Silviase/jgov_v1.4_full_ann huggingface dataset:
de-facto standard. Thus the company that controls the standard can be a monopolist. $ ^{2} $

Network externalities in the router market come from two sources: incompleteness of compatibility of routers and availability of operational and technical expertise.

## (1) Incompleteness of compatibility

Computers in the Internet network communicate using a protocol called TCP/IP. TCP/IP protocols are standardized by the IAB (Internet Architecture Board) and IETF (Internet Engineering Task Force), which are subordinate organizations of the non-profit association called ISOC (Internet SOCiety). Therefore, TCP/IP is an open standard and all routers can be compatibly connected to each other. When standards are open, one company does not become dominant because of network externalities.

However, since implementation of TCP/IP protocol depends on vendors, interconnection is easier among the same vendor's routers. Therefore users tend to choose the vendor with the highest market share because outside routers to be connected are more likely to be the same vendor's routers.

## (2) Availability of operational and technical expertise

Techniques and know how are necessary to operate routers. Techniques and know how depend on vendors, because operations (command line) and trouble shooting are different among vendors. $ ^{3} $ Users can easily utilize operational techniques of the dominant vendor because there are many supporting materials such as mailing lists, BBS, and guide books pertaining to the vendor. On the other hand, supporting materials for
small vendors' routers are not easy to obtain. Users therefore prefer the dominant vendors' products. Also, when the vendor is dominant there are plenty of technical experts in the market. Therefore users easily find technical experts if they use the dominant vendors. $ ^{4} $

In addition, the presence of switching costs might contribute to stability of the share of Cisco. It is a natural hypothesis that switching costs are present in the router market because of incomplete compatibility. When compatibility is not perfect, it is rational for users to employ the same vendor through their network equipment. Extra adjustment costs are needed to connect one vendor's router to other vendor's. Theoretically speaking, switching costs are distinguished from network externalities because the degree of switching cost depends on users' router equipment history, not market share of the vendors. Even if the vendor's market share is only 2-3%, switching costs might be large when the user has purchased all routers from the vendor.

We tested network externalities in the router market in two ways: user survey and econometric analyses.

### 3. Survey of Vendor Choice

First we investigate through questionnaire surveys whether or not users recognize network externalities in router markets.

We chose heavy users of routers and sent a multiple-choice
questionnaire asking about their reasons for vendor choice. We obtained 756 responses (64.1% response rate) from 1179 firms, Internet service providers (ISPs) and universities (see Table 1). The list of firms is composed of 207 finance companies and 649 listed firms with assets of more than 100 billion yen. The list of ISPs comprises 50 firms with no less than 10,000 subscribers and 51 randomly selected firms that do business nationwide. The list of universities comprises 99 national universities and 123 private universities. We conducted this survey in December 2002.

We showed them eight candidates of vendor choice reasons:

1 Compatibility with outside routers

2 Getting information from other users

3 Compatibility with inside routers

4 Accumulation of vendor expertise in your company

5 Functions or stability

6 Low price

7 Vendor support

8 Others

Choices 1 and 2 are network externality factors. Choices 3 and 4 are switching cost factors and choices 5 to 8 are quality and price; that is, ordinary characteristics of the product. We expect that choices 1 and 2 are chosen if network externalities work. The results for Cisco users and other vendor users are shown in Figure 4.

Figure 4 shows that about 20% of Cisco users choose network externality factors (choices 1 and 2), but only 5% of other vendor users choose these factors. Switching cost factors (choices 3 and 4) also are more important for Cisco users than for other users. On the other hand, function, stability and support (choices 5 and 7) don't show large differences. Twenty percent of other users choose low price (choice 6), as opposed to only 2.5% of Cisco users. In summary, users choose Cisco because of network externality factors and switching costs factors, and users choose other vendors because of their low price. This result implies that network
externalities work in the router market.

To see the behavior of independent expert users, we limited samples to only ISPs, which have needs for the latest technology. Large companies tend to choose affiliate vendors and universities might choose routers for special reasons such as education or research. But ISPs are independent and choose the most excellent product.

