Silviase/jgov_v1.4_full_ann huggingface dataset:
### 4.3 Econometrics

In this subsection, I address the identification problem of demand parameters.. According to the literature, some of the explanatory variables in the regression model will be correlated to the unobserved theater characteristic, $ \xi $ . In this study, the average admission price, p, and the market share conditional on going to any of the theaters, $ s_{|1|} $ , are potentially endogenous and correlated to $ \xi $ ; however, the observable theater characteristics, Z, are assumed to be exogenous and orthogonal to $ \xi $ . As Berry (1994) and Berry, Levinsohn, and Pakes (1995) proposed, the rivals' exogenous theater characteristics are candidates of valid instruments for endogenous variables. In this study, the mean of the number of screens, number of seats, number of screened films, and Japanese film ratio, for the rival theaters within the same market are used as instruments. There is another endogenous variable, the density of travel distance, C. I follow a strategy similar to that proposed by Davis (2006b), and the mean of C for the rivals within the same market is used as an instrument. GMM is used as the estimation method, and the heteroskedastic-efficient GMM estimator of standard error is used for the statistical inference. Finally, the admission price is deflated by the Consumer Price Index (general, year 2000 = 100).

### 4.4 Estimation Results

Table 4 reports the estimation results of the share equation. The first column is the result of the basic specification without location. The estimated coefficient of the admission price is negative and that of the within market share conditional on going to any of the theaters is positive, and smaller than unity; hence, both of these satisfy the theoretical requirement. Moreover, these estimates are statistically significant at the 1% level. For other theater characteristics, such as the number of screens, number of seats, number of screened films, and Japanese film ratio, all of the estimated coefficients are statistically significant; moreover, while the estimates of the first two characteristics are positive, those of the number of screened films and the Japanese film ratio are negative. Thus, consumers preferred to watch foreign movies as compared with Japanese ones; the large number of screened films implies that the theater, on average, screened relatively inferior movies, which ran for a very short time. According to the uncentered R-squared, the model explains a large part of variations observed in the dependent variables. The Hansen J statistics show that the endogeneity problems are not so serious.

$$ =Table4= $$
The second column presents the results of the model that adds the squared number of screens to the first model; the estimated coefficient of the new variable is statistically significant. Moreover, the effect of the number of screens on individual utility is nonlinear. The third model adds the dummy for vertically integrated theaters owned by producers/distributors, as well as other dummies, such as the independent establishment dummy. The estimated coefficient of the vertical integration is positive and statistically significant at the 1% level, while the other new dummies are not significant except for the first-run theater dummy, which is significant at the 1% level. Hence, compared to others, the services offered by vertically integrated theaters are highly evaluated by consumers. In addition, other estimated coefficients are not so different from those of the first model. The uncentered R-squared and the Hansen J statistics suggest that the models are almost successfully estimated.

The fourth and fifth columns report the result of the models that add the location, or the density of consumers' travel distance, to the second and third models. The estimated coefficients of the density of travel distance are positive and statistically significant at the 1% level. This implies that the shorter the average travel distance between consumers and the theater, the higher is the evaluation by the consumers. Furthermore, the addition of the location variable affects the magnitude of the coefficients of other variables. For example, the estimates of the admission price become smaller than the models without the location variable in the absolute measure, while the estimates of the within market share are larger than those of the previous models. However, they are still statistically significant and show the expected signs. Again, the uncentered R-squared implies that the model can explain almost all the variations in the dependent variables, and that the Hansen J statistics suggest that the model is successfully identified.

### 4.5 Additional Regression Results

The above results suggest that consumers have highly evaluated the vertically integrated theaters. I perform the additional descriptive regression and investigate the relationship between vertical integration and price and the rental rate for films, which is defined as the total film cost divided by the box office revenue. Table 5 shows the results of reduced-form regression of these variables on the various theater characteristics. The estimation method is the OLS. The first column is the results of the regression of the log admission price, and the coefficient of the vertical integration dummy is positive and significantly estimated at the 1% level. This suggests that the vertical integration resulted in a higher price than the nonintegrated theaters by 0.02%. The second column reports the regression result of the film rental rate on the vertical integration dummy as well as other theater characteristics, and the vertical integration dummy has a positive and statistically significant
coefficient at the 1% level Thus, the average film rental rate of integrated theaters was 2.2% point higher than that for others. $ ^{14} $ With respect to some theaters, the estimated average film rental rates exceed one. This does not imply that such theaters are problematic samples, because, in a certain contract between theaters and distributors, the film rental cost would be over the box office revenue. I perform the same regression for theaters with a film rental rate that is greater than zero and smaller than one. The result is reported in the third column and is almost the same as the results for the full sample: The rental rate of integrated theaters was 2.4% point higher than that for others. $ ^{15} $

