Silviase/jgov_v1.4_full_ann huggingface dataset:
$ \theta_{t}\equiv(国民医療費)/(生産年齢人口(15~64)) $

=(生産年齢人口の医療費 + 従属人口(0～14 および 65～)の医療費) / (生産年齢人口)

=(生産年齢人口の医療費) / (生産年齢人口) + (従属人口の医療費) / (生産年齢人口)

≡θ_{1,t} + θ_{2,t}

## 仮定

高齢者の医療費自己負担は4%程度であり，さらに高齢者が支払う一般税からの老人保健制度への繰り入れ分を加えて，高齢者の実際の負担が計算される。しかしここでは高齢者の自己負担はないとして試算を試みる。

## 結果 (表 2-1)

現役世代(15～64歳)の負担する医療費は現在1人当たり年間23万円(自身に12万円，被扶養世代に11万円)，2025年には全体で69万円(同じく22万円+46万円)で約3倍となり，被扶養世代に対する負担は約4倍となる(ケース4)。仮に，1人当たり医療費が93年水準で止まれば，現役世代の負担は約1.5倍となる(ケース1)。

## 政策的含意

1. 少子・高齢社会の進行は，医療や年金などの世代間の扶助システムを，その根幹から揺るがすことになる。現役世代1人当たりの被扶養世代に対する医療費の負担は約4倍になると推定され，抜本的な改革なしに制度の存続は困難だろう。

2. 前項の状況から，制度の存続のためには，例えば老人医療費の定率制の導入など高齢者の応分の負担は避けられない。

## I II 都道府県別医療費の現状

## 問題

都道府県別の1人当たりの医療費は，「西高東低」といわれ，国民健康保険給付に係る高齢者の入院医療費上位5県(北海道，高知，福岡，熊本，長崎)と下位5県(長野，山形，千葉，栃木，宮城)の比は平均して約2.1倍である。そこで，都道府県別の1人当たり医療費の差異を，1人当たり所得，医師数，病床数，CTスキャナーの設置数，ICUの病床数などで説明を試みる。モデル

都道府県別の1人当たり医療費 $ M_{ij} $ を，入院と外来及び高齢者(70歳以上および65歳以上の寝たきり老人)と非高齢者に分類し，クロス・セクションでの回帰分析を行う。

$$ \mathrm{ln}M_{ij}=c+\alpha_{1}\mathrm{ln}X_{ij,1}+\alpha_{2}\mathrm{ln}X_{ij,2}+\cdots+DUM_{ij}+u_{ij} $$

ここで $ X_{ij} $ は以下の独立変数であり，i は高齢者／非高齢者，j は入院／外来を示し， $ u_{ij} $ は誤差項である。また，c は定数項である。
独立変数（比率以外は特記のない限り人口または対象者1000人当たりに基準化）

NPI	都道府県別 1 人当たり所得[千円]	DOC	医師数
CT	CT スキャナー設置数	ICU	集中治療室及び冠動脈治療室の病床数
BED	医療機関の総病床数	BED2	BED より老人病院病床を除いた総数
PUBBED	公的病院病床のシェア[%]	PUBMED	総数に占める公的医療機関のシェア[%]
SUB	老人保健施設及び特別擁護老人ホーム入所者数	SNH	特別養護老人ホームのベッド数
POP60	70 歳未満人口に占める 60 代人口比率[%]	DUM	ダミー変数(東京は 1, 他は 0)

## 結果 1 (表 3-1)

都道府県別 1 人当たり医療費のバラツキを示す変動係数の値は，高齢・非高齢を問わず入院の値が高い。また入院医療費の差異は，1 人当たり医療費や 1 件当たりの治療日数よりも，1000 人あたり受診件数の差異による。この傾向は，91 年から 93 年までの 3 年間（実際は 80 年代以降）ほぼ安定している。

