Silviase/jgov_v1.4_full_ann huggingface dataset:
population will be different from those shown in the table. The 1993 amount $ (\theta_{2,93}=113,535\ \text{yen}) $ is not charged totally by the working-age or the age group 15-64.

14) See the MHW[1995] in detail.

15) According to the documents of the Council on the Health and Welfare for the Aged at the MHW on October of 1996, among all the elderly waiting for the SNHs, 48 percent are those who have been staying in hospitals. As a result, the decrease in inpatient expenditure for the elderly would be overestimated. In other words, the future figures for the NME would be underestimated.

16) The total number is 307,362 (211,253 and 95,909 respectively for HSFs and SNHs). This is the additional capacity for the facilities which are expected to be built from 1993 to 1999.

17) Particularly for home care, considering opportunity costs of the family labor will be crucial (Tokita $ ^{[1995]} $ ). But this is a theme beyond the scope of this paper. As for an economic evaluation of long-term care, see Yashiro et al $ ^{[1996]} $ .

18) In addition, the reason may also be that family size has been getting smaller and the number of wives getting jobs outside of their homes has been increasing. However, the fundamental reason is that the government has delayed policy responses to this problem.

19) The HSSA covers bedridden people over 65 as well as those over 70 years old. Most of the contributions come from other insurance schemes and tax revenues from national and local governments, so that the system may function as an instrument for income transfer. See MHW $ ^{[1995]} $ .

20) These documents are "Survey on Medical Benefits of the National Health Insurance (Kokumin-Kenkou-Hoken Iryo-Kyufu Jittai-Cyosa Houkoku)", (MHW) and "Survey on Medical Care by Medical Institution (Iryo-Kikan betsu Shinryo Jokyo Shirabe)", (Social Insurance Medical Fee Payment Funds). These are different in the coverage of the patients. The former deals with the enrollees covered under the National Health Insurance, the latter with the persons covered under the Employee's Health Insurance, i.e., government-managed health insurance (Seikan), society-managed health insurance (Kumiai), sea-men's insurance (Sen-in Hoken), and mutual aid associations (Kyosai) (see MHW $ ^{[1995]} $ in detail). Therefore, we estimate the ratios by considering their respective weights in terms of the total reimbursement payments.

21) There are some problems regarding the usage of $ \delta_{ijk} $ . Clearly, as described in section II, the utilization rates among medical institutions have drastically changed (i.e., from clinics
to hospitals). Therefore, it may be inappropriate to use the figures of $ \delta_{ijk} $ in 1993. Furthermore, $ \delta_{ijk} $ would be greatly influenced by the current and future health policies, for example, changes in copayment or fee reimbursement, regulations on hospital beds and so forth.

(22) As data on the number of doctors is not available in 1993, we use the figures in 1994 to calculate the required number of doctors in the future. We here assume that the required number of doctors in 1994 balances the actually supplied doctors. Therefore, we take the respective assumptions about the retirement age into consideration after the year 2000. The future figures of doctors in supply are estimated in the following way. At first, we classify doctors into age groups by five years old and apply to each age group its mortality rate according to the abridged life table. Consequently, the future figures of doctors for each age group are obtained. From these figures, we can calculate the future number of doctors by considering the retirement age.
Chino, T., "Time Costs to Kanja no Sentaku-Koudo" ("Time costs and the patient behavior in Japan"), Iryo to Shakai, Vol. 4, No. 1, 1994, pp. 1-25.

Gerdtham, Ulf-G., Sogaard, Jes, Andersson, F., and B., Jonsson, "An econometric analysis of health care expenditure: A cross-section study of the OECD countries", Journal of Health Economics, Vol.11, 1992, pp.63-84.

Hitiris, Theo and John Posnett, "The determinants and effects of health expenditure in developed countries," Journal of Health Economics, Vol.11, 1992, pp.173-181.

Ministry of Health and Welfare, Annual Report on Health and Welfare, 1995.