$$ =Table5= $$

### 5. Simulating the Paramount in Japan

According to the empirical results in the previous section, while vertical integration results in higher prices, vertically integrated theaters are preferred by consumers. This section simulates the Paramount ruling in the Japanese movie industry, and assesses its effects on welfare by using the estimated demand model for movie attendance. Operationally, the simulation strategy is very simple. First, I consider the hypothetical case in which the Japanese court required major distributors to separate from their exhibition branches; following this decision, they divested their theater chains. This is represented by changing the vertical integration dummy to zero for all ex-integrated theaters. As explained in the previous section, this change will reduce the individual utility attained from the theaters previously owned by the major distributors; in the present study, this effect is called the structural change effect.

The vertical separation will decrease the admission price of previously vertically integrated theaters by about 0.02%, according to the previous regression analysis. This is referred to as the price change effect. The change in the consumers' surplus (CS) or the compensated variation (CV) caused by the divestment is estimated by using the following formula.

$$ C V_{_{m t}}=-\frac{\ln(\exp(\delta_{0t})+(D_{1m t}^{1}(p_{_{m t}}^{1},w_{_{m t}}^{1}))^{1-\sigma})-\ln(\exp(\delta_{0t})+(D_{m t}^{0}(p_{_{m t}}^{0},w_{_{m t}}^{0}))^{1-\sigma})}{\alpha} $$

where 0 and 1 denote the stages before and after the divestment, respectively. $ p_{j}^{1} = (1 - 0.0002) p_{j}^{0} $ , if the theater was owned by the distributors; otherwise, $ p_{j}^{1} = p_{j}^{0} $ . w is the indicator variable for vertical integration, and $ w_{j}^{1} = 0 $ after the divestment, if the theater was previously vertically integrated.
On the other hand, the theaters’ profit function provides the following model

$$ \pi_{_{jmt}}=((1-\eta_{_{jmt}})p_{_{jmt}}+e_{_{jmt}}-mc_{_{jmt}})s_{_{jmt}}(p_{_{jmt}})X_{_{mt}}-F_{_{jmt}} $$

where X is the potential market size, and e denotes the expenditure per attendee on confectionery or other goods, which is not shared with distributors. mc and F represent marginal and fixed costs. $ \eta $ is the average rental rate for films. Following the results in the previous section, the film rental rates of integrated theaters are about 2.4% point larger than others. Hence, the theater divestment tends to reduce the rental rate for films of vertically integrated theaters as well as price, and change the share distribution among theaters. $ ^{16} $ The change in the producers' surplus (PS) per consumer due to the divestment is computed as follows:

$$ \begin{aligned}\Delta P S_{_{mt}}&=\sum_{j\in J_{mt}}((1-\eta^{_{1}}_{_{jmt}})p^{_{1}}_{_{jmt}}s_{_{jmt}}(p^{_{1}}_{_{jmt}},w^{_{1}}_{_{jmt}})-(1-\eta^{_{0}}_{_{jmt}})p^{_{0}}_{_{jmt}}s_{_{jmt}}(p^{_{0}}_{_{jmt}},w^{_{0}}_{_{jmt}}))\\&+\sum_{j\in J_{mt}}(e_{_{jmt}}-mc_{_{jmt}})(s_{_{jmt}}(p^{_{1}}_{_{jmt}},w^{_{1}}_{_{jmt}})-s_{_{jmt}}(p^{_{0}}_{_{jmt}},w^{_{0}}_{_{jmt}}))\\ \end{aligned} $$

where $ \eta_{j}^{1}=\eta_{j}^{0}-0.024 $ , if the theater is vertically integrated; otherwise, $ \eta_{j}^{1}=\eta_{j}^{0} $ . As mentioned above, the changes in $ \eta $ and w are the structural changes. The second term depends upon e-mc, as well as the change in the share distribution. Table 1 reports the mean and the standard deviation of the expenditure on confectionery per attendee, and it ranges from about 125 to 184 JPY. This study considers three cases in which e-mc=0, 100, and 150, respectively.

