Discussion Paper No.

490

CONJOINT
FOR

THE

DEMANDRELATEDTOANALYSISOFHEALTHCOMMON

CARE

COLD

Wataru Suzuki
andYasushi Ohkusa

December 1999

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

November 1999

Conjoint Analysis for the Demand of Health Care related
to Common Cold
Wataru Suzuki
Graduate school of Economics, Osaka University
andYasushi Ohkusa
Institute of Social and Economic Research, Osaka University
This article examines by means of Conjoint Analysis(CA) the health care choicein Japanese people suffering from the common cold.

CA is a stated preferencetechnique that uses a survey and involves the hypothetical scenarios.
Estimates obtained from CA might be biased because it is difficult to keep thequestionnaire accurate under hypothetical scenarios. This article is the firstattempt to evaluate the magnitude of the bias in CA, by comparison with actual
behavior.

Using information about people who have actually no healthinsurance, our empirical result show that CA biases are around 12%.

Keyword: Conjoint Analysis, CA bias, Demand elasticity for medical care,
Minor illness
Correspondence: Yasushi Ohkusa, Osaka University, Institute of Social andEconomic Research 6-1, Mihogaoka Ibaraki, Osaka 567-0047,
JAPAN
TEL:+81-6-6879-8566

FAX:+81-6-6878-2766

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

1.Introduction
Because of the rapid aging societies such as Japan, the control of medical
expenditure has been among the most prominent issues in the policy debate. In
discussing medical cost-control policy, information about the price elasticity ofdemand for medical services is of paramount importance.

Unfortunately, veryfew

topicsresearch

has

been

(Ogura(1990),Nishimura(1987)).

undertaken

on

this

in

Japan

Because of the difficulty in accessing data at

the hospital or household level in Japan, these researches consist of estimatesderived from cross-sectional data aggregated in the prefecture level, or timeseries data aggregated at the national level. These researches have, therefore,
suffered from various statistical problems associated with aggregate data, suchas cross-section bias and spurious regression.

Moreover, the data on healthcare expenditure comprises expenditure related to both minor and serious
illnesses.

Minor illnesses are defined as illness such as common cold anddiarrhea, which are curable by standard treatment at reasonable cost, and byalternative treatment such as OTC medicine, natural recovery, and remedies
based on folklore.

Research using aggregate measures of health careexpenditure cannot really contribute to policy formation, because the luck of
substitution for non-minor illnesses such as cancer or heart attacks makes it
impossible to determine price elasticity.
In the United States, despite extensive research in health economics, only afew studies focus on demand for medical care for minor illnesses or thesubstitution between prescribed and OTC medicine(Stuart and James, 1995;
Fillenbaum et al.,1995).

The significant exception to this is Newhouse(1993),

who uses data from RAND Health Insurance Experiment.
the various problems associated with aggregate data.

This data set avoids

Moreover, as it consistsof household rather than hospital data, it may clarify the decisions that patientsmake before visiting a doctor.

Leibowits(1989) undertakes important researchthat focuses on minor illnesses and studies the substitution between prescribed
and OTC medicine.
Ii and Ohkusa(1999a) and Ii and Ohkusa(1999b) are the first attempts toestimate the price elasticity of the demand for health care for minor illnesses in1

Japan.

Their analyses explicitly take into account the substitution between

formal medicine and OTC or natural recovery.

Using micro data from the

Living Standard Survey in Japan, Ii and Ohkusa (1999a) find that elasticity is
distributed in the range 0.0123 - 0.149.

On the other hand, Ii and Ohkusa(1999b) use original data when specializing in the common cold.
reveals price elasticity in the range 0.23 - 0.36.

This research

However, these studies, which

control as much as possible for individual properties, may not be immune fromthe contamination of cross-sectional bias because, unless panel data is used,
they cannot completely eliminate the individual effect.

If the questionnaireasks the patient’s choice under several hypothetical situations over crosssectional data collection, it can be regarded as a kind of panel data.

