Discussion Paper No.

585

UPDATED HORIZONTAL INEQUITYIN HEALTH CARE UTILIZATION IN JAPAN:
COMPARISONS WITH OECD COUNTRIESUSING AN ORIGINAL SURVEY

Yasushi Ohkusa
andChika Honda

June 2003

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

9 June, 2003

Updated Horizontal Inequity in Health Care
Utilization in Japan: Comparisons with OECDCountries using an Original Survey
Yasushi Ohkusa
Institute of Social and Economic Research, Osaka University
andChika Honda
Yao Public Health Center, Osaka Prefecture
Abstract
We compare health care inequity in Japan with that in other OECD countries in2002 and 2003. To overcome Japanese data problems, we conducted an original
survey. Although some problems remain, we obtained internationally comparable
results on health care inequity for Japan. We test the utilization measure by the
number of outpatients, the number of days of inpatient utilization in the previous
year, out-of-pocket payments in the previous year and other measures, such as a
yes/no indicator for outpatient or inpatient utilization in a lifetime. The results
show that there is no inequity in outpatient or inpatient utilization, but out-ofpocket payments show significant pro-rich inequity.
Keywords: Horizontal Inequity, Japan, International Comparisons, Concentration Index, Kakwani Index, Needs

1

Introduction

Inequity in health care has recently become one of the most pertinent and relevant
issues in health economics and health policy. Much research on methodology andinternational comparisons has been carried out by Wagstaﬀ et al. (1989, 1991),
Van Doorslaer and Wagstaﬀ (1992), Wagstaﬀ and Van Doorslaer (1993, 1994),
Van Doorslaer et al. (1997, 2000), and Kakwani et al. (1997). In particular,
research on horizontal inequity has been undertaken by Van Doorslaer et al.
(2000) and, most recently, by Wagstaﬀ and Van Doorslaer (2000).
Eleven OECD countries have been studied on the basis of reasonably comparable definitions of health inequity. Unfortunately, Japan has not been included
in previous studies. While Ohkusa and Honda (2003a) use the Comprehensive
Survey of Living Conditions for Japan (CSLC), this survey only reports whether
individuals are currently visiting a doctor, rather than the frequency of visits toa doctor or hospitalization during the previous year, as surveys for other OECD
countries do. Unfortunately, no national survey contains both this informationand other socio-demographic information. Hence, an original survey is needed.
Mainly due to financial limitations, the sample size of our original survey was far
less than that of a national survey. However, it may still be representative eventhough it uses two-stage strata, as does the CSLC. Our survey supplements the
CSLC and is comparable with those for other countries.
We performed the survey in March of 2002 and 2003, and we obtained about
3,000 observations. There were various reasons why we could not complete thesurvey in one phase. The most important reason was financial diﬃculty. We
had funds to survey about 1,500 individuals in one year but it was insuﬃcient tosurvey 3,000 individuals. Fortunately, the funding was available in the succeeding
year and thus we could survey over two years. The second reason was related to1some concerns about bias due to the small sample. Of course, we analyzed the2002 data of our original survey (Ohkusa and Honda 2003b), but some problems
seemed to remain, which might have been due to the small sample or othersurvey procedures. To overcome the small sample or other problems, we neededa more appropriate survey once again. The third reason concerns the definition ofvariables that indicate inpatient utilization. In the 2002 survey, we had surveyed
only inpatient utilization in a lifetime until the year of the survey or within ayear. However, in other OECD countries, the number of hospitalized days is also
surveyed and analyzed. Hence, we added some questions about hospitalized daysin the 2003 survey.
Before considering the measurement of health care, the institutional background in Japan is summarized. In 1961, Japan completed the introduction ofcompulsory public health insurance with coverage for all residents. In 1997, a
new law was introduced requiring coinsurance rates of 20% for the employed and
30% for others, such as the self-employed and dependents. For people over 70
years of age, out-of-pocket payments (OPP) are limited to approximately 4000
yen (about US$36 in 2001 prices) per month. However, large firms sometimes
subsidize their employees by reducing their co-payments to less than the legal
requirement. Medical services are provided as welfare to very poor people who
cannot aﬀord to pay the premiums. Thus, everybody can access medical services
in Japan.
The public health insurance system provides reimbursement on a fee-forservice (FFS) basis. Although the government regulates 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 Managed Care. Unlike the National Health Service (NHS) and Sickness Fund, the insurer cannot control thebudget ex ante.
2

