Principals and Their Car Dealers:
What Do Targets Tell about Their Relation?
Presented by
Dr Eddy Cardinaels
Professor
University of Leuven
#2016/17-03
The views and opinions expressed in this working paper are those of the author(s) and
not necessarily those of the School of Accountancy, Singapore Management University.
Principals and Their Car Dealers: What Do Targets Tell about Their
Relation?
Jan Bouwens*
University of Amsterdam, Amsterdam Business School
[email protected]
Eddy Cardinaels
KU Leuven, Department of Accountancy, Finance and Insurance,
Tilburg University, Department of Accountancy,
[email protected]
Jingwen Zhang
Tilburg University, Department of Accountancy,
[email protected]
March 2016
* Corresponding author. This study received generous support from the Thomas Henry Carroll-Ford
Foundation at Harvard Business School. We thank our colleagues at the accounting department of Tilburg,
seminar participants at Frankfurt School of Finance & Management, Michigan State University, LudwigMaximilian-University of Munich and IÉSEG School of Management, participants of the 2015 MAS
conference and 2015 EAA conference, and Karen Sedatole, Will Demere, and Michael Matějka for their
comments on previous versions of the paper.
Principals and Their Car Dealers: What Do Targets Tell about Their
Relation?
Abstract
The paper tries to establish for a large European car dealership whether target ratcheting and
target achievements are associated with the type of contract offered to the franchisee in the
dealership. We find that the franchisor sets different targets conditional on whether or not the car
dealer (franchisee) exclusively acquire the cars they sell from the franchisor. We also find that
compared to non-exclusive dealers exclusive dealers work harder to achieve their targets and are
less likely to mute their effort when their target achievement is relatively high. We argue that the
evidence we present is consistent with the ideas put forward in relational-contracts theory.
Keywords: Target setting; Target ratcheting; Ratchet effect; Target achievement; Relationalcontracts theory.
1. Introduction
This paper examines how choices in target setting affect relations within a franchise organization,
where the franchisor contracts with two types of franchisees. The first type of franchisee
exclusively sells the franchisor’s brand of cars (exclusive franchisee), while the second type of
franchisee also sells brands of other manufacturers (non-exclusive franchisee). We test whether
the franchisor treats the two types of franchisees differently in terms of target updating and whether
franchisees respond differently to the targets conditional on their type.
Our inquiry is motivated by relational-contracts theory. According to this theory, cooperation
between parties is a function of the de facto contracts these parties are engaged in (Gibbons and
Henderson, 2012a). These contracts arise from a chain of interactions between the parties through
which these parties show their commitment to meet each other’s expectations. These expectations
are contingent on circumstances and may differ over time, and thus a formal contract can capture
only some of the effort necessary to meet each party’s expectations. To the extent one party meets
the other’s expectations time after time, the second party will increase its faith in the first’s
commitment. In return, the second party will then be more likely to show commitment to the first
party. Over time, mutual trust arises, with both parties believing that implicit obligations will be
honored at each engagement.
By their very nature, these sorts of contracts preclude the parties from upholding their implicit
agreements in court. As trust grows through repeated interactions, it is difficult for all parties
involved to build and refine their relational contract. Since it takes time for signals to become
mutually credible within an organizational setting, relations are difficult to copy by other parties
once trust is instilled (Gibbons and Henderson, 2012a). As a consequence, two competing firms
may look the same on surface and face the same conditions except that one firm has built a
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sophisticated implicit understanding as to what constitutes the contract within the firm, while the
other is waiting for these particulars to emerge. The latter faces potentially a bigger challenge in
assuring that coordination occurs between all parties included in the relational contract.
We propose that this idea from relational-contracts theory can help us explain why seemingly
similar firms may perform in markedly different ways (Gibbons and Henderson, 2012a). We argue
that as long as trust has not grown in the relational contract, parties will refrain from sharing the
benefits that accrue from their cooperation. 1 An empirical challenge for studying relational
contracts is the difficulty of finding firms that lend themselves to comparison and allow for the
identification of relational contracting. Firms in the same industry may look superficially similar
but often differ as far as relations and formal contracts between principals and agents comprising
the firm are concerned (Gibbons and Henderson, 2012a).
Using the data from a car dealer network, we study the target setting and updating process in which
the principal (franchisor) offers the same formal contract to its agents (franchisees). Regardless of
dealership type, all dealers face a common stair-step incentive scheme, where they receive larger
discounts on the list price of cars, conditional on the dealer achieving the target, The observable
difference between the agents in our franchise organization is that they either exclusively sell only
the brand of cars provided by the franchisor (exclusive dealers) or they sell brands they acquire
via the franchisor and sell in addition other brands offered by other suppliers than the franchisor
(non-exclusive dealers). Because the franchisees are similar in many other aspects, the setting
offers anopportunity to identify how these two types of agents relate to their principal and vice
versa. We measure whether our treatment variable dealership type (exclusive/nonexclusive dealer)
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Standard agency theory would predict that agents exposed to the same incentive scheme should respond the same.
One distinguishing feature of relational contract theory entails that it can explain why agents subject to the same
incentive system respond differently given that trust has yet to arise or already has arisen in the organization.
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is associated with a notable difference in how the franchisor and franchisees treat each other in
terms of target setting (franchisor) and target achievement (franchisee). We propose that, in terms
of establishing a relational contract, the franchisor may treat non-exclusive dealers differently
compared to exclusive dealers, because the franchisor is more confident about the loyalty of their
exclusive dealers. Non-exclusive dealers can direct effort away from the franchisor’s brand
towards other brands; that is, they can direct it to where they expect the highest profit. That may
be the brand of the franchisor or the brand of a competitor.
A second stream of literature that motivates our study focuses on target setting. While the targetsetting literature has examined target ratcheting (e.g., Leone and Rock, 2002; Bouwens and Kroos,
2011), only recently have studies start to investigate the conditions that lead firms to ease targets
(e.g., Indjejikian et. al., 2014b). Indjejikian et al. (2014b) argue that firms may decide to mute
target ratcheting to signal to managers that they can trust the principal to share the proceeds from
high performance. That is, firms may still ratchet targets but not to the extent that would give rise
to describe as the “perverse incentive effect where managers mute their effort or misreport their
performance to have a lower future target” (Aranda et al., 2014). Indjejikian et al. (2014a)
demonstrate that targets of high performers are ratcheted less than those of low performers. This
comports with the idea that firms try to offer high performers a fair share of the proceeds they
realize for the company. These actions would also discourage high performing agents from
shirking in attempts to lower their future targets. Bol and Lill (2015) also show that the principal
does not fully revise targets for agents with whom she has an implicit agreement.
We find that ratcheting levels differ between exclusive and nonexclusive dealers. Results indicate
that the franchisor uses past performance realizations to a lesser extent when updating the sales
target for the exclusive dealer. Hence, the exclusive agent has to fear less that the principal will
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seize all of the benefits of the additional effort by setting very high targets (Baron and Besanko,
1984; Indjejikian et al., 2014b). This result suggests that the principal, who expects an ongoing
relationship with agents, is more likely to commit to an informal contract in which the agents can
trust her to allow them to reap benefits that accrue from performance improvements. We also show
that non-exclusive dealers mute their effort more once they achieve their target and are less likely
to meet or exceed an equally difficult target compared to exclusive dealers. The latter results show
that franchisees may direct their effort to other (more profitable) brands, despite the fact that the
franchisor communicates to franchisees the importance of achieving sales targets.
We argue that the lower level of engagement from non-exclusive dealers signals to the franchisor
that these dealers may not reciprocate an easy target by actually meeting the target. Exclusive
dealers, in contrast, can more credibly communicate to the franchisor that they will do their best
to achieve or exceed the target, because they have fewer opportunities to redirect effort. This, in
turn, can motivate the franchisor to honor this signal, by abstaining from excessive target
ratcheting and thus allowing the agent to receive a larger cut of the sum of the discounts made
available by the franchisor. To our knowledge this is the first study that provides empirical
evidence to show that companies untighten controls once both parties have come to a mutual
(implicit) understanding of what is expected within their relationship. Previous analytical research
has shown that imposing controls can be costly in terms of effort choice (e.g., Falk and Kosfeld,
2006).
The next section reviews the literature and motivates our hypotheses. Section 3 describes the
research setting and empirical measures. Section 4 examines our hypotheses and presents some
additional analyses. Finally, the last section concludes with remarks on limitations of our study
and suggestions for future research.
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2. Literature review and hypotheses
We study a firm where the agents are employed via a franchise contract. We examine two types
of agents: agents who exclusively sell the product (cars) from the principal (exclusive agents) and
agents who sell both the product of the principal and other brands provided by other firms (nonexclusive agents). In our setting, agents can increase their income by selling more cars. In addition,
they are subject to a common incentive scheme in which they receive a volume discount on the
cars they purchase from the principal. To earn these discounts, the agent needs to achieve sales
targets set by the principal (i.e., the franchisor). Our inquiry focuses on how target setting and
target achievement play out.
Setting targets
The target setting literature shows that principals ratchet targets to assure that agents continue to
try to increase profitability. However, because agents know that the principal will use current
performance to set the next-period target, agents are often reluctant to show performance
improvements. Agents thus must balance how much they benefit from current performance
improvements and how much target updating their current performance entails (e.g., trading off a
high bonus now against the prospect of a lower bonus in the future or vice versa). To ensure a
larger bonus later, the agent may reduce his effort in the current period. This perverse effect is
empirically demonstrated by Leone and Rock (2002) and Bouwens and Kroos (2011). Indjejikian
et al. (2014b) argue that, to mute the impact of perverse effects associated with target ratcheting,
principals can commit to a target-updating scheme that allows the agents to keep some of the rents
created through their (high) effort. Their evidence suggests that principals commit to lower levels
5
of target ratcheting for the high performing agents, in comparison to agents who have yet to
achieve high performance, to assure that the high performers continue to work hard.
