Quant Mark Econ (2008) 6:235–256

DOI 10.1007/s11129-008-9039-7

Effort or timing: The effect of lump-sum bonuses

Thomas J. Steenburgh

Received: 16 July 2007 / Accepted: 21 May 2008 / Published online: 26 June 2008
# Springer Science + Business Media, LLC 2008

Abstract This article addresses the question of whether lump-sum bonuses motivate
salespeople to work harder to attain incremental orders or whether they induce
salespeople to play timing games (behaviors that increase incentive payments
without providing incremental benefits to the firm) with their order submissions. We
find that lump-sum bonuses primarily motivate salespeople to work harder—a result
that is consistent with the widespread use of bonuses in practice, but that contradicts
earlier empirical work in academics.

Keywords Sales force incentives . Compensation methods

JEL Classification G30 . J31 . J33 . L23 . M31 . M41 . M52

1 Introduction

Those who manage salespeople commonly believe that lump-sum bonuses are
effective motivators. A recent field survey (Joseph and Kalwani 1998) finds that
72% of firms use bonuses in their sales incentive contracts, whereas only 58% use
commission rates, the next most common form of incentive pay.1 Moynahan (1980,
p. 149) states in his book on designing effective sales incentive contracts that “for
the majority of industrial sales positions, [lump-sum bonuses are] probably the
optimum form of compensation.” While lump-sum bonuses are not considered to be
the only sound way to motivate salespeople, they are widely regarded in the trade

1
Joseph and Kalwani (1998) also find that 35% of firms include both bonuses and commission rates and
5% offer salary alone.
Thomas J. Steenburgh is an Associate Professor at the Harvard Business School. He would like to thank
Andrew Ainslie, Subrata Sen, and K. Sudhir for comments and suggestions that greatly improved the
quality of this article. He is especially grateful for the advice and encouragement of his late thesis advisor,
Dick Wittink. The author, of course, is solely responsible for remaining errors.
T. J. Steenburgh (*)
Harvard Business School, Soldiers Field, Boston, MA 02163, USA
e-mail: tsteenburgh@hbs.edu
236 T.J. Steenburgh

literature (Agency Sales Magazine, Sep 2001; Bottomline, Oct 1986) and in textbooks
on sales compensation planning (Churchill et al. 2000) as effective motivators.
Given the business world’s preoccupation with lump-sum bonuses, it is interesting
to note that academics are divided as to their effectiveness. Two main arguments are
advanced against their use. First, as Holmstrom and Milgrom (1987) and Lal and
Srinivasan (1993) point out, the motivational effects of lump-sum bonuses disappear
once sales quotas have been met and incentives have been earned. “It is not
uncommon,” write Lal and Srinivasan, “to hear of salespeople spending time playing
golf or indulging in other leisurely activities if their past efforts have been unusually
successful.”2 A flat commission rate, on the other hand, should not induce such
fluctuations in behavior since the incentive to work is constant over time and
independent of how well or poorly an individual has performed in the past.
Second, as Oyer (1998) and Jensen (2003) point out, lump-sum bonuses tempt
salespeople to manipulate the timing of orders to meet sales quotas without having to
expend additional effort. This type of behavior can take two forms. Salespeople who
have already made quota are encouraged to push out new orders to the next period to
make attaining future quotas easier to accomplish, a behavior termed delayed selling.
On the other hand, salespeople who would otherwise fall short of their current quota
are encouraged to pull in orders from the next period, a behavior termed forward
selling. These behaviors are in conflict with the firm’s interest because they result in
higher incentive costs without returning concomitant gains.
Adverse consequences notwithstanding, some academics maintain that lump-sum
bonuses are effective motivators. Darmon (1997), among others, makes the point
that providing bonuses encourages individuals to reach for sales targets that they
otherwise might not attain.
The rationale for such plans is simple and well known: Quotas are set so as to
provide salespeople with objectives that are challenging and worth being
achieved. In order to enhance salespeople’s performance, management grants
them some reward when they reach a pre-specified performance level (the
quota) which is higher than the level they would have achieved otherwise.
Attention to the study of how goals, such as sales quotas, affect motivation dates
to the experimental work of Hull (1932, 1938) and Mace (1935). Latham and Locke
(1991) present the findings of hundreds of subsequent studies in the goal-setting
literature. McFarland et al. (2002) discuss how multiple quotas affect sales call
selection; Darmon (1997) discusses what influences management to select specific
bonus contract structures; and Mantrala et al. (1994) use agency theory to develop an
approach for determining optimal bonus contracts.
These arguments for and against lump-sum bonuses suggest the basic question
that must be asked by firms considering whether to offer them: will the productive
gains from increased effort outweigh the counterproductive losses? This question is
not entirely new to marketing since the same basic concern applies to the promotion
of consumer packaged goods. Just as bonuses can motivate either productive effort
or unproductive timing games, consumer promotions can increase demand either

2
This argument is not limited to bonuses; other nonlinear incentive contracts, such as tiered commission
rates, share the disadvantage of not offering constant motivation to work.
Effort or timing: The effect of lump-sum bonuses 237