There is no regulation of the medical services chosen by patients, as undertaken by the gatekeeper in the NHS, or diﬀerent coverage as in the HMO. In
other words, there is no practical diﬀerence between general practitioners andspecialists. The coinsurance rate is the same for services provided in hospitals
and clinics (either public or private), but congestion may implicitly impose anopportunity cost. The number of beds is strictly regulated, but provision of
outpatient services is virtually unregulated.
Private insurance plays only a minor role because public insurance has sucha 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 andEuropean countries, which is why Japan is usually excluded from international
comparisons in health economics.

2

Data

Our original survey was conducted in March of 2002 and 2003 for the whole ofJapan. In 2002, 640 questionnaires were distributed and 570 were completed
and returned, which provided information on about 1,450 adults. In 2003, thecorresponding figures were 900, 783 and 1,596 respectively.
These households voluntarily contracted with the firm that conducted thesurvey to complete the various surveys. The households surveyed were randomly
sampled by two-stage strata, but decisions to cooperate were deliberate. Therefore, particular attention should be paid to the sampling bias that can arise
from this type of sampling. In fact, the survey has no unemployed and few
self-employed respondents, and there is a slight bias towards richer households.
However, this bias could be controlled for by appropriately weighting informa-3

tion. Hence, not only are subsequent regression results weighted by income andby region, so are the summary statistics. The sample excludes institutionalized
individuals.
Unfortunately, even after combining the two years, our sample of 3,046 respondents is the smallest used for a health care inequity study for any OECD
country. The second smallest is a sample of 3,374 respondents for Sweden used by
Van Doorslaer et al. (2000). Other countries for which sample sizes of less than
4,000 have been used are East Germany (3,844) and Denmark (3,955). These
were conducted in the early 1990s and so could be updated. Although comparable, our data set for Japan is much smaller than the surveys for other OECD
countries. This is due primarily to financial problems, which cannot be fixed in
the short term. Therefore, we have to use our small data set to analyze healthcare inequity in Japan, even though smaller samples may lead to bias.
The basic framework of the surveys conducted in the two years are almost
identical. However there are some diﬀerences in sampling and in the questionnaire. First, in the 2002 survey, we selected a few prefectures to survey. By
comparison, in 2003, the survey was distributed randomly to the whole of Japan.
Second, the categories of income and OPP are finer in the 2003 survey than in
the 2002 survey; in the first year, there were nine and aright categories in income
and OPP respectively, while in the second year this was changed to 23 and 22
categories respectively. This change may improve the preciseness of income and
OPP, and thus contribute to gaining a more reliable estimator. The third point
concerns days of hospitalization, i.e. the 2002 survey did not ask about this issue,
but the 2003 survey did. Since it is used in OECD studies other than Japan, thisimprovement can contribute to be more convenient comparison between Japanand other OECD countries.
The main variables are defined as follows. Outpatient utilization is defined in4two ways. The first indicator of outpatient utilization is whether an individual
visited a doctor in the previous year, while the second definition uses the numberof visits. Although the latter is used in international comparisons, the former
is similar and has been used previously (see, e.g., Ohkusa and Honda, 2003a).
Note again that there is no practical diﬀerence between general practitioners andspecialists.
Inpatient utilization is defined in a similar way to outpatient utilization, but
we set two reference periods, i.e. utilization in the previous year, which is usedin OECD studies, and utilization in the lifetime until the survey year. Basedon these reference periods, the first indicator of inpatient utilization is whether
an individual was hospitalized in the reference period and the second definition
uses the number of hospitalized days. Hence, we use four types of inpatients
utilization. Hereafter, the diﬀerence between reference periods is indicated by asuperscript, i.e. ”Inpatienta ” indicates utilization of inpatient service when thereference duration is the previous year and ”Inpatientb ” indicates utilization of
inpatient services when the reference duration is the lifetime until the survey
year.
OPP is defined at the household level. Note that because the questionnaire
defines OPP as payment for medication, it is not limited to co-payments for medical services, but also includes non-prescribed drugs and other medical services
that are not covered by public health insurance. Thus, we assume that its modein each category is the number. Since the highest category is open-ended, we use
the same interval as in the second highest category. Income is also measured at
the household level in nine categories. Hence, we make the same adjustment as
for OPP. Moreover, income is adjusted to household structure as follows.