Relational-contracts theory suggests that principals may not exclusively lower target ratcheting for
their high performers. The theory predicts that principals are likely to commit to lower levels of
target ratcheting when they expect their agents to strive to achieve their target (Gibbons and
Henderson, 2012a). Based on this literature, we argue that principals are more inclined to impose
difficult targets on non-exclusive agents than on exclusive agents. The reason is that the firm may
have fewer incentives to invest into the relationship with non-exclusive agents.
Baron and Besanko (1984) and Levin (2003) argue that agency costs in a principal-agent relation
can decrease if the principal is willing to commit to a long-term stationary contract. In the extreme,
such a contract, under risk neutral conditions, would entail that the principal commits to a fixed
target regardless of the performance improvements an agent achieves. This raises a question: to
whom is the firm more likely to offer such a contract, the non-exclusive agent or the exclusive one?
If a non-exclusive agent receives such a contract, the firm runs the risk of investing in an agent
who may resort to selling other brands or may even decide to end the relationship with the firm
altogether. The reason is that the firm cannot work out in advance how much better her deal is
compared to the reservation income (of complete or partly switching to the other brand) of the
agent. Gibbons and Henderson (2012b) show analytically that, under such conditions, the principal
will not trust the agent to cooperate in period 2. Therefore the non-exclusive agent will not be
offered such a contract.
The costs for the exclusive agent to discontinue his relationship with the principal are arguably
higher (Fein and Anderson, 1997). The fact that an exclusive agent limits his portfolio to the brand
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of the franchisor strongly signals that he is committed to meeting the principal’s expectations (Fein
and Anderson, 1997). Given these signals, the principal considers how much she wants to invest
in the relationship (Fein and Anderson, 1997). In expectation she may expect that investments in
in relations with exclusive agents yield higher returns than investments in relations with nonexclusive agents.
From the perspective of the exclusive agent, their stronger commitment may prompt them to work
harder, which, in turn, would allow the principal to set more difficult targets to reap the full benefits
of the relationship. However, doing so would provide the exclusive agent with the incentive to
become a non-exclusive agent because this would open the opportunity to redirect effort from one
brand to the other when it was more profitable for the agent to do so. In addition, such a decision
may counter the idea of the principal’s willingness to honor her implicit promise to share benefits
that accrue from consistently meeting high expectations. As long as the principal can commit to
keep compensating the agent for additional sales, the mutual understanding will lead the agent to
keep working hard to achieve high levels of performance (Baron and Besanko, 1984). Relational
contracts vest themselves in the extent that principals refrain from exploiting the other party
(Morgan and Hunt, 1994). The above forces would suggest that the principal would commit to
easier target updates for the exclusive agent, to ensure that the exclusive agent maintains his
commitment to the brand. The principal can achieve this by allowing the agent to earn some rents
when he is performing well (Indjejikian et al., 2014b).
When dealing with the non-exclusive agents, principals have more questions to work through when
the agent fails to meet or exceed the target: is it because the agent could not meet the target, because
the agent is trying to avoid harder targets in the next period, or because the agent redirects effort
to another brand? Based on this increased complexity, we predict that higher levels of mutual
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understanding is harder to attain when the franchisor deals with non-exclusive agents. That is, for
the principal it is easier (more difficult) to correctly attribute achievement to effort for exclusive
(for nonexclusive) agents. As a consequence the principal will be more inclined to lock in an
achievement in the next-period target for the nonexclusive agents than for exclusive agents.
To summarize, we argue that it is more difficult for the principal to work out whether performance
levels of non-exclusive agents are due to effort directed to pursue the franchisor’s objective. As a
consequence it is more likely that the franchisor mistakenly attributes good performance in the
current period as a demonstration of low effort choice in the past. She is therefore more likely to
ratchet the target using past (proven) performance for nonexclusive agents than for exclusive
agents. Given our discussion, we propose to test the following hypothesis in the null form:
Hypothesis 1: Type of dealer is not associated with the extent to which past performance is used
for future target updates.
The effort response to targets
According to the target setting literature, agents will protect themselves against target ratcheting
(Milgrom and Roberts, 1992, p. 232-235). Empirical evidence speaks to this idea by demonstrating
that agents who perform well either manage earnings (e.g., Leone and Rock, 2002) or manage real
earnings even downwards to reduce target ratcheting (Bouwens and Kroos, 2011). However, what
will agents do if they have the opportunity to redirect their effort? Will they do so even at the cost
of their relationship with the principal? This subsection aims at the identification of conditions that
would prompt the agent to cease the redirection of effort.
Agents must decide how much effort they are willing to exert to increase their income. When the
target is met, further exceeding the target entails two income sources to the agent: (1) sales minus
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the gross purchase price and (2) the discount on the purchase price. Hence, once the agent meets
the target, he is incentivized to keep increasing the number of products sold. At first glance, there
seem to be few reasons to assume that exclusive agents will behave differently than non-exclusive
dealers in terms of target achievement. However, as the market for the franchisor’s brand
approaches its level of saturation, it may become attractive for the non-exclusive agent to switch
to another brand because the effort required from the agent to continue selling the product may be
larger than the effort required by another (nonsaturated) brand. Non-exclusive agents can relatively
inexpensively resort to alternative income sources and are therefore more likely to give up on
meeting or exceeding a difficult target than are exclusive agents. Exclusive agents have no
alternative profit source; their income is bound to the principal’s brand. If we assume that it is
equally difficult for both types of agents to achieve the target, nonlinearity in the effort-sales
function will likely feature an inflection point where it becomes more attractive for the nonexclusive agent to switch to another brand. Such an inflection point does not exist for the exclusive
agent, who might as well continue to exert effort.
In addition, there is the target ratcheting argument. Non-exclusive agents are in a better position
than exclusive agents to taper their performance to reduce target ratcheting because the nonexclusive agents can switch to the other brand. The exclusive agent, however, may still fear that
his next-period target will be too high. To keep the future target achievable, he can reduce his
effort in the current period, too. Bouwens and Kroos (2011) demonstrate that very well performing
store managers mute effort by putting a hold on how much they exceed their target over an
accounting period. In doing so, these agents protect themselves against a principal setting
unachievable targets for the next period. Indjejikian et al. (2014a), on the other hand, find that
high-performing agents do not need to fear unachievable target updates. This suggests that
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companies commit themselves by allowing the agents to keep a larger share of the rents gained
with achieving higher levels of performance (Baron and Besanko, 1984; Indjejikian et al., 2014b).
If the exclusive agent and the principal develop a mutual informal understanding, whereby the
principal commits to ratchet targets to a lesser extent, she implicitly offers a higher compensation
level to agents who are loyal to the brand. In that case the exclusive agent may be more willing to
continue to work hard for the principal (Indjejikian et al., 2014b) and is less likely to mute effort
in an attempt to protect themselves against target ratcheting. This leads to the following hypothesis
(in the null form):
Hypothesis 2: Dealer type is unrelated to target achievement.
3. Research setting
To test our hypotheses, we use data from a franchise organization, which is responsible for a
network of car dealerships in a Western European country. The franchisor is a national subsidiary
of a car manufacturer with worldwide operations. The franchisor specifies wholesale and retail
prices, sets incentive schemes for the franchisees and arranges marketing promotions for dealers
in the network.
The sales network consists of franchisees (i.e., car dealers) who acquire their cars from the
franchisor. The dealers are separate private companies whose formal relations with the franchisor
are contractual. The dealers have both exclusive and non-exclusive sales relationships with the
franchisor. Exclusive dealers sell only the brand of cars provided by the franchisor. Non-exclusive
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dealers also sell other brands, which can be supplied by other franchisors.2 The decision about
being an exclusive or a non-exclusive dealer can be affected by factors such as the number of
dealers in a sales area, market conditions and strategies of the franchisor and the franchisees. It is
a mutual choice made by both the franchisor and the franchisees.3
All the dealers are represented in a council through which they can express their needs and advise
the franchisor on business operations and strategies. Franchisees make their own pricing decisions
within a range set by the franchisor. Hence dealers can quote different prices for the same car.
Franchisees have full decision rights over their internal organization, staffing and how much they
wish to invest. They can also make dealer-specific advertisement decisions.
Consistent with the policy of a sufficiently dense network, the franchisees (i.e., dealerships)
covered all the sales areas of the domestic market over the sample period 2004–2012. During the
period, sales and revenues slightly increased from 2004 to 2008, and achieved the highest levels
in 2011, but decreased dramatically in 2012. Panel A of Table 2 reproduces the yearly composition
of dealer network. The composition of these franchisees slightly changes over time, with the
percentage of exclusive dealerships declining and the percentage of non-exclusive ones increasing.
Panel A of Table 2 further shows that non-exclusive dealers compared to exclusive dealers more
often enter into the franchise network or end their relations with the franchisor.
2
The manufacturer is a subsidiary of a holding company, which sells other car brands, too. Hence the holding company
offers a family of brands. We focus on one brand offered by the franchisor. However, some dealers also sell other
brands in the family. We define exclusive dealers as the ones who sell the focal brand offered by the franchisor and
consider dealers who also sell other family brands offered by the franchisor as non-exclusive sales. The reason is that
the franchisor does not set targets for the other cars that belong to the family. We also run the data where we consider
the family dealers also as exclusive dealers. The results, however, are largely consistent. Each dealer also receives a
unique identifier. Based on these identifiers we do not observe switches of dealerships. However, the franchisor may
have assigned new identifiers to a dealer after the dealer switched. This may also indicate that the franchisor will treat
them differently if a franchisee changes his type.
3
Franchisees do not need to pay franchise fees to the franchisor. They do have to invest in franchisor-specific show
rooms, displays, services, etc.
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We conduct our empirical analyses using dealer-level performance data of all the dealers in the
network. This data comprises almost 100 percent of the sales the network realized in the national
market because all the sales of the brand are realized through this network.
The incentive system
The franchisor offers three main incentives to their dealers: the difference between the advised
consumer price and the purchase price, a bonus for meeting qualitative standards based on
qualitative measures and a bonus for the volume based on quantitative measures. The data we
study is the volume bonus, which car dealers receive conditional on them achieving the targeted
sales volume. In our sample, the volume bonus accounts for 25%–30% of the dealer’s total margin.