Table 1 Bonus plan effects

across industries Industry Increase Due Decrease Due
to Bonus to Bonus

Office Machines 4.3% −4.4%

Computers 5.3% −6.4%
Optical Supplies 6.2% −4.1%

through increased consumption (primary demand) or through brand switching

(secondary demand). Gupta (1988), Van Heerde et al. (2003), and Steenburgh (2007)
are among many others who have addressed this issue in the promotions literature.
Although much attention has been given to consumer promotions, little empirical
work has been devoted to the effects of sales incentive contracts. A notable
exception is Oyer (1998), who provides empirical evidence that nonlinear incentive
contracts induce temporal variation in firms’ output. Using firm-level data across
many industries, Oyer finds that firms’ reported revenue tends to increase in the
fourth quarter and to dip in the first quarter of their fiscal years. This result is
consistent with the notion that some agents of the firm, whether salespeople or
executives, are varying effort, manipulating the timing of sales, or both in response
to annual incentive contracts. As the magnitude of the spikes and dips are roughly
equivalent in Oyer’s analysis (see Table 1 for estimates from a few industries), we
might infer that timing games play a particularly important role.
In contrast, this study suggests that lump-sum bonuses primarily motivate
salespeople to work harder. Our results are based on a unique dataset that differs
from Oyer’s (1998) in several important respects. First, it offers a more refined view
of how salespeople behave because it is based on individual-level rather than firm-
level output. This is an important distinction because theory suggests that some
salespeople should game the system by delaying sales and others by moving sales
forward at a given point in time. It is not possible to simultaneously observe the
effects of both behaviors on sales using aggregate data.3 Second, whereas Oyer does
not directly observe the firms’ incentive contracts and reasonably assumes that
incentives are offered at the fiscal-year end, we do observe the contracts under which
salespeople work. We show that directly observing the structure and timing of
incentives is critical to understanding whether greater effort or timing games explain
the resulting variation in output. Finally, we observe the output of a group of
salespeople who lack incentives to concentrate production at the end of quarters, and
we use this group’s output use to control for temporal variation not attributable to the
incentive contract, such as customer buying cycles.
Our results suggest that salespeople respond rationally to incentives because
individuals work harder when they have more to gain by doing so. They also suggest
that the widespread practice of using lump-sum bonuses may not be as detrimental to
firms as some believe because their primary effect can be to motivate people to work
harder rather than to play timing games.

3
At best, it may be possible to observe the effect of the dominant behavior, either delayed or forward
selling, on sales using aggregate data; still, given that these two behaviors produce opposing effects, it
may also be possible that both behaviors occur, but their effects on sales cancel out when the data are
aggregated.
238 T.J. Steenburgh

2 Literature review

An extensive theoretical literature in marketing and economics, usually focused on

finding an optimal incentive contract under a given set of conditions, explores how
various incentive contracts affect worker motivation. Basu et al. (1985), Rao (1990),
Lal and Srinivasan (1993), Joseph and Thevaranjan (1998), Gaba and Kalra (1999),
and Godes (2004), among others, examine issues directly related to sales incentive
strategy. Several of these studies examine how sales incentive contracts influence
effort, but none explore timing effects. Gaba and Kalra’s (1999) experimental
evidence supports theoretical predictions about how salespeople should respond to
lump-sum bonuses, but they focus on whether salespeople should engage in more
risky selling behavior rather than whether salespeople should put forth more effort.
Chevalier and Ellison (1997) suggest that a relatively small empirical literature on
how people respond to incentives exists because the direct observation of incentive
contracts is rare. Coughlan and Sen (1986), John and Weitz (1989), Coughlan and
Narasimhan (1992), and Misra et al. (2005) explore sales force incentive issues using
survey data, but focus on firms’ decisions (e.g., what mix of salary and incentive to
offer) rather than the behavior of salespeople. Banker et al. (2000) and Lazear (2000)
find, respectively, that salespeople and factory workers increase productivity in
response to pay-for-performance incentive contracts. These studies, being based on
piece-rate incentive contracts that should curb such behavior, do not explore timing
effects. Healy (1985) finds that managers alter accrual decisions (a timing effect) in
response to their incentive contracts, but does not examine how these contracts affect
the managers’ productivity. Our study provides a more comprehensive view of
behavior by examining workers’ effort and timing decisions under a directly
observed incentive contract.

3 Institutional details

The focal firm is a Fortune 500 company that manufactures, sells, finances, and
maintains durable office products. Its products range in complexity from relatively
simple machines that sit on a desktop to fairly sophisticated ones that fill a room.
Prices range from less than one thousand dollars to several hundred thousand dollars
per machine. In addition to its physical products, the firm offers services such as
equipment maintenance, labor outsourcing, and systems consulting. The firm’s
customers include major corporations, small businesses, and government agencies.
The firm directly employs4 the salespeople in this study, and it broadly classifies
them as either account managers or product specialists. The account managers are
responsible for selling basic products and for spotting opportunities in which the
product specialists may be able to sell more sophisticated ones. There are several
types of product specialists, each having distinct product-line expertise. Organiza-
tionally, the account managers make up one sales force, and the specialists are

4
An indirect sales channel exists to reach small and rural accounts. It is composed of roughly eight
hundred smaller firms that resell the focal firm’s products through “arm’s length” transactions. The focal
firm, for example, cannot directly compensate the salespeople that work in the indirect channel.
Effort or timing: The effect of lump-sum bonuses 239

divided into the remaining sales forces by their product expertise. Although several
salespeople may serve an account, each has unique responsibility and, as a rule, only
one salesperson receives credit for the sale of a given product. The firm’s culture
frowns upon team compensation, and very few salespeople share territories.
The structure of the incentive contract, which is consistent across all of the sales
forces, is outlined in Table 2. The salespeople’s incentive pay is based on the amount
of revenue that they produce for the firm. The contract includes three quarterly
bonuses, a full-year bonus, a base commission rate, and an overachievement
commission rate. The values of the commission rates and bonuses are common
within a sales force, but vary across them. The sales quotas are specific to individual
salespeople. The bonuses and tiered commission rates create a nonlinear relationship
between the output of the salesperson and the incentive pay that they earn. Roughly
half of the salespeople’s pay is distributed through salary and the other half through
incentives. We make no claim that this is an optimal incentive contract, but rather
take it as given. Given the survey work of Joseph and Kalwani (1998), this structure
appears to represent what is commonly found in practice.
The firm views a salesperson as having had a successful year if the full-year sales
quota has been met, and the incentive contract places the greatest emphasis on this
target. The sum of the three quarterly bonuses is worth just slightly more than the
single full-year bonus, and the overachievement commission rate further emphasizes
its importance to the firm. Long-term incentives outside of the sales incentive
contract, such as promotions to better job assignments, grade-level increases, and
salary increases, also depend in part on whether the full-year quota has been met.
These extra-contractual incentive decisions do not depend on the satisfaction of
quarterly quotas.

4 Preliminary aggregate analysis

Taking a preliminary view of the problem, we estimate a model based on sales-force

level data. The intent of this analysis is twofold. First, it helps explain why Oyer’s
(1998) results, which are based on a dataset in which the incentive contracts are not
directly observed, do not necessarily provide evidence of timing games. Specifically,
we show that we cannot draw meaningful conclusions about whether gains in
revenue at the fiscal-year end exceed losses in the subsequent period unless we also
account for the effects of bonuses from interim periods (such as quarterly bonuses).

