Discussion Paper No.

478

IS

THERE

BETWEEN
AND
IN

ANY

SUBSTITUTION

MEDICALSERVICES

OVER-THE-COUNTER

THE

CASE

OF

–ANALYSIS

BASED

ON

MEDICATIONSTHECOMMONCOLD?

AN

ORIGINAL

SURVEY–

Masako Ii
andYasushi Ohkusa

April 1999

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

17 May 1999

Is There Any Substitution Between Medical Services
and Over-the-Counter Medications in the Case of theCommon Cold?
-Analysis Based on an Original Survey- ∗)Masako Ii
Yokohama National University
andYasushi Ohkusa
Osaka University
Abstract
This article examines choice of health care in Japan when patients suﬀer from relatively
minor illnesses. Data was obtained from Basic Survey on People’s Life for 1986, 1989,
1992 and 1995. Patients’ heath information enable authors to make a distinction between
acute and chronic illnesses, and mild and serious illnesses. Authors exclude acute andserious illnesses from the analysis and deﬁne minor illnesses if illness is not chronic or notserious. Empirical results show that in the case of these minor illnesses, price sensitivities(changes in the probability due to price change) for the medical service are between 0.144∼ 0.149. Authors also estimate price sensitivities for medical service demand separately for
43 symptoms. Among these symptoms, for almost half of the symptoms, estimated pricesensitivities are below 1.
JEL Classiﬁcations:I11,I12,I31
Keywords: Minor illnesses, medical service demand, medical insurance system, Over-theCounter Medication

Correspondence: Yasushi Ohkusa, Osaka University, Institute for Social and Economic
Research, 6-1 Mihogaoka, Ibaraki, Osaka, Japan
tel: 06-6879-8566

fax: 06-6878-2766e-mail: ohkusa@iser.osaka-u.ac.jp

1

Introduction

Japan’s health care system has been relatively successful. Gross health indicators are thebest in the world: the infant mortality rate is 0. 46 per cent of live births and the life
expectancy is 75.9 years for males and 81.8 years for females (World Bank, 1998).
There is universal coverage with virtually unlimited access to all health care facilities.
As a ratio of national income in Japan, national medical costs were 7.28 per cent in 1993.
This ﬁgure is a little more than half of the same ratio in the United States. However, in
1997, national medical costs exceeded 28 trillion yen (about 230 billion US dollars) andcontinue to rise by at least one trillion yen every year.
There are at least three factors in this expenditure growth. The ﬁrst factor is the ageing
population. The rate at which the Japanese population is ageing is much faster than inother industrial nations. This is expected to contribute to a continued rise in medical carecosts.
The second factor is the high utilization of hopitals. In Japan there is little functional
diﬀerentiation between clinics and hospitals, and their boundary is further blurred becausea third of the clinics have a small number of beds (the distinction is primarily legal inthat facilities with more than 20 beds are designated as hospitals, whereas those having
fewer than 20 are called clinics). Neither is there much diﬀerentiation between acute andlong-term care (Ikegami,1992). The price of all providers (hospitals and clinics) is uniform,
assuming that their quality is the same. However, patients are increasingly turning awayfrom clinics in favour of hospitals because of their perceived higher quality. As a result,
there are long queues for outpatients’ visits and waiting lists for admissions to hospital.
This situation is diﬃcult to reverse because ”freedom of choice” is regarded as the cardinalprinciple in the delivery of health care in Japan.
The third factor is the high per capita costs for medications. Most physicians in clinics dotheir own dispensing and hospital-based doctors dispense from the institution’s pharmacy.

