Vertical Integration and Market Foreclosure

in the Korean Movie Industry

Yusun Hwang∗

November, 2013

Abstract

I examine the exhibition behavior of movie theaters in the Korean movie industry in orderto investigate the influence of vertical integration on competition. I focus specifically on thechoice of films, screen allocation, and movie run stopping over different vertical structures.Because, in the Korean movie industry, not only can we observe the same movie being shown inboth integrated theaters and unintegrated theaters but also observe the same theater showingmovies from distributors of different vertical structures, I use movie and theater fixed effectsto control for the unobserved quality of movies and theaters. The empirical results suggestthat vertically integrated theaters are more likely to choose their affiliated movies than othercompeting movies, and they choose them more often than other competing theaters do. Inaddition, integrated theaters give their own movies a greater number of screenings over longertime periods. This effect is mostly restricted to company operated theaters, and it is greaterwhen movies are expected to get positive word-of-mouth as well as when underlying demand ishigh such as holidays. I argue that these results are not driven by the matching between movieand theater based on anything other than integration status, and that vertical integration leadsto the foreclosure, denial of access, of independent distributors to integrated theaters, to thedetriment of consumers.

JEL-Classification: L14, L22, L42, L82

Keywords: Vertical Integration, Vertical Foreclosure, Movie Industry

∗Department of Economics, University of Southern California, Los Angeles, CA 90089. Email: yusunhwa@usc.edu

1

1. Introduction

The possibility of vertical foreclosure in vertical mergers has been a major concern of antitrust

authority investigations. Theories suggest that for the purpose of gaining monopoly power, verti-

cally integrated firms may deny an access of competing downstream (upstream) firms to interme-

diate goods (downstream outlets). They also suggest that vertical foreclosure can survive as an

equilibrium in an oligopoly setting, indicating that vertical integration can harm consumer welfare

by raising price of final goods.(Ordover et al. (1990), Salinger (1988), Hart et al. (1990), Choi and

Yi (2000), Chen (2001))

Empirical studies have provided evidence that vertical integration gives rise to foreclosure. For

example, Ford and Jackson (1997), Waterman and Weiss (1996), and Chipty (2001) found that

vertically integrated cable operators in the cable television industry were more likely to carry their

affiliated networks. In particular, Chipty (2001) demonstrated that Time Warner, which owns the

premium movie service, HBO, tends to exclude AMC, the basic movie service, from its basic package

offer. In addition, Goolsbee (2007) found that broadcast networks are more likely to carry their

own shows than independent programming. Regarding to movie industry, Gil (2008) and Fu (2009)

examined the effect of vertical integration between distributors and exhibitors in the Spanish and

in the Singapore movie industry respectively. Both studies found that vertically integrated theaters

showed their affiliated movies longer than unintegrated theaters did.

However, the existence of vertical foreclosure is, by itself, not sufficient to allow for the con-

clusion that vertical integration harms consumers. In fact, in economics, the effects of vertical

integration on consumer welfare have long been a source of debate. Theories predict (Ordover

et al. (1990), Salinger (1988)) that vertical integration may have efficiency-enhancing effects by

reducing transaction costs or eliminating successive monopoly mark-ups, and as a result, vertical

integration can improve consumer welfare by lowering prices. Hence, the welfare effect of vertical

integration depends on the relative importance of anti-competitive effect of vertical foreclosure and

efficiency gains.

A few studies have attempted to assess the consequences of a vertical merger, providing mixed

results.1 Goolsbee (2007) found that broadcast networks apply lower standards to carrying their

1For a survey of empirical studies, refer to Lafontaine and Slade (2007) and Rey and Tirole (2007)

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own shows than to carrying independent programming. Specifically, independent programs need to

generate over 15 percent higher revenues from advertising than comparable in-house programs in

order to get on the air, suggesting that the foreclosure effect outweighs efficiency gains. On the other

hands, Chipty (2001), in her paper on the cable TV industry, concluded that vertical integration

does not harm but rather benefits consumers. By comparing consumer welfare across integrated

and unintegrated markets, she argued that efficiency gains dominate losses from foreclosure. Corts

(2001) studied how vertical integration in the movie industry between producers and distributors

affected competition of movie release-date scheduling. He demonstrated that integrated firms inter-

nalize the negative externality of close release dates, indicating that vertical integration improves

the efficiency. Gil (2008), also, interpreted his findings as efficiency gains in his investigation of

vertical integration between movie distributors and exhibitors. He argued that integrated theaters

run their own movies longer than other movies, and longer than unintegrated theaters do, and

concluded that vertical integration solves the distortion of movie run length created by the revenue

sharing contracts in the movie industry. However, this kind of interpretation should be made with

caution because theaters face capacity constraints caused by having a limited number of screens.

To retain their own movies for a longer period of time, integrated theaters should sacrifice revenues

generated by other movies that otherwise would have been shown, which could be interpreted as a

reduction in total box-office revenues as well as consumer welfare.

A major obstacle in assessing the effects of vertical integration is that we hardly notice that com-

panies with different organizational forms handle the same set of products from both integrated

firms and independent firms in the same market. When each product is a differentiated good,

which holds in many industries, controlling product quality is crucial to demand estimation, but

observables often explain little about product quality. If we can observe that downstream firms do

business with the same set of products, we might attribute observed difference between integrated

downstream and unintegrated downstream to the effects of vertical integration by controlling prod-

uct quality. This is the case in the cable TV industry in which cable TV providers offer different

sets of channels chosen from the same set of channels available. However, the cable TV industry is

virtually monopolized in many markets. Several cable TV providers operate nationwide, but it is

common that a specific provider is the only option that consumers can choose in their residential

area. In that case, the comparison of integrated markets to unintegrated markets could suffer from

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differences in underlying demand over markets in the assessment of the consequences of vertical

integration.

In this spirit, the Korean movie industry provides several advantages for the analysis of vertical

integration. First, because two major domestic distributors own multiplex chains, it is possible

to observe how integrated theaters give preferential treatment to their own movies against other

movies supplied by rival distributors compared to unintegrated theaters. That is, we can observe

four different combinations between movies and theaters: (1) integrated movies shown in integrated

theaters, (2) integrated movies shown in unintegrated theaters, (3) unintegrated movies shown in

integrated theaters, and (4) unintegrated movies shown in unintegrated theaters. This circumstance

enables us to control unobserved movie quality2 as well as theater quality by using movie-theater

fixed effects. Theater fixed effects also control the difference in underlying demand for movies over

geographical markets. With these fixed effect, the effects of vertical integration are determined by

difference in differences approach in the level of movie by theater.

Second, it is distributors and not movie theaters that promote movies nationwide, suggesting

that theaters in the same geographical market face the homogeneous demand for each movie. Al-

though each theater might enjoy some degree of market power because of its membership programs,

it is difficult to conclude that potential consumers at an integrated theater have strong preference

for movies from its affiliated distributor. Moviegoers are usually concerned about the contents they

can see such as trailers, casting, and directors, but not about what is happening behind the film

like which company distributes the film. In addition, movie theaters are located close to each other

in many markets, especially in urban areas in Korea. In the extreme case, two different theaters are

operating within 100-meter distance. It is hard to believe that integrated theaters draw different

sets of consumers based on their preference. Hence, the observed differences in exhibition behavior

between integrated and unintegrated theaters can be attributed to the practice of discrimination

by means of vertical integration under the assumption that in-house promotion by theaters has

little impact on movie demand.3

Third, contracts between distributors and exhibitors are fairly standard and simple in the

Korean movie industry, contrary to the U.S. movie industry where contracts vary with movies and

2In this paper, movie quality means a movie’s box office appeal, not an artistic quality.3Survey shows that most moviegoers choose what movie to see before going to a movie theater.

4

theaters. Distributors and exhibitors make revenue sharing contract for each movie, splitting the

box office revenues that the movie earns. Each party’s share, however, does not vary across movies

or during the weeks after the release with a few exceptions. Therefore, this analysis does not suffer

from a lack of data availability on contracts, which are often problematic in many other studies.

If contracts between distributors and exhibitors do vary, then the observed differences should not

be interpreted as the effects of vertical integration because the discrepancy in contracts generates

different incentives for theaters to each movie. With virtually no variations in contracts, vertical

integration between distributor and exhibitor does not generate cost asymmetry between theaters,

which are often considered as a main force in generating the efficiency gains of vertical integration.

Lastly, movie ticket prices are quite uniform. Ticket prices are higher on the weekend and lower

for the matinee, but do not vary with movies and theaters.4 With ticket prices being fixed, total

consumer welfare depends only on the total number in attendance. As the capacity of every theater

is constrained by a limited number of screens, integrated theaters should reduce screens devoted to

other movies in order to show their movies more and for longer periods. Hence, there is no gain in

consumer welfare from vertical integration unless the increase of box office revenues from integrated

movies outweighs what rival movies would have earned.

With the consideration of these facts, this article examines the effects on competition and

market performance of vertical integration between distributors and exhibitors in the Korean movie

industry. I use the data for movies released from 2006 to 2008 in the Korea movie industry where

integrated firms are major players both in the distribution sector and in the exhibition sector.

Some anecdotes in this industry suggest that access to integrated theaters may be restricted to

some degree for independent distributors when integrated firms have their own movies to show. In

this paper, I focus on three different aspects of the exhibition behavior of movie theaters that are

crucial to box office revenues: the allocation of screens (or screening times), the decision to stop

movie run, and film choice decisions.

