JOINT RESEARCH CENTER FOR PANEL STUDIESDISCUSSION PAPER SERIES
DP2012-001
August, 2012

Relationship between Out of Pocket Pay and Expenditure on
Marketed Drug

Kazuki Kamimura*【Abstract】
In this paper, using Japanese Household Panel Survey (JHPS), we empirically examine
whether there is substitutability between doctor's treatment and commercial drugs.
And moreover, we also examine how large the substitutability is.
First, as expected, there is some substitutability between commercial drugs anddoctor's treatment. The degree of substitutability is not negligible.
Second, the substitutability depends on household income. The degree of subst
itutability is higher at the lowest income group than the other group. Thus to promote
the policy to cut medical cost trough improving the accessibility of commercial drug,
some attention to the poor is necessary.
*Graduate School of Economics, Keio University

Joint Research Center for Panel Studies
Keio University

Relationship between Out of Pocket Pay andExpenditure on Marketed Drugs∗
Kazuki Kamimura†

Abstract
In this paper, using Japanese Household Panel Survey (JHPS), we empiricallyexamine whether there is substitutability between doctor’s treatment and commercial
drugs. And moreover, we also examine how large the substitutability is.
First, as expected, there is some substitutability between commercial drugs anddoctor’s treatment. The degree of substitutability is not negligible.
Second, the substitutability depends on household income. The degree of substitutability is higher at the lowest income group than the other group. Thus to promote
the policy to cut medical cost trough improving the accessibility of commercial drug,
some attention to the poor is necessary.

JEL classification numbers: I12, C30
Keywords: Medical expenditure, Bivariate Model, Substitute goods∗

We would like to thank Kohei Komamura, Colin McKenzie and Akihiko Noda for their helpful comments. All programs used for this paper are available on request.
†
Corresponding author: Graduate School of Economics, Keio University, 2-15-45 Mita, Minato-ku,
Tokyo, 108-8345, Japan (E-mail: k kamimura@2010.jukuin.keio.ac.jp).

11

Introduction

In Japan, explosion of medical care cost has been pressing issue. In 2008, medical expenditure of Japan is about three and a half trillion yen and equals about 10% of national
income. Only twenty years ago, medical expenditure of Japan was about two trillion yen
and equalled about only 6% of national income. Thus both index has doubled in thistwenty years. In addition, most experts of social security predict further increase in thenear future.
The reason why medical care cost has been risen is severalfold. The ﬁrst and mostimportant reason is aging of society. In 1990, fraction of who are equal or over 65 isonly about 12%, however, in 2010, about 23% of Japanese are equal or over 65 and thus
population aging rate has doubled through this twenty years. At least on average, the elder
are less healthier than the young and the elder need more medical care than the young.
The second reason is breakthrough in medical technology. Tuberculosis is a good example. Just thirty or forty years ago, tuberculosis is considered as fatal disease, however,in recent years, “Early detection and rapid cure” can completely overcome tuberculosis.
Many people’s lives are saved itself a blessing event, however, how we ﬁnance the cost ofsuch cutting-edge medical technology remains to be resolved.
The third reason is so called physician-induced demand in the literature. There are
already vast amount of literature in Japan (Noguchi et al. (2005)). Physicians have more
medical knowledge and/or information than other people. Medical technology has become
more and more highly-developed and speed of the progress has become more and more
rapid. Consequently, degree of asymmetry becomes more severe. Under such a symmetry,
physician can (not necessarily do so) provide excessive care which is actually not in need.
Too-much care is problematic at least in terms of exploding medical care cost.
Above argument indicates the mechanism behind exploding medical care cost is severalfold and hard to be dealt with. However, some eﬀort has been already made. The ﬁrst
and perhaps most famous policy is comprehensive payment system. The second example ispulling out coinsurance rate of medical costs for the elderly. The third eﬀort is concerningmarket for a drug.
While the eﬀect of the ﬁrst (Nawata et al. (2009)) and the second (Kan and Suzuki(2006)) trial have been empirically examined, the third trial has not been fully examined
yet compared to the other two trial. The main reason why the registered salesclerk system
has not been empirically examined is lack of suitable data.
Aim of the registered salesclerk system is improve availability of drugs and promote
substituting commercial drugs for doctor’s treatment as already noted. Thus examining
the substitutability of commercial drugs and doctor’s treatment is an implicit veriﬁcation of
the eﬀectiveness of the third trial. Suzuki and Okusa (2000) and Okusa and Ii (2002) are theexamples of empirical studies which examine the substitutability. While the existence of thesubstitutability is veriﬁed, the degree is not. To quantitatively evaluate the substitutability,
information about the degree of the substitutability is indispensable.
Thus in this paper, using Japanese Household Panel Survey (JHPS), we empiricallyexamine whether the registered salesclerk system has impact on actual behavior. To be
more precise, we empirically examine whether there is substitutability between doctor’streatment and commercial drugs. In addition, we examine the relationship between income
and substitutability. As we will note later, implication of substitutability depends on therelationship.
Main results in this paper are as below. First, our estimation results is very strong2

