Silviase/jgov_v1.4_full_ann huggingface dataset:
(Evans[1974]). Even if empirical relationships between the stock of physicians and the rate of utilization for medical resources $ ^{9)} $ are observed, these findings are often not accepted as an unambiguous evidence in favor of the hypothesis. The observed relationships may reflect true demand factors, for instance, more doctors may mean improvement in availability to health care, i.e., less distance to travel and less time to wait $ ^{10)} $ . Therefore, the empirical results found in the next section need to be treated with considerable caution.

The ratio of public medical institutions or the number of beds in them to the total number (PUBMED or PUBBED) is employed to investigate the impact of public provision on health care expenditure. Leu[1986] argued that public provision increases health care expenditure due to the bureaucratic inefficiency and less intensive competition in public sector. There are some arguments against his view. For example, Culyer[1989] notes that medical costs in private sector may be larger due to advertising and selling costs, and that market pressures may be less reliable than professional ethics and governmental regulations. In addition, there are some complicated conditions specific to the Japanese health delivery system. Public institutions are responsible for ensuring medical services to every citizen anywhere and, consequently, some should be located in remote or sparsely populated areas since private institutions could not deliver medical services there. Considering such a complementarity between public and private sectors, the sign of PUBMED or PUBBED on health expenditure could not be determined a priori.

As an indicator of the development of medical technology, the number of CT-scanners (CT) is employed, which is expected to be one of the major cost-increasing factors. In Japan the purchase of CT-scanners is left to the discretion of individual medical institutions and, moreover, reimbursement fee for this service is also believed to be set at a generous level. Consequently, we have now too many CT-scanners, and even the small clinics have them. It is expected to contribute to higher medical cost but its impact on inpatient care costs cannot be determined a priori because it could be applied for outpatient care as a diagnostic technique. Therefore, the total number of Intensive Care Units and Coronary Care Units (ICU) is also employed to be a substitute for CT although ICU may be a factor specific to inpatient care.
The age structure of the population is generally considered to be one of the major determinants since the consumption of health care is unevenly distributed over the life-cycle $ ^{11} $ . In our model, the elderly and non-elderly equations are estimated separately, so that the age structure is taken into consideration to some extent. But, since the non-elderly include a wide range of age structure, i.e., those people aged under 69 years old, it is necessary to include an indicator reflecting such age structure for the non-elderly equation with the ratio of those aged from 60 to 69 in the non-elderly population (POP60) included.

## 2 -b) Specific variables

Let us explain the specific independent variables which could be included in our model. First, the number of total beds (BED) in hospitals and clinics may be specific to inpatient care case and its sign is expected to be positive $ ^{12} $ . This paper also tries to employ the alternative to the variable BED for the non-elderly: the number of beds except those equipped by "Geriatric Hospitals" (BED2). There variables have the same characteristics to the number of doctors in that it is very difficult to interpret their economic implications. In the outpatient equations, BED or BED2 is included as a substitute for outpatient care, so that, in this case, its sign is expected to be negative.

Next, there are important variables specific to inpatient care for the elderly: the number of admission to Health Services Facilities (HSF), Special Nursing Homes (SNH), or both facilities (SUB). These variables could be employed as a substitute for inpatient services in hospitals. In Japan, among the elderly inpatients staying in hospitals, there are many who do not always need medical cure but need long-term care $ {}^{13)} $ . This phenomenon could be considered to stem mainly from the paucity of such care-providing facilities as HSF or SNH for the elderly. Therefore, these variables are included in the elderly inpatient equation since they are expected to affect inpatient expenditures.

There may be some controversy about the sign of HSF, SNH or SUB. These variables may be expected to have positive effects on inpatient expenditure. An increase in these facilities will cause to admit the elderly inpatients who need only cares but not cures. Hospitals and clinics may have room to admit the other elderly with more serious illness who have been obliged to wait for entering hospitals. As a result, inpatient expenditure not only per patient but even per elderly
people will rise, because the cost of SNH is not included in NME.

## I II Empirical Results

All of the equations are estimated in both linear and log-linear form based on 1991 through 1993 data $ ^{14} $ . With respect to the selection of regressors, non-significant regressors are fundamentally excluded one by one and the model re-estimated. In Tables 2-3 and 2-4 are the results of the log-linear equations in 1993 $ ^{15} $ .

Let us examine the results of inpatient equations for the elderly and non-elderly. All the regressors except for PUBBED have positive signs as expected. Clearly, NPI, POP60 and BED (or BED2) are the major factors that determine inpatient expenditures. As far as PUBBED is concerned, its sign is different between the elderly and non-elderly equations: the former is positive and the latter negative although both equations are not statistically significant. The result is not straightforward to interpret theoretically, so it should be investigated further through gathering new information and data.

