings, future research

opportunities, and conclusions.

The structure of the paper may suggest that

the research process was linear from literature re-

view, propositions, case selection, data gathering,

to hypothesis testing. However, this structure is

adopted to present the results of the field study.The research was an iterative process going back

and forth between the case study and the litera-

ture. We selected our research site because of

the detailed archival information on the budget-

ing process and performance. As we progressed,

we identified the richness embedded in the com-

pany�s use of budgetary slack. Throughout the

research process, we kept on going back to theliterature to refine our understanding of the phe-

nomenon, shape the propositions, inform our

observations, and guide our research efforts dur-

ing the interviews and the collection of archival

data.

Literature review and propositions

Budgetary bias and budgetary slack

The literature identifies several characteristics

to explain managers� propensity to build dysfunc-

tional budgetary slack (Dunk & Nouri, 1998). (1)

Budget emphasis (Dunk, 1995; Hopwood, 1972;

Merchant, 1985; Otley, 1978) enhances theimportance of budget targets to responsibility

center managers� social and economic rewards;

empirical evidence indicates that it is positively

related to higher levels of budgetary slack (Mer-

chant, 1985; Onsi, 1973; Walker & Johnson,

1999). (2) Budgetary participation (Merchant,

1985) may lead to better performance because

of subordinates� experience, increased morale,sense of control, commitment (Locke & Latham,

1990), and information exchange between superi-

ors and subordinates (Shields & Shields, 1998).

However, budgetary participation provides man-

agers with an opportunity to intentionally influ-

ence budget targets (Lukka, 1988) and build

budgetary slack (Young, 1985). (3) Information

asymmetry (Fisher et al., 2002a; Fisher, Maines,Peffer & Sprinkle, 2002b) describes settings where

subordinates and superiors have different private

information (Lambert, 2001). When subordinates

have an informational advantage, they can misrep-

resent the information to negotiate easier targets

and create budgetary slack (Kirby, Reichelstein,

Sen, & Palk, 1991). (4) Uncertainty refers to the

lack of information for planning (Chapman,1997; Galbraith, 1973; Macintosh, 1985) and cre-

ates an incentive for the managers to create

budgetary slack as a way of hedging against lack

of predictability (Brownell & Dunk, 1991; Lukka,

1988; Merchant, 1985).

Previous literature describes budgetary bias as

a way of affecting the standards against which

performance will be assessed. It has been definedas a ‘‘deliberately created difference between the

budgeting actor�s forecast about the future (�hon-

est budget estimate�), and his or her submitted


budget figure (budget proposal)’’ (Lukka, 1988).

Budgetary bias can lead to optimistic upward

biasing––setting standards higher than a neutral

estimate of performance––or to budgetary

slack––when standards are lower than the budg-eting actor�s honest estimate of performance.

Dunk and Nouri (1998) review the literature on

budgetary slack and by drawing the various

perspectives in the literature define it as ‘‘the

intentional underestimation of revenues and pro-

ductive capabilities and/or overestimation of

costs and resources required to complete a budg-

eted task’’.These definitions do not expressly imply a dys-

functional element, although the ideas of deviat-

ing from honest estimates, of making budgets

easier to attain, and overestimating costs some-

what suggest such an element. Nor do these def-

initions consider the role of uncertainty and

multiple goals.

The dysfunctional element emerges more clearlyin the presence of private information when subor-

dinates use budgetary slack to ease their work or

obtain personal rents. Budgetary slack facilitates

appropriating excess resources (resource motiva-

tion) and simplifying performance achievement

(performance evaluation intention) (Lukka, 1988).

In agency models, budgetary slack reflects a devia-

tion from maximum efficiency due to informationasymmetry (when the agent is better informed than

the principal). This is especially the case where

maximum efficiency would be achieved if supervi-

sors had all the information and they could set

the budget to provide subordinates with enough

resources to perform their activities. The deviation

from the maximum efficiency translates into a

reduction of managerial effort or an increase inperk consumption (Kirby et al., 1991). In these

models, the efficiency loss associated with budget-

ary slack is unavoidable and part of the second

best solution. Budgetary slack can also be detri-

mental because it isolates the subordinate from

the motivational properties of budgets leading to

lower effort and inefficient use of resources (Bour-

geois, 1981).This literature identifies budgetary slack as dys-

functional. However, the presence of budgetary

slack may be beneficial to the organization (Lillis,

2002; Lukka, 1988; Merchant & Manzoni, 1989;

Onsi, 1973). It facilitates managerial performance

in the presence of high budgetary emphasis and

uncertainty (Dunk, 1995), isolates risk-averse sub-

ordinates from excessive risk, and increases jobsatisfaction as budgetary slack avoids the perform-

ance consequences of missing the budget––such as

loss of bonuses, loss of job (Merchant & Manzoni,

1989), or the social pressure associated with

underachievers.

More importantly, budgetary slack may reduce

budget emphasis and allow subordinates to allo-

cate attention to goals other than meeting the bud-get––such as quality or customer service––when

meeting the budget becomes harder. In work set-

tings with multiple goals, such as cost, efficiency,

productivity, quality, customer service, and

responsiveness, tradeoffs between these goals exist

(Lillis, 2002). When these multiple goals cannot be

simultaneously attained, managers may sacrifice

goals that should have priority. Budgetary slackfacilitates pursuing objectives other than meeting

the budget without fully ignoring the relevance

of budgets. For example, budgetary slack may ease

(but not eliminate) the constraint imposed through

the budget and give the manager the ability to

meet non-financial goals when production volumes

are unexpectedly high.

The notion that organizations may use budget-ary slack purposefully leads to a more elaborate

understanding of how budgetary slack may be cre-

ated. It is not always the case that organizations

prefer an easily achievable target; the objective is

not to make cost targets always easily achievable,

but only when it is particularly difficult to meet

financial and non-financial goals simultaneously.

The process of creating budgetary slack becomessubtler than just using relatively higher levels of

allowable costs. The deliberate design of easier

cost targets when conditions are demanding con-

ceptually represents budgetary slack; although this

budgetary slack is purposeful, motivational, and

contingent on the environment. This concept of

budgetary slack, which includes a performance-

enhancing element in a setting with multiple goals,incomplete performance measures, and uncer-

tainty, is used in the development of our

propositions.

2 An alternative reason why budgetary slack may be present

at higher volumes is higher uncertainty about costs when

volume is higher. The company may protect the manager

through budgetary slack. The evidence that we gathered does

not allow us to test this alternative argument. We thank one of

the reviewers for suggesting this argument.3 It is important to notice that our empirical study was

conducted in a cost center setting, where increasing budgetary

slack does not necessarily lead to lower profits, the reason being

that meeting non-financial performance measures may enhance

the revenue line. This focus on cost centers is in contrast to

other empirical studies that have focused on profit centers

(Otley, 1978). We thank one of the reviewers for pointing out

this important distinction.