Finally, the change in the total welfare $ (TW) $ is defined as the sum of the change in the CS and the PS. In the simulation, I focus on the sample in which the film rental rate is greater than zero and smaller than one $ (0 < \eta < 1) $ —4,198 theaters. In addition, as described above, the parameters $ \delta_{0} $ and $ \theta $ cannot be separately identified. Hence, there are various ways to distribute the combined variations in $ \delta_{0} $ and $ \theta $ , captured by the year and selected market dummies. In the base case, I set $ \theta $ such that it is equal to the estimated constant term and assign the remaining variations to $ \delta_{0} $ .

$$ =Table\;6= $$

Table 6 presents the simulation results. Every welfare effect per consumer is reported in the constant year 2000. Panel (a) of the table presents the results of e - mc = 0. First, the effect of all the structural changes is negative, and the change in the PS is larger than the CV. The structural change, on average, reduces the TW about 116.181 JPY. Second, although the price effects are all
positive, they are very small. Even the change in the PS is larger than that in the CS. In the end, the total effect is dominated by the structural change effects, and the divestment of theaters decreases the TW by almost the same amount as the structural change (115.939 JPY). Panels (b) and (c) show the results of the cases in which e - mc = 100 and 150, respectively; one can see that the estimated welfare effect becomes large as e - mc increases. The reductions in TW are 139.571 JPY and 151.387 JPY, respectively.

### 6. Discussion

In this study, the vertical integration dummy is assumed to be orthogonal to the unobserved theater characteristic, $ \xi $ . However, someone may say that vertically integrated theaters tend to be able to show ‘big’ movies relative to other theaters. Moreover, they can probably make investments and upgrade their facilities, such as seats and screens, or they can potentially be in good locations, because of sufficient financial ability of upstream distributors/producers. $ ^{17} $ This implies that $ \xi $ of vertically integrated theaters may be larger than those of others, and the estimated coefficient of the dummy for vertical integration partly captures such differences in terms of theater quality.

One possible interpretation is that the choice and arrangement of movies as well as large screens, luxury seats, and good access, may be important services of vertically integrated theaters, and these services will be beneficial to consumers. Although I agree with this interpretation, I further assess the effect of vertical integration, applying the following two different strategies. $ ^{[18]} $ First, relaxing the assumption that the vertical integration dummy is exogenous, the ratio of vertically integrated theaters to the total number of theaters in each market is defined as an instrument for the vertical integration dummy. Then, the full model is estimated by adding the interaction terms of the vertical integration dummy and the mean deviation of other theater characteristics with the ratio of vertical integrated theaters, and the interaction terms of the ratio and other theater characteristics as instruments. In another method, the probit model of the determinant of vertical integration with the vertically integrated theater ratio in addition to exogenous characteristics, and instruments as exploratory variables. Thereafter, the full model is estimated as adding the interaction terms of the vertical integration dummy and the mean deviation of other theater characteristics, $ w(\varphi/\Phi) $ and $ (1-w)(\varphi/(1-\Phi)) $ . w is the vertical integration dummy, and $ \Phi $ and $ \varphi $ are estimated from the probit
estimation result. $ ^{19} $ The sixth and seventh columns of Table 4 present the results. The estimates are not so drastically different from those provided in the fifth column; however, the estimated coefficient of the dummy for vertical integration becomes larger, for example, in the sixth model, from 0.212 to 0.917. These results suggest that our inference that vertically integrated theaters were highly evaluated by consumers is not so problematic. $ ^{20} $

### 7. Concluding Remarks

Empirical results show that the price of vertically integrated theaters tends to be higher than others. According to Motta’s checklist, it satisfies the first condition of anticompetitive vertical integration. However, theaters owned by producers/distributors are highly evaluated by consumers. This implies vertical integration can possibly solve the distortion created by complex revenue-sharing contracts, which is pointed out by Gil (2006c), as well as various service provisions by integrated theaters. In addition, the simulation analysis reveals that the positive effect of resolving the distortion due to complex revenue-sharing contracts as well as service provision encouraged by vertical integration will outweigh the negative effect of price increase in the sense of Motta’s checklist, and the order for major distributors to divest their theaters will possibly reduce the social surplus. In other words, the vertical integration in the Japanese movie industry may enhance welfare. These results illuminate the importance of careful investigation of competition effects of respective integrations.