In thissense, the research would supplement that which uses micro data, as do Ii and
Ohkusa(1999b).
In this research, Conjoint Analysis(CA) is one of the most reliable techniques
for dealing with the individual effect by using a questionnaire survey.
questionnaire services hypothetical choice.

The CAThus, the researchers can analyse

choice by reference to the scenarios, and individual properties in statisticalmodels fully control out the individual effect.
To evaluate the benefit of a new medicine or a new technology in the field ofhealth economics, researchers have conventionally employed the Willingness to
Pay (Tolley et al (1994)) or Standard gamble, Time trade-off and Rating Scale
methods.

However, these methods suffer from various theoretical problems.

For instance, the price obtained by Willingness to Pay obviously differs fromutility.

The other three methods also display this shortcoming, which is not

utility.

Under the theoretical concept of ordinal utility, utility cannot becompared or aggregated among individuals.

Compared with Willingness toPay, CA has the advantage that respondents react as the price is given and makeno prior assumptions about their choices.

Furthermore, the statistical modelusing CA can experiment with policy simulations by changing the explanatory
variables.
First, CA has conventionally been widely used in the field of environment economics andtraffic economics.

In health economics, CA has recently been frequently applied2

to the evaluation of new medical treatment technology(Ryan, 1999a; Ryan, 1999b;
Bryan, 1999; Telser and Zweifel, 1999; Ratcliffe, 1999; San Miguel and Ryan, 1999;
Johnson et al, 1999; Ryan and Farrar; 1994; Ryan and Hughes, 1997;, Van der Pol and
Cairns, 1997; Van der Pol and Cairns, 1999; Bryan et al., 1998).

For example, it has

been applied to examinant fertilization(Ryan , 1999a), dentistry correction
technology(Ryan and Farrar, 1994), suspension technology(Ryan and Hughes,
1997), blood transfusion technology(Van der Pol and Cairns, 1997) and theapplication of magnetic resonance imaging(MRI) todamage caused by knee

injuries(Bryan et al., 1998).
Although CA may be more excellent method than conventional ones, it
potentially suffers from bias arising from two sources: hypothetical choice andhypothetical scenarios.

The purpose of this research is to distinguish andevaluate each kind of bias.

If CA is applied more frequently to other fields, themagnitude of bias should be evaluated 1 ) .

Especially with regard to the latter,

we estimate the impact of bias by using the information about people who haveno health insurance.
This paper is organized as follows.
from an original survey.

Section 2 describes the data obtained

Section 3 presents demand for health care using CA.

The biases arising from hypothetical choice and scenario are evaluated in
section 4 and 5, respectively.

Section 6 summarizes the result and discusses

further research.

2.DataThe data used here were obtained from research conducted by the authors.
The respondents were 20-60 years who live in western Japan.
chosen randomly from a telephone book.
and February 1998.

The sample was

The survey was conducted in January

We sent the questionnaire to respondents after they had

confirmed by telephone that they would cooperate.

Some 544 respondents

replied to the questionnaire.
The question mainly used in this research is: “When you have the symptom ofa cold, such as a temperature of 38 degrees, mucus, and a sore throat, and you
think that you have caught a cold, what action do you decide to take? ”.
3Therespondents have three choices: “visit to a doctor right away”, “take OTC
medicine”, and “do nothing”.

They make this hypothetical choice under the

hypothetical scenario of coinsurance rate such as 30%, 40%, 50%, 70%, 100%and under that of actual rate 2) .
While conventional researches (Ii and Ohkusa, 1999b) analyses the demand
for medical services under the actual coinsurance rate alone, by using CA, weanalyse the demand under hypothetical coinsurance rates as well as the actualone.
Since the Japanese public health insurance system is compulsory, it is illegal
in Japan to be uninsured.

However, some people do not change their insurance

when they move from employed-based insurance scheme to a regional one.
Moreover, people who fail to pay the insurance premium is suspended theirinsurance or utilize it only by visiting the regional insurance office and paying
the amount in arrears.