Figure 5 shows that over 50% of Cisco users choose vendors based on compatibility with other users. Non-Cisco users choose functions or stability in addition to low price. This implies that the quality of the product is an important factor for expert users and the network externalities are working even for the expert users with much technical knowledge of routers.

We should note that switching cost factors are also important for users. The results show that Cisco's switching costs are larger than that of other users. One of the reasons for this is that Cisco's products were introduced to the market long before other vendors. Cisco users use Cisco products longer than other users, and switching costs depend on how long the user has used those vendor's products so far.

### 4. Hedonic Price Model

Some empirical researches examined network externalities using a hedonic price model. Brynjolfsson and Kemerer (1996) and Gandal (1994) studied network externalities of spreadsheet software in the United States, and Tanaka (2002) examined the mobile phone market in Japan. Asai and Tanaka (2003) studied the personal computer market in Japan. Finally, Tanaka, Yasaki and Murakami (2003) estimated network externalities of spreadsheet software, word processing software, and routers in Japan. If there are network externalities, users have more willingness to pay for products with a larger share. Thus, if there is positive correlation between the share and the price, we can assume evidence of network externalities.
(1) Data

Details about variables used for estimations are as follows

PRICE: We obtained price from price lists of vendors in 2002. The price of routers depends on functional options. In this case, dependent variable is list price with full functional options. We chose products with system band width which indicates the ability of managing IP packets over 25 Mbps. Although retail prices are better data, we could not obtain these prices because the retail price is determined in the private negotiations between the vendor and users. Price variable is logged because its distribution is skewed to the right.

SHARE: Share represents network externalities. Unit base share (percentage) by vendor come from a user survey. To avoid the simultaneous problem we use one-year-lagged share. We expect that coefficient of share is significantly positive to support the presence of network externalities. The value of the coefficient indicates how much impact the network externalities have.

Characteristic variables: Functions of routers are broadly classified as basic functions, lines that the product supports, redundancy and quality of service (QoS) (see Table 2). All variables except for system bandwidth are dummy variables. The system band width variable is logged.

74 samples were obtained and about half of them are Cisco products (see Table 3).

(2) Results of regressions

The results of regressions are presented in Table 4. In case 1 all independent variables are included. In case 2 we reduced the independent variables until all coefficients were significant and with the expected sign.
The coefficient of SHARE, which measures network externalities, was positive and significant in both cases. The result of a robustness check of T value of SHARE coefficient is presented in Figure 6, which shows distribution of T value of SHARE coefficients for all combination of characteristic dummy variables. T value of SHARE coefficients is over 2 in most cases. Accordingly, network externalities as measured by vendor share significantly increase the price of router products. The coefficient of SHARE is 0.004 in case 2. That is, a one percent increase in share is associated with a 0.4% increase in price (70% difference in share means a 28% difference in price.) That difference of price is consistent with the results of our user survey.

However, the effect of network externalities is not overwhelming compared with other characteristic variables. The latest interface, POS OC-192, is highly evaluated by users and the coefficient of OC192 is 0.533 in case 2. That means introducing this new technology raises price roughly by 50%. Because coefficients of EGB, REDUNP and MPLS are also large, competitors could overcome Cisco by introducing such technologies in a timely manner. Juniper is said to have adopted such a strategy. It allocated resources to development of an ultra-high-end router and introduced new products with MPLS technology. Such a strategy succeeded and as a result Juniper gained some share, although it is a new entrant (see Figure 1: high-end router market structure). According to hearings to the vendors, carriers and very large ISPs needed a seriously high-end router, and Juniper met such needs first. $ ^{5} $

This result suggests that network externalities work in the router market in Japan, although the effect is not overwhelming compared with new technologies.
### 5. Logit Model

In recent years there are some empirical studies of network externalities using the logit model. Ohashi (2003) and Park (2003) estimated network externalities of the VCR market in the United States. Rysman (2003) showed that network externalities work in the market for Yellow Pages. They all employ the logit model and estimate its reduced form using share variable as a dependent variable. This form of the logit model was developed by Berry (1994). Because we have an individual data set from our user survey, we use an original, conditional logit model (see MacFadden (1974), Amemiya (1985)).