## 結果 2 (表 3-2・3-3)

1. 都道府県別 1 人当たり所得は，高齢・非高齢や入院・外来を問わず，概ね有意である。その係数の推定値は医療支出の所得弾力性を示すが，値は 0.5 前後で医療サービスは「奢侈財」ではなく，従来の時系列データによる I を超える結果と対照的である。

2. 人口当たり医師数の係数の推定値は，高齢・非高齢共に外来医療費で有意で，入院医療費では有意でない。

3. 人口あたり病床数の係数の推定値は ,高齢・非高齢共に入院医療費では有意性が高く，頑健である。また外来医療費では係数は負であり，入院サービスとは代替的と解釈される。医師数と病床数が有意なことは，医師誘発需要の理論と整合する。

4. 医療のハイテク化の代理変数である ICU は，高齢者入院で有意だが非高齢者では有意でなく，CT は高齢・非高齢の外来でほぼ有意である。

5. 人口構成の高齢化の代理変数である 70 歳未満人口に占める 60 代のシェアは，非高齢者医療費の入院・外来共に有意である。

## 政策的含意

1. 高齢者入院の場合に, 上位 5 県と下位 5 県の 1 人当たり医療費の平均値は, 2 倍以上の差があり, かつ双方の道県の高齢者の健康状態に有意な差異は報告されていない。従って患者の健康を低下させずに上位の道県の医療費を抑制する余地があると考えられる。

2. これらの差異は病床数や医師数にも依存し，地域ごとの病床を規制する地域医療計画は，病床数などのコントロールを通じて医療費の抑制に貢献するといえる。

3. ICU の設置や CT スキャナーの普及などの医療のハイテク化が，医療費の差異に有意に影響しており，これらの設置を医療機関の機能分化などで調整することが望ましい。
図 1-1：年齢階層・入院／外来別国民医療費の将来推計

注：推計値はケース4の場合。 $ ^{*} $ は1993年の実績値。

出典：厚生省(1997)「日本の将来人口推計(平成9年1月推計)」，厚生省「患者調査」，厚生省「国民医療費」

表 1-1：国民医療費(実質)の年平均増加率

(%)

入院	外来	合計
0-14	15-64	65+	0-14	15-64	65+
ケース 1	-0.9	-0.6	2.1	-0.9	-0.6	2.1	0.8
ケース 2	0.7	0.9	3.8	1.6	1.2	4.0	2.6
ケース 3	-0.7	-0.3	2.4	-0.7	-0.3	2.2	1.1
ケース 4	0.8	1.2	4.0	1.8	1.6	4.1	2.8

注：数値は，国民医療費の将来推計をもとに，1993～2025年にかけての平均増加率を算出したもの。
表 1-2 a：将来の必要医療施設数

病院	診療所
ベッド	施設	ベッド	施設
1993*	1,680,952(100.0)	9,844(100.0)	265,083(100.0)	84,128(100.0)
2000	1,893,069(112.6)	10,773(109.4)	306,509(115.6)	91,375(108.6)
2005	2,046,122(121.7)	11,427(116.1)	336,836(127.1)	96,420(114.6)
2010	2,165,340(128.8)	11,920(121.1)	360,856(136.1)	100,182(119.1)
2015	2,271,426(135.1)	12,334(125.3)	382,889(144.4)	103,252(122.7)
2020	2,397,883(142.7)	12,805(130.1)	409,683(154.5)	106,686(126.8)
2025	2,410,078(143.4)	12,782(129.8)	414,022(156.2)	106,276(126.3)

注：推計値は，一人当たり原単位が1993年の水準から変化しないと仮定して，将来のベッド及び医療施設の必要数を推計したものである。また，0内は，1993年を100とした場合の必要資源量の比である。 $ ^{*} $ 1993年は実績値。