My estimates suggest that integrated theaters discriminate against competing distributors in

favor of their own movies in all three different aspects of exhibition practice. First, integrated

theaters allow approximately two more additional screenings for their own movies than for other

4Second-run theaters charge a lower price, but their market share is negligible, so I include only first-run theatersin my sample. The prevalence of uniform pricing in the movie industry is, in fact, somewhat puzzling. Orbach andEinav (2007) documented rationales for uniform pricing in the movie industry

5

competing movies, and than other unintegrated theaters do. This effect is mostly restricted to

company operated theaters, and it is greater when movies are expected to have positive word-of-

mouth as well as when underlying demand is high such as holidays. Second, integrated theaters

are less likely to drop their own movies than comparable movies from competing distributors.

Integrated theaters are also more likely to choose their own movies, suggesting that independent

distributors are partially foreclosed in the theatrical exhibition market. These results indicate that

vertically integrated theaters apply lower standards to their own movies, which implies that vertical

integration between distributor and exhibitor reduces total box office sales, as a result, harming

consumers.

This paper proceeds as follows. In Section 2 and 3, I provide an overview of the Korean movie

industry and a simple model to provide testable implications. In Section 4 and 5, I describe the data

and the empirical findings on three different aspects of the exhibition practice of movie theaters.

In section 6, robustness check is provided and I conclude the paper with the a discussion on welfare

implications in section 7.

2. Korean Movie Industry

Movie industry consists of three sectors such as production, distribution and exhibition. Dis-

tributors supply films to exhibitors (theaters) and ancillary windows such as DVD, cable, broadcast

TV market and so on. Main decisions of distributors include scheduling the release timing of movies

to theatrical window as well as ancillary windows, acquiring enough screens through negotiating

with theaters, and promoting movies nationwide. In the Korean movie industry, distributors do not

have their own production companies, rather they play an role as main stake holders in production

stage. Exhibitors maximize box office revenues from movies and revenues from other sources such

as concession sales. Since new movies are released almost every week throughout the entire year,

exhibitors must make decisions regarding the replacement of movies playing in their screens every

week. Theaters also promote movies through in-theater advertisements, but its impact is limited

because most of consumers make decisions of what movie to watch before coming to theaters. Dis-

tributors and exhibitors use revenue sharing contracts which specify the weekly share of box office

revenues that each party takes. Contracts generally do not specify either requirement days of movie

6

run or which screen the movie should be screened on, although theaters usually have different size

of screens in terms of the number of seats.

Korean movie industry is one of a few markets in which domestic movies are fairly competitive

against Hollywood movies. The market share of domestic movies fluctuate across years, but they

usually enjoy around 50% market share against foreign movies including mainly Hollywood movies.5

Market share of each distributor also fluctuates across years. Three domestic distributors such as CJ

Entertainment(hereafter CJ ), Showbox, and Lotte Entertainment(hereafter Lotte), and subsidiaries

of Hollywood studios are dominant players in distribution sector in Korea. CJ and Lotte own their

multiplex chains such as CGV and Lotte Cinema respectively. Another major domestic distributor

Showbox had its own theater chain, Megabox, but they were disintegrated on July, 2007.6 No

subsidiaries of Hollywood major studios have their own theaters in Korea.

While declining revenue-sharing term is common in US movie industry, the Korean movie

industry observes the fairly fixed revenue sharing rate. Distributors take 50% of total box office

revenues for domestic movies and 60% for foreign movies regardless of expected demand each movie

has.7 This is an important feature which enables us to attribute observed variation in exhibition

practice between integrated theaters and unintegrated theaters to the effects of vertical integration.

If contracts vary over movie by theater, there is no reason to believe that comparable theaters should

make the same decision regarding to film choice, screen allocation, and stopping movie run even

when market observes no integrated firms.

Figure 1 describes how box office revenues are split into players involved. For example, suppose

that a movie generates 100 million dollars as its total box-office revenues. Movie theaters take 50

millions as its share according to revenue sharing contract. For the rest of 50 millions, the distributor

recoups its costs of prints and advertising as well as distribution fee8 first, and the production

company takes its production costs before residual holders claim their shares. Distributors generally

play as major stake holders, although their shares differ across movies.

Tables 1 and 2 show the overview of the Korean movie industry in each sector.9 An integrated

5In 2006, market share of domestic movies is 94% in India, 53.2% in Japan and 63.8% in Korea.6Showbox and Megabox are defined as integrated firms before disintegration and as independent firms afterwards

in the analysis750/50 split rule is applied to theaters located in regions other than Seoul for both domestic movies and foreign

movies.8Distribution fee is on average around 10% of total box-office revenues.9A multiplex is generally defined as a theater having more than 5 screens. Multiplexes account for around 90%

7

Figure 1: How box-office revenue is split

distributor, CJ is the leading company in distribution sector, accounting for around 30% of market

share, while another integrated distributor, Lotte, is the third player among domestic distributors in

terms of market share. Lotte is a young distributor which started its business in movie distribution

in 2003. Table 2 shows that distributor-owned theaters, CGV and Lotte Cinema were growing

fast, accounting for almost 50% of screens in theatrical exhibtion market in 2008. It is worthwhile

noting that vertical relationship is somewhat different in these two integrated firms. Lotte and Lotte

Cinema belong to the same corporate entity under the same CEO, and it is very flexible in the

transfer of personnel between two departments. Meanwhile, CJ and CGV are subsidiary companies

of the conglomerate, CJ Corporation, and there is no transfer of workers between two companies.

Therefore, the vertical relationship would be much stronger between Lotte and Lotte Cinema than

CJ-CGV. In fact, regression results below suggest that the effects of vertical integration are larger

for Lotte-Lotte Cinema than CJ-CGV.

Given the fact that each movie is a differentiated product and the multiplex is a dominant

of screens in 2008

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form of theaters in Korea, complete foreclosure by integrated firms is hardly observed, while partial

foreclosure is reported to be present. Market practitioners often complain of unfair treatments by

vertically integrated firms. For instance, independent distributors insist that they have a difficulty

in acquiring sufficient number of screens because integrated theaters show their affiliated movies

aggressively, providing less screens to movies of competing distributors in order to protect their

distributors’ profits.

Table 1: Market shares of distributors in 2006∼2009

Market share (%)Distributor 2006 2007 2008 2009

CJ Entertainment 23.2 29.7 30.1 29.1Showbox 20.1 12.3 10.2 15.2

Lotte Entertainment 5.6 8.6 8.3 11.8UPI Korea 7.6 3.5 10.0 2.1

Sony Pictures Releasing Buena Vista Korea 10.0 9.8 6.8 8.5Warner Bros. Korea 5.8 11.3 6.1 5.6

20th Century Fox Korea 5.9 5.6 5.1 7.8Others 21.8 19.2 23.4 19.9Total 100.0 100.0 100.0 100.0

Table 2: Number of Theaters and Screens by Exhibitor

N of theaters N of screensExhibitor 2006 2007 2008 2006 2007 2008

MultiplexCGV 44 57 63 351 461 511Primus 33 38 36 226 276 259Lotte Cinema 36 41 47 273 316 360Megabox 20 15 13 166 123 116Cinus 15 23 25 109 160 178Others 50 36 39 437 344 352Total 198 210 223 1562 1680 1776

Non-Multiplex 123 104 86 318 295 228

Total 321 314 309 1880 1975 2004

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Since admission price is constant across movies within a theater, a main strategy for distributors

to maximize their profits is to show their movies longer in as many of screens as they can. Besides

movie quality, the number of screens and the length of movie run also affect the overall box office.

Getting more screens is important especially in the first week of its release when the information

about movie quality is not yet fully revealed and the highest revenues, around 40% of overall box

office, are usually achieved. Since 3∼5 movies are released every week, theaters have to make

decisions about what movies to show and to drop. It is hard to believe that vertically integrated

theaters have the same pattern with what independent theaters do because independent theaters

only consider their own profits but vertically integrated theaters take into account of joint profits

with affiliated distributor.

3. A simple model

A theater needs to choose what movies to show and to replace with the limited ‘shelf space’

every week. For the tractability of model, I assume that the number of screens allocated to each

distributor (or movie) is a continuous variable chosen by the theater. In this model, the theater

maximizes its current profits, ignoring dynamics of box-office revenues over the life of movie run.

This setting can be justified by the fact that a typical contract between distributor and exhibitor

does not specify the minimum length of movie run.10

Consider the case where a multiplex chooses the combination of movies from two distributors

such as DA and DB under the capacity constraint with N screens.

π =(p− γ) [βAτA (NA)NA + βBτB (NB)NB]

s.t NA +NB = N

π : profit of the theater

10In the US movie industry, minimum length of movie run is usually specified in the contract. This difference mayreflect the fact that distributors do not have strong bargaining power over movie theaters in Korea while big studios- distributors - are dominant players in US.

10

βi : theater’s share of box-office revenue from Di’s movies (i = A,B)

p : ticket price (fixed)

γ : marginal cost of theater (fixed)

τi : admissions per screen allocated to Di’s movie

Ni : number of screens to show Di’s movie

τi is assumed to be a decreasing function of Ni, implying that additional screen dedicated to

the same distributor generates less demand because it would be a less popular movie. The optimal

decision of film choice would be determined at

βA

(∂τA∂NA

NA + τA

)= βB

(∂τB∂NB

NB + τB

)

This condition implies that marginal revenues from showing the least popular movie from each

distributor would be equal at the optimal decision of the multiplex. However, when the multiplex

is merged with DA, vertical merger takes into account revenues from both its distributor sector

and its exhibitor sector, having an increased share with βA > βB. Due to the increase in perceived

marginal revenues from its own movies, the multiplex has an incentive to choose its own movies

more frequently than DB’s movies compared to what it would be under independent ownership. As

a result, industry profits and DB’s profits decrease while vertical merger has increased its profits.

In the previous literature, cost asymmetry through vertical integration and commitment prob-

lem play an critical role to drive the results. However, with the fixed ratio in revenue sharing

contract across movies, vertical integration does not generate cost advantage to movie theaters in

acquiring movies. As screening schedule of each theater is open to the public, there is no com-

mitment problem in contracts between distributor and exhibitor either. With fixed price across

movies and theaters, consumer welfare depends only on the total number of admissions, implying

that vertical merger reduces consumer welfare in this setting. Vertical merger does not want to

foreclosure completely DB, but DB’s ability to reach customers would be limited by the vertical

merger.

