Silviase/jgov_v1.4_full_ann huggingface dataset:
図2 都道府県別 第1号被保険者に占める認定者の割合

## （5） 居宅介護（介護予防）サービス受給者数

居宅介護（介護予防）サービス受給者（以下「居宅サービス受給者」という。）数は、平成23年度累計（※平成23年3月から平成24年2月サービス分）で総数3,829万人（延人月。以下同じ。）、うち第1号被保険者数は3,704万人、第2号被保険者数は124万人となっている。

※介護保険制度のサービス給付（受給者数及び保険給付）は、3月から翌年2月を年度単位としている。（以下同じ。）

要介護（要支援）状態区分別では、要支援1：481万人、要支援2：586万人、要介護1：814万人、要介護2：818万人、要介護3：518万人、要介護4：359万人、要介護5：253万人となっており、要介護2の受給者数が21.4％と最も多くなっており、軽度（要支援1～要介護2）の受給者が約70.5％を占めている。（4表、図3）

1 ヶ月あたり平均でみると、総数で319万人となり、前年度（302万人）に比べ17万人（5.7%）増となっている。（図4）（東日本大震災の影響により数値がない福島県内5町1村を除いた比較）。

4表 居宅サービス受給者数（年度累計）

(单位：千人)

区 分	要支援1	要支援2	經過的要介護	要介護1	要介護2	要介護3	要介護4	要介護5	計
第1号被保険者	4,735	5,698	0	7,927	7,851	4,983	3,454	2,395	37,044
第2号被保険者	73	166	0	211	329	194	136	133	1,243
総 数	4,809	5,865	0	8,138	8,181	5,177	3,589	2,528	38,287
構 成 比	12.6%	15.3%	0.0%	21.3%	21.4%	13.5%	9.4%	6.6%	100.0%

※過年度の訂正請求等が年度中に発生したため、経過的要介護についても若干数の受給者が計上されている。
図 3 要介護（要支援）状態区分別 居宅サービス受給者数の割合（第1号被保険者、第2号被保険者別）

図４ 居宅サービス受給者数の推移（１ヶ月平均）

※東日本大震災の影響により、22年度の数値には福島県内5町1村の数値は含まれていない。
## （6） 地域密着型（介護予防）サービス受給者数

地域密着型（介護予防）サービス受給者（以下「地域密着型サービス受給者」という。）数は、平成23年度累計で総数353万人、うち第1号被保険者数は349万人、第2号被保険者数は5万人となっている。

要介護（要支援）状態区分別では、要支援1：3万人、要支援2：5万人、要介護1：64万人、要介護2：85万人、要介護3：92万人、要介護4：63万人、要介護5：42万人となっており、要介護3の受給者数が25.9％と最も多く、軽度（要支援1～要介護2）の受給者が約44.4%、重度（要介護3～要介護5）の受給者が約55.6％を占めている。（5表、図5）

1 ヶ月あたり平均でみると、総数で29.5万人となり、前年度（26.4万人）に比べ3.0万人（11.4%）増となっている。（図6）（東日本大震災の影響により数値がない福島県内5町1村を除いた比較）。

5表 地域密着型サービス受給者数（年度累計）

(单位：千人)

区 分	要支援1	要支援2	要介護1	要介護2	要介護3	要介護4	要介護5	計
第1号被保険者	29	46	637	840	904	618	413	3,487
第2号被保険者	0	1	7	9	12	9	10	47
総 数	29	46	644	850	916	627	422	3,534
構 成 比	0.8%	1.3%	18.2%	24.0%	25.9%	17.7%	11.9%	100.0%

図5 要介護（要支援）状態区分別 地域密着型サービス受給者の割合（第1号被保険者、第2号被保険者別）
図6 地域密着型サービス受給者数の推移（1ヶ月平均）

※東日本大震災の影響により、22年度の数値には福島県内5町1村の数値は含まれていない。

## （7） 施設介護サービス受給者数

施設介護サービス受給者（以下「施設サービス受給者」という。）数は、平成23年度累計で総数1,026万人となっている。（6表）

要介護（要支援）状態区分別では、要介護1：55万人、要介護2：120万人、要介護3：212万人、要介護4：306万人、要介護5：332万人となっており、要介護5の受給者数が32.4％と最も多く、重度（要介護3～要介護5）の受給者が約82.9％を占めている。（図7）