出典：厚生省「医療施設調査」

表 1-2 b：将来の必要医師数

必要
医师数
RD	供給医师数	不足医师数
S_{70}	S_{75}	S_{80}	S_{85}	RD-S_{70}	RD-S_{75}	RD-S_{80}	RD-S_{85}
1994*	230,519	230,519	230,519	230,519	230,519	0	0	0	0
2000	251,292	222,447	240,756	250,681	254,699	28,845	10,536	611	-3,407
2005	265,827	243,198	255,285	270,855	278,341	22,629	10,542	-5,028	-12,514
2010	276,735	264,662	275,628	285,885	297,617	12,073	1,106	-9,150	-20,882
2015	285,754	284,851	296,294	305,594	313,294	903	-10,541	-19,840	-27,541
2020	295,952	297,931	315,364	325,086	332,060	-1,980	-19,412	-29,134	-36,108
2025	295,105	305,966	327,712	342,539	349,853	-10,861	-32,607	-47,434	-54,748

注：必要医師数は，表1-2aと同じ手続きで推計した。供給医師数は，医師の引退年齢を70,75,80,85歳のそれぞれに設定して推計した。

$ ^{*} $ 1994年は実績値。

出典：厚生省(1994)「医師・歯科医師・薬剤師調査」，厚生省(1994)「簡易生命表」
表 2-1：現役世代への負担推計(ケース 4)

(円・%)

$ \theta $ t	$ \theta $ 1,t	$ \theta $ 2,t	$ \theta $ t/ $ \theta $ 93	$ \theta $ 1,t/ $ \theta $ 1,93	$ \theta $ 2,t/ $ \theta $ 2,93
1993*	231,766	118,230	113,535	100.0	100.0	100.0
2000	322,814	147,688	175,126	139.3	124.9	154.2
2005	386,029	163,130	222,899	166.6	138.0	196.3
2010	460,944	178,572	282,371	198.9	151.0	248.7
2015	558,025	194,014	364,011	240.8	164.1	320.6
2020	632,591	209,457	423,134	272.9	177.2	372.7
2025	685,194	224,899	460,295	295.6	190.2	405.4

注：数値はケース4を想定。 $ ^{*} $ 1993年は実績値。

出典：厚生省(1997)「日本の将来人口推計(平成9年1月推計)」，厚生省「患者調査」，厚生省「国民医療費」

表 3-1：一人当たり医療費の構成要素(1993)

平均	STD	CV
高龄者	入院医療費	(M11)	332,529	78,823	0.237
N11	92.845	24.471	0.264
T11	21.614	1.443	0.067
D11	16,836	2,009	0.119
外来医療費	(M12)	267,102	34,698	0.130
N12	1,311.299	84.104	0.064
T12	3.208	0.472	0.147
D12	6,429	783	0.122
非高齡者	入院医療費	(M21)	74,137	15,301	0.206
N21	23.920	5.435	0.227
T21	20.391	1.195	0.059
D21	15,366	1,526	0.099
外来医療費	(M22)	69,872	7,380	0.106
N22	530.684	42.243	0.080
T22	2.243	0.192	0.086
D22	5,897	503	0.085

注：Nは，1000人当たり受療者数，Tは1件当たり受診日数，Dは1日当たり医療支出を表す。従って，MはN,T,Dの積に等しい。STDは標準偏差，CV（=標準偏差/平均）は変動係数を表す。

出典：厚生省「国民健康保険事業年報」
表 3-2：入院患者の医療費関数の係数推計値(1993)