The effect of vertical integration on the decision of screen allocation and stopping movie run

can be analyzed in the same manner. For example, Ni can be thought to be the number of screens

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to show the Di’s movie if each distributor has one movie. It is common for a multiplex to show a

movie on several screens when it is expected to have higher demand like in the case of blockbusters.

Additional screen dedicated to the same movie can increase the total admission because it provides

more frequent screening times to attract moviegoers facing time constraints, but its additional

increase would decrease as the number of screens increases. As in the case of film choice, the

increase in perceived marginal revenues makes integrated theaters allocate more screens to their

own movies. This result is depicted in figure 2 where NA′ is the number of screens devoted to DA’s

movie when the theater is integrated with DA.

Figure 2: Effects of Vertical Integration on Screen Allocation

Number of screens

MRA

NA

MRB

NB

MRA’

NA’

In summary, this model gives three testable implications about the effects of vertical integration:

(1) Integrated theaters show their own movies longer than other movies and than other unintegrated

theaters do. (2) Integrated theaters allocate more screens to their own movies. (3) Integrated

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theaters are less likely to drop their own movies.

3.1. Externality through Word-of-Mouth

Movie, as a product in theatrical window, is stylized as having a short life, reaching at its

peak mostly at the opening weekend and declining over the rest of its life in terms of its box-office

revenues. It is rare that weekly box-office revenues are higher in the second or third week than the

first week, but some movies show relatively slow decay rates as positive word-of-mouth enhances

demand in subsequent weeks. This is not a ’buzz’ effect generated by marketing efforts or by other

movie characteristics which are known before its release such as star casting, director, sequel, and

trailer etc. It is rather a process of social learning under which potential consumers update their

beliefs on movie quality through the information from who experienced the movie.

Industry executives in movie business consider word-of-mouth as one of major driving forces

for the success of movie. In a companion paper, I showed that word-of-mouth has a significant

and considerable impact on box-office revenues. In the presence of word-of-mouth, the return to

attracting a consumer is greater than the direct effect - ticket price - that the consumer has on

box-office revenues because it may increase the demand of other potential consumers. Since word-

of-mouth can prevail through online user ratings and reviews, this social multiplier effect can be

even bigger than what is measured within the geographical area. While independent theaters do

not consider this spillover effect as well as the multiplier effect, integrated theaters can internalize

the benefits from these effects nationwide that their own movies can generate. When early box-

office revenues are expected to boost positive word-of-mouth, integrated theaters have an incentive

to sacrifice current revenues on behalf of future sales by allowing more screens to their own movies.

Hence, we should observe bigger impact of vertical integration for movies with higher quality.

3.2. Seasonality and Competition

Movie industry observes a clear seasonality. Underlying demand for movies is relatively high in

holidays and school vacations as shown in Figure 3.11

11Einav (2007) decomposed observed pattern of box-office revenues into underlying demand and amplified effects.He found that underlying demand has a similar pattern of observed one, but movies were too clustered in high seasonscompared to underlying demand, suggesting that studios could increase revenues by adjusting release timing of their

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Figure 3: Seasonality in the Korean Movie Industry

In order to capture high demand, distributors tend to release their biggest hits in peak seasons,

resulting in an amplified seasonality in box office revenues as well as an intense competition among

strong movies.12

It is not obvious how this intense competition and high underlying demand influence the effects

of vertical integration on exhibition behavior of movie theaters. With the fixed number of screens

available, competing theaters would not allow many screens to integrated distributors so that one

might expect that integrated theaters have strong incentives to show their own movies when com-

petition is intense. However, integrated theaters also face increased opportunity costs of showing

their own movies because of the presence of other good quality movies from competitors. On the

other hands, integrated firms have strong incentives to boost early box-office revenues by allowing

more screens to their own movies when positive word-of-mouth is expected. Higher underlying

demand can amplify multiplier effects, inducing integrated theaters to show their own movies with

an aggressive manner. Increased competition with good-quality movies from other distributors,

movies more into off-peak seasons.12Corts (2001) argued that vertical integration between production and distribution sector help movies not being

clustered too much although it does not achieve the optimal level.

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however, attenuates the possibility to top competitors and capture higher demand, diminishing the

incentive to sacrifice current revenues of theaters for expected revenues from subsequent weeks.

Therefore, the effects of vertical integration can be either higher or lower depending on relative

importance of each incentive.

3.3. Company-operated theaters vs Dealer-run theaters

There are two different types of integrated theaters, company-operated theaters and dealer-run

theaters. Film choice decision is still under the control of parent company which assigns their staffs

to dealer-run theaters as theater managers. Hence, it is hard to say that dealer-run theaters are

independent from parent companies. However, because the parent company takes a fixed share of

’total’ box-office revenues from dealer-run theaters, dealer-run theaters have less incentives to favor

movies from their affiliated partners than company-operated theaters.

Regarding to differential effects of vertical integration between dealer-run and company-op

downstream firms, Hastings (2004) examined retail gasoline market in Southern California. She

found that vertical integration caused the increase in retail gas price and this effect was not confined

to company-op stations, concluding that it is consistent to a model of differentiated products

with consumer brand royalty. In other words, it is the consumer brand royalty, not the vertical

integration that derives the surge of retail gas price in her paper.

In movie industry, it is also possible that some of consumers have a brand royalty in favor

of a distributor as well as a multiplex chain. Every multiplex chain encourages consumers to be

enrolled with its own reward program which makes program members be locked in. It is also

possible that some of consumers may prefer movies from a specific distributor, if that distributor

has good reputation on specific genre (e.g. Disney). However, for the brand loyalty to explain the

favor of integrated theaters to their own movies, a group of consumers should have brand loyalty

for both sectors of the integrated firm. That is, those who prefer Lotte Cinema should prefer

movies of Lotte Entertainment. This is not the case, I think, here because moviegoers are going to

make their movie-going decisions by what is presented in the marketing, not by what’s happened

behind the camera. It is not likely that consumer brand loyalty plays a role in observed differences

between integrated theaters and independent ones in exhibition behavior. Therefore, I expect that

15

the effects of vertical integration would be stronger for company operated theaters.

4. Data

The data comes from three different sources in this paper. First, I collected daily screening

records of theaters during 2006∼2008 in the Korean movie industry from Korea Film Council

(KOFIC), a governmental agency. KOFIC collects daily screening records from registered theaters

which consist of over 95% of theaters in Korea. This data enables us to examine what movies

were shown in each theater, how long each movie was shown in a specific theater, and how many

screens were allocated to each movie within a theater. This paper focuses on first-run theaters and

feature films released nationwide. The full sample includes 590 movies and 250 theaters, counting

to 83419 observations of combinations between movies and theaters. For each movie, I compiled

information on its characteristics including its distributor, nationality, rating, average score of

online user ratings, and total box office revenues. I also matched information on the number of

screens, location and the identity of multiplex chain to each theater.

Second, I use the unique data from a movie research marketing company which surveys about

expectation on coming movies as well as satisfaction rate on movies people watched. In particular,

the data provides the number of people who are aware of a movie coming to be released, the number

of people who answered that they would go to see the movie in a theater, the number of people who

are satisfied with a movie.13 This data is available for movies released since 2008, so the sample of

the analysis using this data will be restricted to movies released in 2008 in this paper.

Weekly box office revenues each movie generates would be the most important factor to the

decision of movie run stopping. With the help of two major distributors, I collected the data of

weekly box office revenues of each movie from these distributors in every theater in 2008. Since

one distributor is an integrated firm and the other unintegrated, I can still examine the effects of

vertical integration on movie stopping decision in the sense of difference in differences approach.

Descriptive statistics for theaters by integration status are presented in Table 3. Both integrated

theaters and independent theaters have statistically the same number of screens and seats, and the

same average days of movie running. Interestingly, integrated theaters not only show larger number

13The sample of the data includes around 2,000 individuals every week, and the sample is replaced every 6 months.The survey is made through online.

16

Table 3: Theater Characteristics by Integration Status

Total Nonintegrated Integrated Diff

number of screens 7.743 7.510 7.868 -0.358(1.789) (1.927) (1.708) (-1.13)

number of seats 1439.2 1425.3 1446.6 -21.32(573.0) (699.8) (495.8) (-0.21)

number of movies 294.1 274.0 304.9 -30.90**(59.70) (70.50) (50.15) (-3.00)

days of running 19.99 20.05 19.97 0.0803(1.314) (1.725) (1.038) (0.34)

screenings at opening 7.736 7.455 7.887 -0.432*(0.995) (0.735) (1.084) (-2.50)

own movies 0.107 0 0.164 -0.164***(0.0810) (0) (0.0247) (-46.40)

movies per screen 38.44 36.83 39.31 -2.480**(5.240) (6.709) (4.027) (-2.73)

domestic movies 0.408 0.427 0.398 0.0284**(0.0555) (0.0661) (0.0463) (2.97)

US movies 0.418 0.409 0.422 -0.0132(0.0396) (0.0493) (0.0326) (-1.90)

other countries 0.174 0.164 0.179 -0.0152***(0.0256) (0.0285) (0.0222) (-3.49)

Observations 140 49 91 140

Sample includes theaters which do not have any interruption in their operation for the entire periodof the data. Parentheses include t-statistics for Diff, SD for mean values. *Significant at 5%, **at1%, ***at 0.1%

