Discussion Paper No.

561

HORIZONTAL INEQUITYIN HEALTH CARE UTILIZATION IN JAPANChika Honda
and
Yasushi Ohkusa

January 2002

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan22 January , 2002

Horizontal Inequity in Health Care Utilization inJapan
Chika Honda
Yao Public Health Center, Osaka PrefectureandYasushi Ohkusa
Institute of Social and Economic Research, Osaka University
Abstract
International comparisons of horizontal inequity in health have recently become one of
the most pertinent issues in health economics. Japan has not been included in theseinternational comparisons. This omission is rectified in this paper, which focuses on Japan.
Moreover, we consider its dynamics over six years from 1992 to 1998. The dynamics has
never considered in this fields. In a rigorous international comparison, we cannot find any
horizontal inequity in health in Japan and almost similar to Belgium.
JEL Classifications:
Keywords: Horizontal Inequity, Japan, International Comparisons, Concentration Index,
Needs
Correspondence: Yasushi Ohkusa, Institute of Social and Economic Research, Osaka
University, 6-1 Mihogaoka Ibaraki Osaka, Japan
Tel:81-6-6879-8566

Fax: 81-6-6878-2766

E-mail: ohkusa@iser.osaka-u.ac.jp

1

Introduction

Inequity in health has recently become one of the most pertinent and relevant issues inhealth economics and health policy. Much research on methodology and on international
comparisons has been carried out by Wagstaﬀ, Doorslaer and Paci (1989), Wagstaﬀ, Paci
and Doorslaer (1991), Doorslaer and Wagstaﬀ (1992), Wagstaﬀ and Doorslaer (1993),
Wagstaﬀ and Doorslaer (1994), Doorslaer, Wagstaﬀ et al. (1997, 2000), and Kakwani,
Wagstaﬀ and Doorslaer (1997). In particular, research on horizontal inequity has been
done by Doorslaer, Wagstaﬀ et al. (2000), and most recently, by Wagstaﬀ and Doorslaer(2000).
Eleven OECD countries were studied on the basis of reasonably comparable definitions.
Unfortunately, Japan has been excluded in all previous studies. This paper adds Japan tothe current list of countries studied. It adopts the same or comparable definitions of socialand economic conditions, health, and estimation methods as other studies. In addition, thispaper provides some evidence on the dynamics of inequity, which has not been investigatedfully in other studies.
Before considering the measurement of health, the institutional background in Japan issummarized. In 1961, Japan completed compulsory public health insurance and coverage
for all residents. In 1998 (which our paper covers), a new law was introduced, requiring
co-insurance rates of 20% for the employed, and 30% for others, such as the self-employed
and dependents. For people over 70 years of age, the co-payment rate is 10%, and is limited
to approximately 4000 yen (about US$36 in 2001 prices) per month. However, big firms
sometimes subsidize their employees by reducing their co-payments to less than the legal
requirement. If very poor people cannot pay the premiums, medical services are provided
as welfare. Thus, everybody can enjoy accessing to medical services in Japan, even though
there may be exceptional cases.
The public health insurance system provides reimbursement on a fee-for-service (FFS)1basis. Although the government controls the price of treatment and drugs almost every
year, it cannot directly control the choice of treatment and/or drugs, unlike the Utilization
Review at HMO. The insurer cannot control the budget ex ante like NHS and sickness
benefits.
There is no regulation of the medical services chosen by patients, as in the gatekeeper in
NHS or diﬀerence coverage in HMO. The co-insurance rate is the same for services provided
in hospitals or clinics (either public or private), but congestion may implicitly impose anopportunity cost. Of course, the number of beds is strictly regulated, but provision of
outpatient services are virtually unregulated.
Although private insurance exists it plays only a minor role, because public insurance
has such a comprehensive coverage of medical services. Shigeno (2000) shows that private
insurance appears to complement public insurance only through its income eﬀect. Hence,
private insurance in Japan is very diﬀerent from that in the USA and in European countries, and this is why it is usually excluded in health economics research of international
comparisons.