We refer to such people as the ‘actually uninsured’,since they act as if they are not covered by health insurance.

The actuallyuninsured consists of people who are without health insurance at present andthose who were without it in the past.

A sample number of 19 people belonged

to at least one category.
The questionnaire includes additional questions, relating to dependents,
household income, household assets, household debt, labor income in the lastyear, age, gender, education level, working status and so on.

Summary

statistics of these data are shown in Table1.

3.Estimates of the Demand for Health Care using Conjoint
Analysis
In this section, the demand by people with the common cold for medical
services and that for OTC medicine are estimated under several hypotheticalcoinsurance rates using CA.
Table2 indicate a sample distribution around the actual coinsurance rate.

Inthis paper, the term “actual” means “not hypothetical”.
While the actual coinsurance rates for most persons are 20% and 30%, aslegally required, between 1% and 2% of the population have an actual4coinsurance rate of 0%, 10%, 15% and 100%.

Of these rates, the first three

imply insurer subsidies above the legal level, while the 100% rate implies theabsence of insurance.
First of all in this section, the methodology of estimation is described and theestimated results given of the demand for medical services of people with thecommon cold.

Second, the bias arising from hypothetical choice is discussed

by the way of comparison with the estimates of Ii and Ohkusa(1999b).

Third,

the bias arising from hypothetical scenario is evaluated by comparison with theactually uninsured and the hypothetical scenario of the uninsured.
The dependent variables are trinominal: T i,k =1 if a patients go to visit a
doctor, T i,k =2 if a patient takes OTC medicine, and T i, k =0 in other cases.

The

explanatory variables are age A i , gender (G i =1 if female, G i =0 if male),education E i (E i =1 if graduated from a university or college, F i =0 if not),working status in the last year (Wi =1 if working, Wi =0 if not), labor income inthe last year L i , household income in the last year other than own labor income
I i , household assets S i and real coinsurance rate R i, k .

A subscript k in T i,k and

R i,k corresponds to the real coinsurance rate that consists of actual coinsurance
rate and hypothetical coinsurance rate(30%, 40%, 50%, 70% and 100%).
Needless to say, where the actual rate is 30%, the 30% hypothetical scenario isomitted.
Following Ii and Ohkusa(1999b), the estimation equation is
j*
i ,k

T

= α 0j + α Aj Ai + α Gj Gi + α Ej Ei + α Wj Wi + α Lj log Li + α Ij log I i + α Sj S i + α Rj Ri ,k+ vij,k ( j = 0,1,2)1Ti ,k = 2
0



if

Ti1,k*＞Ti ,2k* andTi1,k*＞Ti ,0k*

if

Ti1,k*＜Ti ,2k* andTi ,2k*＞Ti ,0k*

（1）otherwisevij, k = ε ij,k + u ij
by using a multinomial probit estimation method with random effect2).

Because the probability of choice j sum to 1, we can estimate the differencesamong alternative choices.

Here, we set j=0, the choice of “do nothing” as the5basis of our comparison.

The error term vij, k ( j = 0,1,2) consists of ε i,j k and u ij .

The error term ε ij,k ( j = 0,1,2) is a random variable and Ω follows a threedimensional normal distribution over alternatives.

Since the error terms in theestimation also redefined by the difference from ε i0,k and ε ij,k ( j = 1,2) , andnormarizing the (1,1) element of Ω should be 1, its transformed variancecovariance matrix should be− 1 1 0 − 1 1 0 1 ρσ 
− 1 0 1 Ω − 1 0 1 = 2  ρσ σ 
The error term u ij implies the i-th individual random effect.

This specified

model is known as a probit model with random effect (Butler and Moffitt, 1982).
The error term u ij is normally distributed, so thatu ij 〜N (0, σ u2 j )Overall, the variance-covariance matrix of vij, k should beσu21 J K ⊗ I N + I KN ρσI KN
σu2 2 J K ⊗ I N + I KN 

ρσI KN

where K represents the maximum number of the actual coinsurance ratio, N isalso the maximum number 4) of individual number i, I m is m × m identity matrix,
J m is the m × m matrix that all elements are one.
The estimation procedure is the BHHH method with grid search overρ, σ, σ u21 , σ u2 2 5) .
Table3 shows estimation results.

