Silviase/jgov_v1.4_full_ann huggingface dataset:
service provider is ASTEL; however, it plans to shrink its business in the near future. Hence, there are five carriers among the seven brands in the Japanese mobile telecommunications market. Figure 2 depicts the evolving Japanese mobile telecommunications market during the latter half of the 1990s.

$$ =Figure2= $$

Another important issue is the definition of the network factor. With regard to voice transmission services, networks appeared compatible with each other. However, with respect to packet or data transmission services, networks were incompatible because they operated different mobile Internet service networks such as i-mode by NTT DoCoMo and EZweb by KDDI. $ ^{7} $ Thus, in this paper, the installed base of each brand is basically defined as the network factor or network size, although the results with the market-level installed base will also be presented for a comparison.

For the application, we have to set the values of the demand parameters, $ \beta $ , $ \gamma $ , and $ \sigma $ . This paper employs the estimates of the regression using the data of the dominant carrier in the Japanese mobile telecommunications market—the NTT DoCoMo group—because it is relatively easy to obtain sufficient information on this carrier. The NTT DoCoMo group consists of nine regional operating companies; each operating area can be regarded as a separate mobile telecommunications market because most of the mobile phone calls are made within a certain area. $ ^{8} $ Based on the ALM/NE, the relative demand function to the outside alternative of NTT DoCoMo’s cellular phone services is expressed as follows:

$$ \ln(x_{m t}^{N T T}\;/\;x_{m t}^{0})=a^{N T T}+\beta p_{m t}^{N T T}+\gamma\ln N_{m t}^{N T T}+Z_{t}^{N T T}\theta+\sigma\ln s_{m t}^{N T T|C e l}+u_{m t}, $$

where t and m denote the time and operating area. $ x^{NTT} $ and $ x^{0} $ are the number of subscribers to NTT DoCoMo's cellular phone services and the number of non-mobile users, respectively. In this paper, the potential market size is defined as the population of each area, and $ x^{0} $ is defined as the population minus the total sum of mobile users for each operating area. $ Z^{NTT} $ is the vector
of the service characteristics of NTT DoCoMo's cellular phone services, and $ s^{NTT|Cel} $ is the share of NTT DoCoMo in cellular phone users. $ p^{NTT} $ and $ N^{NTT} $ denote the price and the installed base of NTT DoCoMo's cellular phone services, respectively.

## Data

For the estimation, the data is collected from various sources. This paper regards the average (monthly) revenue per user (ARPU) as the price of mobile telecommunications services. The reason why this paper focuses on the ARPU is that the Japanese mobile telecommunication carriers offer several differentiated service menus and options, but it is impossible to deal with these menus because the number of subscribers to each menu is not revealed to the public. Alternatively, this paper defines the ARPU as a price, given the assumption that the use of mobiles is the same across all users during a particular time period. Indeed, it is a very strong assumption; however, given the assumption, the ARPU reflects the average of various service menus and discount options used by subscribers. However, we cannot obtain information on the ARPU for each carrier in each operating area. Alternatively, only the ARPU in terms of the national average and annual revenues for each regional NTT DoCoMo operating company is available. These figures are made available for each fiscal year from each edition of “Terekomu Deta Buku” (Telecom Data Book), which is published by the TCA, “Joho Tushin Handobuku” (Information & Communications in Japan), which is published by InfoCom Research, Inc., and the financial reports of the respective regional NTT DoCoMo operating companies. Assuming that almost all the revenues are generated from the mobile business, the ARPU of NTT DoCoMo’s cellular phone services for each regional operating company is estimated as follows:

$$ ARPU=\frac{Total Revenue-2,700\times\# of Subscribers to\quad NTT DoCoMo^{\prime}s PHS}{\# of Subscribers to NTT DoCoMo^{\prime}s Cellular Phone\times12}, $$