Propositions

The manager of a department pursuing multiple

financial and non-financial objectives––such as

operational costs, customer service, and productquality––decides how to trade off these multiple

goals when they cannot be achieved simultane-

ously. The organization may influence these deci-

sions through the budgetary slack available to

the manager. Organizations may purposefully in-

crease budgetary slack when external conditions

are more demanding, in an attempt to ease meet-

ing budget goals and freeing up managers� atten-tion to achieve alternative organizational goals––

like service or quality. Conversely, organizations

may decrease budgetary slack when conditions

are such that budget emphasis becomes more

relevant.

Unexpected events such as unplanned surges in

demand are likely to lead to decision points where

the manager cannot simultaneously meet multiplegoals. To react to these events, the manager may

choose to increase operating costs in order to

maintain customer service or meet the budget at

the expense of delaying some of the shipments.

The company can influence these trade-off deci-

sions through the budgetary slack that it allows

when these unexpected events happen. A large

budgetary slack encourages the manager to focuson non-financial objectives; conversely a low

budgetary slack focuses attention on financial

objectives. The following proposition reflects this

argument:

Proposition 1. Unexpected higher levels of activity

are associated with larger budgetary slack.

Another situation where managers may facetrade-off decisions is when the expected demand

is large. As demand gets closer to capacity, manag-

ers may have to rely on more expensive variable

costs––such as overtime, temporary employees, re-

work, and express shipments. The closer to full

capacity, the more often the sites will need to use

these expensive resources. While it is uncertain

how often these resources will be needed, higherexpected demand increases the number of times

that the site hits capacity constraints. If goals such

as product quality or customer service are impor-

tant, the company may decide to increase budget-

ary slack when demand is expected to be large.

Increasing budgetary slack reduces budget empha-

sis and allows managers to focus more attention to

meeting these alternative goals. 2 The larger thebudgetary slack, the lower is the priority of bud-

gets. The following proposition captures this

argument: 3

Proposition 2. Increased level of activity is associ-ated with larger budgetary slack.

The previous propositions focus on how cost

budgets can be managed in the presence ofdemanding conditions. However, these conditions

also affect the firm�s ability to achieve non-finan-

cial objectives such as customer service. This holds

unless the company is willing to meet one of its

goals at any price (requiring the company to pro-

vide enough extra resources to completely meet

this alternative goal in any state of the world)

and then we would not expect any effect of in-creased level of activity on this particular goal.

In our research setting, one of the alternative goals

is customer service. If demanding conditions also

hamper the ability of the company to meet these

alternative goals, then we expect the following

propositions to hold:

Proposition 3a. Lower customer service is associ-

ated with unexpected higher levels of activity.

Proposition 3b. Lower customer service is associ-

ated with increased levels of activity.


Qualitative evidence: Company PCC and its

budgeting process

This section describes the company, its budget-

ing process, and qualitative evidence informing theprevious propositions. As part of the data collec-

tion process, we interviewed and e-mailed manag-

ers in the following departments: budgeting,

financial accounting, quality control (including

non-financial data management), logistics per-

formance measurement, supply chain engineering,

and logistic sites. The initial interviews lasted be-

tween 30 and 60 minutes and we had several addi-tional interviews and e-mail exchanges throughout

the course of two years to clarify our understand-

ing of the data and the processes, and to help us

interpret the results.

The company

The study focuses on the four logistic centers ofcompany PCC (a disguised name) located in North

America, Europe, Asia, and Southeast Asia be-

tween April 1998 and March 2000. Company

PCC designs and produces disk drives for comput-

ers. The industry grew rapidly during the 1980s

and went through a period of intense competition

and restructuring during the 1990s. Out of the 43

companies that received venture capital, less than10% survived into the year 2000. The market is

highly competitive in several dimensions. Accord-

ing to PCC�s director of Supply Chain Engineer-

ing: ‘‘Consistently advancing technology through

product development is one of the most critical as-

pects; battles are won at the product development

stage’’. This point is further illustrated by the fact

that manufacturing is outsourced.Once a new product has been introduced, PCC

subcontracts the manufacturing process to an

OEM that delivers the products to the logistic cen-

ters. At the logistic centers, about 50% of the units

undergo a small customization and testing, such as

adding customer labels or adjusting switches to the

requirements of each client. The rest of the units

are simply repackaged. Units are then shipped tothe customer. When a product enters the produc-

tion stage, competition happens in three dimen-

sions: quality, cost, and customer service.

Quality

The importance of quality is highlighted in the

following quote from PCC�s director of Supply

Chain Engineering:

‘‘Our largest customers include Dell, IBM, Apple,HP, and Compaq. They all require the highestlevel of product and service quality because theymanufacture under JIT systems where the cost oflow quality is very high. . . .We identify each singleunit with a unique serial code and we carefullydocument its movement through the supply chain.Whenever a customer rejects a unit because ofquality problems, we test the unit and use individ-ual unit information to trace back the problem. . . .The importance of quality puts a lot of emphasison having a clear understanding of the wholecause-effect relationships through the supply chainfrom the component suppliers, the OEM manufac-turer, our own logistic centers, and the delivery tothe client. When units with quality problems comeback we determine what kind of failure occurredand our new diagnostic software finds 32 differenterror codes’’.

The company tracks the number of failed prod-

ucts per million units, where in the supply chain

the failure occurred (within the manufacturing

partner, the logistic centers, the customer during

the assembly process, or the end user), and whattype of failure happened.

‘‘For warranty costs we accrue a certain amountper unit that goes out of the door, and the actualcosts are incurred because of repairs, replace-ments, and credits. We do not have cost data dee-per than product family, but the systems trackserial numbers of returned products, when it wasshipped, and to which customer’’, explained amember of the controller�s office. We followed thisup with a director of the quality department whoexplained that ‘‘using the serial number we tracewhen it was built, in what factory, and on whichline’’.

Operational costs

Cost is the second competitive dimension in a

market characterized by intense competition and

small gross margins (less than 20% for fiscal year


2000). Because product costs are mainly deter-

mined at the product development stage, once

the product is launched, PCC focuses on reducing

the cost of operating its supply chain. According

to the director of Supply Chain Engineering:

‘‘We have seen large price reductions over the lastyears, while at the same time we and our compet-itors have increased product performance dramat-ically. . . . Cost reduction is vital and we have veryrigorous cost targets to design costs out of theproduct; not just material cost, but manufactur-ing cost and logistics have also become veryimportant. . . . At the logistic sites we focus oncontrolling the costs of operating the sites andinventory costs. Good inventory management isparticularly important because of the rapid priceerosion that requires a negative inventory revalu-ation every quarter. We use the unique serialnumber of each unit not only to trace qualityproblems, but also to monitor how many dayseach unit stays at each phase of the supply chain,and improve throughput . . . We are currentlyimplementing a supply chain initiative to reducethe small customization tasks that we do at thelogistic sites. These tasks are costly because wehave to unpack, make a small change like addingstickers, and repack. The simple task of unpack-ing and repackaging also affects quality. Weexpect that by shipping generic (non-customized)products, we will improve customer service andtake advantage of cost reduction opportunitiesat the logistic sites’’.

