Silviase/jgov_v1.4_full_ann huggingface dataset:
### 2. Toho-Subaru Case

### 2.1 Agreement between Toho and Subaru

The Toho–Subaru case concerned a merger in the Japanese movie theater market in the postwar years of recovery following World War II. $ ^{5} $ Toho Co. Ltd. (Toho) was a major film studio in Tokyo and its business field was film production, distribution and exhibition of movies as well as other performing arts. On the other hand, Subaru Enterprise (Subaru) was a film distribution and exhibition company that had other entertainment and recreation businesses as well as real estate dealing and leasing. $ ^{6} $

On January 26, 1950, Toho and Subaru signed a contract for the tentative joint administrative agreement that Toho would rent Subaru's two theaters in the Yurakucho area, Subaru Za and Orion Za. The contract consisted of six articles as follows. (i) Toho and Subaru would jointly operate Subaru Za and Orion Za. (ii) Toho would lend 30 million yen to Subaru without interest, and the repayment term would be five years. (iii) The contracted term of joint administration would be five years. However, whenever Subaru repaid the loan in full, Subaru could cancel the contract with a notice period of three months. (iv) The operating policy for the two theaters would be determined by consultation between Toho and Subaru. (v) Concerning box office revenues, 15% of monthly box office revenues was to be allocated to Subaru, and the remainder to Toho. Toho would incur the entire cost of operation. (vi) The contract would take full effect upon the decision of a Subaru shareholders' meeting as well as the completion of process required by the Anti-Monopoly Act (AMA). $ ^{7} $

### 2.2 Intervention by JFTC and Hearing

According to provisions of the AMA, Toho submitted notification of the agreement with Subaru to
the JFTC. In those days, movies were a popular leisure activity; hence, potential effects on consumers were not negligible. After the investigation, the JFTC challenged the case because of concerns that according to the fourth article of the agreement, Toho could control both Subaru Za and Orion Za; hence, the agreement would reduce competition between these and the Toho theaters. After the hearing, the JFTC prohibited Toho from making agreements that would enable Toho to control these two theaters.

JFTC (1951) found that the Marunouchi and Yurakucho area in which Subaru Za and Orion Za were located formed a relevant geographic market with a group of potential attendees. In addition, the JFTC also found that if the Marunouchi and Yurakucho area was too small as a geographic market, it would be included in the area occupied by the Ginza branch of the Tokyo Association of Theater Owners (TATO), excluding the following theaters: Shinbashi Enbujo, Mitsukoshi Gekijo, Shiraki Gekijo, and Ningyocho Shochiku Eiga Gekijo (area occupied by the Ginza branch, excluding four theaters), can be regarded as a relevant geographic market. $ ^{8} $

$$ =Table1= $$

Because Toho had seven theaters in the Tokyo metropolitan area, and six of these were located in the Marunouchi and Yurakucho area (see the first panel of Table 1), assessment of the market power of Toho depended on how the relevant geographic movie theater market in Tokyo was defined. $ ^{9} $ If the Marunouchi and Yurakucho area or the Ginza branch-occupied area excluding four theaters was defined as the relevant geographic market, the market power of Toho might be increased by the agreement. On the other hand, if a larger area than these was relevant as a local movie theater market, Toho was unlikely to obtain additional market power before or after the deal. In the end, the wider the defined geographic market was, the smaller the expected anticompetitive effect of the merger between Toho and Subaru had to be. $ ^{10} $

The capacity (number of seats) of Toho’s theaters, including Subaru’s two, was 9,742 and their share of total capacity of theaters within the Marunouchi and Yurakucho area (10,787) was 90.31% (see Table 2). On the other hand, there were 20 theaters within the Ginza branch-occupied area, and the share of Toho’s theaters including Subaru’s two in terms of total capacity of theaters within the
Ginza branch-occupied area (16,807) was 57.96% (see Table 2).