1The government sets no limits on the quantity of medications dispensed. Market prices
are surveyed periodically by the government and reimbursement prices are accordingly
adjusted. However, providers continue to make proﬁts because competition leads to a newround of price-cutting. They maintain that they need this margin to oﬀset the relatively
low reimbursement rates for the medical procedures they provide. Therefore, doctors havean incentive to over-prescribe. Patients’ medical insurance covers medication costs, giving
them a price incentive to favour prescribed products.
The health care reform plan proposes to increase the coinsurance rate for relatively minor
illnesses as well as for prescribed medications for these minor illnesses. Minor illnesses areillnesses such as common cold and diarrhea, which are curable by standard treatment withreasonable cost. Their treatment has several options including medical services provided
by hospital or clinic. Therefore, the eﬀect anticipated of an increase in the coinsurance rateon national medical costs is not clear. If patients are not sensitive to a price change, anincrease in the coinsurance rate may not have an appreciable eﬀect on national costs. On
the other hand, if patients are sensitive to a price increase, they may choose other options
and decide to seek medical services less frequently. An increase in the coinsurance rate willthen decrease the national medical costs.
When patients suﬀer from minor illnesses they face three options in this research: they
can consult a doctor, purchase over-the-counter (OTC) medications or do nothing. The
price sensitivity, therefore, may be relatively high, but this has to be tested empirically1) .
In the United States, where extensive research on health economics is undertaken, only
a few studies focus on demand for medical services related to minor illnesses or on the substitution between prescribed and OTC medications (Stuart and James, 1995; Fillenbaum,
et al., 1995). Of these few studies, particular attention is paid to the research of Leibowitz(1989) because of its originality and, most of all, its research design. The research was partof the study referred to as the Health Insurance Experiment (HIE) that Newhouse (1993)
and others at the RAND institute initiated in 1971, and that lasted for about 10 years. HIE2

is a longitudinal study that experimentally altered the medical care cost-sharing faced byfamilies. Between November 1974 and February 1977, HIE assigned families to insurance
plans that diﬀered in the amount of cost-sharing. According to this study, under a freeplan (deﬁned as no out-of-pocket cost to the family), compared to a 95 per cent coinsurance plan (families paid 95 per cent of their medical expenses), the demand for medical
services increased by about 20 per cent. The spending on medical services under the freeplan was 50 per cent more than that of the 95 per cent coinsurance plan. This means theprice sensitivity (change in the probability due to price change) of medical service was , on
average, 0.21.
Using the HIE, Leibowitz (1989) focused on minor illnesses and analysed the substitution between prescribed and OTC medications. Her empirical results did not support the
expectation that people assigned less generous insurance for prescription drugs substitute
OTC for prescriptions. Although the price sensitivity/price elasticity was not accounted forin either study, Newhouse (1993) indicated that, in the case of inﬂuenza, price sensitivity
was around unity. This suggests that for minor illnesses, the demand for medical services
is relatively sensitive to price.
In Japan, there are only a few studies that estimate the demand for medical services.
Among these studies, Yamada (1997) used hospital-based data to estimate the demand for
medical services. Without the use of a household-based survey, he was not able to analysepatients’ preferences, decisions and behaviour regarding medical services.
Ii and Ohkusa (1998a) were the ﬁrst to attempt an estimation of the demand for medical
services related to minor illnesses. They modelled explicitly the incidence of minor illnesses
and, using micro household data, measured the price sensitivity of the demand for medicalcare for minor illnesses in Japan. Their data, from the Basic Survey on People’s Life
(BSPL), were not experimentally or extensively collected like the data in the HIE. However,the sample size in the BSPL - about 600,000 - is much larger compared to that of the HIE,
which is about 2,000. BSPL includes household and individual characteristics such as
3

gender, age, income and assets, and detailed health information. Their results show thatthe price sensitivity for medical services was 0.208, which is comparable to that for theUnited States, and that medical services and OTC medications are substitutes.
This paper, using patients’ heath information, deﬁnes minor illnesses and estimates pricesensitivity for these minor illnesses. The data set also includes detailed information on
subjective symptoms. Authors also estimate price sensitivty for medical service demand
separately for 43 symptoms. Empirical results show that in the case of these minor illnesses,
the price sensitivity for the medical service are between 0.144 ∼ 0.149.
Section 2 presents this study’s major hypotheses. Section 3 describes the data. The
empirical model is explained in Section 4. Empirical results are given in Section 5. A
summary and conclusion are given at the end.