1 ヶ月あたり平均でみると、介護老人福祉施設45万人、介護老人保健施設33万人、介護療養型医療施設8万人、総数86万人であり、前年度に比べ、介護老人福祉施設1.3万人（2.9%）増、介護老人保健施設0.5万人（1.5%）増、介護療養型医療施設0.5万人（6.1%）減となっている。（図8）（東日本大震災の影響により数値がない福島県内5町1村を除いた比較）。

6表 施設サービス受給者数（年度累計）

区分	要介護1	要介護2	要介護3	要介護4	要介護5	計
介護老人福祉施設	166	466	1,089	1,726	1,934	5,380
介護老人保健施設	378	711	965	1,067	838	3,959
介護療養型医療施設	11	28	79	281	562	962
総 数	554	1,202	2,124	3,060	3,323	10,263
構 成 比	5.4%	11.7%	20.7%	29.8%	32.4%	100.0%

※ 同一月に2施設以上でサービスを受けた場合、施設ごとにそれぞれ受給者数を1人と計上するが総数には1人と計上しているため、3施設の合算と総計が一致しない。
図7 要介護状態区分別 施設サービス受給者の割合（総数）

図8 施設サービス受給者数の推移（1ヶ月平均）

| 老人福祉 | 老人保健 | 療養型
12年度 | 285 | 219 | 100
13年度 | 308 | 239 | 109
14年度 | 325 | 250 | 125
15年度 | 339 | 261 | 132
16年度 | 356 | 274 | 133
17年度 | 373 | 287 | 127
18年度 | 393 | 297 | 116
19年度 | 409 | 305 | 109
20年度 | 419 | 311 | 100
21年度 | 428 | 318 | 92
22年度 | 435 | 325 | 85
23年度 | 448 | 330 | 80

（注）19年度からは、同一月に2施設以上で施設サービスを受けた場合、施設ごとにそれぞれ受給者数を1人と計上するが、総数には1人と計上しているため3施設の合算と総計が一致しない。

※東日本大震災の影響により、22年度の数値には福島県内5町1村の数値は含まれていない。
### 2. 保険給付（介護給付・予防給付）

## (1) 総数

保険給付関係の平成23年度累計の総数は、件数1億2,490万件、単位数7,759億単位、費用額7兆9,409億円、利用者負担を除いた給付費7兆1,936億円となっている。（7表）

なお、高額介護サービス費、高額医療合算介護サービス費、特定入所者介護サービス費を含む費用額は、8兆2,253億円となっている。

給付費について内訳をみると、居宅介護（介護予防）サービス（以下「居宅サービス」という。）は3兆7,828億円、地域密着型介護（介護予防）サービス（以下「地域密着型サービス」という。）は7,010億円、施設介護サービス（以下「施設サービス」という。）は2兆7,097億円となっている。

7表 保険給付 介護給付・予防給付（総数） 平成23年度累計（平成23年3月～平成24年2月サービス分）

件数

(单位：千件)

区分	要支援1	要支援2	經過的要介護	要介護1	要介護2	要介護3	要介護4	要介護5	計
居宅サービス	10,690	14,245	△0	21,392	24,440	16,819	12,866	10,450	110,902
地域密着型サービス	31	48	-	657	867	937	645	435	3,619
施設サービス	0	0	561	1,216	2,150	3,098	3,356	10,381
合計	10,721	14,293	△0	22,609	26,523	19,906	16,609	14,240	124,902
構成比	8.6%	11.4%	0.0%	18.1%	21.2%	15.9%	13.3%	11.4%	100.0%

居宅サービス	13,554	28,041	△0	69,162	88,882	79,037	66,716	56,846	402,238
地域密着型サービス	135	521	-	12,022	17,736	21,140	15,034	10,316	76,904
施設サービス	0	0	13,345	30,695	57,642	89,734	105,388	296,805
合計	13,689	28,562	△0	94,529	137,312	157,820	171,484	172,551	775,947
構成比	1.8%	3.7%	0.0%	12.2%	17.7%	20.3%	22.1%	22.2%	100.0%

居宅サービス	148,148	296,210	△5	715,404	916,776	812,651	685,420	583,075	4,157,679
地域密着型サービス	1,355	5,245	-	121,511	179,452	214,035	152,207	104,555	778,359
施設サービス	0	1	135,049	311,057	583,942	908,543	1,066,299	3,004,891
合計	149,503	301,455	△5	971,965	1,407,285	1,610,627	1,746,170	1,753,929	7,940,929
構成比	1.9%	3.8%	0.0%	12.2%	17.7%	20.3%	22.0%	22.1%	100.0%