Table 2 Elements of the incentive contract

Element Description

First-, Second-, and Third-Quarter A lump-sum, cash bonus awarded if the quarterly revenue
Bonuses exceeds the quarterly quota
Full-Year Bonus A lump-sum, cash bonus awarded if the full-year revenue exceeds
the full-year quota
Base Commission Rate Paid on every dollar of revenue brought in by the salesperson
Overachievement Commission Rate Paid on only the revenue brought in above the full-year quota
240 T.J. Steenburgh

Table 3 Descriptive statistics for the sales forces

Number of Average Average Full- 10th Percentile Full- 90th Percentile Full-
Salespeople Tenure Year Quota ($K) Year Quotas ($K) Year Quotas ($K)
(months)

AM 1,512 77.5 1,298 703 1,868

PS1 370 91.5 2,808 1,221 3,822
PS2 224 116.3 2,911 1,576 4,573
PS3 282 114.0 2,775 1,646 3,995
PS4 92 88.8 3,499 1,863 4,932
PS5 90 130.0 6,543 1,277 20,895

Second, this analysis produces results based on “aggregate” data5 that can be
compared with results based on individual-level data in a more refined analysis.

4.1 The data

Our study is based on 2,570 salespeople who worked in one of six sales forces.6 The
data consist of 50,106 monthly observations taken from January 1999 to December
2001. The maximum number of observations per individual is 36 and the average
number is 19.5. Each month of the observation period, we observe the actual
revenue that an individual produces for the firm, the associated sales quota or quotas
that need to be met, and the individual’s tenure with the firm (measured by the
number of months that a salesperson has been employed).
Summary information about the sales forces is reported in Table 3. Descriptive
statistics include the number of individuals, the average tenure, and the average, 10th
and 90th percentile sales quotas for each sales force. Account managers (AM)
represent more than half of the salespeople in the study. Individuals in this group
tend to have lower sales quotas than the product specialists (PS1–PS5) do because
they sell the most basic products offered by the firm. While the account managers
also tend to have less sales experience, they are not entry-level salespeople. Their
average tenure with the firm is over six years, and most individuals have outside
experience in sales before joining the company. The wide spread between the 10th
and the 90th percentile sales quotas is due to a significant difference in the sales
potential of individual sales territories.
Observing the incentives and the revenue production of individual salespeople is
not enough to determine whether an incentive contract is causing the temporal
variation in output. We need to control for the possibility that customer behavior
explains the peaks and dips in revenue production rather than strategic changes in
the salespeople’s actions. For example, suppose the firm’s customers tend to delay
spending until the last month of every quarter. The spikes and dips in production

5
We use data that are averaged across individuals in the sales forces, rather than sales force aggregates, in
order to control for differences in population size. Sales force aggregates would be sensitive to the number
of people working at any given time.
6
Salespeople who worked in teams, with two or more people sharing quota responsibility and pooling the
revenue for a given territory, were excluded from the study as these individuals’ incentives might differ
from those of the general population owing to the free-riding opportunity.
Effort or timing: The effect of lump-sum bonuses 241

Table 4 Preliminary analysis

assuming year-end effects only Value P value

(Intercept) 11.6061 0.0000

PS1 0.7099 0.0000
PS2 0.6290 0.0001
PS3 0.6217 0.0001
PS4 1.1075 0.0000
PS5 1.2450 0.0000
FY 0.1782 0.1396
POST.FY −0.3393 0.0064
CONTROL.SALES 0.1343 0.0314

might then be attributed to market behavior rather than the salespeople’s response to
the incentive plan.
We use the revenue produced by the indirect sales channel as a covariate to
control for this possibility. The member firms of the indirect channel are
compensated such that the variation in their output over time can be attributed to
market forces rather than to their incentive contracts. In the first two years of the
study, firms worked under a flat commission rate, while in the final year they worked
under a flat commission rate in conjunction with a monthly bonus. Thus, agents in
this channel lack incentive to lump production at the end of quarters.

4.2 Empirical analysis

To analyze the data, we estimate the model

P
5  
yst ¼ a þ Ds *as þ Xt bt þ "st ; "st  N 0; s 2 ð1Þ
s¼1

The independent variable yst represents the log of the average revenue produced
by the salespeople in sales force s in month t. We use log revenue so the effects are
proportional rather then additive. Ds is a dummy variable that takes the value one for
product specialist s 2 f1; :::; 5g; this yields sales-force-specific intercepts. X is
composed of the following explanatory variables: FY is a dummy variable that takes
the value one in December, the month before the full-year bonus period closes.
POST.FY takes the value one in January, the month after the full-year bonus period
closes. These variables are zero in all other months. Similarly, Q is a dummy
variable that takes the value one in March, June, and September, the months before
the quarterly bonus periods close. POST.Q takes the value one in April, July, and
October. These variables are zero in all other months. CONTROL.SALES7 is the
average sales revenue generated by the indirect sales channel, the control population.
This variable is standardized for ease of interpretation.
We present results for the model that include only year-end effects in Table 4. The
revenue production at the end of the bonus period marginally increases (βFY =
0.1782), signifying that the incentive plan has a positive influence on the
salespeople’s behavior, but it is exceeded in magnitude by the decrease in revenue

7
For future research, additional controls might be found by tracking customers’ fiscal calendars, which
could be used to infer their buying cycles. These data, however, were not available for this analysis.
242 T.J. Steenburgh