Some remarks on these results are in order. First, CT and ICU are alternatively employed as a proxy variable for medical technology in the inpatient equations. ICU is comparatively more significant than CT in the elderly and non-elderly equations respectively although the non-elderly are not statistically significant (Appendix Table 2-5). Consequently, only the results of equations used by ICU are reported in the text. Second, as for the elderly equations, HSF, SNH and SUB are alternatively employed as a substitute for inpatient care in hospitals, with the result that HSF is not statistically significant at all. This may stem from the fact that not enough time has passed since the pilot plan for HSF was carried out in 1987. However, the sign still remains controversial as described in Section II.
Table 2-3: Estimated coefficients in inpatient expenditure equations, 1993

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

Notes: Figures in parentheses are t-values. All equations are estimated in log-linear form. As for the definitions of the variables, see Appendix Table 2-4.

Source: As in Appendix Table 2-4.
Table 2-4: Estimated coefficients in outpatient expenditure equations, 1993

Elderly(M12)	Non-elderly(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)	(1.5987)	(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

Notes: Figures in parentheses are t-values. All equations are estimated in log-linear form. As for the definitions of the variables, see Appendix Table 2-4.

Source : As in Appendix Table 2-4.

Next, let us turn to the results of outpatient equations. All the regressors have the same sign as might be expected with the coefficients for NPI, POP60, DOC and CT (or ICU) positive and that for BED (or BED2) negative. NPI, DOC and CT (or ICU) have a dominant role in explaining the outpatient expenditure in contrast with the inpatient case. As described in Section 11, DOC is very difficult to interpret from the theoretical point of view, since the competing hypotheses cannot be identified in our models. Interestingly, as far as medical technology is concerned, CT is more significant than ICU in the outpatient equations. The reason is that CT-scanners may be used for testing both in and out-patients, but ICU be occupied only by inpatients.
In sum, the number of doctors and beds, the age structure, and medical technology are the major factors that determine the medical expenditures at the prefectural level. There are some distinguished differences between medical expenditure equations by age group and by the type of services. In the inpatient equations, the number of beds plays a dominant role, while in the outpatient equation income and the number of doctors do. With respect to medical technology, ICU is so significant in the inpatient equations, but not in the outpatient ones. In contrast, CT is not significant in the former case, but it is in the latter.

## I V Conclusion and Policy Implication

This chapter argues the degree to which the factors relevant to demand and supply of the health care service contribute to the increase of health care expenditure. In general, per capita prefectural income explains the difference of the magnitude among prefectures to some extent. However, it is also endorsed that health care expenditures are affected by supply side factors - such as number of doctors and beds, CT-scanners, and so on. Hereby the following points should be emphasized as the implications:

1. Though an average per capita medical expenditure for elderly patients of the top 5 prefectures is approximately two times as large as that of the bottom 5, broad differences of health and life expectancy between the two groups are not observed. The fact shows that Japan's medical expenditure may have room to be reduced without deteriorating their health.

2. Differences of per capita prefectural medical expenditures are mainly due to disparities of the number of beds and the number of doctors per capita. Thus it suggests that the Regional Medical Plan by MHW which regulates the number of hospital beds in municipalities can contribute to the reduction of National Medical Expenditures.

3. The variation of medical expenditures among prefectures is significantly explained by widespread medical high technology - the number of ICU beds and CT scanners, for example. Therefore medical facilities should be reorganized into at least two classes for primary and secondary care in order to clarify their roles, which will enhance the efficiency of medical resource allocation.
1) Health and Medical Services Law for the Elderly came into force in 1983. This is a system of pooling fund and covers all those over 70 years old and the bedridden people over 65.

2) The names of the three prefectures from the top and from the bottom have been the same at least for the last 3 years (1991-93). Those with the highest ratios are, in decreasing order, Okinawa, Aomori and Wakayama, and those with the lowest ratios, in ascending order, Shiga, Toyama and Kanagawa.

3) Throughout the paper, whenever the terms "health expenditures", "medical spending" or "health spending" are used without any further qualification, they refer to the medical expenditure defined here.

4) Amount per bill does not always mean amount per case. Under the current reimbursement system, the bills of medical treatments for patients have to be calculated and put together at the end of each month by each medical institution and, for reimbursement, sent to third-payers (i.e., Review and Payment Organizations). Therefore, whenever a patient is treated during two consecutive months, the reimbursement is divided into two bills.

5) Medical expenditures for the retired employees aged up to 69 years old are excluded from those for the non-elderly (M $ _{21} $ and M $ _{22} $ ). The reason is that the former are covered by the NHI but are different from other non-elderly persons in their copayment due to the Retiree's Medical Insurance Scheme.

6) These features were also found in 1992 or 1991 (Appendix Table 2-3).