2

Data

The Comprehensive Survey of Living Standards in Japan (CSLSJ) has been conducted
every three years since 1986. The purpose of this survey is to investigate health, medical
services, pensions, welfare, incomes, and other factors aﬀecting living standards. Questionnaires consist of four parts: family, individual, income, and savings. The number ofsubjects, sampled randomly, in the family and individual parts are approximately 780,000
individuals (280,000 families), and the number for the income and savings surveys is approximately 120,000 individuals (40,000 families). The data used in this study are for 1992,
1995, and 1998.
Symptoms and diseases are surveyed in great detail, though there are minor diﬀerences2

among years. For example, in 1998, symptoms are listed as fever, fatigue, sleeplessness,
irritation, failing memory, headache, dizziness, bleary eyes, diﬃculty in seeing, tinnitus, difficulty in hearing, palpitation, diﬃculty in breathing, chest pain, coughs and sputum expectoration, the sniﬄes, noisy breathing, retching, diarrhoea, constipation, appetite loss, stomach ache, haemorrhoids, toothache, dental problems, diﬃculty in chewing, rash, itching,stiﬀ shoulder, back pain, arthralgia, impairments of hands and feet, numbness, frigid hands
and feet, foot oedema, dysuria, frequent urination, incontinence, paramecia/merorrhalgia,
broken bones and sprains, wounds, and other symptoms. Respondents indicate the symptoms they have, but the survey does not collect information about whether respondents
have considered seeing a doctor, or information concerning the seriousness of the symptoms.
The wording of CSLSJ for outpatient utilization is ”Do you currently go to visit tophisician (general practitioner)?” Respondents indicate the diseases which apply to themand which they concern the most, and their duration. However, it does not provide any
information about the number or frequency of visit to the doctor, or medical expenditure.
Note that the CSLSJ asks about the current situation with regard to symptoms and diseases, whereas surveys in other countries define duration explicitly, and do not necessarily
ask about the current situation. Hence, we know only whether they suﬀered from some
symptoms or visit a doctor due to some diseases, and we do not know how long and how
sever it is except for the longest consulting in the disease. This undefined reference period
may be the most important diﬀerence from the survey in the other countries and we haveto remind it to understand the below analyses. Moreover, symptoms and diseases in the
CSLSJ include those other than chronic and/or severe illnesses, whereas other countries
limit questions to chronic or severe illnesses that disrupt daily activity.
Subjective health evaluation responses range from excellent to good, fair, poor, and very
poor. However, there is a problem with this question. Unlike in many countries, such as the
USA and the UK, respondents in Japan are not asked to evaluate their health in relation
to people of a similar age. This diﬀerence may aﬀect the results and may introduce some3

inconsistency in the international comparisons. In fact, Honda and Ohkusa (2001) foundthat, on the basis of this question, subjective health evaluation in Japan is very diﬀerent
from that in the USA and the UK. Such discrepancies are unavoidable in international
comparisons. Therefore, comparisons should be interpreted carefully.
The sample used in the following analysis are limited for the age of 16 or older, but theoriginal surveys cover persons over six years of age, and those who are not hospitalized orin residential care. This should be mentioned for international comparisons.
With income, as in the Dutch and U.S. survey, the survey provides the exact amount of
their household’s income as well as details of income sources.

3

The Measurement of Horizontal Inequity

In measuring horizontal inequity, three aspects need to be clarified, i.e. definition of demand
for medical care; definition of needs; and estimation methods. Definitions and estimation
methods used in this paper are as follows. Social and economic groups (SEG) are definedby household disposable income per equivalent adult as in previous studies.

3.1

Definition of Demand for Medical Care

Several definitions of demand for medical care are employed in existing studies: for instance, medical expenditure (Doorslaer, Wagstaﬀ et al., 2000), visits to a doctor (Doorslaer,
Wagstaﬀ et al., 2000, or Doorslaer, Koolman and Puﬀer,2001 ), and hospitalizations(Doorslaer, Wagstaﬀ et al., 2000). While medical expenditure and hospitalizations are
not available from CSLSJ, visits to a doctor can be used as a measure of demand for
medical care.

3.2

Definition of Needs

Concerning the definition of needs, existing studies use incidences of chronic illness (Doorslaer
and Wagstaﬀ(1992)), and self-assessment of health (Doorslaer, Wagstaﬀ et al.(1997)). Con4

versely, Doorslaer, Wagstaﬀ et al.(2000) define the needs as the estimated demand for medical care which is explained by self-assessment of health (SAH) and/or chronic illness inaddition to demographic characteristics such as age and gender. Although CSLSJ does
not isolate chronic illness, it does survey symptoms as already explained. Moreover, italso provides information on self-assessment of health. Hence we define the needs as theestimated demand for outpatient services of the ith rank in income person Di whether
they go to visit the physicians. The employed explanatory variable are age Ai , gender Gi ,self-assessment of health Hi and/or symptoms Si . Thus the estimated equations in the fullversion isDi∗= α0 +

Di =

(

98
X

j j
αA
Ai +

j

1 if Di∗ > 0
0 otherwise

98
X

αAGj Aji Gi + αG Gi +j

4
X

αlH Hil +l42
X

αSm Sim + εim(1)where the superscript indicates the dummy variables. Age dummy represents from 15 to
98 years old by each age. Since self-assessment of health is classified by 5 categories, there
are four dummies for it. Dummies for symptoms are defined separately for each symptom.
Note that diseases are not used as explanatory variables because these are reported in the
CSLSJ for those who visit a doctor and thus their Di should be always one. In that case,
it is perfect prediction and thus these explanatory variables cannot be identified.
Alternatively, we modified the above equation as using the number of symptoms suffered from instead of symptom dummies, and/or broader categorized in age as Doorslaer,
Wagstaﬀ et al.(2000),i.e. 15-24, 25-44,45-64,65-74 and 75-1) .
The estimation procedure is heteroscedasticity consistent probit. The predicted probability Φ(D̂i ) is the needs n in the following procedures.