居宅サービス	135,420	269,232	△5	653,489	834,917	739,017	622,136	528,621	3,782,828
地域密着型サービス	1,220	4,726	-	109,437	161,639	192,766	137,080	94,156	701,024
施設サービス	0	1	121,805	280,508	526,484	819,232	961,696	2,709,726
合計	136,640	273,960	△5	884,731	1,277,064	1,458,266	1,578,449	1,584,474	7,193,579
構成比	1.9%	3.8%	0.0%	12.3%	17.8%	20.3%	21.9%	22.0%	100.0%

（注）高額介護サービス費、高額医療合算介護サービス費、特定入所者介護サービス費は含まない。

## ◎保険給付（介護給付・予防給付）総括表

平成23年度累計（平成23年3月～平成24年2月サービス分）

(单位：百万円)

区 分	給付費
居宅サービス	3,782,828
地域密着型サービス	701,024
施設サービス	2,709,726
高額介護サービス費	135,224
高額医療合算介護サービス費	16,641
特定入所者介護サービス費	284,365
合 計	7,629,808
## （4） 第1号被保険者分

第1号被保険者分の保険給付関係は、件数1億2,075万件、単位数7,564億単位、費用額7兆7,393億円、給付費7兆0,105億円となっている。

※高額介護サービス費、高額医療合算介護サービス費、特定入所者介護サービス費は含まない。

## （5） 第2号被保険者分

第2号被保険者分の保険給付関係は、件数416万件、単位数195億単位、費用額2,017億円、給付費1,831億円となっている。

※高額介護サービス費、高額医療合算介護サービス費、特定入所者介護サービス費は含まない。

## （6） 高額介護（介護予防）サービス費

高額介護（介護予防）サービス費の累計は件数1,332万件、給付費1,352億円となっている。

## （7） 高額医療合算介護（介護予防）サービス費

高額医療合算介護（介護予防）サービス費の累計は件数61万件、給付費166億円となっている。

## （8） 特定入所者介護（介護予防）サービス費

特定入所者介護（介護予防）サービス費の累計は、給付費2,844億円となっている。

## （9） 市町村特別給付

市町村特別給付の累計は件数55万件、費用額22億円、給付費19億円となっている。
### 3. 第1号被保険者の保険料収納状況

平成23年度の保険料収納状況は、調定額1兆4,241億円、収納額1兆4,030億円、収納率98.5%となっている。(10表)

なお、特別徴収の収納額累計は1兆2,777億円、収納率100.0%、普通徴収の収納額累計は1,253億円、収納率85.6%となっている。

10表 都道府県別保険料収納状況（現年度分）

(单位：百万円)

都道府県	調定額	収納額	収納率	都道府県	調定額	収納額	収納率	都道府県	調定額	収納額	収納率
全 国	1,424,121	1,402,973	98.5%	富山県	15,774	15,644	99.2%	島根県	10,147	10,065	99.2%
北海道	60,288	59,490	98.7%	石川県	15,253	15,080	98.9%	岡山県	25,417	25,131	98.9%
青森県	19,377	19,054	98.3%	福井県	10,125	9,986	98.6%	広島県	36,082	35,692	98.9%
岩手県	15,348	15,197	99.0%	山梨県	9,942	9,792	98.5%	山口県	18,633	18,433	98.9%
宮城県	20,285	19,930	98.3%	長野県	27,730	27,505	99.2%	徳島県	11,192	11,039	98.6%
秋田県	15,502	15,327	98.9%	岐阜県	24,040	23,763	98.8%	香川県	12,562	12,418	98.8%
山形県	14,532	14,399	99.1%	静岡県	44,190	43,633	98.7%	愛媛県	19,727	19,492	98.8%
福島県	18,763	18,532	98.8%	愛知県	72,968	72,180	98.9%	高知県	10,308	10,133	98.3%
茨城県	29,269	28,762	98.3%	三重県	22,244	21,967	98.8%	福岡県	57,584	56,617	98.3%
栃木県	19,525	19,219	98.4%	滋賀県	14,179	14,055	99.1%	佐賀県	10,310	10,177	98.7%
群馬県	22,354	22,076	98.8%	京都府	30,740	30,318	98.6%	長崎県	19,105	18,840	98.6%
埼玉県	67,280	66,051	98.2%	大阪府	102,392	100,282	97.9%	熊本県	22,343	22,031	98.6%
千葉県	59,742	58,700	98.3%	兵庫県	65,401	64,494	98.6%	大分県	14,454	14,249	98.6%
東京都	129,058	126,207	97.8%	奈良県	16,201	16,008	98.8%	宮崎県	12,945	12,749	98.5%
神奈川県	92,928	91,576	98.5%	和歌山県	14,122	13,930	98.6%	鹿児島県	19,704	19,433	98.6%
新潟県	32,909	32,687	99.3%	鳥取県	8,168	8,083	99.0%	沖縄県	12,979	12,549	96.7%
### 4. 介護保険特別会計経理状況