5Adjusted Income =Income(Number of Adults + 0.5 Number of Children)0.75(1)where children are less than 16 years old. Chronic disease is represented by adummy variable that indicates whether individuals suﬀered from symptoms even
if they were not currently visiting a doctor.
Summary statistics are shown in Table 1. In the previous year, 74% of individuals visited a doctor and the average number of visits was 10.4, i.e., almost
once a month. On the other hand, about 5.7% of individuals were hospitalized
in the previous year, but about half of the individuals were hospitalized in their
lifetime until the survey year. The hospitalized days reflect such a diﬀerence,
i.e. the number of hospitalized days in the lifetime is about ten times larger
than hospitalized days in the previous year. Average OPP per year per capita is
about 60 thousand yen (about $50). Per capita income adjusted for the numberof adults in the household is about 3.2 million-yen (about US$25,000).

3The Measurement of Horizontal Inequity

In measuring horizontal inequity, three aspects need to be clarified: the definitionof demand for medical care; the definition of needs; and the estimation methods.
The definitions and estimation methods used in this paper are described below.
Social and economic conditions are defined individually by household disposable
income per equivalent adult, as in previous studies.

3.1

Definition of Needs

Concerning the definition of needs, existing studies use incidences of chronic
illness (Van Doorslaer and Wagstaﬀ, 1992) and self-assessment of health (Van
Doorslaer et al., 1997). Conversely, Van Doorslaer and Wagstaﬀ (2000) define6needs as the estimated demand for medical care, which is explained by selfassessment of health (SAH) and/or chronic illness, in addition to demographic
characteristics such as age and gender.
In this paper, we define needs as the estimated demand for outpatient or
inpatient services, or the OPP of the ith person, which indicates the ith smallest
amount of income adjusted for household structure, with the dependent variable
Di . The explanatory variables used are age Ai , gender Gi , self-assessment ofhealth Hi , and chronic disease Si . Thus the estimated equations in the fullversion are:Di∗ = α0 +
Di =

(

4
X
j

αA Aji +jDi∗ > 04
X

αAGj Aji Gi + αG Gi +

j

4
X

l
αH
Hil + αS Si + εil

1 if
0 otherwise(2)

where superscripts indicate dummy variables. Age categories are divided into
years as follows: 16-24, 25-44, 45-64, 65-74, and 75 plus, as in Van Doorslaer etal. (2000). Since self-assessment of health is classified into five categories, there
are four dummies for this variable.
The model estimated is a heteroscedasticity-consistent probit for whether individuals utilize outpatient and inpatient services. The predicted probability,
Φ(D̂i ), is interpreted as Needs n in the context of this procedure. For the number of visits to the doctor or the number of days hospitalized, the negative binominal model is employed, as in previous research. These utilization variables
are reported on an individual basis, for all household members. Thus, there
may be intra-family correlation through income, lifestyle, or access to medical
institutions. To account for this, we add random household eﬀects to the model.
Since OPP is a continuous variable, we estimate a linear model for the log of
OPP. Since OPP is a household-level variable, we cannot use individual eﬀects.
73.2