2

Hypotheses

Risk aversion and uncertainty about future health events create a demand for medical
insurance. However, once insurance is in place, moral hazard leads to over-utilizationof medical care. This phenomenon may be more serious in the case of relatively minor
illnesses. There is no reason for rates of insurance to be the same for diﬀerent types ofhealth care. In this section, the eﬀects on national medical costs and patients’ consumer
surplus are analysed under an assumed scenario where the new medical insurance reforms
exclude minor illnesses from insurance coverage.
Under the universal medical insurance plan, there is a gap between the price a patient
faces and the price a society faces. Figure 1 shows the market for medical services. The
vertical axis shows social costs of medical services and the horizontal axis shows quantitiesof medical services. In our model, quantity is measured by the proportion of patients who
visited doctors out of the total number of patients. The supply curve (SS) is horizontal in
the short run. This reﬂects the fact that, in Japan doctors cannot refuse patients’ treatment

4and cannot increase prices in times of high demand. Queuing is the rationing device used
to limit demand. The value of waiting time is part of the implicit price.
Suppose DD refers to the demand for patients without medical insurance and D  D  forpatients with medical insurance. Suppose further that the current price level is given by
AO so that the current quantity consumed in the market by patients without medical
insurance is given by QO. Hence, total expenditure on the commodity is given by the areaof the rectangle AOQH. However, if the market demand curve is actually equal to DD,
then some consumers would be willing to pay more for some units of the commodity thanthe market price AO. The total amount that consumers would be willing to pay is equal
to the rectangle AOQH plus the triangle GHA, that is, the consumer surplus.
Under a medical insurance plan, the patient is on the demand curve D  D  and increases
his demand from QO to Q O. Since a coinsurance is BO, he pays BOQ F and the government pays ABF E. The consumer surplus increases by ABF H and the national medical
costs increase by HQQ E. If an increase in consumer surplus (ABF H) is greater than
the increase in national medical costs (HQQ E), then medical insurance is beneﬁcial toconsumers. In order to measure the eﬀect of a rise in the coinsurance rate on national
medical costs, a demand function for medical services needs to be estimated.
The demand for medical services by a patient i suﬀering from a minor illness at period tdepends on the price of the medical service or coinsurance, pM
i,t , the price of a close substitutesuch as OTC medications pOt , income Ii,t , the opportunity cost to visit a doctor Ci,t , andother variables that aﬀect the demand for medical services Zi,t . Therefore, the demand forO
medical services is expressed as d(pM
i,t , pt , Ii,t , Ci,t , Zi,t ). The variable Zi,t includes healthcapital, employment status, and attitudes toward medical services.
In this paper, the hypotheses to be tested are that: 1) medical services are normal goods
in an economic sense and 2) medical services are substitutes for OTC medication. Using the∂d∂d∂d> 0, ∂C<
above notations, these hypotheses can be expressed as ∂p∂dM < 0, ∂p
O > 0, ∂I
i,t
i,t
i,t

5

t

0, and ∂Z∂di,t < 0, that is, an increase in coinsurance decreases the demand for medical care,an increase in the price of OTC medications increases the demand for medical care, anincrease in income level increases the demand for medical care, an increase in the time costdecreases the demand for medical care, and, ﬁnally, an increase in health capital decreases
the demand for medical care. When relatively healthy persons suﬀer from an illness they
are more likely to recover sooner. Therefore, the marginal cost in receiving medical services
is considered to be low.

3

Data

The data for this research were obtained from Basic Survey on People’s Life for 1986,
1989, 1992, and 1995 by the Ministry of Health and Welfare, Japan. The questionnaire forthis survey consists of three sections: household, health, and income and saving. All thehouseholds answered the household and health sections while only a sixth of the households
answered the income and saving section. The survey covered all the 47 prefectures in Japan.
Among these prefectures, 2,000 areas were randomly chosen, and within the chosen areas,
all the households were surveyed.
Although the data are not panel since areas were randomly chosen each time, this data
set provides rich information especially on income and consumption, job status, the type of
medical insurance, and health condition. The analysis focuses on those between 22 and 59
years old, neither hospitalized nor permanently bed-ridden2) , and who suﬀer from a minor
illness.
The variables used in this analysis are age, gender, symptoms (period, seriousness),type of occupation (managerial position, government employee, employee at the large, or
medium-sized enterprise, non-regular worker), labor income, per capita household income
minus own labor income, net ﬁnancial asset, real asset (tax payment on tangible asset),
and medical insurance enrollment (type, insured or dependent).