When choice probability of product j of user i is $ P_{ij} $ , $ P_{ij} $ is given by

$$ P_{ij}=\frac{\exp(\nu_{j})}{\displaystyle\sum_{k=1}^{J}\exp(\nu_{k})},\quad\nu_{j}=\beta X_{j}-\alpha p_{j}+\gamma S_{g|j\in g}. $$

$ X_{j} $ represents characteristic variables of product j. In this case, they are system bandwidth, number of supported lines, and quality of services of the product j. $ p_{j} $ is the price of the product j, and $ S_{g|j \in g} $ is share in a previous year of vendor g which produces product j. In summary $ V_{j} $ is the net utility for product j. We choose $ \alpha $ , $ \beta $ and $ \gamma $ to maximize the joint choice probability (most likelihood method). If the coefficient of share is significantly positive, it implies that network externalities work in the router market.

It is assumed that there is no correlation between utilities obtained from each choice in the conditional logit model. But this assumption is not realistic. For example, users who chose Cisco's product A will choose other Cisco products, not other vendors' products, when users cannot get product A. In such a case, the nested logit model is appropriate.

Products whose utilities are highly correlated are classified as the same group g. We assume that the grouping is based on the vendors. The
choice probability of group g, $ P_{ig} $ , and the choice probability of product j after choosing group g, $ P_{i,j|g} $ , are given by

$$ P_{i g}=\frac{\left[\sum_{j\in g}\exp\left(\mathsf{v}_{j}/\lambda\right)\right]^{\lambda}}{\displaystyle\sum_{g=1}^{G}\left[\sum_{j\in g}\exp\left(\mathsf{v}_{j}/\lambda\right)\right]^{\lambda}},\quad P_{i,j\mid g}=\frac{\exp\left(\mathsf{v}_{j}/\lambda\right)}{\displaystyle\sum_{k\in g}\exp\left(\mathsf{v}_{k}/\lambda\right)}. $$

The choice probability of product j, $ P_{ij} $ , is given by

$$ P_{i j}=P_{i,j\mid g}\cdot\ P_{i g}. $$

$ \lambda $ is a parameter of correlation within group g. $ \lambda $ lies between 1 to 0. As the parameter $ \lambda $ approaches zero, the within group correlation goes to one. And as the parameter $ \lambda $ approaches 1, the within group correlation goes to zero, which means that the nested logit model becomes equal to the conditional logit model. $ \sigma $ ( $ =1-\lambda $ ) can be interpreted as a correlation coefficient.

(1) Data

Details about data used for estimations are as follows

Product j : Products with minor version changes are classified as the same product. For example Cisco 3600, 3602, 3620 are regarded as the same product as Cisco36xx series. As a result we have 17 products.

PRICEj: The list price of product j is used (unit is 10 million yen). Average price is used when the product consists of several minor change versions.

SHAREg : One-year-lagged share of vendor g is used. We obtained share
data from our user survey.

Characteristic variables : We use four characteristic variables whose coefficients in the hedonic price model are significant, LWAN, EGB, PRQ and MPLS (see Table 2). $ ^{6} $

## (2) Results of regressions

The results show that the coefficients of nested logit model are more significant than those of the conditional logit model (see Table 5). Since the ramda, $ \lambda $ , is significant and $ \sigma $ is high ( $ \sigma = 1 - \lambda = 1 - 0.232 = 0.768 $ ), there is a correlation within the same vendor choices. This implies that nesting makes sense.

The coefficient of SHAREg is positive and significant. Vendor's share of the previous period increases the current choice probability of the routers. We can interpret it as evidence of network externalities.

The coefficient of LWAN is not significant. EGB and PRQ increase significantly choice probability as expected. However, MPLS decreases significantly choice probability in contrast to expectations. That might be because MPLS is needed only by carriers and very large ISPs, not by most large firms.