Estimation Method

First, the Concentration Index for medical care or needs is defined following
Kakwani et al. (1997):

2σR2

µi
= α0 + α1 Riµ(3)where µi is the demand for medical care, µ is the average of µi over persons, Ri
is the cumulative proportion up to the ith person in order of income adjusted forhousehold structure, and σR2 is its variance. The estimated α1 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 Van Doorslaer (2000), the variance of the Concentration Index is adjusted as follows:

N
1 X
(4)
{ fi a2i − (1 + Concentration Index)2 }
N i=1
µi
=
qi
(2Ri−1 − Concentration Index) + 2 − qi−1 −(5)
µ
i
1X
=
µs fs
µ s=1

V ar(Concentration Index) =atqi

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

2σR2

"

µi ni
−
µ
n

#

= β0 + β1 Ri
(

(6)
)

N
1 1 X
(aµ − ani )2 − Horizontal Inequity2(7)
V ar(Horizontal Inequity) =N N i=1 i
µi
µ
aµi =
− qiµ
(2Ri − 1 − Concentration Index for µ) + 2 − qt−1
µ

8

t
1X
µs fs
µ s=1
ni
n
=
− qin
(2Ri − 1 − Concentration Index for n) + 2 − qt−1
n
t
1X
=
ns fs
n s=1

qi =
ani
qi

The estimated coeﬃcient of β1 is interpreted as horizontal inequity (Wagstaﬀ etal., 2000, Van Doorslaer et al., 2000).

4Empirical Results

The first panel in Table 2 shows the empirical results for ”Needs”. Note that thesenumbers are the estimated coeﬃcients and not the marginal eﬀects, and hence
they cannot be interpreted directly. It is apparent that SAH and age significantly
aﬀect outpatient utilization, but may not aﬀect inpatient utilization and OPP.
Overall, Wald tests and F tests indicate a good fit.
Table 3 indicates the distribution of actual ”Needs”, while predicted ”Needs”
are shown in Table 2. Actual utilization is higher in the highest and the lowest
income groups, but this is not the case in the predicted Needs. Overall, predicted
Needs do not seem to reflect income classes, except for the highest income class.
The Concentration Index, which measures inequality in utilization, is summarized in the first and third rows of Table 4. Clearly, these numbers indicate no
inequality in utilization in terms of the number of outpatients or inpatient days.
However, there are some progressive cases in the yes/no indicator of outpatient
service or inpatient utilization in the lifetime. Moreover, the Index suggests evidence of progressiveness in OPP. In other words, the rich have tended to spend
more than the poor do. However, since ”Needs” have not been taken into account,
we cannot discuss inequity.
9Figures 1 to 7 show the Concentration Curve of each variable in the case of
the without year dummy. Since these lines measure the deviation of the Concentration Index from the diagonal line, positive (negative) numbers indicate that
the Concentration Curve passes above (below) the diagonal line. Thus, positive(negative) numbers imply that the poor (rich) have relatively more utilization or
OPP. The Concentration Curve for utilization in Figures 2 to 6 moves aroundzero, but the curves in Figure 1 and 7 clearly move below zero. This leads to thepositive Concentration Index in Table 4. These properties are unchanged if year
and/or prefecture dummies are added as explanatory variables in equation (3).
Next, we move to the Kakwani Index, which is the Concentration Index above
minus Needs. Figures 8 to 14 illustrate the diﬀerence between the actual utilization and Needs. At a glance, the lines in Figures 8 and 14 move below zero, but
in the other figures, the line moves around zero. This is similar to the properties
of Figures 1 to 7.
To confirm and test the impression given by the Figures, we undertake empirical investigations. The bottom row of Table 4 summarizes the empirical resultsfor β1 in equation (5), and the lower panel summarizes horizontal inequity adjusted for regions.
As the results indicate, the null hypothesis of no inequity cannot be rejected
for the number of outpatients and the number of days of inpatient utilization.
However, for the yes/no indicator of outpatients and inpatient utilization in the
lifetime or OPP, the results indicate pro-rich inequity, as suggested by the figures.
In addition, in the case of no year or regional dummies, inpatient utilization in
the previous year indicates pro-rich inequity, even though it does not indicate
any inequality in Table 4. Conversely, the Kakwani Index of OPP without year
and regional dummies indicates inequity, but when we add a year dummy into
equation (5), the index is significantly positive. This may reflect the changing
10