Elderly (M11)	Non-elderly (M21)
NPI	0.3842	0.2925	0.5547	0.6925	0.3749	0.3996	0.2236	0.2210
(1.2844)	(1.0969)	(1.9097)	(2.5440)	(2.2153)	(2.4299)	(1.1786)	(1.2629)
DOC	0.2253	0.1939	0.2180	-----	0.0769	0.0858	0.1646	0.1661
(1.2210)	(1.0911)	(1.2884)	(-----)	(0.7431)	(0.8408)	(1.4258)	(1.5400)
ICU	0.1091	0.1199	0.1292	0.1363	0.0267	-----	0.0473	0.0471
(1.8495)	(2.1209)	(2.3201)	(2.4390)	(0.7039)	(-----)	(1.1051)	(1.1244)
PUBBED	-0.1242	-0.1175	-0.1121	-0.0739	0.0473	0.0446	0.0019	-----
(-1.5874)	(-1.5238)	(-1.5861)	(-1.1424)	(1.0381)	(0.9885)	(0.0382)	(-----)
POP60	-----	-----	-----	-----	0.4118	0.3711	0.4098	0.4117
(-----)	(-----)	(-----)	(-----)	(3.1074)	(3.1316)	(2.5521)	(2.7358)
BED	0.3952	0.3958	0.3713	0.5222	0.5772	0.6084	-----	-----
(2.1039)	(2.1211)	(2.2557)	(4.4800)	(5.4087)	(6.3103)	(-----)	(-----)
BED2	-----	-----	-----	-----	-----	-----	0.4896	0.4875
(-----)	(-----)	(-----)	(-----)	(-----)	(-----)	(3.7243)	(4.1375)
SUB	0.1760	0.1609	-----	-----	-----	-----	-----	-----
(1.5264)	(1.4301)	(-----)	(-----)	(-----)	(-----)	(-----)	(-----)
SNH	-----	-----	0.3342	0.3139	-----	-----	-----	-----
(-----)	(-----)	(2.8112)	(2.6422)	(-----)	(-----)	(-----)	(-----)
DUM	-0.1239	-----	-0.2484	-0.1940	-0.2940	-0.3119	-0.2833	-0.2836
(-0.6943)	(-----)	(-1.4108)	(-1.1258)	(-2.8988)	(-3.1966)	(-2.4574)	(-2.4973)
C	8.4959	9.2895	6.8350	5.3980	5.4751	5.2271	7.1106	7.1367
(2.9869)	(3.5899)	(2.4706)	(2.1148)	(3.3886)	(3.3359)	(3.9884)	(4.3877)
F-value	14.31	16.84	16.99	19.23	43.60	51.43	32.51	38.90
Adj.R²	0.6695	0.6738	0.7088	0.7040	0.8663	0.8680	0.8275	0.8318

注：( )内は t 值を表す。各変数の定義については，本文を参照。
表 3-3：外来患者の医療費関数の係数推計値(1993)

高齡者(M12)	非高齡者(M22)
NPI	0.5541	0.5679	0.4709	0.4860
(2.5761)	(3.3045)	(2.6253)	(2.6995)
DOC	0.4092	0.4101	0.3140	0.3111
(3.7220)	(3.7892)	(3.3343)	(3.2678)
CT	0.1445	0.1447	0.1479	0.1398
(1.5677)	(1.5897)	(1.9254)	(1.8230)
PUBMED	-0.4113	-----	0.0135	0.0167
(-0.1095)	(-----)	(0.3602)	(0.4477)
POP60	-----	-----	0.4554	0.4837
(-----)	(-----)	(3.5981)	(3.6465)
BED	-0.1678	-0.1674	-0.1788	-----
(-1.3841)	(-1.3982)	(-1.7379)	(-----)
BED2	-----	-----	-----	-0.1873
(-----)	(-----)	(-----)	(-1.5971)
DUM	-0.4627	-0.4639	-0.3559	-0.3589
(-3.9239)	(-4.0012)	(-3.6305)	(-3.6414)
C	8.7666	8.6478	7.0344	6.8264
(4.2579)	(5.0035)	(4.1058)	(4.0117)
F-value	8.86	10.89	9.61	9.44
Adj. R²	0.5062	0.5181	0.5670	0.5621

注：( )内は t 值を表す。各変数の定義については，本文を参照。
Japan is approaching an aged society. In 1995 those aged 65 years old and over occupy 14.6 percent (18.3 million) in the population. The ratio is expected to rise to 19.6 percent by 2005, to 25.2 percent by 2015, and to 27.4 percent by 2025. Over the next 30 years, the proportion of elderly persons will be increasing so rapidly that they are bound to pose serious challenges to the country. In the field of health care there would be several major problems caused by such rapid demographic changes in the population. These problems will be addressed in the following chapters.