The upper table is the estimation result of

the demand for medical services, while the lower table is that of the demand for
OTC medicine.

The coefficient of the real coinsurance rate in the upper table

is significantly negative.

The marginal effect of this coefficient indicates thata 10% increase in the real coinsurance rate, for example from 30% to 40%,decreases the demand for medical service by 6.27 percentage points.

This

results is much higher than the 3.61 percentage points estimated by Ii and
Ohkusa(1999b).

Age has a significantly positive effect on the demand for

medical services, and its marginal effect implies that 10 additional years of age
increases demand by 3.07 percentage points.
The coefficient of the real coinsurance rate in the lower table is significantly
6positive.

Its marginal effect shows that a 10% increase in the actual

coinsurance rate increases the demand for OTC medicine by 4.08 percentage
points.

This results is barely lower than the 2.95 percentage points estimatedby Ii and Ohkusa(1999b).

The coefficient of age also has a significant effect:

its marginal effect indicate that 10 additional years of age decreases demand by
4.08 percentage points.

4.Bias arising from Hypothetical Choice
Certain factors might be thought to account for the difference between theestimation results of Table3 and those of Ii and Ohkusa(1999b).

First, theestimation model used by Ii and Ohkusa(1999b) controls more individual
properties than that used in this paper, such as seriousness of illness andknowledge of medication.
samples.

Moreover, Ii and Ohkusa(1999b) do not include random effects, as

they do not use CA.
difference.

Another factor is the difference between the

In addition, the methodology may be thought to make a

While the data used in Ii and Ohkusa(1999b) are based on actual

choice, the data used in this paper are based on hypothetical choice.

Sincehypothetical choice need not be consistent with actual choice, there is a bias
arising from hypothetical choice in Table3.
To confirm the impact of this bias, we estimate the model 1 with the sample
excluded from the hypothetical scenario.

Since this estimation is based on theactual coinsurance rate, it is possible to compare with Ii and Ohkusa(1999b).
If there is little difference in the sample property and the estimation model ofthe two research projects, the difference in the results can be regarded as thebias arising from hypothetical choice.

Needless to say, this estimation has norandom effect, since there is only one sample for one individual.
Table 4 reports the results of estimation.

First, the actual coinsurance rate

has a significantly positive effect on the demand for medical services, and its
marginal effect is 8.41 percentage points.

This results is similar to Table3 andis also much higher than the 3.61 percentage points estimated by Ii and
Ohkusa(1999b).
Second, the actual coinsurance rate has a significantly negative effect on the7demand for medical services, and its marginal effect is 3.27 percentage point.
This result is almost the same as(or a little bigger than) the 2.95 percentage
points estimated by Ii and Ohkusa(1999b).

These results suggest that theestimation result of the demand for medical services may be more contaminatedby bias than that for OTC medicine, because of the difficulty of imaginating thedemand for medical services.

5.Bias arising from Hypothetical Scenario
We try to evaluate the bias arising from hypothetical scenarios.

Generally

speaking, it is more difficult to imagine choices under the highly hypotheticalcoinsurance rates such as 70% and 100%, which are far from the actual rates,
than those under rates such as 30% and 40%, which are near the actual rate, as
higher rates are more distant from reality than lower ones.

This bias arisingfrom hypothetical scenarios is a problem, not only for this research project, butalso for every research project using CA or Willing to Pay.
To measure the magnitude of this bias, we estimate the following model:
Ti j* = β 0j + β Aj Ai + β Gj Gi + β Ej Ei + βWj Wi + β Lj log Li + β Ij log I i + β Sj S i + β Nj N i + β Mj M i+ ε ij ( j = 0,1,2)1Ti = 2
0



if

Ti1*＞Ti 2* andTi1*＞Ti 0*

if

Ti1*＜Ti 2* andTi 2*＞Ti 0*（2）otherwiseusing the limited sample where the hypothetical scenario is a 100% coinsurance
rate for the actually insured and the actual coinsurance rate for the actuallyuninsured.