$$ =Table2= $$

### 2.3 Court of Appeals

Toho appealed the ruling of the JFTC at the Tokyo High Court. In the appeal, Toho alleged that the Marunouchi and Yurakucho area could not be a geographic market. Additionally, Toho argued that the Tokyo metropolitan area should be defined as a geographic market, because the Marunouchi and Yurakucho area was in the center of Tokyo and easily accessible from all neighboring areas. Toho also claimed that if the Tokyo metropolitan area was too large as a geographic market, it was appropriate for the area occupied by all member theaters of the Ginza branch of the TATO. $ ^{11} $

On September 19, 1951, the Tokyo High Court upheld the decision of the JFTC and dismissed the appeal. In the decision, the Tokyo High Court judged that, while the Marunouchi and Yurakucho area was too small as a geographic movie theater market, the Tokyo metropolitan area was too large. In the end, the Ginza branch of the TATO-occupied area was defined as an appropriate geographic market, excluding the following theaters: Shinbashi Enbujo, Mitsukoshi Gekijo, Shiraki Gekijo, and Ningyocho Shochiku Eiga Gekijo. Finally, the Tokyo High Court concluded that the agreement between Toho and Subaru would effectively lessen competition in the market. $ ^{12} $

The implication for the geographic market by the Tokyo High Court seems somewhat arbitrary and inconsistent: for example, according to JFTC (1952), although the Tokyo High Court accepted that the idea defining the Tokyo metropolitan area as a relevant geographic movie theater market was undeniable, after a few sentences it ruled that an area narrower than the Tokyo metropolitan area, the Ginza branch-occupied area excluding the above four theaters, was suitable as a geographic market.

### 3. Data

### 3.1 Main Data Sources

The main data source for this study is Jiji Tsushin-sha (1951), which reports numbers of attendees of selected theaters in each prefecture from January to June of 1950. I use data for theaters in the Tokyo metropolitan area (70 theaters). In addition, the almanac contains a directory of theaters operating in
September 1950 (184 theaters). The directory lists the name, address, telephone number, name of owner and manager, capacity (number of seats), and program pattern for each theater that was active in September 1950.

I make the following adjustments. First, there is an inconsistency: a theater, Asakusa Shochiku Eiga Gekijo, is not listed in the directory, but data for its numbers of attendees are reported. Hence, I substitute the information on the theater from the directory of Jiji Tsushin-sha (1952). Second, the Jiji Tsushin-sha directory (1954) reports the establishment date of theaters, according to which eight other theaters were active in 1950. $ ^{13} $ These theaters are included in my dataset. Therefore, the total number of theaters is 192, and for 70 of these the monthly number of attendees from January to June of 1950 is available.

### 3.2 Ownership

Table 3 reports the ownership structure of movie theaters in the Tokyo metropolitan area. According to this, Shochiku Co., Ltd. (one of the major film studios in Japan and its business fields are very similar to Toho) was dominant in terms of number of theaters within the Tokyo metropolitan area: Shochiku owned 17 theaters, which accounted for 8.85% of the total number of theaters. In contrast, Toho owned only seven theaters (3.13% of the total) not including Subaru Za and Orion Za. Moreover, six of theaters owned by Toho were located within a very narrow area, that is, the Marunouchi and Yurakucho area.

$$ =Table3= $$

### 3.3 Program Pattern

In the appeal, Toho argued that the movie quality was an important factor in assessing the effect of the agreement. In Japan, block booking was permitted by law and was popular in the movie distribution market. $ ^{14} $ If a distribution company tended to distribute the same movies to contracting theaters in the same month, movie quality largely depended on which distributor a theater contracted with. Table 4 reports the program patterns of movie showings of each theater in terms of contracted
distributors. $ ^{15} $ For example, 29 theaters showed only movies distributed by Central Motion Picture Exchange (15.10%). $ ^{16} $ The second major pattern of programs is ‘Mixed,’ which means that theaters screen movies from various distributors (27 theaters (14.06%)). We can see this type of contract as a type of free booking. Ninety theaters bought only Shochiku’s films (19 theaters: 9.90%). On the other hand, the theaters that exclusively contracted with Toho accounted for only 2.08% of the total (four theaters). In addition, two theaters screened movies distributed by both Daiei and Tokyo Eiga Haikyu. $ ^{17} $