For dealers to receive a quarterly bonus, they need to meet their quarterly targets. The bonus takes
the form of a discount on the invoice price of the cars that franchisees acquire from the franchisor.
As shown in Panel B of Table 2, this target setting system does feature a “steps schedule”, which
is common in the industry. The stair-steps schedule purportedly incentivizes dealers to work harder
to achieve a higher discount. The discount system does feature a minimum threshold, while there
is no cap on how much bonus a car dealer can earn. The specific bonus scheme of the franchisor
features different steps where each step leads, conditional on the target achievement of a dealer, to
higher bonus percentages (i.e., higher discounts). The firm defines bonus areas based on the
percentage of sales realized relative to the target. For the “at target” area, sales are at least equal
to the target (target achievement ≥ 100%). In general, the dealer gets no additional cash payment
or discount for sales levels much below the sales target. The bonus increases from the Threshold
level till the Maximum level. The highest bonuses are granted to dealers who convincingly exceed
their sales target.
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Target-setting and revision process
The franchisor sets the annual sales target for each dealer in December for the next budget year,
which coincides with the calendar year. Sales targets are denominated in number of cars, not in
euros. The franchisor decides on the annual target for each dealer according to an estimation
method developed by the national dealer organization. This method includes a set of factors
deemed relevant by the franchisor: historic sales performance of the dealers, the firm’s
development strategies, expected local sales conditions and nationwide market conditions. To
gauge these sales conditions, the franchisor uses its discretion to account for specific market
conditions facing a dealer. Once the annual target for each dealer is established, the firm sets
quarterly sales targets. Bonuses are awarded quarterly. A simulated example of target setting is
given in Appendix A.
While these targets are set for the year, they can still be adjusted if market conditions change
dramatically. That is, the franchisor may alter the quarterly targets in the course of the current
quarter. We do not have detailed information about these intra-period changes. What we have is
the final quarterly and annual targets for each dealer over our sample period.
Sample composition
We collect daily sales information of each individual dealer over the period from 2004 to 2012.
We also gather quarterly sales targets and annual sales targets for each dealer. However, 21 dealeryear observations and 44 dealer-quarter observations are excluded due to the missing information
about quarterly targets, resulting in 538 dealer-year observations and 2,090 dealer-quarter
observations with both sales and target data. Some dealer-year observations are incomplete as the
dealers are not actively selling the franchisor’s brand during the full year. This happens when a
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new dealer enters into a contract with the franchisor or when a dealer decides to discontinue his
contract with the franchisor during the year. For our analyses, however, we only use the data of
dealers for which we have complete information over the four quarters of each year. This reduces
the sample size to 497 dealer-year observations and 1,988 dealer-quarter observations. Since we
need past performance as controls in our model, only dealers with more than one year data are
included in the analysis. This results in a final sample of 410 dealer-year observations and 1,640
dealer-quarter observations for our empirical tests.
Variable measurement
Measure of target update: The franchisor considers target setting an important control instrument
to motivate its dealers to achieve desired sales levels. Sales targets are based on the dealers’ past
sales performance and on local and national market conditions. How these conditions translate into
expected sales levels is subject to the franchisor’s assessment. We test in hypothesis 1 whether
these assessments result in different target revisions for non-exclusive dealers compared to the
exclusive dealers. The resulting target update is captured by the variable 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 ,
which represents the difference between last year’s target and the current target and comprises
both objective and subjective factors. A larger percentage of target update is perceived as a more
difficult target.
Dealer performance: Effort choice of a dealer is reflected in his sales performance. Sales
achievements proxy for the dealer’s willingness to invest in the relationship with the franchisor.
We capture dealership performance in three variables: 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 , 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 and
𝑁𝑈𝑀𝑄𝑇_𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 . 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 represents the sales performance of dealer 𝑖 compared
to his sales target in a particular period 𝑡. 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 is a dummy variable, which indicates
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whether the dealer meets his sales target in period 𝑡 . 𝑁𝑈𝑀𝑄𝑇_𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 represents the
frequency with which a dealer meets his quarterly target in a full year (0–4).
Exclusive or non-exclusive dealer: To capture the differences between two types of dealers in how
targets are set and how much effort dealers exert, we create a dummy variable 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 to
indicate dealer who exclusively sells the specific brand of the franchisor (1) or a non-exclusive
dealer who also deals in other brands (0).
Good performers: Indjejikian et al. (2014a) show that a principal may display more commitment
to well-performing agents by assigning them easier targets or by protecting them from ratcheting.
We focus on three different variables to classify a dealer as a good performer: a) if a dealer’s target
deviation is among the top 25 percent of all the dealers (𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡 = 1; 0 otherwise), b) if his
sales are among the top 25 percent (𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 = 1 and 0 otherwise), and c) if he achieves
all the four quarterly targets in the last period (𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡 = 1 and 0 otherwise). With the
analyses, we examine whether the good performers are treated differently in terms of target setting
by the franchisor, depending on their dealer type.
Difficulty: To examine whether nonexclusive dealers differ from exclusive dealers in terms of
effort levels, we must control for target difficulty. The dummy variables 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
and 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 capture whether the target is difficult (1) or easy (0) for a dealer, in the
comparison with the median and mean of target updates of the dealers located in the same area. A
target is difficult if the target update for a dealer is larger than his peers coping with similar market
situations. To conduct this analysis, we need a sufficient number of dealers to make a meaningful
comparison. We require therefore that a particular dealer has at least three dealers in its vicinity in
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any particular year. This procedure reduces our final sample to 291 observations for regressions
where target difficulty enters into the model.
Control variables: As the study aims at identifying whether type of dealer affects the franchisor’s
decision and a dealer’s effort, we include several controls in our model that may also affect the
outcomes: target levels and target achievement. We use the natural logarithm of the target, i.e.,
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 to control for the size of the dealership.4 Sales growth of the brand in the domestic
market can also affect each dealer’s sales potential. We therefore measure 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
to represent the year-to-year changes in the market sales of the brand. In addition, we use
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 to trace year-to-year sales changes of all the car brands in the domestic
market. Past performance of each dealer is also included in our main tests. Some dealers represent
the collective dealers in a dealer committee. They more often communicate directly with the
franchisor than other dealers. To control for this effect, we create the dummy variable
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 , indicating whether the dealer is a delegate to the dealers committee. In addition,
some dealers have an individual agreement with the franchisor. This agreement may result in
benefits or privileges that can affect dealer’s effort. We therefore create the dummy
variable 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 , which equals one if an individual agreement applies and zero otherwise.
Table 1 contains an overview of the variables used in this study.
Descriptive statistics
Panel A of Table 3 summarizes the statistics of the main variables we use for our empirical tests.
As shown in the table, 40.7% (59.3%) of the dealers are exclusive (nonexclusive) dealers. The
4
We also control for size effects by using the logarithm of sales, and results do not change significantly.
16
actual sales at the dealer level are on average lower than the sales target. The mean (median) of
sales-target deviation is about −2.24% (−6.18%), i.e., dealers show an adverse deviation from the
annual sales target. Less than half (40.5%) of the dealers achieve a sales level that exceeds the
sales target. The mean (median) target update of the sales target is 7.898% (6.217%), and sales
targets are adjusted downward in 166 out of 410 cases (not tabulated).
Panel B of Table 3 offers a comparison between exclusive dealers and non-exclusive dealers on
some of the key variables we use in our analyses. Target levels of exclusive dealers are lower in
terms of volume than the target levels of non-exclusive dealers. On average, targets are adjusted
upward to a smaller extent for exclusive dealers (3.6%) compared to the non-exclusive dealers
(10.85%), indicating that non-exclusive dealers are treated differently. The table also shows that
more than half of the exclusive dealers (53.3%) achieve their sales targets, while only 31.7% of
the non-exclusive dealers do.
4. Empirical Results
Test of hypothesis 1: Target updates and the type of dealership
Hypothesis 1 states that a franchisor will use past performance to an equal extent for non-exclusive
and exclusive dealers when considering target revisions to set the target for the next period. To
test H1, we estimate the following regression model:
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 = 𝛽0 + 𝛽1 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 + 𝛽2 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 + 𝛽3 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜀𝑖,𝑡 (1)
17
where 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 denotes the difference between the new target and the current target,
which is decided by the franchisor for dealer 𝑖 in year 𝑡 based on a mixture of objective and
subjective factors; 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is a dummy variable equal to one if the dealer signs the exclusive
contract with the franchisor and zero otherwise; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 indicates the sales
performance of dealer 𝑖 in period 𝑡 compared to the sales target. The variable of interest in this
model is the interaction term between 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 and 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 indicated by the
coefficient 𝛽3. A significant loading for 𝛽3 on target update suggests that the franchisor tends to
use past sales data for target updating differently conditional on dealership type. A negative sign
on 𝛽3 indicates that exclusive dealers are given easier targets than non-exclusive dealers. Since the
firm sets yearly targets, we use the annual data for this estimation.5
Our estimates are based on our regression equation using OLS over the sample period of 2004 to
2012. The t-statistics are based on clustered standard errors to adjust for heteroskedasticity and
autocorrelation.
In Table 4, we reproduce the results of our tests by using model (1).6 As shown in column 1 of
Table 4, the coefficients 𝛽1 of 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is negative and significant, meaning that overall
target updating is easier for exclusive dealers. Past performance (𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 ) is positively
related with target updates (coefficient=0.482, p<0.01). This is consistent with the concept of target
ratcheting, documented previously (e.g., Leone and Rock, 2002; Bouwens and Kroos, 2011). The
negative interaction term between 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 and 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 (coefficient=−0.233,
5
Quarterly targets are decided after the yearly targets have been set. Since we do not have information about how the
yearly targets are further divided into four quarters, we only use yearly data for the test of hypothesis 1.
6
We also run ours tests by winsorizing 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 at 1 percent and 99 percent (not tabulated) to control
for the effects of large target updates, and we find the same results (not tabulated). The large values of
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 shown in Table 3 are probably caused by mergers between dealers or expansion of a particular
dealer. These large values are usually applied to non-exclusive dealers.