17

Table 4: Movie Characteristics by Integration Status

Total Independent Integrated Diff

number of theaters 141.4 135 154.1 -19.12***

average weeks 2.62 2.459 2.941 -0.482***

N of screens 1.304 1.25 1.41 -0.159***

N of screenings 7.377 6.913 8.301 -1.388***

days of running 18.03 16.91 20.27 -3.363***

domestic movies 0.376 0.267 0.594 -0.327***

US movies 0.419 0.501 0.254 0.247***

other countries 0.205 0.232 0.152 0.0793*

boxoffice 769295.1 599116.4 1108788.6 -509672.2***

naver rating 7.17 7.17 7.168 0.00203

N of reviews 2478.9 2119.9 3195.1 -1075.3**

Pre expectation 11.005 9.847 14.476 -4.629**

Satisfaction rate 48.639 47.91 50.795 -2.879

action 0.268 0.262 0.279 -0.0171

adventure 0.158 0.181 0.112 0.0690*

animation 0.078 0.084 0.066 0.018

comedy 0.344 0.333 0.365 -0.0321

crime 0.141 0.132 0.157 -0.025

drama 0.447 0.438 0.467 -0.0293

family 0.117 0.12 0.112 0.00792

fantasy 0.115 0.122 0.102 0.0206

horror 0.102 0.104 0.0964 0.00788

romance 0.219 0.214 0.228 -0.0147

sf 0.0797 0.0967 0.0457 0.0510*

thriller 0.273 0.285 0.249 0.0363

Observations 590 393 197 590

For pre-expectation and satisfaction rate, the sample includes 206 movies released in 2008. *Sig-nificant at 5%, **at 1%, ***at 0.1%

18

of movies, but also allocate more screens to newly released movies than independent theaters do.

Integrated theaters are also less likely to show domestic movies than unintegrated theaters. This

difference suggests that integrated theaters are systematically different from unintegrated theaters

although they have similar characteristics in terms of size, highlighting the need to control theater

quality in the analysis.

Table 4 indicates that a clear distinction exists between movies over integration status. Movies

of integrated distributors are shown longer in more theaters with more screens. Average per-

formance of movies by integrated firms overwhelms unintegrated movies, but large variation in

performances is also observed even among movies of one distributor. This implies that estimation

can be never successful without controlling for movie quality. There is no significant difference in

genre, suggesting that integrated distributors are not specialized to a specific genre.

5. Empirical Results

In this paper, I focus on three important aspects of the decision by movie theaters: allocation

of screens, movie run stopping, and film choice decision. Consider the specification where δi and

ηj are unobserved characteristics for movie i and theater j respectively, and Yij is the decision of

theater j on movie i. Dij [Own Movie] takes one if movie i’s distributor is integrated with theater

j, zero otherwise. Xij includes movie characteristics and theater characteristics.

Yij = α+β1Dij [Own Movie]+β2Dij [Own Movie]∗Xi+β3Dij [Own Movie]∗Xj+δi+ηj+εij (1)

Since equation (1) includes fixed effects for both movie and theater, the effects of movie and

theater characteristics cannot be determined. However, the existence of both independent theaters

and integrated theaters allows for the identification of the effect of vertical integration because

the same movies are observed showing under different vertical structures. I also investigate the

differential effects of vertical integration depending on movie and theater characteristics. For this

analysis, I include interaction terms of vertical integration with relevant variables (Xi and Xj)

including a proxy for movie quality, a dummy for high seasons, and a dummy for company-operated

theaters.

19

With this fixed-effects specification, the effect of vertical integration is determined by compar-

ing the difference of exhibition behavior between integrated movies and unintegrated movies in

vertically integrated theaters to those in independent theaters. Therefore, the identification comes

from the omission of interactions between movie and theater under the assumption that there is

no matching between movies and theaters based on their characteristics other than whether they

are integrated partners or not. I will relax this assumption and show that regression results do not

change qualitatively in the section of robustness check below.

5.1. Allocation of Screening Times

First, I examine the effects of vertical integration on the decision of allocation of screening times

at the opening weekend. I focus on screening times rather than the number of screens devoted to

a movie in order to consider the possibility that theaters make multiple movies share a screen, by

showing a movie after another movie in the same screen.14 How many screens each movie is shown

on is important to the overall success of the movie in its box-office revenues because almost every

week observes newly released movies so that movies’ popularity quickly declines after the first week.

Table 5: Screening times at the Opening Weekend (Saturday) (CJ-CGV)

DistributorTotal Non-CJ CJ Diff

Theater Non-CGV Mean 7.864 7.598 9.104 1.506SD 4.13 3.89 4.88 0.04

Obs 50635 41677 8958

CGV Mean 8.052 7.607 10.050 2.442SD 4.35 3.89 5.58 0.06

Obs 32784 26817 5967

Diff Mean 0.187 0.009 0.946 0.936***SE 0.03 0.03 0.08 0.14

Difference in differences estimators for two integrated firms are provided in Table 5 and 6

respectively. The results clearly show that integrated theaters give more screening times to movies

14Distributors often complain that theaters do not allow entire time slots of a screen for a movie even at theopening week. In addition, I assume that each screen has the same number of seats. However, it is possible thatintegrated theaters locate their own movies to bigger screen having more seats and at show times when demand ishigh such as evening. This kind of discrimination is not considered in this paper because of the lack of data.

20

Table 6: Screening times at the Opening Weekend (Saturday) (Lotte-Lotte Cinema)

DistributorTotal Non-Lotte Lotte Diff

Theater Non-Lotte Mean 7.950 8.070 7.057 -1.012SD 4.17 4.30 2.83 0.05

Obs 66537 58652 7885

Lotte Mean 7.891 7.829 8.291 0.461SD 4.40 4.51 3.64 0.1

Obs 16882 14611 2271

Diff Mean -0.059 -0.241 1.233 1.474***SE 0.03 0.04 0.07 0.17

they own than movies they do not have ownership stake in compared to other theaters. CJ is

likely to give around one additional screening time per day to its own movie while Lotte tends to

increase screening times by around 1.5 per day for its own movies. It is worth noting that this

effect is significant even for Lotte and Lotte Cinema, among which Lotte does not have many hits

in its line-up while Lotte Cinema is the second largest multiplex chain.15 This result provides the

evidence that observed pattern does not result from the matching between movies and theaters

depending on their quality.

Table 7 represents the results of estimating the effects of vertical integration on allocation

of screening times at the opening weekend (Saturday) using the full sample. Movie and theater

fixed effects are included in the second half of the table to control unobserved characteristics of

movie and theater as well as differences in underlying demand by week and location. The result

in column (1) indicates that integrated theaters allocate approximately one more screening time

to their own movies.16 As expected, the larger the number of screens is, the more screening times

are. Column (2) shows that company operated theaters are even more aggressive in the decision of

screen allocation in favor of their own movies than deal-run theaters. Integration effect in deal-run

theaters disappears when including the interaction of vertical integration with dummies for high

seasons in column (3), meaning that no integration effect is observed in dealer-run theaters at low

seasons. This is not surprising because integrated firms take a fixed share of box office revenues from

15Market share is 30% for CJ and 8% for Lotte in 200816Results do not change when the number of screens is used as a dependent variable instead of the number of

screening times

21

Table 7: The Effects of Vertical Integration on Screening Times: Full sample

(1) (2) (3) (4) (5) (6)

Own movie 1.096*** 0.583*** 0.0243 1.185*** 0.641*** 0.370***(8.66) (4.21) (0.10) (29.92) (13.83) (7.21)

Own X Company op 0.897*** 0.929*** 0.957*** 0.969***(3.80) (3.86) (14.65) (14.83)

Own X High season 1.571** 0.768***(3.12) (8.53)

Company op 0.402*** 0.275* 0.272*(3.39) (2.37) (2.34)

Online user ratings 0.171 0.172 0.163(1.89) (1.89) (1.80)

Number of screens 0.455*** 0.454*** 0.454***(10.42) (10.40) (10.40)

Constant 4.377*** 4.447*** 4.338*** 8.032*** 8.026*** 7.831***(3.78) (3.85) (4.06) (22.76) (23.00) (22.77)

Movie and Theater FE No No No Yes Yes Yes

Observations 83419 83419 83419 83419 83419 83419Adj R-squared 0.315 0.316 0.318 0.646 0.647 0.648

This table reports OLS coefficients. All of specifications count 83419 observations with 590 movies. Thedependent variable is the number of screening times of movie i at theater j on Saturday of its opening weekend.Robust standard errors are clustered by movie and theater in columns (1)∼(3). Movie and theater fixed effectsare included in the second half of the table. t statistics are in parentheses. *Significant at 5%, **at 1%, ***at0.1%

22

Tab

le8:

Th

eE

ffec

tsof

Ver

tica

lIn

tegr

atio

non

Scr

een

ing

Tim

es:

Res

tric

ted

sam

ple

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Ow

nm

ovie

1.11

5***

0.3

18

-0.2

42

-0.3

18

1.1

36***

0.3

62***

0.0

34

-0.0

236

(5.8

0)

(1.2

3)

(-0.7

8)

(-0.9

4)

(16.5

3)

(4.5

8)

(0.3

7)

(-0.2

5)

Ow

nX

Com

pan

yop

1.4

35***

1.4

47***

1.4

52***

1.3

98***

1.4

02***

1.3

94***

(3.3

3)

(3.3

5)

(3.3

3)

(12.6

9)

(12.7

6)

(12.6

5)

Ow

nX

Hig

hse

ason

1.3

30**

1.2

06**

0.7

78***

0.6

82***

(2.9

6)

-2.9

5(5

.60)

(5.0

7)

Ow

nX

Pre

Exp

ecta

tion

-0.0

019

0.0

0641

(-0.0

8)

(0.6

6)

Ow

nX

Sat

isfa

ctio

nra

te0.0

325**

0.0

163***

(2.7

9)

(3.7

3)

Pre

Exp

ecta

tion

0.22

6***

0.2

26***

0.2

25***

0.2

25***

(12.