In chapter 1, we will forecast the future health care expenditures from various aspects and discuss the major issues our society is going to face. With a rapidly ageing population, the amount of our society's resources being devoted to health care will increase since the average medical expenses per capita for the elderly are four or five times higher than those for the non-elderly. Therefore, it is requisite to estimate the burdens of health care to the society and distributive transfer between the working and non-working generations under the current health care system over the next 30 years. These quantitative results encourage us to visualize the future situation and to discuss what health care reforms should be carried out.

However, some remarks on the projections are in order, since in this study the current health care system is assumed to continue in existence throughout the time period in question. Even a slight change in the institutional framework for estimation would affect the future health care expenditure. For example, let us take the (new) Gold Plan, i.e., the ten-year strategy to promote health care and welfare for the aged. With the development of this program, some of the elderly inpatients now staying in hospitals may be transferred to other care-providing facilities such as nursing homes. Some may be transferred to their homes and be taken care of by their families. In both cases, such a transfer would cause "cost-shifting". That is, medical expenditures decrease in the hospital sector while additional expenses increase in the other sectors. Similar changes would be brought about by the Nursing Care Insurance scheme. At present (in 1996), the bill is still under discussion in the Diet, but it would influence our estimates of medical spending when it is approved and comes in force.
Next, bearing in mind the rapid increase of the aged in the population, greater emphasis should be placed on inefficiency problems caused by the current health care delivery system. We try to analyze and discuss two major issues on efficiency in production and in consumption. Under the current system, the consumers (patients) can choose hospitals or clinics according to their willingness or judgment. That may result in inefficiencies in production of hospital care (secondary or tertiary care). Actually, many patients, particularly the elderly, are accustomed to visit too often hospitals rather than clinics and, consequently, causing congestion in the hospital sector. In this respect, it may be necessary to encourage hospitals and clinics to divide their roles between primary and secondary care. Before discussing these respective roles in the delivery of services from the normative point of view, the following fundamental problems should be investigated: How many patients will hospitals and clinics deal with respectively? How much of resources will they use in the future? In chapter 1, we attempt to answer these questions.

It is often pointed out in Japan that the per capita medical expenditure varies very much across prefectures and is higher in the western areas than in the eastern. This phenomenon also illustrates the inefficiency in the delivery of services and, therefore, high medical costs. Chapter 2 will deal with these problems. First, it is requisite to identify the factors that may affect the medical expenditures across prefectures. The empirical results will clarify the impact of individual factors on medical spending and lead us to find policy choices that could improve the efficiency or alleviate wastefulness in use of medical resources.

Over the last decade, the government has implemented some measures in order to improve production efficiency particularly in the hospital sector: the regulation on the number of beds in each medical region (the Regional Medical Care Plans), the introduction of fixed payment system for geriatric care, the incentive strategies to encourage some of the beds in hospitals into those for long-term care, and so forth. From the viewpoint of economic efficiency, the empirical results in chapter 2 will also shed light on the economic implications of these policies, among other things, regulations on the number of beds and doctors, and the current reimbursement fee schedule favorable to the services of medical instruments such as CT-scanners.
CHAPTER 1

THE ESTIMATION OF NATIONAL MEDICAL EXPENDITURE IN JAPAN
## I ntroduction

The purpose of this paper is to analyze the impact of a rapidly ageing population upon the National Medical Expenditure in Japan over the next 30 years. The ageing of the Japanese population is clear from the following figures. In 1993, those aged 65 years or over occupy 13.5 percent (16.9 million) in the population. This ratio is expected to rise to 19.6 percent by 2005, to 25.2 percent by 2015, and to 27.4 percent by 2025. Therefore, our society faces several serious problems, as will be made clear through the following simulation models.