5

3.3

Estimation Method

First, the Concentration Index for medical care or needs should be defined following Kakwani, Wagstaﬀ and Doorslaer (1997) asµ+2 +i = α0 + α1 Rt
µ σR

(2)

where the subscript i indicates the individual of the ith SEG, which means ith smallest
amount of income adjusted for household structure, and µ+
i is the demand for medical care.
+
Adjustment for demographic characteristics for µ+i and µ is made by using the average

health condition that applies to the people of the same age (9 categories spanning 10 years),gender, and other demographic characteristics as the ith person.
µ+ is the average of µ+
i over persons, Ri is the cumulative proportion up to the ith personin order of income adjusted for household structure, and σR is its variance. The estimatedα1 is the Concentration Index of the demand for medical care. Similarly, the Concentration
Index of needs is defined by replacing µ by n, which is a measure of needs.
Following Wagstaﬀ and Doorslaer(2000), its variance is adjusted for

V ar(Concentration Index) =
at
qi

10
1 X
{ fi a2i − (1 + Concentration Index)2 }10 t=1(3)µ+i=(2Ri−1 − Concentration Index) + 2 − qi−1 − qi (4)
µ+
i
1 X
=
µ+ fs
µ+ s=1 s

The horizontal inequity measure is obtained by using the following estimation method.

2σR2

"

µ+
n+
i
− i+
+
µ
n

#

= β0 + β1 Ri

(5)

6

1
V ar(Horizontal Inequity) =
N

(

N
1 X
(aµ − ani )2 − Horizontal Inequity2
N i=1 i

)

(6)

µ+
µ
i
(2Ri − 1 − Concentration Index for µ) + 2 − qt−1
− qiµ
+
µ
t
1 X
=
µ+ fs
µ+ s=1 s

aµi =
qi

n+
i
n
(2Ri − 1 − Concentration Index for n) + 2 − qt−1
− qin
n+
t
1 X
=
n+
fs
+
n s=1 sani =qiThe estimated coeﬃcient of β1 is interpreted as horizontal inequity (Wagstaﬀ, Doorslaer
et al.(2000), Doorslaer, Wagstaﬀ et al.(2000)).

4

Empirical Results

Summary statistics are shown in Table 1. Almost 30% of individuals suﬀered some symptoms or were outpatients. The per capita income adjusted for the number of adults in thehousehold is about 3.2-3.7 million-yen (about US$2,700-3,000) per year, and this increases
a little over the six years. Even the smallest sample year has more than 70,000 samples.
Table 2’s show the estimation results for ”Needs” in several specifications. Each table
has ten specifications, i.e. age (classes or dummies) or health condition (using SAH, using
information of symptoms (list of dummies or the number of symptoms) , or using both as
explanatory variables, for three years. Namely, the upper panels show the results in thecase of age classes and the lower panels show the case of age dummies. The first to thirdcolumn in both panels indicate the results of SAH only, dummies for symptoms only, andboth of them, respectively. The results in the case of using the number of symptoms insteadof symptom dummies are summarized in the fourth and fifth columns. Note that thesenumbers are the estimated coeﬃcients and not the marginal eﬀects, and thus it cannot beinterpreted directly. Obviously, almost all explanatory variables are significant and Wald7

