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At the Movies: The Economics of Exhibition Contracts
Darren Filson, David Switzer, and Portia Besocke∗
June 17, 2004
∗
Contact info: Darren Filson (Corresponding Author), Associate Professor of Economics, Department
of Economics, Claremont Graduate University, 160 E. Tenth St., Claremont, CA 91711; ph: (909) 621-8782;
fax: (909) 621-8460; email: Darren.Filson@cgu.edu. David Switzer is a Ph.D. Candidate at Washington
University in St. Louis. Portia Besocke is a Ph.D. Candidate at Claremont Graduate University. We thank
Fernando Fabre, Alfredo Nava, and Paola Rodriguez for background research that contributed to this paper.
We thank Darlene Chisholm and Doug Whitford for comments. Filson thanks the Fletcher Jones Foundation,
the John M. Olin Foundation, and the National Association of Scholars for financial support.
At the Movies: The Economics of Exhibition Contracts
Abstract: We describe a real-world profit sharing contract - the movie exhibition contract -
and consider alternative explanations for its use. Two explanations based on difficulties with
forecasting fit the facts better than asymmetric information models. The first emphasizes
two-sided risk aversion; the second emphasizes measurement costs. Transaction costs and
long-term relationships also affect contractual practices. We use an original data set of all
exhibition contracts involving thirteen theaters owned by a prominent St. Louis exhibitor
over a two-year period to inform our theories and test hypotheses.
JEL Codes: L14: Transactional Relationships and Contracts; D45: Licensing; L82: Indus-
try Studies: Entertainment
Keywords: risk sharing, relational contract, principal agent, motion picture, film
1. Introduction
The literature on profit sharing stresses asymmetric information. Profit sharing occurs when
a party has private information that cannot be credibly revealed or when a party’s actions
cannot be observed. Economists avoid taste-based explanations such as risk sharing (Stigler
and Becker 1977) and other factors that may lead to sharing, such as measurement costs.
We describe a real-world sharing contract that is widely used - the movie exhibition
contract - and argue that asymmetric information is not the main cause of sharing. Two
explanations based on difficulties with forecasting revenue fit the facts better. The first is
that movie distributors (studios or independent distributors) and exhibitors (theater owners)
are both risk averse and exhibition contracts are designed to share risk. The second is that
the sharing rules accompanied by ex post adjustments economize on measurement costs.
Transaction costs and long-term relationships also affect contractual practices. We use an
original data set of 2,769 exhibition contracts to inform our models and test hypotheses.
The data includes all contracts involving thirteen theaters owned by Wehrenberg Theatres,
a prominent St. Louis exhibitor, over roughly two years. Our models explain the sharing
2
that occurs and the conditions that lead to adjustments, and our findings may be relevant
for other contracting environments.
Our explanations for the features of exhibition contracts complement those of De Vany
and Eckert (1991) and De Vany and Walls (1996), who emphasize that difficulties with
forecasting demand necessitate the use of short-term contingency-rich contracts. Some other
work on non-exhibition aspects of the movie business also compares asymmetric information
models to alternatives. Ravid (1999) tests and rejects a model of asymmetric information at
the project selection stage. Ravid and Basuroy (2004) consider risk aversion at the project
selection stage. Chisholm (1993, 1997) and Weinstein (1998) compare principal agent models
to alternatives in studies of contracts between studios and talent.
In the next subsection we describe the basic features of modern exhibition contracts.1
In
the subsection after that we describe how the sharing rules evolved over time and argue that
asymmetric information does not explain the sharing rules. Section 2 contains our models,
Section 3 contains our empirical work, and Section 4 concludes.
1.1. The Modern Movie Exhibition Contract
The unit of analysis for the contracts we describe is a single movie in a single theater (a
theater is a building which may contain multiple auditoriums). While there is typically a
boilerplate contract between each distributor and exhibitor that specifies general conditions
that apply to all individual contracts, terms such as sharing rules and run lengths vary by
movie and theater. A typical run ends after four to eight weeks, but the run may be adjusted
after early revenues are observed, and holdover clauses may be used to extend the run as
long as revenue is sufficiently high. De Vany and Eckert (1991) and De Vany and Walls
(1996) attribute adjustable runs to imprecise forecasts.
1
Modern exhibition contracts have been described by De Vany and Eckert (1991), De Vany and Walls
(1996), and Borcherding and Filson (2001). We also benefited from several conversations with industry
participants, particularly D. Barry Reardon, past-president of Warner Bros. Distributing Corporation and
a former executive of both Paramount Pictures and General Cinema Corporation; Doug Whitford, the
executive at Wehrenberg Theatres in charge of negotiating film rental contracts; and Mike Doban of Trans-
Lux Cinema Consulting. We also benefited from several articles in The Movie Business Book, edited by
Jason E. Squire. The authors of the articles were, at the time of writing, prominent industry participants,
and include among others D. Barry Reardon; Stanley H. Durwood, chairman and chief executive officer of
AMC Entertainment Inc.; A. Alan Friedberg, chairman of Loews Theatres, a subsidiary of Sony Pictures
Entertainment; and A. D. Murphy, financial editor and reporter for Daily Variety and Variety.
3
We focus on explaining the peculiar revenue sharing rule that is common in modern
contracts. While some contracts are “aggregate” deals, in which each side’s percentage
share remains fixed throughout the run, most are “sliding scale” deals. In a sliding scale
deal, each week, the distributor gets the maximum of two possible payments: 1) 90% of the
movie’s weekly ticket revenue over the “house nut,” which is a flat payment to the exhibitor;
2) a “floor payment,” some percentage of the weekly ticket revenue that typically declines
according to a “sliding scale” as the weeks go by - perhaps 70% in the first week, 60% by
the third week, and as low as 30% at the end of the run. If the parties anticipate that
revenue might peak in the second or later weeks (which can occur when the movie opens
before a holiday weekend, for example) the contract includes a “best weeks” clause that
ensures that the high floor payments are associated with the high demand weeks. Most of
the time the floor is relevant; the 90/10 provision applies only for hits early in their runs. The
exhibitor’s payoff function for one week associated with such a contract is graphed in Figure 1.
Interestingly, concession revenue is not shared - the exhibitor gets it all. This is not a trivial
oversight as concessions typically account for approximately half of an exhibitor’s profit.
Most deals are “firm term,” which indicates that both parties expect to be compensated
according to floors or aggregate shares that are specified at the beginning of the run. In
contrast, “flexible” deals have boilerplate terms that are rarely enforced, and the exhibitor
determines the appropriate shares as revenue is observed. In either case, terms may be
adjusted during the run or after it ends; for flexible deals this is common and for firm
deals it is rare. We ignore the firm-flexible distinction in most of our analysis and focus on
explaining floors with sliding scales, best weeks clauses, the 90/10 provision, and the nature
of adjustments.
Other factors affect our modeling assumptions. Most contracts result from negotiations
(Friedberg 1992; Reardon 1992). The courts favored competitive bidding at the time of
the Paramount decrees of the 1940s and 50s, but even when bids are used they are points
of departure for negotiations. Given this, our models emphasize bargaining rather than
auctions. Friedberg (1992) notes that distributors cannot simply choose the “highest” bid
because multiple factors matter, including local demographics, the location, and the decor.
Exhibitor bids typically include 1) a schedule of ticket prices; 2) the number of shows for
4
weekdays and weekends; and 3) the screen number and the number of seats in the auditorium
in which the picture will play. Practitioners tell us these are guidelines only; 2) and 3) are
difficult for distributors to monitor and none of the three terms are enforced. However, ticket
prices are typically constant across movies and time at the theater level (except for daily
matinee prices).2
Given this, ticket prices are exogenous in most of our analysis. Our models
consider a theater with only one auditorium. Given this, we do not endogenize the allocation
of movies to time slots and auditoriums.3
However, in our empirical analysis we consider the
impacts of such allocation decisions.
1.2. The Evolution of Revenue Sharing
Our conversations with practitioners (see fn.1) and historical analyses such as Hanssen (2000,
2002) allow us to describe how sharing rules evolved and explain why sliding scale rules are
used today. Originally movies were short, silent, low-cost, relatively non-differentiated prod-
ucts that were sold to exhibitors outright. As feature films were introduced, production
budgets rose and consumers became more selective. Avoiding downside risk became impor-
tant. Murphy (1992) notes that percentage rentals were introduced to “justify the investment
risk.” With the arrival of sound in the late 1920s, production budgets rose more and the vari-
ance of movie revenue (and profit) increased (Sedgwick and Pokorny 1998; Hanssen 2002).
Revenue sharing became increasingly common.4
The distributor’s share of ticket revenue
rose as budgets rose, from roughly 20% early on, to 25% in the 1920s, 33% in the 1960s, and
45% in the 1990s. Shares varied by movie and theater.
2
Practitioners provide several explanations for inflexible ticket prices. Exhibitors want to avoid menu costs
and eliminate consumer uncertainty about what the movie will cost. Exhibitors do not increase prices of hits
because they are engaged in repeat business with local consumers, and the potential loss of goodwill from
increased prices outweighs the potential gain. Charging different prices for different movies at multiplexes
necessitates employing monitors to ensure that consumers see the movies they pay for. Even offering mid-
week discounts may lead to more time shifting than new demand. Not all analysts or practitioners agree
that inflexible prices are optimal (see Orbach and Einav 2001), although it seems unlikely that such an easy-
to-exploit profit opportunity would persist. Some practioners have experimented with non-uniform prices in
the U.S. in the recent past but inflexible prices remain the norm.
3
Filson (2004) provides a dynamic model that includes allocation decisions within a multiplex.
4
Hanssen (2002) describes how exhibitor inputs were less important for big-budget movies, particularly
after sound. As a result there was less need to make the exhibitor the residual claimant. However, this leaves
open the question of why the distributor was not made the full residual claimant. Hanssen (2002) argues
that exhibitors needed incentives to provide local inputs. We evaluate this argument below - it may have
been relevant in the 1920s but does not appear to explain revenue sharing today.
5
The modifications to the earliest sharing rules reflect attempts to minimize downside
risk. As noted in the previous subsection, in most modern contracts the distributor gets
90% over the house nut during high-revenue weeks. The nut is a fixed dollar amount that
the distributor pays to the exhibitor. It was originally included in contracts with the pretext
of covering the exhibitor’s costs, which effectively limits the exhibitor’s downside. Murphy
(1992) describes how floor payments originated in the late 1960s “as a countermove by
distributors who questioned the validity of house nuts and who no longer could afford to
absorb most of the losses incurred by a failed film.” A floor payment limits the distributor’s
downside - it ensures that the distributor obtains some minimum percentage of the movie’s
revenue. In practice, the floor is the relevant payment the vast majority of the time.
In sum, practitioners claim that downside risk motivates revenue sharing. This is not
surprising; several authors argue that risk is important in the movie industry (De Vany
and Eckert 1991; De Vany and Walls 1996; Weinstein 1998; Borcherding and Filson 2001).
Production involves high upfront sunk costs, and demand is difficult to predict.5
Historically,
distributors and exhibitors relied heavily on internal financing, and neither party could afford
to bear the full impact of a big-budget flop (Murphy 1992). As budgets rose and uncertainty
increased, contracts became more sophisticated.
In contrast, asymmetric information about movie quality does not appear to be impor-
tant. As De Vany and Eckert (1991) and De Vany and Walls (1996, 1999) note, the main
information problem is that it is difficult to forecast demand prior to release because every
movie is different. This does not give one side an informational advantage. The movie must
be shown in the theater, and then demand is discovered. De Vany and Eckert (1991) suggest
that the information provided by pre-release screenings is quite limited - exhibitors often do
not attend screenings. Further, evidence suggests that screenings do not affect sharing rules.
Regulations vary by U.S. state: blind bidding ( contracting before screening) is allowed by
some but not others. Where blind bidding is prohibited, distributors must allow exhibitors
to see the movie before contracting (although exhibitors may choose not to go). The sharing
5
On average, movies made by Motion Picture Association of America members in 2003 had production,
distribution, advertising, overhead and interest costs of $102.9 million. The average movie does not earn a
positive return on investment - high profits on scarce hits make up for losses on the rest (Vogel 1998).
6
rules are similar whether blind bidding is used or not.6
When comparing contracts in states
with blind bidding to those in states without Blumenthal (1988) “focuses on the guarantee
because it is the most variable element of the bid vector.”
Is incentive provision important? Bhattacharyya and Lafontaine (1995) show that simple
sharing rules can be optimal when double-sided moral hazard exists, and their model could
be applied to movie exhibition. Distributors must advertise and promote the movie, while
exhibitors must hire employees and do some local advertising. Neither party’s activities
are easy to monitor. Kenney and Klein (1983) and Hanssen (2002) point out that sharing
contracts provide incentives for exhibitors to keep theaters clean and take other actions
which are hard to monitor but that may increase ticket revenue. Are these effects the reason
for revenue sharing? We argue that long-run relationships between exhibitors, customers,
and distributors reduce the need for incentive contracts - reputational concerns provide
incentives. For example, an unclean theater loses repeat business with local consumers.