The effect of SHAREg of the nested logit model is much larger than that in the hedonic price model. The coefficient of SHAREg in the nested logit model is 3.246. It means that if the difference of share is 70%, the effect of network externalities is 2.3 $ (=3.246 \times 0.7) $ . Since it is too large compared with the effect of price, EGB and PRQ, new technologies or price reduction cannot overcome the network externalities effect. This result is not consistent with the results of the hedonic price model, which shows that new technologies can overcome network externalities effects.
One possible interpretation is that the value of estimation of the logit model is larger than true values, because switching cost effects are mixed. Users have difficulty changing vendors in the presence of switching costs, so users choose the same vendors in the next period with high probability even if there are not network externalities. The share variable here includes switching cost effects beside network externalities. Switching cost effects should be estimated separately to examine network externalities precisely. $ ^{7} $

### 6. Conclusion

This paper conducted three analyses to examine the network externalities in the router market of Japan.

A user survey shows that users choose Cisco because of network externalities.

A hedonic price model shows that a 1% point larger share increases the list price by 0.4%. This can be interpreted as evidence of network externalities. Innovation, however, can beat the network externalities effect.

The logit model shows that share in the previous period increases the choice probability, which can be evidence of network externalities. But the estimated coefficient is much larger than that of the hedonic price model. This is because the switching cost effect is mixed. We should separate network externalities from switching cost.

In summary, we conclude that the dominant share of Cisco in the Japanese market for routers is partly explained by network externalities.
## References

Amemiya, Takashi (1985), Advanced Econometrics, Boston, Mass.: Harvard University Press.

Asai, Sumiko and Tatsuo Tanaka (2003), “Paso-kon Shijou ni Okeru Nettowaku Gaibusei no Kenshou (Testing for Network Externalities in the Personal Computer Market),” (in Japanese) InfoCom Review, Vol.30, pp.29-37.

Berry, Steven T. (1994), “Estimating Discrete-Choice Models of Product Differentiation,” Rand Journal of Economics, Vol.25, pp.242-262.

Brynjolfsson, Erik and Chris F. Kemerer (1996), “Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market,” Management Science, Vol.42, pp.1627-1647.

Forman, Chris and Pei-Yu Chen (2003), “Network Effects and Switching Costs In the Market for Routers and Switches,” NET Institute Working Paper #03-03.

Gandal, Neil (1994), “Hedonic Price Indexes for Spreadsheets and an Empirical Test for Network Externalities,” Rand Journal of Economics, Vol.25, pp.160-170.

Gawer, Annabelle and Michael A. Cusumano (2001), Platform Leadership, Boston, Mass.: Harvard Business School Press.

Katz, Michael L. and Carl Shapiro (1994), “Systems Competition and Network Effects,” Journal of Economic Perspectives, Vol.8, pp.93-115.

McFadden, Daniel (1974), “Conditional Logit Analysis of Qualitative Choice Behavior,” Chapter 4 in Frontiers in Econometrics, edited by Paul Zarembka, New York: Academic Press, pp.105-142.

Ohashi, Hiroshi (2003), “The Role of Network Effects in the U.S. VCR Market, 1978-86,” Journal of Economics and Management Strategy, Vol.12, pp.447-494.

Park, Sangin (2003), “Quantitative Analysis of Network Externalities in Systems Competition: the VCR Case,” unpublished manuscript, available from http://ms.cc.sunysb.edu/~sanpark/wrkps.html.
Rohlfs, Jeffrey H. (1974), “A Theory of Interdependent Demand for Communication Service,” Bell Journal of Economics, Vol.5, pp.16-37.

Rysman, Marc (2003), “Competition between Networks: A Study of the Market for Yellow Pages,” forthcoming in Review of Economic Studies.

Tanaka, Tatsuo (2002), “Keitai Denwa Sangyo de no Nettowaku Gaibusei no Jisshou (Empirical Analysis of Network Externalities in the Mobile Telecommunications Industry),” (in Japanese) Mita Gakkai Zasshi, Vol.95, pp.139-152.