平成23年度の保険者における介護保険特別会計（介護保険事業勘定）について、歳入合計8兆2,093億円、歳出合計8兆1,110億円、差引残額983億円となっている。（11表）

この差引残額のうち、国庫支出金精算額等を精算した後では730億円となる。

また、平成23年度末現在で介護給付費準備基金の保有額は2,848億円（1,452保険者）となっている。

11 表 介護保険特別会計経理状況（保険事業勘定・全国計）

(单位:百万円)

嚴 入	嚴 出
科 目	決 算 額	科 目	決 算 額
保険料	1,411,741	総務費	214,633
分担金及び負担金	3,870	保険給付費	7,641,785
使用料及び手数料	513	地域支援事業費	165,330
国庫支出金	1,837,113	財政安定化基金拠出金	-
介護給付費負担金(再揭)	1,369,344	相互財政安定化事業負担金	-
調整交付金(再揭)	382,564	保健福祉事業費	413
地域支援事業交付金(再揭)	61,484	基金積立金	32,538
支払基金交付金	2,307,848	公債費	1,397
介護給付費交付金(再揭)	2,290,220	予備費	-
地域支援事業支援交付金(再揭)	17,628	諸支出金	54,944
都道府県支出金	1,164,207
都道府県負担金(再揭)	1,125,443
地域支援事業交付金(再揭)	30,416
相互財政安定化事業交付金	-
財産収入	619
寄附金	63
線入金	1,369,987
一般会計線入金12.5%(再揭)	951,973
総務費に係る一般会計線入金(再揭)	212,376
介護給付費準備基金線入金(再揭)	148,528
地域支援事業線入金(再揭)	30,682
線越金	96,161
市町村債	9,846
諸収入	7,362
合 計	8,209,330	合 計	8,111,041

歲入歲出差引殘額（A）	98,290
国庫支出金精算額等（B）	25,289
国庫支出金精算額等差引額（A-B）	73,001	介護給付費準備基金（年度末現在）	284,815

※数値は単位未満四捨五入のため、合計が一致しない場合がある。
# CPRC Discussion Paper Series Competition Policy Research Center Japan Fair Trade Commission

Territorial Restrictions and Consumer Welfare in a Mixed Oligopoly: The Japanese Gas Supply Market

Eiji SATOH

Graduate School of Economics,

Hitotsubashi University, and CPRC

CPDP-48-E January 2011

1-1-1, Kasumigaseki, Chiyoda-ku, TOKYO 100-8987 JAPAN

Phone:+81-3-3581-1848 Fax:+81-3-3581-1945

URL: http://www.jftc.go.jp/cprc/index.html

E-mail:cprcsec@jftc.go.jp
# Territorial Restrictions and Consumer Welfare in a Mixed Oligopoly: The Japanese Gas Supply

Market $ ^{*} $

Eiji SATOH $ ^{\dagger} $

January 2011
## Abstract

This paper assesses the consumer welfare effect of territorial restrictions in the Japanese gas supply market between 1998 and 2005. Territorial restrictions virtually prohibit the expansion of town-gas service areas in response to demand. Estimation reveals that characteristics of gas services play a significant role in demand substitution between town-gas and propane-gas. Simulation exercises indicate that if town-gas service expands to areas where town-gas pipelines have not been laid, some households switch to town-gas. In a static Cournot competition between propane-gas suppliers, this switch triggers a decline in the price of propane-gas, and it improves consumer welfare.