evidence for substitutability between doctor’s treatment and commercial drugs. The substitutability implies there is a possibility improvement of the availability of commercial
drugs somehow cut medical cost in Japan. Second, we ﬁnd the degree of substitutability
depends on income. To be more precise, the poor people are, the larger the substitutability
is. Considering the rich are on average healthier than the poor, there is a possibility thericher consume too large amount of medical resources or the poor consume too little.
This paper is organized as follows. In the next section, we brieﬂy refer to theoreticalbackground of this paper. And in the third section, we provide analytical framework. We
present empirical results in the fourth section and the last section is our conclusion.

2

BackgroundIn this paper, we empirically examine the relationship between medical expenditure. We
provide brief explanations below.
In some earlier studies, existence of substitutability is already conﬁrmed. The reason
why there may be substitutablity exists is as follows. For example, when we catch a cold,roughly speaking, two alternatives are at our hand1 .
The ﬁrst and more careful (sometimes anxious) behavior is seeing a doctor. Prescription drug often more rapidly and eﬃciently remedy your cold and in addition, medical
examination provides us sense of security. Thus we are apt to see a doctor even when it is
not necessary.
The second and more optimistic (sometimes rational) behavior is going to drugstore
and buying commercial drugs. In most cases commercial drugs also suﬃciently remedy ourcold. Sometimes commercial drugs are not enough to remedy our cold, however, in mostcases it is not too late to see a doctor then. Opportunity cost is also a problem.
In sum, there may be substitutability between doctor’s treatment and commercial drugs
when state of disease is not so severe. In Japan, some earlier studies written in Japanese has
already exaimned the existence of the availability. Suzuki and Okusa (2000) and Okusa andIi (2002) are examples. However, they have not examined the degree of the substitutability.
For quantitative discussion, some information about the degree is necessary.
In addition, relationship between the substitutability and income is also an important
issue. The reason why the relationship between them is important is as follows. If there ispositive correlation between the substitutability and income, it seems not so problematic.
In contrast, if there is negative relationship, it is not the case.
Under such situation, at least either of the two cases below hold. The ﬁrst case is the richconsume too large amount of medical resource. This means the rich do not quit consulting
a doctor even if commercial drugs they have bought fully cure them. On the contrary, thesecond case is the poor consume too little amount of medical resources. This means thepoor quit consulting a doctor even if an additional examination besides commercial drugs
is necessary.
There are some problems to be solved to conduct an empirical analysis. The ﬁrst andmost important problem is endogeneity. Some sources of endogneity are there.
First, it is no wonder unhealthier people see a doctor more often and buy commercial
drugs more often. In such a case, spurious correlation is there and if we employ a simple
regression model, this attenuate the eﬀect of the expenditure on commercial drugs on
1

There is also a possibility we do not anything. However, we exclude such a possibility here to makethe argument more simple.

3

expenditure to doctors. To avoid such a case, controlling the health conditions of anindividual as possible is necessary.
The second source is other characteristics of people. For example, educational background of people aﬀect health investment behavior of people (Cutler and Lleras-Muney(2006)). Needless to say, people’s income aﬀect their investment to health. Other thaneducation and income, people’s risk attitude, gender, and age also may have some eﬀect.
In Japan, number of large scale socioeconomic researches including detailed information
about people’s health (both self rated and objective) , socioeconomic status (e.g. education), and preference (e.g. risk attitude). Thus even if we control eﬀect of all available
individual characteristics which may aﬀect medical expenditure, some source of endogneity
remains.
In such a case, instrumental variable approach is eﬀective. The problem is choice of
the instrument. Many earlier studies ﬁnd density of hospital per capita or per area aﬀects
individuals’ probability of taking medical treatment. An empirical example in Japan is
Izumida et al. (1998). Léonard et al. (2009) is a comprehensive survey of the literature.
Municipality level density of doctor is available and we use this measure as instrumental
variable.
There is a factor which earlier studies has overlooked though may have eﬀect on medical
expenditure. Even if people’s health become worse, large amount of time is necessary to
see a doctor. And available time crucially depends on labor hour of them. Thus we makehypothesis as follows. People’s labor hour has positive eﬀect on expenditure to commercial
drugs and negative eﬀect on to doctor’s treatment. The mechanism behind is too busy
people buy commercial drugs instead of doctor’s treatment because this alternative is moretime-saving.