Table 5 Preliminary analysis

with effects for all bonus periods Value P value

(Intercept) 11.4090 0.0000

PS1 0.7028 0.0000
PS2 0.5685 0.0000
PS3 0.5522 0.0000
PS4 1.0678 0.0000
PS5 1.1482 0.0000
FY 0.5694 0.0004
POST.FY −0.3749 0.0077
Q 0.2476 0.0118
POST.Q 0.1054 0.2257
CONTROL.SALES 0.2624 0.0000

after the bonus period ends (βPOST.FY =−0.3393). The coefficient for the year-end
increase is not statistically significant. From a broad perspective, these estimates
appear similar to Oyer’s and may lead us to conclude that the incentive contract
encourages salespeople only to forward sell, not to work harder.
Given that the quarterly bonuses are of lesser value than the full-year bonus, we
might not expect their inclusion to make a significant impact. Yet, when looking at
the results in Table 5 for the model including both quarterly and full-year effects, the
picture is now quite different. The positive effects during the bonus periods
outweigh the negative effects afterwards. In fact, we do not find evidence of a dip in
revenue after the quarterly bonus periods end, as βPOST.Q is insignificant. For the
full-year bonus, the dip in revenue after the period explains 41% of the increase in
revenue during the period.8 These results suggest the primary influence of the
incentive contract is to encourage salespeople to work harder, not to play timing
games.
What causes the results to change so dramatically? The baseline sales level is
overestimated by omitting the quarterly effects because the productive increases in
revenue at quarters’ end are not followed by counterproductive decreases. Since the
quarterly effects do not merely cancel each other out, but rather are positive, the
intercept of the log of revenue drops from 11.6 to 11.4 when we include them. As
can be seen in Fig. 1, this affects the parameter estimates and changes our
interpretation of the year-end effects. By not accounting for the quarterly bonuses,
we underestimate the spike in revenue caused by the full-year bonus and
overestimate the dip in revenue following it.
The preliminary analysis illustrates the need for careful modeling, but it brings up
as many questions as it answers. Most importantly, we have not accounted for
differences in individuals’ circumstances that may have an equally important impact
on our results. For example, while an individual who has already made a bonus is
encouraged to delay sales, an individual who has not yet made it is encouraged to
forward sell. Do these effects cancel one another out or does one effect tend to

8
The proportion of the spike
 in revenue
. during the
 bonus period explained by the dip in revenue
 FY :POST  FY
afterwards is calculated as e b  1 e b  1 .
Effort or timing: The effect of lump-sum bonuses 243

Fig. 1 Preliminary model

comparison
$110 K

$90 K
Mar Jun Sep Dec

Jan

dominate? How does this affect our analysis of whether effort or timing effects are
more important? We now discuss how an individual’s sales history can influence her
or his actions and build an individual-level model to capture these effects.

5 Theoretical motivation

Principal-agent models give us an appreciation of how individuals respond to

various circumstances. In this section, we discuss the ways we would anticipate a
salesperson responding given various levels of accumulated sales within a bonus
period. Specifically, we focus on how past performance influences an individual’s
decision to work and to play timing games. Our conclusions will suggest that an
accurate decomposition of effort and timing effects cannot be made without
accounting for individual-level behavior. This motivates the development of a
statistical model based on individual-level data.

5.1 Effort

Lal and Srinivasan (1993) point out that past performance influences the level of
effort exerted when a salesperson is working under a bonus contract. A simple
example helps clarify this relationship. Consider a salesperson who is working to
achieve a quarterly bonus. Each month she has the opportunity to sell one unit of a
good. By working harder she increases the probability of a sale, but greater effort
comes at an increasing marginal cost. Let θt be the probability of a successful sale
2
and qt 2 be the associated cost of effort in month t 2 f1; 2; 3g. Suppose that the
salesperson’s utility for wealth is u(w). Suppose further that the firm offers a salary
of a no matter what the salesperson produces and a bonus of b if the salesperson
meets or exceeds a quota of q=2 units. Let Δ  uða þ bÞ  uðaÞ be the difference in
utility between earning and not earning the bonus without regard to the cost of effort.
Figure 2 illustrates how the salesperson’s past performance affects the level of
effort exerted in the final month of the quarter. First, consider a salesperson who
does not complete a sale in either of the first two months. She has no chance of
making her quota and earning the bonus; consequently, she chooses not to work in
month three, a marginal decrease in effort from the second period level. Next,
consider a salesperson who completes sales in both of the first two months. She has
already made quota and earned the bonus; consequently, she also chooses not to
work in month three, a marginal decrease in effort from the second period level.
Finally, consider a salesperson who completes one sale in the first two periods. The
244 T.J. Steenburgh

Fig. 2 Effort in month three

given accumulated sales

Effort in Month Three

θ 3⏐X1 + X2=s2
θ 2 | X1 = 0

θ2 | X
1 =1

0
0 1 2
Performance Prior to Month Three (s2)

third period provides the final opportunity for her to make quota; consequently, she
marginally increases effort from the second period level. (Proof in Appendix I.)
Despite being simple, this model provides the basic intuition of how individuals
vary effort when working under a bonus contract. As illustrated in Fig. 2, those who
are within reach of the bonus work harder; those who have already earned the bonus
relax; those who cannot earn the bonus give up.
We summarize the predictions of how salespeople will behave and the
corresponding influence of this behavior on revenue production (which we observe
in the data) as follows:
Suppose a lump-sum bonus is the only incentive offered for quota attainment. In
the final month of the bonus period:
a) salespeople who can make quota if they stretch will increase effort and their
revenue production will marginally increase.
b) salespeople who either
1. have already made quota, or
2. are unlikely to make quota
will decrease effort and their revenue production will marginally decrease.
The firm analyzed in this paper offers an overachievement commission rate in
conjunction with the full-year bonus. This commission rate will modify how a
salesperson who has already made quota behaves, but it will not influence the other
salespeople. Returning to the previous example, suppose the firm offers an
additional incentive c if the salesperson sells one unit more than her quota. She
now will exert positive effort in the third month if she sold a unit in each of the first
two periods, but she still exerts no effort if she did not sell a unit in each of the first
two periods. (See Fig. 3) Given the overachievement commission rate, we make no
prediction about whether salespeople who have met quota will marginally increase
or decrease effort.

5.2 Timing games

Just as past performance influences how hard an individual is willing to work for a
bonus, it affects the types of timing games that he or she plays with orders. Oyer
(1998) builds a simple theoretical model to predict how individuals manipulate the
Effort or timing: The effect of lump-sum bonuses 245

Fig. 3 The effect of an

overachievement commission

Effort in Month Three

rate

θ 3⏐X1 + X2=s2
0
0 1 2
Performance Prior to Month Three (s2)

timing of sales, essentially showing that salespeople will pull in orders from future
periods if they would otherwise fall short of a sales quota and they will push out
orders to future periods if quotas are either unattainable or have already been achieved.
The timing-game predictions correspond to the effort predictions as follows:
Suppose a lump-sum bonus is the only incentive offered for quota attainment. In
the final month of the bonus period:
a) salespeople who can make quota if they stretch will pull in sales from future
periods. Their revenue production will marginally increase in the month before
and will marginally decrease in the month after the bonus period closes.
b) salespeople who either
1. have already made quota, or
2. are unlikely to make quota
will push out sales to future periods. Their revenue production will marginally
decrease in the month before and will marginally increase in the month after the
bonus period closes.
The timing-game predictions raise the issue of whether it is even possible to
decompose the effort and timing effects using aggregate data. For instance, suppose
one group of salespeople is forward selling and another, of equal size, is delaying
sales. In aggregate, we would see no change in output, as the spikes in output of one
group are perfectly balanced by the dips in output of another. Orders are being
moved across periods, but we cannot identify the counterproductive behavior from
the data because they move equally in both directions. We now turn to developing a
statistical model that takes into account an individual’s distance from quota so as to
accurately identify the timing and effort effects.