Thus, cost management discipline within the

supply chain goes as far as tracing the time each

single unit spends in the supply chain. According

to a business analyst in the quality control depart-ment, ‘‘Every month we measure for each unit that

we shipped to customers �how long did we have it?�and we report the average number of days and the

standard deviation by part number and by site’’.

Logistic centers are managed as cost centers and

the importance of costs makes budgets a key con-

trol system. While small variances do not attract

significant management attention, the budget of-fice carefully investigates large unexplained vari-

ances that also impact the performance

evaluation of logistic center managers. The cost

of moving a unit through the supply chain (cost

per unit) is also carefully monitored as an impor-

tant performance measure.

In addition to the quality control procedures

that give a lot of information about the actual ac-tions performed at the logistic sites, additional

control systems complement the budget. These

controls include approval for new investments

and new hires, monthly reports on headcount per

department, and the negotiation at headquarters

of freight contracts. The budgeting manager de-

scribed their discretion regarding freight: ‘‘All the

freight carriers are pre-negotiated with (the com-pany). Site managers cannot choose the carrier.

If there are any deviations from carriers and terms,

it is due to meeting customers needs. Freight costs

are determined based on a few different variables

. . . weight, size (bulk), type of freight (overnight,

ocean liner, air, etc.) that the site manager deci-

des’’. According to the controller: ‘‘We do not rely

only on the budget, but have additional tight con-trols over decisions that may have a significant im-

pact on costs’’.

Customer service

In addition to quality and costs, delivering a

high level of service to customers (most on JIT

manufacturing systems) is the third, albeit more

intangible, piece to the competitive landscape. Asthe director of Supply Chain Engineering

described:

‘‘We measure customer service using two objectiveperformance measures, one reflecting the on-timedelivery of products compared to the confirmeddate, and one reflecting the confirmation of deliv-ery dates compared to the initial request of the cus-tomer. . . . We also have every quarter a quarterlybusiness review (QBR) where the top eight cus-tomers evaluate the level of service received fromPCC and from the logistic centers in particular.The evaluation includes different service dimen-sions and the overall customer satisfaction. TheQBR is the main criterion to evaluate the logisticcenters� performance and determine the bonus oflogistic center managers and other executives inthe company involved in the supply chain. . . .The QBR is very visible for everyone’’.


Logistic centers pursue multiple objectives

simultaneously: cost, service, and quality. Even if

PCC�s products rely on advanced technologies

and accordingly the industry is considered high-

tech, the technology is well-known and the prod-uct is considered to be in the mature part of its life

cycle (Foster, 1986). Production tasks at the logis-

tic centers are simple, well-understood repetitive

processes, with a large amount of non-financial

information available for quality purposes. This

environment significantly reduces the information

asymmetry among different hierarchical levels in

the company. The challenge for sites� managers be-comes difficult performance goals––on-time deliv-

ery, product quality, and cost control––as well as

demand uncertainty. Fig. 1 plots the percentage

difference in actual volume compared to budgeted

volume. The difference is close to ±50%.

The budget setting process

The budgeting process is on a yearly cycle.

However, the budget for the second half of the

year is updated during the second quarter. Accord-

ing to the manager in charge of the budgeting

process, the objective of this update is ‘‘to include

new market information available through the

early part of the year as well as to update financial

expectations, maintain the accuracy of the budget,and include the impact that unexpected changes in

the environment may have on the budget. We want

the budget to be as useful as possible as a guide for

performance expectations’’.

The budgeting process for the logistic centers

starts with an estimate of quarterly and monthly

-50

-30

-10

10

30

50

0 5 10 15 20 25

Per

cen

tag

e d

iffe

ren

ce

Months

Fig. 1. Percentage difference in actual versus budgeted volume.

volume from the Fulfillment Planning Group that

brings together customers� needs, OEM capacity,

and market expectations. According to the con-

troller, ‘‘Supply teams work with each major cus-

tomer to learn about expected demand and thefulfillment people work with the manufacturer to

know about capacity problems’’. The budget man-

ager described it as follows:

‘‘At the Financial Planning Group, we use the esti-mations from the supply teams, fulfillment people,and industry statistics to define expected demand.Then we have analytical models to estimate work-force loads, freights, and costs trends together withthe profit expectations that come from the top ofthe organization (and given as a guide to WallStreet) to delineate the first draft of the budgetfor the logistic centers. At the same time, the logis-tic centers receive the volume estimates from theFulfillment Planning Group and prepare theirown detailed cost budget based on their under-standing of the cost of activities. We review thecost budgets that the logistic centers submit, andcompare them with our own budget numbers,looking at trends, comparing with the actual num-bers of last quarter, and by confronting the budg-ets with a target for maximum spending based onaffordability, which we do not reveal to the logisticcenters in advance. Finally, we talk with the logis-tic sites to reconcile both budgets . . . This last stepis not difficult, because we have done this exercisemany times and we all have a pretty good ideaabout how things work at the logistic sites . . .The focus of the exercise is to give an expense bud-get to site managers, but also to keep a tight con-trol on the costs of delivering one unit of product. . . The last line in the budget is the (logistic) costper unit that we follow very closely’’.

In order to maintain customer satisfaction dur-

ing peak demands or during periods when quality

problems require additional configuration and

testing activities, budgetary slack is common and

accepted. According to a senior manager of

Worldwide Operations, ‘‘we typically allow over-

budgeting at the logistic sites to plan for emer-gency overtime to maintain customer satisfaction

during something like a quality issue or crisis, even

if it is not usually needed’’. Thus budgetary slack is


not usually ‘‘used’’ and translates into a favorable

budget variance. One of the logistic managers con-

firmed the idea of including budgetary slack to re-

act to emergencies: ‘‘We budget enough resources

to be able to keep our service level even in emer-gency situations’’. The budget setting process

incorporates budgetary slack to react to ‘‘emergen-

cies’’, which happen more often for months with

higher expected volumes. The budget manager de-

scribed her awareness about the existence of this

budgetary slack:

‘‘When budgets are put together in Logistics, thevolume and cost structures are set some time inadvance based on forecasted information. Thecosts usually come in lower than forecasted on aregular basis. We have about a 5-cent pad (perdisk drive) in the numbers on a regular basis. Thisis for unforeseen events that might come up. Inaddition to that, the volume that we use in thebudget is a low-risk volume. So, if the actual vol-ume comes in higher than the budgeted volume,the cost per drive is lower’’.

This ‘‘padding’’ does not mean that the com-

pany systematically adds 5 cents per disk to the

budget, but that on average actual cost per unit

comes 5 cents below budgeted cost per unit. In

fact, because ‘‘emergency situations’’ are more

likely in high demand months, budgetary slack islarger in these months and lower in low volume

months.

Budget reports for the logistic sites separate

fixed from variable costs. The main items in the

fixed cost category are indirect labor, facilities,

and equipment. The two main items in the variable

cost category are direct labor and freight; the rest

of the items are comparatively smaller. Logisticcenter managers have some discretion over varia-

ble costs and can influence them to a larger extent

than fixed costs. For example, site managers can

decide to keep people, even if there is not enough

work, to quickly react to unplanned demand.

The budget review process

During the year, the budget office issues

monthly budget control reports detailing itemized

quarter-to-date actual expenses, quarter-to-date

static budget, and the variance between the two.