6

Testing the hypothesis stated in the previous section requires information on the prices
of medical services, pM
i,t . These price data are not available from the survey. However,everyone answers the type of medical insurance (National Health Insurance or Employees’
Insurance) and their insurance status (insurer or dependent) in the questionnaire. Under
the Japanese health insurance system for self-employed persons, pensioners and their dependents (National Health Insurance), coinsurance rate is 30 percent for both in-patientand out-patient care. Under the insurance system for employees (Employees’ Insurance),coinsurance rate is 10 percent of the medical costs. For their dependents, coinsurance rateis 20 percent for inpatient care and 30 percent for outpatient care.
Moreover, prices of OTC medications, pOt , are not available. Year dummy is used since
everyone faces the same price within the same year. Also, prefecture dummy represents
the regional variations such as competition among pharmacies or attitude toward OTC
medications.
Income variables Ii,t include own labor income, total household income net of own labor
income, net ﬁnancial asset, and real asset. For time cost, the employment status is used.
For example, when a patient holds managerial position, he or she has a higher time costcompared to those who do not hold such a position. When a patient holds only a shorttime contract, he or she might have a lower time cost.
Except for the 0.6 percent of the population receiving public health care assistance, allJapanese are mandatorily covered by National Health Insurance or Employees’ Insurance
plans. Therefore, in either plan, patients without jobs face a coinsurance rate of 30 percent.
Patients who are not employed face higher coinsurance rate but lower time costs ofdemand for medical services than patients with jobs. Therefore, in the following analysis,
there is a need to distinguish whether the change in demand comes from diﬀerent level oftime costs or diﬀerent level of coinsurance rates. In the next section, the authors separately
estimate the demand for the whole sample - both workers and non-workers - and the demand
only for those who have jobs - workers.
7

Sample size for the workers is 60,487. Sample size for both employed and not-employedis 86,065. The ﬁrst half of table 1 shows the sample statistics for workers. It reveals thefollowing characteristics: 33 percent sought medical service, 30 percent purchased OTC,
and 37 percent did nothing. The average age is 43.4 years, and 43 percent of the patientsare female. On average, the patients stay in bed for 0.45 days. Less than four percent of
the workers hold managerial positions. Among patients, 6.4 percent work as government
employees, 12.7 percent work for the large enterprises those which have 1,000 or more
employees, and 16.3 percent work for the medium-sized enterprises those which have 100 to
999 employees. About seven percent are non-regular employees whose employment period
is from one to 12 months. The average annual labor income is 1.31 million yen, otherincome is 0.32 million yen, and net ﬁnancial asset is 1.84 million yen. For 59 percent of thepatients, coinsurance rate is 10 percent. Of the total sample, 70.3 percent have jobs. The
second half of table 1 shows the sample statistics for the total population, both workers andnon-workers. In this sample, a proportion of the female is higher at 57 percent comparedto that in the workers only sample. For the 42 percent of the patients, coinsurance rate is
10 percent, and only 70 percent are workers. Therefore, it is not surprising that averageannual labor income is only 0.31 million yen while other income is 0.48 million yen, higher
than that for workers.

4

Estimation Model

The dependent variable takes three values: Ti,t = 1 if a patient demands medical services3) ,Ti,t = 2 if a patient demands OTC, and Ti,t = 0 in other cases. Independent variables are
age Ai,t , gender (Gi,t = 1 if female, Gi,t = 0 if male) his or her own labor income Ii,t , percapita household income net of own labor income, Iˇi,t net ﬁnancial asset Fi,t , real asset(ﬁxed property tax payment) Ri,t , Employment status dummy (Wi,t = 1 1 if a patientM= 1 if holds managerial
is employed and Wi,t = 0 if otherwise), type of occupation (Ji,t

8

M
G
G
position, Ji,t
= 0 otherwise, Ji,t
= 1 if works as a government employee, Ji,t
= 0 otherwise,LLM= 1 if works for a large enterprise, Ji,t
= 0 otherwise, and Ji,t
= 1 if employed by aJi,tMP
= 0 otherwise) , length of employment (Ji,t= 1 if employment
medium-sized enterprise, Ji,tP
= 0 otherwise), coinsurance rate Pi,t , and prefecture
period is from one to 12 months, Ji,tand year dummy variables. Since there is no information on OTC prices, prefecture dummy
is used as a proxy for the OTC price.
We estimate:
j∗
j
j
j
j
j
j
j
2
ˇ
= α0j + αA
Ai,t + αATi,t2 Ai,t + αG Gi,t + αI log Ii,t + α ˇ log Ii,t + αF Fi,t + αR Ri,tI