7) Several different hypotheses have been put forward to explain these differences(OECD [1995]).

8) However, OECD $ ^{[1995]} $ showed that the income elasticity lay between 0.7 and 0.8.

9) The following indicators are employed as the rate of utilization: hospitalization rates, the number of surgical operations, average length of stay in hospital or medical spending per patient and so forth.

10) As for the costs of access to medical services, see Chino[1994].

11) According to the Ministry of Health and Welfare [1995], the per capita medical expenditure during the lifetime for the average Japanese amounts to about 17 million yen in 1992 price. About 62 percent of this amount is spent after the age of 60.
12) The number of medical facilities (MED), i.e., hospitals and clinics, may also be specific to the outpatient care case since a rise in MED is expected to decrease the costs of access. However, as the estimated results are not significant, this variable is not included.

13) According to the national Patient Survey (Kanja Chosa) in 1993, there are about 270,000 inpatients in hospitals who are 65 years old or over and have been staying for more than six months. As a result, the average length of stay in hospital is so much longer than in other developed countries (OECD $ ^{[1995]} $ ).

14) A dummy variable is introduced for Tokyo since the per capita prefectural income is too high in Tokyo and other variables are also very different compared with those in other prefectures.

15) In the paper, only the results in 1993 are reported since they are almost the same for the other two years.
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.

Culyer, A., "Cost containment in Europe", Health Care Financing Review, annual supplement, 1989, pp.21-32.

Evans, R., "Supply-induced demand: some empirical evidence and implications", in M., Perlman, ed., The Economics of Health and Medical Care, John Wiley, 1974, pp.162-173.

Gerdtham, Ulf-G., and B., Jonsson, "Conversion factor instability in international comparisons of health care expenditure", Journal of Health Economics, Vol.10, 1991, pp.227-234.

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.

Leu,R., "The public-private mix and international health care costs," in A.J.Culyer and B., Jonsson, eds., The Public and Private Health Services, Basil Blackwell, Oxford, 1986, pp.41-63.

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

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

Nishimura, S., Iryo no Keizai-Bunseki (Economic Analysis of Health Care), Toyo Keizai Shinpo-Sha, 1987.
OECD, New Directions in Health Care Policy, Health Policy Studies No.7, OECD, 1995.

Schieber, George and Jean-Pierre Poullier, "Overview of international comparisons of health care expenditures", Health Care Financing Review, annual supplement, 1989, pp.1-7.

Wagstaff, A., "The demand for health: some new empirical evidence", Journal of Health Economics, Vol.5, 1986, pp.195-233.
Appendix Table 2-1: Participation ratio in the NHI, 1991-93

Mean	STD	CV	Mini	Max
1991	32.535	4.885	0.150	24.862	45.978
1992	32.019	4.689	0.146	24.682	45.381
1993	31.761	4.526	0.143	24.644	45.064

Notes: Figures are the ratio of those insured by the NHI to all the residents at the prefectural level (%). STD is the standard deviation and CV the coefficient of variation (=STD/Mean).

Source: "Annual Report on the National Health Insurance Scheme", (MHW)

Appendix Table 2-2: Correlation between medical expenditures by age group and by type of services, 1991-93

1991	M11	M12	M21	M22
Elderly	inpatient care	M11	1
outpatient care	M12	0.278	1
Non elderly	inpatient care	M21	0.711	0.078	1
outpatient care	M22	0.269	0.765	0.479	1
1992	M11	M12	M21	M22
Elderly	inpatient care	M11	1
outpatient care	M12	0.260	1
Non elderly	inpatient care	M21	0.697	0.085	1
outpatient care	M22	0.250	0.765	0.496	1
1993	M11	M12	M21	M22
Elderly	inpatient care	M11	1
outpatient care	M12	0.249	1
Non elderly	inpatient care	M21	0.693	0.075	1
outpatient care	M22	0.229	0.749	0.491	1

Notes: M is the per capita medical expenditure at the prefectural level.

Source: As in Appendix Table 2-1.
Appendix Table 2-3: Trends of CVs of three components, 1991-93

1991	1992	1993
Elderly	Inpatient care	(M11)	0.247	0.240	0.237
N11	0.277	0.270	0.264
T11	0.057	0.061	0.067
D11	0.120	0.120	0.119
Outpatient care	(M12)	0.134	0.131	0.130
N12	0.069	0.066	0.064
T12	0.155	0.151	0.147
D12	0.131	0.122	0.122
Non-elderly	Inpatient care	(M21)	0.198	0.200	0.206
N21	0.219	0.222	0.227
T21	0.055	0.056	0.059
D21	0.096	0.099	0.099
Outpatient care	(M22)	0.104	0.103	0.106
N22	0.077	0.075	0.080
T22	0.091	0.089	0.086
D22	0.094	0.086	0.085

Notes : Figures are the coefficients of variation for N, T and D. 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.