Finally, I address the remaining concerns pertaining to competition in the Japanese movie industry. In the movie industry, there are many industry-specific business practices. For example, in Japan, in addition to vertical integration, block booking, which packs the licensing of several movies into a single agreement, is also legal. Block booking acts like bundling or exclusive dealing because a screen can show only one film at a time. $ ^{[21]} $ Eventually, Toho and Toei, two of the major Japanese distributors, distribute their movies using block booking. According to Gil (2006b), only block booking banning was the source of the positive effects of the Paramount ruling on competition, while vertical divestment did not have any effect; thus, the effect of such vertical controls must also be empirically tested. In addition, uniform pricing is another typical business practice in the movie exhibition market. $ ^{[22]} $ While the average admission price in Figure 2 shows some variation, the general admission price for adult has been fixed at 1,800 JPY for many years; this price does not
vary with films. Hence, the variation in the average admission price among theaters in Table 2 may be attributed to the discount for morning or midnight shows, coupons, and the point system, in which, for example, a theater issues a point per attendance and attendees can exchange a certain amount of points for a ticket. $ ^{23} $ The pricing strategy of theaters is somewhat puzzling. Future studies should include the analysis that explicitly incorporates these business practices.
## References

Berry, S. T. (1994) “Estimating Discrete-Choice Model of Product Differentiation,” RAND Journal of Economics, 25(2), 242–262.

Berry, S. T., J. Levinsohn, and A. Pakes (1995) “Automobile Prices in Market Equilibrium,” Econometrica, 63(4), 841–890.

Cooper, J. C., L M. Froeb, D. O'Brien, and M. G. Vita (2005a) "A Comparative Study of United States and European Union Approaches to Vertical Policy," George Mason Law Review, 13(2), 289–308.

Cooper, J. C., L. M. Froeb, D. O'Brien, and M. G. Vita (2005b) "Vertical Antitrust Policy as a Problem of Inference," International Journal of Industrial Organization, 23(7–8), 639–664.

Corts, K. S. (2001) “The Strategic Effects of Vertical Market Structure: Common Agency and Divisionalization in the US Motion Picture Industry,” Journal of Economics & Management Strategy, 10(4), 509–528.

Chipty, T. (2001) “Vertical Integration, Market Foreclosure, and Consumer Welfare in the Cable Television Industry,” American Economic Review, 91(3), 428–451.

Davis, P. (2005) “The Effect of Local Competition on Admission Prices in the U.S. Motion Picture Exhibition Market,” Journal of Law & Economics, 48(2), 677–708.

Davis, P. (2006a) “Measuring the Business Stealing, Cannibalization and Market Expansion Effects of Entry in the U.S. Motion Picture Exhibition Market,” Journal of Industrial Economics, 54(3), 293–321.

Davis, P. (2006b) “Spatial Competition in Retail Market: Movie Theaters,” forthcoming in RAND Journal of Economics.

De Vany, A. (2004) Hollywood Economics: How Extreme Uncertainty Shapes the Film Industry, Abingdon, U.K.: Routledge.

Einav, L. (2006) “Seasonality in the US Motion Picture Industry,” forthcoming in RAND Journal of Economics.

European Commission (2007) “Draft Commission Guidelines on the Assessment of Non-horizontal Mergers,”

(http://ec.europa.eu/comm/competition/mergers/legislation/draft_nonhorizontal_mergers.pdf)

Gil, R. (2006a) “Make-or-Buy in Movie: Integration and Ex-post Renegotiation,” International Journal of Industrial Organization, 25(4), 643–656.

Gil, R. (2006b) “An Empirical Investigation of Paramount Antitrust Case,” mimeo.

Gil, R. (2006c) “Revenue Sharing Distortions and Vertical Integration in the Movie Industry,” mimeo.