3

Analytical framework

3.1

Empirical model

In this paper, we simultaneously estimate expenditure to doctor’s treatment and to commercial drugs. As to how we simultaneously estimate two equations, roughly speaking, twoalternatives are there. The former is two stage estimation and the latter is simultaneous
estimation. Implementing the former is more straightforward than the latter, however, thiseasiness is in exchange for eﬃciency loss. Concretely speaking, two stage estimation canbe implemented with some simplifying assumption as to structure of variance-covariancematrix. The eﬃciency loss can be avoided with some correction, however, if we concern
not only consistency but eﬃciency, simultaneously estimate from the outset is better alternative. Thus we employ simultaneous estimation and estimation equations are as below.
In the ﬁrst speciﬁcation, we include expenditure to commercial drugs as an explanatory
variable of expenditure to doctor’s treatment.
∗yd,i= yc,i ′ α + x′d,i β d + ϵd,i , i = 1, · · · , n,∗
= x′c,i β c + ϵc,i , i = 1, · · · , n,yc,i][[ ] [
]]
[
0
1 ρ
ϵd,i
∼N
,
,
ϵc,i
0
ρ 1

4

{
yl,i =∗∗yl,i
if yl,i>0, l = d, c.
0 if yl,i ≤ 0

Where subscript d indicates expenditure to doctor’s treatment, c indicates expenditure to commercial drugs, respectively. Our ﬁrst concern is whether estimate of α in theabove equation is signiﬁcantly diﬀerent from zero or not. In the former case, estimation
results is evidence for substitutability and in the latter case, estimation results is evidence
against substitutability. Other concern is whether estimate of ρ in the above equation issigniﬁcantly diﬀerent from zero or not.

3.2

Data

We use the ﬁrst (2009) and the second (2010) waves of the Japanese Household Panel
Survey (JHPS) to examine whether there are substitutability between doctor’s treatment
and commercial drugs. JHPS is conducted ﬁrst by Keio University Joint Research Center
for Panel Studies in January 2009 and conducted in subsequent years. Advantage of using
this survey in our study are as below.
First, it provides us about individuals’ expenditure to both doctor’s treatment andcommercial drugs. In JHPS, expenditure to both doctor’s treatment and commercial drugs
are asked as follows.
“Did you spend some money to remedy your disease and/or injuries in the last year?
Please put a ring around relevant number. If you spent any, please also write how muchyour copayment is in the last year. ( Even if you receive refund of expensive medical charge,
please write how money much you paid to clerk of medical agency. )”(a)“How much money did you spent to remedy your disease/injuries in the hospital/clinic?
(directly payment to the hospital/clinic, prescription charge through a doctor’s prescription
and so on)”
1.spent some money 2.spent no money
amount of money

ten thousand(“man”) andthousand(“sen”) yen(b)“How much money did you spent to remedy your disease/injuries by other way? (buycontact lens, medicine for a cold without prescription and so on)”
1.spent some money 2.spent no money
amount of money

ten thousand(“man”) andthousand(“sen”) yen

As is evident from the above question form, both question are twofold. Thus we readily
and certainly distinguish who spent no money and who spent some money.

5

Second, KHPS provides variety of characteristics of surveyed households. For example,
economic situation, family structure, education, employment history, health condition,living environment and so on are asked in KHPS. Health condition is particularly importantin this paper. Self-rated health (SRH), chronic conditions and so on are included. We use
these variables as explanatory variables. What is worth while to note is there is a suspicion
of multicollinearity between SRH and chronic conditions. As SRH is more comprehensive,
we estimate with and without chronic condition. In both cases, SRH is controlled.
Third, JHPS is comprehensive panel survey of households in Japan. A few designatedstatistics and household surveys also provide information about medical expenditure. However, to authors’s knowledge, there is no survey provides us expenditure to both doctor’streatment and commercial drugs. Without both information, we cannot accomplish our
purpose. In addition, those data don’t contain information on their entire life and consequently is not suitable for our purpose it other reason.
Several drawbacks are also there. JHPS is ﬁrst conducted in 2009 and only two waves are
there to date. This drawback mainly matters in terms of sample size. A second drawback
is we cannot know respondents’ past health status in detail. In an ideal world, respondents’
health status from their birth to date is necessary. However, in Japan, no comprehensive
household panel studies contain detailed information as to children’s health. JHPS also
suﬀers from this drawback and thus we control only current health condition of respondents.
(Table 1 around here)
We provide deﬁnition of each variable in table 1. Subsequently, we explain the deﬁnitionof explanatory variables. Each dummy variable take the value one if respondents apply
to the corresponding situation. Labor hour is amount of labor hour per week. Next,
individual’s subjective risk aversion is deﬁned from the answer for following question.
“Usually, when you go out, you go out with an umbrella if the probabilityof rain is over x%”
We transform the answer for the above question as follows. The reason why we adopt
the following transformation is we can interpret empirical results is more easily.
SubjectiveRiskAversion = 100 − theanswerf ortheabovequestion(%)
What need attention most is accessibility. The reason is there is no available statistic
which collects number of pharmacies in each municipality. We therefore gain approximate
value of number of pharmacies in each municipality as follows. Firstly, we gain number ofpharmacists in each prefecture from Survey of Physicians, Dentists and Pharmacists(SPDP)(a). Secondly, we gain number of pharmacies in each prefecture from Report on Public
Health Administration and Services (b). Thirdly, from (a) and (b), we calculate average
number of pharmacists in each pharmacy by prefecture (c). Next, we gain number ofpharmacists in each municipality from SPDP (d). At last, we multiply (c) by (d) and gain
approximate value of number of pharmacies in each municipality. As to number of beds,
SPDP tells us exact numbers and no device is in need.
(Table 2 around here)6Descriptive statistics of variables included in the analysis are provided in table 2. A
few points are worth while to note. Firstly, average expenditure to doctor’s treatment islarger than to commercial drugs. And secondly, variance of availability is very large. As
is expected from the debate concerning doctorless municipalities, some people live in town
or village where there is no hospital and/or drugstore.