Keywords: consumer welfare, propane-gas, simulation analysis, territorial restriction, town-gas

JEL Classification: L43, L95
## 1 Introduction

Many recent studies focus on a mixed oligopoly market where a public firm cohabits with a private firm. Such a market includes financial and education markets. In mixed oligopoly markets, public firms are usually assumed to maximize welfare. However, counterintuitive results are often obtained in many theoretical literatures including De Fraja and Delbono (1989) and Cremer et al. (1991). Empirical literatures (for example, Dewenter and Malatesta; 2001) support the theoretical perspective. Till date, the role of public firms in these markets has been discussed.

The Japanese gas market is a mixed oligopoly market. In this market, propane-gas suppliers can be considered private firms because their prices and service areas have not been regulated. Although propane-gas is substitutable with which town-gas, town-gas suppliers can be considered public firms because their prices and service areas have been regulated. Since town-gas suppliers are considered to be natural monopolists, the Japanese government has regulated town-gas prices and service areas to maximize welfare. However, previous literatures cannot adequately ascertain whether regulations for town-gas suppliers can maximize welfare $ ^{1} $ .

This study focuses on territorial restrictions of town-gas service. The Gas Business Act stipulates that town-gas suppliers provide gas services only within their predetermined service areas. The Japanese government also stipulates an agreement with propane-gas suppliers for expanding town-gas service areas. However, the Japan Fair Trade Commission (1999) reports that no consensus has been reached over such an agreement. In fact, town-gas service areas have rarely been expanded, and territorial restrictions virtually prohibit the expansion.
sion of town-gas service areas in response to demand. Although the territorial restrictions can prevent conflicts between propane-gas suppliers and town-gas suppliers, it is still not obvious whether they can maximize welfare $ ^{2} $ .

This study explores counterfactual scenarios that could arise if town-gas service expands to areas where town-gas pipelines have not been laid. First, I estimate a demand model using Japanese gas market data between 1998 and 2005. This demand model has a nested logit structure to describe consumer decision making. Similar to Berry (1994) and many other literatures, I derive a linear regression model from the demand model and estimate it with a two-stage least squared (2SLS) method. The estimation reveals that the characteristics of gas services play a significant role in demand substitution between town-gas and propane-gas.

Furthermore, this study simulates a counterfactual expansion of town-gas service to 16 areas where town-gas pipelines have not been laid. In these counterfactual scenarios, some households switch from propane-gas to town-gas. In a static Cournot competition between propane-gas suppliers, this switch triggers intense competition among propane-gas suppliers, and decreases propane-gas prices (5.2%); this improves consumer welfare (10.8%). The simulation result indicates that frequent readjustments regarding town-gas service areas should improve consumer welfare. In other words, this result suggests that maintaining territorial restriction for town-gas suppliers softens competition among propane-gas suppliers.

This paper is organized as follows. Section 2 gives a brief overview of the Japanese gas market. Section 3 explains a demand model, our dataset, variable definitions, and demand estimation results. Section 4 conducts simulations re-
Table 1: The Japanese gas market in 2005

Propane-gas	Town-gas
Number of suppliers	21780	212
Market concentration	Small	High
Supply system	Canisters	Pipelines
Number of consumers	25 million	23 million
Service areas	Unregulated	Predetermined
Prices	Unregulated	Full-cost pricing rule

Note: The number of consumers, an approximate value, is defined as the number of households. Data Sources: Yellow Pages and Town-Gas Annual Report.

gardening an expansion of town-gas service areas. Section 5 concludes this paper.

## 2 Overview of the Japanese Gas Market

Table 1 summarizes the Japanese gas market.

### 2.1 Propane-gas

Propane-gas is supplied to homes by about twenty-two thousand firms. Approximately 58% of the propane-gas suppliers are firms with three or fewer employees. Many propane-gas suppliers are small and tiny companies (Okada and Hayashi; 2009). They usually fill their canisters with liquefied petroleum gas at gas stations. Thereafter, they carry their canisters and connect them to the gas rubber hoses of individual households.

Approximately twenty-five million households used propane-gas in 2005. With canisters and portable grills, propane-gas can be used efficiently at any location. Propane-gas consumers live not only in urban regions but also in rural regions. Therefore, propane-gas could be supplied from all over Japan.
### 2.2 Town-gas

Town-gas was supplied to homes by 212 firms in 2005. Town-gas suppliers are regional monopolists in a certain area. However, there exists a great difference in the size of their firms. The three-firm concentration ratio is 77%, with the market share of Tokyo gas, Osaka gas, and Toho gas at 36.2%, 30.1%, and 10.7%, respectively. The top three town-gas firms provide gas services in Tokyo, Osaka, Nagoya, and their environs.