Even if distributors paid flat payments to the exhibitor, a new contract would be negotiated
each time a new movie was released. Thus, payments to the exhibitor could quickly fall if
the theater lost customers. Therefore, the exhibitor would have the incentive to keep the
theater clean in the absence of a sharing rule. Given this, the distributor could simply rent
the auditorium at a flat rental rate using a “four walls” contract. Such contracts have been
used, but only rarely - sharing is the norm. In our conversations with practitioners, we found
absolutely no support for the notion that revenue sharing encourages theater cleanliness or
any other standard good business practices.7
Our models of risk sharing and measurement costs are consistent with what firms claim
6
Exhibitor objections contributed to regulations prohibiting blind bidding, but De Vany and Eckert (1991
fn. 77) explain that the main objections were to bidding itself (exhibitors preferred negotiations) and the
accompanying guarantees, which were nonrefundable upfront payments from the exhibitor to the distributor.
Like many contract terms, guarantees were originally introduced to protect one party against downside risk.
In this case, the risk was that the exhibitor would go bankrupt, taking the revenues and the print (the copy
of the movie). Guarantees are almost never used today and even refundable advances (which ensure that the
exhibitor does not lose money on a movie that does not earn its advance) are rare. Information technology
facilitates paying the distributor early in the run from revenues, so advances are unnecessary.
7
Of course, building a reputation may not be costless, and it may take time before a new exhibitor
develops a reputation for cleanliness and other good business practices. Our point is that the exhibitor has
the incentive to bear these costs regardless of whether the exhibition contract uses a sharing rule or a flat
payment; the exhibitor’s main concern is to build and maintain customer goodwill.
7
they are doing and explanations based on asymmetric information appear inadequate. How-
ever, our explanations raise a question. Given that movie-specific risk is primarily idiosyn-
cratic, and given that standard finance theory suggests that firms should ignore idiosyncratic
risk, why do distributors and exhibitors care about movie-specific risk? We note that in con-
trast to standard finance theory, most firms care about idiosyncratic risk. For example,
risk-neutral firms would not buy insurance because insurance premiums are not actuarially
neutral, and yet corporations spend more money on insurance premiums than they pay out
in dividends (Mayers and Smith 1982; Martin 1988). It is beyond the scope of this paper
to explain why firms alleviate idiosyncratic risk, but most analyses emphasize stakeholder
risk aversion (see Smith and Stulz 1985, De Alessi 1987, DeMarzo and Duffie 1995, and
Tufano 1996). For example, insurance shifts the risk of bankruptcy away from employees
who cannot diversify toward shareholders who can, and by doing so it encourages employees
to make firm-specific investments. Alleviating idiosyncratic risk also encourages some share-
holders to become large undiversified shareholders, who then perform monitoring that aids
all investors. In the movie industry, revenue sharing contracts play this role.8
2. Models
2.1. The Risk Sharing Model
Here we present a simple risk sharing model that explains the basic features of the sharing
rule. The sharing rule evolved when single-auditorium theaters were the norm, and our
model has one distributor with one movie and one exhibitor with one auditorium. We ignore
costs and focus on revenues; this is reasonable because when the movie is placed in the
theater most of the distributor’s costs are sunk and most of the exhibitor’s costs are fixed.
For now, suppose the distributor designs a contract for a single week, and assume that both
players take the ticket price p as given (this is reasonable; see Subsection 1.1). Given p and
8
Note that movie-specific risk may be very hard to insure using third-party insurance. Distributors
and exhibitors have industry know-how that third parties lack, and as a result they may be best-suited to
bear movie-specific risk. Dekom (1992) summarizes the industry attitude: “In the case of major studios,
avoiding risks (by taking serious downside protection) is simply not a business plan.... If the management
has insufficient confidence in its own abilities to choose and distribute motion pictures, perhaps they should
find solace in another industry.”
8
the theater’s weekly capacity N, attendance during the week, nt, is determined according to
a probability density function Pr(nt|p, N, t). The game proceeds as follows: The distributor
proposes a contract wt(pnt). If the exhibitor accepts the contract then it shows the movie
and gets paid accordingly. If it does not accept the contract it receives its reservation utility
U∗
e . In reality U∗
e would be determined by competiting distributors’ movies (opportunity
costs), but for simplicity assume that U∗
e is exogenous. For now, ignore concession revenue;
we discuss it below in Subsection 2.3.
The distributor chooses wt(pnt) to maximize its expected utility subject to the exhibitor’s
participation constraint:
max
{wt(pnt)}N
nt=0
E(Ud) =
N
X
nt=0
Ud(pnt − wt(pnt)) Pr(nt|p, N, t) (2.1)
s.t. E(Ue) =
N
X
nt=0
Ue(wt(pnt)) Pr(nt|p, N, t) ≥ U∗
e (2.2)
where Ud(.) and Ue(.) are the distributor’s and exhibitor’s utility functions and E(.) is the
expectation operator. The first-order condition implies that at each value of nt,
U
0
d(pnt − wt(pnt)) = γtU
0
e(wt(pnt)), (2.3)
where γt is the Lagrange multiplier.9
Differentiating both sides with respect to nt yields the
slope of the revenue-sharing rule:
w0
t(pnt) =
U
00
d (pnt − wt(pnt))
U
00
d (pnt − wt(pnt)) + γtU00
e (wt(pnt))
(2.4)
Risk aversion implies that the second derivatives of both utility functions are negative,
9
Expression (2.3) is similar to the first-order condition in several classic papers on risk sharing. Borch
(1962) was the first to characterize the first-order condition for optimal risk sharing. Stiglitz (1974) and
Leland (1978) consider constant relative risk aversion, which we discuss presently. A first-order condition
similar to (2.3) can be derived from a Nash bargaining model where both players have exogenous outside
options. Nash bargaining solves: max{wt(pnt)}N
nt=0
[E(Ud)−U∗
d ]ψ
[E(Ue)−U∗
e ]1−ψ
, where U∗
d is the distributor’s
reservation utility and ψ measures relative bargaining power. At the solution the marginal utilities are
proportional to each other as in (2.3). Thus, the shape of the optimal contract does not depend critically on
relative bargaining power. When ψ = 1, the distributor has all of the bargaining power and the problem is
identical to solving (2.1) subject to (2.2).
9
which implies that the slope of the sharing rule is positive. However, the change in the slope
of the sharing rule as nt changes depends on the third derivatives of the utility functions.
Thus, we require additional assumptions to replicate the pattern in Figure 1. Suppose both
parties have constant relative risk aversion (CRRA): Ud(x) = xαd
and Ue(x) = xαe
, where x is
money and αd and αe are the coefficients that measure relative risk aversion. These functions
have several plausible properties: the marginal utility is positive and diminishing as long as
αi < 1; relative risk aversion, −xU00(x)
U0(x)
, is constant; and absolute risk aversion, −U00(x)
U0(x)
, is
decreasing in the money payoff as long as αi < 1. We follow the standard principal-agent
model (Mas-Colell et al. 1995) and assume that the contract proposer (the distributor) is
less risk averse than the other party (the exhibitor). However, we assume that both parties
are risk averse.10
Assumption 1: Both parties have CRRA utility and 0 < αe < αd < 1.
Assumption 1 must hold for the results in the remainder of this subsection. We do not
have a strong justification for the assumption that αe < αd, but it seems reasonable. We
argue in the conclusion that distributors have a higher ratio of variable to fixed costs and
are in a better position to absorb fluctuations in revenue. Further, distributors often reduce
their exposure on particular movies through co-production arrangements and revenue sharing
contracts with talent; exhibitors lack similar options. Note that the principal-agent model
also lacks a strong justification for the assumption that the principal is less risk averse than
the agent in many settings where it is used. However, this does not imply that the models
are not useful, and even when we cannot verify assumptions we can evaluate models based on
how well they predict. Our simple model predicts many of the features of modern exhibition
contracts.
Given Assumption 1, (2.4) can be expressed as
w0
t(pnt) =
1
1 +
h
γtαe
αd
i 1
αd−1 αe−1
αd−1
wt(pnt)
αe−αd
αd−1
. (2.5)
Expression (2.5) shows that wt(pnt) is increasing in pnt and that w0
t(pnt) diminishes as
10
If both players are risk neutral then neither cares about who bears the risk and an infinite variety of
contracts are optimal. If one player is risk neutral and the other is risk averse then it is optimal for the risk
neutral player to bear all of the risk and pay the other a flat payment.
10
wt(pnt) rises. Thus, the optimal sharing rule has a diminishing slope as in Figure 1.11
We cannot solve for wt(pnt) analytically, but we can compute it numerically using realistic
parameters for urban theaters (provided by the sources in fn. 1). Assume the capacity
per showing is 250 and there are four showings per day: N = 7000. The ticket price p = 7.
Given N and p, the maximum weekly ticket revenue is $49,000. Friedberg (1992) and Murphy
(1992) suggest this is a reasonable upper bound. We choose the remaining parameters, αd,
αe, and γt, in a rough attempt to minimize the distance between the payments the model
generates and the real-world payments graphed in Figure 1. We set αd = .75, αe = .5, and
γt = 21. The optimal contract is graphed in Figure 2.
Result 1: The optimal sharing rule has a diminishing slope, as in the real-world contract.
If we consider multiple weeks, the model explains floors with sliding scales and best-weeks
clauses. Suppose that at the beginning of the movie’s run, the distributor solves (2.1) for
each week based on a forecast of Pr(nt|p, N, t). The solution is an optimal wt(pnt) for each
week. Typically, pnt is expected to fall over time.12
Given this, the exhibitor’s share of pnt
must rise in order for the exhibitor to obtain utility U∗
e each week. Formally, this results
from an increase in γt, since the risk aversion parameters, prices, and other variables do not
change over the life of the movie.13
When γt rises the entire sharing rule becomes steeper,
whereas in reality only the floors do and the 90/10 split remains a possibility. However,
the floor is virtually always the relevant payment in later weeks so it is reasonable that the
real-world parties reduce transaction costs by not revising the 90/10 provision.
Result 2: If attendance is expected to fall over time, the exhibitor’s share of revenue must
rise over time in order for the exhibitor to obtain utility U∗
e each week (which is required in
order for the exhibitor to continue to show the movie). Thus, the optimal sharing rule has
a floor with a sliding scale, as in the real-world contract.
Result 3: If attendance is expected to peak in the second or later weeks, the exhibitor’s
share of revenue initially falls and then rises in order for the exhibitor to obtain utility U∗
e
11
Note that if αd = αe, the exhibitor receives a constant share of ticket revenue.
12
De Vany and Eckert (1991), De Vany and Walls (1996, 1999), Sawhney and Eliashberg (1996), and
Eliashberg et al. (2000) examine time series of ticket revenue. Typically, revenue per screen falls over time.
13
The multiplier γt measures the distributor’s expected marginal utility from a change in U∗
e . During
low-revenue weeks the distributor gets less revenue at each level of nt (because the exhibitor must continue
to receive U∗
e ). Given that Ud(.) is strictly concave, this implies that γt is higher during such weeks.
11
each week. Thus, the optimal sharing rule explains the “best weeks” clause that occasionally
appears in real-world contracts.
Note that even with perfect forecasting, the exhibitor’s payment would adjust over time
in order for it to obtain U∗
e . Thus, conditional on sharing, adjustments to the floor may
be explained by exhibitor opportunity costs that are roughly constant. However, with per-
fect forecasting there would be no need for revenue sharing; flat payments would suffice.
Even with unpredictability, if one party is not risk averse, a flat payment could be used to
compensate the risk averse party, as long as the appropriate payment could be calculated.
In sum, our model explains concave sharing rules, floor payments with sliding scales,
and best weeks clauses. However, the real-world contract is simpler than the model so far
suggests because it typically specifies a single floor percentage for each week rather than a
mapping from revenues to the floor percentage.14
Thus, transaction costs and the possibility
of ex post adjustments matter. As in many other contractual environments, parties use
simple fractions (see Young and Burke 2001). Most contracts include the 90/10 provision,
and floors adjust by five or ten percentage points, not the one or two that marginal analysis
would suggest.15
Suppose that both parties agree on an interval that is likely to contain the
weekly revenues and choose the floor using (2.5) adjusted to the nearest whole 5%. Both
parties agree that if, once they observe revenue, they learn that their forecast was grossly in
error, they will re-evaluate the shares using (2.5). This contract economizes on transaction
costs if negotiating appropriate payments for unlikely contingencies ex ante is costly relative
to the expected cost associated with ex post adjustments (which takes into account both
how unlikely the contingency is and the ex post transaction costs). In this case, the model
yields two predictions:
Result 4: Ex post adjustments occur only when the movie performs much better or much
worse than expected.
Result 5: Adjustments favor the distributor when the movie performs better than expected
14
Occasionally more complex contingent shares are used and they have the features our model suggests:
the exhibitor’s share is higher when revenue is lower. Also, flexible deals allow the percentage shares to
depend on revenues.
15
Difficulties with forecasting partly account for coarse percentages. However, even flexible deals involve
coarse percentages. Thus, minimizing transaction costs associated with quibbling also plays a role.
12
and favor the exhibitor when the movie performs worse than expected.
This approach also explains the 90/10 provision. Ex post transaction costs would be
higher when there is more profit to fight over - when profit is low not much can be gained
by quibbling. In order to avoid high ex post transaction costs in the event of a surprise
blockbuster hit, industry participants have adopted the standard practice (and therefore the
low-transaction-cost practice) of including the 90/10 provision in most contracts.