4

Empirical results

Empirical results are in table3. We provide detailed results and possible explanation below.

4.1

Without detailed health condition

Firstly, we provide detailed results and possible explanation when we employ the ﬁrst speciﬁcation. As is already noted, in the ﬁrst speciﬁcation, expenditure to doctor’s treatment
is “main” equation and expenditure to commercial drugs is “sub” equation.
(Table 3 around here)
Empirical results when we use pooled sample is in second and fourth columns of table3
and marginal eﬀects are in third and ﬁfth columns. What we should note ﬁrst is whether
there is substitutability between commercial drugs and doctor’s treatment. From second
column of table3, estimate of commercial drug is signiﬁcantly negative and signiﬁcant level
is 5%. This result imply there is in fact substitutability between commercial drugs anddoctor’s treatment. We must calculate marginal eﬀects to argue the result quantitatively.
Marginal eﬀects are provided in table3. Unit of expenditure to doctor’s treatment andcommercial drugs are both Japanese yen. Thus if one’s expenditure to commercial drugs
increase by ten thousand yen, same person’s expenditure to doctor’s treatment decreaseby about ﬁve thousand yen. Whether one regards this eﬀect large or not depends on one’sview, however, this is not negligible results.
Secondly important result is substitutability depends on income. In terms of marginal
eﬀect, if one’s household income is equal or under two million yen, ten thousand yen increase of expenditure to commercial drugs decreases same person’s expenditure to doctor’streatment decrease by about nine thousand yen. This is about two times larger than thoseof people in other income classes. This result implies there is a possibility the poor invest
too less amount of money in their health.
Next, error terms of both equations are correlated and signiﬁcant level is 0.1%. This
seems plausible. The problem here is not whether there is correlation but what is behind the correlation. As to the problem, providing complete and unanimous answer isbeyond the scope of this paper. Nevertheless, we exclude some alternatives. Individual or
household characteristics as gender, age, income, educational background, and total healthcondition and so on are all included in explanatory variables and thus cannot be cause ofcorrelation. Other than them, individual knowledge or attitude as to health is likely scenario though JHPS does not provide such information. Consequently, we cannot conﬁrm
whether knowledge and/or attitude is behind the correlation.
Thirdly, educational background indeed matters. In both equations, estimates of educational background dummies are signiﬁcant and the sign is positive as expected. Marginal
eﬀects are order of thousand yen in both equations. As we also control household income,some factors other than income is necessary to interpret the result. The ﬁrst and most7