Sources : As in Appendix Table 2-1.
Appendix Table 2-4: Variables and their definitions

Variable	Definition
Dependent variables
1. M11	Medical expenditure of inpatient care for the elderly
2. M12	Medical expenditure of outpatient care for the elderly
3. M21	Medical expenditure of inpatient care for the non-elderly
4. M22	Medical expenditure of outpatient care for the non-elderly
Independent variables (common factors)
5. NPI	Prefectural income
6. DOC	Number of total doctors per 1,000
7. CT	Number of CT-scanners
8. ICU	Number of Intensive Care Units and Coronary Care Units
9. POP60	Proportion of those aged 60-69 to total of the non-elderly (%)
10. DUM	Dummy variable for Tokyo
(specific factors)
11. BED	Number of total beds in medical facilities (i.e., hospitals and clinics)
12. BED2	Number of beds except those in Geriatric Hospitals
13. PUBBED	Public share in total beds (%)
14. MED	Number of medical facilities
15. PUBMED	Public share in total medical facilities (%)
16. HSF	Number of admission to Health Services Facilities
17. SNH	Number of admission to Special Nursing Homes
18. SUB	Number of admission to HSF and SNH

Notes: All variables are measured in terms of per capita except ratios.

Source: 1.2.3.4. "Annual Report on the National Health Insurance Scheme", (MHW)

5. "Annual Report on Prefectural Accounts", (Economic Planning Agency)

6. "Survey of Doctors, Dentists and Pharmacists", (MHW)

7.8.11.12.13.14.15. "Survey of Medical Care Institutions", (MHW)

9. "Population Estimates", (Management and Coordination Agency)

16. "Annual Report on Health Services Facilities for the elderly", (MHW)

17. "Survey of Social Welfare Institutions", (MHW)

18. "Annual Report on Health Services Facilities for the elderly", (MHW) and "Survey of Social Welfare Institutions", (MHW)
Appendix Table 2 -5: Estimated coefficients in inpatient expenditure equations, 1993

Elderly (M11)	Non-elderly (M21)
NPI	0.6561(2.1535)	0.4377(1.5751)	0.7741(2.5408)	0.9500(3.2808)	0.4639(2.7288)	0.3996(2.4299)	0.2894(1.4756)	0.3006(1.6522)
DOC	0.2999(1.6153)	0.2277(1.2399)	0.2817(1.5993)	——(——)	0.1026(1.0071)	0.0858(0.8408)	0.1911(1.6337)	0.1848(1.6901)
CT	-0.2337(-1.6774)	-0.1765(-1.2857)	-0.1926(-1.4065)	-0.1705(-1.2279)	-0.1043(-1.3245)	——(——)	-0.0423(-0.4600)	-0.0404(-0.4487)
PUBBED	-0.1679(-2.2005)	-0.1592(-2.0521)	-0.1563(-2.1877)	-0.1092(-1.6460)	0.0416(0.9298)	0.0446(0.9885)	-0.0083(-0.1668)	——(——)
POP60	——(——)	——(——)	——(——)	——(——)	0.3652(3.1081)	0.3711(3.1316)	0.3205(2.1954)	0.3106(2.3566)
BED	0.6763(3.0621)	0.6458(2.8787)	0.6578(3.1954)	0.8405(4.8152)	0.6977(5.9680)	0.6084(6.3103)	——(——)	——(——)
BED2	——(——)	——(——)	——(——)	——(——)	——(——)	——(——)	0.5878(3.9507)	0.5967(4.3465)
SUB	0.1728(1.4864)	0.1412(1.2085)	——(——)	——(——)	——(——)	——(——)	——(——)	——(——)
SNH	——(——)	——(——)	0.2734(2.1921)	0.2484(1.9696)	——(——)	——(——)	——(——)	——(——)
DUM	-0.2879(-1.6048)	——(——)	-0.3805(-2.0836)	-0.3106(-1.7186)	-0.3469(-3.4617)	-0.3119(-3.1966)	-0.3299(-2.8127)	-0.3286(-2.8426)
C	4.7407(1.5399)	6.8221(2.3967)	3.7161(1.2167)	1.9535(0.6728)	4.1928(2.4128)	5.2271(3.3360)	6.3212(3.2101)	6.2148(3.3772)
F-value	14.03	15.33	15.26	16.73	45.16	51.43	31.56	37.73
Adj. R²	0.6647	0.6515	0.6846	0.6723	0.8705	0.8680	0.8230	0.8273

Source: As in Appendix Table 2-4.

Notes: Figures in parentheses are t-values. All equations are estimated in log-linear form. As for the definitions of the variables, see Appendix Table 2-4.