Tanaka, Tatsuo, Yoshihito Yasaki and Reiko Murakami (2003), “Nettowaku Gaibusei no Keizai Bunseki: Gaibusei ka deno Kyousou Seisaku ni tsuiteno Ichian (Economic Analysis of Network Externalities: Possibilities for Competition Policy in the Presence of Externalities),” (in Japanese) Research Report CR01-03, Competition Policy Research Center, Fair Trade Commission of Japan.
Figure 1 Share of Router Market in Japan (unit base)

High-End Router Market

Mid-Range Router Market

Source: Fuji Kimera Research Company
Figure 2 Profit Rate by Vendor, 1996-2002

Note: Profit rate = (income before tax)/sales

Source: Data is available at http://www.sec.gov.
Figure 3 Mean and Standard Deviation of Profit Rate by Vendor

Standard Deviation
Figure 4 Results of User Survey (all samples)

□ Cisco users □ Non Cisco users
Figure 5 Results of User Survey (ISPs only)

☐ Cisco users ☐ Non Cisco users
Figure 6 Robustness Check: T Value of SHARE Coefficient

Note: Constant, SHARE and LWAN are always included. $ 131071 (= 2^{17}-1) $ cases.
Table 1 Sample Distribution

Sent	Replies
Large Companies and Universities	1078	712
ISP	101	44
Total	1179	756 (64.1%)

Table 2 Definition of Variables

Variables	Code
Log of list price	$ \ln $ (price (unit = 1,000 yen))	PRICE

Network externalities	lagged share (unit = percent)	SHARE

Basic	$ \ln $ (system band (unit = Mbps))	LWAN
Line	POS-OC3	OC3
POS-OC12	OC12
POS-OC48	OC48
POS-OC192	OC192
ATM-OC3	ATMOC3
ATM-OC12	ATMOC12
ATM-OC48	ATMOC48
Fast Ether	E100
Gigabit Ether	EGB
10 Gigabit Ether	E10GB
Redundancy	Power	REDUNP
Routing	REDUNR
QoS and others	RSVP	RSVP
IPv6	IPv6
MPLS	MPLS
Priority Queing	PRQ
Class or Weighted Queing	CWQ
RED	RED
NEBS Reference	NEBS
Table 3 Sample Distribution by Vendor

Vendor	Data Points
Cisco	39
Fujitsu	9
Juniper	13
NEC	7
Hitachi	8
Total	76

Table 4 Results of Hedonic Price Model

Case 1	Casa 2
Variable	Coeff.	T stats.	Coeff.	T stats.
C	5.6126	13.62	***	11.93	55.89	***
SHARE	.0055	2.62	***	.004	2.85	***
LWAN	.2377	2.91	**	.335	7.62	***
OC3	.2901	1.47
OC12	.1947	1.03
OC48	.0423	0.15
OC192	.7735	3.22	***	.533	2.50	**
ATMOC3	.0774	0.32	**	.223	4.38	***
ATMOC12	.2533	1.56
E100	.003	0.01
EGB	.444	2.25	***	.595	4.24	***
REDUNP	.8772	4.64	***	.602	3.69	***
REDUNR	.1483	0.58
IPV6	-.9837	-2.83	***
MPLS	-.9490	3.16	***	.522	2.97	***
RSVP	.253	.81
PRQ	.7601	2.41	***
CWQ	-.1388	-.32
RED	-.3671	-.77
NEBS	-.2156	-1.12
n	76	76
F	81.6503	162.715
R²	.9652	.9437
Adjusted R²	.9533	.9379

** Significance at 5% level

*** Significance at 1% level
Table 5 Results of Logit Model

Conditional Logit	Nested Logit
Variable	Coeff.	T stats.	Coeff.	T stats.
LWAN	-.004	-.07	.027	1.06
PRICEj	-.148	-4.31	***	-.038	-3.32
EGB	.162	1.05	.327	6.90
PRQ	.781	2.05	**	.589	2.76
MPLS	-.528	-3.22	***	-.494	-8.70
SHAREg	2.866	11.55	***	3.246	18.64
$ \lambda $	.232	8.95
n	356	356
Log-likelihood	-2.5346	-2.3292

