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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, Departmentof Economics, Claremont Graduate University, 160 E. Tenth St., Claremont, CA 91711; ph: (909) 621-8782;fax: (909) 621-8460; email: [email protected]. David Switzer is a Ph.D. Candidate at WashingtonUniversity in St. Louis. Portia Besocke is a Ph.D. Candidate at Claremont Graduate University. We thankFernando 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.


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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 partys 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

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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 industryparticipants, particularly D. Barry Reardon, past-president of Warner Bros. Distributing Corporation anda former executive of both Paramount Pictures and General Cinema Corporation; Doug Whitford, theexecutive 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 byJason 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 ofAMC Entertainment Inc.; A. Alan Friedberg, chairman of Loews Theatres, a subsidiary of Sony PicturesEntertainment; and A. D. Murphy, financial editor and reporter for Daily Variety andVariety.

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We focus on explaining the peculiar revenue sharing rule that is common in modern

contracts. While some contracts are aggregate deals, in which each sides 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

movies 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

exhibitors payofffunction 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 exhibitors 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

theParamountdecrees 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

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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 profi

t) increased (Sedgwick and Pokorny 1998; Hanssen 2002).Revenue sharing became increasingly common.4 The distributors 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 costsand eliminate consumer uncertainty about what the movie will cost. Exhibitors do not increase prices of hitsbecause they are engaged in repeat business with local consumers, and the potential loss of goodwill fromincreased prices outweighs the potential gain. Charging different prices for different movies at multiplexesnecessitates 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 agreethat 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 inthe 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 leavesopen the question of why the distributor was not made the full residual claimant. Hanssen (2002) arguesthat exhibitors needed incentives to provide local inputs. We evaluate this argument below - it may havebeen relevant in the 1920s but does not appear to explain revenue sharing today.

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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 partys 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 (1991fn. 77) explain that the main objections were to bidding itself (exhibitors preferred negotiations) and theaccompanying 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 copyof the movie). Guarantees are almost never used today and even refundable advances (which ensure that theexhibitor does not lose money on a movie that does not earn its advance) are rare. Information technologyfacilitates 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 exhibitordevelops a reputation for cleanliness and other good business practices. Our point is that the exhibitor hasthe incentive to bear these costs regardless of whether the exhibition contract uses a sharing rule or a flatpayment; the exhibitors main concern is to build and maintain customer goodwill.

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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 distributors costs are sunk and most of the exhibitors costs are fixed.

For now, suppose the distributor designs a contract for a single week, and assume that both

players take the ticket price pas 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. Distributorsand exhibitors have industry know-how that third parties lack, and as a result they may be best-suited tobear 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 managementhas insufficient confidence in its own abilities to choose and distribute motion pictures, perhaps they shouldfind solace in another industry.

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the theaters 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

Ue . In reality U

e would be determined by competiting distributors movies (opportunity

costs), but for simplicity assume that Ue is exogenous. For now, ignore concession revenue;

we discuss it below in Subsection 2.3.

The distributor chooseswt(pnt)to maximize its expected utility subject to the exhibitors

participation constraint:

max{wt(pnt)}Nnt=0

E(Ud) =N

Xnt=0

Ud(pnt wt(pnt))Pr(nt|p, N, t) (2.1)

s.t. E(Ue) =NX

nt=0

Ue(wt(pnt)) Pr(nt|p, N, t) U

e (2.2)

where Ud(.) andUe(.) are the distributors and exhibitors utility functions and E(.) is the

expectation operator. The first-order condition implies that at each value ofnt,

U0

d(pnt wt(pnt)) = tU0

e(wt(pnt)), (2.3)

where t is the Lagrange multiplier.9 Differentiating both sides with respect to nt yields the

slope of the revenue-sharing rule:

w0t(pnt) = U

00

d (pnt wt(pnt))

U00

d (pnt wt(pnt)) + tU00

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) andLeland (1978) consider constant relative risk aversion, which we discuss presently. A first-order conditionsimilar to (2.3) can be derived from a Nash bargaining model where both players have exogenous outsideoptions. Nash bargaining solves: max{wt(pnt)}Nnt=0

[E(Ud)U

d ][E(Ue)U

e ]1, where Ud is the distributors

reservation utility and measures relative bargaining power. At the solution the marginal utilities areproportional to each other as in (2.3). Thus, the shape of the optimal contract does not depend critically onrelative bargaining power. When= 1, the distributor has all of the bargaining power and the problem isidentical to solving (2.1) subject to (2.2).