$$ =Table4= $$

### 3.4 Local Market Structure

Following Davis (2005; 2006a), I quantify the local market competition faced by each theater, counting the number of theaters owned by the same owner and the number of rival theaters within a specified distance ring

$$ \mathbf{o w n}_{j}^{h}=\#\{k\in G_{j}\mid a^{h}
Based on the addresses of theaters, the distance of each theater from others can be computed. First, each address is geocoded (identified by a combination of longitude and latitude). Then the distance between two theaters is computed as a great circle distance. $ ^{18} $ Table 5 reports the descriptive statistics of own and rival theater counts for 192 theaters in Tokyo in 1950. According to this table, the mean own theater count is less than one, except for the third radius (1.292), and fewer than half of the theaters had other theaters owned by the same owner within any radius, because most theaters were independent. On the other hand, most theaters faced competition from rival theaters located within various distance rings. For example, about half of the theaters had 2, 31, 73, and 50 theaters within the first, second, third, and fourth distance rings, respectively.

$$ =Table5= $$

### 4. Empirical Methodologies

### 4.1 Regression Model

This study estimates the following reduced-form regression equation relating the structure of local market competition to the number of attendees at each theater

$$ \ln(\mathrm{attend}_{jt})=a_{1}\mathrm{^{\prime}own}_{j}+a_{2}\mathrm{^{\prime}rival}_{j}+z_{j}b+c\mathrm{distance}_{j}+\mathrm{month}_{t}+\mathrm{cons}+u_{jt}. $$

where $ attend_{jt} $ is the number of attendees of theater j in month t. $ own_{j}' = [own_{j}^{1}, \ldots, own_{j}^{4}] $ , and $ rival_{j}' = [rival_{j}^{1}, \ldots, rival_{j}^{4}] $ . $ z_{j} $ denotes observable theater characteristics of theater j. This study takes (the log of) the number of seats as the capacity of each theater. In addition, as explained below, program patterns are also controlled using dummy variables. The term $ month_{t} $ is a fixed effect of month t. $ a_{1} $ , $ a_{2} $ , b, and c are parameters to be estimated. $ u_{jt} $ is an error term.

Davis (2006b) shows that moviegoing demand depends on the distance between a consumer and a theater, utilizing a discrete-choice model of product differentiation. We cannot observe the distance of each consumer from respective theaters. Hence, in this study, it is assumed that the population of each ward is situated at the location of each local governmental headquarters, and the mean distance from consumers to theater j is calculated as the weighted mean of the distance between headquarters of each ward and theater j, as follows
$$ \mathrm{d i s t a n c e}_{j}\cong\sum_{k=1}^{23}\left(\frac{\mathrm{p o p}_{k}}{\mathrm{p o p}^{\mathrm{T o k y o}}}\right)d(L_{k},l_{j}), $$

where $ pop_{k} $ is the population of ward k, and $ pop^{\mathrm{Tokyo}} (= \Sigma_{k} pop_{k}) $ is the total population of the Tokyo metropolitan area in 1950. $ ^{19} $ $ L_{k} $ is the location of the headquarters of ward k, and $ d(L_{k}, l_{j}) $ is the (great circle) distance between $ L_{k} $ and $ l_{j} $ .

### 4.2 Effects of Local Market Structure

Although Davis (2006a) finds negative correlations of the numbers of own and rivals' screens with box office revenues (cannibalization and business stealing effects), Chisholm and Norman (2009) find a positive correlation between the box office revenues of theaters in Boston and South Florida and the number of theaters within a specified radius, and they conclude that agglomeration effects dominate business stealing (or cannibalization) effects in the movie theater markets. $ ^{20} $

The main objective of this study is to verify the extent to which movie theaters affected each other in Tokyo in 1950, and this study does not incorporate agglomeration effects. Even if agglomeration effects are explicitly incorporated, it is difficult to distinguish agglomeration effects from business stealing (or cannibalization) effects because the empirical model is a reduced form. Therefore, I only test whether estimated $ a_{1}' $ and $ a_{2}' $ are statistically significant or not, in order to assess a relevant geographic market for movie theaters in Tokyo. In this study, I call $ a_{1}' $ and $ a_{2}' $ competitive effects of own and rival theaters on the central theater. I will briefly discuss agglomeration effects in the empirical results of this study below.