The report also includes actual and budgeted logis-

tic costs per unit, which is an additional important

criterion to evaluate the logistic sites� financial per-formance, and actual and budgeted units shipped.

This monthly feedback cycle helps to quickly de-

tect variances and correct them by the end of the

quarter, when performance evaluation happens.

Variances go back to zero at the end of each quar-

ter to start a new cycle. According to a member of

the controller�s office, ‘‘Variances are an important

indication of how we are going to exit the quar-ter’’. The logistic site manager described how he

used the budget reports: ‘‘I follow closely the

monthly budget reports, I look at the actual costs,

the variances and the cost per unit. If I see large

variances I quickly call my controller to under-

stand where they come from and what we can do

to make sure that they go back to a reasonable

level before the end of the quarter. Costs are animportant part of our job, although not the only

one; I also keep track of customer service that

impacts my QBR and make sure that it does not

suffer even in peak demand periods’’.

The monthly control reports are homogeneous

for all sites with the same format and accounts.

The accounts are classified as fixed and variable

costs. For purposes of the quantitative analysis,we grouped fixed (or indirect) labor cost ac-

counts––salary and fringe benefits and other em-

ployee benefits––as one variable and other fixed

costs––travel expenses, engineering, supplies, con-

sulting, equipment, facilities, and recruiting––as a

separate variable. For variable costs we define

three variables: variable labor costs––salary and

fringe benefits, freight––sum of all freight ac-counts, and other variable costs––manufacturing

expense, rework and scrap, miscellaneous.

The budgeting model

The importance of costs, the fact that the

logistic sites are one of the few steps in the supply

chain that PCC directly controls, treating thesesites as cost centers, and the relevance of variance

analysis for performance evaluation indicate that


budgetary emphasis is relevant. Note from the pre-

vious sections, that variance analysis is focused on

(1) the difference between actual total cost and the

static total cost budget, as well as (2) the actual

versus budgeted cost per unit, the latter being themore important measure for evaluating the per-

formance of the sites. However, customer satisfac-

tion and quality are also very relevant to the

performance of the logistic sites. To manage these

multiple objectives, budgetary slack is acknowl-

edged and accepted in the organization to react

to unforeseen events and demanding conditions.

During the research process, as we examinedqualitative and quantitative evidence (described

in Section ‘‘Results’’), the budgeting model

appeared as a key element to generate slack only

under demanding conditions. The budgeting mod-

el makes certain assumptions regarding cost

behavior that do not reflect the actual cost behav-

ior (as the quantitative analysis that we present in

Tables 5 and 6 confirms). In particular, variablecosts are assumed to be fully variable and linear

in the budget model, while in reality they have a

fixed cost component and are likely to be non-

linear, with higher variable cost per unit as volume

increases. This latter behavior is consistent with

comments from various interviewees regarding

the need for overtime, express shipments, and tem-

porary employees as volumes reached the capacityof the site.

stsoc robal tcerid latoT

detcepxE

tegdub citatS

elbarovafnUecnairav emuloV

egdub elbixelF t

soc lautcA t

Fig. 2. Creation of budgetary slack

These disparities create a mechanism, illus-

trated in Fig. 2, that speaks to Proposition 1.

Two forces are at play in Fig. 2. First, budgeted

costs are modeled as fully variable costs, while ac-

tual costs contain a fixed element. This assumptiongenerates a favorable efficiency variance when ac-

tual volume is higher than budgeted volume

(demanding conditions): because the budgeted

‘‘variable costs’’ actually contain a fixed element,

budgeted total costs grow faster with volume than

actual total costs do. This favorable efficiency var-

iance has two purposes. First, it compensates the

unfavorable volume variance that the higher-than-expected volume generates, as indicated in

Fig. 2. Second, and most important, it eases meet-

ing this cost-per-unit target––the main focus for

performance evaluation. The organization accepts

that total variable costs are higher as long as the

cost per unit does not exceed the budgeted cost

per unit, hence the static budget is not the only ref-

erence point, and it is used in combination with aform of flexible budgeting for variable costs. The

mechanism ‘‘engineers’’ slack into this perform-

ance measure through the favorable efficiency

variance. Conversely, this mechanism ‘‘engineers’’

slack out of the budget when volume is unexpect-

edly low to tighten the budget.

A second force that works in the opposite direc-

tion (tightening cost targets) is the linearityassumption for variable costs. Actual variable

emuloV

enil stsoc detegduB

emulov emulov lautcA

enil stsoc lautcA

elbarovaFecnairav ycneiciffE

in the budget setting process.

4 Studies using questionnaire data usually measure slack

based on managers� perception of how easily their budgets are

achievable (Dunk, 1995; Van der Stede, 2000). For example, the

items used by Van der Stede (2000) include: ‘‘I succeed to

submit budgets that are easily attainable’’, ‘‘Budget targets

induce high productivity in my business unit’’ (reverse coded),

‘‘Budget targets require costs to be managed carefully in my

business unit’’ (reverse coded), ‘‘Budget targets have not caused

me to be particularly concerned with improving efficiency in my

business unit,’’ and a question ranging from whether the budget

is ‘‘(1) very easy to attain’’ to ‘‘(5) impossible to attain’’. In our

study we measure budgetary slack as the difference between

actual and budgeted costs as a measure capturing how easy it

was to attain the budget. Only in laboratory experiments can

budgetary slack be directly measured as the difference between

subjects� best estimates of performance with the targets set in


costs per unit are likely to increase with volume

(the actual cost line in Fig. 2 bends upward as

the site reaches full capacity). Therefore, the line-

arity assumption creates an unfavorable efficiency

variance. The quantitative evidence suggests thatthis effect is small compared to the fixed cost effect,

otherwise the favorable efficiency variance with

higher-than-expected volume would disappear

completely.

This evidence indicates that when unexpected

demand makes meeting quality and customer serv-

ice goals harder to achieve, the company eases effi-

ciency goals. This mechanism affects the standardsagainst which performance is assessed in such a

way that meeting efficiency targets becomes easier

only when managers are faced with unexpected de-

mand surges.

When confronted with this analysis of the budg-

eting model, the budgeting manager mentioned,

‘‘We knew that although we labeled variable costs

as �variable�, they were semi-variable costs with afixed component. You can�t reduce direct labor

to zero even if volume goes down to zero; but we

still assumed so in our budget’’. Other managers

from the controller�s office were also aware that

part of what was called ‘‘variable costs’’ had a

fixed component and that this assumption in unex-

pectedly busy times helped logistic managers to

pay attention to customer service and quality whilekeeping the ‘‘variable costs’’ per unit at an accept-

able level. In discussing the results, it became clear

that managers in the controller�s office knew the

benefits to the logistic managers of the fixed com-

ponent of the so-called variable costs but they had

not ‘‘walked through’’ the actual functioning of

the mechanism (as described in Fig. 2). In a sense,

the knowledge of how the system worked was tacit(Nonaka, 1994).

the budget (Fisher et al., 2000, 2002a, 2002b; Young, 1985).5 An unfavorable variance indicates that profits are nega-

tively affected. Conversely, a favorable variance makes actual

profits better than budgeted profits (Horngren, Foster, &

Datar, 2000). Following traditional variance analysis, variances

are estimated at the cost account level and do not include their

impact on revenues. So, for example, a higher volume

compared to budget increases costs and this is labeled an

unfavorable variance, even though the additional revenues may

cause the overall profits to be higher than budgeted. Logistic

centers were treated as cost centers, thus revenue implications

were not included in their reports and evaluation.