06)

(12.0

6)

(12.1

5)

(12.4

0)

Sat

isfa

ctio

nra

te0.

0197**

0.0

196**

0.0

196**

0.0

173**

(3.0

0)

(2.9

9)

(3.0

1)

(2.7

6)

Com

pan

yop

0.34

0*

0.1

56

0.1

55

0.1

54

(2.2

8)

(1.1

2)

(1.1

1)

(1.1

0)

Nu

mb

erof

scre

ens

0.59

4***

0.5

94***

0.5

93***

0.5

92***

(11.

31)

(11.3

4)

(11.3

2)

(11.2

7)

Con

stan

t-1

.008

-0.9

24

-0.7

41

-0.8

48

2.2

18***

2.2

39***

2.3

39***

2.4

30***

(-1.

13)

(-1.0

4)

(-0.8

4)

(-0.9

4)

(5.1

7)

(5.2

0)

(5.4

3)

(5.6

8)

Mov

iean

dT

hea

ter

FE

No

No

No

No

Yes

Yes

Yes

Yes

Ob

serv

atio

ns

33835

33835

33835

33835

33942

33942

33942

33835

Ad

jR

-squ

ared

0.54

10.5

43

0.5

45

0.5

47

0.6

50.6

52

0.6

53

0.6

54

Th

ista

ble

rep

ort

sO

LS

coeffi

cien

ts.

All

of

spec

ifica

tion

sco

unt

wit

h206

movie

sre

lease

din

2008.

Rob

ust

stan

dard

erro

rsare

clu

ster

edby

movie

an

dth

eate

rin

colu

mn

s(1

)∼(4

).M

ovie

an

dth

eate

rfi

xed

effec

tsare

incl

ud

edin

the

rest

half

of

the

tab

le.

Pre

-exp

ecta

tion

an

dsa

tisf

act

ion

rate

are

mea

sure

das

dev

iati

on

sfr

om

thei

rm

ean

valu

es.t

stati

stic

sare

inp

are

nth

eses

.*S

ign

ifica

nt

at

5%

,**at

1%

,***at

0.1

%

23

deal-run theaters based on total box office revenues. Results do not change much, but get more

significant when movie and theater fixed effects are included, as shown in the columns (4)∼(6).

Integrated and company operated theaters allow around two screening times more to their own

movies at high seasons.

In order to see the relationship between integration effects and movie quality, I restrict the

sample including only movies released in 2008, for which I can observe variables of pre-expectation

and satisfaction rate. Satisfaction rate is used as a proxy for movie quality, which can be justified

by the fact that distributors have information from pre-screening to their focused groups as well

as to the public in some cases. Programmers of movie theaters are also able to watch and evaluate

movies before the decision of film choice. Both pre-expectation and satisfaction rate are included as

the deviations from their mean values throughout this paper. Regression results with this restricted

sample are presented in Table 8. Results in columns (1)∼(4) indicate that both pre-expectation and

satisfaction rate are positively correlated with the number of screening times, while the relationship

of each factor with integration differs. Columns (4)∼(8) show that the interaction of integration

with satisfaction rate is strongly significant while the interaction with pre-expectation is not once

unobserved movie and theater characteristics are controlled. This implies that integration effects

are concentrated for movies expected to generate positive word-of-mouth consistent to what the

model predicts. When satisfaction rate is evaluated at 75 percentile, the results show that vertically

integrated and company operated theaters allocate 2.4 screening times per day more to their own

movies at peak seasons when the underlying demand is relatively high.

5.2. Movie Stopping Decision

In this section, I examine how organizational form affects the decision to stop movie run.

Every week, movie theaters decide what movies to drop in order to show newly released movies.

From the model discussed earlier, I expect that integrated theaters are less likely to stop their own

movies, so that integrated theaters show their own movies for a longer period of time.

Tables 9 and 10 suggest that integrated theaters show their own movies longer than other movies

when compared to unintegrated theaters and integrated rival theaters, but the extent of this effect

is different across multiplex chains. Lotte Cinema shows a stronger favor in its own movies, while

24

Table 9: Total Days of Movie Running (CJ-CGV)

DistributorTotal Non-CJ CJ Diff

Theater Non-CGV Mean 19.515 18.458 24.434 5.976SD 11.16 10.62 12.21 0.12

Obs 50635 41677 8958

CGV Mean 19.677 18.434 25.264 6.829SD 11.36 10.62 12.872 0.16

Obs 32784 26817 5967

Diff Mean 0.162 -0.023 0.830 0.862**SE 0.08 0.08 0.20 0.14

Table 10: Total Days of Movie Running (Lotte-Lottecinema)

DistributorTotal Non-Lotte Lotte Diff

Theater Non-Lotte Mean 19.632 20.118 16.016 -4.102SD 11.33 11.65 7.69 0.13

Obs 66537 58652 7885

Lotte Mean 19.369 19.197 20.476 1.278SD 10.84 11.04 9.42 0.24

Obs 16882 14611 2271

Diff Mean -0.262 -0.921 4.459 5.380***SE 0.09 0.10 0.19 0.55

25

the same pattern is observed for the largest multiplex chain, CGV.17

Table 11 examines the effect of vertical integration on the length of movie run using the full

sample. In all of specifications, vertically integrated theaters show their own movies for a longer

period of time, at least two additional days. Columns (1)∼(3) present the regression results with

observed characteristics of movies and theaters. As expected, the number of screens in theaters and

average score of user ratings are positively related to the length of movie run. Regarding to genre

of movies, action, adventure and thriller movies tend to be held longer in theaters.18 Interestingly,

company operated theaters are found to show movies for relatively shorter time periods. It might

imply that these theaters are systematically different from other theaters, highlighting the need to

control theater quality. In the second half of table, I re-estimate the effect of vertical integration on

the length of movie run with movie and theater fixed effects. The coefficient of own movie does not

change much in its magnitude, but the effect of vertical integration turns out to differ depending on

movie and theater characteristics, contrary to the regression results in the first half of table in which

unobserved characteristics are not taken into account. Results from fixed effects models indicate

that company operated theaters are more aggressive in carrying their own movies like what we see in

the analysis of screen allocation. It is worth noting that the favor of integrated theaters in their own

movies is also stronger for domestic movies. Integrated distributors are all domestic firms, but they

distribute both domestic movies and foreign movies. While integrated distributors play as major

stake holders for most of domestic movies that they distribute, they take only the distribution fee

for most of foreign movies so that they take only small portion of box-office revenues that foreign

movies generate.19 This provides the evidence that observed favor of integrated theaters in their

partners does not result from the reduction of transaction costs which is often referred as a source

of efficiency gains from vertical integration. If the reduction of transaction costs is what drives the

observed pattern, then the effects of vertical integration should not depend on the nationality of

movies as long as movies are distributed by the same distributor. Therefore, the large impact on

domestic movies of vertical integration suggests that integrated theaters hold their own movies for

longer time periods because of its increased share in perceived box-office revenues. 20

17Results do not change when the dependent variables is defined as the number of weeks of movie-running.18Each movie is classified as having multiple genre.19In both cases, distributors recoup the costs of prints and advertising in advance.20The estimation results with the restricted sample are provided in Appendix, confirming the same results.

26

Table 11: The Effects of Vertical Integration on the Length of Movie Run: Full sample

(1) (2) (3) (4) (5) (6)

Own movie 2.258*** 2.379*** 2.351** 2.566*** 2.330*** 2.076***(7.05) (4.46) (2.90) (37.16) (20.99) (14.58)

Own X Company op 0.127 0.128 0.388** 0.393***(0.39) (0.39) (3.26) (3.30)

Own X High season -0.562 -0.559 0.0462 0.0705(-0.48) (-0.47) (0.33) (0.50)

Own X Domestic movie 0.0442 0.399**(0.04) (2.84)

Company op -0.853*** -0.871*** -0.871***(-7.04) (-7.13) (-7.14)

Average of user ratings 2.336*** 2.339*** 2.339***(8.27) (8.29) (8.29)

Number of screens 0.374*** 0.374*** 0.374***(7.46) (7.46) (7.46)

Action 3.127** 3.130** 3.130**(3.23) (3.23) (3.23)

Adventure 3.357** 3.355** 3.355**(2.86) (2.86) (2.86)

Thriller 4.218*** 4.218*** 4.218***(3.98) (3.99) (3.98)

Constant -4.124 -4.063 -4.07 23.39*** 23.38*** 23.32***(-1.09) (-1.08) (-1.07) (40.90) (40.78) (40.53)

Movie and Theater FE No No No Yes Yes Yes

Observations 83419 83419 83419 83419 83419 83419Adjusted R-squared 0.42 0.42 0.42 0.831 0.831 0.831

This table reports OLS coefficients. All of specifications count 83419 observations with 590 movies. The dependentvariable is the total days of movie i’s run at theater j. Robust standard errors are clustered by movie and theaterin columns (1)∼(3). Movie and theater fixed effects are included in the second half of the table. t statistics are inparentheses. *Significant at 5%, **at 1%, ***at 0.1%

27

One might be concerned about the possibility that results are driven from the difference in

revenues across movie by theater. If integrated theaters are better at promoting own movies so that

revenues their own movies generate are higher than competitors’ movies in integrated theaters, then

we should also observe the same pattern in movie stopping decision. Most of previous literatures try

to confirm that integrated firms are in favor of products or contents they have ownership stake in and

to see whether it is efficiency gains or strategic foreclosing move that determines observed pattern.

Therefore, it is very important to see if integration effect can be observed even after controlling

weekly box office revenues. In fact, realized weekly box-office revenues would be the most deciding

factor when the decision to stop movie run is relevant. Movie’s true quality is almost realized to the

public through word-of-mouth so that multiplier effect is not crucial to movie stopping decision.