In the paper two models will be presented. The first model is to investigate the impact of changes in the age structure upon medical expenditures for each age group. With a rapidly ageing population, the share of health expenditure for each age group will be expected to change drastically over the time period in question. Income transfers from the shrinking working-age population to the growing retired will increase significantly. Consequently, a serious problem of income transfer will surface unless further fundamental reforms are carried out in the medical insurance system.

The second model is to classify medical institutions into hospitals and clinics, and to project the National Medical Expenditure under these categories. Through this simulation, we will consider what role hospitals and clinics should play respectively in the delivery of medical services in the future. Under the current health system, the consumers (patients) have the freedom to choose medical institutions. They make their choices according to their own willingness and judgement, which results in inefficiency in production and ineffectiveness in consumption.

The plan of this paper is as follows. The next section clarifies the term National Medical Expenditure and overviews two models for the projections. Section II presents empirical results from the first model and discusses the distributive transfers among age groups. Section III presents empirical results from the second model and discusses the roles of medical institutions and medical resources in the future.
I National Medical Expenditure (NME) and its estimation

1) Definition of NME

In this paper, the National Medical Expenditure (NME) is defined to be all the reimbursement for medical services provided by hospitals and general clinics except dental ones, so that our definition is slightly different from the term "National Medical Expenditure" $ ^{1)} $ as usually defined and used by the Ministry of Health and Welfare (MHW). In 1993, the former is about 21 trillion yen, and the latter 24 trillion yen. This difference is due to the exclusion in the former of reimbursement payment for dental services, dispensing by pharmacists, nursing and rehabilitation services in health service facilities for the elderly and home health care by nurses for the elderly. Therefore, the following several problems might occur regarding the estimation of the NME in the future.

The projected figures of the NME under our definition may be over- or underestimated compared with the actual future figures since the institutional framework for estimating the NME is based on the current Japanese health care system in 1993. For example, let us take the (new) Gold Plan, i.e., the ten-year strategy to promote health care and welfare for the aged, which has been under way since 1990. One of its major purposes is to increase Health Services Facilities (HSFs) and Special Nursing Homes (SNHs) for the elderly in order to improve the elderly inpatients' welfare as well as to reduce production inefficiency in hospitals. Therefore, when elderly inpatients in hospitals who only need care and not cure $ ^{2)} $ are transferred into these new facilities, the costs incurred by hospitals will decrease and so will the NME under our definition $ ^{3)} $ .

Whatever method is taken for estimating the future figures with respect to health care expenditure, this problem cannot be avoided. From now, we employ the NME as described above because it contains almost all medical services which are provided under the current health care system.
2) Models and key issues

In this section we overview two models concerning the estimation procedures of the NMEs in the future: Model I and Model II. The purpose of both models is to investigate the impacts of a rapidly ageing population upon the NMEs over the next 30 years. This is requisite not only for policymakers but also for the insured and taxpayers in order to discuss the health care reforms. This research will present the quantitative foundations for their discussions. The two models are different in their characteristics because they focus on different issues respectively, but they complement each other. Each model will be explained briefly.

Model I is to investigate the impact of changes in the age structure upon medical expenditures for each age group. Therefore, we place a great emphasis on categorizing health care expenditures by age group and provide the estimates of the future NMEs under alternative assumptions. There are four cases according to the assumptions about medical expenses and the utilization of medical services. Of these, case 4 is the most likely with the per capita medical expenses growing at certain rate. Using the estimated results of this case, the benefits and burdens between age groups are discussed quantitatively. What burden should the working-age population bear on behalf of the non-working population in the future?