Approximately twenty-three million households used town-gas in 2005 $ ^{3} $ . Town-gas is supplied through pipelines to individual households. However, as most town-gas pipelines are not laid from all over Japan but from urban regions, few areas can benefit from piped town-gas. Only 5% of the land in Japan has town-gas service areas.

The Japanese government regulates not propane-gas prices but town-gas prices. Town-gas prices are set on the full-cost pricing rule. They have been maintained much lower than propane-gas prices. Furthermore, town-gas suppliers need to obtain permission from the government when they change their prices. Town-gas price setting is not left to the discretion of town-gas suppliers.

### 2.3 Territorial restrictions on town-gas service areas

The Gas Business Act stipulates that town-gas suppliers provide their gas services only within their predetermined service areas. It also states that town-gas suppliers should apply to the Ministry of Economics, Trade and Industry (METI) for permission to change their service areas $ ^{4} $ .

Furthermore, METI has stipulated an agreement with propane-gas suppliers
when town-gas suppliers expand their service areas. This can prevent conflicts between propane-gas and town-gas suppliers (Management and Coordination Agency; 1993). However, by expanding town-gas service areas, the propane-gas suppliers would lose their consumers to town-gas suppliers and suffer a decrease in profits. They thus have no incentive to agree on the expansion. Therefore, the territorial restrictions virtually prohibit the expansion of town-gas service areas in response to demand. In fact, current town-gas service areas are similar to previous areas.

## 3 Demand Estimation

### 3.1 Demand model

I assume that a household uses one type of gas; that is, it uses either one type of gas or none at all. Each household is assumed to maximize the following utility function by using gas type g in area j:

$$ u_{i j g}=\beta_{0}+\beta_{1}p_{j g}+\beta_{2}M J_{j g}+\beta_{3}T e m p_{j}+\beta_{4}D e n s i t y_{j}+\xi_{j g}+\epsilon_{i j g}, $$

where $ u_{ijg} $ is household i's utility by using gas type g (either propane-gas or town-gas) in area j. $ p_{jg} $ is the average real price of gas g in area j (adjusted by the Consumer Price Index: 1998 = 100). $ MJ_{jg} $ is a calorific value of gas g in area j. High-calorific gas has high thermal efficiency. $ Temp_{j} $ is the annual mean temperature in the prefecture including area j. Gas consumption should increase in cold weather. $ Density_{j} $ is the population density in area j. Gas suppliers usually have their sales offices in densely populated areas. They reach customers through their sales offices. In areas with high population density, customer support should be enhanced. Let $ \xi_{jg} $ be the mean of consumers' valuations of unobserved qualities of gas type g in area j. Following Berry
Figure 1: Nested logit model

(1994), $ \delta_{jg} $ is defined as the mean utility of using gas type g in area j:

$$ \delta_{j g}:=\beta_{0}+\beta_{1}p_{j g}+\beta_{2}M J_{j g}+\beta_{3}T e m p_{j}+\beta_{4}D e n s i t y_{j}+\xi_{j g}. $$

$ \epsilon_{ijg} $ denotes the distribution of consumer preferences about this mean utility, and $ u_{ijg} $ is decomposed into $ \delta_{jg} $ and $ \epsilon_{ijg} $ .

I impose an assumption on $ \epsilon_{ijg} $ that generates the following nested logit structure. On the first node, a household decides its area of residence. On the second node, given the choice of a dwelling area, the household decides which type of gas it uses. In certain areas where town-gas pipelines are not laid, a household should inevitably use propane-gas. An image of this nested logit model is drawn in Figure 1.

The nested logit formula for the market share of gas type g in area j is

$$ s_{j g}=\frac{e^{\delta_{j g}/(1-\sigma)}}{D_{j}^{\sigma}\sum_{j^{\prime}}D_{j^{\prime}}^{1-\sigma}}, $$
where $ D_{j} := \sum_{g \in \vartheta_{jg}} e^{\delta_{g} / (1 - \sigma)} $ and the set of gas type in area j is $ \vartheta_{j} $ . Parameter $ \sigma $ measures the correlation in unobserved utility from the different gas types in the same area. This parameter also provides a test of whether it is appropriate to have a second-stage choice in the model. If $ \sigma $ is 0, the model becomes equal to a simple logit model. The conditional market share of gas type g given the area j is