Hanssen, F. A. (2000) “The Block Booking of Films Reexamined,” Journal of Law & Economics,
43(2), 395–428.

Hastings, J. S., and R. J. Gilbert (2005) “Market Power, Vertical Integration and the Wholesale Price of Gasoline,” Journal of Industrial Economics, 53(4), 469–492.

Hayashi, S. (2007) “Meiji no Koyo to Genkai: Oshu no Hi-suihei—gata Kigyo Ketsugo Gaidorain An (Benefit and Limitation of Clarification: Draft Commission Guidelines on the Assessment of Non-horizontal Mergers)” Kosei Torihiki, No. 679, 19–26. (in Japanese).

Kaneyama, K. (1994) Eiga Kai ni Shinro wo Tore (Take the course toward Movie Business), Tokyo, JAPAN: Synergy Kikagaku (in Japanese).

Kenney, R. W., and B. Klein (2000) “How Booking Facilitated Self-Enforcing Film Contracts,” Journal of Law & Economics, 43(2), 427–435.

Kinema Junpo Film Institute (2005) Eiga Produsa no Kiso Chishiki (A Basic Guide for the Producer), Tokyo, JAPAN: Kinema Junpo (in Japanese).

Ministry of Economy, Trade and Industry (each edition) Tokutei Sabisu Sangyo Jittai Chosa Hokokusho: Eigakan Hen (Report on Survey of Selected Service Industry: Movie Theaters), Tokyo: Keizai Sangyo Tokei Kyokai (in Japanese).

Motion Picture Producers Association of Japan (2007) Statistics of Film Industry in Japan: Year 1955–1999 & Year 2000–2006, (http://www.eiren.org/toukei/index.html) (in Japanese).

Motta, M. (2004) Competition Policy: Theory and Practice, New York, NY: Cambridge University Press.

Murakami, Y., and N. Ogawa (1999) Nihon Eiga Sangyo Saizensen (Japan Movie Now), Tokyo, JAPAN: Kadokawa Shoten (in Japanese).

Nalebuff, B. (2004) “Bundling: GE–Honeywell (2001),” in J. E. Kwoka, Jr., and L. J. White eds., The Antitrust Revolution, the fourth edition, Case 16, 388–412, Oxford, UK: Oxford University Press.

Ohashi, H. (2003) “The Role of Indirect Network Effects in the U.S. VCR Market, 1978–86,” Journal of Economics & Management Strategy, 12(4), 447–497.

Orbach, B. Y., and L. Einav (2006) “Uniform Prices for Differentiated Goods: the Case of the Movie–Theater Industry,” forthcoming in International Review of Law and Economics.

Seim, K. (2006) “An Empirical Model of Firm Entry with Endogenous Product-Type Choice,” RAND Journal of Economics, 37(3), 619–640.

Shepard, A. (1993) “Contractual Form, Retail Price, and Asset Characteristics in Gasoline Retailing,” RAND Journal of Economics, 24(1), 58–77.

Slade, M. (1998), “Beer and the Tie: Did Divestiture of Brewer-owned Public Houses Lead to Higher Beer Prices?” Economic Journal, 108(448), 565–602.

Suzuki, A. (2006) “Vertical Integration in the U.S. Cable Television Industry,” ISER Discussion Paper Series, No. 675.
Tanaka, T., Y. Yasaki, R. Murakami, M. Funakoshi, and M. Sunada (2007) “Media Contentsu Sangyo deno Kyoso no Jittai Chosa (Research on Competition in Media Contents Industry),” forthcoming as CPRC Report, Competition Policy Research Center, Japan Fair Trade Commission (in Japanese).

Vita, M. (2000) “Regulatory Restrictions on Vertical Integration and Control: The Competitive Impact of Gasoline Divorcement Policies,” Journal of Regulatory Economics, 18(3), 217–233.

Waterman, D., and A. A. Weiss (1996) “The Effects of Vertical Integration between Cable Television Systems and pay Cable Networks,” Journal of Econometrics, 72(1–2), 357–395.

Wooldridge, J. M. (2002) Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press.
Appendix

$$ =\mathrm{T a b l e}7= $$

$$ =Table\;8= $$

$$ =Table9= $$

$$ =Table10= $$

$$ =Table11= $$
Figure 2: Attendance and Average Admission Price