Vertical Integration and Market Foreclosure in the Korean Movie Industry

Home , A2 (theater), CJ Entertainment, Lotte Entertainment, Theater in Korea

Yusun Hwang∗

November, 2013

Abstract I examine the exhibition behavior of movie theaters in the Korean movie industry in order to investigate the influence of vertical integration on competition. I focus specifically on the choice 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 in both integrated theaters and unintegrated theaters but also observe the same theater showing movies from distributors of different vertical structures, I use movie and theater fixed effects to control for the unobserved quality of movies and theaters. The empirical results suggest that vertically integrated theaters are more likely to choose their affiliated movies than other competing movies, and they choose them more often than other competing theaters do. In addition, integrated theaters give their own movies a greater number of screenings over longer time periods. This effect is mostly restricted to company operated theaters, and it is greater when movies are expected to get positive word-of-mouth as well as when underlying demand is high such as holidays. I argue that these results are not driven by the matching between movie and theater based on anything other than integration status, and that vertical integration leads to the foreclosure, denial of access, of independent distributors to integrated theaters, to the detriment 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: [email protected]

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, vertically 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 conclusion 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)

2 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 internalize 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 companies 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 product 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

3 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 theaters in my sample. The prevalence of uniform pricing in the movie industry is, in fact, somewhat puzzling. Orbach and Einav (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 analysis 750/50 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

8 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 29.1 Showbox 20.1 12.3 10.2 15.2 Lotte Entertainment 5.6 8.6 8.3 11.8 UPI Korea 7.6 3.5 10.0 2.1 Sony Pictures Releasing Buena Vista Korea 10.0 9.8 6.8 8.5 Warner Bros. Korea 5.8 11.3 6.1 5.6 20th Century Fox Korea 5.9 5.6 5.1 7.8 Others 21.8 19.2 23.4 19.9 Total 100.0 100.0 100.0 100.0

Table 2: Number of Theaters and Screens by Exhibitor

N of theaters N of screens Exhibitor 2006 2007 2008 2006 2007 2008 Multiplex CGV 44 57 63 351 461 511 Primus 33 38 36 226 276 259 Lotte Cinema 36 41 47 273 316 360 Megabox 20 15 13 166 123 116 Cinus 15 23 25 109 160 178 Others 50 36 39 437 344 352 Total 198 210 223 1562 1680 1776

Non-Multiplex 123 104 86 318 295 228

Total 321 314 309 1880 1975 2004

9 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 may reflect 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

∂τA ∂τB βA NA + τA = βB NB + τB ∂NA ∂NB

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 problem 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 commitment 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

11 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 NA0 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

MRA’

MRA MRB

NA’

NA NB Number of screens

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

12 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 seasons compared to underlying demand, suggesting that studios could increase revenues by adjusting release timing of their

13 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 competition 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.

14 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 period of the data. Parentheses include t-statistics for Diﬀ, SD for mean values. *Signiﬁcant at 5%, **at 1%, ***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 performance 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 comparing 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)

Distributor Total Non-CJ CJ Diﬀ Theater Non-CGV Mean 7.864 7.598 9.104 1.506 SD 4.13 3.89 4.88 0.04 Obs 50635 41677 8958

CGV Mean 8.052 7.607 10.050 2.442 SD 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 the opening week. In addition, I assume that each screen has the same number of seats. However, it is possible that integrated theaters locate their own movies to bigger screen having more seats and at show times when demand is high 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)

Distributor Total Non-Lotte Lotte Diﬀ Theater Non-Lotte Mean 7.950 8.070 7.057 -1.012 SD 4.17 4.30 2.83 0.05 Obs 66537 58652 7885

Lotte Mean 7.891 7.829 8.291 0.461 SD 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 2008 16Results 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 83419 Adj 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. The dependent 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 effects are included in the second half of the table. t statistics are in parentheses. *Significant at 5%, **at 1%, ***at 0.1%