Can a principal-agent model generate our results? Yes, but the required assumptions
are not realistic. We argued in Subsection 1.2 that exhibitors do not require contractual
incentives to incur effort. However, even if we assume incentives are required, in a principal
agent model the slope of the optimal sharing rule is steeper when it is more important for
the exhibitor to incur effort. Thus, Result 1 requires that exhibitor effort is more important
at low revenue levels. Results 2 and 3 require that exhibitor effort is more important in low
revenue weeks than in high revenue weeks, whereas the opposite is true in reality. Further,
typical piecewise linear sharing rules in principal agent models include flat payments from
one party to the other - shares provide incentives and the flat payment adjusts to ensure that
the agent receives its reservation utility. Other than the house nut in the 90/10 provision,
flat payments are not a feature of modern exhibition contracts.
2.2. The Measurement Costs Model
Here we show that a simple cooperative model that emphasizes ex ante and ex post measure-
ment difficulties can also generate Results 1-5. Again we consider one distributor with one
movie and one exhibitor with one auditorium, and we begin by considering a single week. To
ease the notational burden we suppress the time subscripts. Suppose both parties agree the
movie is expected to earn µ = µd +µe, where µd is the distributor’s contribution and µe is the
exhibitor’s contribution; µd is due to the movie per se, whereas µe is due to theater-specific
attributes, such as location, size, decor, and so on. Suppose there are two shocks that affect
revenue. The first is a movie-specific shock εd that does not depend on any theater-specific
attributes; it reflects difficulties with forecasting how the movie will be received by audiences
in general. The second shock ε accounts for all other sources of randomness that affect movie
attendance. The movie’s weekly revenue pn is given by
13
pn = µd + µe + εd + ε (2.6)
Now suppose the parties agree that the distributor should receive εd if it is positive and
compensate the exhibitor if it is negative. This is reasonable because εd depends on movie-
specific attributes provided by the distributor. However, ex post the parties cannot be sure
whether any gain or loss relative to µ is due to εd or ε. As neither party is responsible for
ε, suppose that both parties agree that shares of revenue should be based on the posterior
estimate of µd + εd and µe. To compute these, assume that both shocks are normally
distributed with zero means and variances σ2
d and σ2
. The posterior estimate of εd is
σ2
d(pn − µd − µe)
σ2
d + σ2
(2.7)
Given this, the exhibitor’s share of revenue (the posterior estimate of µe
µd+εd+µe
) is
(σ2
d + σ2
)µe
σ2(µd + µe) + σ2
dpn
(2.8)
Clearly (2.8) is decreasing in pn. Thus, the exhibitor’s share of revenue falls as revenue rises,
as in the real-world contract. If revenue is exactly µd +µe, the exhibitor gets the share µe
µd+µe
.
Other things equal, the exhibitor’s share is increasing in µe and σ2
and decreasing in µd and
σ2
d. If as the weeks go by, µd falls (as we argued in the previous subsection) and σ2
d falls (the
movie’s quality gets revealed), the exhibitor’s share of revenue rises. On the other hand, if
µd is anticipated to rise, then the model predicts a “best-weeks” clause would be used. Thus,
this model generates Results 1-3 discussed above.
As we argued in the previous subsection, transaction costs, preferences for simple frac-
tions, and the possibility of ex post adjustments affect contract terms. Suppose that both
parties initially agree to share revenue each week according to their ex ante estimates of rel-
ative contributions as given by µd and µe. That is, the contract specifies that the exhibitor
receives the share µe
µd+µe
. Both parties agree that if, once they observe revenues, they learn
that their forecast was grossly in error, they will re-evaluate the shares using (2.8). In this
case, the model yields Results 4 and 5.
14
2.3. Concession Revenue, Pricing, and Long-term Relationships
Why is concession revenue not shared? In the aggregate, concession revenue is close to
being a deterministic function of attendance. Given this, a sharing rule based on attendance
(or ticket revenue, given that p is fixed) approximates one that includes concession revenue.
Further, concession revenue is difficult for distributors to monitor, particularly in a multiplex
where it is difficult to attribute concession revenue to the various movies showing at once.
When the monitoring cost is weighed against the small gain from sharing concession revenue,
the parties prefer contracts based solely on ticket revenue.16
Interestingly, leaving concession revenue out of the contract creates a problem: the ex-
hibitor may wish to reduce p after signing the contract. Given the sharing rule, the exhibitor
bears only its percentage of lost ticket revenue but obtains the entire gain from increased
concession revenue that results from higher attendance. On the other hand, the distributor
wants to maximize ticket revenue and does not care about concession revenue, so it prefers
a higher p. The parties cannot set p in the contract because courts frown on vertical price
restraints.17
The parties could divorce the distributor’s payment from p by basing it on
attendance rather than ticket revenue, but such per capita clauses are rare (De Vany and
Eckert 1991; Friedberg 1992). The main solution relies on long-term relationships. The
exhibitor gains from adjusting p only if the adjustment occurs after the contract is signed.
If the exhibitor adjusts p before the contract is signed then the distributor will simply argue
to change the sharing rule to ensure that the exhibitor still gets its reservation utility. Given
that exhibitors include their proposed p in their initial bid, a subsequent adjustment would
violate an implicit contract. Such behavior could cause the distributor (and perhaps others
as well) to punish the exhibitor in the future. This encourages exhibitors to keep p up.
16
Including concession revenue and monitoring costs in the above models is straightforward. Consider the
risk sharing model. Denote concession revenue by c, the exhibitor’s share by wc(c), and assume that c is
a (possibly random) function of attendance n. Suppose that including concession revenue in the contract
requires a monitoring cost of m. In this case, the distributor compares its expected utility from maximizing
(2.1) subject to (2.2) to the modified problem where its utility is based on pn + c − w(pn) − wc(c) − m, the
exhibitor’s is based on w(pn) + wc(c), and the expected utility calculations consider any randomness in the
determination of c. Clearly if c is a deterministic function of n, there is no gain to including c in the contract.
Even if this is not the case, if m is sufficiently high the distributor prefers leaving c out of the contract.
17
Many economists believe that vertical restraints should be permitted, partly because they can help
resolve agency problems (for a discussion see Carlton and Perloff 1994).
15
3. Wehrenberg Theatres Contracts
Our data is provided by Wehrenberg Theatres of St. Louis, Missouri. To avoid revealing
proprietary information, all of the dollar values have been rescaled. Relative comparisons
across theaters, contracts, time, and so on are entirely valid, but the levels are deliber-
ately misstated. Further, we do not reveal distributor identities. Wehrenberg Theatres was
founded in 1906 and currently operates thirteen theaters of various sizes in the St. Louis
area, primarily in the suburbs. Wehrenberg has two main competitors that are concentrated
in the city center: AMC operates four theaters and St. Louis Cinema operates two. In
2001-2002, Wehrenberg accounted for approximately 68% of the ticket revenues collected by
the 19 theaters, which is in proportion to its capacity.
We have data on all movies playing in a Wehrenberg theater as of 8/31/01 and all movies
that opened on or before 5/8/03. There are 308 movies and 2,769 contracts (each theater
has a separate contract for each movie). Table 1 lists by theater the number of screens,
the seating capacity, the number of contracts (which equals the number of movies shown),
the average revenue per contract, and the exhibitor’s average percentage share. Table 1
also provides a breakdown of the contracts into the four possible types. Clearly, the vast
majority of contracts employ sliding scales, and most are firm term. The sharing rules vary
substantially by movie and theater. For example, in sliding scale deals the exhibitor’s share
ranges from 23-70% in week 1, 30-70% in week 4, and 40-70% in week 8. Run lengths depend
on performance and vary from 1 to 28 weeks in our data; the average run length is 5 weeks.
During the period, 21 distributors supply movies to Wehrenberg’s theaters. The group in-
cludes all of the large distributors (Buena Vista, Fox, Miramax, Paramount, Sony, Universal,
Warner Brothers) and several smaller ones. Table 2 lists by distributor the number of movies
placed in Wehrenberg’s theaters during the period and the total number of contracts. The
distributors are ranked according to the number of movies provided, from largest to smallest.
The fifth column shows that average ticket revenue per contract varies substantially across
distributors. This is to be expected; every movie is different, theater demographics differ,
seasons differ, competition from other movies differs, and so on. The seventh column shows
that distributors tend to receive slightly higher average shares when average ticket revenue
16
is higher. This is consistent with our models; distributors receive most of the gains when
ticket revenue is high. On average, the distributor obtains 54% of cumulative ticket revenue.
Table 2 also provides a breakdown of what types of contracts each distributor uses. Most
use sliding scale deals exclusively and display a preference for either firm or flexible deals.
3.1. Concave Sharing Rules
The 90/10 provision is included in 86% of the contracts. Thus, most contracts exhibit the
curvature suggested by Result 1 and depicted in Figures 1 and 2. However, 90/10 rarely
applies. The distributor was compensated according to 90/10 for at least one week during the
movie’s run in only 3% of the contracts. The provision applies for only the biggest hits in the
biggest theaters, and even then for only one or two weeks of the run. The set of contracts that
lack the 90/10 provision includes virtually all of the aggregate deals and a small percentage
of the sliding scale deals. In our models, the 90/10 provision is a transaction-cost minimizing
device that anticipates the adjustment that would occur if it were not in place. Given this,
our models suggest parties would leave the provision out only if they anticipate low revenues.
The data on sliding scale deals supports this view. For example, the average total revenue
per contract for sliding scale firm-term deals with the 90/10 provision is $22,455; for those
without, $13,233. This difference is statistically significant at the 1% level (t stat 4.87;
1% critical value 2.58). For brevity we do not report additional comparisons, but results
are similar for the sliding scale flexible deals and for comparisons at the theater level. We
discuss aggregate deals below in Subsection 3.4.
3.2. Revenue Sharing and Run Lengths
Our models suggest that revenue sharing is used to share risks and economize on measure-
ment costs. Of course, many sharing rules could accomplish these goals; Result 2 suggests
that sliding scale rules are prevalent because they provide the exhibitor with the incentive
to keep the movie longer. In this subsection, we evaluate this claim. We must be careful
when applying the models to data, because the models assume the exhibitor has a constant
reservation utility, whereas in a modern multiplex, U∗
e (or µe) changes systematically over
the life of the contract. When a movie first opens, it competes for the best auditoriums
17
and time slots; U∗
e is quite high. Later in the run, the movie is relegated to the smaller
auditoriums where the opportunity cost is much lower.18
However, our arguments here do
not depend on U∗
e being constant over a movie’s run.
We show that if run lengths are unaffected by the form of the sharing rule, the par-
ties could achieve essentially the same stream of revenues using a much simpler rule that
eliminates transaction costs. Table 3 reports results from OLS regressions of the exhibitor’s
portion of cumulative revenue for each movie (after any adjustment) on a constant and the
movie’s total revenue, by theater. By construction in OLS, the estimated residuals sum to
zero. Thus, each theater (and the distributors at each theater, as a group) would have re-
ceived exactly the same cumulative money payoffs during the sample period if compensated
using the regression line instead of the real-world contracts. The regression line suggests a
simple linear rule that would be constant across movies and time for each theater: a flat
fee (given by the constant term) plus a share of the movie’s revenue (given by the slope
coefficient). For example, for every movie placed in the Arnold theater, Wehrenberg could
require a flat payment of $876 and 42% of the movie’s revenues. There would be no need to
negotiate terms for each movie or adjust terms ex post.
If run lengths are unaffected, why not use this linear rule? Perhaps distributors would
not want a one-size-fits-all rule. We re-ran the regressions in Table 3 while including dummy
variables to allow the flat payments and shares to vary by distributor for the largest ten
distributors. Wald tests of the null hypothesis that all of these effects were zero were accepted
in all but one of the theaters. This suggests that each distributor’s cumulative revenues would
not be affected much by a switch to the linear rule, holding run lengths constant.
Another possibility is that while cumulative revenues would be unaffected, the flow of
revenues would be drastically altered. This is not the case. The R-squared is at least .95 in
every case in Table 3; the unexplained variance is small. As an example, Figure 3 compares
Wehrenberg’s weekly revenue from the Arnold theater to what it would have been under
our linear rule with the flat fee paid in four weekly installments. It is difficult to distinguish
18
The change in the opportunity cost is sometimes reflected in the house nut. The house nut declines over
the run in about 6% of the contracts. The decline in the nut also reflects the decline in the number of prints
(copies of the movie) required as the movie is shown less often.
18
the two cash flow series. Given the wide fluctuations in theater revenue, it is unlikely that
the relatively small deviations associated with switching to the linear rule would deter the
exhibitor from adopting it. Figure 4 shows that the same is true for Distributor 1 at the
Arnold theater. These figures are representative - moving from the relatively complex movie-
by-movie rules to our simple linear rule has little effect on any theater’s or distributor’s cash
flows, and the effects remain small when aggregated to the firm level.
Finally, note that our rule could only improve resource allocation, because it ensures
that the exhibitor optimizes by maximizing total revenue. Under the current sharing rules,
it is possible that more favorable terms on a worse movie might encourage the exhibitor to
allocate movies to screens and run times in an inefficient (non total-revenue maximizing)
way. In conclusion, it seems likely that the main reason why our simple linear rule is not
used is that such a rule would encourage the exhibitor to shorten the run length.
3.3. Best Weeks Clauses
Result 3 suggests that best weeks clauses are used when it is possible that movie performance
might improve over time. Best weeks clauses are relatively rare; they are used in only 8% of
the contracts in our data, and only five distributors use them during the period we examine.