6 Model development

6.1 Defining the sales history variables

The theoretical discussion highlights why we need to account for past performance if
we are to accurately decompose the effort and timing effects. The implementation
246 T.J. Steenburgh

of this, however, is made difficult by the nonlinear relationship between past

performance and how an individual behaves. For example, if prior outcomes are
poor, the salesperson reduces effort near the end of a bonus period. If he or she is
within striking distance of quota, the salesperson increases effort. Yet, if the quota
has already been made, the salesperson reduces effort.
We use categorical variables to capture how past performance affects an
individual’s revenue production. The variables are created using the individuals’
performance to date (PTD) against quota immediately prior to the final month of a
bonus period. For every month that a salesperson works, we observe the sales quota
or quotas that need to be met and the actual amount of revenue produced by the
individual. An individual’s PTD is defined as the ratio of cumulative revenue
produced in a bonus period to the quota that needs to be met. For example, if a
salesperson’s first-quarter quota is $400K and she has produced $200K in total at the
end of February, the PTD is 50% against the first quarter quota at that point in time.
Two sets of categorical variables are needed to capture the effects of sales history
on revenue production: one set of variables for the month before a bonus period and
one set for the month following a bonus period. The categorical variables are:
EXCEEDED, NEAR, STRETCH, FAR, and REMOTE in the month before the end
of an incentive period; and POST.EXCEEDED, POST.NEAR, POST.STRETCH,
POST.FAR, and POST.REMOTE in the month after it. (Note: we add two additional
categories, VERY.FAR and POST.VERY.FAR, for the full-year bonus period
because distribution of past performance is wider.) We refer to these as the sales
history variables and their definitions, which are based on the PTD measure, are
given in Table 6. We estimate the quarterly and full-year effects separately because
the amount of compensation at stake is greater at the end of the year than it is at the
end of a quarter. The observed frequency of occurrence for each of the categories is
given in Fig. 4.
An example clarifies how these variables are defined. Suppose a salesperson has
done very well and her PTD is 120% at the end of February. In March, the variable
EXCEEDED associated with the quarterly quotas takes the value one and variables
NEAR, STRETCH, FAR, and REMOTE take the value zero for this salesperson. In
April, all of the aforementioned variables take the value zero; the variable POST.
EXCEEDED associated with the quarterly quotas takes the value one; and the
variables POST.NEAR, POST.STRETCH, POST.FAR, and POST.REMOTE take
the value zero. (The POST variables take the value zero in March, and all of the
variables take the value zero in months not surrounding a quarterly bonus.) A similar
process is used to define the quarterly variables in June, July, September, and
October and to define the full-year variables in December and January.

Table 6 Definition of Sales

History Variables Variable Quarterly Full-year
performance to date performance to date

EXCEEDED ≥1 ≥1
NEAR 2=  1 11=  1
3 12
Several alternative definitions of STRETCH 1=  2= 8=  11=
3 3 12 12
these variables were tested; none FAR 0  1=3 4=  8= and 0  4=
12 12 12
resulted in substantive changes REMOTE ≤0 ≤0
to the findings.
Effort or timing: The effect of lump-sum bonuses 247

Fig. 4 Observed frequency of Quarterly Quotas

categories 0.40

0.35

Percent of Observations
0.30

0.25
0.20

0.15
0.10

0.05
0.00
REMOTE FAR STRETCH NEAR EXCEEDED

Full-Year Quota
0.35
Proportion of Observations

0.30
0.25
0.20
0.15
0.10
0.05
0.00

ED
H
TE

R
R

EA

D
FA

FA
O

ET

EE
EM

N
Y.

C
R

ST
R

EX
VE

How do we know whether individuals are playing timing games or exerting

greater effort? Timing games imply that salespeople move orders from one period to
the next. Subsequently, spikes (dips) in revenue production in the month prior to the
close of a bonus period are followed by equivalent dips (spikes) in production in the
month after it. On the other hand, if the salespeople are just varying effort, spikes or
dips in production exist in the month prior to the close of an incentive period, but not
in the month after it. In other words, we infer whether timing games are being played
by the sign of the coefficient of the POST variables.

6.2 The regression model

We model the revenue production of salesperson i from sales force s in month t as

follows:
 
ysit ¼ asi þ Xsit bs þ "sit ; "sit  N 0; s 2si ð2Þ

 
asi  N xs ; s 2as
248 T.J. Steenburgh

 
xs  N g; s 2x

bs  MVNp ðd; Σ Þ

where s ¼ 1; . . . ; 6; i ¼ 1; . . . ; ns ; t 2 f1; . . . ; 36g. An individual is identified by two

subscripts, s and i, in this notation. The constant ns denotes the number of
individuals in sales force s. The month t refers to a specific calendar month; this is
necessary to identify the market sales, a variable in the vector Xsit. A salesperson’s
output is measured in thousands of dollars of revenue produced for the firm. The
variance of the error term is assumed to be individual specific. (See Appendix II for
the full conditional posterior distributions.)
Differences among the individual salespeople are accounted for through the
random intercepts αsi. Since individuals within a sales force have many common
characteristics—for example, they sell the same types of products, share common
managers, undergo similar training, etc.—we model the intercepts as arising from a
sales-force-specific distribution. In turn, the means of the sales-force-specific
distributions, ξs, are modeled as arising from a common population distribution.
The intercepts αsi are interpreted as an individual’s baseline revenue production.
The vector of explanatory variables, Xsit, includes tenure with the firm, market sales
(measured by the revenue produced in the indirect channel), and the categorical
variables describing an individual’s sales history at that point in time. The sales-force-
specific parameters βs quantify the influence of these variables. Since the sales history
variables are categorical, we can interpret the coefficients associated with these variables
as marginal changes in an individual’s revenue production from her or his baseline. We
model the parameters βs as arising from a common population distribution. Our
specification allows us to draw inference at both the sales force and population levels.
We decompose the marginal changes in revenue production into effort and
timing-game components using the following relationships: Let Δ be the marginal
change in revenue production attributable to effort and let Λ be the change
attributable to timing games. For any given sales history, say for individuals in the
STRETCH classification, Δ and Λ are defined as:

ΔSTRETCH ¼ d STRETCH þ d POST :STRETCH

ΛSTRETCH ¼ dPOST :STRETCH 

It is straightforward to find these quantities through the Markov chain Monte

Carlo (MCMC) output.