The actually insured, who face a 20% or 30% coinsurance rate,

have great difficulty imagining choice under this scenario, whereas the actuallyuninsured can easily imagine it.
The explanatory variable N i is the present actually uninsured dummy and M i isthe all uninsured dummy.

If these dummies are significant, this should be8interpreted as evidence of bias arising from hypothetical scenarios.

Other

explanatory variables and the estimation method are the same, as in the previous
subsection.
Table5 shows the estimation results.

The all uninsured dummy has asignificantly positive effect on demand for medical services, but a significantly
negative effect on demand for OTC medicine. The marginal effect of thesecoefficients indicates that the bias is about 12.6 percentage points for thedemand for medical service and 12.7 percentage points for the demand for OTC
medicine.

We therefore conclude that this bias is very large and cannot beignored.

6.Concluding Remarks
This paper examines by means of Conjoint Analysis the choice of treatment
by people suffering from the common cold in Japan.

A 10% increase in thereal coinsurance rate decrease by 6.27 percentage points the demand for medical
services, and increases 4.08 percentage points the demand for OTC medicine.
Both these estimated results are higher than those of Ii and Ohkusa(1999b).

To

account for the differences between the two research projects, we have analysed
the bias arising from hypothetical choice and the bias arising from hypotheticalscenarios.

Our empirical results show that the former bias in the demand for

medical services is bigger than that for the demand for OTC medicine, and thatthe later grew by up to 12 percentage points.
The problems arising from these biases are common to all research projects
using CA.

We believe that the analysis of these biases and the method ofcontrolling them is an important issue in future research.

9

Footnote:*)We would like to acknowledge the cooperation of Prof. Shigeno in Osaka City
University with respect to the overall design of the questionnaire.

Needlessto say, any remaining errors are ours.
1) Bryan et al(1998) also focused on the biases arising from the design of CA.
They checked the robustness of results by comparing two estimators fromdifferent samples.

However, they don’t investigate the biases arising fromhypothetical choice and hypothetical scenarios.
2)In Japan, there are two types of health insurance: regional and employmentbased.

In the employment-based health insurance, the coinsurance rate of

the insured is 20% for all services, while their dependents’ rete are 20% forinpatient service, and 30% for their outpatient services. However their actual
coinsurance rates may be lower than the legal rate, since employees of large
companies are subsided.

In the regional health insurance, a coinsurance rateof all subscribers is 30% for all service.
3)Multinominal Probit model with random effect was used for estimates in
Daganzo(1979) and Hauseman and McFadden(1984).
4)For example, because the coinsurance rate of regional health insurance does
not become 30% or less, the number of k differs between an individuals.
5)This procedure is as accurate as Keane(1992)has shown.

10Reference:
Butler,J. and R.Moffitt(1982), “A Computatinally Efficient Quadrature Procedure for the
One Factor Multinominal Probit Model.”, Econometrica,50,pp.347-364
Bryan,S., M.Buxton, R.Sheldon, A.Grant (1998), “The Use of Magnetic ResonanceImaging for The Investigation of Knee Injuries: A Discrete Choice Conjoint Analysis
Exercise.”, Health Economics 7 ,pp.595-604
Bryan,S.(1999), “Structural Reliability of Conjoint Analysis Applied to Health Care : an
Empirical Investigation” papers presented in IHEA 2 nd World Conference (Rotterdam,
6-9 June 1999)
Daganzo,C.(1979),Multinominal Probit:The Theory and Its Application to Demand
Forecasting(Academic Press,New York)