Nakaizumi, M., "Iryo-niokeru Gaibusei to Primary Care no Katsuyo ("Externality in medical care and the usefulness of primary care"), in Tokita, T., ed., Nihon no Iryo-Keizai (The Japanese Health Economy), Toyo Keizai Shinpo-Sha, 1995.

Newhouse,J., "Medical care expenditure: a cross-national survey", Journal of Human Resources 12, 1977, pp.115-125.

OECD, The Reform of Health Care Systems: A Review of Seventeen OECD Countries, Paris, 1994.

Tokita, T., "Zaitaku-Kaigo no Keizai-Bunseki" ("An economic analysis of community care"), Iryo to Shakai, Vol.5, No.3, 1995, pp.1-17.

Yashiro, N., Oshio, T., Terasaki, Y., and M. Miyamoto, "Kaigo-Hoken no Keizai-Bunseki" ("Economic analysis of the Nursing Care Insurance scheme"), Keizai-Bunseki, 1996.
Appendix Figure 1-1: Medical expenses per patient by age group

Notes: Medical expenses per patient are the amounts of reimbursement which medical institutions could receive in the case of continuing to

provide medical care with one patient during one year. Data on the Patient Survey is published only every three years since 1984.

Source : "National Medical Expenditure", (MHW)

"Patient Survey", (MHW)
Appendix Figure 1-2: Utilization of medical care in a day by age group

Source : "Patient Survey", (MHW)
(trillion yen: 1990 prices)

Case 1

Inpatient	Outpatient	Total
0-14	15-64	65+	0-14	15-64	65+
1993*	0.38	4.48	4.65	0.85	5.81	4.00	20.17
2000	0.34	4.45	6.02	0.76	5.76	5.17	22.51
2005	0.33	4.35	6.88	0.74	5.63	5.92	23.86
2010	0.34	4.18	7.74	0.75	5.42	6.65	25.08
2015	0.33	3.95	8.78	0.73	5.11	7.54	26.44
2020	0.31	3.80	9.18	0.69	4.92	7.89	26.79
2025	0.29	3.71	9.12	0.64	4.80	7.83	26.39
Case 2
Inpatient	Outpatient	Total
0-14	15-64	65+	0-14	15-64	65+
1993*	0.38	4.48	4.65	0.85	5.81	4.00	20.17
2000	0.37	4.90	6.88	0.96	6.68	6.07	25.85
2005	0.40	5.25	8.59	1.07	7.24	7.69	30.25
2010	0.44	5.50	10.48	1.22	7.65	9.49	34.78
2015	0.47	5.61	12.81	1.33	7.87	11.72	39.80
2020	0.48	5.81	14.35	1.39	8.21	13.25	43.49
2025	0.48	6.07	15.22	1.41	8.61	14.16	45.94
Case 3
Inpatient	Outpatient	Total
0-14	15-64	65+	0-14	15-64	65+
1993*	0.38	4.48	4.65	0.85	5.81	4.00	20.17
2000	0.35	4.89	6.54	0.80	6.37	5.34	24.30
2005	0.35	4.78	7.47	0.79	6.23	6.11	25.72
2010	0.35	4.60	8.41	0.79	5.99	6.87	27.00
2015	0.34	4.34	9.53	0.77	5.65	7.79	28.42
2020	0.32	4.18	9.96	0.73	5.44	8.14	28.78
2025	0.30	4.07	9.90	0.68	5.31	8.09	28.35
Case 4
Inpatient	Outpatient	Total
0-14	15-64	65+	0-14	15-64	65+
1993*	0.38	4.48	4.65	0.85	5.81	4.00	20.17
2000	0.39	5.38	7.47	1.01	7.38	6.26	27.90
2005	0.42	5.77	9.33	1.14	8.01	7.94	32.60
2010	0.46	6.04	11.38	1.29	8.46	9.80	37.42
2015	0.49	6.16	13.90	1.40	8.70	12.10	42.76
2020	0.50	6.39	15.59	1.47	9.07	13.68	46.69
2025	0.50	6.67	16.52	1.49	9.52	14.62	49.32

Notes: All figures are estimated by model I in each case. For the definition of each case, see the text. The projected population is assumed to be the medium estimates.

