Silviase/jgov_v1.4_full_ann huggingface dataset:
### 1. Introduction

There is an old but important antitrust case referred to as the Paramount. In the Paramount, following the long litigation, five major U.S. studios were required to divest their theater chains, in addition to the prohibition of block booking. The Paramount is still a controvertible case that provides rich debates on the effects of vertical integration and vertical restraints on competition and welfare, while the Supreme Court decision was made about sixty years ago. On the other hand, major Japanese distributors operate their own theaters as well as production divisions, because vertical integration is not prohibited by law in Japan. Hence, we can observe both of vertically integrated theaters and non-integrated ones.

This study examines the vertical integration in the Japanese movie industry. For that purpose, I estimate the discrete choice model of demand for movie attendance, utilizing the unique data of Japanese theaters, in which one can identify whether or not a theater is owned by producers/distributors. Moreover, in the demand model, vertical integration will affect the individual utility level, and if vertical integration improves the quality of movie exhibition services, integrated theaters will have more attendance than they otherwise do. While previous empirical works on vertical integration tend to use the reduced-form regression model, I utilize a variant of the discrete choice model used in Einav (2006) and Davis (2006c). Finally, I conduct a simulation analysis of the welfare effect of the hypothetical court decision like the Paramount, where major distributors are ordered to divest their theaters.

It is well known that vertical integration and vertical restraints resolve inefficiencies such as double marginalization, downstream moral hazard, transaction cost, and free-riding. Especially, according to Gil (2006c), vertical integration can solve distortion on the movie run length, which is similar to the inefficiency created by double marginalization, and vertical integration will improve consumers' welfare. However, such practices may also have anticompetitive effects in certain circumstances. Hence, competition authorities should be cautious with regard to intervention, and should carefully investigate the net effect of certain integration or restraints on competition and welfare. $ ^{1} $

Motta (2004) argues that vertical integration will be anticompetitive only if all of the three
conditions are satisfied. First, vertical integration results in price increases. Second, the effect of price increase dominates the positive effect of the effort made by integrated firms to provide services. Third, the social welfare becomes exactly smaller than the case without vertical integration. There are many empirical studies that investigate the competition effect of vertical integration. $ ^{2} $ While some of them obtain results consistent with the anticompetitive effect of vertical integration, others suggest that the vertical integration appears to be procompetitive. Hence, the effect of vertical integration must be empirically examined on a case-by-case basis.

The results of this paper suggest that, although vertical integration leads to higher prices, theaters owned by producers/distributors are highly assessed by consumers. This implies vertical integration can possibly solve the distortion created by contractual complexity as well as promotion service effort by integrated theaters, such as pre- and post-release information of movies, and improving the direct service provided to customers, and so on. In addition, according to the simulation results, it is concluded that the order for major distributors to divest their theaters will possibly reduce the social welfare in Japan. This study will be an important case study of vertical integration in the Japan, because the empirical works analyzing vertical integration in Japanese industries are scarce.

While empirical studies of the Japanese movie industry are relatively rare, there are many empirical works on the movie industry in the US and Europe. For example, Corts (2001) examined the effects of the vertical market structure between movie production and distribution on release data competition, and found that complex vertical structures do not lead to efficient movie scheduling. Gil (2006a) studies the relationship between integration and the frequency of costly renegotiation in the Spanish movie industry; the results suggest that movies renegotiated ex-post tend to be distributed by integrated distributors ex-ante, and are more likely to show in vertically integrated theaters. Gil (2006c) analyzes the effect of vertical integration in the Spanish movie industry on movie run length, which will be affected by the revenue-sharing contracts, and finds that integrated theaters run their own movies longer than others. Gil (2006b) studies the effects of the Paramount case on the U.S. movie industry. $ ^{3} $ The present study will also contribute to the development of the
economic analysis of Japanese media industries.