Evidence supports Result 3. In contracts without best weeks clauses, weekly revenue peaks
after the opening week in only 6% of the cases. In contracts with best weeks clauses, weekly
revenue peaks after the opening week in 23% of the cases. This difference is statistically
significant at the 1% level (t stat 9.08, 1% critical value 2.58). Thus, there is a strong
positive correlation between the use of a best weeks clause and the likelihood that a movie
reaches its peak performance after the opening week.
3.4. Aggregate Deals
As we noted above, aggregate deals are used in a small percentage of cases. Table 2 shows
that three large, one medium size, and three small distributors occasionally use aggregate
deals; no distributor relies on them exclusively and most never use them. Practitioners tell
us that some distributors use aggregate deals when the evolution of revenue is particularly
difficult to predict, and our data supports this view. In such a case it may be difficult to
19
determine an appropriate schedule of floor payments in advance, and the distributor may
not want to leave the choice up to the exhibitor, as in a flexible sliding scale deal.19
For the
distributors who sometimes use aggregate deals, we computed the ratio of week2 to week1
total revenues and week3 to week1 total revenues for every contract. Then we grouped the
contracts into sliding scale deals vs. aggregate deals. While the mean ratios are virtually
identical (.63 vs. .64 for week2 to week1; .39 vs. .40 for week3 to week1) the variances are
substantially higher for aggregate deals (.040 vs. .053 for week2 to week1; .042 vs. .090 for
week3-week1). F tests of the null hypothesis that the variances are equal are rejected at the
1% level in both cases (F statistics: 1.32, 2.14; 1% critical value <1.30).
Our models suggest three implications of aggregate deals. First, an aggregate deal forces
the exhibitor to bear a greater share of downside risk; in our models the exhibitor would
have to be compensated with a greater share of the upside. This explains the absence of
the 90/10 provision in virtually all aggregate deals. Second, our risk sharing model suggests
that an exhibitor will accept non-optimal risk sharing only if it is compensated with higher
expected revenue. The data provides some support for this view. On average, the exhibitor
received lower revenue in sliding scale deals ($9,803 vs. $13,238 for aggregate deals) and the
same percentage share in both types of deals (46%). However, this finding is not conclusive
because movies with aggregate deals earned higher revenues and the exhibitor may have had
higher opportunity costs (see Subsection 3.2).20
Third, aggregate deals are similar to our
proposed linear deals; Result 2 suggests that they should lead to shorter runs. Unfortunately,
we cannot assess this because we cannot isolate the effect of deal type on run length. In our
data, average run lengths are slightly longer for movies with aggregate deals. However, this
occurs because movies with aggregate deals were on average better than others, not because
of the sharing rules.
19
A very small percentage of deals are “aggregate flexible,” but these may be based on an exceptionally
good long-term relationship between the distributor and the exhibitor. The next subsection shows that the
overwhelming majority of such deals are adjusted after the run is over.
20
Also, the finding does not mean that aggregate deals are used when expectations are high. For distribu-
tors who use aggregate deals there is no clear association between higher-than-average revenues and the use
of an aggregate deal.
20
3.5. Ex Post Adjustments
All types of deals may be adjusted, and adjustments may favor either party. Here we count
any departure from the initial schedule of floors or aggregate shares as an adjustment.21
This differs from actual adjustments only in that we count cases where 90/10 applies as
adjustments favoring the distributor. This is sensible given our models; the 90/10 provision
is a transaction-cost minimizing device that anticipates the adjustment that would occur if
it were not in place. Flexible deals are more likely to be adjusted. Given our definition, 11%
of sliding scale firm-term deals get adjusted, along with 41% of sliding scale flexible deals,
13% of aggregate firm-term deals, and 82% of aggregate flexible deals.
In our models, adjustments occur when a movie does much better or worse than expected.
Expectations vary by movie and theater and we cannot measure them directly. However,
we can assess Results 4 and 5 by comparing revenue outcomes. We divide contracts into
three categories: no adjustment, an adjustment favoring the distributor, and an adjustment
favoring the exhibitor. If the probability of an adjustment is independent of the initial
expectation, then the average expected revenue in each category should be the same in a
large sample. Given this, we can compare the average actual revenue in each category to
measure the departures from expectations. More realistically, departures from expectations
may be i.i.d. zero-mean shocks with heteroscedastic variances, where the variance tends to
be higher when expected revenue is higher. In this case, contracts with higher expected
revenues are more likely to be adjusted. However, even in this case we can assume that the
average expected revenue is the same in the two cases where adjustments occur.
The exhibitor’s average revenue per contract from sliding scale firm-term deals with
no adjustment is $8,933; with an adjustment favoring the distributor, $26,641; with an
adjustment favoring the exhibitor, $7,723. The last two averages are significantly different
at the 1% level (t stat 6.86; 1% critical value 2.58). The facts are consistent with Results
4 and 5: adjustments favor the distributor when the movie does much better than average
21
Measuring adjustments for flexible deals requires some subtlety. As we noted in Subsection 1.1, in
a flexible deal neither party expects to be compensated using the formal boilerplate terms. Instead, the
exhibitor determines the payments as revenue is observed, and we use this schedule as our measure of the
initial schedule rather than the boilerplate terms. Final settlements occur 21-30 days after the run is over,
and adjustments may occur at that point.
21
and favor the exhibitor when the movie does worse than average. Note that the average
revenue when adjustments favor the exhibitor is only slightly below the average revenue
when no adjustments occur. This is consistent with our argument in the previous paragraph
that adjustments are more likely when expectations are high. For brevity we do not report
additional comparisons, but results are similar for the other types of deals, comparisons at
the theater level, and comparisons where we exclude cases where 90/10 applies.
4. Conclusion
Our results suggest that exhibition contracts evolved to help distributors and exhibitors share
risks and overcome measurement problems and not to overcome asymmetric information
problems. Our models explain several contractual features including concave revenue sharing
rules, floors with sliding scales, best weeks clauses, and ex post adjustments. Other features
of the environment also affect contracts: concession revenue is not shared because it is
difficult for distributors to monitor, and long-run relationships that exhibitors have with
distributors and consumers explain several practices.
Why were modern sharing rules not used when the industry began? Initially movies
were low-cost non-differentiated products; demand was fairly predictable and the cash flow
consequences of a flop were not serious. Given this, the transaction costs associated with
complex sharing rules were not worth bearing. Revenue sharing requires that distributors
monitor exhibitors, and monitoring is worthwhile only if the expected benefit is sufficiently
high. Even after sound, cheap B movies and movies shown in third, fourth, and fifth run
theaters were not leased using sharing rules (Hanssen 2002). Over time movies became more
differentiated, budgets grew, and risks increased. This trend continued through the 1950s
when studios stopped making B movies in response to the emergence of television, and it
became more worthwhile to adopt increasingly complex sharing rules.
Although our models are designed to explain exhibition contracts in the movie business,
many of the insights obtained apply to other goods with large upfront costs and uncer-
tain demand such as new books and music. Other contracting environments also involve
unpredictability, two-sided risk aversion, and measurement costs. Future research should in-
22
vestigate the effects of two-sided risk aversion and measurement problems on contract terms
in greater depth and explain how levels of risk aversion differ across firms. An explana-
tion may require considering firm-level cash flows in a dynamic environment. Firms avoid
downside risks, and the stability of their cash flows determines how important it is to avoid
downside risks. In the movie exhibition market, exhibitors have less control over their cash
outflows than distributors. Operating a theater involves mainly fixed costs that cannot be
avoided without exiting. On the other hand, distributors can avoid costs by delaying new
movie projects, adjusting production and promotion budgets, sharing costs with outside in-
vestors, or compensating talent using sharing rules. These differences may make exhibitors
more reluctant to bear downside revenue risk than distributors.22
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27
28
Figure 3. Wehrenberg Weekly Revenue Comparison for Arnold Theater
Arnold Theater - Weekly Revenue Comparison
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
$40,000
8/31/01
9/30/01
10/31/01
11/30/01
12/31/01
1/31/02
2/28/02
3/31/02
4/30/02
5/31/02
6/30/02
7/31/02
8/31/02
9/30/02
10/31/02
11/30/02
12/31/02
1/31/03
2/28/03
3/31/03
4/30/03
Actual Linear Rule
Figure 4. Distributor 1 Weekly Revenue Comparison for Arnold Theater
Distributor 1 - Weekly Revenue Comparison
$0
$5,000
$10,000
$15,000
$20,000
$25,000
8/31/01
9/30/01
10/31/01
11/30/01
12/31/01
1/31/02
2/28/02
3/31/02
4/30/02
5/31/02
6/30/02
7/31/02
8/31/02
9/30/02
10/31/02
11/30/02
12/31/02
1/31/03
2/28/03
3/31/03
4/30/03
Actual Linear Rule
29
Table 1. Theater Characteristics
Percent of Contracts that are:
Average Exhibitor
Sliding Scale Aggregate
Theater Screens Total
Seating
Contracts
Revenue
per
contract*
% Share
per
contract
Firm Flexible Firm Flexible
Arnold 14 2,164 243 7,982 47 59 30 9 1
Clarkson 6 1,357 154 6,136 46 59 31 8 2
DesPeres 14 2,447 256 11,354 46 61 29 9 1
Eureka 6 1,039 179 2,021 49 61 28 10 1
HallsFerry 13 2,526 127 2,063 50 65 29 6 1
Jamestown 14 2,254 265 11,534 46 58 31 9 1
Kenrick 8 1,899 136 6,482 48 54 35 10 1
MidRivers 14 2,505 250 12,775 46 59 30 9 2
Northwest 9 2,254 223 3,035 48 58 30 11 1
O’Fallon 15 2,583 200 16,618 45 57 32 11 2
Ronnies 20 3,625 294 20,986 45 59 31 9 1
St. Charles 18 3,720 284 12,865 45 59 31 9 1
St. Clair 10 2,197 158 8,020 48 56 32 10 2
Wehrenberg 161 30,570 2,769 10,342 46 59 31 9 1
* Revenues have been rescaled to preserve proprietary information.
All percentages and rankings remain intact.
30
Table 2. Distributor Contract Characteristics
Distributors Total Average Percent of Contracts that are:
Total Distributor
Contracts Revenue Revenue Rental Sliding Scale Aggregate
per Rate
Movies Contracts movie
per
contract*
per
contract* (%) Firm Flexible Firm Flexible
1 44 441 10 21,184 11,643 55 98 2 0 0
2 39 396 10 21,772 12,243 56 71 1 29 0
3 34 325 10 30,567 16,685 55 12 88 0 0
4 34 336 10 24,789 14,063 57 99 1 0 0
5 31 202 7 15,627 7,773 50 12 77 0 10
6 27 275 10 16,727 8,414 50 25 75 0 0
7 27 229 8 24,191 13,175 54 96 0 4 0
8 19 166 9 34,717 18,522 53 25 0 75 0
9 14 131 9 13,586 7,199 53 8 92 0 0
10 13 130 10 31,683 17,866 56 100 0 0 0
11 7 47 7 4,830 2,145 44 40 43 17 0
12 5 41 8 8,401 3,998 48 20 56 0 24
13 3 8 3 16,049 7,351 46 88 13 0 0
14 2 14 7 46,714 21,881 47 0 57 0 43
15 2 10 5 3,767 1,729 46 100 0 0 0
16 2 3 2 16,566 7,514 45 33 67 0 0
17 1 2 2 6,128 2,778 45 0 100 0 0
18 1 1 1 5,505 2,697 49 0 100 0 0
19 1 4 4 1,180 526 45 100 0 0 0
20 1 3 3 2,592 1,192 46 0 100 0 0
21 1 5 5 12,715 5,295 42 0 100 0 0
All Distributors 308 2,769 9 22,651 12,309 54 59 31 9 1
* Revenues have been rescaled to preserve proprietary information.
All percentages and rankings remain intact.
31
Table 3. Linear Regressions
Dependent Variable: Exhibitor’s Cumulative Revenue per Movie by Theater
Table entry: Estimated Coefficient (White Std. Errors in parentheses)
Theater Constant
Total Revenue
per Movie by
Theater R2
Arnold 876*** 0.42*** 0.98
243 Obs (115) (0.0092)
Clarkson 274** 0.44*** 0.95
154 Obs (126) (0.015)
DesPeres 812*** 0.43*** 0.98
256 Obs (204) (0.012)
Eureka 109*** 0.46*** 0.97
179 Obs (31) (0.012)
HallsFerry 86* 0.48*** 0.98
127 Obs (45) (0.015)
Jamestown 853*** 0.43*** 0.97
265 Obs (197) (0.011)
Kenrick 339*** 0.46*** 0.98
136 Obs (93) (0.0095)
MidRivers 1,329*** 0.41*** 0.98
250 Obs (163) (0.0079)
Northwest 101** 0.47*** 0.97
223 Obs (49) (0.012)
O’Fallon 1,909*** 0.39*** 0.97
200 Obs (368) (0.013)
Ronnies 2,075*** 0.40*** 0.97
294 Obs (405) (0.012)
St. Charles 1,158*** 0.41*** 0.97
284 Obs (303) (0.015)
St. Clair 636*** 0.44*** 0.97
158 Obs (227) (0.018)
* Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level

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At The Movies The Economics Of Exhibition Contracts

  • 1. At the Movies: The Economics of Exhibition Contracts Darren Filson, David Switzer, and Portia Besocke∗ June 17, 2004 ∗ Contact info: Darren Filson (Corresponding Author), Associate Professor of Economics, Department of Economics, Claremont Graduate University, 160 E. Tenth St., Claremont, CA 91711; ph: (909) 621-8782; fax: (909) 621-8460; email: Darren.Filson@cgu.edu. David Switzer is a Ph.D. Candidate at Washington University in St. Louis. Portia Besocke is a Ph.D. Candidate at Claremont Graduate University. We thank Fernando Fabre, Alfredo Nava, and Paola Rodriguez for background research that contributed to this paper. We thank Darlene Chisholm and Doug Whitford for comments. Filson thanks the Fletcher Jones Foundation, the John M. Olin Foundation, and the National Association of Scholars for financial support.