where the base charge for NTT DoCoMo’s PHS services is set at 2,700 yen. The number of subscribers to NTT DoCoMo and other carriers in nine operating areas are downloaded from the TCA Web site in the form of a monthly data from April 1996 to March 2005. Figure 3 compares the arithmetic and weighted means of the estimated ARPU for nine areas with the actual ARPU series of the NTT DoCoMo group at the national level. On the other hand, the installed base is defined as the lag of the number of subscribers to NTT DoCoMo’s cellular phone services: While the number of subscribers to each service at the end of each fiscal year (March of the following year) is regarded as the current users, the number of subscribers each April is defined as the installed base. Based on the data of current users, the share of NTT DoCoMo within each regional cellular phone market is also calculated. To control for the improvement in the business of NTT DoCoMo’s cellular phone services during this time period, two dummy variables are
introduced as product characteristics: the i-mode dummy, which is 1 if the year is 1999 or later and 0 otherwise, and the i-appli & 3G dummy, which is 1 if the year is 2001 or later and 0 otherwise. $ ^{9} $

$$ =Figure3= $$

## I nstruments

According to previous works, the price and share within the cellular phone market may be endogenously determined and correlated with the error term. For this purpose, we need the appropriate instruments. The first possible candidate is a cost-side variable that is excluded from the product characteristics. This paper uses the monthly salary of service industries in each region from “Chingin Kozo Kihon Chosa” (Basic Survey on Wage Structure). Hausman (1997) introduced another candidate, that is, prices of the product in other regional markets. The present paper employs the average prices that exclude the prices of the region being instrumented. Other variables, which represent the competition condition in each regional mobile market, are used as valid instruments, such as the Hirschman-Herfindahl Index (HHI) and the share fluctuation index (SFI). While the former index is commonly used, the latter may not be well known. The SFI was introduced by Izumida, Funakoshi, and Takahashi (2004) and used by Motohashi, Funakoshi, and Tohei (2005) in the analysis of the dynamic relationship between competition and productivity. The SFI is calculated as follows:

$$ S F I_{m r}=\frac{1}{J}\sum_{J}\left(s_{j m r}-s_{j m r-1}\right)^{2}, $$

where $ s_{jmr} $ denotes the market share of brand j in market m in time r. Hence, the SFI may capture the dynamic aspect of competition within a market. Finally, the prices and wages are deflated by the CPI $ (2000 = 100.0) $ . Table 1 reports the descriptive statistics of the regression variables. $ ^{10} $
## Estimation Results

Table 2 reports the estimation results. This paper uses the generalized method of moments (GMM) as the estimation method. $ ^{11} $ The efficient GMM estimates of standard errors with both arbitrary heteroskedasticity and arbitrary intertemporal within-area correlation are within parentheses and those with small sample corrections are within square brackets. The first column presents the estimation of the simple logit model ( $ \sigma = 0.0 $ ). The parameters are significantly estimated and show the expected signs. The Hansen J test statistic is equal to 3.790, and the assumption that the instruments are orthogonal to the error term is not rejected (p-value = 0.285).

$$ =Table2= $$

The second column shows the results of the nested logit model. Again, all the parameters except for $ \sigma $ are significantly estimated and show the expected signs. The Hansen J test statistic is equal to 2.613, and the orthogonal assumption is not rejected (p-value = 0.270). The estimate of price coefficient $ \beta $ is negative and satisfies the theoretical requirement. The marginal effect of network factor $ \gamma $ is positively estimated in both the models, and thus, an increase in the network size is expected to lead to an improvement in welfare. This result is contrary to that in Iimi (2005), who could not find significant direct network effects and concluded that the network size was not a crucial factor in the Japanese cellular phone market. $ ^{12} $ On the other hand, Okada and Hatta (1999) found significant network effects in the mobile phone demand from 1992 to 1996. Thus, despite the controversy surrounding the network effects of the Japanese mobile services, this paper conducts the following simulations, given the presence of the network effects. While $ \sigma $ is not precisely estimated (p-value = 0.174 and 0.250 with and without small sample correction, respectively), it lies between 0 and 1. As described above, this paper follows the market delineation reported by the Ministry of Internal Affairs and Communications (2005) and we use the estimates of the nested logit model. $ ^{13} $
### 4. Simulation Analysis of the Japanese Mobile Telecommunications Market