statistics show they fit very well. Moreover, pseudo R2 ’s are very high despite of large
sample.
Table 3 indicates the distribution of outpatient utilization in the actual and the predicted”Needs” in the many specifications in Table 2’s. These numbers imply that there are notsubstantial diﬀerences in the pattern of the outpatient utilization over six years. Namely,
the utilization rate is the highest in the top income group and the lowest in the middle
income group. On the other hand, the distribution of the estimated ”Needs” with age
classes does not fit well and shows the positive relationship with income monotonically.
Conversely, if we use the age dummies instead of age classes, it fits very well. In other words,
diﬀerences in age is much more informative than the adopted classes for their ”Needs.”
The horizontal inequity measures are displayed in Figures 1, 2, and 3 for the three years
in the case of Needs defined by the first three columns in the upper panel in the three
Table 2’s. The solid line is horizontal inequity defined only by SAH, the dotted line ishorizontal inequity defined only by dummies of symptoms, and the dashed line indicatesthe horizontal inequity defined by the both in eq. (1). Three figures exhibit the same
pattern. Namely, the solid lines deviate at most by 1% at 0.6-0.7 income classes and tend
to be pro-poor, which means over the 0% line. The dotted lines do not deviate by 0.2%
and show the particular pattern. These two types of lines are calculated by using SAH.
Conversely, the broken lines, which do not adjusted for SAH, indicate heavily pro-richinequity. It reaches about 6% at maximum in the 0.3-0.4 income classes. This suggests
Needs definition without SAH does not seems to be reliable.
While figures provide much information about horizontal inequity, the empirical results
should confirm and test it. Table 4 summarizes the empirical results of β1 in eq. (3), andTable 5 summarizes horizontal inequity adjusted for regions. As figures indicate, there are
significant pro-rich in the case of using age classes and without SAH. These are significant
pro-rich inequity by 0.08 to 0.12. However, these inequities disappear by using SAH or age
dummies. All other cases without such exceptional cases imply that the null hypothesis
8

of no inequity cannot be rejected. The most similar estimators with Doorslaer, Wagstaﬀ
et al.(2000) and Doorslaer, Koolman and Puﬀer(2001) are -0.0006, .0019 and -0.0009 in
1992, 1995 and 1998, respectively. Hereafter, these three figures would be thought of as
the estimators of horizontal inequity in Japan.
Even though we controlled regions in eq.(3), there are no substantial change in thenumbers. Namely, there are significant pro-rich in the case of using age classes and without
SAH, and the similar figures for the previous studies are -0.0007, .0017 and -0.0005 for three
years.
We can summarize our findings as follows: First of all, the null hypothesis of no inequity
cannot be rejected. Secondly, there is not significant change in inequity over six years
and no clear trend. Thirdly, the estimated horizontal inequity is heavily aﬀected by thedefinition of ”Needs.” Especially, the omitting SAH contaminates the results heavily. It
casts the diﬃculty of international comparison. Finally, regional adjustment in eq.(3) dose
not aﬀect the estimated inequity.

5

Concluding Remarks

Our findings are very straightforward. The null hypothesis of no inequity cannot be rejected
and Japan would have enjoyed one of the greatest equity in health among OECD countries.
In fact, these point estimator are larger than Spain(-0.0137), Ireland(-0.0098) and Italy(0.0098) , less than Austria(0.0389), Portugal(0.0524), UK(0.0074), Canada(0.0072) andUSA(0.0532)2) . It is almost the same as Belgium(-0.0001). Comparison with Belgium, thepro-rich inequity in Japan is larger than Belgium in 1995, but less in 1992 and 1998. The
null hypothesis that horizontal inequity in Japan is diﬀerent form Belgium is not rejectedin the most detailed specifications.
However, we have to remind that the demand for outpatient services is defined whether
they visit to physicians currently, but in the previous OECD studies, it is defined by

9the number of visits to physicians (general practitioners) in a certain period (typically,
one year). In other words, CSLSJ does not provide any information about the numberof visits in a certain period in the past. It seems to be obvious to reduce the eﬀects ofincome inequality for health care utilization and thus it makes inequity measures verysmall. Therefore, complete comparison with other OECD countries is remained for future
research.
Since there is not other comparable Japanese data to other OECD countries, we will haveto conduct a survey originally to obtain completely comparable data, for more rigorouscomparison.

10

Acknowledgments

The data used in this paper have been obtained from one of the research meetings of theaging society, directed by Prof. Tachibanaki of Kyoto University, as one of the New Millennium Projects of the Economics and Social Research Institution, Cabinet Oﬃce of Japan.
I would like to acknowledge fruitful discussions with anonymous referee, Prof.Doorslaer,
Prof. Tachibanaki of Kyoto University and with other members of the research group. I
also thank to assistance provided by Tomoko Nagase. Needless to say, all remaining errors
are ours.

Footnotes

1) Doorslaer, Wagstaﬀ et al.(2000) and Doorslaer, Koolman and Puﬀer(2001) are correspondence to the case of broader age categories and symptoms dummies precisely.
2) These numbers in other OECD countries are cited from Doorslaer, Koolman and
Puﬀer(2001) in the case of all physician visit as utilizatin and with region information.