Quantitative evidence: Research design

Besides the qualitative data gathered through

interviews and company visits, we also collected

the monthly budget control reports (described inSection ‘‘The budget review process’’) for each of

PCC�s logistic centers from April 1998 to March

2000 and customer service statistics from Septem-

ber 1998 to March 2000. Customer service statis-

tics come from 58,528 transactions capturing

each shipment that occurred during the period

for the seven main product families (that account

for 91% of the volume).Budgetary slack has been equated with budgets

being achieved more than 50% of the time (Mer-

chant & Manzoni, 1989). 4 The assumption is that

uncontrollable factors follow a symmetric random

distribution where the median is an unbiased esti-

mation of performance. However, this measure

does not take into account how far away from

the original budget actual results are. Varianceanalysis facilitates the inclusion of this additional

information,

Variance ¼ Budget cost � Actual cost

Budget cost

A positive variance indicates a favorable vari-

ance because actual costs are below budgeted

costs. 5 Even if PCC does not estimate volume,

price, and efficiency variances, the reports gener-

ated allow us to identify variances due to changes


in expected volume and variances due to price and

efficiency (jointly). Because prices are stable, we la-

bel this latter variance as efficiency variance and

we define it following the common definition

(Horngren et al., 2000) and standardize it:

Efficiency variance ¼ Budgeted variable cost

Budgeted volume� Actual volume � Actual variable cost

� ��Budgeted variable cost

A positive number indicates a favorable effi-

ciency variance. 6 Because efficiency variances ease

the achievement of budget goals when demand is

higher (as illustrated in Fig. 2) and allow managers

Volume variance ¼ Budgeted variable cost � Budgeted variable cost

Budgeted volume� Actual volume

� ��Budgeted variable cost

to focus on achieving goals other than costs, we

use this variable as our measure of budgetary

slack. Note that efficiency variances are not re-

ported separately, so local managers can compen-

sate unfavorable volume variances with favorable

efficiency variances to absorb cost increases due

to higher volumes.

We measure the expected level of activity in aparticular month through the budgeted volume

of units shipped (budgeted volume). Months with

higher expected volume are those with increased

level of activity (Propositions 2 and 3b). Because

of the differences in size across sites, we normalize

this variable by the average units shipped over the

period studied per site. We measure unexpected

levels of activity as the difference between budg-eted and actual units shipped (change in volume)

(Propositions 1 and 3a):

6 Standardizing the variance leads to empirical tests of the

relative importance of budgetary slack. In other words, the tests

of Proposition 1 and Proposition 2 are tests of whether the

relative budgetary slack increases with expected and unexpected

levels of activity.

Change in volume

¼ Budgeted volume � Actual volume

Budgeted volume

¼ 1 � Actual volume

Budgeted volume

Notice that this variable is negative when actual

volume is greater than budgeted volume. Notice

further that this variable is equal to volume vari-

ance as traditionally defined:

Customer service is measured as on-time deliv-

ery. For each of 58,528 transactions, we code

whether the shipment is shipped before or on the

commit date. For each month, we measure on-time

delivery as the number of shipments shipped before

or on the commit date (late shipments) over the

total number of shipments.

On-time delivery

¼ Shipments on or before commit date

Total number of shipments

Results

Table 1 presents descriptive statistics for the

four logistic sites. The sites are significantly differ-

ent in size, with North America being the largest

and Asia the smallest. To control for size effects,all variances are scaled by budgeted amounts.

The largest expense account is freight; otherwise

the cost structure differs mainly in terms of fixed


and variable labor. We also include non-financial

measures of performance. On-time delivery is

close to 100%, except for Europe where it is

74%.

Table 2 presents further descriptive statistics onhow the sites succeed in meeting the budget. Total

variance is positive for 63 out of 96 logistic center-

Table 1

Descriptive statistics�

Budgeted costs (in thousands of dollars or units per month) N

Fixed costs

Mean 71

Standard deviation 21

Fixed labor costs

Mean 47


Other fixed costs

Mean 23


Variable costs

Mean 50


Direct labor costs

Mean 17


Freight costs

Mean 26


Other variable costs

Mean 72


Operational measures (per month)

Actual volume (in th.)

Mean 11


Budgeted volume (in th.)

Mean 97


On-time delivery

Mean 0.

Standard deviation 0.

* Cost figures have been multiplied by a constant to preserve confi

month variances (65.6%) and on average is 5.9%

(actual cost below budgeted cost) although after

controlling for serial correlation this number is

only significant at the 15% level. Looking at the

cost components, we find that the overall fixed costvariance and its components are not significantly

different from zero, either using a binomial test

orth America Europe Asia Southeast Asia

1 439 85 489

8 58 6 50

9 179 50 151

6 22 5 35

1 259 35 338

51 3 29

36 2512 293 1189

01 396 74 319

01 471 117 152

3 118 37 46

08 1595 137 888

1 287 42 252

8 446 38 150

4 207 12 67

74 772 163 753

1 159 35 201

0 638 135 622

0 144 32 182

92 0.74 0.94 0.96

02 0.14 0.05 0.03

dentiality.

Table 2

Meeting the budget

Variable description # of favorable variances Mean Standard deviation Z-statistic Min. Max.

Total variance 63a 0.059 0.29 1.43 �1.23 0.70

Fixed cost variance 41 �0.005 0.25 0.29 �0.76 0.44

Fixed labor cost variance 51 �0.024 0.25 0.47 �0.86 0.93

Other fixed costs variance 50 �0.025 0.44 0.12 �1.65 0.95

Variable cost variance 63a 0.061 0.38 1.41 �1.86 0.92

Direct labor variance 67a 0.155 0.38 5.59�� 1.71 1.77

Freight variance 50 �0.068 0.47 �0.78 �2.21 0.73

Other variable costs variance 70a 0.125 0.69 1.41 �2.94 1.52

Volume varianceb 42 �0.027 0.29 �1.42 �0.71 0.74

Efficiency variance 63a 0.102 0.42 1.97�� 2.10 1.21

Direct labor efficiency variance 64a 0.182 0.49 3.43�� 1.96 2.07

Freight efficiency variance 51 �0.041 0.47 �0.16 �2.46 0.99

Other variable costs efficiency variance 67a 0.152 0.71 1.54 �2.53 1.82

We run four separate regressions (one per site) with a constant and an AR(1) error term to control for serial correlation. The mean and

standard deviations reported are the average pooled all observations; the Z-statistic is the average Z-statistic, its significance is assessed

using Z ¼ �z=ðstdevðzÞ=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðN � 1Þ

pÞ where N is the number of regressions (N = 4).