$ ^{*} $ The figures in 1993 are actual.

Source : "Population Projections for Japan" (estimated in Jan. 1997), (MHW)

"Patient Survey", (MHW)

"National Medical Expenditure", (MHW)
Appendix Table 1-2: Projected National Medical Expenditure, model I, case 4

Inpatient	Outpatient	Total
0-14	15-64	65+	0-14	15-64	65+
1993*	0.38	4.48	4.65	0.85	5.81	4.00	20.17
2000	0.39	5.38	7.47	1.00	7.38	6.26	27.89
2005	0.40	5.77	9.33	1.10	8.01	7.94	32.54
2010	0.43	6.04	11.38	1.19	8.46	9.80	37.29
2015	0.44	6.15	13.90	1.25	8.68	12.10	42.53
2020	0.44	6.33	15.59	1.28	8.99	13.68	46.31
2025	0.43	6.54	16.52	1.29	9.35	14.62	48.75

Notes: All figures are estimated by model I in case 4. The projected population is assumed to be the lowest estimates.

$ ^{*} $ The figures in 1993 are actual.

Source : As in Appendix Table 1-1

Appendix Table 1-3: Burdens to the working-age population, model I, case 1

$ \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	260,464	118,230	142,234	112.4	100.0	125.3
2005	282,544	118,230	164,313	121.9	100.0	144.7
2010	308,857	118,230	190,627	133.3	100.0	167.9
2015	345,024	118,230	226,794	148.9	100.0	199.8
2020	362,993	118,230	244,763	156.6	100.0	215.6
2025	366,694	118,230	248,464	158.2	100.0	218.8

Notes: Figures are estimated by equation (2) in case 1. The NME is divided by the population of the age group 15-64, so that they are not the accurate amounts paid by the working-age population. See the text in detail.

$ ^{*} $ The figures in 1993 are actual.

Source: As in Appendix Table 1-1.
Appendix Table 1-4: Distributive ratios of medical expenditures between hospitals and clinics, 1992 and 1993

$ \delta $ 111	$ \delta $ 112	$ \delta $ 121	$ \delta $ 122	$ \delta $ 211	$ \delta $ 212	$ \delta $ 221	$ \delta $ 222
1992	95.7	4.3	50.3	49.7	94.2	5.8	51.7	48.3
1993	95.8	4.2	51.2	48.8	94.8	5.2	54.4	45.6

Notes: Figures are estimated by considering the two documents. See the text and 21) for the procedure in detail.

Source: "Survey on Medical Benefits of the National Health Insurance", (MHW)

"Survey on Medical Care by Medical Institution", (Social Insurance Medical Fee Payment Funds)

Appendix Table1-5: Projected National Medical Expenditure by medical institution, model II (trillion yen: 1990 prices)

Inpatient	Outpatient	Total
0-69	Hospital	0-69	Clinic	Hospital	0-69	Clinic
1993*	5.55	3.53	0.24	0.19	3.95	1.60	3.76	1.34	20.17
2000	5.47	4.78	0.24	0.26	3.89	2.17	3.70	1.82	22.33
2005	5.36	5.73	0.24	0.31	3.81	2.60	3.63	2.18	23.86
2010	5.24	6.51	0.23	0.36	3.73	2.95	3.55	2.48	25.04
2015	5.07	7.26	0.22	0.40	3.60	3.30	3.43	2.77	26.06
2020	4.81	8.22	0.21	0.45	3.42	3.73	3.26	3.13	27.24
2025	4.61	8.49	0.20	0.47	3.28	3.86	3.13	3.23	27.27

Notes: All figures are estimated by model II. This model assumes that the distributive ratios $ (\delta\ \mathrm{ijk}) $ of medical expenditures between hospitals and clinics are their 1993 figures and the case 1 as described in section I holds. See the text in detail.