The remaining parts of this study are organized as follows: The next section briefly reviews the movie industry. In the third section, I explain the data used in this study. The fourth section describes the model of demand for movie attendance, variables and estimation issues, as well as estimation results. In the fifth section, I simulate the Paramount case in Japan by using the estimated demand model. The sixth section provides further discussion of results. The seventh section is the concluding remarks.

### 2. Movie Industry

### 2.1 Industry Characteristics and Paramount

The movie industry comprises three different sectors: production, distribution, and exhibition. $ ^{4} $ Movies are generally created by production companies, and supplied by distributors to the exhibition market. Exhibitors operate single or multiple theaters and show films on their screens and generate box office revenue. The box office revenue is distributed by the three agents depending on the contract between each of them. For example, according to De Vany (2004), theaters rent a film from distributors for exhibition and pay depending on the contracted rental rate, which varies with the movie run. In addition, the revenue-sharing contract usually contains various arrangements and collateral conditions. Corts (2001) introduces some types of financing and distribution arrangements between production companies and distributors, which determine the ownership structure of copyright and the distribution right to the film.

The successful movie run involves many key strategic factors such as screenplay, casting, film footage, scheduling release and end timing, and the number and location of theaters. Moreover, at each step of the process, agents must contract with counterparts; because there is uncertainty, appropriate arrangements are required ex-post. According to Gil (2006c), vertical integration can solve distortion on the movie run length due to revenue-sharing contracts; that is similar to the distortion created by double marginalization. $ ^{5} $ In addition, theaters are expected to provide appropriate promotion service effort, such as pre- and post-release information of movies, morning and midnight shows, ensuring that facilities are clean and convenient, and improving the direct service provided to customers. As seen in the case of other retailers, theaters are differentiated to
some extent in terms of geographic location and have regional market power. Since movies themselves are highly differentiated products, there exists the possibility of double marginalization. The downstream moral hazard problem may be another source of inefficiency. Hence, to avoid inefficiencies that are inherent in the complicated business nature, there are incentive to various types of vertical integration, in addition to facilitating vertical controls, such as block booking and blind license.

In the U.S., however, vertical integration between distribution/production and exhibition has been illegal after the Supreme Court ruling on the Paramount case in 1948. Before that time, major studios, such as RKO, MGM, Twentieth Century Fox, Warner Brothers, and Paramount, operated their own theater chains and engaged in the production, distribution, and exhibition of their own films. The DOJ regarded such an industry structure as anticompetitive. In 1938, the DOJ brought suit against major studios and started a long litigation that lasted about ten years. Although the first decision was reached in 1946, both sides appealed to the Supreme Court and in 1948 the Supreme Court reversed the 1946 decision of the District Court; in particular, the Supreme Court recommended that the District court ensure divestment of theaters owned by the five major studios. $ ^{6} $ First, following the decision, RKO divested its theater branch in 1948; finally, MGM separated from its theaters.

### 2.2 The Japanese Movie Exhibition Market

This subsection briefly reviews the Japanese movie market using data from the website of the Motion Picture Producer Association of Japan, Inc. (MPPAJ). $ ^{7} $ Figure 1 depicts the aggregate pattern of the box office revenue in Japan from 1955 to 2006. According to this, the box office revenue increased drastically during the 1970s and the early 1980s; it took a downward turn toward the mid 1990s. Thereafter, from 1996 onward, the box office revenue started to grow: in 2006, the total box office revenue was 202,934 million JPY. Further, Figure 1 shows the long-run change in the number of screens. The number of screens increased rapidly during the latter half of the 1950s; however, from 1960 onward, it started to decline sharply, with there being only 1.734 screens in 1993. However, the first multiplex theater was opened in Ebina, Japan, the same year; this city is located in the center of Kanagawa Prefecture, about 50 km from Tokyo. Thereafter, the number of screens has been increasing annually. According to Figure 2, the aggregate movie attendance indicates a similar pattern for the number of screens. However, in the late 1990s and early 2000s, the
attendance did not refloat the same way as the number of screens. Thus, the attendance per screen is observed to have a downward trend.