18
p=0.067) provides weak evidence to suggest that franchisors use past sales information to a lesser
extent to update next period targets for exclusive dealers. The effect is that non-exclusive dealers
face more demanding targets than do exclusive dealers and hence that the latter stand a better
chance to qualify for a (higher) discount on the purchase price of cars that the non-exclusive dealer.
We propose that this difference in target setting reflects how much the franchisor is willing to
invest in building a relation with its franchisees, conditional on them being exclusive or nonexclusive dealers.
In the second column of Table 4, we follow the target-setting process as it was described to us by
the firm. Representatives of the franchisor said that they also considered a dealer’s performance in
𝑡 − 2 and 𝑡 − 3.7 The results in column 2 again provide evidence to suggest that target ratcheting
occurs to a lesser extent for exclusive dealers than for non-exclusive dealers (coefficient=−0.242,
p=0.065), when using 𝑡 − 2 and 𝑡 − 3 next to 𝑡 − 1 sales data for updating targets. We primarily
find evidence that performance in the last period affects the target setting for the next period. This
suggests that, to the extent that sales achievements of 𝑡 − 2 and 𝑡 − 3 affect the target updates,
this data is impounded only to the extent it is related to 𝑡 − 1 sales.
Based on these results, we reject hypothesis 1: past performance is not used to the same extent for
exclusive and non-exclusive dealers. Our results suggest that the exclusive dealers face easier
targets because past sales realizations are impounded to a lesser extent in next year targets.
Target update, dealership and target achievement
As indicated by the example in Appendix A, we are told that the franchisor considers the previous three years’
performance to set the new target. However, we find that only the last year’s performance plays a significant role.
Therefore, for the rest of our tests, we only include last year’s performance. By doing so, we also keep a reasonable
sample size for analyses.
7
19
A second test for hypothesis 1 is to examine how target achievement affects target updates.
Previous literature finds that principals set targets differently depending on whether agents meet
their target (e.g., Leone and Rock, 2002; Bouwens and Kroos, 2011).8 In this section, we examine
whether the franchisor sets different targets for dealers who achieve their targets conditional on
dealer type, exclusive or non-exclusive. We rerun our basic model (1) but now create two
subsamples: one is comprised of observations where dealers exceed their targets in the last year,
while the other is comprised of observations where dealers fail to meet their target last year. The
results reported in Table 5 resemble those reported in Table 4. The coefficient on the interaction
term is negative (coefficient=−0.408, p<0.05). This result indicates that, compared to nonexclusive dealers, exclusive dealers get a better deal (less tough target updating) when they reach
their targets. However, as shown in the second column of Table 5, if targets of the previous year
are not met, the franchisor does not differentiate between the two types of dealers. These results
suggest that exclusive dealers who show loyalty to the franchisor by achieving the target are given
easier access to discounts on the purchase price of cars compared to non-exclusive dealers who
express the same level of loyalty. This evidence again leads us to reject H1. More specifically, the
results suggest that the franchisor puts less weight on past performance to set a target for the
exclusive dealers, i.e., target ratcheting occurs to a greater extent for the non-exclusive dealers.
Target update, dealership and good performer
8
We also test whether targets are updated asymmetrically, i.e., where positive target deviations lead to a higher targets
while adverse deviations are ignored or only come through in the target to a very limited extent. However, we do not
find evidence for asymmetric target updating in our sample. Table 5 also shows that the main effect of
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 is significant for target updating, indicating a more symmetric updating scheme. Nevertheless,
exclusive dealers relative to non-exclusive dealers are treated differently. A possible explanation for symmetric
updating may lie in the fact that we study franchisee-franchisor relations, rather than employee-supervisor relations.
20
Indjejikian et al. (2014a) find that principals show their commitment to good performers by
assigning them easier targets. They argue that principals do this because these agents have limited
scope to increase their performance much further. It stands to reason that, should further
improvements occur, management will grant to the franchisees a fair share of the proceeds incurred
from that achievement. This action taken by the principal is also supposed to mitigate target
ratcheting effects such that agents are less likely to mute their efforts.
In our setting, we expect exclusive dealers to be more likely to be rewarded for good performance
without having to fear a stark target update. A dealer qualifies as a good performer if (1) his sales
deviation, compared to the target level, is among the top 25 percent of the dealers (𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡 ),
(2) if his sales volume belongs to the top 25 percent of all the dealers (𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 ), or (3) if
he achieved all the quarterly targets in the last period (𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡 ). To test whether the data
supports our expectations, we focus on the interaction term between these three indicators for good
performance and the variable 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 because our interest lies in different treatments among
better-performing dealers depending on their dealership type. Results are reproduced in Table 6.
The interaction terms in Table 6 all have negative and significant coefficients ([1]
coefficient=−15.721, p<0.05; [2] coefficient=−19.506, p<0.01); [3] coefficient=−27.946; p<0.01).
In sum, our theory seems to be supported by the data in that different treatments surface for good
performers conditional on the type of dealership, i.e., the franchisor shows his commitment to
exclusive dealers by refraining from target ratcheting if they perform well, while such commitment
is shown to a lesser extent to non-exclusive dealers.
Relationship building
21
Gibbons and Henderson (2012a) argue that trust between parties can grow over time. We construct
a test that refers to the history of the relation between the franchisor and the franchisee. Our
variable of interest is the percentage of times that a franchisee achieved the target in the past
(𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡 ). We argue that this measure proxies for the relation because it
measures the extent to which the franchisee has strived to meet the target and the extent to which
the franchisor saw to it that the target was achievable—and hence whether a history of mutual
understanding ensued. We expect that mutual understanding is more likely to develop between the
franchisor and exclusive franchisees, since the franchisor is more confident that exclusive dealers
will follow his guidance in sales. Thus the history of past achievements loads negatively for
exclusive dealers on how much their targets will be updated in the next period. Such a muted
update signals to the franchisee that the franchisor is sharing a part of the gain that accrues from
the (increased) effort the franchisee exerts to meet the target. The results in Table 7 are consistent
with our prediction. We find negative and significant coefficients for the interaction terms of
𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡 and 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 in both column 1 (coefficient=−15.392, p<0.05)
and column 2 (coefficient=−15.460, p<0.05). These results suggest that, compared to exclusive
dealers, non-exclusive dealers must have a longer history of achieving their targets before the
franchisor rewards the dealers’ loyalty shown by achieving the target.
In sum, the four tests indicate that the franchisor uses past performance to a higher extent to set
targets for non-exclusive dealers. Based on this evidence, we reject hypothesis 1 and conclude that
franchisors does distinguish between types of dealers when updating targets. The evidence would
also suggest that it takes longer for the non-exclusive dealer to convince the franchisor of his good
behavior. As a result, the franchisor ratchets targets to a lesser extent for the exclusive dealers,
which could reflect a willingness to invest in the relationship with the exclusive dealer.
22
Mutual understanding in relation building: the side of the dealer
For hypothesis 1, we examine the franchisor-franchisee relation mainly from the franchisor’s
perspective. This section examines relationship building from the franchisee’s point of view (H2).
We expect that exclusive dealers are more inclined to invest in their relation with the franchisor.
Hence we expect that exclusive dealers try harder than non-exclusive dealers to increase sales,
with the aim of building a long-term relationship where both parties commit to act in a predictable
fashion. We expect that exclusive dealers express their commitment to a greater extent than nonexclusive dealers. We measure commitment by examining whether we observe that target
ratcheting occurs. We expect that ratcheting will be present to a lesser extent for exclusive dealers,
i.e., non-exclusive dealers are more likely to mute their effort and are less likely to reach their sales
targets.
The net effect of target levels
Before we turn to the how franchisees respond to their target, we want to estimate the net effect of
the decision of the franchisor to require non-exclusive dealers to achieve larger sales improvements.
The purpose of this test is to estimate whether exclusive and non-exclusive dealers differ in their
target achievement, given that the franchisor has decided that the non-exclusive dealers must meet
higher targets to access discount levels.
We run the following logit regression to test whether the likelihood of meeting the targets differs
across exclusive and non-exclusive dealers. We run these tests both for quarterly (target
achievements per quarter) and yearly data (achievements per year).
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 = 𝛽0 + 𝛽1 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 + 𝛽2 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜀𝑖,𝑡 (2)
23
where, 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 is a dummy variable capturing whether dealer 𝑖 exceeds the sales target in a
given year 𝑡 (quarter 𝑡). We examine whether the type of dealer relates to target achievement, i.e.,
whether the coefficient of 𝛽1 (𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ) loads on dealer performance measured in terms of
target achievement. We reproduce our results in Table 8, Column 1 (annual data) and Column 2
(quarterly data). The coefficients of 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 for the two samples are both positive. However,
for the year data, the coefficient is close to the 10% level of significance (i.e., p=10.9). When
analyzing the quarterly data, the effect of 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is significant at the 5% level of
significance. The different significance levels of our tests would suggest that power issues affect
our results. From the evidence, we conclude that the non-exclusive dealers are less likely to achieve
their targets.
To acquire a first idea of whether non-exclusive dealers can achieve higher targets, we test in panel
B of Table 8 whether the sales progress (percentages) differs for exclusive dealers. The results in
panel B suggest that non-exclusive dealers realize more sales progress. However, these differences
are not significant. Taken together the latter result suggests that the non-exclusive dealers are not
in a position to meet the higher target requirements the franchisor imposes on them. 9 Overall, the
analysis summarized in panel A suggests that non-exclusive dealers are less likely to achieve their
targets.
Tests of hypothesis 2: dealer type is unrelated target achievement
To test our second hypothesis, we first examine whether types of dealers are equally likely to push
their sales levels beyond their targets. Our identification strategy aims at establishing whether non-
9
We also ran a regression controlling for other factors to test differences in sales progress of exclusive and nonexclusive dealers. However, this analysis did not change the conclusion we drew from the univariate analysis.
24
exclusive dealers differ from exclusive dealers in their likelihood to mute their effort when this is
conducive from the standpoint of the franchisees. We then continue to examine whether achieving
the target is related to dealer type. In these tests, we control for target difficulty to create a level of
playing field between the two types of dealers.