  • 2. At the Movies: The Economics of Exhibition Contracts Abstract: We describe a real-world profit sharing contract - the movie exhibition contract - and consider alternative explanations for its use. Two explanations based on difficulties with forecasting fit the facts better than asymmetric information models. The first emphasizes two-sided risk aversion; the second emphasizes measurement costs. Transaction costs and long-term relationships also affect contractual practices. We use an original data set of all exhibition contracts involving thirteen theaters owned by a prominent St. Louis exhibitor over a two-year period to inform our theories and test hypotheses. JEL Codes: L14: Transactional Relationships and Contracts; D45: Licensing; L82: Indus- try Studies: Entertainment Keywords: risk sharing, relational contract, principal agent, motion picture, film 1. Introduction The literature on profit sharing stresses asymmetric information. Profit sharing occurs when a party has private information that cannot be credibly revealed or when a party’s actions cannot be observed. Economists avoid taste-based explanations such as risk sharing (Stigler and Becker 1977) and other factors that may lead to sharing, such as measurement costs. We describe a real-world sharing contract that is widely used - the movie exhibition contract - and argue that asymmetric information is not the main cause of sharing. Two explanations based on difficulties with forecasting revenue fit the facts better. The first is that movie distributors (studios or independent distributors) and exhibitors (theater owners) are both risk averse and exhibition contracts are designed to share risk. The second is that the sharing rules accompanied by ex post adjustments economize on measurement costs. Transaction costs and long-term relationships also affect contractual practices. We use an original data set of 2,769 exhibition contracts to inform our models and test hypotheses. The data includes all contracts involving thirteen theaters owned by Wehrenberg Theatres, a prominent St. Louis exhibitor, over roughly two years. Our models explain the sharing 2
  • 3. that occurs and the conditions that lead to adjustments, and our findings may be relevant for other contracting environments. Our explanations for the features of exhibition contracts complement those of De Vany and Eckert (1991) and De Vany and Walls (1996), who emphasize that difficulties with forecasting demand necessitate the use of short-term contingency-rich contracts. Some other work on non-exhibition aspects of the movie business also compares asymmetric information models to alternatives. Ravid (1999) tests and rejects a model of asymmetric information at the project selection stage. Ravid and Basuroy (2004) consider risk aversion at the project selection stage. Chisholm (1993, 1997) and Weinstein (1998) compare principal agent models to alternatives in studies of contracts between studios and talent. In the next subsection we describe the basic features of modern exhibition contracts.1 In the subsection after that we describe how the sharing rules evolved over time and argue that asymmetric information does not explain the sharing rules. Section 2 contains our models, Section 3 contains our empirical work, and Section 4 concludes. 1.1. The Modern Movie Exhibition Contract The unit of analysis for the contracts we describe is a single movie in a single theater (a theater is a building which may contain multiple auditoriums). While there is typically a boilerplate contract between each distributor and exhibitor that specifies general conditions that apply to all individual contracts, terms such as sharing rules and run lengths vary by movie and theater. A typical run ends after four to eight weeks, but the run may be adjusted after early revenues are observed, and holdover clauses may be used to extend the run as long as revenue is sufficiently high. De Vany and Eckert (1991) and De Vany and Walls (1996) attribute adjustable runs to imprecise forecasts. 1 Modern exhibition contracts have been described by De Vany and Eckert (1991), De Vany and Walls (1996), and Borcherding and Filson (2001). We also benefited from several conversations with industry participants, particularly D. Barry Reardon, past-president of Warner Bros. Distributing Corporation and a former executive of both Paramount Pictures and General Cinema Corporation; Doug Whitford, the executive at Wehrenberg Theatres in charge of negotiating film rental contracts; and Mike Doban of Trans- Lux Cinema Consulting. We also benefited from several articles in The Movie Business Book, edited by Jason E. Squire. The authors of the articles were, at the time of writing, prominent industry participants, and include among others D. Barry Reardon; Stanley H. Durwood, chairman and chief executive officer of AMC Entertainment Inc.; A. Alan Friedberg, chairman of Loews Theatres, a subsidiary of Sony Pictures Entertainment; and A. D. Murphy, financial editor and reporter for Daily Variety and Variety. 3
  • 4. We focus on explaining the peculiar revenue sharing rule that is common in modern contracts. While some contracts are “aggregate” deals, in which each side’s percentage share remains fixed throughout the run, most are “sliding scale” deals. In a sliding scale deal, each week, the distributor gets the maximum of two possible payments: 1) 90% of the movie’s weekly ticket revenue over the “house nut,” which is a flat payment to the exhibitor; 2) a “floor payment,” some percentage of the weekly ticket revenue that typically declines according to a “sliding scale” as the weeks go by - perhaps 70% in the first week, 60% by the third week, and as low as 30% at the end of the run. If the parties anticipate that revenue might peak in the second or later weeks (which can occur when the movie opens before a holiday weekend, for example) the contract includes a “best weeks” clause that ensures that the high floor payments are associated with the high demand weeks. Most of the time the floor is relevant; the 90/10 provision applies only for hits early in their runs. The exhibitor’s payoff function for one week associated with such a contract is graphed in Figure 1. Interestingly, concession revenue is not shared - the exhibitor gets it all. This is not a trivial oversight as concessions typically account for approximately half of an exhibitor’s profit. Most deals are “firm term,” which indicates that both parties expect to be compensated according to floors or aggregate shares that are specified at the beginning of the run. In contrast, “flexible” deals have boilerplate terms that are rarely enforced, and the exhibitor determines the appropriate shares as revenue is observed. In either case, terms may be adjusted during the run or after it ends; for flexible deals this is common and for firm deals it is rare. We ignore the firm-flexible distinction in most of our analysis and focus on explaining floors with sliding scales, best weeks clauses, the 90/10 provision, and the nature of adjustments. Other factors affect our modeling assumptions. Most contracts result from negotiations (Friedberg 1992; Reardon 1992). The courts favored competitive bidding at the time of the Paramount decrees of the 1940s and 50s, but even when bids are used they are points of departure for negotiations. Given this, our models emphasize bargaining rather than auctions. Friedberg (1992) notes that distributors cannot simply choose the “highest” bid because multiple factors matter, including local demographics, the location, and the decor. Exhibitor bids typically include 1) a schedule of ticket prices; 2) the number of shows for 4
  • 5. weekdays and weekends; and 3) the screen number and the number of seats in the auditorium in which the picture will play. Practitioners tell us these are guidelines only; 2) and 3) are difficult for distributors to monitor and none of the three terms are enforced. However, ticket prices are typically constant across movies and time at the theater level (except for daily matinee prices).2 Given this, ticket prices are exogenous in most of our analysis. Our models consider a theater with only one auditorium. Given this, we do not endogenize the allocation of movies to time slots and auditoriums.3 However, in our empirical analysis we consider the impacts of such allocation decisions. 1.2. The Evolution of Revenue Sharing Our conversations with practitioners (see fn.1) and historical analyses such as Hanssen (2000, 2002) allow us to describe how sharing rules evolved and explain why sliding scale rules are used today. Originally movies were short, silent, low-cost, relatively non-differentiated prod- ucts that were sold to exhibitors outright. As feature films were introduced, production budgets rose and consumers became more selective. Avoiding downside risk became impor- tant. Murphy (1992) notes that percentage rentals were introduced to “justify the investment risk.” With the arrival of sound in the late 1920s, production budgets rose more and the vari- ance of movie revenue (and profit) increased (Sedgwick and Pokorny 1998; Hanssen 2002). Revenue sharing became increasingly common.4 The distributor’s share of ticket revenue rose as budgets rose, from roughly 20% early on, to 25% in the 1920s, 33% in the 1960s, and 45% in the 1990s. Shares varied by movie and theater. 2 Practitioners provide several explanations for inflexible ticket prices. Exhibitors want to avoid menu costs and eliminate consumer uncertainty about what the movie will cost. Exhibitors do not increase prices of hits because they are engaged in repeat business with local consumers, and the potential loss of goodwill from increased prices outweighs the potential gain. Charging different prices for different movies at multiplexes necessitates employing monitors to ensure that consumers see the movies they pay for. Even offering mid- week discounts may lead to more time shifting than new demand. Not all analysts or practitioners agree that inflexible prices are optimal (see Orbach and Einav 2001), although it seems unlikely that such an easy- to-exploit profit opportunity would persist. Some practioners have experimented with non-uniform prices in the U.S. in the recent past but inflexible prices remain the norm. 3 Filson (2004) provides a dynamic model that includes allocation decisions within a multiplex. 4 Hanssen (2002) describes how exhibitor inputs were less important for big-budget movies, particularly after sound. As a result there was less need to make the exhibitor the residual claimant. However, this leaves open the question of why the distributor was not made the full residual claimant. Hanssen (2002) argues that exhibitors needed incentives to provide local inputs. We evaluate this argument below - it may have been relevant in the 1920s but does not appear to explain revenue sharing today. 5
  • 6. The modifications to the earliest sharing rules reflect attempts to minimize downside risk. As noted in the previous subsection, in most modern contracts the distributor gets 90% over the house nut during high-revenue weeks. The nut is a fixed dollar amount that the distributor pays to the exhibitor. It was originally included in contracts with the pretext of covering the exhibitor’s costs, which effectively limits the exhibitor’s downside. Murphy (1992) describes how floor payments originated in the late 1960s “as a countermove by distributors who questioned the validity of house nuts and who no longer could afford to absorb most of the losses incurred by a failed film.” A floor payment limits the distributor’s downside - it ensures that the distributor obtains some minimum percentage of the movie’s revenue. In practice, the floor is the relevant payment the vast majority of the time. In sum, practitioners claim that downside risk motivates revenue sharing. This is not surprising; several authors argue that risk is important in the movie industry (De Vany and Eckert 1991; De Vany and Walls 1996; Weinstein 1998; Borcherding and Filson 2001). Production involves high upfront sunk costs, and demand is difficult to predict.5 Historically, distributors and exhibitors relied heavily on internal financing, and neither party could afford to bear the full impact of a big-budget flop (Murphy 1992). As budgets rose and uncertainty increased, contracts became more sophisticated. In contrast, asymmetric information about movie quality does not appear to be impor- tant. As De Vany and Eckert (1991) and De Vany and Walls (1996, 1999) note, the main information problem is that it is difficult to forecast demand prior to release because every movie is different. This does not give one side an informational advantage. The movie must be shown in the theater, and then demand is discovered. De Vany and Eckert (1991) suggest that the information provided by pre-release screenings is quite limited - exhibitors often do not attend screenings. Further, evidence suggests that screenings do not affect sharing rules. Regulations vary by U.S. state: blind bidding ( contracting before screening) is allowed by some but not others. Where blind bidding is prohibited, distributors must allow exhibitors to see the movie before contracting (although exhibitors may choose not to go). The sharing 5 On average, movies made by Motion Picture Association of America members in 2003 had production, distribution, advertising, overhead and interest costs of $102.9 million. The average movie does not earn a positive return on investment - high profits on scarce hits make up for losses on the rest (Vogel 1998). 6
  • 7. rules are similar whether blind bidding is used or not.6 When comparing contracts in states with blind bidding to those in states without Blumenthal (1988) “focuses on the guarantee because it is the most variable element of the bid vector.” Is incentive provision important? Bhattacharyya and Lafontaine (1995) show that simple sharing rules can be optimal when double-sided moral hazard exists, and their model could be applied to movie exhibition. Distributors must advertise and promote the movie, while exhibitors must hire employees and do some local advertising. Neither party’s activities are easy to monitor. Kenney and Klein (1983) and Hanssen (2002) point out that sharing contracts provide incentives for exhibitors to keep theaters clean and take other actions which are hard to monitor but that may increase ticket revenue. Are these effects the reason for revenue sharing? We argue that long-run relationships between exhibitors, customers, and distributors reduce the need for incentive contracts - reputational concerns provide incentives. For example, an unclean theater loses repeat business with local consumers. Even if distributors paid flat payments to the exhibitor, a new contract would be negotiated each time a new movie was released. Thus, payments to the exhibitor could quickly fall if the theater lost customers. Therefore, the exhibitor would have the incentive to keep the theater clean in the absence of a sharing rule. Given this, the distributor could simply rent the auditorium at a flat rental rate using a “four walls” contract. Such contracts have been used, but only rarely - sharing is the norm. In our conversations with practitioners, we found absolutely no support for the notion that revenue sharing encourages theater cleanliness or any other standard good business practices.7 Our models of risk sharing and measurement costs are consistent with what firms claim 6 Exhibitor objections contributed to regulations prohibiting blind bidding, but De Vany and Eckert (1991 fn. 77) explain that the main objections were to bidding itself (exhibitors preferred negotiations) and the accompanying guarantees, which were nonrefundable upfront payments from the exhibitor to the distributor. Like many contract terms, guarantees were originally introduced to protect one party against downside risk. In this case, the risk was that the exhibitor would go bankrupt, taking the revenues and the print (the copy of the movie). Guarantees are almost never used today and even refundable advances (which ensure that the exhibitor does not lose money on a movie that does not earn its advance) are rare. Information technology facilitates paying the distributor early in the run from revenues, so advances are unnecessary. 7 Of course, building a reputation may not be costless, and it may take time before a new exhibitor develops a reputation for cleanliness and other good business practices. Our point is that the exhibitor has the incentive to bear these costs regardless of whether the exhibition contract uses a sharing rule or a flat payment; the exhibitor’s main concern is to build and maintain customer goodwill. 7