22 Table 8: The Eﬀects of Vertical Integration on Screening Times: Restricted sample

(1) (2) (3) (4) (5) (6) (7) (8)

Own movie 1.115*** 0.318 -0.242 -0.318 1.136*** 0.362*** 0.034 -0.0236 (5.80) (1.23) (-0.78) (-0.94) (16.53) (4.58) (0.37) (-0.25)

Own X Company op 1.435*** 1.447*** 1.452*** 1.398*** 1.402*** 1.394*** (3.33) (3.35) (3.33) (12.69) (12.76) (12.65)

Own X High season 1.330** 1.206** 0.778*** 0.682*** (2.96) -2.95 (5.60) (5.07)

Own X Pre Expectation -0.0019 0.00641 (-0.08) (0.66)

Own X Satisfaction rate 0.0325** 0.0163*** (2.79) (3.73)

Pre Expectation 0.226*** 0.226*** 0.225*** 0.225*** 23 (12.06) (12.06) (12.15) (12.40)

Satisfaction rate 0.0197** 0.0196** 0.0196** 0.0173** (3.00) (2.99) (3.01) (2.76)

Company op 0.340* 0.156 0.155 0.154 (2.28) (1.12) (1.11) (1.10)

Number of screens 0.594*** 0.594*** 0.593*** 0.592*** (11.31) (11.34) (11.32) (11.27)

Constant -1.008 -0.924 -0.741 -0.848 2.218*** 2.239*** 2.339*** 2.430*** (-1.13) (-1.04) (-0.84) (-0.94) (5.17) (5.20) (5.43) (5.68)

Movie and Theater FE No No No No Yes Yes Yes Yes

Observations 33835 33835 33835 33835 33942 33942 33942 33835 Adj R-squared 0.541 0.543 0.545 0.547 0.65 0.652 0.653 0.654 This table reports OLS coefficients. All of specifications count with 206 movies released in 2008. Robust standard errors are clustered by movie and theater in columns (1)∼(4). Movie and theater fixed effects are included in the rest half of the table. Pre-expectation and satisfaction rate are measured as deviations from their mean values. t statistics are in parentheses. *Significant at 5%, **at 1%, ***at 0.1% 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)

Distributor Total Non-CJ CJ Diﬀ Theater Non-CGV Mean 19.515 18.458 24.434 5.976 SD 11.16 10.62 12.21 0.12 Obs 50635 41677 8958

CGV Mean 19.677 18.434 25.264 6.829 SD 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)

Distributor Total Non-Lotte Lotte Diﬀ Theater Non-Lotte Mean 19.632 20.118 16.016 -4.102 SD 11.33 11.65 7.69 0.13 Obs 66537 58652 7885

Lotte Mean 19.369 19.197 20.476 1.278 SD 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 83419 Adjusted 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 dependent variable is the total days of movie i’s run at theater j. Robust standard errors are clustered by movie and theater in columns (1)∼(3). Movie and theater fixed effects are included in the second half of the table. t statistics are in parentheses. *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 (2) + β4Ageit + β)5Revijt + wt + εijt

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 revenues are not controlled in that analysis because of the lack of data. The results conﬁrm that integrated theaters are less likely to stop their own movies.

28 Table 12: The Eﬀects of Vertical Integration on Movie Stopping Decision using Probit

(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 Yes Obs 17629 17629 17629 17611 This table reports probit marginal eﬀects evaluated at means. Sample includes movies released in 2008 by two major domestic distributors, one integrated and the other independent, counting up to 36 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%, ***at 0.1%

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 Table 13: The Eﬀects of Vertical Integration on Film Choice Decision using Probit

(1) (2) (3) (4) (5) (6) (7) (8) Own movie 0.09749*** 0.29923*** 0.10773*** 0.08752** 0.11119*** 0.10624*** 0.10535*** 0.07906*** (0.00871) (0.03212) (0.01133) (0.03265) (0.01219) (0.02238) (0.00926) (0.01256)