CUTijt =α+ β1Dij [Own Movie] + β2Dij [Own Movie] ∗Xi + β3Dij [Own Movie] ∗Xj

+ β4Ageit + β)5Revijt + wt + εijt

(2)

Equation (2) considers the decision of movie theaters to stop movie run. CUTijt takes one if

theater j drops movie i at week t, zero otherwise. The regression includes dummies for weeks, wt,

the number of weeks since movie’s release, Ageit, and most importantly, weekly box-office revenues

of movie i at theater j at week t, Revijt.

For the present purpose, I use the data for movies released in 2008 by two domestic distributors,

one integrated and the other unintegrated of which I can observe weekly box office revenues in the

level of theater. This restricts the sample into 38 movies, 17% of movies released nationwide in

2008.

Table 12 shows the results of estimating a probit using this data.21 The results show that

integration effect does not change when I control for weekly box office revenues. The results in

column (4) show that integrated theaters are 13% less likely to stop their own movies than other

theaters. This is almost the same as the effect of log of box office revenues (measured in tickets

sold) when evaluated at means. Given the mean value of log weekly box office revenues (measured

in ticket sold) equal to 6.47, this result means that independent movies should sell approximately

21Results of linear probability model with the full sample are presented in Appendix. Weekly box-office revenuesare not controlled in that analysis because of the lack of data. The results confirm that integrated theaters are lesslikely to stop their own movies.

28

Table 12: The Effects of Vertical Integration on Movie Stopping Decision usingProbit

(1) (2) (3) (4)

Own movie -0.1124157*** -0.1051095*** -0.0982011*** -0.1301268***(0.0283738) (0.0269054) (0.0273358) (0.027307)

Own X Company op -0.0136823 0.0516953*** 0.0287785*(0.0117365) (0.0133844) (0.0142846)

Log of weekly Box office -0.16252*** -0.1354981***(0.0121578) (0.0080698)

Weeks since release 0.0621331*** 0.0621296*** 0.0292059** 0.035189***(0.0132436) (0.0132456) (0.0100515) (0.0110278)

Integ Movie -0.0056788 -0.0056142 -0.0075139 0.017196(0.0505577) (0.0505582) (0.0320827) (0.0247029)

Number of screens -0.0140767*** -0.014159*** 0.0017948 -0.0027645(0.0023775) (0.0023778) (0.0029262) (0.0022669)

Company op 0.0319289*** 0.0348441*** 0.0865314*** 0.0810435***(0.0047049) (0.0058494) (0.0085432) (0.009387)

Pre Expectation -0.0125497*** -0.0125544*** -0.0015773 -0.0055612*(0.0030179) (0.0030189) (0.0028858) (0.002767)

Satisfaction rate -0.0078744*** -0.0078745*** -0.0039852** -0.0020167(0.0014221) (0.0014223) (0.0013162) (0.001451)

Week dummies No No No YesObs 17629 17629 17629 17611

This table reports probit marginal effects evaluated at means. Sample includes movies released in2008 by two major domestic distributors, one integrated and the other independent, counting up to36 movies. Pre-expectation and satisfaction rate are measured as deviations from their mean values.Robust standard errors are in parentheses and clustered by movie. *Significant at 5%, **at 1%, ***at0.1%

29

1,100 tickets more than integrated movies to be dropped at the same week.

Overall, the significant effect of vertical integration in this model suggests the evidence of partial

market foreclosure, but not the existence of efficiency gains. Integrated theaters discriminate movies

from other distributors in favor of movies they own in the decision of movie run stopping. With

the fixed number of screens available in the short run, this foreclosure not only makes independent

distributors difficult in their business, but also reduces the total box-office revenues in theatrical

market.

5.3. Film choice decision

Now, I turn to the relationship between organization structure and film choice decision. As

pointed out earlier, vertically integrated theaters are more likely to show their own movies as they

internalize revenues from both distributor and exhibitor. However, this effect would be relevant

mainly for movies at the margin which usually have difficulty in finding theaters to be shown.

Table 13 shows the results of estimating a probit model for the decision of film choice.22 Columns

(1)∼(4) include all the movies released from 2006 to 2008, while columns (5)∼(8) include movies

released in 2008 only. In all of specifications, the effect of vertical integration is significant, implying

that integrated theaters are more likely to choose their own movies. In columns (1) and (2), I present

marginal effects evaluated at means from a probit model that does not include movie and theater

fixed effects. Results show that the effect of vertical integration is higher in peak seasons, but

negatively correlated with online user ratings. I include average score of online user ratings as a

proxy of movie quality, but it seems not to be a good measure for two reasons in this specification.

First, average score of user ratings does not represent opening attractiveness of the movie, but it

is ex ante expectation about movie quality rather than true quality of movie that determines the

performance at opening weekend. Without specifying the minimum length of movie run in the

contract with distributor, movie theaters would consider pre-expectation about movies as the most

important factor for their film choice decisions. Second, those who leave online ratings are not

representative to the population. It is often observed that movies receiving stellar reviews from

critics fail to do strong business in box-office. Likewise, online user ratings for some of movies might

represent preference of specific groups rather than overall popularity.

22Results from linear probability model are in Appendix.

30

Tab

le13:

Th

eE

ffec

tsof

Ver

tica

lIn

tegr

atio

non

Fil

mC

hoi

ceD

ecis

ion

usi

ng

Pro

bit

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Ow

nm

ovie

0.0

9749***

0.2

9923***

0.1

0773***

0.0

8752**

0.1

1119***

0.1

0624***

0.1

0535***

0.0

7906***

(0.0

0871)

(0.0

3212)

(0.0

1133)

(0.0

3265)

(0.0

1219)

(0.0

2238)

(0.0

0926)

(0.0

1256)

Num

ber

of

scre

ens

0.0

6322***

0.0

6318***

0.0

5837***

0.0

5886***

(0.0

0207)

(0.0

0215)

(0.0

0255)

(0.0

0267)

Com

pany

op

0.0

1485

0.0

2539**

0.0

0445

0.0

1641

(0.0

0828)

(0.0

0738)

(0.0

1090)

(0.0

0910)

Aver

age

of

use

rra

tings

0.0

0870***

0.0

1011***

(0.0

0110)

(0.0

0114)

Pre

exp

ecta

tion

0.0

1947***

0.0

1929***

(0.0

0054)

(0.0

0055)

Sati

sfact

ion

rate

0.0

0121***

0.0

0126***

(0.0

0014)

(0.0

0014)

Ow

nX

Com

pany

op

0.0

1498

0.0

4404***

0.0

4316

0.0

5900***

(0.0

1545)

(0.0

1025)

(0.0

2314)

(0.0

1481)

Ow

nX

Hig

hse

aso

n0.0

8591***

0.0

1260

0.0

3599*

0.0

0143

(0.0

1280)

(0.0

1122)

(0.0

1642)

(0.0

1688)

Ow

nX

Use

rra

tings

-0.0

3171***

-0.0

0051

(0.0

0443)

(0.0

0428)

Ow

nX

Pre

exp

ecta

tion

0.0

0469*

0.0

0060

(0.0

0205)

(0.0

0134)

Ow

nX

Sati

sfact

ion

rate

-0.0

0115*

-0.0

0052

(0.0

0056)

(0.0

0050)

Mov

ieand

Thea

ter

FE

No

No

Yes

Yes

No

No

Yes

Yes

Obse

rvati

ons

109259

109259

102752

102752

42395

42395

40797

40592

Th

ista

ble

rep

ost

sp

rob

itm

arg

inal

effec

tsev

alu

ate

dat

mea

ns.

Th

ed

epen

den

tvari

ab

leis

ad

um

my

vari

ab

leth

at

takes

valu

eon

eif

the

thea

ter

show

the

movie

,an

dze

rooth

erw

ise.

Th

efi

rst

half

of

tab

leco

unts

wit

hall

of

590

movie

sre

lease

dd

uri

ng

the

enti

red

ata

per

iod

,w

hile

the

seco

nd

half

cou

nts

206

movie

sre

lease

din

2008.

Pre

-exp

ecta

tion

an

dsa

tisf

act

ion

rate

are

mea

sure

das

dev

iati

on

sfr

om

thei

rm

ean

valu

es.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

an

dcl

ust

ered

by

movie

inre

gre

ssio

ns

wit

hou

tfi

xed

effec

ts(c

olu

mn

s(1

),(2

),(5

),(6

)).

*S

ignifi

cant

at

5%

,**at

1%

,***at

0.1

%

31

In columns (3) and (4), I employed fixed effect models in the dimension of movie and theater

which enable us to control unobserved quality of movie as well as the quality of theater.23 Integra-

tion effect is still significant and its magnitude is quite stable. Fixed effect model, however, shows

that integration effect does not depend on either seasonality or movie quality represented by online

user ratings, while company operated theaters are more likely to choose their own movies than

dealer run theaters. These results hold the same when I restricted the sample to movies released

only in 2008 in order to use the data about pre-expectation and satisfaction rate for each movie

from a movie marketing company as shown in columns (5)∼(8). They increase the probability to be

chosen, while the effect of integration does not vary along the level of these variables once movie-

theater fixed effects are controlled. This might be because the sample does not include movies

failed to be marketed, but only movies released nationwide. Movie characteristics such as casting,

director and scenario provide some degree of forecast for the demand of movie in advance, but the

success of a movie is hardly predictable before a film is made for the release.24 As a result, some of

movies are not released to the public and the decision to market a movie would depend on movie’s

expected demand. Meanwhile, conditional that a movie is released nationwide, whether to show it

in its own theater does not depend much on movie quality and its potential for opening scores. In

fact, over 90% of movies are shown in their own theaters in the sample. Movie’s expected popularity

would be more relevant to the decision of screen allocation which was investigated above.