measurement in the two survey years. With regional dummies, the magnitudes
are 0.014, 0.028 and 0.082 for outpatients, inpatient in a lifetime and OPP respectively. Without regional dummies, the corresponding magnitudes are 0.020,
0.037 and 0.074. The Kakwani Index of inpatient utilization in the previous year
without year or regional dummies is 0.08, which is very high compared to OPP.

5

Concluding Remarks

We found that the hypothesis that there is no inequity cannot be rejected, andJapan would have enjoyed one of the greatest degrees of equity in health careamong OECD countries. However, the point estimate for the number of outpatient services without regional dummies (0.0002 to 0.0011) is larger than thosefor Spain (-0.0137), Ireland (-0.0098), Italy (-0.0098), and Belgium (-0.0001), andsmaller than those for the UK (0.0074), Canada (0.0072), Greece (0.0273), Austria (0.0389), Portugal (0.0524), and the USA (0.0532)1) . Thus, Japan enjoys thebest equity not only in the statistical sense, but also in the economic sense.
We can compare inpatient utilization measured in days in Japan with otherOECD countries (Van Doorslaer et al., 2000). Our point estimate -0.123 is thesmallest among Belgium, Denmark, Finland, the Netherlands, Sweden, Switzerland, the UK and USA2) . Thus, Japan has the greatest most pro-poor inequity.
However, the Kakwani Index is not significant in Japan. As Belgium and the
UK have significant pro-poor inequity, the extent of pro-poor inequity in Japanis behind these countries in the statistical sense.
From this study, we can learn about the huge diﬀerences between the yes/noindicator and the number of utilization days. The yes/no indicator in outpatient
service always indicated pro-rich inequity, but utilization in terms of the numberof outpatients never showed significant inequity. Moreover, yes/no indicators of11

inpatient utilization sometimes showed pro-rich inequity, but the number of daysof inpatient utilization did not. Therefore, our previous research result (Ohkusa
and Honda 2003a), which reported pro-rich inequity before 1997, but equity in1998 in the yes/no indicator of outpatients, might be misleading for horizontal
inequity in Japan. We cannot reconsider this result as we did not perform asurvey like the one in this study before 1998. Nevertheless, we have to check therobustness of the result obtained in this research. This remains a topic for future
study.

12

Acknowledgments

This research was supported by a 2002 grant from the Ministry of Science and
Education represented by Dr. Fumio Hayashi (12124207). We are also gratefulfor the assistance of Mr. Kunio Tsuyuhara, a graduate student at the UBC. Allremaining errors are ours.

Footnotes

1) The figures for other OECD countries are from Van Doorslaer et al. (2001).
They represent utilization as total visits to a physician and incorporate
regional information.
2) The eight OECD countries are from Van Doorslaer et al. (2000) in the caseof inequity adjusted by age, sex, SAH, and a dummy for chronic illness.
For purposes of comparison, we adopt estimated inequity without regionaldummies.