Number of screens 0.06322*** 0.06318*** 0.05837*** 0.05886*** (0.00207) (0.00215) (0.00255) (0.00267)

Company op 0.01485 0.02539** 0.00445 0.01641 (0.00828) (0.00738) (0.01090) (0.00910)

Average of user ratings 0.00870*** 0.01011*** (0.00110) (0.00114)

Pre expectation 0.01947*** 0.01929*** (0.00054) (0.00055)

Satisfaction rate 0.00121*** 0.00126*** (0.00014) (0.00014) 31 Own X Company op 0.01498 0.04404*** 0.04316 0.05900*** (0.01545) (0.01025) (0.02314) (0.01481)

Own X High season 0.08591*** 0.01260 0.03599* 0.00143 (0.01280) (0.01122) (0.01642) (0.01688)

Own X User ratings -0.03171*** -0.00051 (0.00443) (0.00428)

Own X Pre expectation 0.00469* 0.00060 (0.00205) (0.00134)

Own X Satisfaction rate -0.00115* -0.00052 (0.00056) (0.00050)

Movie and Theater FE No No Yes Yes No No Yes Yes

Observations 109259 109259 102752 102752 42395 42395 40797 40592 This table reposts probit marginal effects evaluated at means. The dependent variable is a dummy variable that takes value one if the theater show the movie, and zero otherwise. The first half of table counts with all of 590 movies released during the entire data period, while the second half counts 206 movies released in 2008. Pre-expectation and satisfaction rate are measured as deviations from their mean values. Robust standard errors are in parentheses and clustered by movie in regressions without fixed effects (columns (1),(2),(5),(6)). *Significant at 5%, **at 1%, ***at 0.1% 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 screenings 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 include 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 Yes Pre expectation X multiplex chain No No Yes No No Yes Satifaction rate X multiplex chain No No Yes No No Yes

Observations 33835 33835 33835 33835 33835 33835 Adj 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 inclusion of interactions between movie and theater characteristics. The dependent variable is the number of screening times dedicated to movie i in theater j on Saturday of the opening week. Pre expectation and satisfaction rate are measured as deviations from their mean values as before. The sample includes movies released in 2008, counting to 206 movies. t statistics are in parentheses and standard errors are clustered 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 Distributors Others 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 0.063 Obs 28662 4806 39099 4384

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

DID (b)-(a) 1.227 (b)-(a) 1.339 0.111 SE 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 Distributors Others 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 0.187 Obs 28662 4806 39099 4384

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

DID (b)-(a) 1.717 (b)-(a) 0.731 -0.986 SE 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 distribution 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 disintegration 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 estimate 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 Table 17: Disintegration of Showbox and Megabox

±3 months dropped ±6 months dropped (1) (2) (3) (4) (5) (6) (7) (8)

Showbox at Megabox (β) 1.640*** 1.005*** 0.531 1.286* 1.980*** 1.185*** 0.498 1.061* (11.72) (5.51) (1.61) (2.22) (11.43) (5.81) (1.94) (2.18)

Showbox at Megabox X Company op (γ) 1.934*** -0.114 1.568*** -0.0641 (5.14) (-0.19) (5.29) (-0.12)

Constant 1.419* 1.083 -1.441* 0.122 -1.445* 0.122 1.420* 1.083 37 (1.98) (1.67) (-2.21) (0.18) (-2.21) (0.18) (1.98) (1.67)

Observations 24999 28273 16929 23942 16929 23942 24999 28273 Adj R-squared 0.643 0.651 0.648 0.661 0.647 0.661 0.644 0.651 F 107.5 129.6 101.8 128.2 101.9 128.5 107.5 129.2