On the other hands, these results contradict the view that integrated firms force independent

theaters to show their own movies when movies are expected to have lower quality, by which

integrated theaters can increase their profits by allowing more screens to movies with better quality

from other distributors. If integrated firms have better bargaining power over independent theaters

and the above view holds in the market, then the lower movie quality is, the less the probability to

show own movies should be, which is not what we observe here. Overall, integrated and company

operated theaters are at least 10% percent more likely to show their own movies when using movie-

theater fixed effects.

23Probit model with fixed effects suffer from incidental parameters problem, generating inconsistent estimators.Hence, the interpretation of these results should be limited with caution.

24This is why we observe flops every year.

32

6. Robustness check

The identification of the effects of vertical integration is, so far, justified by the assumption

that there is no systematic matching between movies and theaters based on their characteristics

other than integration status. This assumption can be violated if both integrated distributors

and integrated theaters are of better quality so that integrated theaters prefer their own movies

because of higher movie quality their movies have. This is not likely to drive the results for following

reasons. First, integrated distributors show better performance on average, but each distributor has

both hits and bursts in its line-up. Second, among two integrated distributors, CJ is the leading

company, explaining around 30% of market share, but the other integrated distributor, Lotte has

8% of market share on average during the data period and does not have many hits in its line-up.

Nevertheless, to address this concern, I re-estimate the effects of vertical integration on screen-

ings times with the inclusion of interactions between movie and theater characteristics in Table

14. Columns (1) and (4) repeat the estimation results without interaction terms for the purpose

of comparison. In columns (2) and (5), the number of screens in the theater is interacted with

measures of movie quality represented by pre-expectation and satisfaction rate. In addition, I in-

clude the interactions between these movie quality measures and dummies for multiplex chain in

columns (3) and (6). Positive and significant effects of interactions between the number of screens

and movie qualit measures indicate that there exists some degree of matching between movies and

theaters based on other than integration status. However, in all of specifications, the effects of

vertical integration are quite stable and significant, and the size of integration effects does not

change much.

Product Differentiation One might still concern that observed patterns reflect the results of

product differentiation strategy of each theater. In fact, Chisholm et al. (2010) found that the

closer theaters are located to each other, they tend to choose different film-choice programming to

lessen competition in US motion picture industry. This result can be seen as a strategic product

differentiation, where the product is defined as a set of movies to show. What we are observing in

data might be a mixture of the effect of vertical integration and product differentiation.

I address this issue with the use of the disintegration of a distributor Showbox and a multi-

33

Table 14: Integration Effects on Screenings Times at the Opening week

(1) (2) (3) (4) (5) (6)

Own movie -0.353 -0.294 0.0227 -0.0236 0.0255 0.381***(-1.09) (-0.91) (0.07) (-0.25) (0.29) (4.32)

Own X Company op 1.458*** 1.187** 1.002** 1.394*** 1.124*** 0.948***(3.37) (3.11) (2.79) (12.65) (10.78) (9.32)

Own X High season 1.235** 1.262** 1.217** 0.682*** 0.712*** 0.621***(2.87) (3.04) (3.05) (5.07) (5.63) (5.10)

Own X Pre expectation 0.0261 -0.00135 -0.0088 0.00641 0.00309 -0.00555(1.16) (-0.06) (-0.42) (0.66) (0.37) (-0.70)

Own X Satisfaction rate 0.0330** 0.0309** 0.0163*** 0.0166*** 0.0111**(2.78) (2.61) (3.73) (4.03) (2.73)

N of screens X Pre expecation 0.0429*** 0.0420*** 0.0434*** 0.0428***(8.84) (8.99) (32.96) (33.18)

N of screens X Satisfaction rate 0.00172 0.00151 0.00213*** 0.00187***(1.26) (1.17) (4.45) (3.91)

Constant -4.381*** 1.372 -0.0175 2.430*** 3.803*** 4.254***(-4.19) (1.25) (-0.02) (5.68) (11.46) (13.41)

Movie and Theater FE No No No Yes Yes YesPre expectation X multiplex chain No No Yes No No YesSatifaction rate X multiplex chain No No Yes No No Yes

Observations 33835 33835 33835 33835 33835 33835Adj R-squared 0.545 0.592 0.61 0.654 0.7 0.718

This table reproduces OLS coefficients for the effects of vertical integration on screening times at the opening week with the inclusionof interactions between movie and theater characteristics. The dependent variable is the number of screening times dedicated to moviei in theater j on Saturday of the opening week. Pre expectation and satisfaction rate are measured as deviations from their mean valuesas before. The sample includes movies released in 2008, counting to 206 movies. t statistics are in parentheses and standard errors areclustered by movie and theater in columns (1)∼(3) while robust standard errors are used with movie-theater fixed effects in columns(4)∼(6). *Significant at 5%, **at 1%, ***at 0.1%

34

Table 15: Change in screening times before and after disintegration

Before disintegration After disintegration

Distributors DistributorsOthers Showbox Difference Others Showbox Difference DIDID

Theaters (1) (2) (2) - (1) (3) (4) (4) - (3)Others (a) Mean 7.880 8.827 0.947 7.729 8.155 0.426

SD 4.307 4.469 0.068 3.906 4.135 0.063Obs 28662 4806 39099 4384

Megabox (b) Mean 8.436 10.611 2.174 8.380 10.145 1.765SD 5.237 6.276 0.268 4.873 5.981 0.293Obs 2826 475 2842 325

DID (b)-(a) 1.227 (b)-(a) 1.339 0.111SE 0.337 0.468 t=0.19

Table 16: Change in total days of movie run before and after disintegration

Before disintegration After disintegration

Distributors DistributorsOthers Showbox Difference Others Showbox Difference DIDID

Theaters (1) (2) (2) - (1) (3) (4) (4) - (3)Others (a) Mean 19.160 20.531 1.371 19.757 21.119 1.362

SD 10.348 12.521 0.167 11.464 13.815 0.187Obs 28662 4806 39099 4384

Megabox (b) Mean 18.108 21.196 3.088 18.464 20.557 2.093SD 10.178 12.498 0.523 10.252 11.393 0.607Obs 2826 475 2842 325

DID (b)-(a) 1.717 (b)-(a) 0.731 -0.986SE 0.459 0.729 t=1.15

35

plex chain Megabox which happened in 2007. This disintegration between Megabox and Showbox

provides the opportunity to disentangle the pure effect of vertical integration from observed distri-

bution of movie allocation. This disintegration was not forced by any other policy like Paramount

decree in U.S., which provokes the concern about endogenous treatment problem. The use of movie

and theater fixed effects is believed to attenuate this concern.

Tables 15 and 16 describe how Megabox changes in its treatment of Showbox after the dis-

integration in the manner of DIDID approach. Before the disintegration, Megabox allows 1.227

screening times more at the opening Saturday to their own movies compared to other movies and

other theaters. This tendency does not seem to change even after Megabox was disintegrated from

Showbox (1.339). The difference is very small and insignificant (t=0.19). The length of movie run

reduced after the disintegration, but it is not significant either. To get more precise results, I esti-

mate the effect of Showbox movies in Megabox theaters on screening times at the opening Saturday

before and after the disintegration separately. Table 17 shows these results. In all of specifications,

I include movie and theater fixed effects to control unobserved characteristics of movie and theater.

My estimates suggest that Megabox allocates more screening times to Showbox movies even after

the disintegration. However, the size of the effects is significantly lower than what was before the

disintegration and Wald test rejects that these effects are equal before and after the disintegration

at least 5% level, and at 0.1% level when interacting with company operated theaters, suggesting

that product differentiation does not explain the entire picture of screen allocation.

Enodeneity The topic of vertical integration is related to firm boundaries, specifically to the

question of which transactions to carry out in-house and which to buy through the market. Lit-

erature of transaction cost economics argues that vertical integration may be an efficient way to

organize when contracts are incomplete and ex post renegotiation is costly. In the study of the

US airline industry, Forbes and Lederman (2009) show that airlines are more likely to use owned

regionals on routes on which ex post adaptation needs to be made frequently and the costs of

adaptation are more costly. They use the average weather patterns at the endpoint airports of a

city pair to measure the probability to have adaptation on routes and the degree to which a given

city is integrated into the major’s overall network to measure costs of adaptation. Gil (2007) also

shows that vertically integrated distributors are more likely to distribute movies of contractual

36

Tab

le17

:D

isin

tegr

atio

nof

Showbox

andMegabox

±3

month

sd

rop

ped

±6

month

sd

rop

ped

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Sh

owb

oxat

Meg

abox

(β)

1.6

40***

1.0

05***

0.5

31

1.2

86*

1.9

80***

1.1

85***

0.4

98

1.0

61*

(11.7

2)

(5.5

1)

(1.6

1)

(2.2

2)

(11.4

3)

(5.8

1)

(1.9

4)

(2.1

8)

Sh

owb

oxat

Meg

abox

XC

omp

any

op(γ

)1.9

34***

-0.1

14

1.5

68***

-0.0

641

(5.1

4)

(-0.1

9)

(5.2

9)

(-0.1

2)

Con

stan

t1.4

19*

1.0

83

-1.4

41*

0.1

22

-1.4

45*

0.1

22

1.4

20*

1.0

83

(1.9

8)

(1.6

7)

(-2.2

1)

(0.1

8)

(-2.2

1)

(0.1

8)

(1.9

8)

(1.6

7)

Ob

serv

atio

ns

24999

28273

16929

23942

16929

23942

24999

28273

Ad

jR

-squ

ared

0.6

43

0.6

51

0.6

48

0.6

61

0.6

47

0.6

61

0.6

44

0.6

51

F107.5

129.6

101.8

128.2

101.9

128.5

107.5

129.2

Wal

dte

stβ(1

)=β(2

)β(3

)+γ(3

)=β(4

)+γ(4

)β(5

)=β(6

)β(7

)+γ(7

)=β(8

)+γ(8

)

χ2

5.5

611.1

06.0

011.4

5P

-val

ue

0.0

184

0.0

009

0.0

143

0.0

007

Th

ista

ble

rep

ort

sO

LS

coeffi

cien

tsfr

om

mod

els

wit

hm

ovie

an

dth

eate

rfi

xed

effec

ts.