$$ =Figure1= $$

$$ =Figure2= $$

In Japan, the vertical integration in the movie industry is not prohibited by the law, and there are some large distributors who own theaters as well as production divisions, such as Toho, Shochiku, and Toei, which are large Japanese distributors, have had affiliated movie theaters for a long time. The recent growth in the number of screens was driven by the increase in multiplex theaters. The Kadokawa Group and Tokyu Group, as well as three large distributors, are operating their own multiplex theater chains. Even foreign movie companies, such as Warner Brothers and United Pictures, have their own multiplex theater chains. There are also many independent exhibitors. Figure 3 depicts the number of screened films, and its decomposition into Japanese and foreign films. During the 1950s and 1960s, the Japanese film ratio was around 60% or more and then the ratio began to decline. After 1986, the Japanese film ratio drifted below 50%. However, the number of Japanese films released in a year increased dramatically in 2006, and the Japanese film ratio reached 50.8%. Accordingly, Japanese films seemed to have faced the competitive pressure from foreign films.

### 3. Data

In this study, I use the micro-data of the movie theater file of Tokutei Sabis Sangyo Jittai Chosa (Survey of Selected Service Industries) (hereinafter, SSSI). $ ^{8} $ The SSSI is an annual survey conducted by the Ministry of Economy, Trade and Industry (METI) for several selected industries. However, given the rotation of industries selected for the survey, different industries are surveyed each year. The SSSI of the movie theater industry was conducted in 1975, 1976, 1980, 1991, 1994, 1997, 2001, and 2004; however, the micro-data of the first three surveys are not available. Hence, the data of the 1990s and 2000s are used in this study.

According to the report on the SSSI, establishments engaged in services included in Japan Standard Industrial Classification, category 841, (motion picture theaters) are treated as the sample unit of the SSSI of the movie theater industry. In principal, a theater is assumed to have one screen;
thus, if an owner operates a number of screens in the same building, each screen is treated as different establishments. However, if multiple screens have a common entrance and ticket office, then it becomes a typical case of multiplex theaters. Such an overall system is regarded as a single establishment. $ ^{9} $ Eventually, after observing the dataset, the definition of establishment by the METI may not be consistent on a yearly basis. For example, a certain multiplex theater is reported as a single establishment, while screens of the same theater are regarded as different establishments in one of the previous surveys. Therefore, based on the name and address of the original sample, I produce a new ID number. In the end, I construct a unique dataset of the Japanese movie theater, in which one can consistently identify each establishment on a yearly basis. Thus, the observation unit of this study is theater (establishment).

Since the SSSI on the movie industry does not always report the admission price, I calculate the annual average admission price of each theater by dividing the total box office revenue by the total number of attendance. Since there are some abnormal values, theaters outside the range of the mean of the estimated average admission price plus/minus the standard deviation multiplied by three, are defined as outliers and excluded from the sample. In the end, the total sample size is 4,254. Table 1 presents the details of my dataset, as well as the information from the website of the MPPAJ (bottom panel). According to this, on average, the sample accounts for about 87% of the total number of screens, about 76% of the total attendance, and about 81% of the total box office revenue, respectively. Table 1 also reports the number and share of vertically integrated theaters and screens in the Japanese movie industry, and the integrated theaters and screens account for about one fourth to one third of the total. In addition, one can see that the number and share of integrated theaters declined gradually during this time period: while the share of integrated theaters is about 31% in 1991, it decreased to about 23% in 2004. On the other hand, those of integrated screens have recently taken an upward turn: although the share of integrated screens declined from about 33% (1991) to about 24% (2001), it recovered to about 28% in 2004,