Model II is to project and analyze the future NMEs by medical institution as well as by age group. Medical institutions are divided into hospitals and clinics according to the Medical Services Law, with the former equipped with more than 20 beds and the latter otherwise. What role should they take respectively in the delivery of medical services particularly in the future? Under the current health system, the consumers (patients) can choose hospitals or clinics according to their willingness or judgments, which results in inefficiencies of production in hospital care (secondary or tertiary care). Actually, many patients, particularly the elderly, are too often accustomed to visit hospitals rather than clinics and causing congestion in hospitals $ ^{4} $ . Therefore, before proceeding to discuss this issue and reforms in the delivery system, it is requisite to get quantitative information on the NME in the ageing society.
1) Model I

Model I is formulated to calculate medical expenses by age group and by type of services in the future. The equation of model I is defined as follows:

$$ \begin{array}{r}{\mathrm{\boldmath~E~}_{\mathrm{t}}=\Sigma\mathrm{\boldmath~E~}_{\mathrm{i j,t}}=\Sigma\mathrm{\boldmath~C~}_{\mathrm{i j,t}}\mathrm{\boldmath~N~}_{\mathrm{i j,t}}=\Sigma\mathrm{\boldmath~C~}_{\mathrm{i j,t}}\mathrm{\boldmath~R~}_{\mathrm{i j,t}}\mathrm{\boldmath~P~}_{\mathrm{i j,t}}}\end{array} $$

i (= 1,2 or 3) representing the age group (0-14,15-64 or 65 years old and over)

j (= 1 or 2) representing the type of services (in-or outpatient care)

E is the NME, C the medical expenses per patient and N the number of patients during a year. N is composed of the number of patients in a given day for a population of 100,000 (R) and the projected population (P) $ ^{5)} $ . P is taken from the figures projected by the National Institute of Population and Social Security Research at the MHW. In the "Population Projection for Japan", three estimates (low, medium, and high estimates) for the projected population are made based upon three alternative assumptions concerning fertility rates. The medium estimation projection is used in this paper $ ^{6)} $ .

Then, what figures should be used for C and R in the calculation of the future NMEs (E)? In general, C is supposed to be subject to the development of medical technology, the organizational characteristics of medical institutions, delivery system of medical services and so forth. R reflects the utilization of medical resources and is supposed to be influenced by changes in the disease patterns (e.g., an infectious disease or a degenerative one) as well as in policy measures (e.g., copayment). Considering these conditions about C and R, it seems to be very difficult to predict their future figures.

2) Some assumptions and cases

This section examines the past trends of C and R over time, and provides the
procedures for estimating their future figures over the next 30 years. First, the per capita expenditure for medical services, i.e., inpatient and outpatient care, seems to grow slowly at certain rates irrespective of the age group (Appendix Figure 1-1). As described above, medical expenses are influenced by many uncertain and unpredictable factors, so that, as a preliminary approach, we assume that the past trends of $ C_{ij} $ could be represented by time trends only. Through the regression analysis by time trends $ ^{7} $ , six equations for medical expenses by the age group and by the type of services are estimated. From these equations we can obtain the projected medical expenses for the six categories respectively.

In contrast, R is relatively stable over time. Each utilization of medical care (i.e., the number of patients) in a day by the age group and by the type of medical services seems to be comparatively stable except in the case of the elderly inpatient care (Appendix Figure 1-2). We assume that the average of utilization rates during the last ten years could be carried forward to the future. However, the utilization of inpatient care for the elderly should be subjected to further investigation by gathering information and data.

As far as the projection of C and R is concerned, there may still remain some inevitable uncertainties since they are projected respectively over the next 30 years. Thus, it is more reasonable to assume a certain range of projected figures as an alternative. The following cases are adopted to estimate the NME in the future:

Case 1: $ C_{ij} $ and $ R_{ij} $ are their respective 1993 figures.

Case 2: $ C_{ij} $ is projected by the time trend equation, while $ R_{ij} $ is its 1993 figure.

Case 3: $ C_{ij} $ is its 1993 figure, while $ R_{ij} $ is the average during the last 10 years.

Case 4: $ C_{ij} $ is projected by the time trend equation, and $ R_{ij} $ the average during the last 10 years.

3) Empirical results and discussions

This section presents the future figures of the NME estimated by model I in the four