+ αW Wi,t +


 1

Ti,t

 j k

αk Ji,t + αpj Pi,t +47jβk Areaki,t +

3

j

t
γt Y eari,t+ εji,t (j = 0, 1, 2)

t=1

k
k=1
1∗
2∗
1∗
0∗
Ti,t
> Ti,tandTi,t> Ti,t
1∗
2∗
2∗
0∗
Ti,t
< Ti,tandTi,t> Ti,t

if
=
2 if


0 otherwise(1)

by using multinomial probit estimation method is used (Daganzo, 1979) 4) . Because theprobability of choosing j adds up to 1, we can estimate the diﬀerences in probability.
Here, we set j = 0, the choice of ”do nothing”, as the basis for comparison. The error
term, εji,t (j = 0, 1, 2), is a random variable and Ω follows a three-dimensional normal
distribution. Since the error terms in the estimation also redeﬁned by the diﬀerence from
ε0i,t and εji,t (j = 1, 2), and normarizing the (1,1) element be 1, its variance-covariance matrix
should be−1 1 0−1 0 1Ω−1 1 0−1 0 1





=

1 ρ
ρ σ



.

(2)

The estinmation procedure is BHHH method with grid research over ρ and σ. This procedure is as accurate as Keane(1992) has shown.

5

Estimation Results

The data set includes detailed information on the type of illness, therefore it allows theauthors to distinguish whether an illness is acute or chronic, and mild or serious. Authors
9

exclude acute and serious illnesses from the analysis and deﬁne that a patient suﬀers from aminor illness if he reports at least one of the following illnesses: hypertension, hypotension,common cold, gastritis, decayed tooth, gingivitis, autonomic nerve, troubles with eyes or
ears, nasal catarrh, skin trouble, neurosis, osteoporosis, or women’s diseases. If a patientdoes not see a doctor and does not know a type of illness he suﬀers from, it is also deﬁned
as a minor illness.
Tables 2 and 3 show the determinants of the workers’ demand for medical services and
OTC when they suﬀer from minor illnesses as deﬁned above. Sample size is 40,895. The ﬁrst
columns show marginal eﬀects. These eﬀects tell how a unit change in explanatory variablechanges probabilities of choosing medical services or OTC. For example, a probability ofchoosing medical service is 4.7 percent higher, and of choosing OTC 3.1 percent lower forgovernment employees than for others.
Other studies have found that females demand medical care more than males do (Leibowitz, 1989; Ii and Ohkusa, 1998). However, when analyses are focused on the case ofminor illnesses, not on illnesses in general, there seems to be no clear gender diﬀerences inthe demand behavior.
Coinsurance rate is signiﬁcantly positive. This implies that a demand curve is downward
sloping, and increasing coinsurance rate from 10 to 30 percent, medical demand decreases
by 2.98 percent. This means, on average, price sensitivity is 0.149 for workers. US data
show that the lowest estimate of the price sensitivity of medical service in general is 0.21,
which is equivalent to our estimate5) . However, in the US, a price sensitivity of medical
service for minor illnesses (inﬂuenza) is around 1 and much larger than ours 6) .
Tables 4 and 5 show estimation results for the total sample. All of them in the sample
suﬀer from the minor illnesses as deﬁned above. The sample size is 57,521. Increasing coinsurance rate from 10 to 30 percent, medical demand decreases by 2.88 percent, therefore,on average, price sensitivity is 0.144.
The data set also includes detailed information on subjective symptoms. Authors esti10

mate price sensitivities for medical service demand separately for the following 43 symptoms: fever, languidness, insomnia, headache, dizziness, sluggishness, numbness, mucus,
dimness of sight, pain in ear, ringing in ear, toothache, bleeding gums, nasal congestion,
running nose, soar throat, cough, phlegm, wheezing, palpitation, short of breath, pain inchest, nausea, diarrhea, constipation, heartburn, indigestion, diminished appetite, stomachache, hemorrhoid, eruption, pruritus, pain in urination, frequent urination, incontinenceof urine, morning sickness, menstrual irregularity, vaginal discharge, incised wound, sprain,stiﬀ shoulders, backache, and arthralgia 7) . Among these symptoms, for almost half of thesymptoms, estimated price sensitivities are below 1. For example, a price sensitivity forworkers is 0.55 for fever, 0.27 for sore throat, and 0.25 for stomachache. These results arealmost the same as the total sample.