Hauseman,J. and D.Mcfadden(1984), “Specification Test for the Multinominal Logit
Model.”,Econometrica 52,pp.1219-40
Ii,M and Y.Ohkusa (1999a), “Kei-iryo ni okeru juyo no danryokusei no sokutei(Estimating the price elasticity of demand for health care related to minor illness)” ,forthcomming in Iryo keizai kenkyu(Japanese Journal of Health Economics andPolicy)Ii,M and Y.Ohkusa(1999b), “Is There Any Substitution Between Medical Services andOver-The-Counter Medications In The Case of The Common Cold? –Analysis Basedon an Original Survey-.”,ISER Discussion Paper No.478(Osaka)
Johnson,F,

W.Desvousges

andM.Ruby(1999),“Willingness

to

Pay

to

Avoid

Cardiovascular and Respiratory Conditions : Consensus Estimates of Task-SpecificStated Preferences” papers presented in IHEA 2 nd World Conference (Rotterdam, 6-9
June 1999)
Keane,M.(1992),"A Note on Identification in the multinominal Probit Model.",Journal ofBusiness and Economic Statistics 10,pp.193-200.
Leibowits,A(1989), “Substitution Between Prescribed and Over-the-Counter Medication.”,
Medical Care 27,pp.85-94
Newhouse,J(1993),Free For All(Harvard University Press,Boston)Nishimura,S(1987), “Ishiyuhatsujuyo wo megutte(Esseys on Physician Induced Demand)”in Nishimura,S “Iryo no keizaibunseki(Analysis of medical service)” (Toyo keizai
Shinposha,Tokyo)
Ogura,S(1990), “Iryo juyo no kakaku danryokusei ni kansuru yobiteki kousatsu(Someconsiderations about price elasitisity of demand for medical service)” in Kanamori,Hand H,Ibe ed., “Koureika shakai no keizaigaku(Economics for Aging Society)”(Tokyo University Press,Tokyo)

11

Ryan,M.(1999a), “Using Conjoint Analysis to Take Account of Patient Preferences and Go
Beyond Health Outcomes. An Application to In-Vitro Fertilization.”, Social Scienceand Medicine 48 ,pp.535-546
Ryan,M.(1999b), “Measuring Benefits in Health Care : The Role of Discrete conjoint
analysis”,papers presented in IHEA 2 nd World Conference (Rotterdam, 6-9 June
1999)
Ryan,M. and S.Farrar (1994),“A Pilot Study Using Conjoint Analysis to Establish theViews of Users in the Provision of Orthodontic Services in Grampian.”,Health
Economics Reserch Unit Discussion Paper No 07/94, Aberdeen,University ofAberdeen
Ryan,M and J.Hughes (1997), “Using Conjoint Analysis to Assess Women’s Preference for
Miscarriage Management.”,Health Economics 6 ,pp.261-274
Ratcliffe.J(1999), “Patients Prefrences Regarding the Process and Outcomes of Life
Saving Technology : An Application of Conjoint Analysis to Liver Transplantation”
papers presented in IHEA 2 nd World Conference (Rotterdam, 6-9 June 1999)
San Miguel,A and M.Ryan(1999), “Testing the Assumptions of Completeness and Stabilityof Preferences in Health Care Using Discrete Choice Models” papers presented in
IHEA 2 nd World Conference (Rotterdam, 6-9 June 1999)
Telser,H and P.Zweifel(1999), “Conjoint Analysis as a Method for Measuring Wilingness
to Pay for Risk Reductions” papers presented in IHEA 2 nd World Conference(Rotterdam, 6-9 June 1999)
Tolley,G.,D.Kenkel and R.fabian(1994),Valuing Health Policy:An Economic Approach,(University of Chicago Press,Chicago)
Van der Pol,M. and J.Cairns (1997), “Establishing Patient’s Prefernces for Blood
Transfusion Support: An Application of Conjoint Analysis” Journal of HealthServices Research and Policy 3,pp.70-76
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for Other’s Health” papers presented in IHEA 2 nd World Conference (Rotterdam, 6-9
June 1999)

12

Table1: Descriptive Statistics
Mean Standard Deviation Minimum Maximum
Age47.53631