13

References[1] Van Doorslaer, E., Wagstaﬀ, A., 1992. Equity in the delivery of health care:some international comparisons, Journal of Health Economics 11, 389-411.
[2] Van Doorslaer, E., Wagstaﬀ, A. et al., 1997. Income-related inequalities inhealth: some international comparisons, Journal of Health Economics 16,
93-112.
[3] Van Doorslaer, E., Wagstaﬀ, A., et al., 2000. Equity in the delivery of healthcare in Europe and the US, Journal of Health Economics 19, 553-583.
[4] Van Doorslaer, E., Koolman, X., Puﬀer, F., 2001. Equity in the use of physician visits in OECD countries: has equal treatment for equal need beenachieved?, Equity II project, Working Paper No. 3.
[5] Kakwani, N., Wagstaﬀ, A., Van Doorslaer, E., 1997. Socioeconomic inequalities in health: measurement, computation and statistical inference,Journalof Econometrics 77, 87-103.
[6] Ohkusa. Y., Honda, C, 2003a. Horizontal inequity in health care utilizationin Japan, Health Care and Management Science, forthcoming.
[7] Ohkusa. Y., Honda, C., 2003b. Horizontal Inequity in Health Care Utilization in Japan: Comparisons with OECD Countries using an Original Survey,
manuscript[8] Shigeno, Y., 2000. The demand for private health insurance and public healthinsurance (in Japanese), Quarterly Journal of Social Security Research 36,
378-390.

14

[9] Urbanos-Garrido, R. M. 2001. Explaining inequality in the use of publichealth care services: evidence from Spain, Health Care Management Science4, 143-157.
[10] Wagstaﬀ, A., Van Doorslaer, E., Paci, P., 1989. Equity in the finance anddelivery of health care: some tentative cross-country comparisons, Oxford
Review of Economic Policy 5, 89-112.
[11] Wagstaﬀ, A., Paci, P., Van Doorslaer, E., 1991. On the measurement ofinequalities in health, Social Science and Medicine 33, 545-557.
[12] Wagstaﬀ, A., Van Doorslaer, E., 1993. Equity in the delivery of health care:methods and findings, in: E. Van Doorslaer, A. Wagstaﬀ and F. Rutten,(Eds.), Equity in the Finance and Delivery of Health Care: An International
Perspective, Oxford University Press, Oxford.
[13] Wagstaﬀ, A., Van Doorslaer, E., 1994. Measuring inequalities in health in
the presence of multiple-category morbidity indicators, Health Economics 3,
281-291.
[14] Wagstaﬀ, A., Van Doorslaer, E., 2000. Measuring and testing for inequalities
in the delivery of health care, Journal of Human Resources, 716-733.

15

Table1: Summary Statistics
Average
Outpatient
Yes/No.7439265
Number
10.42587aInpatient
Yes/No.0574524Days
2.15856
Inpatientb
Yes/No.4794694
Days
21.66077
OPP
62155.3
Adjusted Income 319.4429
Chronic disease.0029547
SAH
Excellent
.1404
Good
.2608985
Fair.4805324
Poor.1001664
Very Poor.01797Age class35-44.2166776
45-64.3260013
65-74.0577807
75.0541694
Female.5330266
Age class in female
35-44.1041735
45-64.16562665-74.0276043
75.0364772

Standard

Minimum

Maximum.4365346
21.04485

0
0

1
240

.2327432
18.20843

0
0

1
365

.4997362
59.74993
108338.7
175.1763.054285700652.1186
0
0

1
1200
1096728
2500
1

.1207
.4391978.499704.3002717.1328646

0
0
0
0
0

1
1
1
1
1

.4120487.4688247.2333666.2263888.49899

0
0
0
0
0

1
1
1
1
1

.3055357.3718057.1638632.187505

0
0
0
0

1
1
1
1

Note: ”Inpatienta ” indicates utilization of inpatient services when the reference
duration is the previous year and ”Inpatientb ” indicates utilization of inpatient
services when the reference duration is the lifetime until the survey year.