6

Concluding Remarks

In this paper, using data from Basic Survey on People’s Life, the price elasticity of demand
for medical services in case of relatively minor illnesses is estimated. The data set includes
detailed information on the type of illness, therefore it allows the authors to deﬁne minor
illnesses in more accurate manner. With this deﬁnition, authors obtain the price sensitivity
around 0.144-0.149.
In the sample, 32 percent sought medical services, therefore, the estimated price sensitivity suggests that if the new medical insurance reform plan should exclude minor illnesses
from the insurance coverage, almost half of the patients - 13.5 percent point - may stopseeking medical services. The data set also includes detailed information on subjective
symptoms. Authors are able to estimate price sensitivity for medical service demand separately for 43 symptoms. Estimation results show that among these symptoms, for almost
half of the symptoms, estimated price sensitivities are below one.
These results are, however, obtained from a household survey, including medical infor-

11

mation, which was conducted on a given day. Therefore, extreme care must be exercisedin interpreting the results. Ideally, longitudinal survey will improve the results. This is the
subject matter of further investigation.

12

Footnote*) This research has beneﬁted from a research meeting called ”Social Welfare and Economics”. We would like to acknowledge the comments of the participants at themeeting, particularly Masahiro Toriyama and Maki Yoshida of the Nomura ResearchInstitute and Makoto Satito, Yasushi Iwamoto, and Kotaro Hitomi from Kyoto University and other seminar participants at Miyatsu conference. The views expressedherein are the authors’ own and do not reﬂect that of their institutional aﬃliations.
We would also like to acknowledge the assistance provided by Kazuko Matsumoto.
1) Surprisingly, there is no documentation which explains the amount of national medical costs spent on relatively minor illnesses because nationwide surveys are done only
on particular days. For example, the Ministry of Health and Welfare conducts apatient survey on June 1 of every year, and a hospital and clinic survey on October
1 of every year. These surveys, conducted in summer or fall, will not give us a reliable estimate on medical cost used, for example, for cold or ﬂu. A rough estimateshows that about half of the total medical cost is for the outpatients. Out of thesecosts however, no information is given on the shares of the cost for minor illnesses
or for chronic diseases including geriatric diseases. Therefore, in this paper, using an
estimate of the price sensitivity of medical demand for minor illnesses, we can only
measure a reduction in medical cost by proportion, not by value. Medical cost used
for minor illnesses should be also estimated by research.
2) If a patient is bed-ridden for more than a month, then we deﬁne the patient aspermanently bed-ridden.
3) Note that the data are not panel.

134) Multinomial logit is easier to estimate than multinomial probit. But in multinomial logit, odds ratio have to be independent of the other alternatives. HausmanMcFadden (1984) test suggests that if a subset of the choice set is truly irrelevant,omitting the choice set in our model Ti,t = 0, from the model will not change parameter estimates systematically. Hausman-Mcfadden test statistics for workers are
124.12 and for the whole sample 156.84. With the degree of freedom, 64 and 63,
respectively, chi-squared test rejects the null hypothesis of independence with 99
percent probability. Therefore, we are not able to use multinomial logit model.
5) Our estimate is obtained from a one-day survey for the patients who reported illness
on the particular day, while in the US, the estimate is from a longitudinal, one-year
survey.
6) There is no account on the price sensitivity or price elasticity of medical service forminor illnesses in either Newhouse (1993) or Leibowits (1989). This price sensitivity
is inferred from their tables.
7) Estimation results can be obtained from authors upon request.