11.901320

78

Gender

0.424630.49474

01

Education status

0.56801

0.495810

1Presence of dependents

0.83824

0.36857

01Working status in this year

0.76326

0.4254901Working status in the last year

0.71140.45353

0

1

Health status

0.95327

0.21126

0

1

Labor income in the last year

510.6329

387.02904

02250Household income in the last year

718.0985

450.144402250Household income-Labor income

339.1892

400.93148

0

1950

Household assets

2619.392284.96782

0

7500

Attendance of life insurance

0.88603

0.31807

0

1

Attendance of private health insurance

0.569850.49555

0

1

The actually uninsured in this year

0.007353

0.085512

01

The actually uninsured
Actual coinsurance ratio

0.034926
0.234465

0.183760.0891138

0
0

1
1

Notes:
Gender takes 1 if female and 0 if male.
Education takes 1 if graduated from a
university or collage and takes 0 if not.
Presence of dependents takes 1 if they havedependents and 0 if not.
Working status in the last year and Working status in thisyear take 1 if working and 0 if not.
Attendance of private insurance andAttendance of life insurance take 1 if they attend the insurance and 0 if not.
The
actually uninsured takes 1 if they were the actually uninsured in last year or are the one
in this year and takes 0 if otherwise.
Health status takes 1 if they reports fine ,usually and not fine much and takes 0 if otherwise.
Labor income andHousehold income are showed at 10,000 yen.

Table2: Distribution of Coinsurance Rates

Actual Coinsurance Rate Number of Samples Percentage Cumulative Percentage
0

5

1.03

1.03

0.1

9

1.85

2.88

0.15

7

1.44

4.32

0.2

303

62.35

66.67

0.3

158

32.51

99.18

1

4

0.82

100

total

486

100

Note: 0.1 of Actual coinsurance rate means 10%.

Table3: Estimation Result of Demand for Health Care using Conjoint Analysis
Coefficient

Marginal Effect t -values p -values[Demand for medical service]Age0.0096816

0.00307657

1.816

0.069

Gender0.1699789

0.05401531

1.202

0.229

Education

0.0067574

0.00214733

0.165

0.869

Working status in this year

-0.1447855-0.04600944-0.498

0.619

log(Labor income in the last year)

0.0153518

0.00487843

0.286

0.775

log(Household income in the last year)

0.0259869

0.00825803

0.567

0.571

Household assets

0.0000282

8.97E-06

1.296

0.195

Real coinsurance rate
constant-1.974177-0.54303-0.62734708-11.001-1.23800

0.000
0.216

Age

-0.0111743-0.0041939-2.3270.020

Gender

-0.0918302-0.03446525-0.734

0.463

Education

0.0197817

0.00742438

0.521

0.603

Working status in this year

-0.0626257-0.02350436-0.204

0.838

log(Labor income in the last year)-0.0082176-0.00308418-0.1470.883

log(Household income in the last year)

0.0808351

0.03033863

1.443

0.149

Household assets-0.0000185-6.94E-06-0.8830.377

1.087356-0.029780.40810122

9.781-0.06500

0.000
0.948

[Demand for OTC medicine]Real coinsurance rate
constant

Note:(1)Estimation method is Trinominal Probit Model with random effect. The number of thesamples is 2733(486 house), log likelihood is -2198.3691 and Pseudo R2 is 0.0273.
The differnce of an error term of medical service and that of doing nothing is correlated withthe differnce of an error term of medical service and that of OTC medicine. Theircorrelation coefficient(t -value) is 0.77(3.17). Variance of the difference of an error term of
medical service and that of doing nothing is 1.21(6.96) while variance of the difference of anerror term of medical service and that of OTC medicine is normalized as one. Variance ofrandom effect in demand for medical service is 1.17(5.32) and that in demand for OTC
medicine is 1.01(9.78).
(2)Explanatory variables are same as Table1.