plausible explanation is individuals’ unobserved preference. Among variety of preferences,most hopeful one is time preference. Some earlier studies point out there is not negligible
connection between time preference and educational background and also strong connection between time preference and health related behaviors. There is also a possibility risk
attitude used as to rain and as to health considerably diﬀer. The second explanation ishealth knowledge. If highly educated people have, at least on average, more abundant
health knowledge and health knowledge enhance investment in health, our result can be
explained. Regardless of what is a most appropriate scenario, the implication is educational disparity also leads to health disparity. We should remember this disparity is notnecessarily undesirable from equality. There is a possibility highly educated people simply
waste medical resources and less educated people consume appropriately. If this is the case,
how we hamper wasteful spending is an issue.
And next, availability also matters to each expenditure. Number of beds for generalpatients per unit habitable area in each municipality has positive eﬀect on expenditure to
doctor’s treatment and number of pharmacies per unit habitable area in each municipality
has positive eﬀect on expenditure to commercial drugs. This is a plausible result. Whatplausible means is if medical resources per unit habitable area is large, people can easily
access them.
One’s labor hour has signiﬁcantly negative eﬀect on expenditure to doctor’s treatment
and positive but not signiﬁcant eﬀect on expenditure to commercial drugs. Thus shorttime worker see a doctor more often than long time worker. People usually work daytime
and hospitals also opens daytime is the reason.
Eﬀects of self rated health is also as expected. The worse their health condition become,the more amount of money they spend to their health. What we should take care of is,
total eﬀect of health condition is incomprehensible if we look marginal eﬀects of the ﬁrst
equation alone. If one’s SRH goes from best (SRH=1) into worst (SRH=5), direct eﬀectof health condition is about sixty nine thousand yen. Indirect eﬀect is about 4.5 thousand(8.9 times 0.50) yen. Total eﬀect is over seventy thousand yen. From the same calculation,
If one’s SRH gets a little worse (SRH=1 to SRH=2), total eﬀect on expenditure to doctor’streatment is about nine thousand yen. From a viewpoint of households, this is perhaps
negligible, however, from a viewpoint of national health expenditure, this is not negligible.
The result thus implies importance of health maintenance as possible to hold down national
health expenditure.

4.2

With detailed health condition

Comparing results in table 3 and table 3, there are a few diﬀerences but results are almost
the same. Thus we mainly focus on what diﬀers from table 3 and brieﬂy remaining results.
Firstly, and most importantly, estimate of expenditure to commercial drugs is signiﬁcantand the sign is negative. The magnitude is also almost the same. Thus our results strongly
support the substitutability.
Secondly, the relationship between income and the substitutability also remains thesame. Thus this result is also robust.
Thirdly, estimate of ρ is signiﬁcant and the sign is positive. Signiﬁcant level is over
0.1% and all results are almost the same results as results in table??.
Fourthly, as availability, results are almost the same. However, the eﬀect of accessibility of doctor’s treatment turns into insigniﬁcant. This result implies true accessibility is8

diﬀerent from per area medical resources in each municipality. Amount of time required
to nearest clinic or hospital is possibly the desirable alternative.
Most striking change is eﬀect of SRH. First issue is eﬀect of SRH. Firstly, as to expenditure to doctor’s treatment, eﬀect of self assessed health seems almost the same as resultsin table3. Reduction rate of eﬀect is about twenty percent and seems modest. On thecontrary, as to expenditure to commercial drugs, there is a striking diﬀerence as to eﬀectof self assessed health compared to table3.
What is diﬀerent when people see a doctor and buy commercial drugs is severity of
their health problems. On average, health problems is more severe in the former casethan the latter case. Thus these result implies in case of severe health problems, degree ofmulticollinearity is modest. On the contrary, in case of severe health problems, degree ofmulticollinearity is strong.
Eﬀect of SRH does not entirely disappear even if we control more detailed conditions.
Therefore, we reconﬁrm the two implication as to SRH. The ﬁrst one is SRH tells us some
aspect of health what we cannot know from other indicators. This is often pointed out
in medical studies. The second one is, on the contrary, SRH is an integrative indicator ofhealth and more detailed conditions tell us some fraction of information SRH tells us.
Next issue is eﬀect of chronic conditions themselves. We ﬁrst check up the ﬁrst equation.
Among chronic condition variables, having back problem or cold have signiﬁcant eﬀect. The
sign of these variables are as expected. What are behind this result is those conditions, incomparison with other conditions, are more strongly connected with speciﬁc diseases.
We subsequently check up the second equation. What have signiﬁcant eﬀect are headache,cold, and back problem. Except headache, this is the same with the case of doctor’s treatment. The reason is possibly also the same. As for mild or middle degree of headache,headache medication may suﬃciently do good.