### 4.3 Fixed Effects

Because directories are only available once a year, information about theater location does not change during the sample period. Therefore, fixed effects for respective theaters cannot be included. I include the following fixed effects in the reduced form regression equation.
Einav (2007) points out large seasonal or sometimes weekly fluctuations of box office revenues in the US movie industry. In this study, because of data limitation, to deal with possible seasonal fluctuation in the total movie attendance, I simply include the monthly dummies (February to June), that is, $ time_{t} $ in (3).

Quality of movies screened by a theater is expected to depend on the distributor(s) with which the theater contracts. Therefore, to control movie quality, I estimate the models including the dummies for patterns of program (program pattern dummies, hereinafter). Another source of quality difference among theaters is a good location effect that managers know more about than researchers and had built theaters at such good places where demand was high. To deal with such an effect, I incorporate the ward dummies. Thus, the $ u_{jt} $ is decomposed as follows

$$ \boldsymbol{u}_{j t}=f_{c}+f_{w}+\Delta\boldsymbol{u}_{j t}, $$

where $ f_{c} $ and $ f_{w} $ is the fixed effects of program pattern c and ward w of theater j. $ \Delta u_{jt} $ is a residual error term.

### 4.4 Instruments

To control the possible correlation between $ \Delta u_{jt} $ and local market structure variables, and correlation between $ \Delta u_{jt} $ and the mean distance from consumers, following Berry et al. (1995), Thomadsen (2005), and Davis (2006b), I estimate models utilizing the following variables as instruments.

First, as in previous studies, I assume that own theater characteristics (capacity of each theater) are exogenous. $ ^{[21]} $ Second, the maximum capacity of rival theaters within each distance ring is also used because theaters' managerial decisions were affected by competitive pressure from rivals. Third, I use the number of theaters belonging to the same owner, and dummies for theaters owned by Shochiku, Toho, and Nikkatsu. These change the cost of entry and operation. Moreover, the distance from the nearest rival theater is utilized as an instrument because it is a demand shifter. Fourth, I use the average of the mean distance of rival theaters within a 15 km radius as an instrument for the mean distance from consumers.

In addition to these variables, population within each radius in 1947 is used as an instrument,
because the past potential market size of each location would partly affect the location decision for theaters. Population within each radius of theater j is calculated as the sum of population of wards whose headquarters are located within 1 km (5 km, 10 km, or 15 km) of theater j. Table 6 reports the descriptive statistics of regression variables. $ ^{22} $

$$ =Table\;6= $$

### 5. Results

Table 7 reports the estimation results. The first and second columns are the OLS results. The first model includes the program pattern and ward dummies, but not the month dummies. Concerning the structure of local market competition, except for own theaters within the second distance ring, the estimated coefficients of all local market competition variables are negative, and the estimated coefficients of own theaters within the fourth (10–15 km) distance ring and the coefficients of rivals within the second (1–5 km), and third (5–10 km) distance rings are statistically significant. The estimated coefficient of capacity is positive, that of mean distance from consumers is negative, and both are statistically significant at the 1% level. The second model adds the month dummies into the first model. The results of the second model are similar to those of the first, but the estimated coefficients of own theaters within the third distance ring are statistically significant.