11

References[1] Doorslaer, E.V. and A. Wagstaﬀ, 1992, Equity in the delivery of health care:
Some international comparisons, Journal of Health Economics 11, 389-411.
[2] Doorslaer, E.V., A. Wagstaﬀ et al., 1997, Income-related inequalities in health:Some international comparisons,Journal of Health Economics 16, 93-112.
[3] Doorslaer, E.V. and A. Wagstaﬀ et al., 2000, Equity in the delivery of healthcare in Europe and the US,Journal of Health Economics 19, 553-583.
[4] Doorslaer, E.V., X.Koolman and F.Puﬀer,2001, Equity in the use of physician
visit in OECD countries: has equal treatment for equal need been achieved ?,ECuity II project, WOrking Paper No.3.
[5] Gunji,A. 2001, Regional Variation in Medical Expenditure (in Japanese),Toyokeizai-shinpousha.
[6] Honda, C. and Y. Ohkusa, 2001, International comparison of subjective healthevaluation - USA, UK and Japan, ISER Discussion Paper No.546.
[7] Ii, M. and Y. Ohkusa, 2001, Estimating price sensitivity for the medical services
in the case of minor ailments using information on illness and symptom, Japan
Economic Review, forthcoming.
[8] Kakwani, N., A. Wagstaﬀ and E.V. Doorslaer, 1997, Socioeconomic inequalities in health: Measurement, computation and statistical inference,Journal of
Econometrics 77, 87-103.
[9] Shigeno, Y., 2000, The demand for private health insurance and public healthinsurance (in Japanese), The Quarterly of Social Security Research 36, 378-390.
[10] Wagstaﬀ, A., E.V. Doorslaer and P. Paci, 1989, Equity in the finance and deliveryof health care: Some tentative cross-country comparisons, Oxford Review of
Economic Policy 5, 89-112.
12

[11] Wagstaﬀ, A., P. Paci and E.V. Doorslaer, 1991, On the measurement of inequalities in health, Social Science and Medicine 33, 545-557.
[12] Wagstaﬀ, A. and E.V. Doorslaer, 1993, Equity in the delivery of healthcare: Methods and findings, in: E.V. Doorslaer, A. Wagstaﬀ and F. Rutten,
eds.,Equity in the Finance and Delivery of Health Care: An International PerspectiveOxford University Press.
[13] Wagstaﬀ, A. and E.V. Doorslaer, 1994, Measuring inequalities in health in thepresence of multiple-category morbidity indicators, Health Economics 3, 281-291.
[14] Wagstaﬀ, A. and E.V. Doorslaer(2000), Measuring and Testing for Inequalities
in the Delivery of Health Care, Journal of Human Resources , pp.716-733.

13

Table 1: Summary Statistics
Year

Utilization
Adjusted Income
SAH
Excellent
Good
Fair
Poor
Very Poor
femaleNo. of symptoms
35-44
45-64
65-74
75No. of Samples

1998
1995
1992
Average Standard Average Standard Average Standard
Deviation
Deviation
Deviation
0.3156 0.4648 0.3271 0.4692 0.2996 0.4581
360.0
263.6
358.9
266.9
326.9
256.8
0.25270.17460.45000.11150.0111
0.52311.31600.15150.35470.12770.073771999

0.18880.14410.24750.09910.01100.49952.76170.35860.47840.33370.261414

0.31270.17600.4091
0.09300.0092
0.52220.99090.06270.38850.13400.118585526

0.21490.14500.24170.08440.00910.4995
2.11250.24250.48740.34070.32320.33740.16080.3956
0.09580.01040.52550.89880.06480.36850.13030.133099518

0.22360.13490.23910.08660.01030.49942.07480.2462
0.4824
0.3366
0.3396

Table 2-1: The Estimation Result for Need in 1998

Age Classes
No. of Symptoms
SAH
Good
Fair
Poor
Very Poor
Age Class
35-44
45-64
65-74
75Female
Female · Age Class
35-44
45-64
65-74
75ConstantSymptom Dummies
Wald statistics
p-valueLog-likelihood
Pseudo R2
Age Dummies
No. of Symptoms
SAH
Good
Fair
Poor
Very Poor
FemaleConstant
Symptom Dummies
Wald statistics
p-valueLog-likelihood
Pseudo R2.101∗∗∗.458∗∗∗.593∗∗∗
1.40∗∗∗
1.78∗∗∗.291∗∗∗.367∗∗∗.811∗∗∗
1.10∗∗∗.396∗∗∗.495∗∗∗
1.02∗∗∗
1.23∗∗∗.150∗∗∗.250∗∗∗.718∗∗∗
1.32∗∗∗
1.50∗∗∗.177∗∗∗.283∗∗∗.714∗∗∗
1.25∗∗∗
1.40∗∗∗.159∗∗∗.263∗∗∗.695∗∗∗
1.23∗∗∗
1.37∗∗∗.161∗∗∗.239∗∗∗.683∗∗∗
1.25∗∗∗
1.40∗∗∗.141∗∗∗.268∗∗∗.712∗∗∗
1.28∗∗∗
1.46∗∗∗.143∗∗∗-.182∗∗∗-.087∗∗∗-.077∗∗∗-.103∗∗∗-1.72∗∗∗
No
524202≤0.001-358020.1993-.202∗∗∗-.101∗∗∗-.055∗∗∗-.044∗∗-1.56∗∗∗
Yes
207300≤0.001-337530.2451-.206∗∗∗-.103∗∗∗-.075∗∗∗-.078∗∗∗-1.80∗∗∗
Yes
4135.15≤0.001-329970.2620-.183∗∗∗-.087∗∗∗-.058∗∗∗-.067∗∗∗-1.70∗∗∗
No
101900≤0.001-348010.2217-.178∗∗∗-.087∗∗∗-.036∗∗∗-.034∗-1.35∗∗∗
No
181816≤0.001-360530.1937.148∗∗∗.099∗∗∗