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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) =xd and Ue(x) =xe, where xis

money and dand eare the coefficients that measure relative risk aversion. These functions

have several plausible properties: the marginal utility is positive and diminishing as long as

i < 1; relative risk aversion, xU00(x)

U0(x) , is constant; and absolute risk aversion, U

00(x)U0(x)

, is

decreasing in the money payoffas long as i < 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 < e < d


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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 Nand 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, d,

e, and t, 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 d = .75, e = .5, and

t= 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 movies run, the distributor solves (2.1) for

each week based on a forecast ofPr(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 exhibitors share ofpnt

must rise in order for the exhibitor to obtain utility Ue each week. Formally, this results

from an increase in t,since the risk aversion parameters, prices, and other variables do not

change over the life of the movie.13 When t 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 exhibitors share of revenue must

rise over time in order for the exhibitor to obtain utility Ue 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 exhibitors

share of revenue initially falls and then rises in order for the exhibitor to obtain utility Ue

11 Note that ifd= e, 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 t measures the distributors expected marginal utility from a change in U

e . Duringlow-revenue weeks the distributor gets less revenue at each level ofnt (because the exhibitor must continueto receive Ue ). Given that Ud(.) is strictly concave, this implies that t is higher during such weeks.

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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 exhibitors payment would adjust over time

in order for it to obtain Ue

. 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,

andfloors 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 exhibitors share is higher when revenue is lower. Also, flexible deals allow the percentage shares todepend on revenues.

15 Difficulties with forecasting partly account for coarse percentages. However, even flexible deals involvecoarse percentages. Thus, minimizing transaction costs associated with quibbling also plays a role.

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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 = d+e,wheredis the distributors contribution and eis the

exhibitors contribution; d

is due to the movie per se, whereas e

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 d 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 accounts for all other sources of randomness that affect movie

attendance. The movies weekly revenue pnis given by

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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 setp in the contract because courts frown on vertical price

restraints.17 The parties could divorce the distributors payment from p by basing it on

attendance rather than ticket revenue, but such per capitaclauses 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 pin 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 pup.

16 Including concession revenue and monitoring costs in the above models is straightforward. Consider the

risk sharing model. Denote concession revenue by c, the exhibitors share by wc(c), and assume that c isa (possibly random) function of attendance n. Suppose that including concession revenue in the contractrequires a monitoring cost ofm. 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 + cw(pn)wc(c)m,theexhibitors is based on w(pn) + wc(c),and the expected utility calculations consider any randomness in thedetermination ofc. Clearly ifc is a deterministic function ofn, there is no gain to includingc in the contract.Even if this is not the case, ifm is sufficiently high the distributor prefers leaving cout of the contract.

17 Many economists believe that vertical restraints should be permitted, partly because they can helpresolve agency problems (for a discussion see Carlton and Perloff1994).

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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 exhibitors 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 exhibitors 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 Wehrenbergs 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 Wehrenbergs 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

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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

movies 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 thatlack 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 diff

erence is statistically signifi

cant 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 suggeststhat 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, Ue (or e) changes systematically over

the life of the contract. When a movie first opens, it competes for the best auditoriums

17


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and time slots; Ue 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 Ue being constant over a movies 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 exhibitors

portion of cumulative revenue for each movie (after any adjustment) on a constant and the

movies 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 movies 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 movies 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 distributors 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

Wehrenbergs 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 overthe 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.