14

References[1] Keans,M.(1992),”A Note on Identiﬁcation in the multinominal Probit Model,” Journalof Business and Economic Statistics, 10, pp.193-200.
[2] Daganzo,C.(1979),Multinominal Probit: The Theory and Its Application to Demand
Forecasting,Academic Press.
[3] Fillenbaum G., J. T. Hanlon, E. H. Corder, T. Ziqubu-Page, W. E. Wall, Jr. and D.
Brock. (1993),”Prescription and Nonprescription Drug Use among Black and White
Community Residing Elderly,”American Journal of Public Health,83,pp.1577-1582.
[4] Hausman,J. and D.McFadden(1984), ”Speciﬁcation Tests for the Multinomial Logit
Model,”Econometrica, 52, pp.1219-40.
[5] Ii,M. and Y.Ohkusa(1998), ”Is There Any Subustitution Between Medical Services
and Over-the-Counter Medications?”, manuscript.
[6] Ikegami, N.(1992), ”The Economics of Health Care in Japan,” Science, 258,pp.614618.
[7] Leibowitz,A.(1989),”Substitution Between Prescribed and Over-the-Counter Medecation,”?Medical Care,27,pp.85-94.
[8] Newhouse, J.(1993),Free For All,Harvard University Press.
[9] Stuart, B. and G. James(1995),”Are Prescribed and Over-the-Counter Medecines Economic Substitutes? A Study of the Eﬀect of Health Insurance on Medicine Choicesby the Elderly,”Medical Care,33,pp.487-501.
[10] Yamada,T.(1997), ”Iryo sabis no jyuyokannsru no suitei (Estimation of Demand for
Medical Service)” (unpublished, in Japanese).

15[11] World Bank(1998),World Development Report, Oxford University Press.

16

Table 1 : Descriptive Statistics

Workers Only (N=60,487)
Medical service
Over-the-Counter medication
Do nothing
Age
Length of stay-in-bed
FemaleManagerial position
Government employee
Medium-sized enterprise
Large enterprise
Non-regular employee
Labor income (in log ）Other income (in log)
Net ﬁnancial asset
Real asset (in log)
Coinsurance rate (dummy)

MeanStandard
deviation

Minimum

Maximum.3285445.2999614.3714941
43.38016.4517851.4339566.0389613.0640921.1273265.1626855.0696458
4.875487
3.478245
183.8823
2.393375.5877219.469689.4582456.4832093
10.48763
1.408925.4956244.1935049.2449196.333342.3690824.2545518
2.053332
2.011145
973.8945
2.571293.49225

0
0
0
22
0
0
0
0
0
0
0
0
0
-2250
0
0

1
1
1
60
10
1
1
1
1
1
1
9.305568.729397
3250
11.512931(continued(

17

(contiuned)
Both Workers and Non-Workers (N=86,065)
Medical service
Over-the-Counter Medication
Do nothing
Age
Length of stay-in-bed
FemaleManagerial position
Government employee
Medium-sized enterprise
Large enterprise
Non-regular employee
Labor income (in log)
Other income (in log)
Net ﬁnancial asset
Real asset (in log)
Employment status (dummy)
Coinsurance rate (dummy).3430768.297897.3590262
43.68973.5215702.5737092.0273913.0450592.0895153.114374.0489636
3.427651
3.873235
201.6496
2.319853.7030376.4158438.4747405.4573375.4797185
10.74956
1.558349.4945408.1632219.2074355.2854883.3182674.2157936
2.815468
1.864505
978.447
2.565762.4569233.4928706

0
0
0
22
0
0
0
0
0
0
0
0
0
-2250
0
0
0

Note: (1) ”Medical service”, ”Over-the-Counter Medication”, and ”Do nothing” are dummy
variables which take the value of one if a patient chooses the choice, and 0 if otherwise.
(2) ”Female” is a dummy variable which takes the value of one if a patient is female and0 if male.
(3) ”Managerial position”, ”Government”, ”Large enterprise”, and ”Medium-size enterprise” are dummy variables which take the value of one if a patient holds a managerial
position, works at government, works at large enterprise with 1,000 or more employees,
and works at medium-size enterprise with 100 to 999 employees, respectively.
(4) ”Non-regular employee” is a dummy variable which takes the value of one if employment period is between one to 12 months.
(5) ”Labor income”, ”Other income”, ”Net ﬁnancial asset” and ”Real asset (Tax payment
on tangible asset)” are in 10,000 yen, in 1995 real term.
(6) ”Coinsurance rate” is a dummy variable which takes the value of one if coinsurance
rate is 10 percent (employees), and 0 if it is 30 percent.
18

1
1
1
60
10
1
1
1
1
1
1
9.305568.729397
3250
11.51293
1
1

(7) ”Employment status” is a dummy variable which takes the value of one if a patientis employed and 0 if otherwise.