1.38∗∗∗-1.33∗∗∗
No
7849.9≤0.001-355600.2047.395∗∗∗.492∗∗∗
1.02∗∗∗
1.22∗∗∗
1.27∗∗∗-1.64∗∗∗
No
23021≤0.001-343240.2324.456∗∗∗.589∗∗∗
1.40∗∗∗
1.77∗∗∗-5.97∗∗∗-1.67∗∗∗
No
18500≤0.001-352680.2113

1.73∗∗∗-1.55∗∗∗
Yes
70531≤0.001-333100.2551.291∗∗∗.365∗∗∗.814∗∗∗
1.10∗∗∗-5.52∗∗∗-1.75∗∗∗
Yes
90521≤0.001-325630.2718

Note: These number are estimated coeﬃcients and not marginal eﬀects.

15

Table 2-2: The Estimation Result for Need∗∗∗in 1995 ∗∗∗No. of Symptoms
SAH
Good
Fair
Poor
Very Poor
Age Class
35-44
45-64
65-74
75Female
Female · Age Class
35-44
45-64
65-74
75ConstantSymptom Dummies
Wald statistics
p-valueLog-likelihood
Pseudo R2
Age Dummies
No. of Symptoms
SAH
Good
Fair
Poor
Very Poor
FemaleConstant
Symptom Dummies
Wald statistics
p-valueLog-likelihood
Pseudo R2.127.459∗∗∗.566∗∗∗
1.39∗∗∗
1.82∗∗∗.299∗∗∗.351∗∗∗.812∗∗∗
1.18∗∗∗.396∗∗∗.464∗∗∗.999∗∗∗
1.32∗∗∗.186.130∗∗∗.562∗∗∗
1.12∗∗∗
1.38∗∗∗.205∗∗∗.169∗∗∗.618∗∗∗
1.12∗∗∗
1.36∗∗∗.183∗∗∗.134∗∗∗.574∗∗∗
1.07∗∗∗
1.29∗∗∗.182∗∗∗.127∗∗∗.547∗∗∗
1.09∗∗∗
1.33∗∗∗.178∗∗∗.176∗∗∗.603∗∗∗
1.16∗∗∗
1.43∗∗∗.179∗∗∗-.044-.124∗∗∗-.147∗∗∗-.136∗∗∗-1.59∗∗∗
No
15632≤0.001-428290.1805-.039-.150∗∗∗-.135∗∗∗-.097∗∗∗-1.50∗∗∗
Yes
19816≤0.001-402460.2300-.038-.154∗∗∗-.151∗∗∗-.117∗∗∗-1.69∗∗∗
Yes
20328≤0.001-393660.2468-.048-.132∗∗∗-.139∗∗∗-.114∗∗∗-1.60∗∗∗
No
15542≤0.001-417080.2020-.055-.124∗∗∗-.118∗∗∗-.088∗∗-1.32∗∗∗
No
12743≤0.001-431380.1746.183∗∗∗.124∗∗∗.856-1.04∗∗∗
No13825≤0.001-425720.1854.396∗∗∗.464∗∗∗
1.00∗∗∗
1.31∗∗∗.998-1.28∗∗∗
No
16493≤0.001-411160.2125.458∗∗∗.563∗∗∗
1.39∗∗∗
1.81∗∗∗.982∗∗-1.28∗∗∗
No
16565≤0.001-422200.1922.975-1.24∗∗∗
Yes
20549≤0.001-39746
0.239516.301∗∗∗.352∗∗∗.817∗∗∗
1.18∗∗∗
1.08-1.39∗∗∗
Yes
21059≤0.001-388710.2563