5

Conclusion

This paper empirically investigate whether commercial drugs are substitute goods of doctor’s treatment using KHPS. Main ﬁndings of this paper and implication of these ﬁndings
are as follows.
First, empirical results in this paper strongly support the hypothesis commercial drugs
are substitute goods of doctor’s treatment and the result does not depend on how we control
individuals’ health condition. More precisely speaking, when we estimate simultaneously
both equations we include either of dependent variables as explanatory variables for theother (but not simultaneously so), estimate of those are signiﬁcantly negative.
Implication of this substitutability is as follows. First, substitutability as conﬁrmed
in this paper make contribution to save limited medical resources. The reason is verystraight forward. To be checked by a doctor, at least a few medical service workers arenecessary while to purchase commercial drug, only a clerk is in need. From the viewpointof manpower, if most of Japanese refrain from consulting a doctor more than is necessary
and purchase commercial drugs instead, considerable amount of medical resources is saved.
What remains to be solved is whether commercial drugs can truly complement doctor’streatment from medical point of view. In more medical words, whether commercial drugs
remedy one’s injury and disease as doctors and prescription drug do. The answer perhaps
crucially depends on the meaning of injury and disease. If one’s health is severely damaged, usage of commercial drugs without advice from medical professionals may sometimes
9harm health. However, if one’s health is only somewhat damaged, commercial drugs cancomplement doctors and prescription drug.
Secondly, the substitutability depends on income. The substitutability is about two
times larger among the poor (whose household income is equal or less than two millionyen). Considering pure income eﬀect is also signiﬁcant, there is a possibility the poor
invest too little amount of money in health. Health disparity between the poor and therich may stem from this fact.
Thirdly, when estimating both expenditures, error terms of both equations are signiﬁcant in all cases. The sign are positive in all cases and thus this result make some
contribution to the literature from the viewpoint of studying complected relationship between health behaviors. Some previous literature already ﬁnd correlation of error termsof various health related behaviors though expenditure to commercial drugs and doctor’streatment is not an issue of them. In terms of health production function, there is a strong
possibility some invest in health excessively while others not. As Grossman (1972) notes,
production of health is recursive process and thus if diﬀerence of health investment behavior in single year is not too large, it will result in much diﬀerence of health in the longrun. Relationship between medical expenditure and harmful behavior such as smoking
and overeating (obesity) is also an important issue. If addicts are who spend less relative
to their health condition, their health will more and more worsen and consequently their
medical cost will also run up.
Fourthly, this paper reconﬁrms who are highly educated invest more in health. Thisﬁnding is almost in line with the literature (Cutler and Lleras-Muney (2006)). For example,
Cutler and Glaeser (2005) ﬁnds highly educated people smoke less than others and Molla
et al. (2004) ﬁnds highly educated people on average live longer than others. Results in thispaper indicates results as the latter can be somewhat explained by diﬀerence of medical
expenditure behavior between highly educated and less educated. This result has several
implications both to human capital policy and socioeconomic disparity.
As to human capital policy, implication is as below. If education promote one’s healthfrom somewhat reason, decision making as to how much one invests in education without
considering eﬀect on health leads to underinvestment in health. What is available to policy
makers is limited though reconsidering health education in school is example of possible
strategy. If future studies succeed in explaining more precisely what is behind relationshipbetween health and education, number of alternative policies will increase. As to thelatter issue, if health disparity is not only the result but the cause of disparity in otheraspects, health is the key factor behind glowing (or to be glow in the future) socio economic
disparity.
There are also few issues to be resolved in future research. The ﬁrst and perhaps mostimportant issue is we should conduct more studies to examine what kind of treatment
can be substituted by commercial drugs and what cannot be. In this paper, for instance,
whether individuals have caught a cold has signiﬁcant eﬀect of both expenditure. Therefore,at least if degree of cold is not severe, commercial drugs is substitutable goods for doctor’streatment. However, there are a large amount of disease of which this paper cannot takeaccount. Examining whether there is a substitutability and how this substitutability isstrong for each disease is important future research area.
The second issue is dynamics of individuals’ medical expenditure behavior. Typically,
our behavior as to what type of medical service we purchase is as follows. First, we go topharmacies and buy some commercial drugs. If we get well, the process ends here. However,
if we still feel ill, most people see a doctor and the rest of people buy more eﬀective (at10least it seems to them) drugs. And moreover, through such sequence, we learn what typesof drugs are eﬀective in what cases and in what cases we should immediately see a doctor.
Analytical framework of this paper does not incorporate such a process.
The third issue is what the correlation of error terms means. Many studies including
this study empirically conﬁrm error terms of health related behaviors are signiﬁcantly
correlated, however, these papers fail to suﬃciently exhibit what is behind the correlation.
Some possible explanations are already proposed and a few studies empirically test whether
the explanations are valid though results are mixed. This paper also tests whether risk
attitude can fully explain the correlation but the results is negative.
The fourth issue is validity of self medication. Some recent studies examine causes andresults of self medication. These paper indicates depending too much on self medication
may be harmful for health. If promoting availability of drugs without description as apolicy tool to pull in medical expenditure, empirically examining how much degree of self
medication is allowable is indispensable part of such a policy. This issue is also related tothe ﬁrst issue.
The ﬁfth issue is as to behavior of elderly people. In this paper, our datasets consist of
who are under 70. The reason is there is a diﬀerence between coinsurance rate of who areunder 70 and of who are over 69. Though limitation of sample size hinders analysis withdataset which consists of people over 69, such extension is in need. Needless to say, theolder they become, the more their medical expenditure become.