$ ^{*} $ The figures in 1993 are actual.

Source : As in Appendix Table 1-1 and in Appendix Table 1-3.
CHAPTER 2

# THE DETERMINANTS OF MEDICAL EXPENDITURE ACROSS PREFECTURES IN JAPAN
## I ntroduction

The purpose of this paper is to identify the factors that explain variations in medical expenditures across prefectures in Japan and to examine the differences or similarities between the elderly and non-elderly and between in- and outpatient care. In general, most of studies on the determinants of health expenditure have employed international cross-section data (Newhouse [1977], Gerdtham et al [1992], OECD [1995]). However, several problems on the OECD data appear in the results (Gerdtham and Jonsson[1991], Schieber and Poullier[1989]).

This paper continues this line of research but differs from previous studies in some distinct ways. First, by using Japanese data, we can avoid the problems related to the OECD data. Second, the per capita medical expenditure is categorized by age group and by the type of medical services. Therefore, the fundamental health expenditure functions for estimation are of four types. Finally, owing to this categorization, some additional variables that are of interest are included in each equation: those reflection in- or outpatient care, and those reflecting institutional characteristics with respect to the Japanese health care system.

The plan of this paper is as follows. Next section provides data characteristics and key issues related to the differences in medical expenditure across prefectures. Section II presents analytical framework for econometric approach and introduces common and specific independent variables for estimation. Section III shows the empirical results and presents their economic implications.

## I Differences in medical expenditure across prefectures

This section employs 1991-93 data on the National Health Insurance Scheme to shed light on the differences in medical expenditure across prefectures in Japan. Some interesting findings are arrived at through categorizing medical expenditures and analyzing variations in their components.
1) Data characteristics and key issues

The paper focuses on the elderly and non-elderly people covered by the National Health Insurance Scheme (NHI), whose features will be explained later. Through the descriptive statistics on the NHI from 1991 to 1993, we shed light on the key issues related to variations of medical expenditure across prefectures. Some interesting characteristics of in-and outpatient care expenditure will be made clear regarding the elderly and non-elderly insured.

First, let us explain the National Health Insurance Scheme (NHI) in brief. NHI is one of the Japanese insurance systems which mainly covers the self-employed, retired employees, and their dependents. In 1993, the total number of the enrollees is about 43 million, a third of the population in Japan. As far as the Health Services Scheme for the Aged (HSSA) $ ^{1)} $ is concerned, approximately 70 percent (7.6 million) of all the enrollees, i.e., all the elderly aged 70 or over and the bedridden people aged 65 or over, belong to the NHI scheme. Therefore, the NHI can be considered as the main scheme that covers the elderly. The ratio of those insured by the NHI to all the residents at each prefecture (the participation ratio) is diversifying (Appendix Table 2-1). Okinawa Prefecture shows the highest ratio (45 percent), and Shiga the lowest (25 percent) $ ^{2)} $ .

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. However, few studies have examined this econometrically despite the importance of its economic implications (Nishimura [1987]). For our research, we first define the per capita medical expenditure as $ M_{ij} $ where i (= 1 or 2) represents the elderly or non-elderly, and j (= 1 or 2) does in- or outpatient care. This expenditure in Japan also means the amount of reimbursement for services provided by medical institutions (i.e., hospitals and general clinics except dental ones) $ ^{3)} $ . Generally, all medical care providers are reimbursed for the expenses incurred through delivering services according to the fee-for-service payment schedule. Individual fees are officially fixed and the same fees apply to all medical institutions around the country. Therefore, differences in medical expenditure, other conditions being equal, would reflect differences in the quantity of individual service.
Let us turn to the analysis of descriptive statistics on $ M_{ij} $ across prefectures. From Table 2-1, the following observations can be made regarding the per capita medical expenditure for the elderly and non-elderly. First, the inpatient expenditure for the elderly ( $ M_{11} $ ) is approximately five times higher than that for the non-elderly ( $ M_{21} $ ), and the outpatient expenditure for the elderly ( $ M_{12} $ ) about four times higher than that for the non-elderly ( $ M_{22} $ ). Second, the coefficient of variation for inpatient care is higher than that for outpatient care and, what is more important, the coefficient for both in- and outpatient care is much higher for the elderly than for the non-elderly.