### 4. Estimation of Demand for Movie Attendance

### 4.1 Model

In this study, I employ the nested logit model. $ ^{10} $ As explained above, this study uses annual data, with a theater (establishment) being regarded as the sample unit. The utility that consumer i attains from going to theater j within market m in year t is assumed to be
$$ \begin{align*}v_{ijmt}&=\alpha p_{jmt}+Z_{jmt}\beta+d(L_{i},L_{jmt})+\xi_{jmt}+\varsigma_{imt}+(1-\sigma)\varepsilon_{ijmt}\\&=\delta_{jmt}+\varsigma_{imt}+(1-\sigma)\varepsilon_{ijmt}\end{align*} $$

where p is the admission price; Z, the vector of observed theater characteristics such as number of screens, number of seats as well as type of ownership; and $ \xi $ , an unobserved theater characteristic. Following Davis (2006b), I introduce the geographic differentiation, or, more concretely, the location of consumers and theaters, $ L_{i} $ and $ L_{j} $ , respectively. $ d(, $ denotes the distance of consumer location from each theater. The details of the measurement of distance and variable definition will be explained below. On the other hand, the mean utility from the outside alternative, i.e., from not going to any theater, is assumed to vary on a yearly basis.

$$ v_{i0m t}=\delta_{0m t}+\boldsymbol{\varsigma^{\prime}}_{i m t}+(1-\sigma)\varepsilon_{i m t} $$

where $ \delta_{0mt} $ is the mean utility level in year t within market m; I treat this as the year fixed effect. $ \zeta_{0} $ is normalized to zero.

$ \varepsilon $ is assumed to be distributed as the i.i.d. type I extreme value distribution. $ \zeta $ is a random variable that has a distribution with the parameter $ \sigma(0\leq\sigma<1) $ ; subsequently, even $ \zeta+(1-\sigma)\varepsilon $ has the extreme value distribution. $ ^{11} $ If $ \sigma $ approaches zero, the within group correlation reaches zero, and the model becomes a simple logit model; however, if $ \sigma $ approaches one, the within group correlation reaches one. Further, the outside alternatives do not substitute for the inside goods.

In this setting, the market share of theater j's condition on going to any of the theaters is

$$ s_{_{jmt|1}}=\frac{s_{_{jmt}}}{s_{_{1mt}}}=\frac{s_{_{jmt}}}{1-s_{_{0mt}}}=\frac{\exp(\delta_{_{jmt}}/(1-\sigma))}{D_{_{1mt}}} $$

where

$$ D_{1m t}=\sum_{j\in J_{m}}\exp(\delta_{j m t}/(1-\sigma)) $$

$ J_{m} $ is the set of theaters within market m in year t. Moreover, the probability of going to any of the theaters is defined as follows:

$$ s_{_{1mt}}=\frac{D_{_{1mt}}^{^{1-\sigma}}}{D_{_{0mt}}^{^{1-\sigma}}+D_{_{1mt}}^{^{1-\sigma}}}=\frac{D_{_{1mt}}^{^{1-\sigma}}}{\exp(\delta_{_{0mt}})+D_{_{1mt}}^{^{1-\sigma}}} $$

and the possibility of not going to any theater is

$$ s_{_{0mt}}=\frac{\exp(\delta_{_{0mt}})}{\exp(\delta_{_{0mt}})+D_{_{1mt}}^{^{1-\sigma}}} $$

Hence, the market share of each theater is expressed as follows:

$$ s_{_{jmt}}=s_{_{jmt|1}}s_{_{1mt}}=\frac{\exp(\delta_{_{jmt}}/(1-\sigma))}{D_{_{1mt}}^{\sigma}(\exp(\delta_{_{0mt}})+D_{_{1mt}}^{1-\sigma})} $$

In the end, one can get the following well-known linear regression equation (hereinafter, share
equation):

$$ \ln(s_{jmt})-\ln(s_{0mt})=\alpha p_{jmt}+Z_{jmt}\beta+d(\overline{L}_{mt},L_{jmt})+\sigma\ln(s_{jmt|1})-\delta_{0mt}+\xi_{jmt} $$

where $ \alpha $ , $ \beta $ , $ \delta_{0} $ , and $ \sigma $ are parameters that must be estimated. The $ L_{mt} $ with a bar denotes the representative consumer's location within market m at t; $ d(\cdot) $ is the average travel distance from consumers' location to theater j.