7 Results

We summarize the results from Eq. 2 using the mean and standard deviation of the
posterior distributions. The population-level results are reported in Table 7 and the
sales-force-level results in Table 8. The incentive contracts generally motivate
Effort or timing: The effect of lump-sum bonuses 249

Table 7 Population parameter

estimates Variable Coefficient SD

Intercept 70.8 11.0

Quarterly Exceeded 38.7 7.6
Near 24.3 7.4
Stretch 12.7 7.1
Far −2.9 5.9
Remote −7.5 6.9
Post.exceeded 11.6 7.0
Post.near 3.8 7.3
Post.stretch 2.2 6.0
Post.far 0.0 5.6
Post.remote 1.0 6.7
Full-Year Exceeded 92.4 10.1
Near 59.4 12.4
Stretch 80.5 10.9
Far 49.0 9.3
Very.far 22.6 15.3
Remote −25.8 13.1
Post.exceeded −9.6 8.0
Post.near −11.3 12.4
Post.stretch −10.8 8.2
Post.far −0.8 7.4
Post.very.far −2.6 8.7
Post.remote −6.0 14.2
Tenure 0.6 4.6
Control.sales 19.6 5.6

salespeople to produce more revenue during the bonus period. See the EXCEEDED
coefficients, for example, in Table 7. We now turn to discussing whether effort or
timing games lead to the increases.

7.1 Timing games

Very limited support exists for the idea that the salespeople play timing games in
response to bonuses at this firm. When considered individually, none of the POST
variables are statistically significant at the population level (see Table 7). This holds
for both the quarterly and the full-year bonus periods. We also consider the
weighted-average of the post-period effects, where the weights are determined by the
observed frequency of a given sales history. When taken as a group, the 90%
credible intervals of the weighted means are (−1.9, 8.2) for the quarterly effects and
(−12.3, 0.3) for the full-year effects. Since both intervals contain zero, no support
exists for timing games on this measure either.
This is surprising for a few reasons. Salespeople who sell durable goods should
be able to influence the timing of sales more directly than their consumer goods
counterparts because each sale requires considerable time and intense customer
contact. We would expect that these salespeople would have some ability to
manipulate the timing of business. Second, a sizeable portion of the focal firm’s
business comes from customers trading in old equipment. This should make it easier
for salespeople to delay the timing of sales because not all customers have a pressing
need for new equipment.
250 T.J. Steenburgh

Table 8 Sales force parameter estimates

Variable AM PS1 PS2 PS3 PS4 PS5

Intercept 63.5 87.6 66.1 58.3 96.4 54.2

2.0 4.2 6.0 5.8 12.2 11.2
Quaterly Exceeded 38.6 28.8 38.6 43.7 41.5 43.9
4.0 5.1 9.0 8.8 9.1 12.9
Near 24.6 16.7 17.1 29.1 29.9 29.4
4.0 5.3 8.5 8.2 8.5 14.3
Stretch 15.2 8.4 0.5 4.9 18.2 28.1
2.6 4.1 6.0 7.8 6.9 8.3
Far 3.8 −8.4 −3.4 0.5 −8.5 −0.1
1.8 3.3 5.1 5.5 7.4 7.7
Remote −7.9 −18.4 −2.6 −2.2 −8.7 −4.0
3.8 5.5 6.8 6.5 11.4 8.0
Post.exceeded 7.5 13.7 14.9 10.9 15.8 10.1
3.8 4.6 8.8 8.2 10.3 12.6
Post.near 0.4 6.1 8.3 0.7 6.9 5.3
3.3 5.3 9.4 8.7 7.2 13.2
Post.stretch 5.3 1.0 −2.5 4.9 5.5 −3.2
2.5 4.3 4.7 7.7 7.3 9.2
Post.far 0.8 −5.0 −1.8 2.7 3.6 −1.6
2.0 3.8 4.5 5.1 7.1 6.2
Post.remote 1.2 −3.2 0.7 6.5 2.0 −2.2
3.5 5.7 6.1 6.6 9.5 7.6
Full-Year Exceeded 75.2 94.8 99.5 102.6 96.1 81.1
5.5 7.8 13.5 12.6 11.5 17.3
Near 56.7 58.4 68.1 57.9 57.8 55.3
10.3 11.2 15.5 14.1 15.1 17.4
Stretch 55.3 76.9 76.0 82.7 88.1 98.4
4.4 5.7 11.2 10.9 15.0 16.5
Far 29.1 49.0 49.6 68.0 49.4 45.1
4.5 6.1 8.7 11.3 10.3 15.0
Very.far 5.5 32.1 52.1 39.1 23.1 −22.8
3.6 10.8 13.2 11.8 19.6 13.9
Remote −30.5 −18.2 −9.4 −22.2 −26.8 −43.9
8.0 16.0 15.6 14.4 16.4 15.2
Post.exceeded −16.0 −12.3 −10.0 −1.8 −5.2 −5.9
4.6 6.5 8.7 10.2 11.2 14.5
Post.near −16.1 −16.1 2.5 −2.8 −10.1 −18.0
7.5 11.5 12.9 15.6 14.8 19.2
Post stretch −9.1 −11.7 −5.6 −7.6 −10.3 −16.8
4.5 6.0 9.8 11.3 10.9 12.2
Post.far 1.1 −5.4 5.9 1.9 −5.0 −6.8
3.4 7.1 7.4 8.2 10.7 12.9
Post.very.far −0.4 −3.3 −6.7 −1.6 −2.1 1.6
4.3 9.6 12.0 10.0 11.9 12.7
Post.remote −9.6 −3.5 −5.7 −7.1 −4.8 −8.5
9.3 16.5 18.8 18.7 16.5 20.5
Tenure 1.4 0.0 0.3 0.6 −0.1 1.0
0.2 0.4 0.5 0.5 1.3 1.0
Control.sales 15.8 23.9 18.0 24.2 24.9 12.0
1.0 1.8 2.0 2.1 3.8 3.2
Effort or timing: The effect of lump-sum bonuses 251