Table 2-1: Differences in the per capita medical expenditure, 1991-93

1991	Mean	STD	CV
Elderly
inpatient care	M11	313,481	77,333	0.247
outpatient care	M12	249,253	33,312	0.134
Non elderly
inpatient care	M21	63,584	12,591	0.198
outpatient care	M22	63,870	6,659	0.104
1992	Mean	STD	CV
Elderly
inpatient care	M11	330,598	79,498	0.240
outpatient care	M12	255,582	33,593	0.131
Non elderly
inpatient care	M21	71,389	14,298	0.200
outpatient care	M22	67,433	6,924	0.103
1993	Mean	STD	CV
Elderly
inpatient care	M11	332,529	78,823	0.237
outpatient care	M12	267,120	34,698	0.130
Non elderly
inpatient care	M21	74,137	15,301	0.206
outpatient care	M22	69,872	7,380	0.106

Notes: M is the per capita medical expenditure at the prefectural level. STD is the standard deviation and CV the coefficient of variation (=STD/Mean).

Source : As in Appendix Table 2-1.
Next, let us focus on the correlation between medical expenditures by age group and by the type of services. From Appendix Table 2-2, it suggests that the positive correlation is comparatively high between $ M_{11} $ and $ M_{21} $ or between $ M_{12} $ and $ M_{22} $ , but not between $ M_{11} $ and $ M_{12} $ nor between $ M_{21} $ and $ M_{22} $ . In the prefectures where inpatient spending for the elderly is high, so is inpatient spending for the non-elderly. This feature also holds for outpatient spending. However, in the prefectures where inpatient spending for the elderly (or non-elderly) is high, the outpatient one for the elderly (or non-elderly) is not always so high.

In sum, there is a wide range of differences between the per capita medical expenditures classified by age group and by the type of services. Therefore, as we analyze the determinants of medical expenditure at the prefectural level, it is requisite to categorize medical expenditure on the basis of age and services in order to clarify the variations across prefectures.

## 2 ) Variations in the components of medical expenditure

Before proceeding to analyze the determinants of medical expenditure, we have to focus on the following question: what components of medical spending per capita give rise to the wide differences in the coefficients shown in Appendix Table 2-2. By definition, the per capita medical expenditure can be divided into some components as follows:

$$ \textbf{M}_{\mathrm{~ij~}}=\textbf{N}_{\mathrm{~ij~}}\bullet\textbf{C}_{\mathrm{~ij~}}=\textbf{N}_{\mathrm{~ij~}}\bullet\textbf{T}_{\mathrm{~ij~}}\bullet\textbf{D}_{\mathrm{~ij~}} $$

N is defined as the number of bills for reimbursement per patient, and C the medical expenses per bill (or per case) $ ^{4)} $ . C also comprises of two components: the number of days per bill (T) and medical expenses per day (D). Table 2-2 shows the descriptive statistics on these components $ ^{5)} $ .
Table 2-2: Components of the per capita medical expenditure in 1993: descriptive statistics

Mean	STD	CV
Elderly	inpatient care	(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
outpatient care	(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
Non-elderly	inpatient care	(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
outpatient care	(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

Notes: N is the number of bills for reimbursement per 1,000 persons, T the number of days per bill, and D medical expenses per day. Therefore, M=NTD. STD is the standard deviation and CV the coefficient of variation (=STD/Mean).