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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 theaters or distributors 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 peaksafter 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

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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


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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 averageactual 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 tobe 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 exhibitors 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 Results4 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, ina flexible deal neither party expects to be compensated using the formal boilerplate terms. Instead, theexhibitor determines the payments as revenue is observed, and we use this schedule as our measure of theinitial 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


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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-

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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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nants of Contract Form Journal of Political Economy110 no.3 (June 2002): 564-91.

[2] Bhattacharyya, S., and F. Lafontaine. Double-Sided Moral Hazard and the Nature of

Share ContractsRAND Journal of Economics26 no.4 (Winter 1995): 761-781.

[3] Blumenthal, M.A. Auctions with Constrained Information: Blind Bidding for MotionPicturesThe Review of Economics and Statistics (1988): 191-198.

[4] Borch, K. Equilibrium in a Reinsurance MarketEconometrica 30 (1962): 424-44.

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Coming to America - Tales from the Casting Couch, Gross and Net, in a Risky Business

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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 explorethe 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. Hartoget al. (2002) describe how surveys can reveal levels of risk aversion.

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[7] Chisholm, D. Asset Specificity and Long-term Contracts: The Case of the Motion

Pictures IndustryEastern Economic Journal19 no.2 (Spring 1993): 143-55.

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tion Pictures Industry Journal of Law, Economics, & Organization 13 no.1 (1997):

169-201.

[9] De Alessi, L. Why Corporations InsureEconomic Inquiry25 no.3 (July 1987): 429-38.

[10] Dekom, P.J. Movies, Money and Madness in Squire, Jason E., ed.The Movie Business

Book, 2nd ed. (New York: Fireside, 1992).

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ingReview of Financial Studies8:3 (Fall 1995): 743-71.

[12] De Vany, A.S., and R.D. Eckert. Motion Picture Antitrust: The Paramount Cases

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[13] De Vany, A.S., and D.W. Walls. Bose-Einstein Dynamics and Adaptive Contracting

in the Motion Picture Industry The Economic Journal (1996): 1493-1514.

[14] De Vany, A.S., and D.W. Walls Uncertainty in the Movies: Does Star Power Reducethe Terror of the Box Office? Journal of Cultural Economics (Nov. 1999): 285-318.

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[16] Filson, D. Dynamic Common Agency, Vertical Integration, and Investment: The Eco-

nomics of Movie Distribution Claremont Graduate University working paper, 2004.

[17] Friedberg, A.A. The Theatrical Exhibitor in Squire, Jason E., ed.The Movie Business


[18] Hanssen, F.A. The Block-Booking of Films Reexamined Journal of Law and Eco-

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[19] Hanssen, F.A. Revenue-Sharing in Movie Exhibition and the Arrival of SoundEco-

nomic Inquiry40 no.3 (July 2002): 380-402.

[20] Hartog, J., A. Ferrer-i-Carbonell, and N. Jonker. Linking Measured Risk Aversion to

Individual Characteristics Kyklos55 no.1 (2002): 3-26.

[21] Kenny, R.W., and B. Klein. The Economics of Block Booking Journal of Law and

Economics (October 1983): 497-540.

[22] Leland, H.E. Optimal Risk Sharing and the Leasing of Natural Resources with Appli-

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28

Figure 3. Wehrenberg Weekly Revenue Comparison for Arnold TheaterArnold 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

A ct ual Li nea r Rule

Figure 4. Distributor 1 Weekly Revenue Comparison for Arnold TheaterDistributor 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

Ac tua l Linea r R ul e


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29

Table 1. Theater Characteristics

Percent of Contracts that are:Average Exhibitor

Sliding Scale Aggregate

Theater Screens Total

Seating

Contracts

Revenueper

contract*

% Shareper

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

OFallon 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.


30/31

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 RegressionsDependent Variable: Exhibitors Cumulative Revenue per Movie by TheaterTable entry: Estimated Coefficient (White Std. Errors in parentheses)

Theater Constant

Total Revenue

per Movie byTheater R

2

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*** 0.97223 Obs (49) (0.012)

OFallon 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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