Two obstacles may prevent these salespeople from playing timing games. First,
managers have regular one-on-one meetings9 to discuss where in the sales cycle all
prospective customers are. This form of monitoring may make it difficult to delay
the close of business because managers can infer delay tactics when future sales
arrive. Furthermore, many of the managers have worked their way up through the
ranks and have established personal relationships in their salespeople’s accounts. If
they suspect an employee is delaying orders, they may be able to directly contact
customers and learn when the salesperson initiated the sales process. A monitoring
explanation, however, does not account for why salespeople do not appear to be
forward selling. Sales managers have no incentive to prevent this behavior, but we
find no evidence of it either.
An explanation more consistent with the data is that the customers prevent timing
games from being played in this industry. Spikes in market sales during the final
month and dips during the first month of bonus periods bolster this idea. (The
average values of the standardized CONTROL.SALES variable are 0.669 for the
final months of a quarter and 1.61 for the final month of the year, whereas they are
−0.430 for the first month of a quarter and −1.40 for the first month of the year.)
Recall that the CONTROL.SALES variable was taken from an indirect channel that
has no incentive to manipulate the timing of sales. A plausible explanation of the
spikes and dips in these data is that customers require sales to close according to
their own needs, perhaps making purchases only when enough money is available in
their budgets at the end of a quarter. If this is the case, then salespeople face the
prospect of either closing sales when the customers want them to close or losing
them entirely, which precludes the salespeople from moving business across periods.

7.2 Effort

Support does exist for the idea that bonuses motivate salespeople to vary effort, and,
on the whole, they motivate salespeople to work harder. Considered individually, the
EXCEEDED and NEAR coefficients are positive and statistically significant for the
quarterly periods, and the EXCEEDED, NEAR, STRETCH, and FAR coefficients
are positive and statistically significant for the full-year period (see Table 7). Taken
as a group, the 90% credible intervals for the weighted means are (4.6, 16.6) for the
quarterly periods and (52.2, 73.0) for the full-year period. As both these intervals are
strictly positive and all of the POST coefficients are insignificant, we claim that the
incentive contract tends to motivate salespeople to work harder.
This is not to say that the bonuses only have productive effects. While the
coefficients are not statistically significant, the estimates are negative for both of the
REMOTE categories. This suggests that salespeople give up if they feel that they
cannot make the quota. Even if we cannot interpret this as a marginal decrease in
effort, we can certainly claim that these salespeople do not increase effort in an
attempt to earn greater incentives. This supports the idea that salespeople react to the
incentive contract in a rational manner.
How do the results based on individual output compare to the preliminary results
based on sales-force output? The individual-level results provide even less evidence

9
These meetings occur at least monthly and sometimes weekly.
252 T.J. Steenburgh

of timing games. The results for the quarterly bonus periods are consistent across the
two analyses, and neither suggests that timing games occur. Spikes in revenue
production at the end of the quarterly bonus periods are not followed by
counterproductive dips in the subsequent period. The results for the full-year bonus
period, however, are less consistent across the two analyses, and the individual-level
results provide less evidence that the salespeople are playing timing games. In the
preliminary analysis, we find evidence of forward selling because the spikes in
revenue production at the end of the full-year bonus period are followed by dips in
production in the subsequent period. In the individual-level analysis, we do not find
statistically significant evidence of forward selling. Even if we were to use the
weighted mean of the POST effects as a point estimate of the forward selling effects,
it explains very little of the spike in revenue production. Since the weighted mean of
the POST effects is −6.3, and the weighted mean of the bonus period effects is 62.0,
we would estimate that about 10% of the increase is due to forward selling by this
method.
Taken altogether, our results suggest that individual-level data are needed to
determine the magnitude of timing and effort effects. As was seen in the preliminary
analysis, the baseline sales level is crucial in accurately decomposing effort and
timing effects, and the most appropriate baseline is an individual’s sales level. Not
accounting for heterogeneity in the intercepts is bound to bias the analysis.
Furthermore, an individual’s sales history determines which timing game is in her
or his self-interest, and this history is lost if the data are aggregated.

8 Conclusions and future research

In this paper, we find that lump-sum bonuses motivate salespeople to work harder,
not to play timing games—a result that is consistent with the widespread use of
lump-sum bonuses in practice. This is not to suggest that lump-sum bonuses have no
counterproductive effects. We find that bonuses cause some salespeople, those who
are unlikely to make quota, to reduce effort, but this effect is more than compensated
for by productive increases in output by other salespeople. Our results are based on a
unique data source that contains the revenue production of individual salespeople.
Using these data, we bring into question whether models based on aggregate data
sources can accurately decompose effort and timing effects and cast doubt on
previous findings that suggest the primary effect of lump-sum bonuses is to induce
salespeople to play timing games.
This study also provides a basis for future research. We are currently addressing
the issue of how firms should design optimal incentive contracts—combining sales
quotas, bonuses, and commission rates to effectively motivate their sales forces. This
and other studies that explore policy variation need to make assumptions about how
individuals will behave when policies are changed. Our current findings suggest that
salespeople will alter how hard they work, but will not manipulate the timing of
orders in response to incentive contracts. Having identified the key ingredients to a
structural model of salespeople’s behavior, we can now pursue questions of how to
effectively motivate them.
Effort or timing: The effect of lump-sum bonuses 253