Source: As in Appendix Table 2-1.

Some interesting features on the components of medical expenditure emerge from the table $ ^{6} $ . First, as far as the coefficient of variation (CV) for inpatient care is concerned, there is a wide range of CVs within its components. The CV for N is very high not only for the elderly but also for the non-elderly. It suggests that variations in medical expenditure can be attributed to N rather than T or D. Next, with respect to outpatient care, there are also differences in the CVs within its components for both the elderly and non-elderly. The CVs for D and T are comparatively high, while that for N is low. Furthermore, there is not so much difference among the CVs of the three components for the non-elderly, while for the elderly the CVs for T and D are clearly more diverse than that for N.
## I I Model

## 1 ) Analytical framework

What kind of factors would influence medical expenditures at the prefectural level? There is no straightforward theory to rely on in this respect, but most studies on the determinants of health care expenditure across countries suggest some major explanatory variables (Newhouse [1977], Gerdtham et al [1992], OECD [1995]). Among them there are some variables commonly employed by these studies: income, age structure, public provision (or finance) of medical care, the number of doctors and so forth.

Our paper follows this line of research but differs from previous studies in some distinct ways. First, the per capita medical expenditure is categorized by age (the elderly or non-elderly) and by the type of medical services (in- or outpatient care) described in section I. Therefore, the fundamental equations for estimation are of four types. Second, owing to this categorization, some additional variables that are of interest are included in each equation: those representing in- or outpatient care, and those representing institutional characteristics concerning the current health care system.

Our model of medical (or health) expenditure can be written in the following way:

$$ ln\ \mathrm{M}\ _{ij}=\alpha+\alpha_{1}\bullet ln\ \mathrm{X}\ _{ij,1}+\alpha_{2}\bullet ln\ \mathrm{X}\ _{ij,2}+\bullet\bullet\bullet+\mathrm{u}\ _{ij} $$

i (=1 or 2) representing the elderly or non-elderly

j (=1 or 2) representing in- or outpatient care

This regression model is composed of four equations $ (M_{11}, M_{12}, M_{21} $ and $ M_{22}) $ : $ M_{11} $ , for example, is the equation for inpatient expenditure for the elderly. Therefore, regression analysis is applied independently to each equation. As far as the independent variables (X) are concerned, they can be classified into common and specific variables. The former are per capita income, the number of doctors, the share of public provision and medical technology. The latter are the variables characterized by the age structure and by the type of
medical services provided.

## 2 ) Independent variables

Let us specify the independent variables and explain why they should be included in the estimation under the current health care system. They are referred to as common and specific independent variables described above (Appendix Table 2-4). However, this categorization is actually not clear. For example, the public provision variable is included in both in- and outpatient equation, but the data actually employed for the public provision is not the same. The former is measured by public share in total number of beds (PUBBED) and the latter by public share in total medical facilities (PUBMED). In a sense, they might be referred to as specific factors to in- and outpatient care respectively.

## 2 -a) Common variables

Let us explain the common independent variables which should be included in all equations irrespective of age group or the type of services. These variables are income, the number of doctors, the share of public provision, medical technology and the age structure.

First, income is measured by the prefectural income per capita and is expected to have a positive effect on medical expenditure. As far as the income elasticity of health care is concerned, there is a wide range of estimates $ ^{7} $ . According to most of the studies using international cross-section data, the income elasticity exceeds one (Newhouse[1977], Gerdtham et al [1992] and Hitiris and Posnett[1992]) $ ^{8} $ . In contrast, the results of numerous studies on national micro data have revealed a low income elasticity for the utilization of health care across households (Wagstaff[1986], Manning et al [1987]).

The number of doctors (DOC) is expected to have a positive effect on medical expenditure although it is very difficult to interpret its economic implications. This variable has often been employed to test the supplier-induced demand or target income hypothesis.