# observations: 96.*** Significant at 1% (2-tailed test); **Significant at 5% (2-tailed test).

a Significant at 1% level (binomial test).b By construction, volume variance is equal for all accounts.


or a means t-test. On the other hand, variable cost

variance and two of its components––direct labor

and other costs––are significant using the binomial

test. The efficiency variance and two of its compo-

nents are significant, also using the binomial test.

The rest of the paper focuses on the analysis of

variable cost variances where budgetary slack ap-

pears to be created. Fixed costs appear to be esti-mated accurately. 7

Table 3 presents the correlation matrix. Change

in volume is positively correlated with various var-

iable cost variances and negatively with efficiency

variances. This correlation suggests that as unex-

pected volume increases (and change in volume is

negative) efficiency variance improves, thus reflect-

7 For completeness we analyzed fixed costs. None of the

results were significant. Notice also that even if there is no

budgetary slack in fixed costs, the conservative volume that

they use for their budgets means that the actual cost per unit

will be lower than the budgeted cost per unit, thus facilitating

the achievement of cost-per-unit targets.

ing the creation of budgetary slack (Proposition 1).

Budgeted volume is positively correlated with sev-

eral variances suggesting that when higher vol-

umes are expected, favorable variances increase

(Proposition 2). The correlation matrix is unin-

formative about Propositions 3a and 3b. Direct

labor and direct labor efficiency are positively

associated with on-time delivery consistent withlogistic sites being better at meeting its customer

service objectives when having budgetary slack in

direct labor. Also of interest is the lack of correla-

tion between budgeted volume and change in vol-

ume. This observation suggests that the expected

level of activity is not associated with unexpected

changes in the level of activity. Other significant

correlations are engineered relationships.The propositions indicate that budgetary slack

and customer service change with expected volume

and changes in volume. Fig. 1 illustrates the signif-

icant variation in changes in volume. Fig. 3 plots

budgeted units over time. Starting in month 10, ex-

pected volume increases significantly at the end of

Table 3

Correlation matrix

Correlations Change in

volume

Variable

cost variance

Direct labor

variance

Freight

variance

Other variable

costs variance

Efficiency

variance

Direct labor

efficiency

variance

Freight

efficiency

variance

Other

variable

costs

efficiency

variance

On-time

delivery

Budgeted volume 0.03 0.20* �0.02 0.28** 0.35** 0.16 �0.03 0.26*** 0.32** �0.16

Change in volume 0.26** �0.02 0.30** 0.13 �0.46** �0.61** �0.30** �0.28** �0.05

Variable cost variance 0.60** 0.89** 0.66** 0.74** 0.32** 0.73** 0.54** 0.01

Direct labor variance 0.25*** 0.41** 0.57** 0.81** 0.27** 0.41** 0.25***

Freight variance 0.47** 0.61** 0.02 0.82** 0.33** �0.09

Other variable

costs variance

0.52** 0.25*** 0.39** 0.92** �0.04

Efficiency variance 0.72** 0.89** 0.69** 0.04

Direct labor

efficiency variance

0.39** 0.49** 0.22*

Freight efficiency

variance

0.50** �0.07

Other variable

costs efficiency

variance

�0.02

Person correlation reported.

# of observations: 96.* Significant at 10% (two-tailed).** Significant at 1% (two-tailed).*** Significant at 5% (two-tailed).

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0.6

0.8

1

1.2

1.4

1.6

0 5 10 15 20 25

Un

its

(no

rmal

ized

)

Months

Fig. 3. Budgeted volume over time.


each quarter suggesting that logistic processes may

be reaching their full capacity in these months. 8

Table 4 reports the formal test for the proposi-

tions advanced in Section ‘‘Literature review and

propositions’’ using the following regressionmodel:

Variance or on-time delivery

¼ b0 þX

bi � logistic centers’ dummies þ b4

� budgeted volume þ b5 � change in volume

þX

bj � control variables þ e

The dependent variable is the various variable

cost variances or on-time delivery. We include

both total variance as well as efficiency variance. 9

Total variance includes volume variance––the var-

iance due to actual volume being different from

budgeted volume––that is equal to the variable

change in volume, thus the positive sign for the

regressions with total variance as dependent varia-

ble. Total variances are included for completeness.

8 The pattern of the budgeted volume changes significantly

over the last four quarters, becoming a lot more seasonal. The

company mentioned that it was due to their customers� demand

pattern. Customers started to emphasize end-of-quarter targets

more towards the end of the observation period that coincides

with the year 1999 and the first quarter of 2000.9 As one of the reviewers pointed out, if actual costs increase

when meeting alternative goals becomes more difficult, these

additional costs will reduce the extra budgetary slack created

and accordingly will reduce the power of our tests. The reviewer

also pointed out that standardizing the variables might poten-

tially reduce the power of the tests.

Efficiency variance excludes the effect of volume

changes on actual costs and thus is a better meas-

ure of budgetary slack as used at PCC.

We include three dummy variables to control

for differences in the intercept across sites. Forthe variances regression, we include the number

of months to the end of the semester as a control

for potential loss in accuracy as the planning hori-

zon increases (we use the semester because the

budget is updated at the end of the sixth month

period). This variable takes a value of 1 for the

first month in the semester, 2 for the second, 3

for the third month and so forth. For on-timedelivery, we control for months that are the end

of the quarter because shipments in these months

are much higher. 10 This dummy variable takes va-

lue of one for the third month of each quarter and

0 otherwise. For on-time delivery, we also control

for the percentage of generic products (products

that are not manipulated in the logistic sites) be-

cause a higher percentage of generic productsmakes on-time delivery simpler. 11 To control for

autocorrelation in the dependent variables we use

an AR(1) model in the error terms. We also use

a generalized least squares approach to allow for

different variances across sites (Greene, 2000).

Results for Proposition 1

Proposition 1 relates favorable efficiency vari-

ance (higher budgetary slack) with unfavorable

changes in volume (note that higher-than-expected

volumes lead to a negative value for the changes in

volume variable). The evidence in Table 4 for direct

labor, freight, and other variable costs is consistent

with the proposition.

10 We also used end of quarter instead of months to end of

semester in the variance regressions; the results were compara-

ble. We also ran the efficiency regressions defining efficiency

variance on a per-unit basis (rather than total efficiency), and

the inferences remained unchanged.11 The reason being that generic products allow consolida-

tion of the demand of different customers. Holding inventory of

a generic product requires less safety stock (for a particular

service level) compared to holding inventories of several

customer-specific products (because of risk-pooling); or the

same amount of safety stock makes it possible to offer a higher

service level (Lee & Tang, 1997).