Pre-change Market Outcome

First, using this framework, the marginal costs and product qualities for each brand are estimated from the market outcome in 2003. The reason why this paper focuses on 2003 as the pre-change period is that the ARPU of NTT DoCoMo's cellular phone and PHS services, au and Tu-Ka by KDDI, and Vodafone are available from "Joho Tushin Handobuku" (Information & Communications in Japan) for this year. However, since the ARPU of WILL.COM and ASTEL is not available, this is assumed to be the same as that of NTT DoCoMo's PHS services. The number of current users and the installed base are defined as the number of subscribers in March 2004 and April 2003, respectively. The ownership structure in 2003 is as follows: NTT DoCoMo operated cellular phone and PHS services; KDDI had two cellular phone brands, au and Tu-Ka; and there were other independent carriers, Vodafone (cellular phone), WILL.COM (PHS), and ASTEL (PHS).

$$ =Table3= $$

Table 3 presents the pre-change market outcome. The first column reports the market share; it can be seen that NTT DoCoMo captured more than half the Japanese mobile telecommunications market. The second column presents the ARPU/100. According to this column, the prices of NTT DoCoMo and au seemed to be relatively high. The third and fourth columns report the estimate of the marginal cost divided by 100 and the price-cost margin ratio. According to these columns, the carriers gained approximately 20%–30% margins from the cellular phone business and 36%–56% from the PHS business. The estimates of product quality are reported in the fifth column, which shows that NTT DoCoMo provided the highest quality cellular phone services followed by au that accounts for about three-fourths of NTT DoCoMo's cellular phone services. The ratio of the total mobile users to the population is approximately 68.6%, and the share weighted average of prices is 7,255 yen.

$$ =Table4= $$

Table 4 reports the own and cross price elasticity matrix $ E^{p} $ . According to this matrix,
every own price elasticity was greater than 1.0 in absolute value, and those of au and Vodafone were relatively larger. With respect to cross price elasticity, those of NTT DoCoMo's cellular phone services and au were large, but those of the PHS services were small. $ ^{14} $ On the other hand, Table 5 presents the estimates of own and cross network size elasticity matrix $ E^{N} $ . According to this table, all the own price elasticities were smaller than 1.0 and those of au and Tu-Ka were larger than the others. In the case of cross price elasticity, those of NTT DoCoMo's cellular phone services and WILL.COM were relatively large.

= Table 5 =

## Merger Simulation

To illustrate the ALM/NE, we consider a hypothetical merger among cellular phone carriers. $ ^{15} $ For each merger, three types of operations are considered in order to verify the importance of network effects and investigate the welfare effects of network integration. In addition, the results with the market-level network effect model will be reported for a comparison.

The first operation is the separate operation, which means that the merged brands are run as separate networks by the single merged firm. In this case, the network product is regarded as other usual differentiated products. This situation is represented only by the change in the ownership structure. The marginal costs are the same as those in the pre-merger period. $ ^{16} $

The second operation is the partial integration, which means that the network is integrated but the product qualities are different. This implies that while two brands are operated and supplied as explicitly differentiated services, the different brands of a merged carrier do not
encounter interconnection problems in calling, sending e-mails or images, and browsing mobile contents. This situation is represented by the change in the ownership structure and the installed base of the merged brands, which is set to the sum of the installed base of each merged brand. In this case too, the marginal costs are the same as those in the pre-merger period.

The last operation is the complete integration, which implies that brands merge to become a single identical service. This situation is represented by setting the product qualities of the merged brands to the maximum level, in addition to the change in the ownership structure and the installed base, as is the case with partial integration. Additionally, the marginal cost of the completely integrated brand is set to the maximum marginal cost value among the original brands. Finally, it should be noted that in all simulations, the PHS services are assumed to be operated separately by merged carriers because of its technological differences, and that WILLCOM is regarded as a single independent carrier since it was generated by the acquisition of the PHS business from KDDI by the Carlyle Group in 2004.