19

Table 2 : Marginal Eﬀect for Medical Services (Workers Only)
Explanatory variables
Age
Length of stay-in-bed
FemaleManagerial position
Government employee
Medium-sized enterprise
Large enterprise
Non-regular employee
Labor income (in log)
Other income (in log)
Net ﬁnancial asset
Real asset (in log)
Coinsurance rate

marginal eﬀect.0001615.0049182-.0030734.0070886.0461069.0109177.0210605-.0104414-.0034317.0014803
5.75e-06-.0022794.0298321

t-value
0.331
1.994-0.333
0.359
2.472
0.819
1.589
-0.654-1.4400.661
1.458-1.026
2.768

Prob Value
0.741
0.046
0.739
0.720
0.013
0.413
0.112
0.513
0.150
0.509
0.145
0.305
0.006

Note:
Number of observation is 40,895.
Marginal eﬀects indicate discrete changes from 0 to 1 for dummy variables.
Pseudo R2 is 0.2141. An estimated coeﬃcient and t-value (in parenthesis) of ρ are
0.3343(2.7697) and of σ are 0.7384(4.3744).
An estimation equation includes a constant term, but in this table of marginal eﬀect, aconstant term cannot be deﬁned.

20

Table 3 : Marginal Eﬀect for Over-the-Counter Medication(Workers Only)
Explanatory variables
Age
Length of stay-in-bed
FemaleManagerial position
Government employee
Medium-sized enterprise
Large enterprise
Non-regular employee
Labor income (in log)
Other income (in log)
Net ﬁnancial asset
Real asset (in log)
Coinsurance rate

marginal eﬀect.0009445.0023762-.0148858-.033997-.0294592.005575-.017644.0190208-.0019374-.0018038-6.54e-06-.0019862-.0103842

t-value
2.152
1.185-1.919-2.003-1.9050.490-1.5811.424-0.973-0.911-1.880-0.998-1.158Prob Value
0.031
0.236
0.055
0.045
0.057
0.624
0.114
0.154
0.330
0.362
0.060
0.318
0.247

Note:
Number of observation is 40,895.
Marginal eﬀects indicate discrete changes from 0 to 1 for dummy variables.

21

Table 4 : Marginal Eﬀect for Medical Services (Both Workers and
Non-Workers)
Explanatory variables
Age
Length of stay-in-bed
FemaleManagerial Position
Government employee
Medium-sized enterprise
Large enterprise
Non-regular employee
Labor income (in log)
Other income (in log)
Net ﬁnancial asset
Real asset (in log)
Employment status (dummy)
Coinsurance rate

marginal eﬀect.0002723.0038797-.007847.0082478.0472706.0115126.0227414-.010579-.0041296.0013366
3.06e-06-.0034369-.0232418.0288198

t-value
0.706
1.918-0.914
0.430
2.573
0.873
1.794
-0.656-1.8060.640
0.862-1.875-1.9742.873

Prob Value
0.480
0.055
0.361
0.667
0.010
0.383
0.073
0.512
0.071
0.522
0.389
0.061
0.048
0.004

Note:
Number of observation is 57,521.
Marginal eﬀects indicate discrete changes from 0 to 1 for dummy variables.
Pseudo R2 is 0.1669. An estimated coeﬃcient and t-value (in parenthesis) of ρ are
0.3132(3.1765) and of σ are 0.6758(5.8765).

22

Table 5 : Marginal Eﬀect for Over-the-Counter Medication(Workers and Non-Workers)
Explanatory variables
Age
Length of stay-in-bed
FemaleManagerial position
Government employee
Medium-sized enterprise
Large enterprise
Non-regular employee
Labor income (in log)
Other income (in log)
Net ﬁnancial asset
Real asset (in log)
Employment status
Coinsurance rate

marginal eﬀect.0008619.0023855-.0085012-.0352704-.030698.0057971-.0183217.0191237-.0012599-.002835-2.52e-06-.0008394.0139663-.0097513

t-value
2.512
1.563-1.207-2.130-1.9830.508-1.7051.431-0.656-1.507-0.814-0.5451.328-1.182

Prob Value
0.012
0.118
0.227
0.033
0.047
0.612
0.088
0.152
0.512
0.132
0.416
0.586
0.184
0.237

Note: Number of observation is 57,521.
Marginal eﬀects indicate discrete changes from 0 to 1 for dummy variables.

23