Table 2-3: The Estimation Result for Need with Age Classes in 1992
No. of Symptoms
SAH
Good
Fair
Poor
Very Poor
Age Class
35-44
45-64
65-74
75Female
Female · Age Class
35-44
45-64
65-74
75ConstantSymptom Dummies
Wald statistics
p-valueLog-likelihood
Pseudo R2
Age Dummies
No. of Symptoms
SAH
Good
Fair
Poor
Very Poor
FemaleConstant
Symptom Dummies
Wald statistics
p-valueLog-likelihood
Pseudo R2.135∗∗∗.496∗∗∗.635∗∗∗
1.46∗∗∗
1.77∗∗∗.331∗∗∗.403∗∗∗.810∗∗∗
1.02∗∗∗.434∗∗∗.532∗∗∗
1.04∗∗∗
1.20∗∗∗.203∗∗∗-.036.357∗∗∗.880∗∗∗
1.20∗∗∗.123∗∗∗.033.432∗∗∗.943∗∗∗
1.24∗∗∗.127∗∗∗-.024.364∗∗∗.866∗∗∗
1.15∗∗∗.119∗∗∗-.042∗.342∗∗∗.856∗∗∗
1.16∗∗∗.104∗∗∗.033.430∗∗∗.963∗∗∗
1.30∗∗∗.109∗∗∗.106∗∗∗-.026-.071∗∗-.085∗∗∗-1.56∗∗∗
No
17904≤0.001-485500.1818.077∗∗-.058∗∗-.091∗∗∗-.090∗∗∗-1.46∗∗∗
Yes
23357≤0.001-45452
0.2341.084∗∗-.060∗∗-.096∗∗∗-.099∗∗∗-1.65∗∗∗
Yes
23975≤0.001-444210.2514.095∗∗∗-.047∗-.074∗∗-.076∗∗-1.56∗∗∗
No
17746≤0.001-472210.2042.087∗∗∗-.043∗-.063∗∗-.057∗-1.28∗∗∗
No
13875≤0.001-490810.1729.201∗∗∗.133∗∗∗-.384∗∗∗-1.01∗∗∗
No
15192≤0.001-484340.1838.438∗∗∗.536∗∗∗
1.05∗∗∗
1.19∗∗∗-.419-1.24∗∗∗
No
18781≤0.001-465880.2149.499∗∗∗.637∗∗∗
1.46∗∗∗
1.76∗∗∗.193-1.23∗∗∗
No
18855≤0.001-478670.1933.195-1.20∗∗∗
Yes
24087≤0.001-448890.243517.334∗∗∗.407∗∗∗.820∗∗∗
1.02∗∗∗-.601-1.34∗∗∗
Yes
24681≤0.001-438580.2609

Table 3: Utilization and Estimated Needs by Income Quantile
AgeClasses

No.
Year
of
Symptoms
1998
1995
1992
No
1998

ClassesNo

1995

ClassesNo

1992

Classes

Yes

1998

Classes

Yes

1995

ClassesYes

1992

Dummies No

1998

Dummies No

1995

Dummies No

1992

Dummies Yes

1998

Dummies Yes

1995

Dummies Yes

1992

Util./Needs Bottom 2020%
40%

4060%

6080%

Top
20%

Utilization
Utilization
Utilization
SAH
Symptoms
Both
SAH
Symptoms
Both
SAH
Symptoms
Both
Symptoms
Both
Symptoms
Both
Symptoms
Both
SAH
Symptoms
Both
SAH
Symptoms
Both
SAH
Symptoms
Both
Symptoms
Both
Symptoms
Both
Symptoms
Both

.184
.195
.175
.294
.287
.296
.312
.314
.319
.328
.346
.345
.276
.294
.295
.310
.310
.321
.174
.173
.173
.193
.193
.193
.184
.182
.182
.173
.173
.184
.185
.185
.184

.235
.230
.218
.334
.320
.335
.344
.332
.342
.328
.309
.316
.300
.330
.323
.346
.316
.332
.218
.218
.219
.231
.231
.231
.234
.234
.234
.219
.219
.231
.231
.234
.234

.569
.539
.505
.513
.444
.507
.515
.440
.507
.547
.468
.541
.423
.511
.424
.510
.452
.545
.505
.505
.505
.538
.537
.537
.569
.569
.569
.505
.505
.538
.538
.569
.569

18

.278
.269
.248
.114
.161
.110
.137
.186
.135
.127
.183
.127
.191
.113
.210
.135
.212
.126
.247
.246
.246
.268
.267
.267
.280
.280
.280
.248
.248
.262
.262
.304
.304

.294
.266
.266
.157
.199
.161
.192
.224
.192
.229
.250
.227
.210
.157
.236
.191
.258
.228
.268
.267
.267
.269
.268
.268
.292
.291
.291
.265
.265
.281
.281
.265
.265