16

Table 2: Estimation Results for Needsa
Chronic disease
SAH
Good
Fair
Poor
Very Poor
Age class
35-44
45-64
65-74
75FemaleAge class in female
35-44
45-64
65-74
75year dummy
constant
Sample size
No. of households
log likelihood
p-value for Wald test

Outpatient
Yes/No-1.045501 ∗Outpatient
Number-.2067976

Inpatient
Yes/No-7.284971

Inpatienta
Days.2249545 ∗.4127749 ∗∗∗.9521107 ∗∗∗.7666047 ∗∗.0529394.2639287 ∗∗∗.7134136 ∗∗∗1.144656 ∗∗∗.1744287.1576405.9689101 ∗∗∗1.917009 ∗∗∗.0460832-.3586596.8027589 ∗∗1.976788 ∗∗∗.060803.1377472.62589 ∗∗∗.7410975 ∗∗.3012328 ∗∗∗.0046188.2943616 ∗∗∗.6513819 ∗∗∗.8443223 ∗∗∗.1603277 ∗∗-.1067994.1081444.579962 ∗∗.6625701 ∗∗.1960088-.7429157 ∗∗-.4674477-.1304366.2316605-1.271226 ∗∗∗-.1273943.1053174-.0719006.036728-.1992257.268076 ∗∗.1466648.1225138-.1683252 ∗-.0845568 ∗∗.4662105 ∗∗∗.1183388
3003
2252
1100
1007
-1538.6975-7722.81≤0.0001≤0.0001(continue).0281784-.2641426-.5192896-.0392491.2595098 ∗∗-2.576085 ∗∗∗
3003
1100
-577.38673≤0.0001

1.137019 ∗∗∗1.339137 ∗∗∗1.815664 ∗∗∗.9183236 ∗∗17

4.721294 ∗∗∗110
95-428.42406≤0.0001SAH
Age classAge class in female
Chronic disease
Good
Fair
Poor
Very Poor
35-44
45-64
65-74
75Female
35-44
45-64
65-74
75year dummy
constant
Sample size
No. of households
log likelihood
p-value for Wald test

Inpatientb
Yes/NoInpatientb
Days

OPP

-.0061803.2374815 ∗∗.4218254 ∗∗∗.9066628 ∗∗∗1.50387 ∗∗∗.0266897.3509446 ∗∗∗1.025813 ∗∗∗1.136715 ∗∗∗-.0471593.3058874 ∗∗.2742656 ∗∗-.4427922 ∗-.2268846.5078101 ∗∗∗-1.225269 ∗∗∗
3003
1100
-1819.9933≤0.0001-.1769932.0142394-.0603613.4318681 ∗∗∗1.039713 ∗∗∗-.0476653.1987412 ∗.0819955.7517713 ∗∗∗-.1521425.3846519 ∗∗.1867501.5262953 ∗∗∗.3458703 ∗-.7762752 ∗-.0170967.1687189 ∗∗.6207846 ∗∗∗1.055295 ∗∗∗-.0404152.082979.3327759 ∗∗-.0605596-.0702421.0931229.0443513-.135499.2762056.1382997 ∗∗∗9.925178 ∗∗∗2950.2212076 ∗755
417-3500.7883≤0.0001

0.0332†≤0.0001‡

Note: The estimation method for need in the yes/no indicator of outpatientand inpatient is probit with random eﬀects. For the number of outpatients or
inpatient days, the method is negative binominal with random eﬀects. For OPP,
a heteroscedasticity-consistent linear model is used. † indicates R2 and ‡ indicatesthe p-value for the F test. ∗∗∗ indicates significance at the 1% level, ∗∗ at the 5%
level , and ∗ at the 10% level. Since hospitalization days of ”Inpatienta ” and”Inpatientb ” is surveyed only in 2003, the size of the sample is about half thatfor other variables.