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12

13

Deﬁnition
Dependent
EXP 1
Number of cigarettes a husband (wife) smokes in a day
EXP 2
Drinking frequency of a husband (wife)
Independent
AGE
Age of a respondent
MALE
takes the value one if a respondent is male
HEALTH2
takes the value one subjective health condition of a respondent is good (2=good)
HEALTH3
takes the value one subjective health condition of a respondent is fair (3=fair)
HEALTH45
takes the value one if subjective health condition of a respondent is bad or very bad(4=bad, 5=very bad)
INCOME 200
takes the value one if annual household income is under two million yen
INCOME 400
takes the value one if annual household income is equal or over two million yen and beyond four million yen
INCOME 600
takes the value one if annual household income is is equal or over four million yen and beyond six million yen
INCOME 800
takes the value one if annual household income is equal or over six million yen and beyond eight million yen
INCOME 1000
takes the value one if annual household income is equal or over ten thousand yen and beyond ten million yen
RASubjective Risk Aversion(detailed deﬁnition is in the body of this paper)
EDCOL
takes the value one if a respondent is graduated from junior college or technical college
EDUNIVtakes the value one if a respondent is graduated from university
Working Hour
How many hours a respondent works in a week
HOSP DENS
number of hospitals per unit area in each second medical area
approximate number of pharmacy per unit area in each city/town/village
PHAR DENSHEL HEAD
takes the value one if degree of headache and dizziness is less than or equal to two(Rarely).
takes the value one if Degree of palpitation and breath shortness (1 ∼ 4)
HEL PALPHEL STOMACK takes the value one if Degree of stomachache (1 ∼ 4)
takes the value one if Degree of backache and shoulder pain (1 ∼ 4)
HEL BACKHEL TIRE
takes the value one if How often each respondent feels tired (1 ∼ 4)
takes the value one if How often each respondent catches a cold (1 ∼ 4)
HEL COLD
HEL SLEEP
takes the value one if Degree of problem with falling asleep (1 ∼ 4)
YD2010takes the value one if observation is from JHPS 2010
Note: 14 big cities are Sapporo, Sendai, Saitama, Chiba, Tokyo, Kawasaki, Yokohama, Nagoya,
Kyoto, Osaka, Kobe, Hiroshima, Kitakyushu, and Fukuoka.

Table 1: Deﬁnitions of Variables

Table 2: Descriptive Statistics

Dependent
EXP 1
EXP 2
IndependentAGE
MALE
HEALTH2
HEALTH3
HEALTH45EDCOLEDUNIVINCOME 200
INCOME 400
INCOME 600
INCOME 800
INCOME 1000
WH
RA
HOSP DENS
PHAR DENSHEL HEAD
HEL PALPHEL STOMACKHEL BACKHEL TIRE
HEL COLD
HEL SLEEP
Number of obs

Mean

Pooled Sample
Std Dev
MinMax

30.60107.473750.8290
15.7908385
132

46.8130
0.50790.26290.32490.08720.13650.30820.06930.21320.24990.19710.134629.25540.30813.45401.94780.30790.15490.32470.58830.55810.21220.270013.8828190.500000.440300.468400.282200.343400.461800.254000.409600.433000.397900.3414022.758500.461804.94780.0113
2.155800.461700.361900.468300.492200.496700.408900.4440
0
4241

14

0
0

69
1
1
1
1
1
1
1
1
1
1
1
96
1
33.132214.0055
1
1
1
1
1
1
1

Table 3: Empirical Results

EXP 1
EXP 1*INCOME 200
EXP 1*INCOME 400
EXP 1*INCOME 600
EXP 1*INCOME 800
EXP 1*INCOME 1000AGE
MALE
HEALTH2
HEALTH3
HEALTH45
INCOME 200
INCOME 400
INCOME 600
INCOME 800
INCOME 1000EDUNIVEDCOL
WHRAHOSP DENS
PHAR DENSHEL HEAD
HEL PALPHEL STOMACHHEL TIRE
HEL COLD
HEL SLEEP
HEL BACKCONSTANTσ1
σ2
ρ
Log Likelihood
N
Number of Clusters