  • 8. they are doing and explanations based on asymmetric information appear inadequate. How- ever, our explanations raise a question. Given that movie-specific risk is primarily idiosyn- cratic, and given that standard finance theory suggests that firms should ignore idiosyncratic risk, why do distributors and exhibitors care about movie-specific risk? We note that in con- trast to standard finance theory, most firms care about idiosyncratic risk. For example, risk-neutral firms would not buy insurance because insurance premiums are not actuarially neutral, and yet corporations spend more money on insurance premiums than they pay out in dividends (Mayers and Smith 1982; Martin 1988). It is beyond the scope of this paper to explain why firms alleviate idiosyncratic risk, but most analyses emphasize stakeholder risk aversion (see Smith and Stulz 1985, De Alessi 1987, DeMarzo and Duffie 1995, and Tufano 1996). For example, insurance shifts the risk of bankruptcy away from employees who cannot diversify toward shareholders who can, and by doing so it encourages employees to make firm-specific investments. Alleviating idiosyncratic risk also encourages some share- holders to become large undiversified shareholders, who then perform monitoring that aids all investors. In the movie industry, revenue sharing contracts play this role.8 2. Models 2.1. The Risk Sharing Model Here we present a simple risk sharing model that explains the basic features of the sharing rule. The sharing rule evolved when single-auditorium theaters were the norm, and our model has one distributor with one movie and one exhibitor with one auditorium. We ignore costs and focus on revenues; this is reasonable because when the movie is placed in the theater most of the distributor’s costs are sunk and most of the exhibitor’s costs are fixed. For now, suppose the distributor designs a contract for a single week, and assume that both players take the ticket price p as given (this is reasonable; see Subsection 1.1). Given p and 8 Note that movie-specific risk may be very hard to insure using third-party insurance. Distributors and exhibitors have industry know-how that third parties lack, and as a result they may be best-suited to bear movie-specific risk. Dekom (1992) summarizes the industry attitude: “In the case of major studios, avoiding risks (by taking serious downside protection) is simply not a business plan.... If the management has insufficient confidence in its own abilities to choose and distribute motion pictures, perhaps they should find solace in another industry.” 8
  • 9. the theater’s weekly capacity N, attendance during the week, nt, is determined according to a probability density function Pr(nt|p, N, t). The game proceeds as follows: The distributor proposes a contract wt(pnt). If the exhibitor accepts the contract then it shows the movie and gets paid accordingly. If it does not accept the contract it receives its reservation utility U∗ e . In reality U∗ e would be determined by competiting distributors’ movies (opportunity costs), but for simplicity assume that U∗ e is exogenous. For now, ignore concession revenue; we discuss it below in Subsection 2.3. The distributor chooses wt(pnt) to maximize its expected utility subject to the exhibitor’s participation constraint: max {wt(pnt)}N nt=0 E(Ud) = N X nt=0 Ud(pnt − wt(pnt)) Pr(nt|p, N, t) (2.1) s.t. E(Ue) = N X nt=0 Ue(wt(pnt)) Pr(nt|p, N, t) ≥ U∗ e (2.2) where Ud(.) and Ue(.) are the distributor’s and exhibitor’s utility functions and E(.) is the expectation operator. The first-order condition implies that at each value of nt, U 0 d(pnt − wt(pnt)) = γtU 0 e(wt(pnt)), (2.3) where γt is the Lagrange multiplier.9 Differentiating both sides with respect to nt yields the slope of the revenue-sharing rule: w0 t(pnt) = U 00 d (pnt − wt(pnt)) U 00 d (pnt − wt(pnt)) + γtU00 e (wt(pnt)) (2.4) Risk aversion implies that the second derivatives of both utility functions are negative, 9 Expression (2.3) is similar to the first-order condition in several classic papers on risk sharing. Borch (1962) was the first to characterize the first-order condition for optimal risk sharing. Stiglitz (1974) and Leland (1978) consider constant relative risk aversion, which we discuss presently. A first-order condition similar to (2.3) can be derived from a Nash bargaining model where both players have exogenous outside options. Nash bargaining solves: max{wt(pnt)}N nt=0 [E(Ud)−U∗ d ]ψ [E(Ue)−U∗ e ]1−ψ , where U∗ d is the distributor’s reservation utility and ψ measures relative bargaining power. At the solution the marginal utilities are proportional to each other as in (2.3). Thus, the shape of the optimal contract does not depend critically on relative bargaining power. When ψ = 1, the distributor has all of the bargaining power and the problem is identical to solving (2.1) subject to (2.2). 9
  • 10. which implies that the slope of the sharing rule is positive. However, the change in the slope of the sharing rule as nt changes depends on the third derivatives of the utility functions. Thus, we require additional assumptions to replicate the pattern in Figure 1. Suppose both parties have constant relative risk aversion (CRRA): Ud(x) = xαd and Ue(x) = xαe , where x is money and αd and αe are the coefficients that measure relative risk aversion. These functions have several plausible properties: the marginal utility is positive and diminishing as long as αi < 1; relative risk aversion, −xU00(x) U0(x) , is constant; and absolute risk aversion, −U00(x) U0(x) , is decreasing in the money payoff as long as αi < 1. We follow the standard principal-agent model (Mas-Colell et al. 1995) and assume that the contract proposer (the distributor) is less risk averse than the other party (the exhibitor). However, we assume that both parties are risk averse.10 Assumption 1: Both parties have CRRA utility and 0 < αe < αd < 1. Assumption 1 must hold for the results in the remainder of this subsection. We do not have a strong justification for the assumption that αe < αd, but it seems reasonable. We argue in the conclusion that distributors have a higher ratio of variable to fixed costs and are in a better position to absorb fluctuations in revenue. Further, distributors often reduce their exposure on particular movies through co-production arrangements and revenue sharing contracts with talent; exhibitors lack similar options. Note that the principal-agent model also lacks a strong justification for the assumption that the principal is less risk averse than the agent in many settings where it is used. However, this does not imply that the models are not useful, and even when we cannot verify assumptions we can evaluate models based on how well they predict. Our simple model predicts many of the features of modern exhibition contracts. Given Assumption 1, (2.4) can be expressed as w0 t(pnt) = 1 1 + h γtαe αd i 1 αd−1 αe−1 αd−1 wt(pnt) αe−αd αd−1 . (2.5) Expression (2.5) shows that wt(pnt) is increasing in pnt and that w0 t(pnt) diminishes as 10 If both players are risk neutral then neither cares about who bears the risk and an infinite variety of contracts are optimal. If one player is risk neutral and the other is risk averse then it is optimal for the risk neutral player to bear all of the risk and pay the other a flat payment. 10
  • 11. wt(pnt) rises. Thus, the optimal sharing rule has a diminishing slope as in Figure 1.11 We cannot solve for wt(pnt) analytically, but we can compute it numerically using realistic parameters for urban theaters (provided by the sources in fn. 1). Assume the capacity per showing is 250 and there are four showings per day: N = 7000. The ticket price p = 7. Given N and p, the maximum weekly ticket revenue is $49,000. Friedberg (1992) and Murphy (1992) suggest this is a reasonable upper bound. We choose the remaining parameters, αd, αe, and γt, in a rough attempt to minimize the distance between the payments the model generates and the real-world payments graphed in Figure 1. We set αd = .75, αe = .5, and γt = 21. The optimal contract is graphed in Figure 2. Result 1: The optimal sharing rule has a diminishing slope, as in the real-world contract. If we consider multiple weeks, the model explains floors with sliding scales and best-weeks clauses. Suppose that at the beginning of the movie’s run, the distributor solves (2.1) for each week based on a forecast of Pr(nt|p, N, t). The solution is an optimal wt(pnt) for each week. Typically, pnt is expected to fall over time.12 Given this, the exhibitor’s share of pnt must rise in order for the exhibitor to obtain utility U∗ e each week. Formally, this results from an increase in γt, since the risk aversion parameters, prices, and other variables do not change over the life of the movie.13 When γt rises the entire sharing rule becomes steeper, whereas in reality only the floors do and the 90/10 split remains a possibility. However, the floor is virtually always the relevant payment in later weeks so it is reasonable that the real-world parties reduce transaction costs by not revising the 90/10 provision. Result 2: If attendance is expected to fall over time, the exhibitor’s share of revenue must rise over time in order for the exhibitor to obtain utility U∗ e each week (which is required in order for the exhibitor to continue to show the movie). Thus, the optimal sharing rule has a floor with a sliding scale, as in the real-world contract. Result 3: If attendance is expected to peak in the second or later weeks, the exhibitor’s share of revenue initially falls and then rises in order for the exhibitor to obtain utility U∗ e 11 Note that if αd = αe, the exhibitor receives a constant share of ticket revenue. 12 De Vany and Eckert (1991), De Vany and Walls (1996, 1999), Sawhney and Eliashberg (1996), and Eliashberg et al. (2000) examine time series of ticket revenue. Typically, revenue per screen falls over time. 13 The multiplier γt measures the distributor’s expected marginal utility from a change in U∗ e . During low-revenue weeks the distributor gets less revenue at each level of nt (because the exhibitor must continue to receive U∗ e ). Given that Ud(.) is strictly concave, this implies that γt is higher during such weeks. 11
  • 12. each week. Thus, the optimal sharing rule explains the “best weeks” clause that occasionally appears in real-world contracts. Note that even with perfect forecasting, the exhibitor’s payment would adjust over time in order for it to obtain U∗ e . Thus, conditional on sharing, adjustments to the floor may be explained by exhibitor opportunity costs that are roughly constant. However, with per- fect forecasting there would be no need for revenue sharing; flat payments would suffice. Even with unpredictability, if one party is not risk averse, a flat payment could be used to compensate the risk averse party, as long as the appropriate payment could be calculated. In sum, our model explains concave sharing rules, floor payments with sliding scales, and best weeks clauses. However, the real-world contract is simpler than the model so far suggests because it typically specifies a single floor percentage for each week rather than a mapping from revenues to the floor percentage.14 Thus, transaction costs and the possibility of ex post adjustments matter. As in many other contractual environments, parties use simple fractions (see Young and Burke 2001). Most contracts include the 90/10 provision, and floors adjust by five or ten percentage points, not the one or two that marginal analysis would suggest.15 Suppose that both parties agree on an interval that is likely to contain the weekly revenues and choose the floor using (2.5) adjusted to the nearest whole 5%. Both parties agree that if, once they observe revenue, they learn that their forecast was grossly in error, they will re-evaluate the shares using (2.5). This contract economizes on transaction costs if negotiating appropriate payments for unlikely contingencies ex ante is costly relative to the expected cost associated with ex post adjustments (which takes into account both how unlikely the contingency is and the ex post transaction costs). In this case, the model yields two predictions: Result 4: Ex post adjustments occur only when the movie performs much better or much worse than expected. Result 5: Adjustments favor the distributor when the movie performs better than expected 14 Occasionally more complex contingent shares are used and they have the features our model suggests: the exhibitor’s share is higher when revenue is lower. Also, flexible deals allow the percentage shares to depend on revenues. 15 Difficulties with forecasting partly account for coarse percentages. However, even flexible deals involve coarse percentages. Thus, minimizing transaction costs associated with quibbling also plays a role. 12