Table4: Estimation Result of Demand for Health Care using Actual Coinsurance Rate Sample
Coefficient

Marginal Effectt -values

p -values[Demand for medical service]Age0.0069413

0.00274025

0.865

0.387

Gender

0.3119696

0.12315743

1.381

0.167

Education-0.0152059-0.00600288

-0.27

0.787

Working status in this year

0.8396021

0.3314529

1.409

0.159

log(Labor income in the last year)-0.1343639-0.05304337-1.2260.220

log(Household income in the last year)-0.0327162-0.01291549

-0.38

0.704

Household assets

0.0000441

1.74E-05

1.371

0.170

Real(Actual) coinsurance rate
constant-2.129579-0.01410-0.84070182-2.326-0.01800

0.020
0.985

Age

-0.0074089-0.00295558-0.9280.353

Gender

-0.2363165-0.09427255-1.074

0.283

Education

0.0288409

0.01150537

0.516

0.606

Working status in this year

-0.8171296-0.32597338-1.385

0.166

log(Labor income in the last year)

0.1153775

0.04602696

1.068

0.285

log(Household income in the last year)

0.0542368

0.0216364

0.619

0.536

Household assets-0.0000384-1.53E-05-1.2010.230

Real(Actual) coinsurance rate
constant

0.8194532
0.10432

0.32690035

0.954
0.13600

0.340
0.891

[Demand for OTC medicine]Note:(1)Estimation method is Trinominal Probit Model and the number of the samples is 486, log likelihood
is -405.81051 and Pseudo R2 is 0.0252.
The differnce of an error term of medical service and that of doing nothing is correlated with thediffernce of an error term of OTC medicine and that of doing nothing. Their correlation coefficient(t value) is 0.64(2.47). Variance of the difference of an error term of OTC medicine and that of doing
nothing is 1.12(4.19) while variance of the difference of an error term of medical service and that ofdoing nothing is normalized as one.
(2)Explanatory variables are same as Table1.

Table5: Estimation Result of Demand for Health Care When Coinsurance Rate is 100%
Coefficient

Marginal Effectt -values

p -values[Demand for medical service]Age0.0165033

0.00275376

2.169

0.030

Gender

0.0320384

0.00534584

0.151

0.880

Education

0.0311655

0.00520021

0.516

0.606

Working status in this year

0.0337776

0.00563604

0.051

0.959

log(Labor income in the last year)-0.0221069-0.0036887-0.1830.855

log(Household income in the last year)

0.0686464

0.01145416

0.546

0.585

Household assets

0.0000117

1.95E-06

0.345

0.730

The actually uninsured in this year

-0.3908659-6.52E-02-0.608

0.543

The actually uninsured
constant

0.7555946-2.70012

0.12607664

2.908-2.98400

0.004
0.003

Age

-0.0131898-0.00417133

-1.98

0.048

Gender

0.0060884

0.00192549

0.036

0.971

Education

0.0602944

0.01906841

1.197

0.231

Working status in this year

-0.164274-0.05195247-0.391

0.696

log(Labor income in the last year)

0.0144182

0.00455982

0.188

0.851

log(Household income in the last year)

0.0842966

0.02665921

1.24

0.215

Household assets

1.06E-06

3.37E-07

0.038

0.970

The actually uninsured in this year

-0.0203321-6.43E-03-0.0380.970

The actually uninsured
constant-0.4015091
0.74397-0.12697924-1.6871.27500

0.092
0.202

[Demand for OTC medicine]Note:(1)Estimation method is Trinominal Probit Model. The number of the samples is 486, log likelihood
is -418.93874 and PseudoR2 is 0.0328.
The differnce of an error term of medical service and that of doing nothing is correlated with thediffernce of an error term of OTC medicine and that of doing nothing. Their correlation coeffient(t value) is 0.56(4.40). Variance of the difference of an error term of OTC medicine and that of doing
nothing is 1.45(5.17) while variance of the difference of an error term of medical service and that ofdoing nothing is normalized as one.
(2)Explanatory variables are same as Table1.