Appendix I. The effort model

First, let us consider a salesperson who has been successful in the first two periods.
This person’s expected utility is
" # " #
q 2 1X 2
q 2 1X 2
q 3 uð a þ bÞ  3 2  q þ ð 1  q 3 Þ uð a þ bÞ  3 2 
2
q 2
2 t¼1 t 2 t¼1 t
" #
2 1 X 2
¼ uð a þ bÞ  q 3 2  q 2
2 t¼1 t

because the bonus is earned whether the salesperson is successful or not. Taking the
first derivative of expected utility with respect to θ3 results in the first-order
condition that θ3 =0. No additional gain comes from working, so the salesperson
chooses not to do so. Letting q3jS2 ¼s represent the effort put in the third period if the
salesperson’s accumulated sales after the second period is s, we find q 3jS2 ¼2 ¼ 0. The
P 2
salesperson’s expected utility is uða þ bÞ  12 q 2t if this decision node is reached.
t¼1
A similar argument holds for a salesperson who has not completed a sale in the
first two periods. This person’s expected utility is
" #
2 1 X2
uð aÞ  q 3 2  q2
2 t¼1 t
because the bonus is not earned whether the salesperson is successful in the third
period or not. Thus, q3jS2 ¼0 ¼ 0 and the salesperson’s expected utility is uðaÞ  12
P2
q2t if this decision node is reached.
t¼1
Now, let us consider a salesperson who has completed one sale after two periods.
This person’s expected utility is
" # " #
2 1 X2
2 1 X2
q3 uða þ bÞ  q3 2  q 2 þ ð 1  q 3 Þ uð aÞ  q 3 2  q2
2 t¼1 t 2 t¼1 t
2 1X 2
¼ uðaÞ þ q3 ½uða þ bÞ  uðaÞ  q3 2  q2
2 t¼1 t
because the bonus is earned only if the salesperson is successful in the last period.
Thus, the first-order condition for a maximum is ½uða þ bÞ  uðaÞ  q 3 ¼ 0. For
convenience, define the change in utility for earning the bonus as Δ ¼
uða þ bÞ  uðaÞ. Thus, q3jS2 ¼2 ¼ Δ (positive effort is exerted to earn the bonus)
P
2
and the salesperson’s expected utility is uðaÞ þ 12 Δ2  12 q 2t if this decision node
t¼1
is reached. We assume the firm chooses a bonus b such that Δ ≤1; that is, the bonus
is set at a reasonable, not an extraordinarily high, level. Otherwise the firm would be
overpaying for the chance of a certain sale in this period. Since a > 0; b > 0,
0 < Δ  1.
The question is how do the third period strategies compare to the second period
strategies?
254 T.J. Steenburgh

Let us first consider a salesperson who completed a sale in the first period. The
expected utility of this person is
" # " #
1X 2
1 2 1X 2
q 2 uð a þ bÞ  q þ ð1  q2 Þ uðaÞ þ Δ 
2
q 2
2 t¼1 t 2 2 t¼1 t
1  q2 2 1X 2
¼ uðaÞ þ q2 Δ þ Δ  q2
2 2 t¼1 t

The first order condition for a maximum is Δ  Δ2  q 2 ¼ 0, which implies

2

q2jS1 ¼1 ¼ Δ  Δ2 .
2

Now consider a salesperson who did not complete a sale in the first period. This
person’s expected utility is
" # " #
1 2 1X 2
1 X2
q2 uð aÞ þ Δ  q 2 þ ð 1  q 2 Þ uð aÞ  q2
2 2 t¼1 t 2 t¼1 t
q 2 Δ2 1 X 2
¼ uð aÞ þ  q2
2 2 t¼1 t

The first order condition for a maximum is Δ2  q 2 ¼ 0, which implies

2

q2jS1 ¼0 ¼ Δ2 .
2

Since q3jS2 ¼1 ¼ Δ > Δ2 ¼ q2jS1 ¼0 and q 3jS2 ¼1 ¼ Δ > Δ  Δ2 ¼ q2jS1 ¼1 when

2 2

0 < Δ  1, the salesperson, if it is necessary to stretch to make the quota,

marginally increases effort in the third period.
Since q3jS2 ¼2 ¼ 0 < Δ  Δ2 ¼ q 2jS1 ¼1 , the salesperson, if the quota has already
2

been made, marginally decreases effort in the third period.

Since q 3jS2 ¼0 ¼ 0 < Δ2 ¼ q2jS1 ¼0 , the salesperson, if the quota has already been
2

made, marginally decreases effort in the third period.

Appendix II. The full conditional distributions

Assume conjugate prior distributions

 2  2 h 2i P  
s si ¼ s as ¼ s x ¼ G½v0 =2; l0 =2 ½ 1  ¼ W r0 Ip ; r0
 
½g  ¼ N 0; t 20 ½d  ¼ MVNp ð0; T0 Þ

For notational convenience, define

P ns P 1
dsi ¼ nsi s 2 2
si þ s as Ds ¼ s 2 T
si Xsi Xsi þ
Pi¼1
ds ¼ ns s 2 2
as þ s x D¼ c 1 þ T01
d¼ cs 2
x þ t 2
0
Effort or timing: The effect of lump-sum bonuses 255

The full conditional distributions resulting from these assumptions are

Pnsi 
 2  l0 þ ðy asi Xsit bs Þ2
s si ysit ; asi ; bs ¼ G v0 þn 2
si
; t¼1 sit
2
 2 2
 1  2 Pnsi 1 
asi jysit ; bs ; s si ; xs ; s as ¼ N
dsi s si t¼1 ðysit   Xsit bs Þ þ s 2
as xs ; dsi
P
 2  l0 þ
ns
ðasi xs Þ2
s as asi ; xs ¼ G v0 þn 2 ;
s i¼1
2
h i  h Pns i 
xs jasi ; s 2
as ; g; s x
2
¼ N ds1 s 2 as
2
i¼1 asi þ s x g ; ds
1

h  i
Pc 
 v0 þc l0 þ ðxs g Þ2
s 2
x x s ; g ¼ G 2 ; s¼1
2
h i  h Pc i 
2 1 2 1
gjxs ; s x ¼ N d s x s¼1 xs ; d
h P1 i  P P1  1 
bs jysit ; asi ; s 2
si ; d; ¼ MVNp D1 s
ns 2 T
i¼1 s si Xsi ðysi  asi insi Þ þ d ; Ds
hP  i P 
1  c T
d; bs ¼ W s¼1 ðb s  d Þðb s  d Þ þ r0 Ip ; c þ r0
h P i  P P  
djbs ; 1 ¼ MVNp D1 1 c
s¼1 b s ; D
1

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