  • 13. and favor the exhibitor when the movie performs worse than expected. This approach also explains the 90/10 provision. Ex post transaction costs would be higher when there is more profit to fight over - when profit is low not much can be gained by quibbling. In order to avoid high ex post transaction costs in the event of a surprise blockbuster hit, industry participants have adopted the standard practice (and therefore the low-transaction-cost practice) of including the 90/10 provision in most contracts. Can a principal-agent model generate our results? Yes, but the required assumptions are not realistic. We argued in Subsection 1.2 that exhibitors do not require contractual incentives to incur effort. However, even if we assume incentives are required, in a principal agent model the slope of the optimal sharing rule is steeper when it is more important for the exhibitor to incur effort. Thus, Result 1 requires that exhibitor effort is more important at low revenue levels. Results 2 and 3 require that exhibitor effort is more important in low revenue weeks than in high revenue weeks, whereas the opposite is true in reality. Further, typical piecewise linear sharing rules in principal agent models include flat payments from one party to the other - shares provide incentives and the flat payment adjusts to ensure that the agent receives its reservation utility. Other than the house nut in the 90/10 provision, flat payments are not a feature of modern exhibition contracts. 2.2. The Measurement Costs Model Here we show that a simple cooperative model that emphasizes ex ante and ex post measure- ment difficulties can also generate Results 1-5. Again we consider one distributor with one movie and one exhibitor with one auditorium, and we begin by considering a single week. To ease the notational burden we suppress the time subscripts. Suppose both parties agree the movie is expected to earn µ = µd +µe, where µd is the distributor’s contribution and µe is the exhibitor’s contribution; µd is due to the movie per se, whereas µe is due to theater-specific attributes, such as location, size, decor, and so on. Suppose there are two shocks that affect revenue. The first is a movie-specific shock εd that does not depend on any theater-specific attributes; it reflects difficulties with forecasting how the movie will be received by audiences in general. The second shock ε accounts for all other sources of randomness that affect movie attendance. The movie’s weekly revenue pn is given by 13
  • 14. pn = µd + µe + εd + ε (2.6) Now suppose the parties agree that the distributor should receive εd if it is positive and compensate the exhibitor if it is negative. This is reasonable because εd depends on movie- specific attributes provided by the distributor. However, ex post the parties cannot be sure whether any gain or loss relative to µ is due to εd or ε. As neither party is responsible for ε, suppose that both parties agree that shares of revenue should be based on the posterior estimate of µd + εd and µe. To compute these, assume that both shocks are normally distributed with zero means and variances σ2 d and σ2 . The posterior estimate of εd is σ2 d(pn − µd − µe) σ2 d + σ2 (2.7) Given this, the exhibitor’s share of revenue (the posterior estimate of µe µd+εd+µe ) is (σ2 d + σ2 )µe σ2(µd + µe) + σ2 dpn (2.8) Clearly (2.8) is decreasing in pn. Thus, the exhibitor’s share of revenue falls as revenue rises, as in the real-world contract. If revenue is exactly µd +µe, the exhibitor gets the share µe µd+µe . Other things equal, the exhibitor’s share is increasing in µe and σ2 and decreasing in µd and σ2 d. If as the weeks go by, µd falls (as we argued in the previous subsection) and σ2 d falls (the movie’s quality gets revealed), the exhibitor’s share of revenue rises. On the other hand, if µd is anticipated to rise, then the model predicts a “best-weeks” clause would be used. Thus, this model generates Results 1-3 discussed above. As we argued in the previous subsection, transaction costs, preferences for simple frac- tions, and the possibility of ex post adjustments affect contract terms. Suppose that both parties initially agree to share revenue each week according to their ex ante estimates of rel- ative contributions as given by µd and µe. That is, the contract specifies that the exhibitor receives the share µe µd+µe . Both parties agree that if, once they observe revenues, they learn that their forecast was grossly in error, they will re-evaluate the shares using (2.8). In this case, the model yields Results 4 and 5. 14
  • 15. 2.3. Concession Revenue, Pricing, and Long-term Relationships Why is concession revenue not shared? In the aggregate, concession revenue is close to being a deterministic function of attendance. Given this, a sharing rule based on attendance (or ticket revenue, given that p is fixed) approximates one that includes concession revenue. Further, concession revenue is difficult for distributors to monitor, particularly in a multiplex where it is difficult to attribute concession revenue to the various movies showing at once. When the monitoring cost is weighed against the small gain from sharing concession revenue, the parties prefer contracts based solely on ticket revenue.16 Interestingly, leaving concession revenue out of the contract creates a problem: the ex- hibitor may wish to reduce p after signing the contract. Given the sharing rule, the exhibitor bears only its percentage of lost ticket revenue but obtains the entire gain from increased concession revenue that results from higher attendance. On the other hand, the distributor wants to maximize ticket revenue and does not care about concession revenue, so it prefers a higher p. The parties cannot set p in the contract because courts frown on vertical price restraints.17 The parties could divorce the distributor’s payment from p by basing it on attendance rather than ticket revenue, but such per capita clauses are rare (De Vany and Eckert 1991; Friedberg 1992). The main solution relies on long-term relationships. The exhibitor gains from adjusting p only if the adjustment occurs after the contract is signed. If the exhibitor adjusts p before the contract is signed then the distributor will simply argue to change the sharing rule to ensure that the exhibitor still gets its reservation utility. Given that exhibitors include their proposed p in their initial bid, a subsequent adjustment would violate an implicit contract. Such behavior could cause the distributor (and perhaps others as well) to punish the exhibitor in the future. This encourages exhibitors to keep p up. 16 Including concession revenue and monitoring costs in the above models is straightforward. Consider the risk sharing model. Denote concession revenue by c, the exhibitor’s share by wc(c), and assume that c is a (possibly random) function of attendance n. Suppose that including concession revenue in the contract requires a monitoring cost of m. In this case, the distributor compares its expected utility from maximizing (2.1) subject to (2.2) to the modified problem where its utility is based on pn + c − w(pn) − wc(c) − m, the exhibitor’s is based on w(pn) + wc(c), and the expected utility calculations consider any randomness in the determination of c. Clearly if c is a deterministic function of n, there is no gain to including c in the contract. Even if this is not the case, if m is sufficiently high the distributor prefers leaving c out of the contract. 17 Many economists believe that vertical restraints should be permitted, partly because they can help resolve agency problems (for a discussion see Carlton and Perloff 1994). 15
  • 16. 3. Wehrenberg Theatres Contracts Our data is provided by Wehrenberg Theatres of St. Louis, Missouri. To avoid revealing proprietary information, all of the dollar values have been rescaled. Relative comparisons across theaters, contracts, time, and so on are entirely valid, but the levels are deliber- ately misstated. Further, we do not reveal distributor identities. Wehrenberg Theatres was founded in 1906 and currently operates thirteen theaters of various sizes in the St. Louis area, primarily in the suburbs. Wehrenberg has two main competitors that are concentrated in the city center: AMC operates four theaters and St. Louis Cinema operates two. In 2001-2002, Wehrenberg accounted for approximately 68% of the ticket revenues collected by the 19 theaters, which is in proportion to its capacity. We have data on all movies playing in a Wehrenberg theater as of 8/31/01 and all movies that opened on or before 5/8/03. There are 308 movies and 2,769 contracts (each theater has a separate contract for each movie). Table 1 lists by theater the number of screens, the seating capacity, the number of contracts (which equals the number of movies shown), the average revenue per contract, and the exhibitor’s average percentage share. Table 1 also provides a breakdown of the contracts into the four possible types. Clearly, the vast majority of contracts employ sliding scales, and most are firm term. The sharing rules vary substantially by movie and theater. For example, in sliding scale deals the exhibitor’s share ranges from 23-70% in week 1, 30-70% in week 4, and 40-70% in week 8. Run lengths depend on performance and vary from 1 to 28 weeks in our data; the average run length is 5 weeks. During the period, 21 distributors supply movies to Wehrenberg’s theaters. The group in- cludes all of the large distributors (Buena Vista, Fox, Miramax, Paramount, Sony, Universal, Warner Brothers) and several smaller ones. Table 2 lists by distributor the number of movies placed in Wehrenberg’s theaters during the period and the total number of contracts. The distributors are ranked according to the number of movies provided, from largest to smallest. The fifth column shows that average ticket revenue per contract varies substantially across distributors. This is to be expected; every movie is different, theater demographics differ, seasons differ, competition from other movies differs, and so on. The seventh column shows that distributors tend to receive slightly higher average shares when average ticket revenue 16
  • 17. is higher. This is consistent with our models; distributors receive most of the gains when ticket revenue is high. On average, the distributor obtains 54% of cumulative ticket revenue. Table 2 also provides a breakdown of what types of contracts each distributor uses. Most use sliding scale deals exclusively and display a preference for either firm or flexible deals. 3.1. Concave Sharing Rules The 90/10 provision is included in 86% of the contracts. Thus, most contracts exhibit the curvature suggested by Result 1 and depicted in Figures 1 and 2. However, 90/10 rarely applies. The distributor was compensated according to 90/10 for at least one week during the movie’s run in only 3% of the contracts. The provision applies for only the biggest hits in the biggest theaters, and even then for only one or two weeks of the run. The set of contracts that lack the 90/10 provision includes virtually all of the aggregate deals and a small percentage of the sliding scale deals. In our models, the 90/10 provision is a transaction-cost minimizing device that anticipates the adjustment that would occur if it were not in place. Given this, our models suggest parties would leave the provision out only if they anticipate low revenues. The data on sliding scale deals supports this view. For example, the average total revenue per contract for sliding scale firm-term deals with the 90/10 provision is $22,455; for those without, $13,233. This difference is statistically significant at the 1% level (t stat 4.87; 1% critical value 2.58). For brevity we do not report additional comparisons, but results are similar for the sliding scale flexible deals and for comparisons at the theater level. We discuss aggregate deals below in Subsection 3.4. 3.2. Revenue Sharing and Run Lengths Our models suggest that revenue sharing is used to share risks and economize on measure- ment costs. Of course, many sharing rules could accomplish these goals; Result 2 suggests that sliding scale rules are prevalent because they provide the exhibitor with the incentive to keep the movie longer. In this subsection, we evaluate this claim. We must be careful when applying the models to data, because the models assume the exhibitor has a constant reservation utility, whereas in a modern multiplex, U∗ e (or µe) changes systematically over the life of the contract. When a movie first opens, it competes for the best auditoriums 17
  • 18. and time slots; U∗ e is quite high. Later in the run, the movie is relegated to the smaller auditoriums where the opportunity cost is much lower.18 However, our arguments here do not depend on U∗ e being constant over a movie’s run. We show that if run lengths are unaffected by the form of the sharing rule, the par- ties could achieve essentially the same stream of revenues using a much simpler rule that eliminates transaction costs. Table 3 reports results from OLS regressions of the exhibitor’s portion of cumulative revenue for each movie (after any adjustment) on a constant and the movie’s total revenue, by theater. By construction in OLS, the estimated residuals sum to zero. Thus, each theater (and the distributors at each theater, as a group) would have re- ceived exactly the same cumulative money payoffs during the sample period if compensated using the regression line instead of the real-world contracts. The regression line suggests a simple linear rule that would be constant across movies and time for each theater: a flat fee (given by the constant term) plus a share of the movie’s revenue (given by the slope coefficient). For example, for every movie placed in the Arnold theater, Wehrenberg could require a flat payment of $876 and 42% of the movie’s revenues. There would be no need to negotiate terms for each movie or adjust terms ex post. If run lengths are unaffected, why not use this linear rule? Perhaps distributors would not want a one-size-fits-all rule. We re-ran the regressions in Table 3 while including dummy variables to allow the flat payments and shares to vary by distributor for the largest ten distributors. Wald tests of the null hypothesis that all of these effects were zero were accepted in all but one of the theaters. This suggests that each distributor’s cumulative revenues would not be affected much by a switch to the linear rule, holding run lengths constant. Another possibility is that while cumulative revenues would be unaffected, the flow of revenues would be drastically altered. This is not the case. The R-squared is at least .95 in every case in Table 3; the unexplained variance is small. As an example, Figure 3 compares Wehrenberg’s weekly revenue from the Arnold theater to what it would have been under our linear rule with the flat fee paid in four weekly installments. It is difficult to distinguish 18 The change in the opportunity cost is sometimes reflected in the house nut. The house nut declines over the run in about 6% of the contracts. The decline in the nut also reflects the decline in the number of prints (copies of the movie) required as the movie is shown less often. 18