Table 4

Drivers of budgetary slack and on-time delivery

Independent variables Dependent variable

Variable cost

variance

Direct labor

variance

Freight

variance

Other variable

costs variance

Efficiency

variance

Direct labor

efficiency

variance

Freight

efficiency

variance

Other

variable costs

efficiency

variance

On-time delivery

Constant

Coefficient �0.059 0.044 �0.177* 0.098 �0.059 0.044 �0.177* 0.098 0.802**

z-statistic �0.99 0.61 �1.86 0.98 �0.99 0.61 �1.86 0.98 22.31

Budgeted volume

Coefficient 0.063 0.505** �0.126 0.255 0.063 0.505** �0.126 0.255 �0.057***

z-statistic 0.65 5.33 �0.90 1.43 0.65 5.33 �0.90 1.43 �2.05

Change in volume

Coefficient 0.622** 0.228** 0.757** 0.452** �0.378** �0.772** �0.242*** �0.548** 0.042*

z-statistic 7.63 3.15 6.64 2.92 �4.64 �10.67 �2.13 �3.54 1.67

Months-to-end-semester

Coefficient 0.031*** 0.018 0.033 0.071** 0.031*** 0.018 0.033 0.071**

z-statistic 2.17 1.33 1.58 2.84 2.17 1.33 1.58 2.84

End of quarter

Coefficient 0.041**

z-statistic 2.64

Percentage generic

Coefficient 0.137***

z-statistic 2.06

Pseudo-R2 0.44 0.36 0.55 0.42 0.56 0.69 0.54 0.48 0.79

# of observations 96 96 96 96 96 96 96 96 72

Regressions include dummy variables to control for differences across sites (not reported).

Pseudo R2 is the correlation between the actual dependent variable and the fitted values.* Significant at 10%.** Significant at 1%.*** Significant at 5%.

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13 The magnitude of these changes is quite sizeable. Given

the simple operations performed at the logistic sites, such


The mechanism for the budgeting model de-

scribed in Section ‘‘The budgeting model’’ relies

on variable costs having a fixed component to engi-

neer the relationship between unexpected volume

and favorable efficiency variance. To examine thisrelationship using quantitative data, we examine

the cost behavior assumed during the budgeting

process and the actual cost behavior. For each of

the sites, we performed the following regression:

Actual or budgeted costs

¼ b0 þ b1 � actual or budgeted volume þ e

We also include an AR(1) error term to control

for serial correlation. We run separate regressions

for each site to fully capture the differences in cost

structures, for actual versus budgeted costs, and

for total variable costs and its three components

(direct labor, freight costs, and other variable

costs). To assess the significance of the coefficients,

we combined the z-statistics of the four sites intoan overall z-statistic estimated as Z ¼ �z=ðstdevðzÞ=

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðN � 1Þ

pÞ (Greene, 2000). Table 5 re-

ports the average z-statistics for the four sites. 12

While all the components of variable costs are

correctly assumed to be fully variable in the budg-

eting system––the fixed component of the budg-

eted costs is not significant while their variable

part is highly significant––actual costs have a fixedas well as a variable component, both of them

significant.


Proposition 2 suggests that favorable efficiency

variance (as the proxy for budgetary slack) in-

creases with expected volume (budgeted volume).We find evidence consistent with this proposition

only for direct labor. When units budgeted are

higher, direct labor costs are lower than budgeted

(and the efficiency variance is favorable), suggest-

ing higher budgetary slack when expected volume

is higher. An increase in 1% expected units (over

12 Table 5 does not report the estimated coefficients but only

their average significance (across sites). In the majority of the

regressions, the coefficient on budgeted volume is significantly

larger than the coefficient on actual volume, consistent with

Fig. 2.

the logistic site average) improves efficiency vari-

ance by 0.5%; for example one standard deviation

for North America (231/1174 = 20% from Table 1)

translates into an increase of 11% of the efficiency

variance. 13 We do not find any significant effecton freight or other variable costs. Thus budgetary

slack built during the planning process is relevant

for variable costs.

These results indicate that the ‘‘padding’’ is not

a constant amount per disk drive. While a constant

‘‘pad’’ per unit would create more budgetary slack

with higher budgeted volume, it would not in-

crease the relative amount of budgetary slack,and constant padding would come up in the con-

stant term of the regression, not in the coefficient

b4 for budgeted units. 14 The qualitative data sug-

gests that the company allows this budgetary slack

because of the importance of customer service and

product quality. However, the quantitative analy-

sis is uninformative about documenting actual

benefits from this policy.These results must be interpreted with some

care. First, the empirical tests are significant for

only one component of variable costs––direct la-

bor. Second, besides the multiple-goals argument,

budgetary slack may also be required when there

is more uncertainty about the variable cost per

unit at higher levels of volume. While this argu-

ment may have also played a role at the researchsite, no evidence in the interviews suggested so.


Proposition 3 predicts that customer service suf-

fers in months with unexpected higher volume (3a)

and in months with high expected volume (3b).

The evidence in Table 4 is consistent with bothpropositions. Notice that change in volume is de-

fined such that a higher-than-budgeted volume

changes are very unlikely to come from economies of scale;

rather it is consistent with budgetary slack as manager

described. We further explore this potential explanation in the

section with Results for Proposition 2.14 The intercept terms for some of the sites (not reported) are

significant, suggesting that there may be some ‘‘constant

padding’’ different for each site.

Table 5

Fixed and variable components of variable cost accounts


Total variable costs Direct labor Freight costs Other variable costs

Variable component of actual costs 3.71�� 1.82� 5.20�� 6.23��

Fixed component of actual costs 25.61�� 7.39�� 4.08�� 8.30��

Variable component of budgeted costs 3.87�� 3.44�� 3.77�� 3.12��

Fixed component of budgeted costs 1.23 �1.08 1.27 0.48

The table reports the average z-statistic of the fixed component (constant) and variable component (slope) for the following regression

model: Actual or budgeted costs ¼ b0 þ b1 � actual or budgeted volume þ e. Each z-statistic is estimated using four regressions (one

per site). Each column presents the average z-statistic for two sets of regressions, the top two rows report the results for actual costs and

the bottom two rows for the budgeted costs, for total variable costs (first column) and its components (remaining columns). The

average z-statistic is estimated as Z ¼ �z=ðstdevðzÞ=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðN � 1Þ

pÞ (Greene, 2000). A significant average z-statistic indicates that the esti-

mated parameter (b0 for the fixed component, and b1 for the variable component) is significantly different from zero (two-tailed tests)

at 10% (*) and 1% (***).


leads to negative change in volume, hence the pos-

itive coefficient.

The evidence suggests that the company allows

a larger budgetary slack to react to unexpected in-

creases in volume (Proposition 1) and when it ex-

pects high demand (Proposition 2). The evidence

regarding Proposition 3 indicates that customer

service suffers in both of these circumstances.However, the question remains whether the

trade-off between customer service and costs exists.

If such a trade-off exists, we expect operational

costs to increase in demanding periods, and site

managers face deteriorating customer service and

increasing costs during these periods leading to

trade-off decisions. To address this question, we

examine whether unexpected increases in volumeor higher expected demand affect variable costs.

Using the same specification as in Table 5:

Actual costs ¼ b0 þ b1 � actual volume þ e

We extended the regression specification with

two interaction terms. The first one interacts actual

volume with changes in volume. This interactionterm captures whether variable costs (that in Table

5 we assumed to be constant across volume through

b1) is different in periods with unexpected demand.