Dealer performance: Muting effort
The ratchet effect implies that agents choose to mute their effort to receive less difficult targets in
the future (Indjejikian et al., 2014b; Bouwens and Kroos, 2011). In our setting, non-exclusive
dealers may benefit more than exclusive dealers from muting their effort once they have exceeded
their target. Target achievement makes the dealer eligible to higher discounts. However, once they
have reached this level of achievement, their expected return from selling even more cars may
yield them less income than expending sales effort on cars of other brands. Non-exclusive dealers
may thus redirect their effort to other brands once they have secured their bonus level for the
current quarter. Exclusive dealers, on the other hand, are expected to continue expending effort
even after they have reached the targets. In doing so, they demonstrate their willingness to invest
in their relationship with the franchisor. In addition, when they expect that they can trust their
franchisor, they are best off when they decide to sell as many cars as possible.
To test whether dealers mute their effort once they have met their targets, we examine whether
both types of dealers keep selling in the last calendar month of the quarter once they have met their
targets in the second month of the quarter. If non-exclusive dealers do differ in sales achievements,
we should observe different sales levels in month three of the quarter.10
10
To explore this idea, quarterly data are more appropriate. In our setting, each dealer is paid quarterly. Therefore
reaching quarterly target matters to them, in addition to reaching the annual target. Given this quarterly incentive
structure, it is likely to find muting behavior if we compare sales in the last quarter of the whole year.
25
We report the results of our tests in Table 9. We first split the sample conditional on whether the
quarterly target is achieved, resulting in a sample comprising of 800 observations that achieve
quarterly targets. We find a negative coefficient of 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡 (coefficient=−6.490,
p<0.05) (Table 9: column 1), suggesting that dealers reduce their effort in the final month of the
quarter if the quarterly target is already achieved. In addition, we find a positive interaction
between 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡 and 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 (coefficient=10.863, p<0.01) suggesting that
exclusive franchisees are more likely to keep extending their sales numbers in the final month than
their non-exclusive counterparts. Our findings are also supported when we run the model for the
full sample (Column 2 of Table 9: the interaction of 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡 and 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ,
coefficient=4.693, p<0.10).11
These results provide us with the first piece of evidence that would lead us to reject the null
hypothesis H2, stating that exclusive and non-exclusive dealers do not differ in achievement. From
the results, we conclude that non-exclusive dealers are more likely to stop trying to further improve
performance once the quarterly target is met (in the first two months).
Dealer performance: controlling for target difficulty
In this section, we test whether dealership type is associated with the likelihood of target
achievement. The challenge of testing this idea is that the types of dealers face different target
difficulty levels. That is, the tests we ran on target updating (hypothesis 1) suggest that targets are
more difficult for non-exclusive dealers. Exclusive dealers may thus have a better chance of
reaching their targets. To check whether exclusive dealers indeed exert more effort than their
11
We also ran the analyses for exclusive and non-exclusive dealers only. The results of these analyses suggest that
non-exclusive dealers do not reduce effort level at an absolute level but rather at a relative level. In other words, they
both try to keep extending their sales, but the exclusive dealers try harder.
26
counterparts, we need to create a level playing field for both kinds of dealers. Our approach is
based on the idea that we should control for the conditions facing each particular dealer. We
propose that dealers from the same area face similar economic and demographic conditions, and
we take advantage of this likelihood in deciding whether a dealer faces a difficult target. We do so
in comparing the target update imposed on an individual dealer compared to the median (average)
target update of his peers located in the same province.12
We require an area to be comprised of at least three dealers. Application of this procedure reduces
our sample to 291 observations. We examine whether exclusive dealers differ from non-exclusive
dealers in their likelihood of meeting the targets. We generate indicator variables
(𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 and 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 ) to represent whether the targets are updated
to the levels that are difficult for the dealers. Based on our criterion, around 43 percent of the
targets out of a total of 291 observations are identified as difficult targets (see Table 3).
We run the following Logit/Poisson regression to test our hypothesis:
𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝐴𝑁𝐶𝐸𝑖,𝑡 = 𝛽0 + 𝛽1 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 + 𝛽2 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇𝑖,𝑡 + 𝛽3 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖,𝑡 ∗
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇𝑖,𝑡 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜀𝑖,𝑡
(3)
where, 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝐴𝑁𝐶𝐸𝑖,𝑡 features 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 and 𝑁𝑈𝑀𝑄𝑇𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 , i.e., the dummy
variable capturing whether dealer 𝑖 exceeds the sales target and the frequency of achieving
quarterly targets in a year. And indicator variable 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇𝑖,𝑡 in our model can be either
12
We also use another measure to define target difficulty. We use our baseline model (1) to predict the expected target
update for each dealer and then compare the real target update to the expected update. If the real update is larger than
the expected update, the target of a dealer is regarded as difficult. Conversely, if the real update is smaller than the
expected update, the target is considered to be easy. Using this definition for difficulty, we still find similar results.
27
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 or 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 . In Table 10 and Table 11, we report the
results for the additional tests we conduct to examine our second hypothesis.13
The variable of interest is whether the interaction between the type of dealer and target difficulty
load on performance. According to our arguments, finding a positive significant coefficient
for 𝛽3 (𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖,𝑡 ∗ 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇𝑖,𝑡 ) suggests that exclusive dealers, on average, are more
likely to achieve a more difficult target.
Table 10 reproduces the results on our tests of whether exclusive dealers try harder to reach their
targets compared to non-exclusive dealers. The evidence would contradict H2 if the coefficient on
the interaction represented by 𝛽3 is significant. A positive sign on the coefficient would suggest
that exclusive dealers are more likely to put in the effort required to meet the target. We run two
separate regressions with the measures 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 (Column 1 Table 10) and
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 (Column 2 Table 10) to capture our difficulty variable.
The results from our analysis are rather weak. That is, we find the interaction to be positive.
Nevertheless, this result is only significant at the 10 percent level for the regression where we use
the mean (coefficient=1.093) as the relative benchmark to estimate target difficulty. We conclude
from this analysis that the likelihood of achieving the target, controlling for difficulty, is
marginally related to the type of dealer.
Likelihood of achieving the target over quarters
13
For tests using 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 or 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 , we only use the annual data rather than the
quarterly data. Because we do not know how the franchisor assigns the annual targets further to the quarterly targets,
it is not easy to assess the difficulty levels of quarterly targets. Therefore only annual data is used for our tests. Table
11 tries to capture quarterly performance by measuring the number of times in a year that quarterly targets are met.
28
The tests in the previous subsection are conducted with yearly data. However, this test potentially
abstracts from the underlying incentive system, where bonus payouts occur with reference to the
quarterly target. This gives dealers incentives to meet the target in as many quarters as possible.
Hence it is entirely possible that dealers choose to switch between quarters to make it to the target
as often as possible. In our setting, the non-exclusive dealers can re-direct their effort from the
focal brand to the brand not offered by the franchisor. The non-exclusive dealer therefore can
choose which action he anticipates to be the most profitable. The exclusive dealer does not have
that option.
Our strategy is to capture quarterly performance by measuring the number of times in a year that
quarterly targets are met. We expect that, controlling for target difficulty, exclusive dealers may
reach their quarterly targets within a year more often than non-exclusive dealers.
In Table 11, we reproduce our results of whether exclusive dealers meet their quarterly targets
within one year more often than non-exclusive dealers. Since the dependent variable is the number
of times that quarterly targets are met in a year, we apply a Poisson model to the whole sample
(column 1 of Table 11). In columns 2 and 3, we use our reduced sample where we control for
target difficulty. We find a positive and significant coefficient for 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
(coefficient=0.159, p<0.05) in column (1) of Table 11, where we abstract from target difficulty.
This result indicates that exclusive dealers more often hit the quarterly targets within a year. Under
the assumption that targets are equally difficult for each type of dealer, these results suggest that
exclusive dealers try harder to reach the target at each point in time. Non-exclusive dealers, in
contrast, may divert energy to other brands if their benefits are higher. When we control for target
difficulty in columns (2) and (3) using the reduced sample, we continue to find that non-exclusive
dealers meet their target less often than exclusive dealers. The focal variables are again the
29
interactions. The results point in the direction which would suggest that non-exclusive dealers are
more likely to redirect their effort when a target becomes more difficult to achieve. We find weak
results when we measure the level of difficulty with the median (coefficient=0.211, p<0.15).
Results are significant when we measure difficulty as the mean (coefficient=0.293, p<0.05).
In sum, we run three tests to gauge whether exclusive dealers’ results differ from those of nonexclusive dealers in terms of the level of effort they put into achieving targets set by the franchisor.
Our first test (Table 9) suggests that non-exclusive dealers are more likely to redirect effort to other
cars once they have hit the mark that gives them eligibility for a discount on the purchase price of
cars. We then test whether exclusive dealers are more likely to meet their target controlling for
difficulty. While we find only weak support for yearly target achievement (Table 10), the data
yields significant results when we consider quarterly sales targets (Table 11). We find evidence to
support the idea that non-exclusive dealers meet their quarterly targets less frequently than
exclusive dealers within one year even if the target is equally difficult for them. Together, these
results would lead us to us to reject null hypotheses 2, which states that non-exclusive dealers do
not differ from exclusive dealers in their likelihood of exceeding the target.
5. Conclusion, discussion and limitations
In this paper, we examine aspects of relational contracts between franchisor and franchisees in the
context of target setting which the franchisor considers as an important control instrument to
motivate dealers to achieve desired sales levels. We predict that franchisors may be less willing to
invest in their relationship with non-exclusive dealers relative to exclusive dealers. Our evidence
supports this idea. Specifically, performance improvements are to a higher extent impounded in
30
the targets for the non-exclusive dealers than they are for the exclusive dealers. Because
performance improvements lead the franchisor to increase the target to a higher extent for nonexclusive dealers, these dealers have to work harder to earn the same bonus discount on the
purchase price of cars they sell. That is, exclusive dealers stand a better chance than non-exclusive
dealers of achieving their target. Given that dealers earn a discount bonus on the cars they purchase
from the franchisor if they achieve their targets, we consider this evidence as consistent with the
relational-contracts theory (Gibbons and Henderson, 2012a).