= Table 6 =

Table 6 reports the results of the merger simulation. The first four columns present the hypothetical merger between NTT DoCoMo and Vodafone, and the next four present that between NTT DoCoMo and KDDI (au & Tu-Ka). These results show similar patterns: the prices of almost all cellular phone brands increase in the post-merger period and the share weighted average also rises in response. In addition, the ratio of the total mobile telecommunications market to population shrinks in this period. The first and second panels of Figure 4 depict the welfare effects of the simulated mergers. According to these panels, CS decreases but PS increases, and the total welfare is affected in cases of separate operation and complete integration. However, in the partial integration case, the total welfare increases slightly because the increases in PS outweigh the decreases in CS.

= Figure 4 =

The final four columns and the final panel of Figure 4 report the results of the hypothetical merger between KDDI (au & Tu-Ka) and Vodafone; these show slightly different patterns. In the case of separate operation, the prices of almost all cellular phone brands increase in the post-merger period and the weighted average rises to approximately 7,456 yen. The share of mobile users in the population declines slightly to 67.4%. While CS decreases, PS increases,
and the total welfare improves very slightly. In the case of partial integration, while the weighted average price increases slightly to 7,351 yen, the ratio of the number of mobile users to the population increases to 69.0%. Increasing prices lead to an improvement in both the CS and the PS and consequently enhances the total welfare. This is because the integrated installed base contributes to the improvement in consumers' welfare. On the other hand, in the case of complete integration, the average price increases to 7,654 yen, and the mobile telecommunications market shrinks in the post-merger period. In regard to the welfare effects, CS decreases and PS increases. Finally, this leads to the deterioration in the total welfare.

These results suggest that there may be mergers that improve both CS and PS when the merged carrier seamlessly integrates its brands while ensuring that the lineup of the available services corresponds to that of each component carrier in the pre-merger period. This is because in comparison with complete integration, this merger enables consumers to exploit the network effects without loss of product (or brand) variety as well as price decline due to competition among mobile carriers. The final bar chart of each panel presents the results taking the market-level installed base as the network size. According to these results, if the market-level installed base is a relevant definition of the network size, mergers among cellular phone carriers will have serious effects on the total welfare.

## Entry Simulation

In July 2005, the Radio Regulatory Council submitted a report on the frequency allocation guideline released by the Ministry of Internal Affairs and Communications of Japan. According to this report, at the most, new frequencies would be assigned to two new entrants in the 1.7 GHz band and to one entrant in 2.0 GHz band. Two broadband access service providers, Softbank and e-Access, applied for entry in 1.7 GHz band, with IP-Mobile applying for entry in the 2.0 GHz band. Three months later, all the applications were approved, and they planned to begin cellular phone services by 2006 or 2007. $ ^{17} $ In this subsection, the hypothetical entry into the Japanese mobile telecommunications market is simulated and its welfare effects are assessed using the ALM/NE.

The following simulations consider three cases with different number of entrants and different sizes of the installed base per entrant: a single entrant with 6 million, two entrants with 3 million, and three entrants with 2 million. Hence, in each case, the total installed base of the
entrants is equal to 6 million. $ ^{18} $ In addition, while the product qualities of the new entrants are assumed to be the mean of those of cellular phone brands, the marginal costs are assumed to be the maximum marginal cost value among the brands. $ ^{19} $ Hence, in the case of two or three entrants, they are completely symmetric. All the entrants are assumed to be independent carriers. The present framework allows entrants with positive network factors to appear suddenly in the post-entry period, where the network size of incumbents is unchanged; this characteristic seems to be slightly unusual. This paper intends to present a picture of the market several years later, rather than to describe the dynamic process of an entry. This simulation does not take into account the entry cost. As is the case with merger simulation, this paper also considers the results with the market-level network effects.