Table 4: The Estimated Concentration Index, Horizontal
Inequity and Their Confidence Interval
AgeClasses

No.
Year
of
Symptoms
1998
1995
1992
No
1998

ClassesNo

1995

ClassesNo

1992

Classes

Yes

1998

Classes

Yes

1995

ClassesYes

1992

Dummies No

1998

Dummies No

1995

Dummies No

1992

Dummies Yes

1998

Dummies Yes

1995

Dummies Yes

1992

Needs Def./CI Estimator

CI
CI
CI
SAH
Symptoms
Both
SAH
Symptoms
Both
SAH
Symptoms
Both
Symptoms
Both
Symptoms
Both
Symptoms
Both
SAH
Symptoms
Both
SAH
Symptoms
Both
SAH
Symptoms
Both
Symptoms
Both
Symptoms
Both
Symptoms
Both

19

.0007
.0075
.0064
-.0048144.0783489-.0008935-.0012385.0825699.001905-.0016404.0849225-.0005696.1118019.0023189.1120658.0005353.1274143.0024351.0008551.0002563.0002635.0001678-.0004148-.0004874.0006951-.0002273-.0003296.0008852.001191-.0007807-.0000584-.0001778.0002361

95%CI
lower
boubd
-.0091-.0082-.0062-.0114498.0724745-.0069083-.0077185.0769057-.0039076-.0082949.0792991-.0063858.1055935-.0041148.1060572-.005711.1214338-.0038694-.0044873-.0048394-.0047627-.0049554-.0052664-.0052775-.004251-.0049104-.0049545-.004466-.0040259-.0058842-.0050583-.0051346-.0046002

95%CI upper bound
.0107
.0233
.0191
.001821.0842234.0051213.0052415.088234.0077177.005014.0905459.0052465.1180104.0087526.1180744.0067816.1333948.0087396.0061976.0053521.0052897.005291.0044368.0043028.0056413.0044558.0042954.0062364.0064079.0043228.0049416.0047791.0050724

Table 5: The Estimated orizontal Inequity and Their
Confidence Interval, cotrolled with Regional Dummies
Age

ClassesClassesClassesClassesClassesClassesDummiesDummiesDummiesDummiesDummiesDummiesNo.
Year Needs Def./CIofSymptoms
No
1998 SAH
Symptoms
BothNo
1995 SAH
Symptoms
BothNo
1992 SAH
Symptoms
BothYes
1998 Symptoms
BothYes
1995 Symptoms
BothYes
1992 Symptoms
BothNo
1998 SAH
Symptoms
BothNo
1995 SAH
Symptoms
BothNo
1992 SAH
Symptoms
BothYes
1998 Symptoms
BothYes
1995 Symptoms
BothYes
1992 Symptoms
Both

20

Estimator-.0044354.0787252-.00056-.0015081.0824537.0017665-.0019883.0847203-.0007749.1123355.002791.1120204.0004334.1271793.0022197.0015379.000738.0008135.0003914-.0003152-.0003547.0010876.0003113.0001369.0014243.0018136-.0005587.0001573.0002488.0005927

95%CI
lower
boubd
-.0110766.0728414-.0065829-.0079876.0767899-.0040452-.0086423.0790964-.0065911.1061217-.0036468.1060145-.0058099.121197-.0040851-.0038023-.0043597-.0042142-.0047274-.0051673-.0051451-.0038597-.0043754-.004492-.0039275-.0034037-.0056617-.0048427-.0047113-.0042472

95%CI upper bound.0022059.0846091.0054629.0049713.0881175.0075781.0046657.0903441.0050413.1185492.0092287.1180264.0066767.1331616.0085245.0068781.0058357.0058412.0055102.004537.0044357.006035.0049981.0047658.0067761.0070309.0045443.0051573.005209.0054325

Fig.1 Concenration Curve of Utilization minus Needs in 1992 −:SAH, −−:Symptoms,...:Both
1

0

−1

%

−2

−3

−4

−5

−6

−7

0

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Proportion of Cumulative Distribution of Income

0.8

0.9

1

Fig.2 Concenration Curve of Utilization minus Needs in 1995 −:SAH, −−:Symptoms,...:Both
1

0

−1

%

−2

−3

−4

−5

−6

0

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Proportion of Cumulative Distribution of Income

0.8

0.9

1

Fig.3 Concenration Curve of Utilization minus Needs in 1998 −:SAH, −−:Symptoms,...:Both
2

1

0

%

−1

−2

−3

−4

−5

−6

0

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Proportion of Cumulative Distribution of Income

0.8

0.9

1