We also examine whether franchisees respond differently to targets. Under the assumption that
non-exclusive dealers know how the franchisor sets targets, we predict that franchisees will adapt
their effort to the target updating policy of their franchisor. In terms of relational contracting theory,
we test this conjecture by examining the extent to which the two types of dealers withhold effort
and beat their targets and whether the two types, when facing the same target difficulty, are equally
likely to achieve their targets. We find that non-exclusive dealers are more likely to mute their
effort once they have achieved their target. We further find that non-exclusive dealers exceed their
targets to a lesser extent and are less likely to achieve their targets. Non-exclusive dealers are in a
better position to redirect their effort than exclusive dealers because exclusive dealers can only
focus on cars that are offered by the franchisor. Hence, to the extent that the franchisor
demonstrates that he continues to ratchet the target, the non-exclusive franchisee will have few
incentives to achieve the performance level the franchisor wants him to meet. Our evidence also
shows that franchisors put up higher barriers to benefit from selling the franchisor’s cars for nonexclusive dealers and that non-exclusive dealers put in lower levels of effort than exclusive dealers
when we control for target difficulty.
31
We consider the different control choices the franchisor makes for the different dealer types as
evidence of relational-contracts theory. When time pasess both the agent and principal develop a
mutual understanding where the firm can credibly convince not to ratchet targets. For the nonexclusive dealer, however, it takes longer to build up a relationship where both parties exhibit
mutual trust of not ratcheting and the extent to which the agent tries to achieve his target.
As with all studies our research has caveats. We focus on one franchise organization that operates
in one business—car sales. It has one common incentive system for its franchisees. These specific
features may limit the external validity of our results. However, the system of stair-step incentives
we describe is fairly common in (European) automobile sales. In Europe, franchisors usually
contract with both exclusive and non-exclusive dealers, and these contracts typically subject
dealers to bonuses that are conditional on achieving targets set by the franchisors. Therefore, while
the target setting and the incentive system may diverge at other car franchisors, we still feel our
findings are relevant to other franchise organizations that have different network contracts in their
portfolio.
To advance knowledge of the functioning of relational-contracts, future research can examine how
relationship building evolves in other principal-agent settings. For instance, Bénabou and Tirole
(2003) identify conditions where it is inefficient to subject inexperienced employees to a
performance-based contract. Understanding would be enhanced if scholars studied this idea in the
context of relational contracts. That is, do we observe that over time the understanding between
the principal and the employees evolves in a direction that allows for a performance-based contract
to be abandoned altogether? Also relational contracts can be important to better understand the
notion of discretion in relation to objective vs. subjective performance measures. Gibbons (2005)
argues that principals exert their discretion to complement objective performance measures,
32
because performance measures are noisy. Such subjectivity is appropriate when employees trust
the principal to adjust the bonus to account for the noise (Gibbons, 2005; Bol, 2008). Nevertheless,
the benefit of objective contract is that the principal will not renege on this principle too much;
that is, he cannot misuse his discretion too much. If trust between employee and principal emerges,
we expect that the use of managerial discretion in evaluations may start to play a stronger role
(Gibbs et al., 2004).
In this study, we show that non-exclusive franchisees respond differently to the franchisor’s
actions. As exclusive and non-exclusive dealers seem to react in different ways, it may be
beneficial to apply different incentive systems to the different types of dealers. Sohoni et al. (2011)
find that stair-step incentives can help firms to increase sales and decrease sales volatility if the
thresholds are set optimally. Our results hint at the fact that inappropriate stair-step incentives can
backfire, because non-exclusive dealers seem to divert their effort to other brands if targets become
too difficult.
33
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35
Appendix A
A simulated example of the target-setting process
At the end of each accounting year, the franchisor assigns a new target to each dealer for the next
budget year. In general, these new targets are based on dealers’ past performance, expected future
sales growth of the brand and the franchisor’s strategies. The objective input for the new target is
the dealers’ past performance. This is further subjectively adjusted by the franchisor. In Table A1,
we reproduce an example of target setting for one dealer. In total, two steps are included to
determine the target.
In step A, the firm calculates the relative sales performance of each individual dealer within the
nationwide dealer network. In this example, the dealer sold 1,000–1,250 cars per year. The
outcome of Step A is 1.29%, which is the average weight of the past three years. Applying this
percentage to the estimated sales forecast of 99,000 gets the franchisor to the expected sales
number for the dealer in the coming year (n=1,277 cars). Step B entails the franchisor (i.e., the
national dealer organization) adjusting the estimated target based on the firm’s strategies and the
market conditions, resulting in the final year target. For the example case, the final target is
adjusted downward compared to the original value, resulting in a lower target (n=941 cars)
compared to his past performance. However, the subjective adjustments can be either upward or
downward in our sample. Thus, to identify each dealer’s sales target, the sample firm uses a method
that incorporates a mixture of parameters.
Table A.1 Simulation target calculation for a single dealer
2009
2010
2011
2012
Step A
Actual sales
1,000
1,125
1,250
Nationwide sales
75,000
87,500
100,000
Relative performance of the dealer
1.33%
1.29%
1.25%
1.29%
1,277
Step B
Subjective adjustments made by the national dealer organization dependent on the aspects, such as the franchisor’s
strategy and market conditions.
Nationwide sales forecast 2012
110,000
Correction of the forecast * (10% for 2012)
99,000
Dealer target (after correction)
0.95%
941
*The firm’s forecast is corrected for self-registrations (dealer/firm) and rentals.
*Sales numbers and target numbers are multiplied by an unrevealed parameter to disguise the identity of the firm
we investigate in this paper, but we use the original numbers for the rest of the analyses.
36
37
Table 1 Variable definitions
Variable
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 /𝑆𝐴𝐿𝐸𝑆𝑄𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 /𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 /
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡
Definition
indicator variable equal to 1 for exclusive dealer and 0 otherwise
actual sales of dealer 𝑖 in year 𝑡/ quarter 𝑡
sales target of dealer 𝑖 in year 𝑡/ quarter 𝑡
deviation from target sales for dealer 𝑖 in year (quarter) 𝑡, which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑆𝐴𝐿𝐸𝑆𝑄𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡
∗ 100 (
); and sales and targets are the real
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡
annual (quarterly) data
annual target adjustments in year 𝑡 considered by the manufacturer, which is
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
calculated as
∗ 100
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 /
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑄𝑇𝑖,𝑡
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡
𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡
𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡
𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡
𝑆𝐴𝐿𝐸𝑆𝐿𝐴𝑆𝑇𝑀𝑂𝑁𝑖,𝑡
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡
𝑁𝑈𝑀𝑄𝑇_𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
indicator variable equal to 1 if actual sales of dealer 𝑖 is equal to or higher than
the annual/quarterly sales target in year 𝑡 and 0 otherwise
Logarithm of annual target for dealer 𝑖 in year 𝑡
year-to-year sales changes of the car brand in the particular European country
where both the franchisor and the franchisees are located
annual changes of total market sales in the particular European country where
both the franchisor and the franchisees are located
indicator variable equal to 1 if the dealer is the committee member and 0
otherwise
indicator variable equal to 1 if the dealer signs additional contracts with the
manufacturer and 0 otherwise
indicator variable equal to 1 if the target deviation of dealer 𝑖 is among the top
25 percent of all the dealers and 0 otherwise
indicator variable equal to 1 if the sales of dealer 𝑖 is among the top 25 percent
of all the dealers and 0 otherwise
indicator variable equal to 1 if dealer 𝑖 achieves all quarterly targets in year 𝑡 −
1 and 0 otherwise
percentage of times that annual targets are achieved before year 𝑡, since 2004 or
since the dealer first appears in the data
indicator variable equal to 1(0) if quarterly target of dealer 𝑖 is (not) achieved
by the end of the second month of quarter 𝑡
sales of dealer 𝑖 in the last month of quarter 𝑡
indicator variable equal to 1(0) if the target update of dealer 𝑖 in year 𝑡 is larger
(smaller) than the median target update of all the dealers located in the same
province with dealer 𝑖
indicator variable equal to 1(0) if the target update of dealer 𝑖 in year 𝑡 is larger
(smaller) than the average target update of all the dealers located in the same
province with dealer 𝑖
number of quarters that the dealer 𝑖 reaches the quarterly sales target in year 𝑡
38
Table 2 Dealer composition and incentive structure
Panel A: Annual composition of exclusive and non-exclusive dealers
Number Exclusive
New dealers
Non-exclusive
Year
of
dealer
dealer (NE)
E
NE
dealers
(E)
2004
55
25
30
2005
57
26
31
2006
57
26
31
2007
62
25
37
2008
67
24
43
2009
59
21
38
2010
62
20
42
2011
59
19
40
2012
60
19
41
3
(0.115)
2
(0.076)
1
(0.040)
0
(0.000)
0
0.000
0
(0.000)
0
(0.000)
0
(0.000)
2
(0.065)
3
(0.097)
7
(0.189)
8
(0.186)
3
(0.079)
6
(0.143)
1
(0.025)
3
(0.073)
Dropout dealers
E
NE
2
(0.080)
2
(0.077)
2
(0.077)
1
(0.040)
3
(0.125)
1
(0.048)
1
(0.050)
0
(0.000)
1
(0.033)
3
(0.097)
1
(0.032)
2
(0.054)
8
(0.186)
2
(0.053)
3
(0.071)
2
(0.050)
Panel B: Incentive Structure
Threshold Area
At Target Area
Maximum Area
0.60%
1.50%
1.75%
2004
0.60%
1.50%
1.75%
2005
0.90%
1.50%
2.10%
2006
0.90%
1.50%
2.10%
2007
0.90%
1.50%
2.10%
2008
0.90%
1.50%
2.10%
2009
1.35%
1.95%
2.55%
2010
1.35%
1.95%
2.55%
2011
0.90%
1.80%
3.00%
2012a
0.90%
3.00%
3.00%
2012b
Note: The discount percentages in Panel B are all multiplied by an unrevealed parameter to disguise the identity of
the firm we investigate in this paper, but we use the original numbers for the rest of the analyses. We also only disclose
the percentages for the threshold area (minimum), the “at target area,” and the maximum bonus area. In certain years,
the firm also defines other intermediate performance brackets for areas a bit below the target or areas that are above
target. The “at target” area can only be reached when sales are at least equal to the target (target achievement ≥ 100%).