$$ =Table7= $$

The results reported in Table 7 and Figure 5 suggest that with both network definitions, a new entry causes price declines, market expansions, and an increase in CS but decrease in PS, thereby improving the total welfare. For example, in the case of a single entry with brand-specific network effects, the new carrier accounts for approximately 7.3% of the market, and the average price decreases to 7,181 yen and the share of the total mobile users in the population becomes approximately 70.0%. In addition, CS increases to approximately 4.07%, but PS decreases to approximately 4.42%. The total welfare improves (approximately a 1.76% increase). Moreover, it can be seen that the welfare gain decreases as the number of entrants increases. This is because the incumbents lose their profits further, although the improvement in consumers' welfare is enhanced with an increase in the number of entrants and the former effect is larger. On the other hand, in the case of market-level network effects, the welfare gain increases as the number of entrants increases and the relative importance of welfare effects on CS and PS is reversed.

$$ =Figure5= $$
### 5. Concluding Remarks

This paper conducted a simple simulation analysis of the hypothetical mergers and entry in the Japanese mobile telecommunications market using the ALM/NE for illustrative purposes. The framework explicitly incorporates network effects, where the increase in the network size is assumed to improve consumers' welfare, and enables us to predict a price change due to the change in the market structure and assess its welfare effects.

To assess the welfare effects of mergers and entry, it is important to focus on the appropriate welfare measure: the total welfare or consumers' welfare. As pointed out by Werden (1996), economists prefer to focus on the total welfare, but consumers' welfare seems to be the yardstick of antitrust enforcement. From the first viewpoint, all hypothetical mergers with partial integration will be regarded as those meant for enhancing the welfare. However, if we focus on the second viewpoint, with the exception of the merger between KDDI and Vodafone with separate operation or partial integration, the hypothetical mergers seem to harm the welfare. In the case of entry, if one pays attention to the total welfare, the single entry case will be preferred, although consumers' welfare may be improved as the number of entrants increases.

Indeed, these results depend on the assumption that consumers' preferences are reflective of the network effects on mobile telecommunication services. If such an assumption is rejected, as seen in the results of Iimi (2005), the hypothetical mergers result in cases of separate operation, with all mergers except for the one between KDDI and Vodafone decreasing the total welfare and consumers' welfare. Therefore, it is important to investigate the nature of the consumption patterns and check the validity of the network effects. With regard to application, it is assumed that the Japanese mobile telecommunications market exhibits brand-specific network effects. However, as stated in the footnote, these days, Japanese mobile carriers attempt to make their mobile Internet services compatible with those of their rivals. Further, the simulation results with market-level network effects are different from those with brand-specific network effects. If their services become perfectly compatible with each other, the analysis based on brand-specific network effects might lead to incorrect policy implications.

This analysis excluded the dynamic aspect of mergers and entry and was conducted in a static setting. As pointed out by Gowrisankaran (1999), ignoring dynamic determinants of firm behavior, such as mergers, entry/exit or investment, and the linkages between them, will lead to incorrect conclusions. Moreover, network effects will shed light on another important dynamics of consumer behavior because the timing of adoption or switching among brands is likely to be intertemporally affected by the change in the market structure and the network size. In order to deal with such problems, we need a completely dynamic model of mergers, where firms' and
consumers’ behavior are endogenously determined. This remains an issue to be dealt with in future studies.

Finally, it should be noted that the simulation results depend heavily on model assumptions such as the nest structure, price definition, potential market size, and parameter values used in the simulation, some of which are poorly estimated. Therefore, we have to take note of the precision of the simulation. In addition, Crooke, Froeb, Tschantz, and Werden (1999) show that the curvature of the assumed demand system significantly affects the predicted price increase. Hence, the results of this study should be compared with the simulation results based on other types of demand systems. However, at the same time, Werden and Froeb (2002) proposed the ALM as a practical, quick, and easy tool for merger simulation. Therefore, given these limitations, the framework will be a benchmark for policy analysis in network industries.