** Significance at 5% level

*** Significance at 1% level
国等における温室効果ガス等の排出の削減に配慮した契約の推進に関する法律(環境配慮契約法)第8条第1項の規定に基づき、平成25年度における復興庁の温室効果ガス等の排出の削減に配慮した契約(環境配慮契約)の締結実績を次のとおり公表します。

### 1. 平成25年度の経緯

環境配慮契約法及び平成19年12月に閣議決定された「国及び独立行政法人等における温室効果ガス等の排出の削減に配慮した契約の推進に関する基本方針に基づき、可能なものから温室効果ガス等の排出の削減に配慮した契約（以下「環境配慮契約」という。）の推進を図ることとした。

### 2. 平成25年度における復興庁の環境配慮契約の締結状況

基本方針において具体的な契約方法が定められている、①電気の供給を受ける契約(裾切り方式)、②自動車の購入及び賃貸借に係る契約(総合評価方式)、③船舶の調達に係る契約(船舶の設計(プロポーザル方式))、④省エネルギー改修事業(ESCO事業)に係る契約、⑤建築物に関する契約(プロポーザル方式)、⑥産業廃棄物の処理に係る契約(裾切り方式)のうち、⑥に関連して以下のとおり契約を締結した。

## ○産業廃棄物の処理に係る契約

復興庁においては入居している民間ビルの指定業者との契約であり、競争できないため環境配慮契約方式による契約を行っていない。
# E-0802 アジア太平洋地域を中心とする持続可能な発展のためのバイオ燃料利用戦略に関する研究

（１）オントロジーを用いた問題の構造化と政策立案支援ツールの開発

大阪大学

産業科学研究所 知識システム研究分野

溝口理一郎

<研究協力者>

大阪大学 産業科学研究所 知識システム研究分野 准教授 古崎晃司

国際連合大学サステイナビリティと平和研究所 学術研究官 齊藤修

平成20～22年度累計予算額：7,443千円（うち、平成22年度予算額：2,641千円）

予算額は、間接経費を含む。

[要旨] バイオ燃料利用に関わる問題領域は、エネルギー供給、食糧生産、産業構造、など多岐に渡っており、それぞれの領域におけるステークホルダーも多数存在する。このような複数領域にまたがる多様な問題を適切に構造化することは、バイオ燃料利用戦略に関わる政策立案を行う上でも重要な課題である。そのような背景のもと、本研究はバイオ燃料に関わる本質的な概念構造を明示化する機能を持ったオントロジーを構築し、バイオ燃料利用に関する問題の構造化および政策立案支援ツールをサブテーマ（2）と連携しつつ開発することを目的とする。

平成20年度はオントロジー構築のための基礎資料の収集・解析と、オントロジーを用いた問題の構造化の実現方法を検討した。その結果、バイオ燃料オントロジーを用いた問題領域俯瞰のためのオントロジー探索ツールを試作し、いくつかの問題領域の俯瞰を試みた。平成21年度は、収集・解析した基礎資料からの問題構造を整理し、その結果を元にバイオ燃料オントロジーを拡充した。その結果、概念数が649から1892、スロット数が1075から2119に拡充された。さらに、サブテーマ（2）と連携して本ツールの評価実験を実施した。その結果、本ツールを用いて専門家にとって十分に意味があるマップやパス（概念連鎖）が生成できることが確認できた。また、それらのパスの約75%がオントロジー構築時に想定していた以外の情報を含んでおり、本ツールが思いがけない内容を提示し利用者の発想を刺激する可能性を持つことが示唆された。平成22年度は、サブテーマ（2）の協力のもと、本ツールを政策立案に必要となるステークホルダー間の合意形成支援ツールとして発展させ、被験者がステークホルダーの役割を演じて（ロールプレイ）議論を行う実験を通して、合意形成支援の可能性を示すことができた。これらの結果を通して、研究目的であるオントロジーを用いたバイオ燃料利用に関する問題の構造化と政策立案支援ツールの開発を達成できたと言える。

[キーワード] オントロジー、問題の構造化、可視化、領域俯瞰、政策立案支援

1. はじめに

国内外でバイオ燃料の生産と利用について、現在様々な研究と実践が活発に行われている。こ