For calculation of our variables, we define achievement of targets when the dealership realizes a sales level that is
equal to or bigger than the specified target for the year (target achievement ≥ 100%) or the quarter when considering
quarterly data, respectively. The franchisor uses this as the reference point when evaluating the sales potential of
dealerships.
39
Table 3 Descriptive statistics and comparisons
Panel A: Descriptive statistics
Variable
N
Mean
Median
S.D.
Min
Max
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡
410
0.407
0.000
0.492
0.000
1.000
410
1731.939
1160.000
1576.944
145.000
11415.000
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
410
1880.244
1187.500
1845.531
170.000
13360.000
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 (%)
410
7.898
6.217
30.333
-61.916
197.952
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 (%)
410
-2.240
-6.178
27.849
-64.286
152.239
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
410
0.405
0.000
0.491
0.000
1.000
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 (%)
410
2.589
5.677
25.910
-36.611
49.165
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 (%)
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
410
1.920
0.697
14.037
-23.960
24.351
410
0.059
0.000
0.235
0.000
1.000
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡
410
0.120
0.000
0.325
0.000
1.000
410
0.232
0.000
0.422
0.000
1.000
𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡
410
0.215
0.000
0.411
0.000
1.000
𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡
410
0.161
0.000
0.368
0.000
1.000
𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡
410
𝑁𝑈𝑀𝑄𝑇𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
410
0.477
1.951
0.500
2.000
0.378
1.324
0.000
0.000
1.000
4.000
291
0.423
0.000
0.495
0.000
1.000
0.436
0.000
0.497
0.000
1.000
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡
291
Panel B: Comparisons across dealership types
Variable
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
Exclusive dealer
1029.970
1033.323
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
3.601
3.824
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
0.533
Non-exclusive dealer
2214.362
2462.284
10.851
-6.408
0.317
Note: Sales numbers and target numbers are multiplied by an unrevealed parameter to disguise the identity of the firm
we investigate in this paper, but we use the original numbers for the rest of the analyses. All variables are defined in
Table 1.
Variable definition: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 represents annual sales for dealer 𝑖 in year 𝑡; 𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 represents the annual target for dealer 𝑖
in year 𝑡; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 denotes the deviation from target sales for dealer 𝑖 in year 𝑡, which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗ 100 ; 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 denotes the annual target adjustments in year 𝑡 considered by the
manufacturer, which is calculated as
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
∗ 100; 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 is an indicator variable equal to 1 if the
dealer achieves the annual sales target in year 𝑡 and 0 otherwise; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales
changes of the car brand in the particular European country where both the franchisor and the franchisees are located;
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual changes of total market sales in the particular European country where both
the franchisor and the franchisees are located; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the
committee member and 0 otherwise; 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional
contracts with the manufacturer and 0 otherwise; 𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡 is an indicator variable equal to 1 if the target deviation
of dealer 𝑖 is among the top 25 percent of all the dealers and zero otherwise; 𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 is an indicator variable
equal to 1 if the sales of dealer 𝑖 is among the top 25 percent of all the dealers and zero otherwise; 𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡 is
an indicator variable equal to 1 if dealer 𝑖 achieves all quarterly targets in year 𝑡 − 1 and zero otherwise;
𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡 represents the percentage of times that annual targets are achieved before year 𝑡, since
40
2004 or since the dealer first appears in the data; 𝑁𝑈𝑀𝑄𝑇𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 indicates number of quarters that the dealer
𝑖 reaches the quarterly sales target in year 𝑡; 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 is an indicator variable equal to 1(0) if the target
update of dealer 𝑖 in year 𝑡 is larger (smaller) than the average target update of all the dealers located in the same
province as dealer 𝑖; 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 is an indicator variable equal to 1(0) if the target update of dealer 𝑖 in year
𝑡 is larger (smaller) than the average target update of all the dealers that are located in the same province as dealer 𝑖.
41
Table 4 Target updates and the type of dealership
Dependent variable:
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖,𝑡 ∗ 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
(1)
(2)
-5.894
(2.44)**
0.482
(4.37)***
-0.233
(1.86)*
8.240
(3.02)***
0.472
(6.10)***
0.380
(1.57)+
-7.104
(1.34)
-8.982
(1.74)*
-1.397
(0.62)
0.435
(4.32)***
-0.242
(1.88)*
5.705
(3.04)***
0.460
(7.37)***
0.465
(2.91)***
-11.006
(3.69)***
-2.194
(0.43)
0.044
(0.80)
-0.048
(0.97)
-34.450
(3.26)***
Yes
Cluster
0.663
253
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−2
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−3
Intercept
-41.103
(2.97)***
Yes
Cluster
0.485
410
Year effect
Error control
Adjusted R2
N
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 denotes the deviation from target sales for dealer 𝑖 in year 𝑡, which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗ 100 ; 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 denotes the annual target adjustments in year 𝑡 considered by the
manufacturer, which is calculated as
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
∗ 100; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm of the annual target
of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales changes of the car brand in the particular
European country where both the franchisor and the franchisees are located; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual
changes of total market sales in the particular European country where both the franchisor and the franchisees are
located; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the committee member and 0 otherwise;
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional contracts with the manufacturer and 0
otherwise.
42
Table 5 Target updates and dealership conditional on target achievement
Dependent variable:
Target is achieved
Target is not achieved
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖
at time 𝑡 − 1
at time 𝑡 − 1
-2.647
0.815
(0.55)
(0.14)
0.597
0.372
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
(3.99)***
(2.14)**
-0.408
0.247
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖,𝑡 ∗ 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
(2.47)**
(0.69)
11.972
5.858
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
(2.28)**
(2.37)**
0.441
0.519
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
(4.91)***
(3.35)***
0.273
0.459
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
(0.80)
(1.37)
-11.129
16.619
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
(2.71)***
(2.15)**
-19.558
-3.674
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
(2.15)**
(0.65)
Intercept
-61.607
-30.398
(2.33)**
(2.25)**
Year effect
Yes
Yes
Error control
Cluster
Cluster
Adjusted R2
0.481
0.485
N
194
216
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖,𝑡
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 denotes the deviation from target sales for dealer 𝑖 in year 𝑡, which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗ 100 ; 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 denotes the annual target adjustments in year 𝑡 considered by the
manufacturer, which is calculated as
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
∗ 100; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm of the annual target
of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales changes of the car brand in the particular
European country where both the franchisor and the franchisees are located; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual
changes of total market sales in the particular European country where both the franchisor and the franchisees are
located; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the committee member and 0 otherwise;
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional contracts with the manufacturer and 0
otherwise.
43
Table 6 Target update and dealership conditional on good performance
Dependent variable:
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡−1
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡−1
Based on target
deviation at t-1
Based on sales
increase at t-1
Whether all
quarters’ targets are
achieved at t-1
-1.246
(0.50)
24.534
(3.41)***
-15.721
(2.22)**
1.341
(0.69)
0.454
(0.19)
𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡−1
22.837
(4.75)***
-19.506
(3.57)***
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡−1
𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡−1
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡−1
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
8.146
(2.71)***
0.459
(5.70)***
0.637
(2.85)***
-7.814
(1.57)+
-7.264
(1.45)
-46.293
(2.94)***
Yes
Cluster
0.449
410
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
Intercept
Year effect
Error control
Adjusted R2
N
5.663
(2.25)**
0.425
(4.81)***
0.693
(2.92)***
-0.101
(0.04)
-5.008
(1.27)
-34.680
(2.67)***
Yes
Cluster
0.454
410
32.928
(3.92)***
-27.946
(3.37)***
7.024
(2.58)**
0.471
(6.16)***
0.457
(1.93)*
-9.077
(1.55)+
-3.627
(0.77)
-40.539
(2.87)***
Yes
Cluster
0.470
410
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 denotes the annual target adjustments in year 𝑡 considered by the manufacturer,
which is calculated as
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
∗ 100; 𝐺𝑃𝑀_𝐷𝐸𝑉𝑖,𝑡 is an indicator variable equal to 1 if the target deviation
of dealer 𝑖 is among the top 25 percent of all the dealers and 0 otherwise; 𝐺𝑃𝑀_𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 is an indicator variable equal
to 1 if the target deviation of dealer 𝑖 is among the top 25 percent of all the dealers and 0 otherwise; 𝐺𝑃𝑀_𝐴𝐿𝐿𝑄𝑇𝑖,𝑡 is
an indicator variable equal to 1 if dealer 𝑖 achieves all quarterly targets in year 𝑡 − 1 and 0 otherwise; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
is the logarithm of the annual target of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales changes of
the car brand in the particular European country where both the franchisor and the franchisees are located;
44
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual changes of total market sales in the particular European country where both
the franchisor and the franchisees are located; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the
committee member and 0 otherwise; 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional
contracts with the manufacturer and 0 otherwise.
45
Table 7 Relationship building and the type of dealership
Dependent variable:
𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
Intercept
Year effect
Error control
Adjusted R2
N
(1)
(2)
1.342
(0.37)
7.696
(1.33)
-15.392
(2.25)**
0.388
(4.22)***
8.414
(3.07)***
0.475
(6.20)***
0.409
(1.74)*
-7.285
(1.29)
-8.466
(1.65)+
-45.838
(3.11)***
Yes
Cluster
0.484
410
1.389
(0.38)
23.018
(3.53)***
-15.460
(2.24)**
7.327
(2.70)***
0.479
(5.62)***
0.608
(2.54)**
-5.076
(0.97)
-5.590
(1.07)
-46.617
(3.09)***
Yes
Cluster
0.430
410
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝑇𝐴𝑅𝐺𝐸𝑇_𝑈𝑃𝐷𝐴𝑇𝐸𝑖,𝑡 denotes the annual target adjustments in year 𝑡 considered by the manufacturer,
which is calculated as
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
∗ 100; 𝑁𝑈𝑀𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_𝑌𝑅𝑃𝐶𝑇𝑖,𝑡 represents the percentage of times that
annual targets are achieved before year 𝑡 , since 2004 or since the dealer first appears in the data;
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 denotes the deviation from target sales for dealer 𝑖 in year 𝑡, which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗
100; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm of the annual target of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-toyear sales changes of the car brand in the particular European country where both the franchisor and the franchisees
locate; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual changes of total market sales in the particular European country
where both the franchisor and the franchisees locate; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer
is the committee member and 0 otherwise; 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs
additional contracts with the manufacturer and 0 otherwise.