Wald test β(1) = β(2) β(3) + γ(3) = β(4) + γ(4) β(5) = β(6) β(7) + γ(7) = β(8) + γ(8) χ2 5.56 11.10 6.00 11.45 P-value 0.0184 0.0009 0.0143 0.0007 This table reports OLS coefficients from models with movie and theater fixed effects. Areas with no Megabox theaters are excluded from the sample. 3 months before and after the disintegration are also dropped in columns (1)∼(4) and 6 months before and after dropped in colums (5)∼(8) to reduce possible noise from the transition in integration status. Regression results using the sample before the disintegration are shown in columns with odd numbers while results from the sample after the disintegration shown in columns with even numbers. t statistics are in parentheses. *Significant at 5%, **at 1%, ***at 0.1% 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: Restricted sample

(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 33835 Adjusted 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 standard errors are clustered by movie and theater in columns (1)∼(4). Movie and theater fixed effects are included in the rest half of the table. Pre-expectation and satisfaction rate are measured as deviations from their mean values. t statistics 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 89153 Adjusted 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. In columns (2), I include variables related to movie quality, and these variables are found to reduce the chance to stop movie run. When I add the age of movie - the weeks after movie’s release - and interactions of vertical integration with movie quality, the results do not change. In columns (5)∼(7), I use movie and theater fixed effects to control for possible difference in revenues across movies and theaters. Week dummies are also included in columns (6) and (7), enabling me to control for the variation in opportunity costs related to newly released movies over weeks. All of specifications suggest that integrated theaters are less likely to stop their own movies than nonintegrated theaters and other rival integrated theaters do. The sample includes movies released in 2008. Robust standard errors are in parentheses and clustered by movie in columns (1)∼(4). *Significant at 5%, **at 1%, ***at 0.1%

43 Table A3: The Eﬀects of Vertical Integration on Film Choice Decision using OLS

(1) (2) (3) (4) (5) (6) (7) (8)

Own movie 0.0608*** 0.213** 0.0612*** 0.0614*** 0.0716*** 0.125*** 0.0711*** 0.0953*** (5.48) (2.86) (18.58) (3.49) (4.90) (4.71) (12.87) (10.01)

Own X Company op -0.0317 -0.0225*** -0.0118 0.0005 (-1.90) (-3.99) (-0.55) (-0.06)

Own X Average of user ratings -0.0194 0.00165 (-1.86) (0.69)

Own X High season 0.0191 0.000886 -0.137*** -0.0162 (0.68) (0.13) (-3.56) (-1.57)

Own X Pre Expectation 0.00272 -0.00345*** (1.12) (-7.60)

Own X Satisfaction rate -0.00124 -0.000698* (-1.16) (-2.41)

Average of user ratings 0.00503 0.00658 0.0179 0.018 44 (0.66) (0.85) (1.40) (1.43)

Number of screens 0.0580*** 0.0580*** 0.0532*** 0.0527*** (13.06) (13.06) (10.33) (10.29)

Company op 0.0197 0.0234* 0.00808 0.0104 (1.91) (2.11) (0.63) (0.75)

Action 0.107*** 0.106*** 0.110** 0.110** (4.38) (4.37) (3.02) (3.09)

Thriller 0.0769** 0.0772** 0.115* 0.117** (2.71) (2.72) (2.52) (2.58)

Constant 0.382*** 0.371*** 0.664*** 0.664*** 0.380** 0.374* 0.433*** 0.432*** (3.93) (3.79) (31.24) (31.17) (2.59) (2.11) (9.83) (9.82)

Movie and Theater FE No No Yes Yes No No Yes Yes

Observations 109935 109935 109935 109935 43015 42810 43015 42810 Adjusted R-squared 0.215 0.215 0.447 0.447 0.194 0.196 0.415 0.416 This table reposts OLS coefficients. The dependent variable is a dummy variable that takes value one if the theater show the movie, and zero otherwise. The first half of table counts with all of 590 movies released during the entire data period, while the second half counts 206 movies released in 2008. Pre-expectation and satisfaction rate are measured as deviations from their mean values. Robust standard errors are in parentheses and clustered by movie in regressions without fixed effects (columns (1),(2),(5),(6)). *Significant at 5%, **at 1%, ***at 0.1% References

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