Are

as

wit

hn

oMegabox

thea

ters

are

excl

ud

edfr

om

the

sam

ple

.3

month

sb

efore

an

daft

erth

ed

isin

tegra

tion

are

als

od

rop

ped

inco

lum

ns

(1)∼

(4)

an

d6

month

sb

efore

an

daft

erd

rop

ped

inco

lum

s(5

)∼(8

)to

red

uce

poss

ible

nois

efr

om

the

tran

siti

on

inin

tegra

tion

statu

s.R

egre

ssio

nre

sult

su

sin

gth

esa

mp

leb

efore

the

dis

inte

gra

tion

are

show

nin

colu

mn

sw

ith

od

dnu

mb

ers

wh

ile

resu

lts

from

the

sam

ple

aft

erth

ed

isin

tegra

tion

show

nin

colu

mn

sw

ith

even

nu

mb

ers.

tst

ati

stic

sare

inp

are

nth

eses

.*S

ign

ifica

nt

at

5%

,**at

1%

,***at

0.1

%

37

complexity in the Spanish movie industry, and more likely to show them in their own theaters. In

fact, the decision of vertical integration is endogenous, and movies of integrated distributors may

be systematically different from movies of independent distributors.

To investigate this issue, I re-examine the decision of movie run stopping with the consideration

of renegotiation ex post. Box-office revenues are split into distributor and exhibitor and its share

is quite uniform in the Korean movie industry as pointed out earlier. However, the distributor

and exhibitor use an ex-post renegotiation to adjust sharing terms. I have information about the

renegotiation of one integrated exhibitor with distributors in 2008. Regression results show that the

effects of vertical integration is quite robust to the inclusion of the interaction of vertical integration

with dummies for renegotiation, suggesting that the possibility of renegotiation ex post is not a

major issue in contracts between distributor and exhibitor.

Interviews with industry executives also reveal that no distributors prefer risky movies. Vertical

integration can reduce costs of ex post adaptation, but it does not mean that integrated firms want

to choose risky packages ex ante. In addition, renegotiation is not common and, if any, usually

happens several weeks after the release when movies generate typically very small amount of box-

office revenues.

7. Summary and Conclusion

This paper studies the effects on exhibition behavior of movie theaters of vertical integration

between movie distributor and exhibitor in the Korean movie industry. Specifically, I examine the

effects of vertical integration on the decision of film choice, allocation of screening times, and movie

run stopping. The use of movie and theater fixed effects control the variations in movie quality

and underlying demand over markets.

Using a rich dataset on movies released during 2006∼2008 in the Korean movie industry, I

find that integrated theaters are more likely to choose their own movies than movies of other

distributors, and than unintegrated theaters do. It implies that independent distributors have a

limited access to theatrical windows at the margin. I find as well that integrated theaters allocate

more screening times to their own movies at the opening week which is the crucial timing for the

38

success in box office revenues. This tendency is getting larger when movies are expected to generate

positive word-of-mouth as well as when underlying demand for movies is high like holidays. In the

analysis of movie run stopping decision, it is shown that integrated theaters are less likely to drop

their own movies. This favor in own movies survives even after controlling the difference in weekly

box office revenues of each movie over theaters. Hence, it should be interpreted as that integrated

theaters discriminate movies of independent distributors in favor of movies they have ownership

stake in. That is, integrated theaters use a lower standard to their own movies against other

competing movies. I argue that these results are not driven by the matching based on movie and

theater quality or other characteristics other than integration status.

Overall, combined with stylized facts in the Korean movie industry, these findings suggest that

independent distributors are partially foreclosed and that vertical integration harms consumer in

theatrical exhibition market. This is consistent with what the Korean movie industry has observed.

Integrated firms have increased their market shares both in distribution sector and in exhibition

sector, while many domestic independent distributors exited from the market. Hence, the vertical

integration might make consumers even worse off in the long run.

Nevertheless, the assessment of consumer welfare should be made with caution. As discussed

earlier, integrated theaters can internalize the dynamic effect as well as the multiplier effect that

early box-office revenues generate, increasing overall performance of movies that are embedded

with good quality. The increase in future revenues from these movies is achieved at the expense of

current box-office revenues, but it might be possible that this effect outweighs the loss of current

revenues. Future research requires precise evaluation of the dynamic effect of vertical integration.

39

8. Appendix

Figure A1: Distribution of Theaters in Korea

40

Figure A2: Distribution of Theaters in Seoul, Korea in 2008

41

Table A1: The Effects of Vertical Integration on the Length of Movie Run: Restrictedsample

(1) (2) (3) (4) (5) (6)

Own movie 2.304*** 1.358 1.208 2.439*** 1.629*** 1.444***(5.22) (1.92) (1.55) (21.44) (8.81) (7.73)

Own X Company op 0.332 0.339 0.582** 0.594**(0.83) (0.78) (3.01) (3.08)

Own X High season 1.831 1.544 1.166*** 1.001***(1.13) (0.99) (5.30) (4.56)

Own X Pre Expectation -0.0192 0.0465***(-0.27) (3.79)

Own X Satisfaction rate 0.0890* -0.0024(2.02) (-0.34)

Pre Expectation 0.500*** 0.498*** 0.499***(8.02) (8.01) (7.75)

Satisfaction rate 0.200*** 0.200*** 0.194***(5.87) (5.87) (5.59)

Company op -0.899*** -0.942*** -0.942***(-5.23) (-5.48) (-5.45)

Number of screens 0.591*** 0.590*** 0.587***(10.58) (10.59) (10.51)

Constant 4.904 5.175 4.914 16.98*** 17.14*** 17.22***(1.05) (1.10) (1.08) (17.10) (17.29) (17.40)

Movie and Theater FE No No No Yes Yes Yes

Observations 33835 33835 33835 33942 33942 33835Adjusted R-squared 0.623 0.624 0.625 0.847 0.847 0.847

This table reports OLS coefficients. All of specifications count with 206 movies released in 2008. Robust standarderrors are clustered by movie and theater in columns (1)∼(4). Movie and theater fixed effects are included in therest half of the table. Pre-expectation and satisfaction rate are measured as deviations from their mean values. tstatistics are in parentheses. *Significant at 5%, **at 1%, ***at 0.1%

42

Table A2: The Effects of Vertical Integration on Movie Stopping Decision using OLS

(1) (2) (3) (4) (5) (6) (7)

Own movie -0.0245∗∗∗ -0.0268∗∗∗ -0.0419∗∗∗ -0.939∗∗∗ -0.0558∗∗∗ -0.0556∗∗∗ -0.0441∗∗∗

(-7.72) (-7.88) (-8.55) (-12.91) (-10.82) (-11.58) (-5.60)

Number of screens -0.00484∗∗∗ -0.00796∗∗∗ -0.0148∗∗∗ -0.0150∗∗∗

(-5.51) (-9.78) (-10.49) (-10.76)

Pre-Expectation -0.00768∗∗∗ -0.0125∗∗∗ -0.0135∗∗∗

(-66.55) (-44.04) (-40.81)

Satisfaction rate -0.00316∗∗∗ -0.00622∗∗∗ -0.00608∗∗∗

(-47.50) (-52.08) (-50.55)

Weeks since release 0.112∗∗∗ 0.112∗∗∗ 0.149∗∗∗ 0.142∗∗∗ 0.141∗∗∗

(34.59) (34.13) (112.00) (73.22) (73.10)

Own X Pre Expecation 0.00482∗∗∗ 0.000274(10.04) (0.72)

Own X Satisfaction rate -0.000383 0.000842∗∗

(-1.88) (3.21)

Own X Company op 0.0174∗ -0.0184∗

(2.19) (-2.36)

Constant 0.102∗∗∗ 0.270∗∗∗ 0.253∗∗∗ 0.255∗∗∗ 0.124∗∗∗ -0.213∗∗∗ -0.212∗∗∗

(4.25) (11.08) (8.85) (9.12) (3.86) (-7.04) (-7.02)

Movie and Theater FE No No No No Yes Yes Yes

Week Dummies Yes Yes Yes Yes No Yes Yes

Observations 89334 89153 89153 89153 89334 89334 89153Adjusted R2 0.147 0.195 0.318 0.320 0.334 0.423 0.423

This table reports OLS coefficients. Dependent variable is CUTijt taking one if theater j drops movie i at week t, zero otherwise. Incolumns (2), I include variables related to movie quality, and these variables are found to reduce the chance to stop movie run. WhenI add the age of movie - the weeks after movie’s release - and interactions of vertical integration with movie quality, the results donot change. In columns (5)∼(7), I use movie and theater fixed effects to control for possible difference in revenues across movies andtheaters. Week dummies are also included in columns (6) and (7), enabling me to control for the variation in opportunity costs relatedto newly released movies over weeks. All of specifications suggest that integrated theaters are less likely to stop their own movies thannonintegrated theaters and other rival integrated theaters do. The sample includes movies released in 2008. Robust standard errors arein parentheses and clustered by movie in columns (1)∼(4). *Significant at 5%, **at 1%, ***at 0.1%

43

Tab

leA

3:T

he

Eff

ects

ofV

erti

cal

Inte

grat

ion

onF

ilm

Ch

oice

Dec

isio

nu

sin

gO

LS

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Ow

nm

ovie

0.0

608***

0.2

13**

0.0

612***

0.0

614***

0.0

716***

0.1

25***

0.0

711***

0.0

953***

(5.4

8)

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44

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46