$$ s_{g|j}=\frac{e^{\delta_{j g}/(1-\sigma)}}{D_{j}}. $$

Following Berry (1994), a linear regression model for this two-stage nested logit is derived as follows:

$$ \begin{aligned}&\log(s_{jg})-\log(s_{0})\\ &=\delta_{jg}+\sigma\log(s_{g|j})-\delta_{0}\\ &=\beta_{0}+\beta_{1}p_{jg}+\beta_{2}MJ_{g}+\beta_{3}Temp_{j}+\beta_{4}Density_{j}+\xi_{jg}+\sigma\log(s_{g|j}).\\ \end{aligned} $$

The option of not using either propane-gas or town-gas is represented by 0. $ s_{0} $ denotes the market share of households in the usage of community-gas $ ^{5} $ . I assume that $ \delta_{0}=0 $ and estimate this model in the following subsection.

### 3.2 Data

The dataset ranges from 1998 to 2005 on a yearly basis, and it has four different data sources. Surveys on propane-gas prices conducted by The Oil Information Center (The Institute of Energy Economics, Japan) report retail prices for 10 cubic meter of propane-gas, including consumption taxes and basic charges. Furthermore, these prices are reported by each administrative district. These administrative districts are narrower than prefectures and wider than munici-
palities. In this study, I refer to these administrative districts as areas $ ^{6} $ .

The number of propane-gas firms is listed in the Japanese telephone directory Townpage. Townpage lists telephone numbers and addresses classified by propane-gas suppliers. I enumerate the numbers of propane-gas firms by area.

The Town-Gas Annual Report by The Agency for Natural Resources and Energy (METI) provides town-gas prices, the number of town-gas meters, and financial accountings for each town-gas firm. Since town-gas supplied by different town-gas firms has different calorific values, I calculate the town-gas prices in calorific value equivalent to 10 cubic meter of propane-gas. Furthermore, I regard the number of town-gas meters as the number of households using town-gas. However, the Town Gas Annual Report does not always show the number of town-gas meters by each area. I supplement the number of town-gas meters using municipality surveys.

Furthermore, I complement relevant government statistics with regard to geographical characteristics by municipalities. Using the number of town-gas households and total households, I define the market shares of town-gas and propane-gas $ ^{7} $ .

### 3.3 Instruments

Three explanatory variables in equation (3) are probably correlated with $ \xi_{jg} $ . Obvious variables are $ \log(s_{g|j}) $ and $ Density_{j} $ , since $ s_{g|j} $ and $ Density_{j} $ contain part of the explained variable $ s_{jg} $ ; $ p_{jg} $ may be correlated with $ \xi_{jg} $ . If $ \xi_{jg} $ is correctly perceived by households and suppliers, a type of gas with a better image may induce higher willingness to pay. Although town-gas suppliers cannot charge higher prices according to regulations, propane-gas suppliers may be able.
to charge higher prices.

To account for the endogeneity of these three variables, I use several instrument variables. I employ some instruments from the cost side. One instrument is free-on-board prices (the data are from the Trade Statistics of Japan). Because propane-gas and town-gas are usually made from imported components, their retail gas price would be affected by free-on-board prices. Another instrument could be kerosene prices including delivery charge. Similar to a kerosene delivery, propane-gas suppliers often carry gas canisters by using motor trucks. Furthermore, average prices in contiguous areas could be instruments. Since the major components of propane-gas and town-gas are usually imported, transportation charges on contiguous areas may be similar.

I have appropriate instruments with regard to geographical characteristics. First, I use the logarithm of road mileage and the habitable area divided by land area. In the set of instruments I also include a population and its square. These characteristics could capture population density. Furthermore, the apartment ratio of new housing could be an instrument. Town-gas is often supplied in areas with many apartment houses. The apartment ratio of new housing may be correlated with the within-area market share $ \log(s_{g|j}) $ .

The basic statistics and variable definitions are summarized in Table 2.

### 3.4 Demand estimation results

Table 3 shows four estimation results of a gas demand. The first specification (3.1) presents the ordinary least squared (OLS) estimates without regard for the endogeneity. From (3.2) to (3.4), I use a 2SLS method to control for the endogeneity in $ p_{jg} $ , $ \log(s_{g|j}) $ , and $ Density_{j} $ . In the specification (3.2), I use the instruments introduced in the above subsection for equation (3). Specifi-