Without chronic conditions
EXP 1
EXP 2
Coeﬃcients
Margins
Coeﬃcients
Margins-0.8810***-0.4986***[0.2554][0.1435]-0.6969*-0.3943*[0.3836][0.2169]-0.1112-0.0629[0.2595][0.1468]
0.17100.0968[0.2673][0.1513]-0.0289-0.0164[0.2848][0.1612]-0.1505-0.0852[0.2951][0.1670]
1.1758***0.6654***-0.1980***-0.0790***[0.1024][0.0585][0.0450][0.0180]-0.2637-0.1492-8.5538***-3.4263***[3.0018][1.6984][1.2650][0.5034]
13.3361***7.7849***4.5738***1.8956***[2.8644][1.7164][1.3372][0.5713]21.2524***
12.4688***4.5560***1.8700***[3.0553][1.8422][1.2810][0.5378]67.4482***
47.7739***
11.7653***
5.1293***[5.2470][4.2363][2.6652][1.1970]-27.1458***-13.5295***-11.7653***-3.9076***[6.7284][2.8969][2.6652][0.7202]-15.2218***-8.2305***-7.5867***-2.8054***[5.1924][2.6738][1.9230][0.6570]-13.4940***-7.3735***-3.3938*-1.3154*[5.0089][2.6376][1.8074][0.6797]-4.4770-2.4986-2.6065-1.0123[5.0569][2.7828][1.8731][0.7078]-9.7173*-5.3003*-3.1924-1.2234*[5.1973][2.7287][1.9891][0.7308]
11.6254***6.7252***
6.4611***2.6923***[3.1259][1.8371][1.2907][0.5586]8.1403**4.7431**3.2931*1.3699*[3.8542][2.3056][1.6867][0.7290]-0.1649**-0.0933**
0.02730.0109[0.0642][0.0363][0.0263][0.0109]-0.0761-0.0431-0.0332-0.0133[0.0538][0.0363][0.0243][0.0097]
0.4751*0.2688*[0.2553][0.1444]0.4894*0.1954*[0.2630][0.1050]-40.7564***-4.3490[8.0809][3.4097]
68.8149***[2.0887]
27.8628***[0.9569]
0.4313***[0.0314]-26296.846

With chronic conditions
EXP 1
EXP 2
Coeﬃcients
Margins
Coeﬃcients
Margins-0.8660***-0.4900***[0.2530][0.1422]-0.7049*-0.3989*[0.3874][0.2191]-0.1110-0.0628[0.2582][0.1461]
0.1651
0.0934[0.2659][0.1505]-0.0371-0.0210[0.1581][0.1600]
0.13960.0790[0.2949][0.1669]
1.1963***0.6769***-0.1639***-0.0652***[0.1040][0.0593][0.0455][0.0182]
1.06140.6001-7.3362***-2.9245***[3.0522][1.7277][1.2750][0.5065]
11.0251***
6.4027***2.6991**
1.0972**[3.0082][1.7889][1.3517][0.5602]17.2377***10.0480***
1.72790.6942[3.2385][1.9383][1.3413][0.5440]
59.6191***
41.4076***
6.2135***2.6954**[5.6087][4.4642][2.4247][1.1398]-27.2135***
13.5519***-11.6442***-3.8519***[6.7442][2.8977][2.6616][0.7162]-15.2524***-8.2448***-7.6625***-2.8172***[5.2154][2.6841][1.9101][0.6477]-13.3895***-7.3175***-3.2646*-1.2610*[5.0022][2.6349][1.7953][0.6735]-3.9804-2.2248-2.4398-0.9450[5.0614][2.7938][1.8648][0.7038]-9.2047*-5.0300*-2.9066-1.1128[5.1939][2.7375][1.9763][0.7280]
11.9614***6.9245***
6.7836***2.8218***[3.1313][1.8424][1.2865][0.5573]
8.2279**4.7962**
3.5078**1.4574**[3.8505][2.3052][1.6820][0.7282]-0.1675***-0.0948***0.02290.0091[0.0644][0.0365][0.0270][0.0107]-0.0750-0.0424-0.0345-0.0137[0.0538][0.0304][0.0243][0.0097]
0.4688*0.2653*[0.2562][0.1449]0.4752*0.1889*[0.2601][0.1034]-0.0888-0.0502
2.1570*-0.8700*[3.0243][1.7108][1.2902][0.5273]
2.23951.2772-1.6874-0.6571[3.7797][2.1724][1.5699][0.5987]
1.75520.99632.3547**0.9497**[2.8018][1.5958][1.1737][0.4796]-2.8585–1.6203-0.7065-0.2812[2.8879][1.6396][1.2599][0.5023]
9.9969***5.8207***5.8808***-2.4784***[3.1001][1.8457][1.2604][0.5593]4.8808*-2.7932*
0.7414
0.2965[2.8831][1.6672][1.2561][0.5054]5.9251**-3.3349**3.7287***1.4655***[2.8631][1.6000][1.2499][0.4852]-46.3740***-0.5328[8.2494][3.5098]
68.5659***[2.0698]
27.6926***[0.9489]
0.4250***[0.0317]-28621.045
4241(2766)

Notes:(1) Standard errors are in brackets.
(2) ***,**,* indicate significance at the 1%, 5% and 10% levels, respectively.

15