The OLS results of the effects of local market competition on attendance are vague. As mentioned in the previous section, there is an endogeneity problem of local market structure variables and mean distance from consumers. The third and fourth columns report the instrumental variable estimation results. These correspond to the first and second models, respectively. $ ^{23} $

In the third model, except for own theaters within the fourth distance ring, the estimated coefficients of all local market competition variables are negative and larger than those in the OLS results in magnitude. Although all estimated coefficients of own theater counts are not statistically significant, the estimates of rival theater counts within the second and third distance rings are statistically significant at the 1% and 10% levels, respectively. The estimated coefficient of mean distance from consumers is larger than that from the OLS results. Moreover, the Hansen J statistics of the third
model do not reject the null hypothesis $ (J = 5.599: df = 5; p\text{-value} = 0.347) $ , and hence the endogeneity problem is less serious.

The fourth model adds month dummies into the third model. Although most results are similar to the third model, all estimated coefficients of local market competition variables are slightly larger than those in the third model. The estimates for own theater counts within the third distance ring and the coefficient of rival theaters within the first distance ring are statistically significant at the 5% and 10% levels, respectively. The estimate for rivals within the third distance ring is more precise. Again, the Hansen J statistics do not reject the null hypothesis of orthogonality of instruments (J = 6.510: df = 5; p-value = 0.260), and the endogeneity problem is not so serious in this specification.

In summary, the results show that nearby rival theaters have competitive effects on the attendance of a centered theater, and these effects do not disappear even at 10 km from a theater. On the other hand, the effects of own theaters are not clear.

$$ =Table7= $$

### 6. The Tokyo Movie Theater Market in 1950

According to Davis (2006a), these results have implications for the delineation of the geographic market and the assessment of market power in the Toho–Subaru case. The econometric analysis in the previous sections shows that in the 1950 Tokyo movie theater market, rival theaters within 10 km had statistically significant competitive effects on the central theater. Therefore, the relevant geographic movie theater market in Tokyo of 1950 can be defined as a circle with a radius of at least 10 km.

$$ =Table\;8= $$

Table 8 reports the share of Toho’s theaters including Subaru Za and Orion Za within each radius of these two theaters as a relevant geographic market, in terms of the number of theaters and capacity. $ ^{24} $ If we can define the area, at least, 10 km radius from Subaru Za and Orion Za as a geographic market, the share of Toho in the number and capacity decreases: for example, within a circle with 10 km radius from Subaru Za and Orion Za, the share of Toho was 6.52% in terms of number of theaters and 13.95% in terms of capacity.
As mentioned in Section 2, the wider the defined relevant geographic market, the smaller the anticompetitive effect of the agreement between Toho and Subaru must be. If a larger relevant geographic market than above can be defined, we can say that the merger would not have had a serious anticompetitive effect. In other words, the geographic market defined by the Tokyo High Court seems to have been relatively small.

### 7. Concluding Remarks

In 2004, the JFTC issued merger guidelines (JFTC (2004; 2007)) that outline the analytical and evaluative framework applied when the JFTC reviews mergers under the AMA, although, at the time of the Toho–Subaru case, there were no such guidelines for merger reviews by the JFTC. $ ^{25} $

The guidelines provide for the concept of definition of a relevant geographic market, and list the geographic business area of a supplier, the area where consumers travel for shopping, features of products and services, and transportation cost as important factors to be considered. $ ^{26} $ The philosophy behind the guidelines harmonizes with the empirical models of previous works and this study. The transportation cost is expected to increase with the distance between a consumer and a store, and the utility attained by consumers from shopping will decrease as the store is located further from them. Therefore, the area where consumers travel for shopping should be bounded. Econometric analyses of local market competition are an effective tool for scientific merger reviews.

The empirical results of this study show that the effects of nearest rivals (0–1 km) are smaller in magnitude than those of more distant rivals (1–5 km, and 5–10 km). One possible explanation is that agglomeration effects reduce the negative effects of competition on attendance. To separately identify these effects, we must incorporate agglomeration effects in the model. This is beyond the scope of this study, and will be a future research topic.

Finally, I must note the limitations of this study. First, because the time span of the data on the number of monthly attendees is only six months and the location of theaters did not vary during the sample period, I cannot control for the fixed effects of theaters. Second, in this study, physical
distance between theaters and consumers is used as a measure of spatial differentiation. However, actual travel time may be more appropriate for accounting for spatial differentiation among movie theaters.