46
Table 8 Target achievement and the type of dealership
Panel A: Likelihood of achieving targets
Dependent Variable:
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
Annual data
Quarterly data
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑄𝑇𝑖,𝑡
0.526
(1.60)+
0.051
(5.76)***
0.423
(2.20)**
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡−4
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
0.012
(4.56)***
-0.546
(4.89)***
0.018
(4.10)***
0.001
(0.12)
1.350
(2.95)**
0.350
(1.56)
3.209
(4.65)***
Yes
Yes
Cluster
0.151
1,640
-1.299
(4.81)***
0.034
(3.50)***
0.004
(0.17)
2.088
(2.50)**
1.158
(2.48)**
6.191
(3.97)***
Yes
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
Intercept
Year effect
Quarter effect
Error control
Pseudo R2
N
Cluster
0.151
410
Panel B: Sales increase (%) between two types of dealers
Sales increase (%)
N
Mean
Median
inincincrease
Non-exclusive
243
6.063
3.356
exclusive
167
3.044
2.183
Total sample
410
4.833
2.498
SD
38.273
27.114
34.166
Min
-65.476
-53.086
-65.476
Max
181.152
113.924
181.152
T-test
0.879
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 ( 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡 ) denotes the deviation from target sales for dealer 𝑖 in year
(quarter) 𝑡, which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗ 100 (
𝑆𝐴𝐿𝐸𝑆𝑄𝑇𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑄𝑇𝑖,𝑡
); 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 (𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑄𝑇𝑖,𝑡 ) is
an indicator variable equal to 1 if the dealer achieves the annual sales target in year (quarter) 𝑡 and 0
otherwise; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm of the annual target of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates
year-to-year sales changes of the car brand in the particular European country where both the franchisor and the
franchisees locate; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual changes of total market sales in the particular European
country where both the franchisor and the franchisees locate; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the
47
dealer is the committee member and 0 otherwise; 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs
additional contracts with the manufacturer and 0 otherwise.
48
Table 9 Muting effort and the type of dealership
Dependent variable:
𝑆𝐴𝐿𝐸𝑆𝐿𝐴𝑆𝑇𝑀𝑂𝑁𝑖,𝑡
Quarterly Target is achieved
Total sample
-3.561
(1.75)*
-6.490
(2.54)**
10.863
(2.78)***
-1.061
(0.71)
-4.224
(2.10)**
4.693
(1.72)*
13.867
(8.41)***
0.123
(6.54)***
22.933
(8.94)***
0.137
(2.99)***
0.012
(0.14)
0.171
(0.07)
3.037
(0.82)
-112.666
(8.30)***
Yes
Yes
Cluster
0.653
1,640
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑄𝑇𝑖,𝑡
𝑆𝐴𝐿𝐸𝑆𝑄𝑇𝑖,𝑡−4
0.186
(4.28)***
21.848
(5.26)***
-0.003
(0.07)
0.232
(1.40)
-3.019
(1.49)+
9.260
(2.07)**
-96.532
(5.09)***
Yes
Yes
Cluster
0.702
800
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
Intercept
Year effect
Quarter effect
Error control
Adjusted R2
N
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝑆𝐴𝐿𝐸𝑆𝐿𝐴𝑆𝑇𝑀𝑂𝑁𝑖,𝑡 is sales of dealer 𝑖 in the last month of quarter 𝑡; 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷_2𝑀𝑂𝑁𝑖,𝑡 is an indicator
variable equal to 1(0) if quarterly target of dealer 𝑖 is (not) achieved by the end of the second month of quarter 𝑡;
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑄𝑇𝑖,𝑡 is an indicator variable equal to 1 if the dealer achieves the annual sales target in quarter 𝑡 and 0
otherwise; 𝑆𝐴𝐿𝐸𝑆𝑄𝑇𝑖,𝑡 is sales of dealer 𝑖 in quarter 𝑡; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm of the annual target of dealer 𝑖
in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales changes of the car brand in the particular European country
where both the franchisor and the franchisees are located; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡 indicates annual changes of total
market sales in the particular European country where both the franchisor and the franchisees are located;
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the committee member and 0 otherwise;
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional contracts with the manufacturer and 0
otherwise.
49
Table 10 Target achievement and dealership: control for difficulty
Dependent variable:
𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
(1)
(2)
-0.223
(0.35)
-1.850
(3.67)***
0.588
(0.78)
-0.413
(0.68)
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡
𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡−1
0.075
(6.18)***
-1.771
(3.99)***
0.047
(3.52)***
-0.012
(0.41)
2.434
(3.21)***
1.099
(1.91)*
9.946
(3.90)***
Yes
Cluster
0.471
291
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
Intercept
Year effect
Error control
Pseudo R2
N
-1.346
(2.83)***
1.093
(1.69)*
0.067
(6.31)***
-1.726
(3.68)***
0.044
(3.75)***
-0.007
(0.28)
2.249
(2.89)***
1.120
(2.05)**
9.516
(3.54)***
Yes
Cluster
0.445
291
*, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 denotes the deviation from target sales for dealer 𝑖 in year 𝑡 , which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗ 100; 𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 is an indicator variable equal to 1 if the dealer achieved the annual sales target
in year 𝑡 and 0 otherwise; 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 is an indicator variable equal to 1(0) if the target update of dealer
𝑖 in year 𝑡 is larger (smaller) than the average target update of all the dealers located in the same province as dealer 𝑖;
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 is an indicator variable equal to 1(0) if the target update of dealer 𝑖 in year 𝑡 is larger (smaller)
than the average target update of all the dealers located in the same province as dealer 𝑖; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm
of the annual target of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales changes of the car brand in
the particular European country where both the franchisor and the franchisees are located; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
indicates annual changes of total market sales in the particular European country where both the franchisor and the
franchisees are located; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the committee member and
50
0 otherwise; 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional contracts with the
manufacturer and 0 otherwise.
51
Table 11 Number of achieved quarters and dealership: control for difficulty
Dependent variable:
𝑁𝑈𝑀𝑄𝑇𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖
(1)
0.159
(2.26)**
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
(2)
-0.017
(0.14)
-0.258
(2.93)***
0.211
(1.60)+
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡
𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 ∗ 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡
𝑁𝑈𝑀𝑄𝑇𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡−1
𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡
𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡
𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡
Intercept
Year effect
Error control
Pseudo R2
N
0.178
(6.24)***
-0.238
(4.58)***
0.007
(3.88)***
0.004
(0.93)
0.339
(3.97)***
0.239
(2.41)**
1.339
(4.04)***
Yes
Cluster
0.102
410
0.195
(6.09)***
-0.297
(3.15)***
0.007
(3.60)***
0.004
(0.82)
0.320
(4.27)***
0.245
(2.15)**
1.796
(3.13)***
Yes
Cluster
0.114
291
(3)
-0.047
(0.40)
-0.244
(2.73)***
0.293
(2.31)**
0.190
(5.90)***
-0.297
(3.10)***
0.007
(3.77)***
0.004
(0.82)
0.318
(4.12)***
0.247
(2.11)**
1.809
(3.09)***
Yes
Cluster
0.113
291
+, *, ** and *** indicate significance at the 15%, 10%, 5% and 1% level (two-tailed). T-values (or Z-values) are
presented in the brackets.
Variable definitions: 𝐸𝑋𝐶𝐿𝑈𝑆𝐼𝑉𝐸𝑖 is an indicator variable equal to 1 if the dealer 𝑖 is an exclusive dealer and 0
otherwise; 𝐷𝐸𝑉_𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 denotes the deviation from target sales for dealer 𝑖 in year 𝑡 , which is measured as
𝑆𝐴𝐿𝐸𝑆𝑖,𝑡 −𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡
∗ 100; 𝑁𝑈𝑀𝑄𝑇𝐴𝐶𝐻𝐼𝐸𝑉𝐸𝐷𝑖,𝑡 indicates number of quarters that the dealer 𝑖 reaches the quarterly
sales target in year 𝑡; 𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐷𝐼𝐴𝑁𝑖,𝑡 is an indicator variable equal to 1(0) if the target update of dealer 𝑖 in
year 𝑡 is larger (smaller) than the average target update of all the dealers located in the same province as dealer 𝑖;
𝐷𝐼𝐹𝐹𝐼𝐶𝑈𝐿𝑇_𝑀𝐸𝐴𝑁𝑖,𝑡 is an indicator variable equal to 1(0) if the target update of dealer 𝑖 in year 𝑡 is larger (smaller)
than the average target update of all the dealers located in the same province as dealer 𝑖; 𝐿𝑁𝑇𝐴𝑅𝐺𝐸𝑇𝑖,𝑡 is the logarithm
of the annual target of dealer 𝑖 in year 𝑡; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐵𝑅𝐴𝑁𝐷𝑡 indicates year-to-year sales changes of the car brand in
the particular European country where both the franchisor and the franchisees are located; 𝐺𝑅𝑂𝑊𝑇𝐻_𝐴𝐿𝐿𝐵𝑅𝐴𝑁𝐷𝑡
indicates annual changes of total market sales in the particular European country where both the franchisor and the
franchisees are located; 𝐶𝑂𝑀𝑀_𝑀𝐸𝑀𝑖,𝑡 is an indicator variable equal to 1 if the dealer is the committee member and
52
0 otherwise; 𝐼𝑁𝐷_𝐴𝐺𝑅𝐸𝐸𝑖,𝑡 is an indicator variable equal to 1 if the dealer signs additional contracts with the
manufacturer and 0 otherwise.
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