The second term interacts actual volume with

budgeted volume to test whether variable costs

are higher for months with higher expected de-

mand. These two interaction terms capture whether

variable costs (assumed to be constant on a per-unit

basis in Table 5 through b1) change with unex-

pected surges in demand (changes in volume) and

expected higher demand (budgeted volume). We fo-

cus the test on variable costs, controlling for fixed

costs, because these costs are expected to vary while

fixed costs are more likely to remain stable.

Actual costs

¼ b0 þ b1 � actual volume þ b2

� actual volume � changes in volume þ b3

� actual volume � budgeted volume þ e

Table 6 reports the results. Each column reports

the average z-statistic of four regressions (one per

site). The average z-statistic on actual vol-

ume * changes in volume is negative and significant;

this result indicates that when actual volume is lar-

ger than budgeted volume, variable costs increase.Similarly, the average z-statistic on actual vol-

ume * budgeted volume is positive and significant

indicating that those months with higher expected

volume also record higher variable costs. This evi-

dence suggests that site managers face a trade-off

between customer service and operational costs.

It is also consistent with the qualitative evidence

indicating that variable costs depend on volume,for reasons such as overtime, express shipments,

temporary employees, and rework.

Finally, and as a robustness check, we examine

the importance of the results being driven not only

Table 6

Change in the variable component of variable cost accounts


Total variable costs Direct labor Freight costs Other variable costs

Fixed component of variable actual costs (constant) 7.67*** 3.49*** 4.71*** 2.52**

Variable component of variable actual costs (actual units) 0.31 �0.63 2.01** �1.20

Variable component of variable actual costs times change

in volume (actual units * change in volume)

�9.04*** �1.63* �1.73* �2.49**

Variable component of variable actual costs times

units budgeted (actual units * budgeted volume)

3.27*** 1.15 2.56** 1.06

The table reports the average z-statistic of the fixed component (constant), variable component (slope) and interaction terms for the

following regression model: Actual costs = b0 + b1 * actual volume + b2 * actual volume * changes in volume + b3 * actual vol-

ume * budgeted volume + e. Each z-statistic is estimated using four regressions (one per site). Each column presents the average z-

statistic for one set of regressions for total variable costs (first column) and its components (remaining columns). The average z-statistic

is estimated as Z ¼ �z=ðstdevðzÞ=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðN � 1Þ

pÞ. A significant average z-statistic indicates that the estimated parameter (b0 for the fixed

component, and b1 for the variable component) is significantly different from zero (two-tailed tests) at 10% (*) 5% (**) and 1% (***).


by budgetary slack but also by actual improve-

ments in operating efficiency. Managers may

increase the operating efficiency through day-to-

day decisions like adjusting direct labor more

quickly or optimizing freight. To test for potential

operational efficiency gains we regressed the varia-

ble cost per unit on actual units shipped (to control

for economies associated with fixed costs), end ofquarter, percentage of generic products, and unex-

pected volume (volume variance). We controlled

for potential serial correlation using an AR(1)

model in the error term. We ran separate regres-

sions for each site and computed the z-statistic

for the coefficient on volume variance as we de-

scribed for Table 5. The z-statistic is 1.20 indicat-

ing that higher unexpected volume does not leadto lower costs, as we would expect if operational

efficiency were present.

The fact that on-time delivery deteriorates even

as the budget becomes less demanding does not in-

form about whether managers are behaving opti-

mally. They may be doing so and providing the

best service, given the resource constraints that they

face. Conversely, they may rely on demanding cir-cumstances to decrease their effort and not deliver

the service that the additional resources provide.

Discussion and conclusions

Budgetary slack is a key concept in the budget-

ing literature. In previous empirical work, it has

been interpreted as dysfunctional. It insulates

managers from the motivational properties of

budgets leading to lower effort and inefficient use

of resources. Previous research has focused on

identifying the conditions leading to the presence

of dysfunctional budgetary slack.

The objective of this study is to empirically test

the prediction, advanced in previous theoreticalwork, that budgetary slack can function as a tool

to help management rather than being solely dys-

functional. The company may allow budgetary

slack, not because incentives to remove slack

would be too expensive––the agency perspective

(e.g. Kirby et al., 1991)––but as a powerful tool

to influence how managers allocate their attention

when performance requires balancing multiplegoals (Lillis, 2002; Merchant & Manzoni, 1989;

Van der Stede, 2000).

Using field data from four logistic sites of a

technology company, we find that the budgeting

process encourages budgetary slack when the

company expects business processes to be under

demanding conditions and managers may require

flexibility to meet non-financial goals. We also findthat the budget model itself supports the creation

of budgetary slack when business processes face

unexpected external events. The mechanism

embedded in the budgeting system is based on

the cost accounting assumptions. The budgeting

system is designed to create budgetary slack when

actual volume is above expected volume by assum-

ing semi-variable costs as fully variable during the


budgeting process. This design feature reinforces

the creation of slack when needed, as explained

in Fig. 2.

While previous studies have investigated budget

design in relation to non-accounting factors, suchas uncertainty of the environments or tasks, or

flexible management styles, this paper relates

cost-budget design to choices of the accounting

system. We discuss how a firm used two design ap-

proaches––variable costs containing fixed compo-

nents and ignoring non-linearity in variable

costs––to facilitate managers focusing on other

goals besides costs by reducing budget emphasiswhen budgeted or unexpected volume is high.

These results may help to better understand

accounting choices that firms may employ to allow

flexible responses to uncertainty.

Our results have implications for the impact of

budgets upon performance. In the face of uncer-

tainty and when multiple short-term goals are

important, budgetary slack enables managers toremain focused on various short-term goals simul-

taneously. Organizations can design budgetary

slack to influence the allocation of organizational

attention. Moreover, the mechanisms can be self-

regulating and can adjust the amount of slack to

the particular circumstances facing a decision-

maker. Thus, budgetary slack is not created indis-

criminately, but only when attention to alternativegoals demand it. These ideas outline a sophisti-

cated use of budgets that goes beyond the more

traditional applications.

Future research could focus on understanding

how organizations design budget systems that

deliberately incorporate budgetary slack under

certain circumstances. This requires subtle design

decisions, such that cost targets are eased onlywhen it is particularly difficult to meet all goals

simultaneously. The process of creating budgetary

slack is more ingenious than just allowing rela-

tively high levels of budgeted costs. There is also

a need to better understand the ways in which

organizations maintain the beneficial effects of

budget emphasis while, at the same time, allowing

some conditional form of budgetary slack to pre-vent these managers from overly focusing on

achieving cost targets, at the expense of non-finan-

cial goals.

Acknowledgements

We thank participants at the AAA Manage-

ment Accounting Conference, 2000, European

Accounting Association Conference, 2001, partici-pants at Michigan State University, the London

School of Economics, Joan Luft, Ken Koga, San-

der van Triest, Kari Lukka, Wim Van der Stede,

and the reviewers for their many helpful and con-

structive comments. Marc Wouters appreciates the

financial support of the Niels Stensen Stichting,

Amsterdam, The Netherlands.

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Documents

Managing Budget Emphasis Through the Explicit Design of Conditional Budgetary Slack