18

Table3: Distribution by Income Quantile
ActualOutpatient
Yes/NoNumberInpatienta
Yes/NoDays
Inpatientb
Yes/NoDays
OPP
Estimated
Outpatient
Yes/NoNumberInpatienta
Yes/NoDays
Inpatientb
Yes/NoDays
OPP

Bottom 20%

20-40%

40-60%

60-80%

Top 20%

11.1
.706

10.2
.753

9.29
.765

10.4
.717

10.9
.777

.044
2.27

.054
2.76

.064
1.95

.068
1.79

.055
2.12

.438
22.8
53410

.477
21.8
57778

.487
23.4
51650

.506
19.5
66900

.493
20.0
80577

9.63
.835

9.80
.827

10.3
.824

9.41
.825

9.76
.820

.038
1.59

.033
2.36

.039
3.07

.031
2.68

.027
2.66

.482
48.6
62957

.475
51.6
61969

.466
49.4
63052

.482
52.8
61736

.479
50.8
61391

Note:

19

Table4: Concentration Index

Year

Index

without regional dummies
Outpatient
Yes/Nono.0133001
yes.0128394
Numberno-.0014481
yes-.003452aInpatient
Yes/Nono.0238123
yes.0291438
Days-.026409bInpatient
Yes/Nono.0207744
yes.02566
Days-.0195206
OPP
no.073927
yes.0748255
with regional dummies
Outpatient
Yes/Nono.0191708
yes.0188095
Numberno-.0158913
yes-.015044
Inpatienta
Yes/Nono.0199909
yes.0181376
Days-.0117427
Inpatientb
Yes/Nono.0321469
yes.0313325
Days.0080052
OPP
no.065327
yes.0659555

pvalue95% Lower
bound

95% Upper
bound

0.036
0.043
0.950
0.881

.0008926.0004304-.0466945-.0484953.0257075.0252484.0437982.0415914

0.561
0.478
0.841

-.0565128-.0514387-.2842658.1041375.1097263.2314479

0.107
0.042
0.584
0.001
0.000

-.0045064.0009741-.0894058.0319765.0328541.0460552.050346.0503645.1158776.116797

0.004
0.005
0.506
0.529

.0061655.005814-.0627827-.0619071.032176.0318049.0310002.0318191

0.642
0.675
0.926

-.0643915-.0665442-.2583283.1043734.1028194.2348428

0.015
0.018
0.818
0.002
0.002

.0062303.0054453-.0602076.0234634.0238636.0580635.0572196.076218.1071906.1080474

Note: ”Year” indicates whether the estimated index takes year dummies into theestimation equation.

20

Year

Table5: Kakwani Index

Index

without regional dummies
Outpatient
Yes/Nono.0140893
yes.0141709
Numberno.0011258
yes.0002138aInpatient
Yes/Nono.0807551
yes.0794159
Days-.1231977
Inpatientb
Yes/Nono.0287579
yes.0279234
Days-.0232047
OPP
no-.0232047
yes.0828898
with regional dummies
Outpatient
Yes/Nono.0201333
yes.0196416
Number
no-.009268
yes-.0087318
Inpatienta
Yes/Nono.0770748
yes.0758128
Days-.0970992
Inpatientb
Yes/Nono.0374557
yes.0373853
Days-.0047643
OPP
no.0739336
yes.0750902

pvalue95% Lower
bound

95% Upper
bound

0.024
0.023
0.956
0.992

.0018742.0019533-.0393327-.0400582.0263043.0263885.0415844.0404858

0.046
0.051
0.182

.0013508-.0003121-.304077.1601594.1591439.0576816

0.021
0.025
0.433
0.433
0.000

.0043901.0035536-.0811936-.0811936.041062.0531256.0522932.0347842.0347842.1247176

0.002
0.003
0.665
0.683

.0073386.0068732-.0512617-.0507228.0329281.03241.0327258.0332591

0.074
0.079
0.279

-.0075017-.0088642-.2728992.1616514.1604898.0787008

0.004
0.004
0.868
0.001
0.001

.0118865.0118218-.061018.0317866.0327101.0630249.0629489.0514893.1160806.117470421