### 4.2 Variables

In this subsection, I explain the variables used in the demand estimation. First, as mentioned above, I define the annual average admission price of each theater as price, and the mean and standard error of the estimated admission price for the sample are reported in Table 2. The number of screens, number of seats, number of screened films, and Japanese film ratio to the total screened films are defined as other theater characteristics.

$$ =Table1= $$

$$ =Table2= $$

In addition, I include the dummy variable for vertical integration, which takes one if the theater is owned by production or distribution companies; otherwise, zero. If the vertical integration of the theater improves the quality of its services, the coefficient of the dummy variable is expected to be positive. Moreover, the following dummy variable is included: dummy variable for a separate theater, which takes one if the theater is located in a building that does not have any other theaters; otherwise, zero. For example, in Tokyo, several theaters owned by different exhibitors are located in the same building. In addition, the dummy variable for the first-run theater is also included as a theater characteristic.

With respect to the dependent variable, one must define the geographical market and potential market size of movie attendance. In this study, forty-seven prefectures are defined as geographic markets. $ ^{12} $ In almost all the markets, the sum of attendance of theaters within a market
exceeds the population. Therefore, I assume that the potential market size minus the sum of attendance, which is the number of consumers not going to any of the theaters, is proportional to the market population. Based on this assumption, the dependent variables are arranged as follows:

$$ \ln(s_{jmt})-\ln(s_{0mt})=\ln(x_{jmt}/(POP_{mt}\exp(\theta)))=y_{jmt}-\theta $$

where $ \theta $ is a constant that will be estimated. On the other hand, the market share of theater j, conditional on going to any of the theaters, is calculated as the number of attendees of j divided by the total number of attendees in the market.

The utility function incorporates the geographic differentiation or the distance from each consumer to the respective theaters. Davis (2006b) captures the features of the density of travel distance by simply counting the number of consumers within some distance rings. In this study, I define the ratio of population within a 20-km circle of theater j to the total market population as the inverse of the average consumer travel distance to theater j.

$$ d(\overline{L}_{mt},L_{jmt})\cong\gamma(POP_{jmt}^{20km}/POP_{mt})=\gamma C_{jmt}^{20km} $$

The idea behind this variable is that if the ratio is large, a relatively large portion of potential attendees within the market will live near the theater and the average travel distance will be small. The population within the 20-km circle of each theater is estimated based on the address of theaters and municipal head offices. The population within the 20-km circle is defined as the sum of the population of municipalities whose government offices located within the 20-km distance from each theater. $ ^{13} $

In the end, the share equation is rearranged to

$$ y_{_{jmt}}=\alpha p_{_{jmt}}+Z_{_{jmt}}\beta+\gamma C_{_{jmt}}^{20km}+\sigma\ln(s_{_{jt|1}})-\delta_{_{0mt}}+\theta+\xi_{_{jmt}} $$

$ \alpha $ , $ \beta $ , $ \gamma $ , $ \delta_{0} $ , $ \theta $ , and $ \sigma $ are parameters that will be estimated; however, $ \delta_{0} $ and $ \theta $ cannot be separately identified. In the estimation, I include the year and selected market dummies for the following relatively large markets: Tokyo, Aichi, and Osaka. This is done to capture the variation in $ -\delta_{0}+\theta $ . Table 3 reports the descriptive statistics of regression variables for vertically integrated theaters and others as well as for the entire sample. According to this table, there are no significant differences in these variables between vertically integrated theaters and others.

$$ \Delta_{a,b}=\sin^{2}((x_{b}-x_{b})/2)+\cos(x_{a})\cos(x_{b})\sin^{2}((y_{b}-y_{b})/2) $$