  • 19. the two cash flow series. Given the wide fluctuations in theater revenue, it is unlikely that the relatively small deviations associated with switching to the linear rule would deter the exhibitor from adopting it. Figure 4 shows that the same is true for Distributor 1 at the Arnold theater. These figures are representative - moving from the relatively complex movie- by-movie rules to our simple linear rule has little effect on any theater’s or distributor’s cash flows, and the effects remain small when aggregated to the firm level. Finally, note that our rule could only improve resource allocation, because it ensures that the exhibitor optimizes by maximizing total revenue. Under the current sharing rules, it is possible that more favorable terms on a worse movie might encourage the exhibitor to allocate movies to screens and run times in an inefficient (non total-revenue maximizing) way. In conclusion, it seems likely that the main reason why our simple linear rule is not used is that such a rule would encourage the exhibitor to shorten the run length. 3.3. Best Weeks Clauses Result 3 suggests that best weeks clauses are used when it is possible that movie performance might improve over time. Best weeks clauses are relatively rare; they are used in only 8% of the contracts in our data, and only five distributors use them during the period we examine. Evidence supports Result 3. In contracts without best weeks clauses, weekly revenue peaks after the opening week in only 6% of the cases. In contracts with best weeks clauses, weekly revenue peaks after the opening week in 23% of the cases. This difference is statistically significant at the 1% level (t stat 9.08, 1% critical value 2.58). Thus, there is a strong positive correlation between the use of a best weeks clause and the likelihood that a movie reaches its peak performance after the opening week. 3.4. Aggregate Deals As we noted above, aggregate deals are used in a small percentage of cases. Table 2 shows that three large, one medium size, and three small distributors occasionally use aggregate deals; no distributor relies on them exclusively and most never use them. Practitioners tell us that some distributors use aggregate deals when the evolution of revenue is particularly difficult to predict, and our data supports this view. In such a case it may be difficult to 19
  • 20. determine an appropriate schedule of floor payments in advance, and the distributor may not want to leave the choice up to the exhibitor, as in a flexible sliding scale deal.19 For the distributors who sometimes use aggregate deals, we computed the ratio of week2 to week1 total revenues and week3 to week1 total revenues for every contract. Then we grouped the contracts into sliding scale deals vs. aggregate deals. While the mean ratios are virtually identical (.63 vs. .64 for week2 to week1; .39 vs. .40 for week3 to week1) the variances are substantially higher for aggregate deals (.040 vs. .053 for week2 to week1; .042 vs. .090 for week3-week1). F tests of the null hypothesis that the variances are equal are rejected at the 1% level in both cases (F statistics: 1.32, 2.14; 1% critical value <1.30). Our models suggest three implications of aggregate deals. First, an aggregate deal forces the exhibitor to bear a greater share of downside risk; in our models the exhibitor would have to be compensated with a greater share of the upside. This explains the absence of the 90/10 provision in virtually all aggregate deals. Second, our risk sharing model suggests that an exhibitor will accept non-optimal risk sharing only if it is compensated with higher expected revenue. The data provides some support for this view. On average, the exhibitor received lower revenue in sliding scale deals ($9,803 vs. $13,238 for aggregate deals) and the same percentage share in both types of deals (46%). However, this finding is not conclusive because movies with aggregate deals earned higher revenues and the exhibitor may have had higher opportunity costs (see Subsection 3.2).20 Third, aggregate deals are similar to our proposed linear deals; Result 2 suggests that they should lead to shorter runs. Unfortunately, we cannot assess this because we cannot isolate the effect of deal type on run length. In our data, average run lengths are slightly longer for movies with aggregate deals. However, this occurs because movies with aggregate deals were on average better than others, not because of the sharing rules. 19 A very small percentage of deals are “aggregate flexible,” but these may be based on an exceptionally good long-term relationship between the distributor and the exhibitor. The next subsection shows that the overwhelming majority of such deals are adjusted after the run is over. 20 Also, the finding does not mean that aggregate deals are used when expectations are high. For distribu- tors who use aggregate deals there is no clear association between higher-than-average revenues and the use of an aggregate deal. 20
  • 21. 3.5. Ex Post Adjustments All types of deals may be adjusted, and adjustments may favor either party. Here we count any departure from the initial schedule of floors or aggregate shares as an adjustment.21 This differs from actual adjustments only in that we count cases where 90/10 applies as adjustments favoring the distributor. This is sensible given our models; the 90/10 provision is a transaction-cost minimizing device that anticipates the adjustment that would occur if it were not in place. Flexible deals are more likely to be adjusted. Given our definition, 11% of sliding scale firm-term deals get adjusted, along with 41% of sliding scale flexible deals, 13% of aggregate firm-term deals, and 82% of aggregate flexible deals. In our models, adjustments occur when a movie does much better or worse than expected. Expectations vary by movie and theater and we cannot measure them directly. However, we can assess Results 4 and 5 by comparing revenue outcomes. We divide contracts into three categories: no adjustment, an adjustment favoring the distributor, and an adjustment favoring the exhibitor. If the probability of an adjustment is independent of the initial expectation, then the average expected revenue in each category should be the same in a large sample. Given this, we can compare the average actual revenue in each category to measure the departures from expectations. More realistically, departures from expectations may be i.i.d. zero-mean shocks with heteroscedastic variances, where the variance tends to be higher when expected revenue is higher. In this case, contracts with higher expected revenues are more likely to be adjusted. However, even in this case we can assume that the average expected revenue is the same in the two cases where adjustments occur. The exhibitor’s average revenue per contract from sliding scale firm-term deals with no adjustment is $8,933; with an adjustment favoring the distributor, $26,641; with an adjustment favoring the exhibitor, $7,723. The last two averages are significantly different at the 1% level (t stat 6.86; 1% critical value 2.58). The facts are consistent with Results 4 and 5: adjustments favor the distributor when the movie does much better than average 21 Measuring adjustments for flexible deals requires some subtlety. As we noted in Subsection 1.1, in a flexible deal neither party expects to be compensated using the formal boilerplate terms. Instead, the exhibitor determines the payments as revenue is observed, and we use this schedule as our measure of the initial schedule rather than the boilerplate terms. Final settlements occur 21-30 days after the run is over, and adjustments may occur at that point. 21
  • 22. and favor the exhibitor when the movie does worse than average. Note that the average revenue when adjustments favor the exhibitor is only slightly below the average revenue when no adjustments occur. This is consistent with our argument in the previous paragraph that adjustments are more likely when expectations are high. For brevity we do not report additional comparisons, but results are similar for the other types of deals, comparisons at the theater level, and comparisons where we exclude cases where 90/10 applies. 4. Conclusion Our results suggest that exhibition contracts evolved to help distributors and exhibitors share risks and overcome measurement problems and not to overcome asymmetric information problems. Our models explain several contractual features including concave revenue sharing rules, floors with sliding scales, best weeks clauses, and ex post adjustments. Other features of the environment also affect contracts: concession revenue is not shared because it is difficult for distributors to monitor, and long-run relationships that exhibitors have with distributors and consumers explain several practices. Why were modern sharing rules not used when the industry began? Initially movies were low-cost non-differentiated products; demand was fairly predictable and the cash flow consequences of a flop were not serious. Given this, the transaction costs associated with complex sharing rules were not worth bearing. Revenue sharing requires that distributors monitor exhibitors, and monitoring is worthwhile only if the expected benefit is sufficiently high. Even after sound, cheap B movies and movies shown in third, fourth, and fifth run theaters were not leased using sharing rules (Hanssen 2002). Over time movies became more differentiated, budgets grew, and risks increased. This trend continued through the 1950s when studios stopped making B movies in response to the emergence of television, and it became more worthwhile to adopt increasingly complex sharing rules. Although our models are designed to explain exhibition contracts in the movie business, many of the insights obtained apply to other goods with large upfront costs and uncer- tain demand such as new books and music. Other contracting environments also involve unpredictability, two-sided risk aversion, and measurement costs. Future research should in- 22
  • 23. vestigate the effects of two-sided risk aversion and measurement problems on contract terms in greater depth and explain how levels of risk aversion differ across firms. An explana- tion may require considering firm-level cash flows in a dynamic environment. Firms avoid downside risks, and the stability of their cash flows determines how important it is to avoid downside risks. In the movie exhibition market, exhibitors have less control over their cash outflows than distributors. Operating a theater involves mainly fixed costs that cannot be avoided without exiting. On the other hand, distributors can avoid costs by delaying new movie projects, adjusting production and promotion budgets, sharing costs with outside in- vestors, or compensating talent using sharing rules. These differences may make exhibitors more reluctant to bear downside revenue risk than distributors.22 References [1] Ackerberg, D.A. and M. Botticini. “Endogenous Matching and the Empirical Determi- nants of Contract Form” Journal of Political Economy 110 no.3 (June 2002): 564-91. [2] Bhattacharyya, S., and F. Lafontaine. “Double-Sided Moral Hazard and the Nature of Share Contracts” RAND Journal of Economics 26 no.4 (Winter 1995): 761-781. [3] Blumenthal, M.A. “Auctions with Constrained Information: Blind Bidding for Motion Pictures” The Review of Economics and Statistics (1988): 191-198. [4] Borch, K. “Equilibrium in a Reinsurance Market” Econometrica 30 (1962): 424-44. [5] Borcherding, T.E., and D. Filson. “Conflicts of Interest in the Hollywood Film Industry: Coming to America - Tales from the Casting Couch, Gross and Net, in a Risky Business” in Davis, M., and A. Stark, eds. Conflict of Interest in the Professions (New York: Oxford University Press, 2001). [6] Carlton, D.W., and J.M. Perloff. Modern Industrial Organization (2nd. ed. New York: Harper Collins, 1994). 22 It is also possible that stakeholder risk aversion differs between firms. Future research could explore the relation between stakeholder risk aversion, firm characteristics, and contracts. Ackerberg and Botticini (2002) provide insight into how this might be done when risk aversion cannot be observed directly. Hartog et al. (2002) describe how surveys can reveal levels of risk aversion. 23
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  • 27. 27
  • 28. 28 Figure 3. Wehrenberg Weekly Revenue Comparison for Arnold Theater Arnold Theater - Weekly Revenue Comparison $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 8/31/01 9/30/01 10/31/01 11/30/01 12/31/01 1/31/02 2/28/02 3/31/02 4/30/02 5/31/02 6/30/02 7/31/02 8/31/02 9/30/02 10/31/02 11/30/02 12/31/02 1/31/03 2/28/03 3/31/03 4/30/03 Actual Linear Rule Figure 4. Distributor 1 Weekly Revenue Comparison for Arnold Theater Distributor 1 - Weekly Revenue Comparison $0 $5,000 $10,000 $15,000 $20,000 $25,000 8/31/01 9/30/01 10/31/01 11/30/01 12/31/01 1/31/02 2/28/02 3/31/02 4/30/02 5/31/02 6/30/02 7/31/02 8/31/02 9/30/02 10/31/02 11/30/02 12/31/02 1/31/03 2/28/03 3/31/03 4/30/03 Actual Linear Rule
  • 29. 29 Table 1. Theater Characteristics Percent of Contracts that are: Average Exhibitor Sliding Scale Aggregate Theater Screens Total Seating Contracts Revenue per contract* % Share per contract Firm Flexible Firm Flexible Arnold 14 2,164 243 7,982 47 59 30 9 1 Clarkson 6 1,357 154 6,136 46 59 31 8 2 DesPeres 14 2,447 256 11,354 46 61 29 9 1 Eureka 6 1,039 179 2,021 49 61 28 10 1 HallsFerry 13 2,526 127 2,063 50 65 29 6 1 Jamestown 14 2,254 265 11,534 46 58 31 9 1 Kenrick 8 1,899 136 6,482 48 54 35 10 1 MidRivers 14 2,505 250 12,775 46 59 30 9 2 Northwest 9 2,254 223 3,035 48 58 30 11 1 O’Fallon 15 2,583 200 16,618 45 57 32 11 2 Ronnies 20 3,625 294 20,986 45 59 31 9 1 St. Charles 18 3,720 284 12,865 45 59 31 9 1 St. Clair 10 2,197 158 8,020 48 56 32 10 2 Wehrenberg 161 30,570 2,769 10,342 46 59 31 9 1 * Revenues have been rescaled to preserve proprietary information. All percentages and rankings remain intact.
  • 30. 30 Table 2. Distributor Contract Characteristics Distributors Total Average Percent of Contracts that are: Total Distributor Contracts Revenue Revenue Rental Sliding Scale Aggregate per Rate Movies Contracts movie per contract* per contract* (%) Firm Flexible Firm Flexible 1 44 441 10 21,184 11,643 55 98 2 0 0 2 39 396 10 21,772 12,243 56 71 1 29 0 3 34 325 10 30,567 16,685 55 12 88 0 0 4 34 336 10 24,789 14,063 57 99 1 0 0 5 31 202 7 15,627 7,773 50 12 77 0 10 6 27 275 10 16,727 8,414 50 25 75 0 0 7 27 229 8 24,191 13,175 54 96 0 4 0 8 19 166 9 34,717 18,522 53 25 0 75 0 9 14 131 9 13,586 7,199 53 8 92 0 0 10 13 130 10 31,683 17,866 56 100 0 0 0 11 7 47 7 4,830 2,145 44 40 43 17 0 12 5 41 8 8,401 3,998 48 20 56 0 24 13 3 8 3 16,049 7,351 46 88 13 0 0 14 2 14 7 46,714 21,881 47 0 57 0 43 15 2 10 5 3,767 1,729 46 100 0 0 0 16 2 3 2 16,566 7,514 45 33 67 0 0 17 1 2 2 6,128 2,778 45 0 100 0 0 18 1 1 1 5,505 2,697 49 0 100 0 0 19 1 4 4 1,180 526 45 100 0 0 0 20 1 3 3 2,592 1,192 46 0 100 0 0 21 1 5 5 12,715 5,295 42 0 100 0 0 All Distributors 308 2,769 9 22,651 12,309 54 59 31 9 1 * Revenues have been rescaled to preserve proprietary information. All percentages and rankings remain intact.
  • 31. 31 Table 3. Linear Regressions Dependent Variable: Exhibitor’s Cumulative Revenue per Movie by Theater Table entry: Estimated Coefficient (White Std. Errors in parentheses) Theater Constant Total Revenue per Movie by Theater R2 Arnold 876*** 0.42*** 0.98 243 Obs (115) (0.0092) Clarkson 274** 0.44*** 0.95 154 Obs (126) (0.015) DesPeres 812*** 0.43*** 0.98 256 Obs (204) (0.012) Eureka 109*** 0.46*** 0.97 179 Obs (31) (0.012) HallsFerry 86* 0.48*** 0.98 127 Obs (45) (0.015) Jamestown 853*** 0.43*** 0.97 265 Obs (197) (0.011) Kenrick 339*** 0.46*** 0.98 136 Obs (93) (0.0095) MidRivers 1,329*** 0.41*** 0.98 250 Obs (163) (0.0079) Northwest 101** 0.47*** 0.97 223 Obs (49) (0.012) O’Fallon 1,909*** 0.39*** 0.97 200 Obs (368) (0.013) Ronnies 2,075*** 0.40*** 0.97 294 Obs (405) (0.012) St. Charles 1,158*** 0.41*** 0.97 284 Obs (303) (0.015) St. Clair 636*** 0.44*** 0.97 158 Obs (227) (0.018) * Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level